world cotton research conference - 5 .session_3

Cotton Production, Physiology and Economics

Cotton Genotypes Performance under Rainfed and Irrigated Conditions in two Regions of Northern Argentina

Marcelo Paytas1 and Jose Tarrago2 1INTA EEA Reconquista, Santa Fe, (3560), Argentina

2INTA EEA Las Brenas, Chaco, (3722), Argentina E-mail: [email protected]

Abstract—Narrow-row cotton production systems have became popular in Argentina in the last few years. It is mainly cultivated under rainfed conditions as a low input crop which is challenging and risky. Irrigation can improve the performance of current genotypes and may reduce the variability in yield produced under rainfed conditions across different environments. This research was aimed to understand the differences in growth, development and yield of two cotton cultivars in a narrow row system under rainfed and irrigated conditions.

Experiments were conducted during 2010-11 under rainfed and irrigated conditions at the Research Station of INTA Reconquista, Santa Fe (29º11´S, 59º42’W) and INTA Las Brenas, Chaco (27º05´S, 61º06’W). The annual rainfall and its distribution, temperatures, evaporative demand and soil types differ between both cotton regions. The experimental design in each location was a split plot design with four replications: two genotypes (NuOpal and DP402) with two moisture levels (rainfed and irrigated). The results indicated differences between genotypes in terms of days to crop maturity. Earliness was found for DP402 for both locations compared with NuOpal. However, no significant differences in terms of phenology were found between rainfed and irrigated conditions due the amount of soil water content available from rainfall for the plant in both systems. Dry matter production and partitioning to reproductive organs was affected by genotypes and moisture levels. DP402 with shorter vegetative and reproductive stages produced significant differences in dry matter between moisture levels than NuOpal with later maturity. Percentage of fruit retention increased by maturity in DP402 compared with NuOpal under both rainfed and irrigated conditions, although NuOpal produced higher number of nodes and fruiting sites but higher fruit abortion in the lower part of the plant.

INTRODUCTION

Narrow-row cotton has become popular in Argentina in the last few years reaching about 90% of the national sowing area. By reducing distance between rows and increasing plant population, plants became smaller and harvested with stripper machines reducing harvesting costs compared with previous traditional cotton systems. Cotton is mainly cultivated under dryland conditions as a low input crop which is challenging and risky. However, irrigation practices can improve the current genotypes performances and may reduce the variability in yield.

Changing row spacing and plant population has been used to increase yield in many other crops. By changing the spacing between plants, competition for light, water and nutrients is altered, which can change fruit number and retention per plant and the size of the plant (Bednarz, 2000). Due to the influence of environmental conditions on plant growth and development, specific row spacing and population recommendations for crops may vary. The optimum plant population for any crop is the population that maximizes yield while optimising resource use (Willey and Heath, 1969). Nowadays, Argentinean cotton farmers are mainly using 52 cm as row spacing and 220,000 plants per hectare. Whether this population is optimal or not to produce high yielding cotton with current Bt varieties is focus of numerous studies.

Boll retention and distribution within a plant play an important role in determining final yield, and are linked to the allocation of assimilate produced during vegetative growth by the plant. If the availability of assimilate is adequate to support the developing bolls, then the bolls will be retained (Constable, 1991; Jenkins et al., 1990a). However, if the demand from growing bolls exceeds the assimilate supply, the retention of bolls will decline as a result of an increase in the boll shedding (Guinn, 1998; Mason, 1922).

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310 World Cotton Research Conference on Technologies for Prosperity

Most of the time, research has been done comparing different crop configuration, while in this work the aim was to maintain the same configuration and vary the inputs of water to increase source availability for maximizing cotton yield, using two different genotypes and two growing environmental conditions. Increased resource availability by irrigation may reduce the variability in yield produced under rainfed conditions across these different environments. This research was aimed to understand the differences in growth, development and yield of two cotton cultivars in narrow row systems under rainfed and irrigated conditions.

MATERIAL AND METHODS

Experiments were conducted during 2010-11 under rainfed and irrigated conditions at the Research Station of INTA Reconquista, Santa Fe (29º11´S, 59º42’W) and INTA Las Brenas, Chaco (27º05´S, 61º06’W), Argentina. The experimental design in each location was a split plot design with four replications: two cultivars (NuOpal and DP402) were sown at a spacing of 0.52 m between rows having 11 plants per meter. Two soil moisture treatments (irrigated and rainfed) were compared. In both the locations, irrigated plots received three irrigations at the time of flowering (about 90 mm in Las Brenas and 60 mm in Reconquista) besudes water from rainfall. Neutron moisture meter measurements were used to monitor soil moisture content (0-150 cm depth). Harvests for total biomass, biomass partitioning, radiation interception and yield, as well as mapping, were done at various developmental stages throughout the season. Meteorological conditions were recorded during the season.

RESULTS AND DISCUSSION

The annual rainfall and its distribution, temperatures, evaporative demand and soil types differ between both cotton regions. Wetter conditions were found at Reconquista, Santa Fe compared with Las Brenas, Chaco. Differences between cultivars were observed in terms of days to crop maturity. Earliness was found for DP402 for both locations compared with NuOpal. However, no significant differences in terms of phenology were found between rainfed and irrigated conditions due the amount of soil water content available from rainfall for the plant in both systems.

Dry matter production and partitioning to reproductive organs was affected by genotypes and moisture levels. DP402 with shorter vegetative and reproductive stages produced significant differences in dry matter production between moisture levels than NuOpal with later maturity. In both locations, similar responses were found in terms of dry matter production and partitioning to reproductive organs. Lower solar radiation interception was found in the lower part of the canopy in NuOpal. Possibly, the greater vegetative growth in NuOpal may have contributed to reduced boll growth and shedding of flowers and young bolls lower in the canopy due to poor light infiltration. Percentage of fruit retention in first fruit positions on the main stem increased by maturity in DP402 compared with NuOpal under both rainfed and irrigated conditions. NuOpal produced higher number fruits abortions in the lower part of the plant. It is likely that solar radiation and photosynthesis in low position fruiting sites become a limitation, with a bigger plant and complete canopy closure resulting in fruit abortions and decrease in the yield potential in conventional cropping systems (Constable and Rawson, 1980b; Wullschleger and Oosterhuis, 1990a; 1990b).

The longer period to maturity in NuOpal may compensate after reproductive organs in the first few positions were aborted, with higher number of nodes and fruiting sites on lateral and upper part of the canopy, increasing final cotton yield in a wet season for both locations. However, DP402 with shorter vegetative and reproductive period produced higher seed cotton yields than NuOpal (Table 1) in both locations, with a better crop performance under narrow row systems in a subtropical environment with humid crop season.

Cotton Genotypes Performance under Rainfed and Irrigated Conditions in two Regions of Northern Argentina 311

TABLE 1: SEED COTTON YIELD (KG*HA-1) FOR TWO GENOTYPES UNDER RAINFED AND IRRIGATED CONDITIONS FOR TWO LOCATIONS IN NORTH ARGENTINA

Location: Reconquista, Santa Fe

Seed Cotton yield (kg*ha-1) Location: Las Brenas, Chaco Seed Cotton yield (kg*ha-1)

NuOpal-I 3.298 NuOpal-I 2.160 NuOpal-RF 3.045 NuOpal-RF 2.156 DP402-I 3.788 DP402-I 3.231 DP402-RF 3.540 DP402-RF 3.340 Significance * *

I: Irrigated treatment RF: Rainfed treatment *Significance (P 0.05)

REFERENCES [1] Bednarz, C.W., Bridges, D.C. and Brown, S.M. (2000) - Analysis of cotton yield stability across population densities.

Agronomy Journal 92, 128-135. [2] Constable, G.A. and Rawson, H.M. (1980b) - Carbon production and utilization in cotton - inferences from a carbon

budget. Australian Journal of Plant Physiology 7: 539-553. [3] Constable, G.A. (1991) - Mapping the Production and Survival of Fruit on Field-Grown Cotton. Agronomy Journal 83:

374-378. [4] Guinn, G. (1998) - Causes of square and boll shedding. Beltwide Cotton Conferences, pp. 1355–1364. [5] Jenkins, J.N., McCarty, J.C. and Parrott, W.L. (1990) - Effectiveness of fruiting sites in cotton - yield. Crop Science 30:

365-369. [6] Mason, T.G. (1922) - Growth and abscission in Sea Island cotton. Annals of Botany 36: 457-484. [7] Willey, R. and Heath, S. (1969) - The quantitative relationship between plant population and crop yield. Advances in

Agronomy 21: 281-321. [8] Wullschleger, S.D. and Oosterhuis, D.M. (1990a) - Photosynthetic and respiratory activity of fruiting forms within the

cotton canopy. Plant Physiology 94: 463-469. [9] Wullschleger, S.D. and Oosterhuis, D.M. (1990b) - Photosynthetic carbon production and use by developing cotton leaves

and bolls. Crop Science 30: 1259-1264.

The Adaptation of Irrigated Cotton to the Tropical Dry Season

S.J. Yeates1,2

1Principal Research Scientist, CSIRO–Plant Industry, Ayr, Qld, Australia 2The Australian Cotton Cooperative Research Centre

Abstract—The reintroduction of cotton to most of the Australian tropics was prevented by insect pests that are dominant during the wet (summer) season and a perception that the crop could only be grown in the wet season. Growing cotton during the dry (winter) season has avoided these pests. However the photothermal pattern of the dry season is the reverse of the wet season and that of spring sown cotton in temperate latitudes. Average night temperatures are cool mid season (12 to 14 oC) with extremes < 6 oC and high temperatures are likely early and late in the season. Solar radiation is 20% less than at temperate latitudes mid season and could also limit crop growth. It was not known what yield or fibre quality was possible. Over three seasons two upland Bt-transgenic cultivars and one Gossypium barbadense cultivar were sown from March to June in field experiments at the Ord River (15.5oS). A pot experiment conducted at Katherine, (14.5oS) over two seasons where average ambient minimum temperatures were 4oC lower than the field experiments during flowering were compared with temperatures 6 oC higher by moving plants into a glasshouse at night. Despite the photothemal constraints, lint yields were at the high end of Australian and international benchmarks when sown in March and April. The lower temperature and radiation during flowering and early boll growth for the March and April sowings combined to reduce the crop growth rate during this phase compared with cotton grown at temperate latitudes. However, assimilate supply was adequate because boll demand was also lower at this time due to early flowers having slower development, lower retention and smaller bolls. Increasing late season temperature and radiation permitted yield compensation via an extended flowering period and a greater contribution to yield from later pollinated flowers on the top and outside of the plant. The Katherine experiment found boll retention and size was correlated (p < 0.01) with minimum temperature during flowering. Full yield recovery occurred because cold minimums were episodic. RUE was negatively correlated with average temperature up to first flower a response not reported previously in cotton and explained some of variation in RUE measured here and elsewhere. Cool temperatures during fibre development reduced fibre length and strength at March and April sowings. Further screening may identify cultivars with suitable fibre length and strength in these conditions.

Introduction

There have been many attempts to grow cotton in the Australian semi-arid tropics (SAT). The region is vast, approximately 30% of the Australian continent, and largely unutilised for cropping of any species. The region contains about 66 drainage basins or river catchments; these account for around 60% of Australia’s surface water runoff, with significant ground water and arable soils (NLWRA 2001). The only significant commercial production of cotton in the region occurred at the Ord River between 1963 and 1974. Cotton was grown during the wet season (November to April) with irrigation supplementing rainfall to finish the crop early in the dry season (April to June) (Hearn, 1975). Despite yields similar to south-eastern Australia during the same period, cotton production became uneconomic due to poor fibre quality and resistance of Helicoverpa armigera to insecticides, which resulted in excessive pesticide usage (Hearn, 1975).

The reintroduction of cotton to the Australian SAT is being assessed via a multidisciplinary study that evaluates a novel production system designed to avoid the pest management problems of the previous cotton industry. The new system involves dry season cropping to avoid peak numbers of the key pests Helicoverpa armigera, Helicoverpa punctigera, Spodoptera litura, Pectinophora gossypiella and Anomis spp., which characterise the wet season, and incorporates Integrated Pest Management and Bt transgenic genotypes (Strickland et al.,, 1998). A comparison of the proposed system with the previous wet season system is shown in Table 1. Sowing of cotton crops from March 1st is desirable in the Ord River and much of the Australian SAT as they flower during the cooler months of May to August avoiding key insect pests (Strickland et al.,, 1998 & 2003). Once the first field is sown in a valley all

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Genetic Diversity Analysis in Cotton Germplasm 313

cotton must be sown within five weeks of that date to minimise the number of generations of Helicoverpa armigeria exposed to Bt proteins (Monsanto and Cotton Australia, 2010). Pest management research to date demonstrates effective insect management was achieved by adopting this system, requiring only 3.5 insecticide sprays per crop (Strickland et al.,, 1998; Annells and Strickland, 2003) compared with 40 for the 1970’s industry (Hearn, 1975).

TABLE 1: KEY ELEMENTS OF A NOVEL COTTON PRODUCTION SYSTEM FOR THE ORIA CONTRASTED WITH THE PREVIOUSLY UNSUCCESSFUL SYSTEM OF THE 1970S (ADAPTED FROM STRICKLAND ET AL., 1998)

1970s Industry New Industry Wet season planting window that was long – November to February.

Dry season (winter) cropping, with a narrow planting window (5 weeks) in March – April.

Flowering from wet season (February) to early dry season (May).

Flowering in low pest months of May to August.

Conventional cultivars Bt transgenic cultivars Broad spectrum insecticides IPM systems No pesticide resistance management Pre-emptive Bt resistance management

The key agronomic change in this proposed production system is the requirement for a five week planting window that can commence on March 1st. Hence it is pertinent to ask why sowing after February was not practiced previously in the Ord? Firstly, there was a perception that cotton growth and development during the coldest months of May-August would be poor, delaying boll set until temperatures increased and pushing harvest into the wet season. Results and recommendations on sowing date were contradictory (Toms, 1963; Stern, 1965; Thomson, 1965; Hearn, 1975) prompting the conclusion ‘the possibility of March sowings warrants further investigation’ (Thomson, 1965). Secondly, larger modern pickers combined with all weather storage of seed cotton are now available and reduce the possibility of a long harvest and ginning season and UV light damage to fibre that occurred in the 1970’s (Hearn, 1975). Thirdly, prior to 1972, water storage capacity was insufficient to irrigate large areas of a fully irrigated dry season crop but the irrigation system capacity is now expanded.

Growing cotton in the dry season creates new challenges for crop growth and the timing of farming operations. A possible growing season of sowing in April, when trafficability is least affected by wet season rain, then picking in October is the reverse of temperate Australia in terms of temperature and daylength where cotton is usually sown in October and picked in April. Figure 1 compares the dry season in the ORIA (Kununurra, 15oS) with temperate summer cotton at Narrabri, NSW (30oS), for monthly rainfall, maximum and minimum temperature and solar radiation. Growing season rainfall is much less at Kununurra (Fig. 1A), although rainfall prior to sowing is higher and may cause difficulties with land preparation and sowing operations. It will be important to pick promptly at Kununurra as rainfall increases significantly each month after October.

Monthly temperatures (Fig. 1B) are higher early and late in the season, while mid season minimum temperatures are cooler averaging 14oC with extremes below 10oC (Cook and Russell, 1983) which could be problematic for fibre quality and boll growth (Gipson and Ray 1970; Hearn 1994) and would delay crop development (Constable and Shaw, 1988). High temperatures during September and October could also be detrimental to boll growth (Hearn, 1994), but should enhance boll desiccation and improve defoliant efficacy.

Potential daily photosynthesis is lower during flowering and boll growth at Kununurra because daily radiation is about 80% of Narrabri during this phase (Fig. 1C) (Hearn, 1994). However, it is not known whether reduced daily radiation will translate into lower yields as cooler temperatures may compensate via slower development rate and less night respiration


Fig. 1: Climatic Comparison the Proposed Tropical Dry or Winter Growing Season in the Ord River (April to October) and the Temperate Summer Growing Season at Narrabri 30oS (October to April) A) Mean Monthly Rainfall; B) Average Monthly Temperatures,

with Possible Development States Shown for the Ord River Based on Degree day Sums (Constable and Shaw 1988); C) mean daily Radiation for each Month. Where − Narrabri, --- Ord River.

There is very little literature reporting cotton grown during the dry season in the SAT worldwide. Cotton is known to be grown during the dry season in several tropical regions, such as eastern Asia, Central America, Colombia, Sudan, and Malawi. In most cases production is near the coast or large lakes where temperature extremes are minimised and these countries have lower economic yield expectations than Australia (Hearn, 1995). Hence, there is a need to develop and evaluate the dry season production system outlined in Table 1, as a prerequisite to assessing the feasibility of reintroducing cotton into the Australian SAT. The Ord River is suitable for this evaluation as it is one of the few valleys north of 21oS developed for irrigation and expansion of the cropping area was planned for the near future (Yeates, 2001; Yeates et al., 2002a).

While insect management and crop husbandry research was being conducted separately to this research (Strickland et al., 1998; Annells and Strickland 2003; Yeates et al., 2002b), the research reported here addresses the following important crop adaptation issues relevant to dry season cotton production:

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• What is the optimum sowing window for yield and quality given sowing must commence after March 1 to avoid insect pests and there must be sufficient time to pick by before the start of the wet season?

MATERIALS AND METHODS

The research described here integrates four papers (Yeates et al., 2010a,b,c and Yeates et al., 2011) and some previously unpublished research into one document.

Field Experiments

Sowing date by cultivar experiments conducted over three seasons at the Frank Wise Institute, 13 km NW of Kununurra WA, Australia (15o39’S, 128o43’E) in the Ord River Irrigation Area were used to collect relevant data. These experiments are described in detail in Yeates et al., (2010a). To summarise, the Gossypium barbadense cultivar Pima S7 was compared with two Bt transgenic Gossypium hirsutum (upland) cultivars: Siokra L23i and Sicot 50i (producing the Monsanto Cry1Ac protein). In the first season, the non Bt transgenic equivalent of the upland cultivars (Siokra L23 and CS50) were sown. Where data are combined for the three seasons these cultivars are referred to as L23 and S50. In each of the 3 seasons these cultivars were sown on 4 occasions (main plots): 27 to 29 March, 21 to 29 April, 15 to 23 May and 9 to 14 June; there were 4 replications. The experiments were furrow-irrigated. The crop was sowing a at 90 cm row spacing on wide beds accommodating two rows per bed. Plots were 6 rows wide and 20m in length. Rows were in an east – west direction.

Boll period was measured by tagging 30 recently pollinated (white or pink) first position flowers in each plot on 3 occasions with the date and node number recorded, which represented the flowers on lower, middle and upper part of the main-stem. The date of tagging was identified by different coloured tags Bolls were hand picked on alternate days, the number of bolls and date picked was recorded and the boll period calculated as the time from tagging to the median open day. Seed cotton was machine harvested from 13m of a centre row of each plot. Above ground biomass from 1m2 from each plot was partitioned into stems, leaves, squares, flowers and bolls prior to drying at 80oC for 3 – 4 days in a fan forced oven. Biomass was partitioned at early squaring, at first flower, at approximately 30 and 60 days after first flower and when approximately 60% of the bolls were open. The final biomass sampling was made prior to chemical defoliation. The measurement of RUE was described in detail in Yeates et al., (2010b). To determine the effect of temperature on RUE, the average RUE calculated for the different growth phases for each sowing month of each variety was plotted against the average minimum, maximum and mean temperature for the duration of the growth phase. Biomass was converted to a glucose equivalent using the method of Wall et al. (1994).

Pot Experiment

The experiments were located at the Katherine Research Station, 4 km east of Katherine (14o28' S, 132o18' E), Northern Territory, Australia (see Yeates et al., 2011, for more details). Due to greater distance from the ocean Katherine has a greater probability of cooler dry season minimums than the Ord River, with similar maximum temperatures, photoperiod and monthly radiation (Cook and Russell, 1983). Minimum temperature was manipulated by protecting plants in a glasshouse at night during flowering (‘warm’ plants) and comparing these with plants grown at ambient temperatures at night (‘cool’ plants). Glasshouse temperatures were maintained approximately 5oC above ambient to ensure similar daily variation in minimum temperature to plants grown at ambient temperatures. The only


exceptions were when ambient temperatures were < 6oC, on these nights glasshouse temperatures were not permitted to fall below 10.3oC and when ambient temperatures were warm, glasshouse minimum temperatures did not exceed 24oC. The experiment was run over two seasons with sowing occurring in late April. To ensure plants were exposed to the same minimum temperatures prior to flowering all plants were grown outside until 6 and 7 days prior to first flower in 2003 and 2004 respectively. When the temperature treatments commenced the warm night plants were moved inside at night for the next 60 and 53 days in 2003 and 2004 respectively; that is at least 15 days after flowering was completed.

Results and Discussion

Lint Yield

For the field experiments Lint yields exceeded 2000 kg/ha when sowing occurred during March and April for the upland cultivars (Fig. 2). For Pima S7 sowing in March produced the highest lint yields which were approximately 1800 kg/ha. Yields declined rapidly when sown after mid May. Despite the photothermal limitations described above these lint yields were at worst in line with recent Australian commercial irrigated yields (ABS 2006) and commonly reported research yields for irrigated cotton in temperate Australia and the USA, where lint yield was inflated by laboratory ginning, (e.g. Fritschi et al., 2003; Hutmacher et al., 2004; Bange and Milroy, 2004) and at the top of international averages elsewhere (ICAC 2002). The lower yield of the Gossypium barbadense cultivar Pima S7 compared to the Gossypium hirsutum cultivars was consistent with research and commercial experience with this species from temperate regions (Fritschi et al., 2003). Hence sowing in March or April of either species should meet current commercial expectations if repeated reliably on a larger scale, provided fibre quality standards are achieved. Moreover sowing before May would ensure picking could occur before the onset of the wet season (Yeates et al., 2010a).

Fig 2: The Effect of Sowing Date on Average Lint Yield for Three Seasons for Upland Varieties and Pima S7. Bars show Range of Yields. Adapted from Yeates et al. 2010a. How were Yields Achieved?

Final crop biomass did not limit yield. For the highest yielding March and April sowings final crop biomass was mostly > 1000 g /m2 (Fig 3). This was similar to the maximum values reported for irrigated cotton in temperate Australia (Sadras 1996; Bange and Milroy 2004) and the USA (Fritschi et al., 2003). These biomasses were also 25 to 60 % higher than for dry season cotton but similar to wet season cotton grown in the 1960’s at this location (Stern, 1965; Thomson, 1965).

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DAS = Days after Sowing. Adapted from Yeates et al. (2010b)

An important finding was that biomass was accumulated differently in the tropical dry season than in temperate (~ 30o lat.) and tropical wet season grown cotton. Fig. 5 shows for the highest yielding March and April sowings, final biomass was accumulated by sustaining a modest growth rate of 6.9 to 12.3 g/m2/d for a long period (78 to 134 days) between first squaring and boll opening. This contrasts with similar yielding crops in temperate climates where growth was characterised by shorter period (25 to 60 days) with a greater maximum growth rate of 15 to 20 g/m2/d (Kennedy and Hutchison, 2001; Bange and Milroy, 2004) and lower yielding wet season cotton, with a similar vegetative biomass to this study, where a maximum growth rate of 7 to 16 g/m2/d was maintained for 50 to 60 days (Stern 1965; Thomson 1965).

There was positive correlation between yield and greater horizontal fruiting, that is more 2nd position (P2) and ≥ 3rd position (P3+) bolls and the lack of correlation between the number of first position (P1) bolls and their retention and yield (Table 2). This is a departure from spring sown upland cotton in temperate climates where P1 bolls and their retention are regularly monitored due to their positive association with yield (Constable, 1991; Kerby and Hake, 1996). The relationship between yield and P1 retention found for temperate regions is related to the need for earlier maturity due to a growing season length that is defined by cool temperature (Kerby and Hake, 1996). In temperate climates the growth of

L23 - 1997

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bottom P1 bolls often coincides with favourable climatic conditions, which are the longest photoperiods and highest solar radiation for the growing season. Hence, P1 bolls were larger at each fruiting branch and had a greater chance of survival (Constable, 1991). There is also greater likelihood of sub-optimal temperatures for later developing bolls making compensation from the loss of lower fruit risky. The tropical dry season is the reverse with the pollination of early flowers coinciding with less favourable climatic conditions, that is, cool to cold night temperatures and relatively low radiation, while later developing bolls were exposed to rising temperatures and radiation (Fig. 1).

TABLE 2: LINEAR REGRESSION COEFFICIENTS FOR THE RELATIONSHIP BETWEEN YIELD COMPONENTS OR PLANT MAPPING VARIABLES AND LINT YIELD. DATA FOR THE THREE SEASONS COMBINED. WHERE, ** P<0.01, * P<0.05 AND NS = NOT SIGNIFICANT,

POSITION 3+ = (POSITION 3 +POSITION 4 + ADVENTITIOUS BOLLS). ADAPTED FROM YEATES ET AL. 2010A

Yield Component / Plant Mapping Variable Lint Yield (n=48 for each cultivar) L23 S50 Pima S7

Position 1 bolls (m-2) ns ns ns Position 2 bolls (m-2) 0.41** 0.36** 0.38** Position 3+ bolls (m-2) 0.34** 0.36** 0.45**

Boll development was slower for bolls from early P1 flowers when sown in March and April compared with May or June sowings (Fig. 4). The boll development period was at least 20 days less for later flowers for the March and April sowings. This pattern of boll period with plant position and sowing date reflected the temperatures during boll development. That is boll growth from the earliest flowers on March and April sown plants coincided with the coolest night temperatures (Fig. 1).

Fig. 4: The Impact of Sowing Month on Boll Periods for: ■ Early Flowering P1 Bolls (1st Three Fruiting Branches), ■ P1 Bolls Mid Canopy □ P1 Bolls From late Flowers or Top of the Canopy

A reduced contribution to yield from P1 flowers combined with a longer boll period may have had a positive impact on yield for the March and April sowings by reducing boll demand for assimilate early in flowering, a time when assimilate supply was limited by low solar radiation and cool night temperatures (Fig 1). Compensation for the loss of early flowers occurred due to a greater production of horizontal fruiting sites that flowered later when temperatures were warmer and radiation higher. The positive relation between yield and time-to-maturity supports this hypothesis (Yeates et al., 2010a).

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Because the field experiments were grown using Integrated Pest Management where pest number thresholds are used as a trigger for control measures it was difficult to determine whether cool minimum temperatures were the only factor in the reduced contribution of early bolls to yield. Other possible causes such as water logging, nutrient stress can also occur. Moreover in the field there was no ‘warm night’ control to compare with.

The pot experiment found that yield was not reduced by ambient minimum temperatures observed during flowering that averaged 10.2oC and 12.6oC compared with minimums that were 5oC higher. This experiment showed the reduced contribution to yield from early first position flowers in the tropical dry season was due to low minimum temperatures near anthesis and not biotic causes such as pests. Fig. 5 shows that boll weight and retention of P1 bolls was reduced in proportion to minimum temperature during flowering. Yield compensation from cold minimums during flowering occurred via two processes. Firstly, for all fruiting positions boll survival was significantly correlated with minimum temperature near anthesis. Because minimum temperatures in the field were variable, greater boll retention and weight followed periods of warmer minimums (Yeates et al., 2011). Secondly, the greater contribution to yield from later pollinated flowers reflected the general increase in temperature and radiation later in flowering that typifies the tropical dry season (Fig. 1).

Fig. 5: The Impact of Average Minimum Temperature During the Flowering Period on Seed Cotton per Boll and Boll Number on First Position Bolls. Data from Experiment at Katherine

For all varieties the glucose equivalent RUE changed significantly (p<0.05) with crop ontogeny ; being highest in the squaring to flowering period (1.88 to 2.34 g/MJ) then declining to a minimum late in boll growth of 1.2 to 1.4 g/MJ (Yeates et al., 2010b). From first square to first flower the linear correlation between RUE and average temperature was highly significant (p<0.01) (Fig. 6). The lines fitted for the upland cultivars Siokra L23i and Sicot 50i were not significantly different hence their responses were combined (Fig. 6A). The correlation between temperature and RUE after early flowering

Seed Cotton = 0.24T + 2.0661R2 = 0.98

0

1

2

3

4

5

6

7

9 10 11 12 13 14 15 16 17 18Average Minimum Temperature oC

See

d C

otto

n (p

er b

oll)

Boll Number = 0.0887T + 1.1783R2 = 0.65

0

0.5

1

1.5

2

2.5

3

9 10 11 12 13 14 15 16 17 18Average Minimum Temperature oC

Bol

l Num

ber (

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was not significant, although the temperature range was smaller (data not presented). This reduction of RUE with average temperature up to first flower has not been reported previously in cotton and explains some of variation in RUE observed here (Yeates et al., 2010b) and elsewhere (Sadras, 1996; Bange and Milroy, 2004). The development of this relationship was possible because the temperature range observed for these sowing months was greater than could be generated reliably over the first square to first flower phase in temperate latitudes (~30o) or in the tropical wet season. Low temperatures have reduced RUE in peanut (Bell et al., 1992), sorghum (Hammer and Vanderlip, 1989) and maize (Andrade et al., 1993). The effect of lower temperatures on RUE would be expected to occur in cotton via their effect on photosynthesis (Peng and Krieg, 1991).

Fig. 6: The Effect of Average Temperature on RUE for A) Upland Cultivars and B) Pima S7 between First Square and First Flower. RUE Was Calculated for Each Sowing Date from the Linear Regression between Accumulated Biomass and Intercepted PAR. Fitted Lines, their Equations and Regression

Coefficients are Shown. Where ** = P < 0.01, Tav = Average Temperature between First Square and First Flower. From Yeates et al. (2010b)

Low temperatures are more likely to reduce RUE when cotton is sown in May or June because the greatest proportion of growth up to early flowering at these sowing dates coincides with the cooler months of June and July (Yeates et al., 2010a). This observation appears to be supported by the generally lower biomass (flowering and maturity) of the May and June sowings (Yeates et al., 2010b). For these experiments, monthly temperatures were near (± 2oC) the long-term average (Yeates et al., 2010a), hence the RUE for cotton sown in April could also be reduced in seasons with lower than average minima during flowering.

For the upland cultivars the range in RUE measured here was similar to elsewhere using the glucose equivalent (GE) and dry weight only methods of calculating biomass (Sadras, 1996; Bange and Milroy, 2004; Rosenthal and Gerik, 1991) but lower than the peak values of 3 g/MJ/m2 reported by Milroy and Bange (2003), where leaf nitrogen concentration was high. The RUE for Pima S7 (Gossypium barbadense) of 1.2 to 2.3 g (GE) / MJ (Fig. 4.2) is the first for this species.

0.0

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20 21 22 23 24 25 26 27

RU

E (g

MJ

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A Upland RUE=0.223*Tav-3.43 R2=0.53**

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20 21 22 23 24 25 26 27Average Temperature (Co)

RU

E (g

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-1)

BPima S7 RUE=0.265*Tav-4.01 R2=0.86**


Yeates et al. (2010c) found fibre length and strength at the highest yielding March and April sowings were low to marginal compared with market preference values. This was due to cool temperatures during fibre development. The cultivar differences observed here suggest wider screening may identify Gossypium hirsutum cultivars with suitable fibre length and strength in these conditions. The commercial prospects for Gossypium barbadense are doubtful unless longer and stronger fibre types are identified.

CROP MANAGEMENT IMPLICATIONS

The emphasis in crop monitoring will need to shift from measuring and ensuring high P1 boll retention and then protecting these bolls because of their high contribution to yield in temperate climates, to monitoring all fruiting positions to ensure the production of new fruiting sites is sufficient to permit yield compensation when needed.

Achieving a balance between yield compensation via fruiting sites toward the top and outside of the plant and appropriate vegetative growth will require careful management. Over use of growth regulators or insufficient irrigation or nutrient deficiency will inhibit compensatory growth and reduce yield. On the other hand a luxurious supply of water and or nutrients combined with an insufficient amount of growth regulator could lead to excessive or ‘rank’ vegetative growth. Concurrent growth regulator research has shown that avoidance of high doses during fruiting site production is essential to permit compensation via the production of additional fruiting sites (Yeates et al., 2002b). Concurrent studies (S.J. Yeates; J. Moulden, AGWA unpublished data) found lowering of plant density to assist the growth of these fruiting sites is unlikely to be an option because it only increased the ratio of out side bolls to top bolls.

ACKNOWLEDGEMENT

The financial support from the Australian Cotton Cooperative Research Centre made this research possible. I would also like to thank Dr Greg Constable CSIRO for his cotton physiology expertise, Stewart Addison, Geoff Strickland and their staff from Agriculture WA for entomological assistance, Warren Muller of CSIRO-MIS for statistical assistance and Kellie Cooper of CSIRO for HVI analysis.

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for Helicoverpa spp. Management - In: Swanepoel A., (Ed.), Proceedings of the World Cotton Research Conference-3: Cotton Production for the new millennium. 9-13 March 2003, Cape Town, South Africa, pp. 905-912.

[4] Bange, M.P. and Milroy, S.P., (2004) – Growth and partitioning of diverse cotton cultivars - Field Crops Res., 87: 73-87. [5] Bell, M.J., Wright, G.C. and Hammer, G.L., (1992) – Night Temperature Affects Radiation-Use Efficiency in Peanut - Crop

Sci., 32: 1329-1335. [6] Constable, G.A. and Shaw, A.J. (1988) – Temperature Requirements for Cotton – Agfact ,P5.3.5, Department of

Agriculture NSW, Australia. [7] Constable, G.A. (1991) – Mapping the Production and Survival of Fruit on Field Grown Cotton - Agron. J., 83: 374-378 [8] Cook, L.J. and Russell, J.S. (1983) – The Climate of seven CSIRO Field Stations in Northern Australia - Division of

Tropical Crops and Pastures, Technical Paper No. 25, CSIRO, Australia. [9] Fritschi, F.B., Roberts, B.A., Travis, R.L., Rains, D.W. and Hutmacher, R.B. (2003) – Response of Irrigated Acala and

Pima S7 Cotton to Nitrogen Fertilisation: Growth, Dry Matter Partitioning and Yield - Agron. J., 95: 133-146. [10] Gipson, J.R. and Ray, L.L. (1970) – Temperature-cultivar Interrelationships in Cotton. 1. Boll and Fiber Development -.

Cotton Grow. Rev., 47: 257-271. [11] Hammer, G.L. and Vanderlip, R.L., (1989) – Genotype-by-environment Interaction in Grain Sorghum. I. Effects of

Temperature on Radiation use Efficiency - Crop Sci., 29: 370-376. [12] Hearn, A.B., (1975) - Ord Valley Cotton Crop: Development of a Technology- Cotton Grow. Rev., 52: 77-102. [13] Hearn, A.B., (1994) - OZCOT: A Simulation Model for Cotton Crop Management, - Agric. Sys., 44: 257 – 299. [14] Hearn, A.B. (1995) – Cotton in the Kimberley: A Note on Varieties For Tropical Winter Production - Unpublished report

for The Australian Cotton CRC, ‘Myall Vale’ Wee Waa Rd Narrabri NSW, Australia, 2p.


[15] Hutmacher, R.B., Travis, R.L., Rains, D.W., Vargas, R.N., Roberts, B.A., Weir, B.L., Wright, S.D., Munk, D.S., Marsh, B.H., Keely, M.P., Fritschi, F.B., Munier, D.J., Nichols, R.L. and Delgado, R., (2004) - Response of Recent Acala Cotton Cultivars to Variable Nitrogen Rates in the San Joaquin Valley of California- Agron. J., 96: 48-62.

[16] ICAC, (2002) – Cotton world statistics - Bulletin of the International Cotton Advisory Committee, September 2002. www.icac.org.com.

[17] Kennedy, C.W. and Hutchinson, R.L. (2001) – Cotton Growth and Development Under Different tillage systems - Crop Sci., 41: 1162-1168.

[18] Kerby, T.A. and Hake, K. D. (1996) – Monitoring Cotton’s Growth- In: Hake, S.J., Kerby, T.A., Hake, K.D., (Eds), Cotton Production Manual. University of California, Oakland, pp. 335-355.

[19] Milroy, S.P. and Bange, M.P. (2003) – Nitrogen and Light Responses for Cotton Photosynthesis and Implications for Crop Growth- Crop Sci., 43: 904-913

[20] NLWRA. (2001) – National Heritage Trust, Australian Agriculture Assessment 2001, Volume 1. National Land and Water Resources Audit, p 8-9, Commonwealth of Australia. ISBN 0 624 37129 6, (www.nlwra.gov.au/atlas).

[21] Peng, S. and Krieg, D.R. (1991) – Single leaf and crop photosynthesis response to leaf age in cotton- Agron. J., 83: 704-708.

[22] Rosenthal, W.D. and Gerik, T.J. (1991) – Radiation use efficiency among cotton cultivars- Agron. J., 83: 655-658. [23] Sadras, V.O. (1996) – Cotton Response to Simulated Insect Damage: Radiation-Use Efficiency, Crop Architecture and Leaf

Nitrogen Content as Affected by Loss Of Reproductive Organs- Field Crops Res., 48: 199-208. [24] Stern, W.R. (1965) – The Seasonal Growth Characteristics of Irrigated Cotton in A Dry Monsoonal Environment- Aust. J.

Agric. Res., 16: 347-66. [25] Strickland, G.R., Yeates, S.J., Fitt, G.P., Constable, G.A. and Addision, S.J. (1998) - Prospects for a Sustainable Cotton

Industry in Tropical Australia Using Novel Crop and Pest Management In: Gilliam F.M., Kechagia, U., Xanthopoulos, F. Tsaliki E. (Eds). Proceedings, World Cotton Conference-2, New Frontiers in Cotton Research, Athens, Greece, September 7-11, 1998, pp850-857.

[26] Strickland, G.R., Annells, A.J. and Ward, A.L. (2003) – Assessing the Feasibility for Cotton in Tropical Australia: Research for the Development of Sustainable Pest Management Systems - In: Swanepoel, A. (Ed.), Proceedings of the World Cotton Research Conference-3: Cotton Production for the new millennium. 9-13 March 2003, Cape Town, South Africa, pp. 975-986.

[27] Thomson, N.J. (1965) – Cotton Cultivar Trials in the Ord Valley North Western Australia. 3. The Effect of Time of Sowing on Cultivars of Different Maturity- Empire Cotton Grow. Rev., 42: 263-278.

[28] Toms, W.J. (1963) – Commercial Cotton Growing in the Ord River Project- J. Dept. Agric., West Aust. 4(4): 754. [29] Wall, G.W., Amthor, J.S. and Kimball, B.A. (1994) – COTCO2: A Cotton Growth Simulation Model for Global Change-

Agric. For. Meteorol., 70: 289-342. [30] Yeates, S.J. (2001) – Cotton Research and Development Issues in Northern Australia: a Review and Scoping Strudy- The

Australian Cotton CRC, ‘Myall Vale’ Wee Waa Rd Narrabri NSW, Australia, August 2001, p6-11, http://www.cotton.crc.org.au/ Publicat/ NthIssue.htm.

[31] Yeates S.J., Lawn R.J., Adkins, S.A. (2000) – Predicting Weather Damage of Mungbean Seed in Tropical Australia. I. Relation between seed quality, weather and reproductive development- Aust. J. Agric. Res., 51: 637-48.

[32] Yeates S., Strickland, G., Murti, S., Wood, B. and Mcleod, I. (2002) - Is There a Future for Cotton in Northern Australia? - In: Field to Fashion, Proceedings 11th Australian Cotton Conference, Brisbane, 13-15 August, 2002 12 pp. Australian Cotton Growers Research Association.

[33] Yeates, S.J., Constable, G.A. and McCumstie, T. (2002b) - Developing management options for mepiquat chloride in tropical winter season cotton- Field Crops Res., 74: 217-230.

[34] Yeates, S.J., Constable, G.A. and McCumstie, T. (2010a). Irrigated Cotton in the Tropical Dry Season. I. Yield, its Components and Crop Development- Field Crops Res., 116: 278-289

[35] Yeates, S.J., Constable, G.A. and McCumstie, T. (2010b) – Irrigated cotton in the tropical dry season. II. Biomass accumulation, partitioning and RUE - Field Crops Res., 116: 290-299.

[36] Yeates, S.J., Constable, G.A. and McCumstie, T. (2010c) – Irrigated Cotton in the Tropical Dry Season. III. Predicting the Impact of Temperature and Cultivar on Fibre Quality - Field Crops Res., 116: 300-307.

[37] Yeates S.J, Kahl M. and Dougall A.J. (2011) – The impact of variable cold minimum temperatures on cotton flower retention, boll growth and yield recovery - Journal of Cotton Science (Forthcoming)

Which Carbon Footprint Tool for the Cotton Supply Chain

F. Visser, P. Dargush and C. Smith

School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia E-mail: [email protected]

Abstract—Appropriate and user-friendly tools are required by members of the textile supply chain, including farmers, to measure the carbon footprint levels of raw materials and products. The purpose of the study is therefore to identify the most appropriate carbon footprint calculator to be used by cotton suppliers to meet the emerging sustainability measurement requirements from brand owners and retailers in the textile supply chain.

The textile sector has adopted a Life Cycle Analysis (LCA) approach to evaluate the sustainability levels of raw materials. Currently there is interest in the development of standards and methods that are suitable for the calculation of the carbon footprint levels of products in particular.

The first step in terms of the methodology was to identify a range of applicable carbon footprint calculators that may be used by industry for a cotton crop. Nine calculators were identified and they were analysed following an outcome-based approach.The next phase comprised of an actual case study on an irrigated cotton farm in the Goondiwindi district in Australia where the field data was gathered to populate the nine calculators. The reporting framework was developed that incorporates both on-farm and off-farm emission sources and that is also aligned to a Life Cycle Analysis (LCA) approach. The results show that total nett carbon footprint for the same cotton from the same farm varies from 828 kg CO2 e/ha to 4703 kg CO2 e/ha according to the different calculators. It appears that that there are big differences in the methodologies that are being applied, especially for soil carbon emissions, and results even vary for common ‘standardised’ emission indicators like fuel use. We conclude there is a compelling need for an internationally standardised format and methodology for crop-level carbon footprint calculations for the textile supply chain. Although these are all ‘reputable’ CFP tools, applied to the same farm data, they generate vastly different results. These variations in outcomes are mainly due to differences in structure and methodologies applied. Further research needs to be undertaken to apply process-based models to validate the accuracy of soil emission results from CFP calculators.

INTRODUCTION

Appropriate and user-friendly tools are required by members of the textile supply chain, including farmers, to measure the carbon footprint levels of raw materials and products. The purpose of the study is therefore to identify the most appropriate tool to meet the emerging sustainability measurement requirements from brand owners and retailers in the textile supply chain, in particular for GHG emissions.

For the purposes of this study a product’s carbon footprint or GHG inventory is defined as the total greenhouse gas emissions caused directly or indirectly by a product, in this case raw cotton up to the farm gate (Russell, 2011)

Considerable interest currently exists in the determination of the carbon footprint levels of products, as borne out by:

• The new GHG Product Accounting and Reporting Standard by The Greenhouse Gas Protocol Initiative published in September 2011(World Resources Institute & World Business Council for Sustainable Development 2011),

• The new ISO 14067 Standard, Carbon Footprints of Products, to be published in 2012 (PCF World Forum 2011),

• The current review of the PAS 2050:2008 Standard (British Standards Institution 2011), which regulates the assessment of GHG emissions of goods and services,

55


• And the working paper issued by the World Resources Institute in January 2011 regarding proposed accounting and reporting steps for GHG inventories for agricultural products (Russell 2011).

WHY THE INTEREST IN PRODUCT CARBON FOOTPRINT (CFP) ANALYSES?

There appears to be four main reasons:

Global Warming/Climate Change

One can only manage that what you can measure, and a number of standards and protocols have been developed internationally to guide the quantification of GHG’s in order to mitigate its effect on global warming and climate change. The initial focus was at national level and organisational or industry level (The Greenhouse Protocol Initiative, 2010) but has now developed to products and the quantification of their contribution to GHG emissions.

Economic and Legal Considerations

Various programmes have been launched at regional, national or global levels to either penalise or encourage GHG emitters to reduce their levels, or to support projects or programmes that have the ability to sequester carbon from the atmosphere. A case in point in Australia is the proposed Clean Energy Bill (Department of Climate Change and Energy Efficiency, 2011b) that will levy a carbon price / tax of $23 ton CO2e against certain categories of emitters. On the supporting side is the Carbon Farming Initiative or Carbon Credits Bill 2011 (Department of Climate Change and Energy Efficiency, 2011a) that will enable farmers to sell offsets or abatements generated under certain conditions or practices. There may also be legal requirements whereby products have to declare their carbon footprints as is now being introduced in France (The Economist, 2011).

Product Differentiation

The quantification and labelling of a product’s carbon footprint has become an established from of differentiation in the market place. As of April 2010 there were 5500 carbon footprint labels in the United Kingdom (Planet Ark, 2010). Among retailers Tesco took the lead in food carbon footprint labels, and the first labels were introduced in their stores in 2007. The first product was a box of ‘Simple’ instant Quaker Oats, which was soon followed by a packet of Walkers Crisps and a range of drinks from Innocent Smoothies (Miller, 2008; Environmental Leader, 2010). Authorities have stepped in to regulate and standardise the introduction of such schemes, in particular in France and Japan, South Korea and Thailand (The Economist, 2011). The measurement of carbon footprints may also be a requirement to meet certain product certification standards.

Part of Sustainability Measurement

Various initiatives and programs have been launched in the recent past to develop sets of appropriate indicators the measure the sustainability levels of products in accordance with the three social, economic and environmental pillars. It is desirable that a scientifically grounded and outcomes based methodology be applied, as opposed to a criteria or practice based methodology (Von Wirén-Lehr, 2001). Of specific relevance to the former is the ongoing Stewardship Index for Specialty Crops (SISC) in the United States where a second round of pilot studies are being planned to test the crop-level metrics that have been developed (Stewardship Index, 2011).

An indication of the different sustainability indicators that have been developed by the main certification programs or standards in the textile supply chain is given in Table 1.

Which Carbon Footprint Tool for the Cotton Supply Chain 325

TABLE 1: SUSTAINABILITY INDICATORS AND STANDARDS IN THE TEXTILE SUPPLY CHAIN

As can be seen above, greenhouse gas emission levels / pollution is one of the key environmental

indicators to be measured in an outcomes based product sustainability calculation. GHG calculations are closely aligned with energy use as the majority of emissions sources are energy use derived, like fuel and electricity consumption for example.

At this point no calculators or mechanisms are available whereby the actual complete sustainability level or rating of a product can be determined by industry on an outcomes or scientific basis. The only outcomes-based sustainability calculator for crops that has been released is the ongoing Fieldprint Calculator from the Field to Market initiative in the US. The five indicators that have been incorporated at this stage are land use, soil loss, irrigation water use, energy use and climate impact (GHG). Further indicators in the pipeline include soil quality, water quality, biodiversity and the required social and economic indicators (The Keystone Alliance for Sustainable Agriculture, 2010). In terms of the Stewardship Index mentioned above, the first calculators that will be trialed are for water use efficiency, soil health, nutrient use and energy use (Stewardship Index, 2011).

Eco Index

Sustainable Apparel Coalition

Patagonia Chron.

ULE 880

BCI Cradle to Cradle

Blue Sign

Oeko-Tex 1000

GOTS

Land Use Intensity *

Water Use & Quality * * * * *

Waste * * * *

Biodiversity * *

Chem. Tox. – People * * *

Chem. Tox. – Environm. * * *

Energy Use * * * * *

GHG * * *

Social / Labour * * * *

Non –Renewables

Pollution

Sust. Governance *

Env. Management * * *

Work Force *

Customers/Suppliers *

Community *

Pesticides *

Soil Health *

Product Quality * *

Product Safety * *

Resource Productivity *

Consumer safety *

Air Pollution * *

Water Pollution * * *

Worker H & S * *

Noise Pollution *

Child Labour *

Chemical Inputs *

Storage, Pack. & Transport

*

Chemical Residues *

Tech. Specifications *


Such practical tools or mechanisms are required by industry to calculate and integrate the outcomes from the various indicators and metrics due to the complexity involved and to provide scientific integrity and rigor to the results. Supply agreements could potentially be negotiated on the basis of such outcomes, and consequently be contested as well.

Greenhouse Gasses (GHG), and closely aligned energy use, are the only indicators for which a number of such tools or calculators have been developed. The purpose of the study is to identify the most appropriate carbon footprint calculator to be used by cotton suppliers to meet the emerging sustainability measurement requirements from brand owners and retailers in the textile supply chain.

CARBON FOOTPRINT CALCULATORS

A number of carbon footprint calculators have been developed for agriculture. The majority of these are designed to generate farm-level results, but generally these can be adapted to provide crop-level outputs by only providing the actual inputs of the crop in question.

Classification of Calculators

There are a number of ways in which CFP calculators can be classified for comparison purposes. The IPCC Good Practice Guidelines distinguishes between three levels of complexity, and corresponding accuracy, for such methodologies:

Tier 1: Simple, emission factor-based approach, where emissions are calculated by multiplying activity data by an appropriate emission factor. Tier 1 emission factors are international or regional defaults.

Tier 2: More specific emission factors or more refined empirical estimation methodologies.

Tier 3: Dynamic bio-geophysical simulation models using multi-year time series and context-specific parameterization (IPCC National Greenhouse Gas Inventories, 2006).

This is not deemed to be an appropriate classification as it appears that the calculators apply a combination of tier level approaches depending on the different emission sources and data availability.

Calculators can also be classified in terms of transparency, methodology, effectiveness of communication, source data, and structure (Kim and Neff, 2009). For the purpose of this study an outcomes-based approach was adopted, in line with recent global trends in product sustainability measurement. Accordingly the outcomes from a range of CFP calculators are compared by populating them with the same, site specific set of data inputs. The aforementioned potential differences are de facto dealt with in analyzing of the variances in the results.

Structure Requirements for CFP Calculators

As mentioned above, in calculating a product’s carbon footprint one needs to include emissions caused directly as well as indirectly by that product. The Greenhouse Gas Protocol defines direct and indirect emissions as follows:

• Direct GHG emissions are emissions from sources that are owned or controlled by the reporting entity (farm).

• Indirect GHG emissions are emissions that are a consequence of the activities of the reporting entity, but occur at sources owned or controlled by another entity (The Greenhouse Gas Protocol Initiative, 2001).

The GHG Protocol further categorizes these direct and indirect emissions into three broad scopes:

• Scope 1: All direct GHG emissions (On farm) • Scope 2: Indirect GHG emissions from consumption of purchased electricity, heat or steam.


• Scope 3: Other indirect emissions, such as the extraction and production of purchased materials) (The Greenhouse Gas Protocol Initiative, 2001)

The following calculator format (Table-2) comparison framework was developed based on the above guidelines and the type of results generated by the calculators in question.

As will be shown, the less advanced calculators do not address all the emission sources indicated above. It is not mandatory to include Scope 3 emissions under the Corporate GHG Standard, but encouraged where these are deemed to be ‘significant’. The calculators generally do include bio-chemical production as the level of use of fertilisers and chemicals are directly under the control of farm management and an integral part of the carbon footprint of a crop, and certainly ‘significant’ in terms of emission levels, particularly in the case of fertilizers (Russell, 2011). The emissions associated with the manufacture of capital equipment (tractors, etc.) is also a Scope 3 emission and could also be included in principle, but none of the calculators make provision for this.

TABLE 2: THE CALCULATOR FORMAT BASED ON GHG PROTOCOL INITIATIVE

Type OF Emissions Scope Comments/ Examples Direct On-Farm Emissions Scope 1 Energy Usage Mainly fuel use like diesel, petrol, gas Soil / Crop Residue Emissions Soil N2O Related to fertilizer application Nett Soil CO2 Related to whole carbon-cycle in soil Crop Residue / Stubble Related to tillage practices Nett Soil / Residue Emissions Nett On-Farm Emissions Indirect Off-Farm Emissions Purchased Electricity Scope 2 Bio-Chemical Production Scope 3 Fertilizer Production Embedded energy in fertilizer production Chemicals Production Incl. herbicide, fungicide and pesticide Total Off-Farm Emissions Total (Nett) Emissions

The one area where there is still a considerable amount of uncertainty and variation in the types of methodologies applied is the level of carbon soil emissions, resulting predominantly from the different types of cultivation practices adopted by farmers. The main ‘gap’ in the methodology is that no standard has been developed for this type of emission as opposed to most of the other emission categories, and appears to be a main contributor to variations in footprint results. “But the emissions from non-mechanical sources, such as soils and livestock, are more challenging. Consensus best practices for dealing with these challenges do not yet exist. The GHG Protocol intends to develop a consensus-based GHG accounting and reporting protocol for the agriculture sector.” (World Resources Institute, 2011) A working paper has been issued to this effect (Russell 2011). Types of methodologies being applied to calculate carbon soil emissions is presented in Table 3.

Most of the calculators appear to be based on empirical models (Table 4) although it is not always possible to ascertain which methodology the results are based on. The above mentioned framework is also aligned with Life Cycle Analysis (LCA) methodology. The life cycle under investigation is the production process on the farm and the product boundary is raw cotton at the farm gate. The upstream / indirect emission sources taken into consideration are the generation of electricity and the emissions involved in the manufacture of the fertilisers and chemicals. The environmental impact category is restricted to GHG emissions.


TABLE 3: METHODOLOGIES APPLIED TO SOIL CARBON EMISSION

Methodology Description Comments Field Measurements Including lab measurements of soil density Time-consuming Expensive Emission Factors-based Models e.g., tonnes CO2 emitted per ha wheat farming Simple and inexpensive Low accuracy

Empirical Models From statistical links between GHG data and management factors

Medium accuracy Inexpensive, most common

Processed-based Models Mathematical simulation models of CO2 emission / fixation process

Expensive, time consuming Too complex for industry tool

Source: (Russell 2011)

TABLE 4: CFP CALCULATORS THEIR DESCRIPTION AND SOURCE

Calculator Description Source Lincoln Uni – Carbon Calc (NZ) Mainly mixed farming tool http://www2.lincoln.ac.nz/carboncalculator/ Cotton GHG Calculator (Aus) For Cotton Ind., mainly farm-level tool http://www.isr.qut.edu.au/tools/index.jsp FarmGas GHG Calculator (Aus) Broad acre GHG tool, no energy calc. http://farmgas.farminstitute.org.au Fieldprint Calculator (US) For commodity crops – sust. calculator http://www.fieldtomarket.org/fieldprint-

calculator/ Cool Farm Tool (UK) Funded by Unilever, global application http://www.unilever.com/aboutus/supplier/su

stainablesourcing/tools/ US Cropland Calculator (US) GIS-based empirical method http://surf.kbs.msu.edu/ghgcalculator/ Veggie Carbon Calculator (Aus) For hort. but can apply to broad acre http://www.vegiecarbontool.com/ NCEA – USQ (Aus) USQ case study on cotton energy use http://energy.ncea.biz/ ACSC – USQ (Aus) USQ case study on cotton GHG levels http://eprints.usq.edu.au/7101/

In the context of this study it is important for any proposed CFP methodology to also adhere to the principles of LCA methodology. The reason being that LCA has been identified as the mechanism (specifically PAS 2050) that will be used to measure product sustainability levels in the textile supply chain by major initiatives like the Eco Index, who has the support of the major brand owners and retailers like Wal-Mart, Marks & Spencer, Levi’s, Adidas, Nike, etc (Eco Index, 2011). Since 2008, PAS 2050 has been the only LCA-based product carbon footprint standard. As far as agriculture is concerned it specifically states that:

• “Changes in the carbon content of soils, either emissions or sequestration, shall be excluded from the assessment of GHG emissions under this PAS”

• “Agricultural emissions include emissions from fertilizers (e.g. N2O emissions arising from the application of nitrogen fertilizer and emissions arising from the production of the fertilizer)”

• “The GHG emissions arising from the production of capital goods used in the life cycle of a product shall be excluded from the assessment of GHG emissions.” (Carbon Trust, 2008)

Therefore, in terms of a comparison between the above framework and LCA principles it appears that it is correct to include both soil N2O emissions and the production emissions of fertilizers, and that it is order not to incorporate emissions from capitals goods manufacturing. Although PAS 2050 does not require the inclusion of soil carbon emissions, it is very significant that the Greenhouse Gas Protocol’s “Product Accounting and Reporting Standard” released recently in September 2011 actually includes ‘carbon stock’ and defines it as “CO2 emissions and removals resulting from a carbon stock change occurring within and between land use categories” (World Resources Institute & World Business Council for Sustainable Development 2011). It will therefore be important to note what the new ISO 14067 Standard on the Carbon Footprints of Products will stipulate when it is released in 2012 (PCF World Forum, 2011).

LCA calculations rely predominantly on various databases for emission data, but these do not typically provide reliable data on soil carbon emissions, as can be explained by the above. As indicated this may need to be improved in future and carbon footprint calculators can play a valuable role in this regard. Soil carbon emissions can represent a significant part of a carbon footprint of a crop, be it as a debit or a credit. It is furthermore important to be able to quantify potential carbon sequestration levels in the interests of promoting conservation agriculture and for farmers to be able to take up potential offset opportunities with programs like the Carbon Framing Initiative (Department of Climate Change and Energy Efficiency, 2011a).


Selection of CFP Calculators

The following criteria were applied in selecting suitable CFP calculators from those applicable to agriculture:

• Recognised and currently in use in agriculture in Australia • Internationally applied in regions where major textile brand owners and retailers are based • Will generate results in a format that will be applicable to LCA evaluation mechanisms in the

textile supply chain, in particular the Eco Index, • Applicable as an industry–based tool • Based on a level of complexity that takes into account most of the emissions sources indicated

above

DATA COLLECTION

The focus of the study is to identify the most appropriate CFP calculator to determine the carbon footprint levels of Australian cotton, mainly to meet sustainability requirements in the textile supply chain. The aim is therefore not to provide a detailed analysis of the actual emission levels cotton production but to identify the most appropriate methodology to achieve this. Nevertheless the cotton farm used for the study was selected on the basis of being fairly representative of Australian cotton farms with regards to soil types, climatic conditions and farming practices.

The procedure was to populate all 9 calculators with the same base data from the farm for the 2010/11 growing season. The calculators differ markedly in the type of information required and a questionnaire was developed to cover the complete range of data required, which were grouped in to the following categories: energy use, fertiliser use, crop chemicals, cultivation practice, residue management, crop rotation, land use change, soil characteristics, and water use / irrigation. Some of the common base data applied are listed in Table 5.

TABLE 5: COMMON BASE DATA

Indicator Value Area cotton production 300 ha Diesel use / crop 40 000 l Tillage practice Reduced till N application rate 220 N / ha Chemicals applications 3 per crop Rainfall 650 mm / a Soil bulk density 1.7g / cm3 Soil carbon content 0.5 % Soil clay fraction 55 % Dry land / Irrigated Irrigated

RESULTS

The methodology to calculate an organization or product’s carbon footprint has been standardized by the initial Greenhouse Gas Protocol, the IPCC guidelines and subsequently the ISO 14067-1 standard, upon which the Carbon Trust Standard and PAS 2050 are also based. Therefore in particular for ‘standard’ items like fuel use and fertilizer applications one would expect that the same base data would render similar results across the calculators. This is not the case. As mentioned earlier the one emission source in agriculture that there has not been a ‘standard’ methodology for is soil emissions, and these results vary considerably as can be expected.

Table 6 below shows the carbon footprint results by emission source and calculator from the selected cotton farm, with the corresponding chart below:


TABLE 6: COMPARATIVE ANALYSIS (KG CO2 / HA OF VARIOUS GHG CALCULATORS

Lincoln Uni. Cotton GHG Farm Gas Field Print Cool Farm US Cropland Veggie Carb NCEA - USQ ACSC - USQSource of Emissions Carbon Calc Calc - QUT GHG Calc Calculator Tool Calc - MSU Calculator Cotton Cotton Direct On-Farm Emissions Energy / Fuel CO2 821 360 141 357 90 360 460 322 Soil / Crop Residue Emissions Soil N2O Emissions 1241 77 828 1665 950 3650 1336 300 2244 Crop Residue / Stubble 66 Nett Soil CO2 Emissions 158 -1141 60 Nett Soil / Residue Emissions 1241 77 828 1823 -125 3710 1336 300 2244 Nett On-Farm Emissions 2062 437 828 1964 232 3800 1696 760 2566 Indirect Off-Farm Emissions Purchased Electricity 1109 1137 555 1329 Bio-Chemical Production Fertiliser Production 844 480 766 572 80 804 803 Chemicals Production 58 62 925 615 Total Off-Farm Emissions 844 480 0 824 1743 80 1137 2284 2747 TOTAL (Nett) EMISSIONS 2906 917 828 2788 1975 3880 2833 3044 5313

Model: 300 ha cotton farm in Goondiwindi - Reduced till production, 220 Kg N per hectare, 3 pesticide applications per crop, 40 000 L diesel per crop, no petrol.

It is immediately apparent and alarming that the calculators generate such a considerable difference in results from the same cropping data. This is mainly due to two reasons: a) Inconsistencies is structure, not all the emissions sources are taken into consideration although these are fairly well defined in the standards, apart from soil carbon emissions; b) Differences in methodologies, again especially in the case of soil emissions, although there are considerable variances even for ‘standardized’ outcomes like fuel use. It must be borne in mind that the calculators in question have all been developed by reputable institutions and their outcomes could all therefore be regarded as ‘credible’ and viable alternatives for farmers to use. Therefore if a cotton ginner includes carbon footprint levels as a buying criteria for a premium grade/ price, farmer A could qualify with a level of 917 kg CO2 e/kg by using the Cotton GHG calculator, whereas farmer B may be excluded at a level of 3880 kg CO2 e/ha by using the US Cropland calculator although they may be following exactly the same farming practices under the same conditions. The specific emission components are dealt with in more detail below.

Energy/ Fuel Use

Fig. 1: Energy used in the Different Calculators

CO2


Fuel use is one of the ‘standardised’ emission indicators. In this case the farm used 40 000L of diesel to grow the 300 hectares of cotton during the 2010 / 2011 planting season (Fig.1). NCEA(USQ) and ACSC(USQ) are actually two cotton case studies that determined the GHG emission levels of two cotton operations. As such these studies can be used as benchmarks for the current operation, and when converted to 300 hectares the NCEA operation would have used 47 700L and the ACSC operation 31 620L which also explains these differences in emission levels. As a standardised item one can actually determine what the emission levels should be for this amount of diesel use by applying the IPCC energy content and emission factors for mobile combustion diesel usage. This equates to 360 kg CO2 e/ha as indicated by the line on the above graph (2006 IPCC National Greenhouse Gas Inventories 2006). It is therefore unclear what the results for the Lincoln University, Fieldprint and US Cropland calculators could be based on.

Soil N2O Emissions

Fig. 2: Nitrous Oxide Emissions

Soil nitrous oxide emissions mainly result from fertiliser applications (Fig. 2). As most of the calculators do not do a separate calculation for soil carbon emissions, some of those emissions may be included under this section, as well as emissions from stubble decay, although these are less significant. If one applies the IPCC default of 1.25% direct N2O emissions resulting from N fertiliser applications, it equates to 341 kg CO2 e/ha. As mentioned although some of calculators may add in some emissions from crop residues and soil carbon, it still remains uncertain what the amount of 3 650 kg CO2 e/ha in the case of the US Cropland calculator could be based on. In the case of the two USQ case studies, the emissions were not measured but derived from industry estimates and reports.

Crop Residue / Stubble

The Cool Farm Tool is the only calculator that specifies the emissions resulting from different crop residue management practices. In the case of this study the ‘reduced till’ option was selected where more than 30% of the crop residue is left on the surface with the next planting. The corresponding emissions are 66 kg CO2 e/ha. The Grains Greenhouse Accounting calculator from the University of Melbourne estimates this to be 48 kg CO2 e/ha (Echard, 2009).

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Purchased Electricity

There is relatively little variance in the emission results from purchased electricity for the four calculators that quantify this. Purchased electricity is a mandatory Scope 2 emission and should be part of any product CFP calculation. In the case of agriculture it is mainly used for irrigation and can make up a considerable part of a farm’s total carbon footprint, as is the case with these results where it is comparable to fuel use.

Although one may expect that is another fairly ‘standardised’ indicator, emission factors vary by country and according to the look-up table in the Cool farm Tool it varies from 0.0 in the case of Norway to 2.5 kg CO2 /kWh for India. In the case of Australia there are regional variances from 0.23 for Tasmania to 1.23 kg CO2 /kWh for Victoria (NGERS (Measurement) Technical Guidelines, 2010).

Bio-Chemical Production

Fertilisers

Fig. 5: Emissions Associated with Fertilizer Production

Most of the calculators account for the Scope 3 emissions associated with the manufacture of fertilisers (Fig 5). The FarmGas calculator is mainly focussed on emissions from farming practices and in the case of the Veggie Carbon calculator it only deals with Scope 1 and 2 emissions, although PAS 2050 requires the inclusion of these emissions.

Again the results are relatively consistent compared to some of the other emission indicators. In the interest of comparability, Urea was used as the standard fertiliser in the calculators as it is commonly used in Australia along with anhydrous ammonia. However, these calculations do not entail a simple application of a ‘standard’ conversion rate for fertilisers in general, as the different fertilisers attract different emission factors. Only the Cool Farm Tool and the Fieldprint calculators make a specific distinction between the different types of fertilisers used. The table below shows the differences in emission levels between using 220 kg N/ha of Urea and Anhydrous Ammonia for soil N2O emissions and those from the production of the fertilisers:

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To gauge to total effect of fertiliser applications one should combine the soil N2O levels with the fertiliser production emissions, which makes it the single biggest contributing factor to a crop’s total carbon footprint. As irrigated cotton in mainly grown in fairly arid environments, electricity (or diesel) use for irrigation will always be an important factor, but can also be affected by rainfall as the growing and rainfall seasons often coincide for cotton.

One of the major contributions that CFP calculators can make is to show the potential benefits that conservation farming practices can have to buffer or offset other emissions associated with growing a crop, as clearly shown above. It was mentioned earlier than in the event of a no-till practice; the carbon credit will have increased to 1750 kg’s CO2 per hectare according to the calculator’s estimate. One will expect fuel use to be a significant contributor in capital intensive farming operations, as is the case with cotton farming in Australia.

CONCLUSION

The following conclusions are drawn from the results:

• There is a compelling need for an internationally standardised format and methodology for crop-level carbon footprint (CFP) calculations for the textile supply chain

• CFP tools, applied to the same farm data, generated vastly different results. Variations in outcomes are mainly due to differences in structure and methodologies applied.

• CFP calculators can play a valuable role in quantifying and acknowledging the positive outcomes that conservation farming practices can have on mitigating the emission levels of farm products

• No common methodology exists in particular for the calculation of soil carbon emissions • Further research needs to be undertaken to apply process-based models to validate the accuracy

of soil emission results from CFP calculators.

REFERENCES [1] IPCC (2006) - IPCC National Greenhouse Gas Inventories 2006, 2006 IPCC Guidelines for National Greenhouse Gas

Inventories, 2006 IPCC National Greenhouse Gas Inventories Programme and Institute for Global Environmental Strategies, Japan

[2] British Standards Institution. (2011) - PAS 2050 Research Report, British Standards Institution, London. [3] Carbon Trust. (2008) - PAS 2050: 2008 - Specification for the assessment of the life cycle greenhouse gas emissions of

goods and services, Carbon Trust, London. [4] DoCCaE Efficiency (2011a) - Carbon Credits Bill. Department of Climate Change and Energy Efficiency, Australian

Government. [5] DoCCaE Efficiency (2011b) - Clean Energy Bill Department of Climate Change and Energy Efficiency, Australian

Government. [6] Echard, R. (2009) - Grains Greenhouse Accounting, University of Melbourne, viewed 21 September 2011. [7] Eco Index. (2011) - Eco Index Apparel Tool, Sustainable Apparel Coalition, viewed 15 September 2011,

<http://www.ecoindexbeta.org/>. [8] Environmental Leader. (2010) - 72% of UK consumers: Give us carbon footprint labels on food, Environmental Leader,

viewed 12 January 2010. [9] Johnson, J.M.F., Franzluebbers, A.J., Weyers, S.L. and Reicosky, D.C. (2007) - Agricultural opportunities to mitigate

greenhouse gas emissions, Environmental Poll., 150: 107–24. [10] Kim, B, Neff, R. (2009). Measurement and communication of greenhouse gas emissions from U.S. food consumption via

carbon calculators, Ecol. Econ., 69: 186–96. [11] Miller, V. (2008) - Weighing in, Guardian, 8 February 2008, p. 2. [12] NGERS (2010) - NGERS (Measurement) Technical Guidelines, Department of Climate Change and Energy Efficiency,

Canberra. [13] PCF World Forum. (2011) - ISO 14067 - Carbon Footprint of Products, PCF World Forum, viewed 12 September 2011. [14] Planet Ark. (2010) - Carbon Reduction Label - Frequently asked questions, Planet Ark, viewed 26 July 2010. [15] Russell, S. (2011) - Corporate Greenhouse Gas Inventories for the Agricultural Sector: Proposed accounting and reporting

steps, World Resources Institute, Washington. [16] Stewardship Index. (2011) - Stewardship Index for Speciality Crops, Stewardship Index, viewed 27 July 2010. [17] The Carbon Trust. (2010) - Footprint Expert, Carbon Trust, viewed 24 May 2010. [18] The Economist. (2011) - Following the footprints, The Economist, vol. Q2 2011.


[19] The Greenhouse Gas Protocol Initiative. (2001) - Corporate Accounting and Reporting Standard, World Resources Institute & World Business Council for Sustainable Development, Washington.

[20] The Greenhouse Protocol Initiative. (2010) - Measurement of GHG emissions, The Greenhouse Protocol Initiative, viewed 24 August 2010.

[21] The Keystone Alliance for Sustainable Agriculture. (2010) - Field to Market, The Keystone Center, Keystone. [22] Von Wirén-Lehr, S. (2001). Sustainability in agriculture - An evaluation of principal goal-oriented concepts to close the gap

between theory and practice, Agric. Ecosys. Environ., 84: 115–129. [23] World Resources Institute & World Business Council for Sustainable Development. (2011) - The Greenhouse Gas Protocol

Initiative: Product accounting and reporting standard, World Resources Institute & World Business Council for Sustainable Development, Washington.

Studies on the Seed Cotton Yield, Growth and Yield Contributing Characters of New Bt Cotton Hybrids

under Varied Agronomic Manipulations

Kulvir Singh, Harmandeep Singh, R.K. Gumber and Pankaj Rathore

PAU, Regional Research Station, Faridkot–151 203, Punjab

Abstract—A field experiment was conducted at Punjab Agricultural University, Regional Research Station, Faridkot during Kharif 2010 in a split plot design. The treatments comprised three Bt cotton hybrids (MRC 7361, Bioseed 6488 and RCH 134) in main, two plant geometries (67.5 x 75 cm & 67.5 x 90 cm) in sub and three nutrient levels {100% recommended dose (RD) (150kg N, 30kg P/ha); 125% RD & 150% RD} in sub-sub plots replicated thrice. The soil of the experimental site was loamy textured with slightly alkaline pH (8.7), normal EC (0.40 m mhos/cm), low in OC (0.48%), low in available P (7.5 Kg/ha) but high in available K (750 Kg/ha). Among Bt hybrids, MRC 7361 had heavier and more bolls per plant and recorded significantly higher seed cotton yield (2795 kg/ha) than Bioseed 6488 (2217 kg/ha) and RCH 134 (1897 kg/ha). Further leaf area index (5.91) was greater in case of MRC 7361 which contributed to improved yield contributing parameters and significantly higher seed cotton yield (SCY). Ginning outturn (%), lint and seed yield was also found to be significantly better for MRC 7361 than Bioseed 6488 and RCH 134 hybrids. Fertilizer use efficiency (4.97 kg SCY/kg fertilizer applied) and water productivity (532.4 g/m3) were significantly higher in MRC 7361 than the other two hybrids RCH 134 had the least values for LAI (3.80), FUE (3.24 kg SCY/kg fertilizer applied) and water productivity (361.3 g/m3). Highest net returns of Rs. 88971/ha were observed with MRC 7361 than Bioseed 6488 (Rs. 67301/ha) and RCH 134 (Rs. 55287/ha). Plant geometry had no significant effect on yield. However, number of bolls/plant was significantly better under wider than closer spacing. Among nutrient levels, 150% RD fertilizer produced 35.3 % higher seed cotton yield (2591 kg/ha) than 100% RD fertilizer (1914 kg/ha) though it was at par with 125% RD fertilizer (2404 kg/ha). Significant improvement in number of monopods and sympods per plant coupled with higher LAI (5.16) with 150% RD was mainly responsible for higher yield than the 100% RD fertilizer level. Thus, significantly highest net returns (Rs. 80912/ha) were recorded for 150% than 100% RD (Rs. 56351) and was similar to 125% RD.

Keywords: Bt cotton, hybrids, Seed cotton yield, Leaf Area Index, Plant geometry, Nutrient levels, Fertilizer use efficiency and Water productivity.

INTRODUCTION

Cotton is an important cash crop in South-western zone of Punjab grown during Kharif season. It plays a significant role in state economy and occupied an area of 511 thousand ha with a production of 2006 thousand bales and lint yield of 667 kg/ha during 2009-10 (Anonymous, 2011). Owing to fast growth and better performance in terms of high seed cotton yield, Bt cotton hybrids have become popular among farmers and is presently covering about 90 per cent of the cotton acreage in the state. Apart from improvement in yield, Bt cotton hybrids have also lowered the pest incidence and reduced environmental pollution by limited use of insecticides. Development of much efficient hybrids has a crucial role to play for increasing the seed cotton yield in the present era of Bt cotton. The productivity of cotton can be improved by identifying high yielding genotypes coupled with suitable agronomic practices. Among different agronomic manipulations, selection of potential genotypes having high yield contributing characters, optimum plant stand and suitable fertilization plays crucial role in increasing the productivity of cotton. Therefore, the present study was undertaken to identify high yielding Bt cotton hybrids and identify their agronomic requirements under the site specific agro-climatic conditions of Faridkot.


The field experiment was conducted during the Kharif 2010 at Research farm of Punjab Agricultural University, Regional Research Station, Faridkot (30040’N and 74044’E), a typical representative of South-Western zone (Zone IV) in Punjab. The soil of the experimental field was loamy in texture, alkaline in soil reaction (pH 8.7), normal EC (0.40 m mhos/cm), low in OC (0.48%), low in available P (7.5 kg/ha) but high in available K (750 kg/ha). The experiment conducted in a split split plot design

56

Studies on the Seed Cotton Yield, Growth and Yield Contributing Characters of New Bt Cotton Hybrids 339

comprised of three Bt hybrids (MRC 7361, Bioseed 6488 and RCH 134) in main plots, two plant geometries (67.5 x 75 cm and 67.5 x 90 cm) in sub plots and three nutrient levels { 100% of RD (150kg N, 30kg P/ha), 125% of RD (187.5kg N, 37.5kg P/ha) and 150% of RD (225 kg N, 45kg P/ha)} in sub-sub plots replicated thrice. The crop was sown in first fortnight of May by dibbling 3-4 seeds/hill which were later thinned to one seedling per hill. Full dose of phosphorus was applied before sowing while nitrogen dose was given in two splits, first half at the time of thinning and the remaining at flowering stage. Other production and protection measures were applied as per recommendations given by Punjab Agricultural University. The data were analyzed statistically following procedures outlined by Cheema and Singh (1991).


Effect of Bt Cotton Hybrids TABLE 1: GROWTH, YIELD AND YIELD CONTRIBUTING CHARACTERS OF BT COTTON HYBRIDS UNDER DIFFERENT PLANT GEOMETRY AND NUTRIENT LEVELS

Treatments Plant Height (cm)

LAI Monopods/Plant Sympods/Plant

Bolls/Plant

Boll Weight

(g)

Seed Cotton Yield

(Kg/ha)

Lint Yield

(kg/ha)

Biomass (q/ha)

GOT(%)

Bt Cotton Hybrids Bioseed 6488 121.5 4.97 2.56 21.8 54.6 3.91 2217 748.5 122.4 33.7 RCH 134 107.0 3.80 2.46 22.2 47.8 4.50 1897 649.8 118.2 34.1 MRC 7361 147.2 5.91 3.08 29.5 57.8 5.41 2795 978.4 162.9 35.0 CD (0.05) 8.1 0.42 0.35 0.92 4.3 0.23 257 97.3 12.0 0.67

Plant Geometry 67.5 x 75 cm 127.4

5.05 2.66 23.2 51.3 4.50 2218 761.1 135.1 34.2

67.5 x 90 cm 123.1 4.74

2.74 25.8 55.5 4.71 2387 823.3 133.9 34.4

CD (0.05) 3.7 NS NS 2.39 3.2 NS NS NS NS NS Nutrient Levels

100 % RDF 121.3 4.42 2.51 22.3 51.7 4.46 1914 660.7 130.5 34.4 125 % RDF 125.1 5.11 2.79 24.8 53.6 4.62 2404 828.7 133.9 34.3 150 % RDF 129.4 5.16 2.80 26.4 55.0 4.75 2591 887.2 139.1 34.2 CD (0.05) 5.0 0.39 0.21 2.41 NS NS 184 61.4 NS NS

The tested Bt cotton hybrids differed significantly for seed cotton yield (SCY) as well as for yield contributing characters. Data in the Table 1 indicated that MRC 7361 produced significantly higher seed cotton yield as compared to Bioseed 6488 and RCH 134 due to more number of monopods, sympods and bolls per plant. Nehra et al. (2006) at Ganganagar, Rajasthan reported significant differences for SCY among the different Bt cotton hybrids due to more number of bolls per plant. Singh et al. (2007) also found significant differences for seed cotton yield among the tested Bt cotton hybrids. The present investigations indicated an increase in yield of MRC 7361 over Bioseed 6488 and RCH 134 to the tune of 26.1 and 47.3 percent, respectively. Among Bt hybrids, MRC 7361 had heavier bolls than the rest. Besides smaller boll size, RCH 134 Bt had significantly fewer bolls per plant compared to the other hybrids. The biomass production was significantly higher for MRC 7361 as compared to other hybrids was probable due to taller plants and vigorous growing habit. As a result of this, MRC 7361 exhibited significantly higher leaf area index than Bioseed 6488 and RCH 134 (Table-2). All of the above said factors further culminated to give higher fertilizer use efficiency (FUE) and water productivity (WP) indices for MRC 7361 than the rest of the hybrids. Ginning out turn (%) for MRC 7361 was found to be significantly better than Bioseed 6488 and RCH 134. Similarly, lint and seed yield was also significantly superior in MRC 7361 than Bioseed 6488 and RCH 134. Significantly higher net returns of Rs. 88971/ha were recorded with MRC 7361 as compared to Bioseed 6488 (Rs. 67301/ha) and RCH 134 (Rs. 55287/ha). Higher B: C ratio also indicated superiority of MRC 7361 (3.88) than rest of the hybrids.


Effect of Plant Geometries

The data given in Table 1 suggest that plant geometries did not effect SCY significantly. Brar et al. (2008) also reported non-significant differences for seed cotton yield with respect to plant geometries. However in present investigations, the number of sympods and boll number per plant was significantly more under wider geometry i.e 67.5 x 90 cm but converse was true for plant height. Narayana et al (2007) reported that the number of bolls/plant was significantly better under wider plant geometry of 120 x 60cm than the closer geometry (90 x60 and 90 x 90cm). However, non-significant differences were observed for LAI, GOT, Lint and seed yield (Table-1) and FUE and WP.

TABLE 2: EFFECT OF DIFFERENT TREATMENTS ON FERTILIZER USE EFFICIENCY (FUE) AND WATER PRODUCTIVITY (WP)

Treatments LAI FUE (kg Seed Cotton Yield/ kg Fert. Applied)

WP (g/m3)

Bt Cotton Hybrids Bioseed 6488 4.97 3.75 422.3 RCH 134 3.80 3.24 361.3 MRC 7361 5.91 4.90 532.4 CD (0.05) 0.42 0.43 48.9 Nutrient levels (150kg N, 30kg P/ha) 100 % RD 4.42 4.09 364.7 (187.5kg N, 37.5kg P/ha) 125 % RD 5.11 4.11 457.9 (225 Kg N, 45kg P/ha) 150 % RD 5.16 3.69 493.5 CD (0.05) 0.39 0.32 35.2

Effect of Nutrient Levels

Among nutrient levels, 150% RD produced 35.3 percent higher yield than 100 % RD fertilizer and was equivalent to 125% RD (Table-1) significantly, increased number of monopods and sympods per plant were also observed with 150% RD than the recommended fertilizer level. Consequently, better indices for LAI (5.16) as compared to 100% RD nutrient levels might have reflected its positive effect on SCY. Singh and Rathore (2007) also reported significant improvement in SCY with increased N level. However, in the present studies, number of bolls per plant, boll weight and dry matter was not affected by nutrient levels. Lint and seed yield under high N levels was significantly better than the 100 % RD level (Table 1). Significantly higher net returns under high nutrition levels (Rs. 74297-80912) than 100% RD (Rs. 56351/ha) and consequently higher B:C ratio (3.34-3.54) also supported the favour of higher nutrient applications. These results are in conformity with the findings of Biradar et al. (2010) who reported higher returns with enhanced level of nutrition (150% RD) than recommended (100% RD) level of nutrition.

REFERENCES [1] Anonymous. (2011) - Punjab Agricultural University, Ludhiana. Package of practices for Kharif Crops. [2] Brar, J.S., B.S. Sidhu, K.S. Sekhon and G.S. Buttar (2008) - Response of Bt cotton (Gossypium hirsutum L.) to plant

geometry and nutrient combinations in sandy loam soil. J. Cotton Res. & Dev., 22(1): 59–61. [3] Biradar, V., Rao, S. and Hosamani, V. (2010) - Economics of late sown Bt cotton (Gossypium hirsutum L.) as influenced by

different plant spacing, fertilizer levels and NAA applications under irrigation. International J. of Agric. Sci., 6(1): 196–198.

[4] Cheema, H.S. and Singh, B. (1991) - Software: Statistical package CPCS-1 Deptt. of Math, Statistics and Physics; Punjab Agricultural University, Ludhiana.

[5] Narayana, E., Hema, K., Srinivasulu, K., Prasad, N.V.V.S.D. and Rao, N.H.P. (2007) - Agronomic evaluation of Gossypium hirsutum hybrids for varied spacings and nitrogen levels in vertisols under rainfed conditions. J. Cotton Res. & Dev. 21(2): 197–200.

[6] Nehra, P.L., Kumawat, P.D. and Nehra, K.C. (2006) - Response of promising Gossypium hirsutum hybrids to fertilizer levels in irrigated north western plain zone of Rajasthan. J. Cotton Res. & Dev. 20(1): 87–88.

[7] Singh, K. and Rathore, P. (2007) - Effect of different spacings and nitrogen levels on growth and yield attributes of American cotton (Gossypium hirsutum L.) genotypes. J. Cotton Res. & Dev. 21(2): 178–179.

[8] Singh, K., Jindal, V., Singh, V. and Rathore, P. (2007) - Performance of Bt cotton hybrids under different geometrical arrangements. J. Cotton Res. & Dev. 21(1): 41–44.

Evaluation of Cotton Genotypes for High Density Planting Systems on Rainfed Vertisols

of Central India

M.V. Venugopalan, A.H. Prakash, K.R. Kranthi, Rachana Deshmukh, M.S. Yadav and N.R. Tandulkar

Central Institute for Cotton Research, P.O. Box. No. 2, Shankarnagar, Nagpur–440010, India

Abstract—Promising genotypes of both Gossypium hirsutum and Gossypium arboreum were evaluated for their performance at higher planting densities in a field trial under rainfed conditions at the Central Institute for Cotton Research, Nagpur, India during 2010-2011. The central region is characterized by a hot, dry sub-humid climate and the soil was a Typic Haplustert, low in organic C and available P but rich in available K. Five genotypes of G. hirsutum (Anjali, CCH 724, NISC 50, PKV 081 and CNH 120MB) evaluated at 5 crop densities viz. 60x30, 45x20, 45x13.5, 30x30 and 30x20 cm. Similarly five genotypes of G. arboreum (AKA 07, CINA 404, PA 255, PA 08, JK 5) were also evaluated at 5 crop densities viz. 60x15, 45x13.5, 45x10, 30x20 and 30x15 cm. Results indicated that among G. hirsutum, genotype PKV 081 was found most suitable for high density planting at 30x20 cm and 45x13.6 cm (both corresponding to 166000 plants/ha) in terms of yield, morphology, earliness, tolerance to sucking pests and boll weight. In G. arboreum, CINA 404 gave the highest yield of 2174 kg seed cotton/ha at 45x10 cm (222000 plants/ha). However, other high yielding genotypes viz., JK5 (1842 kg seed cotton/ha) and AKA07 (1815 kg seed cotton/ha) were dwarf and more compact than CINA 404. Across genotypes, a spacing of 45x13.5 cm for G. hirsutum and 45x10 cm for G. arboreum was optimum for planting short compact plant types. In both G. hirsutum and G. arboreum genotypes the boll weight and harvest index decreased with increasing plant density. The latter in turn, decreased nutrient utilization efficiency. Nevertheless, in all the genotypes of both species, the nutrient uptake efficiency and partial factor productivity for N increased with increase in planting density. The partial factor productivity of N ranged from 10.6 to 21.8 kg seed cotton/kg N applied and 18.3 to 22.8 seed cotton/kg N applied in G. hirsutum and G. arboreum genotypes respectively and PKV 081 and CINA 404 recorded the highest values.

INTRODUCTION

Manipulation of planting density, plant population and spatial arrangement of cotton plants continues to be topics of cotton research worldwide and India is no exception. It is widely accepted that increasing planting density is an option to increase yield or profits and also to improve input use efficiency. The availability and acceptance of effective alternate weed and insect pest management strategies and spindle type pickers has rekindled interest on high density cotton planting systems (Reddy et al., 2009). With increase in planting density the yield per unit area generally to an upper limit or optima later plateaus and ultimately declines. The optimum plant density under this parabolic relationship will depend upon the genotype characteristics, properties of soil, climatic parameters and management regime (Silvertooth et al., 1999). The earliness usually associated with high density planting makes this system suitable for rainfed Vertisols where the cotton crop invariably experiences terminal moisture stress.

Before the advent of cotton hybrid and the adoption of seed dibbling techniques, varieties of G. hirsutum and G. arboreum were planted at populations ranging from 55000 to 89000 plants/ha (Bonde, 1992). Cotton breeding efforts in India were not focused on developing plant types specially suited for high density planting (CICR, 2011). The present study was therefore formulated to evaluate promising genotypes of both G. hirsutum and G. arboreum on rainfed Vertisols with specific objectives of identifying genotypes amenable to high density planting and to optimize planting density/crop geometry for yield maximization under high density planting.


Separate field experiments were conducted under rainfed conditions during the kharif (monsoon) season of 2010-2011 at the experimental farm of Central Institute for Cotton Research, Nagpur. The site is a representative of Agro eco sub region 10.2 (hot dry sub-humid climate) dominated by Vertisols and

57


Vertic intergrades. As against a mean seasonal rainfall of 760 mm, the season received 1005 mm rainfall spread over 53 days. The soil at the experimental site was a Typic Haplustert slightly alkaline in reaction low in organic C (0.39%) low in Olsen’s available P (10.3 kg/ha) and high in available K (630 kg/ha). Two sets of experiments were laid out in split plot design with 3 replications. In the first set the main plot comprised of 5 crop geometries viz., 60x30 cm (55,555 plants/ha), 30x30 cm, 45x20 cm (both 11,111 plants/ha), 30x20 cm and 45x13.5 cm (both 1,66,666 plants/ha) and 5 genotypes of G. hirsutum viz., Anjali, CNH 120MB, PKV 081, NISC 50 and CCH 724 as sub-plots. In the second set, the main plot comprised of 5 crop geometries viz., 60x15 cm (11,111 plants/ha), 45x13.5 cm, 30x20 cm (both 1,66,666 plants/ha), 45x10 cm and 30x15 cm (both 2,22000 plants/ha and 5 genotypes – AKA 7, CINA 404, PA 255, PA 08 and JK 5 formed the sub plots. The crop was sown on and raised using the recommended package of practices.

At maturity, five plants per plot were harvested, separated into leaves, stem, carpels, seed, lint and other floral parts and dried to calculate dry matter yield. N content in various plant parts was estimated following wet and digestion and total N uptake was calculated. Seed cotton yield was recorded for the entire net plot. Nitrogen Use Efficiency (NUE or N response or Partial factor productivity of N) and its components, N uptake efficiency (N Upt E) and N utilization efficiency (N Uti E), were calculated on a kg kg-1 basis according to Moll et al. (1982), where: NUE = N Upt E x N Uti E and in turn N Upt E = Nt/ Na and N Uti E= SCY/ Nt, where Nt, Na and SCY denote N uptake, N applied and seed cotton yield, respectively. Data was subjected to ANOVA (Gomez and Gomez, 1984).


Yield and Yield Attributes

Hirsutum

The effect of spacing, genotype and genotype x spacing interaction on seed cotton yield, number of bolls/m2 and boll weight was significant (Table 1). Averaged across genotypes, the seed cotton yield was significantly higher in the spacings of 45x13.5 cm and 30x20 cm, both accommodating 166000 plants/ha. Jagannathan and Venkitaswamy (1996) also reported that dwarf compact genotypes responded favourably to a population of 1,11,000 plants/ha on Vertisols. Since within a given population both the crop geometries (spacings) produced similar yields, a rectangular spacing (45x13.5 cm or 45x20 cm) which facilities inter-culture was as good as a more square type geometry (30x20cm or 30x30 cm).

The highest mean yield (1203 kg/ha) was obtained with 45x13.5 cm spacing (166000 plants/ha) which in turn was 38% higher than that obtained with the recommended (60x30 cm spacing). Among the 5 genotypes PKV 081 was significantly superior to the other genotypes. Genotype x spacing interaction was also significant indicating that genotypes Anjali, PKV 081 and CCH 724 were more amenable to closer spacings i.e. higher planting densities (166000 plants/ha). Rossi et al., (2007) also reported significant genotype x spacing interaction in Spain.

TABLE 1 EFFECT OF PLANTING DENSITY ON YIELD AND YIELD ATTRIBUTES IN G. HIRSUTUM GENOTYPES

Spacing (cm) 60x30 45x20 30x 30 45x 13.5 30x20 Mean Population/ha 55000 111000 111000 166000 166000 Genotypes Seed cotton yield (kg/ha) Anjali 502 847 853 966 796 793 CNH120MB 1030 976 1138 1250 1289 1137 PKV 081 1200 1714 1418 1921 1967 1644 NISC 50 1056 890 1103 1016 1052 1023 CCH 724 679 843 681 864 835 781 Mean 893 1054 1039 1203 1188 CD at 5% Spacing 97.636 Genotypes 70.205 Interaction 170.710

Table 1 (Contd.)…

Evaluation of Cotton Genotypes for High Density Planting Systems on Rainfed Vertisols of Central India 343

…Table 1 Contd. Number of bolls/m2

Anjali 15.3 24.1 28.4 28.6 25.8 24.4 CNH120MB 34.2 45.0 53.7 82.0 70.1 57.0 PKV 081 34.5 56.1 58.0 98.7 65.5 62.6 NISC 50 28.1 27.1 32.1 42.4 36.0 33.1 CCH 724 15.3 37.0 41.3 31.4 43.3 33.7 Mean 25.7 37.9 42.7 56.6 48.1 CD at 5% Spacing 6.380 Genotypes 5.910 Interaction 13.414

Boll Weight (g)Anjali 3.27 3.52 3.21 3.33 2.74 3.22 CNH120MB 2.31 2.56 2.07 2.33 1.93 2.24 PKV 081 3.49 3.32 2.69 3.25 3.08 3.17 NISC 50 3.53 3.27 3.35 2.52 2.97 3.13 CCH 724 3.33 3.04 2.32 2.78 1.98 2.69 Mean 3.19 3.14 2.84 2.72 2.55 CD at 5% Spacing 0.167 Genotypes 0.133 Interaction 0.314

In all the genotypes the boll number per plant decreased with closer spacing due to greater inter-plant competition. However, the increase in the number of plants per unit area at closer spacing compensated for this decline and hence the boll number/m2 were significantly higher at all the closer spacings compared to the recommended (60x30 cm) spacing. Jost and Cothern (2000) also observed a decrease in boll number per plant but an increase in boll number per unit area at elevated populations.

The highest yielding cultivar, PKV 081 had the highest boll number/m2. The genotype x spacing interaction was also significant and unlike other genotypes PKV 081 produced almost thrice the number of bolls/m2 at 45x13.5 cm over the currently recommended spacing of 60x30 cm. In general the boll weight declined at closer spacing. There was significant variation among genotypes. Genotypes Anjali, PKV 081 and NISC 50 produced significantly bigger bolls than CNH 120MB and CCH 724.

Arboreum

All the genotypes of G. arboreum responded significantly to higher planting density (Table 2) and the mean yield increase the highest planting density (45x10 cm) was 45.4% over the recommended one (60x15 cm). Among the genotypes CINA 404 yielded significantly higher than all other genotypes but in terms of microbiological parameters (height, monopodia etc.) AKA 7 and JK 5 were more amenable to HDPS. Genotypes AKA7 and JK5 were more determinate and the boll bursting was uniform. Earlier (Belot and Salles 2010) suggested that termination in fruiting after the production of 5-7 bolls/plant is essential for the success of high density planting. Genotype x plant density interaction was also significant for yield. The yield increase at higher plant density was primarily due to more number of bolls/m2 since the effect of boll weight was not significant. Across genotypes the boll number/m2 was significantly higher at 45x10 cm spacing. Among genotypes, CINA 404 produced significantly larger bolls over the other genotypes. Low boll weight is an inherent limitation in G. arboreum cotton and an increase in boll weight may directly contribute to an increase in yield.

Nutrient use Efficiency Parameters

Hirsutum

The N response ratio or N use efficiency or partial factor productivity is the simplest measure of efficiency. It provides a measure of crop yield per unit of N applied. In different genotypes of N ranged from 10.6 to 21.8 and PKV 081 recorded the highest values (Table 3). In general there was a gradual increase in N response from 11.9 kg/ha at 55000 plants/ha (60x30 cm) to 13.9-14.1 at 111000 plants/ha and 20.4-23.9 at 166000 plants/ha. This N response is a product of N uptake efficiency (N uptake/N applied) and N utilization efficiency (seed cotton yield/N uptake). Among genotypes, the N uptake efficiency was the highest for PKV 081. The N uptake efficiency as distinctly higher at 111000 plants/ha (0.91-1.00) and 55000 plants/ha (0.91-1.00) compared to 0.73 at the recommended 55000 plants/ha. N utilization efficiency declined slightly with increase in planting density.


TABLE 2: EFFECT OF PLANTING DENSITY ON YIELD AND YIELD ATTRIBUTES IN G. ARBOREUM GENOTYPES

Spacing (cm) 60x15 45x13.5 30x 20 45x 10 30x15 Mean Population/ha 111000 166000 166000 222000 222000 Genotypes Seed cotton yield (kg/ha) AKA-07 1163 1349 1456 1815 1419 1441 CINA-404 1430 1550 1610 2173 1772 1707 PA-255 1259 1595 1349 1625 1226 1411 PA-08 1090 1318 1455 1509 1479 1370 JK-5 1223 1452 1151 1842 1734 1480 Mean 1233 1453 1404 1793 1526 CD at 5% Spacing 71.104 Genotypes 73.617 Interaction 164.612

Number of bolls/m2 AKA-07 50.0 58.1 67.3 76.5 65.4 63.4 CINA-404 59.8 71.0 75.6 82.6 75.2 72.9 PA-255 54.3 77.5 77.5 77.7 71.5 71.7 PA-08 55.5 62.7 58.1 93.7 66.6 67.3 JK-5 46.9 70.1 63.6 117.2 86.3 76.8 Mean 53.3 67.9 68.4 89.5 73.0 CD at 5% Spacing 7.870 Genotypes 8.628 Interaction 19.293

Boll weight (g)AKA-07 2.29 2.37 2.43 2.24 2.07 2.28 CINA-404 2.47 2.45 2.67 2.22 2.41 2.44 PA-255 1.91 1.98 1.80 2.48 2.31 2.10 PA-08 2.30 2.05 2.12 2.05 1.72 2.05 JK-5 2.41 2.10 1.73 2.11 2.07 2.08 Mean 2.28 2.19 2.15 2.22 2.12 CD at 5% Spacing NS Genotypes 0.173 Interaction NS

N utilization efficiency is a product of HI and N biomass production efficiency (Ortiz – Monasterio et al., 1997). In the present study there is a reduction in HI at higher planting density which would have decreased the N utilization efficiency. Darawsheh et al., (2009) also observed that the partitioning of assimilates to reproductive parts was lower in narrow row high plant density systems. For improved N utilization efficiency genotypes which maintain high HI even at increased planting densities would be ideal. Genotype with higher harvest index viz., PKV 081 also had a higher N utilization efficiency.

TABLE 3: EFFECT OF PLANTING DENSITY ON N USE EFFICIENCY PARAMETERS IN G. HIRSUTUM GENOTYPES

Spacing (cm) 60x30 45x20 30x 30 45x 13.5 30x20 Mean Population/ha 55000 111000 111000 166000 166000 Genotypes N use Efficiency (N Response Ratio or Partial Factor Productivity) Anjali 6.69 11.29 12.88 11.37 10.61 10.57 CNH120MB 13.73 13.01 16.67 15.19 17.19 15.16 PKV 081 16.00 22.85 25.61 18.91 25.61 21.80 NISC 50 14.08 11.87 13.55 14.71 13.55 13.55 CCH 724 9.05 11.24 9.08 9.08 11.52 9.99 Mean 11.91 14.05 15.56 13.85 15.70

N uptake efficiencyAnjali 0.46 0.84 0.78 0.72 0.73 0.70 CNH120MB 0.84 0.88 1.02 1.04 1.14 0.98 PKV 081 0.84 1.19 1.34 1.06 1.37 1.16 NISC 50 0.90 0.91 1.10 1.04 1.04 1.00 CCH 724 0.63 0.73 0.75 0.67 0.73 0.70 Mean 0.73 0.91 1.00 0.91 1.00

N Utilization EfficiencyAnjali 14.655 13.47 16.55 15.815 14.54 15.01 CNH120MB 16.366 14.75 16.27 14.557 15.14 15.42 PKV 081 19.006 19.22 19.08 17.841 18.73 18.78 NISC 50 15.697 13.03 12.36 14.102 12.98 13.63 CCH 724 14.392 15.36 12.11 13.619 15.84 14.26 Mean 16.02 15.17 15.27 15.19 15.45

Evaluation of Cotton Genotypes for High Density Planting Systems on Rainfed Vertisols of Central India 345

Arboreum

The N response ratio or partial factor productivity of N ranged from 18.3 to 22.8 and CINA 404 recorded the highest values (Table 4). Typical value of Nitrogen use efficiency under Indian condition was 5.7 kg lint 1kg N corresponding to around 17.1 kg seed cotton/kg N (ICAC 2008). In general there was a gradual increase in N response from 16.4 kg/ha at 111000 plants/ha (60x15 cm) to 19.4-19.5 at 55000 plants/ha and 20.4-23.9 at 222000 plants/ha. This N response is a product of N uptake efficiency (N uptake/N applied) and N utilization efficiency (seed cotton yield/N uptake). Where there was little variation (range 0.95 to 1.18) in N uptake efficiency among genotypes, the uptake efficiency as distinctly higher at 166000 plants/ha (0.97-1.09) and 222000 plants/ha (1.13-1.17) compared to 0.81 at the recommended 111000 plants/ha. Genotype with higher harvest index viz., AKA 7 and JK 5 had higher N utilization efficiency. Increase in population to 222000 plants/ha tended to decrease the N utilization efficiency.

TABLE 4: EFFECT OF PLANTING DENSITY ON N USE EFFICIENCY PARAMETERS IN G. ARBOREUM GENOTYPES

Spacing (cm) 60x15 45x13.5 30x 20 45x 10 30x15 Mean Population/ha 111000 166000 166000 222000 222000 Genotypes N use Efficiency (N Response Ratio or Partial Factor Productivity) AKA-07 15.51 17.99 24.20 19.41 18.92 19.21 CINA-404 19.05 20.67 28.99 21.47 23.63 22.76 PA-255 16.79 21.27 21.67 17.99 16.35 18.81 PA-08 14.53 17.57 20.12 19.40 19.72 18.27 JK-5 16.31 19.36 24.56 19.36 23.12 20.54 Mean 16.44 19.37 23.91 19.53 20.35

N uptake EfficiencyAKA-07 0.75 0.92 1.06 1.01 1.02 0.95 CINA-404 0.99 0.98 1.40 1.22 1.30 1.18 PA-255 0.82 1.01 1.14 1.28 1.07 1.06 PA-08 0.75 0.97 1.05 1.08 1.07 0.98 JK-5 0.73 0.94 1.17 0.85 1.21 0.98 Mean 0.81 0.97 1.17 1.09 1.13

N utilization EfficiencyAKA-07 20.71 19.64 22.86 19.30 18.56 20.22 CINA-404 19.33 21.00 20.73 17.58 18.12 19.35 PA-255 20.60 21.03 18.98 14.01 15.27 17.98 PA-08 19.37 18.03 19.08 18.04 18.43 18.59 JK-5 22.39 20.61 20.93 22.85 19.12 21.18 Mean 20.48 20.06 20.52 18.36 17.90

CONCLUSION

On rainfed Vertisols, the genotype PKV 081 was found most suitable for high density planting system (HDPS) (166000 plants per ha) based on yield (1921 kg/ha), morphological features, earliness, tolerance to sucking pests and boll weight. In G. arboreum, on basis of yield, CINA 404 (2174 kg/ha) performed well under under HDPS (222000 plants/ha). However other high yielding genotypes viz., JK 5 (1842 kg/ha) and AKA 7 (1815 kg/ha) were dwarf and more compact than CINA 404. Across the genotypes the spacing of 45x13.5 cm (166000 plants/ha) was optimum for short compact types. Similar a spacing of 45x10 cm (222000 plants/ha) was optimum for short compact plant types of G.arboreum.

REFERENCES [1] Belot, J.L. and Salles, A.O. (2010) - Second-cycle cotton grown with high plant density in Mato Grosso, Brazil. The ICAC

Recorder 28(2): 9–15. [2] Bonde, W.C. (1992) - Achievements in cotton production technology. Edited by A.K. Basu and S.S. Narayanan, 1992.

Achievements of AICCIP (1967–92), 176 pp. [3] CICR (2011) - CICR vision 2030. Central Institute for Cotton Research, Nagpur.


[4] Darawsheh, M.K., Khah, E.M., Avivalakis, G., Chachalis, D. and Fatbardh Sallaku (2009) - Cotton row spacing and plant density cropping systems I. Effects on accumulation and partitioning of dry mass and LAI. Journal of food, Agriculture and Environment, Vol. 7 (3&4), July-October 2009.

[5] Gomez, K.A. and Gomez, A.A. (1984) - Statistical Procedures for Agricultural Research, 2nd edn. John Wiley, New York, USA.

[6] ICAC (2008) - Nitrogen fertilization in cotton. The ICAC Recorder. 26(1) 8–13. [7] Jagannathan, N.T. and Venkitaswamy, R. (1996) - Effect of plant density and nutrient levels of new cotton varieties.

Madras agric. J. 83(3): 159–161. [8] Jost, P.H. and Cothern, J.T. (2000) - Growth and yield comparisons of cotton planted in conventional and ultra-narrow row

spacings. Published in Crop Sci. 40: 430–435. [9] Moll, R.H., Kamprath, E.J., and Jackson, W.A. (1982) - Analysis and interpretation of factors which contribute to

efficiency of N utilization. Agron. J., 74: 562–564. [10] Ortiz-Monasterio, R.J.I., Sayre, K.D., Rajaram, S., McMahon, M. (1997) - Genetic progress in wheat yield and N use

efficiency under four N rates. Crop Sci., 37, 898–904. [11] Reddy, K.N., Burke, I.C., Boykin J.C. and Williford, R. (2009) - Narrow row cotton production under irrigated and non-

irrigated environment: plant population and lint yield. The Journal of Cotton Science 13: 48–55. [12] Rossi, J., Brajos, E. and Basce vanos, D. (2007) - Varietal response to ultra narrow row cotton in Spain. Paper 1772 Proc.

World Cotton Conference – 4, (Sept. 10-14, 2007). Lubbock, Texas. wcrc.confese.com/wcrc/2007/tech program/p 1772–HTM.

[13] Silvertooth, J.C., Edmisten, K.L., and McCarty, W.H. (1999) - Production practices. In C.W. Smith (ed.) Cotton: Origin, history, technology and production. John Wiley and Sons, New York. pp. 463–465.

Pruning and Detopping Studies in Bt-Cotton

S.S. Hallikeri

Associate Professor of Agronomy, Agricultural Research Station, Dharwad farm, Dharwad–580007, Karnataka, India

Abstract—Excess growth management is a challenging task in cotton because of its indeterminate growth habit. Failure of reproductive phase into seed cotton leads to a sudden spurt of vegetative growth. Various management strategies to minimize cotton growth are detopping, pruning of monopodials at their early period of 40-50 DAS and planting geometry Field studies were undertaken at ARS, Dharwad from 2007 to 2009 on medium deep black soils in a split-split-plot design with detopping at 80 DAS and no detopping as main plots, retaining of only one, two, three or all monopodia as sub-plots and planting geometry of 90x 30 cm, 90 x 60 cm as sub-sub-plots. Averaged over years, detopping had no significant effect on seed cotton yield (SCY) (2116 kg/ha) when compared with no detopping (2149 kg/ha). However, detopping reduced and number of sympodial branches. Further, pruning of monopodials, too had no effect on yield. Planting geometry with closer spacing of 90 x 30 cm produced significantly higher seed cotton yield (2264 kg/ha) than 90 x 60 cm (2001 kg/ha). Productivity per plant, number of bolls per plant and boll weight significantly increased at wider spacing due to reduced population levels. It is concluded that growth-reducing function like detopping can be adopted under excess growth situations without affecting productivity.

INTRODUCTION

Cotton, the ‘white gold' or the 'king of the fibre’, as it is often referred to, still holds its position high. World over, cotton is gradually assuming the status of a preferred fibre even for fashion fabrics. Cotton cultivation needs to be sustainable, offering livelihood security to millions of people in the country. Though release of Bt-cotton in the country increased the productivity of the crop several other production factors may help further upgrade the productivity. Some situations such as high soil fertility and irrigation water management practices lead to more of vegetative growth and resultant competition for natural resources. Thus adjacent plants are affected and reproductive phase gets delayed. Growth modification practices therefore become important by converting its phase of vegetative to reproductive growth. This can be achieved by detoping and pruning. Detopping is removal of top terminal portion after prominent vegetative growth stag. This may encourage growth of already formed sympodials as well as formation and development of fruiting bodies. Detopping may also help reduce sucking pests and bollworm infestation, as it avoids fresh growth out let which is unwanted at particular stage. Hence, detopping of cotton after required growth may result in booming of cotton production. In view of this hypothesis, field studies were conducted for 3 years to study the response of Bt-cotton when pruned and detopped under different planting patterns.


Field experiments were conducted for three years from 2007 to 2009 at Agriculture Research Station, University of Agricultural Sciences, Dharwad, Karnataka. Soils were medium deep black and neutral in their pH (7.6) with low available N, medium in available P and high exchangeable K. Experiment was laid out in split-split plot design with three replications. Main plots comprised detopping and no detopping with different levels of pruning of monopodials in the subplots. Two planting methods (90 x 30 and 90 x 60 cm) were in the sub-sub plots. Experiment was sown every year in the month of June–July. Recommended doses of 80:40:40 kg NPK was applied with half of the N and entire P & K as basal. The remaining N was applied 60 DAS. Detopping of top terminal portion was done at 80 DAS in the detopping treatments. Pruning of monopodials were undertaken at 50 DAS as per the treatments. Standard recommendations for plant protection, weed control, nutrient management were followed. Growth and yield parameters were recorded at the time of harvesting. Data on various parameters recorded from the field experiment were statistically analyzed using MSTAT-C programme and the level of significance used in ‘F’ test was at p=0.05.

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Effect of Detopping

Detopping of cotton at 80 DAS significantly affected the growth parameters viz., plant height and sympodial branches at various crop growth stages (Table-1). Detopping at 80 DAS significantly decreased plant height compared to no detopping. Decrease in plant height was due to termination of apical dominance (Brar et al., 2000 and Vekatakrishnan and Pothiraj, 1994). However, detopping had no effect on the production of monopodial branches. On the other hand, detopping reduced the number of sympodial branches at harvest when compared with no detopping. Effect of detopping was seen at both vertical and horizontal growth of cotton.

TABLE 1: EFFECT OF PRUNING AND DETOPPING ON GROWTH AND YIELD PARAMETERS OF COTTON (MEAN OF 2007 2008 AND 2009)

Treatments Plant ht. MonoPodia

Sym Podia

Bolls/ Plant Boll wt. Yield/ Plant Yield kg/ha

Main plot (D) Detopping at 80 DAS 87.0 1.48 14.1 27.2 3.89 98.4 2116 No Detopping 101.8 1.54 16.9 27.9 3.95 97.5 2149 S.Em. + 0.8 0.03 0.1 0.4 0.02 0.7 42 CD @ 5% 2.9 NS 0.3 NS NS NS NS Sub plot (P) Retaining of 1-monopodium 96.0 1.11 15.8 27.3 3.97 96.6 2098 Retaining of 2-monopodia 94.1 1.40 15.7 26.8 3.94 95.4 2129 Retaining of 3-monopodia 93.1 1.55 15.4 27.8 3.94 98.3 2096 Retaining all monopodia (check) 94.5 1.98 15.2 28.3 3.83 101.5 2209 S.Em. + 1.2 0.06 0.2 0.6 0.04 2.1 37 CD @ 5% NS 0.18 NS NS 0.1 NS NS Sub sub plot (S) 90 x 30 cm 95.8 1.45 15.3 23.0 3.87 80.3 2264 90 x 60 cm 93.0 1.57 15.7 32.1 3.97 115.6 2001 S.Em. + 0.9 0.05 0.1 0.4 0.03 1.5 26 CD @ 5% 2.4 NS 0.41 1.2 0.07 4.1 73 Interaction (DXPXS) NS NS * NS NS NS NS

Canopy modification by detopping of cotton had no influence on seed cotton yield (SCY) as compared to no detopping. Even though the plant was condensed vertically in the detopped treatment, yield per hectare was not affected. Similarly, yield parameters of cotton viz., yield per plant, boll weight, number of bolls per plant, number of green bolls per plant were also not significantly affected due to detopping. These results are similar to those observed by Brar et al. (2002) for cotton cultivars and Rao and Lakshminarayan (1985) for hybrids. This indicates that the cotton crop canopy can be modified by reducing its spread and height without reducing the productivity of Bt-cotton. Excess growth in cotton is a result of high fertility (N-fertilizers) and availability of soil moisture. The effects of detopping are more pronounced in longer duration cottons with fruiting period extending beyond 150-180 DAS. Beneficial effect of detopping may not be possible in cotton with less luxuriant growth (Rao and Lakshminarayan, 1985).

In years of heavy rainfall and under high fertility conditions, hybrid cotton attains unmanageable growth and vegetative crop canopy which has got adverse effect on reproductive phases of cotton directly and encourages more and more incidence of bollworms indirectly and it further results in ineffective pesticide spraying due to higher canopy levels. Application of higher quantity of nitrogenous fertilizers also can cause more vegetative growth and increases the competition for scarce inputs. Canopy management practices thus plays an important role and helps to encourage reproductive phase. As cotton is indeterminate in growth habit detopping of the plant after suitable vegetative stage (80 DAS) encourages the better growth of already developed sympodial branches (Brar et al., 2000) and disallowing the pest of cotton for feeding and egg laying (Singh and Sandhu, 1996).

Pruning and Detopping Studies in Bt-Cotton 349

Effect of Pruning of Monopodials

Unlike detopping pruning of monopodials to retain specific numbers did not significantly affect either plant height or sympodial numbers. Pruning of monopodial branches also had no significant on SCY. However, there was a yield reduction to an extent of 3.6 to 5.0 per cent when compared with no pruning (checks). Similar trend was also observed with yield per plant and number of bolls per plant. Objective of horizontal coverage of monopodials was checked thus allowing the higher populations per unit area. Hence pruning of monopodial branches did not offer any yield advantage in increase yield.

Planting Geometry Relations

Mean of three years data on productivity indicated that Bt-cotton can be accommodated at higher population levels (90x30 cm) when comparing with lower populations (90x60 cm) (Table-2). Closer spaced plants had fever bolls and lower per plant yield than the wide spaced plants. Yield per unit area in closer spaced treatments was greater compared to wider spaced treatments. Plant height significantly increased at 90x 30 cm but reduced it s sympodial numbers when compared with 90x60 cm.

TABLE 2: EFFECT OF PRUNING AND DETOPPING ON YIELD (KG/HA) OF COTTON (MEAN OF 2007 2008 AND 2009)

Treatments Main plot (D) Detopping at 80 DAS No detopping Mean of sub plots 90 x 30 cm 90 x 60 cm Mean 90 x 30 cm 90 x 60 cm Mean

Retaining of 1-monopodium 2178 1983 2080 2245 1984 2115 2098 Retaining of 2-monopodia 2273 2093 2183 2241 1908 2074 2129 Retaining of 3-monopodia 2207 1882 2045 2240 2052 2146 2096 Retaining all monopodia (check) 2307 2007 2157 2422 2099 2261 2209 Mean 2241 1991 2116 2287 2011 2149 Comparisons for means of S.Em. + CD @ 5% Main plot (D) 42 NS Sub plot (P) 37 NS Sub sub plot (S) 26 73 M X PX S 75 NS

The interaction effects were not significant. Detopping and no pruning at 90 X 30 cm spacing recorded the highest seed cotton yield (2422 kg/ha) compared to other treatment interactions. Data of this study indicates that either detopping or pruning of monopodia had no advantage with any of the planting geometry. Hence from the results it can be concluded that detopping had no advantage to produce higher yield. But growth can be checked with detopping without affecting yield. Pruning of monopodia had no advantage and some time reduced the seed cotton yield. Hence retention of all monopodia would be an ideal practice under rainfed conditions.

REFERENCES [1] Brar, A.S., Singh, A. and Singh, T. (2000) - Response of hybrid cotton (Gossypium hirsutum) to nitrogen and canopy

modification practices. Indian J. Agron., 45: 395–400. [2] Brar, A.S., Singh, N. and Deol, J.S. (2002) - Influence of plant spacing and growth modification practices in yield and its

attributing characters of two cotton cultivars (G. hirsutum). J. Research, Punjab Agriculture University, 39 (2): 181–183. [3] Singh, Jai and Sandhu, B.S. (1996) - Effect of detopping in cotton on the efficacy of chemical control of boll worms.

Journal of Indian Society of Cotton Improvement, 21: 56–57. [4] Rao, D.V.M. and Lakshminarayan (1985) - Effect of physical and chemical growth regulation of plant growth on the

development and yield of two cotton hybrids. Cotton Development Journal 15: 55–58. [5] Venkatakrishnan, A.S. and Pothiraj, P. (1994)-Effect of nutrient management, foliar spray and detopping on yield attributes

and seed cotton yield. Madras Agriculture Journal, 81 (9): 519–520.

Input use Efficiency, Productivity, Profitability and Sustainability of Bt Cotton Based Multi Tier

System with Nutrient Levels

K. Sankaranarayanan1, P. Nalayini2, C.S. Praharaj3 and N. Gopalakrishnan4

1,2Senior Scientists, Agronomy, Central Institute for Cotton Research, Regional Station, Coimbatore–641003

3Principal Scientist, Agronomy, Indian Institute of Pulses Research, Kanpur (UP) 4Assistant Director General (Commercial Crops),

Indian Council of Agricultural Research, New Delhi

Abstract—Field studies were conducted to assess the performance of Bt cotton based vegetables multi tier systems with different nutrient levels to intercrops during winter season (Aug-Feb) of 2007-08 and 2008-09 at Coimbatore, Tamil Nadu. The treatment combinations comprised of three Bt cotton based multi tier systems (Bt cotton + coriander + vegetable cowpea + cluster bean) (in addition to recommended level of nutrient to Bt cotton) combined with 50, 75 and 100 % of recommended levels of nutrient to intercrops. Bt cotton + radish + beet root + coriander and.Bt cotton + radish + cluster bean + beet root) were compared with sole Bt cotton. Pooled analysis of two years data revealed that crop growth characteristics, yield attributes, yield and quality parameters of Bt cotton were not significantly affected by multi-tier systems and nutrient levels. Seed cotton yield of ranged from 2565 to 2912 kg/ha, whereas sole cotton yield was 2593 kg /ha. The significantly highest LAI of 2.21, partial factor productivity (24.4 kg/kg) and economics nutrient use efficiency (1.80 kg /Rs) were achieved with multi tier system involving Bt cotton with coriander, vegetable cowpea and cluster bean and application of 50 % of nutrient to intercrops. The results on light interception (78.6%), weed smothering efficiency (43.8%), water use efficiency ( 90.8 kg/ha-cm), water productivity (Rs 20.9/M3), relative production efficiency (135.5%), gross return (Rs1, 40,270/ha), seed cotton equivalent yield (61.0 q/ha), land equivalent ratio (2.43), area time equivalent ratio (1.72), diversity index (3.03), uptake of nitrogen (183.4 kg/ha), phosphorus (36.3 kg/ha) and soil available nitrogen (192.4 kg/ha) were significantly highest with multi tier system involving Bt cotton with coriander, vegetable cowpea and cluster bean with 100 per cent nutrient to intercrops. However, the system is labour intensive, resulted on enhanced cost of cultivation; hence multi tier planting of Bt cotton + radish + beet root + coriander with application of 100 % nutrient to intercrops registered the significantly highest net profit (Rs 93, 098/ha), benefit cost ratio (3.23), per day profitablity (620.7) and relative economic efficiency (191%).

INTRODUCTION

In multiple cropping systems, use of resources like sunlight, nutrient and water is more efficient leading to increased biological diversity and higher production stability. Monocropping is exception while mixture (of species) is the rule of nature. Reviewing cropping system in South Asia, it was observed that intercropping is a well established practice covering over 12 m ha (Woodhead et al., 1994). India ranks first in world acreage (10.2 m ha) with almost 33 % of total cotton area. Since cotton is a crop of relatively longer duration, its slow initial growth offers a vast scope for cultivation of suitable vegetable intercrops. Our requirement on vegetables has increased to about 127.2 million tonnes to meet the nutritional requirement of an estimated 1200 million people expected by 2020-21. Although, the productivity levels of vegetables increased manifolds, it is not sufficient to feed ever increasing population as a result of increased demands. This will complicate the issue of price rise further leading to increased costs of vegetables. Thus, keeping in view of climatic vulnerability, market fluctuation of vegetable crops and better resources use, Bt cotton is chosen as a candidate crop and Bt cotton based system with multiple vegetable intercrops is aimed. The multi tier systems includes different proportion of short duration vegetables, make intensive cropping, hence recommended nutrient levels to sole Bt cotton may not be sufficient to multi tier cropping. Thus, additional nutrition to intercrops is needed to make multi tier systems sustainable.

59

Input use Efficiency, Productivity, Profitability and Sustainability of Bt Cotton Based Multi Tier System 351


A field trial was conducted during winter season (August to February) of 2007-08 and 2008-09 at Central Institute for Cotton Research, Regional Station, Coimbatore. The experimental soil was clay loam in texture, low in available N (214 kg/ha), medium in available P (16.8 kg P2O5 ha-1) and high in exchangeable K (812 kg K2O ha-1) with a pH of 8.5. The experiment was laid out in a randomized block design with three replications. The treatment combinations comprised of three Bt cotton based multi tier systems (with recommended level of nutrient to Bt cotton combined with 50, 75 and 100 % of recommended nutrient levels to intercrops. The three multi-tier system were S1.Bt cotton + coriander + vegetable cowpea + cluster bean (nutrient levels to intercrop, 11.0:21.8:6.4, 16.4:32.7:9.5 and 21.9:43.6:12.6 kg N, P2O5 & K2O/ha respectively for 50, 75 and 100%), S2. Bt cotton + radish + beet root + coriander (nutrient levels to intercrop, 10.7:29.6:17.2, 16.0:44.3:25.8 and 21.3:59.1:34.3kg N, P2O5 & K2O/ha respectively) and S3.Bt cotton + radish + cluster bean + beet root (nutrient levels to intercrop, 13.7:35.5:20.2, 20.5:53.3:30.2 and 27.3:71.0:40.3 kg N, P2O5 & K2O/ha, respectively) were compared with sole Bt cotton. Bt cotton (Gossypium hirsutum L.) hybrid ‘RCH–20 Bt’, coriander (Coriandrum sativum L.) cultivar ‘SURABHI’, radish (Raphenus sativus L.) cultivar ‘PUSA CHETKI’, beet root (Beta vulgaries L.) cultivar ‘DDR’, cluster bean (Cyamopsis teragonolaba L.) cultivar ‘PUSA NAVBAHAR’, and vegetable cowpea (Vigna unguiculata L.) Walp) cultivar ‘CO2’ were included in the trial. The doses of fertiliser level to inter crops were arrived by adjusting the recommended level of nutrients to plant population in a particular intercropping system. N was applied in two equal splits, first at the time of planting and second 40 DAS (days after sowing), while entire P and K were applied as basal at the time of planting. Bt cotton equivalent yields were worked out by equating prices of component crops’ yield as suggested by De et al. (1976). Benefit cost analysis was also made for different systems to select the viable and remunerative combination.

The field was ploughed once with tractor drawn mould board plough and then harrowed twice. Bt Hybrid ‘RCH 20’ was planted at 120 x 45 cm, where two ridges at 60 cm apart were formed, and various intercrops (three crops) were planted on 4 sides of the 2 ridges in sequence. Radish, coriander and other vegetables (beet root, cluster bean and vegetable cowpea) were planted at intra-row spacing of 10, 15 and 20 cm, respectively. Crop received a total rainfall of 438.2 and 436.8 mm during 2007-08 and 2008-09 where as effective rainfall was only 275.9 and 264.2 mm, respectively (measured by the FAO method suggested by Brouwer and Heibloem (1986). In addition to effective rainfall, water need of the crop was supplemented by irrigation. No additional irrigation was provided for the intercropping systems (similar to sole Bt cotton crop). Bt cotton hybrid seeds treated with imidachloprid were taken up for planting. Endosulfan (2.5 l/ha) was used to contain the menace of leaf eating caterpillar in radish at 25 and 20 DAS, profenophos (1.25 l/ha) at 62 and 68 DAS in Bt cotton and larvin (1 kg/ha) at 140 and 136 DAS had been used for Bt cotton for control of sucking pest and boll worms, respectively, in 2007-08 and 2008-09. Growth characters, yield attributes, main crop (seed cotton) and intercrop yields were recorded during the course of investigation. Area time equivalent ratio (ATER) was calculated from the land equivalent ratio (LER) using the formula (Heibsch, 1980):

ATER = LER x Dc x Dt-1 where, Dc is time taken by crop, Dt is time taken by whole system.

Fibre quality parameters (2.5% span length, maturity ratio, uniformity ratio, micronaire, fibre strength and fibre elongation) were also analyzed. Fibre quality index (FQI= LT/√M, where L, 2.5% span length in mm, T, fibre bundle tenacity at 3.2 mm gauge in g tex-1 and M, micronaire Value in µ in-1), count (C = 0.196 x FQI – 16) and count strength product (CSP=1.740 x FQI + 1600) were also worked out. All the quality parameters were analyzed by using the High Volume Instrument (Statex-Fibrotex model HVI).

Relative production efficiency (RPE) was calculated using the following formula:

RPE = (EYD-EYE)*100/EYE, Where EYD is the equivalent yield under improved/diversified system, while EYE is the existing system yield. Relative Economic Efficiency (REE) is a comparative measure of economic gains over the existing system. It is expressed in percentage (REE = (DNR-


ENR)*100 ENR-1), where DNR is the net return obtained under improved/diversified system, while ENR is net return in the existing system. Pooled analysis of the two years data was done to obtain reliable assessment of multi-tier intercropping. Gross return, net return and benefit: cost analysis was also derived on the basis of prevailing market price of inputs and outputs.


Bt Cotton

Growth Characters and Yield TABLE 1: GROWTH CHARACTERS, YIELD ATTRIBUTES AND SEED COTTON YIELD AS INFLUENCED BY MULTI TIER SYSTEMS AND NUTRIENT LEVELS

Treatments Plant Height (cm)

No of Squar

e

No. Sympodia

No. Monopodia

No. of Nodes

LAI No of Bursted

Bolls

Boll Weight (g)

Seed Bt Cotton Yield

(kg/ha) T1.50%RDF(IC).+C+Co+V.C+C.b

107.3 7.9 20.1 2.4 23.4 2.52 27.7 5.3 2643

T2.75%RDF(IC)+C+Co+V.C+C.b

108.3 3.8 20.8 2.5 24.3 2.35 29.1 5.3 2864


117.3 3.5 21.7 2.5 25.4 2.59 31.6 4.9 2912

T4.50% RDF(IC). +C+ R+ B+ Co

99.0 7.3 19.1 2.3 22.7 2.22 28.4 5.4 2781

T5.75% RDF(IC)+ C+ R+ B+ Co

104.6 4.6 19.4 2.3 23.2 2.27 29.1 5.2 2844

T6.100% RDF(IC)+ C+ R+ B+ Co

112.9 8.3 20.5 2.6 24.8 3.01 30.3 5.3 2896

T7.50% RDF(IC)+ C+ R+ C.b+ B

103.5 5.1 19.5 2.1 23.7 2.52 24.1 5.5 2565

T8.75% RDF(IC)+C +R + C.b+ B

113.0 4.9 21.3 2.5 25.5 2.68 26.7 5.5 2735

T9.100% RDF(IC)+ C+ R+ C.b +B

113.7 4.0 19.4 2.3 23.2 2.56 30.1 5.3 2901

T10.Sole Bt cotton 106.6 9.9 20.5 2.6 23.7 2.85 27.6 5.2 2593 SEd 7.1 4.0 1.2 0.3 1.2 0.44 3.9 0.2 110.7 CD (5%) NS NS NS NS NS NS NS NS NS

Analysis of pooled data on biometric observations recorded at 45, 90 and 120 DAS and at harvest, yield attributes and seed cotton yield (Table 1) showed that none of the attributes were significantly influenced by the combination of multi-tier cropping systems and nutrient levels to intercrops. Seed cotton yield for different systems ranged from 2565 to 2912 kg/ ha and sole cotton yield of 2593 kg/ ha was recorded. Since the component vegetable inter-crops were harvested early (coriander within 30- 45 DAS, radish at 45 DAS, vegetable cowpea, cluster beans, and beet root within 75 DAS) and nutrient demand met by fertilizer application to intercrops none of the above crops competed with the main crop of Bt cotton during the growth and development. As a result, statistically similar growth characters, yield attributes and seed cotton yield were recorded in Bt cotton with different inter/ sole crop systems. Thus, intensive cropping systems through crop mixes with application of required nutrient levels to intercrops was successful as the components in the system have different nutrient and moisture requirement, varied feeding zones in the soil profile, differential growth duration for enabling the utilization of natural resources optimally. It was also reported that seed cotton was not adversely influenced by intercropping with cowpea and onion (Chowdhury and Singh, 1983).


Intercrops

Observation on growth characters of intercrop showed that the tallest plant (59.1 cm at 45 DAS) with cluster bean and longest extractable tap root (21.2 cm) and highest root volume (385.2 cc) with radish at 45 DAS and greatest leaf area (5042 cm2 at 45 DAS) with vegetable cowpea. Yield (Kg ha-1) of vegetable intercrops (Table 2) varied for coriander (2465 to 3508), vegetable cowpea (1646 to 2019), cluster bean (2504 to 4985), beet root (639 to 1471), radish (2846 to 4122). Pure crop yield (t/ha) of vegetables, coriander, vegetable cowpea, cluster bean, beet root and radish were 12.2, 3.9, 6.5, 15.2 and 19.9 respectively. As a result, net return (x’000 Rs/ ha) calculated from intercrops varied in coriander (26.65 to 39.33), vegetable cowpea (4.08 to 5.49), cluster bean (6.18 to 14.90), beet root (0.728 to 4.52), radish (8.63 to 13.33). Similarly, B:C ratio varied for coriander (8.5 to 11.8), vegetable cowpea (1.6 ), cluster bean (1.6 to 1.7), beet root (1.22 to 2.1), radish (3.1 to 3.6). Thus, the economics of intercrops alone revealed that coriander harvested under Bt cotton + beet root + coriander + radish with 100 % RDF (T6) registered the highest gross return (42, 973 Rs/ ha), net return (39, 328 Rs/ ha), benefit cost ratio (11.8) and seed cotton equivalent yield (18.68 q/ha). While calculating cost of cultivation for intercrops, cost of seeds, sowing charges, fertiliser and harvest charges of intercrops were taken into count, hence cost of cultivation is less with intercrops, which favoured high net return, benefit cost ratio and seed cotton equivalent yield.

TABLE 2: GROWTH CHARACTERS, YIELD AND ECONOMICS OF BT COTTON AND INTERCROPS OF MULTI TIER SYSTEM AT RECOMMENDED LEVEL OF NUTRIENT APPLICATION

Characters Plant Height

at 45 DAS

(cm)

Root Length (cm) at 45

DAS

Root Volume (ml) at 45

DAS

LA at 45 DAS

Yield (kg/ha)

Gross Return (Rs/ha)

CC (Rs/ha)

Net Return (Rs/ha)

B/C Ratio

SCEY(kg/ha)

T3.Bt cotton 31.5 19.8 11.6 1375 2919 67143 28979 38164 2.3 2912 Coriander 19.6 7.8 23.2 2816.0 2761 33818 3645 30173 9.3 1470 V.cowpea 28.5 16.7 19.6 5012.0 2019 13930 8444 5486 1.6 606 Cluster bean 47.8 15.4 10.3 2064.0 3678 25380 15100 10279 1.7 1103 T6.Bt cotton 30.2 15.7 11.6 1386 2903 66778 28913 37865 2.3 2896 Beet root 25.1 7.7 75.6 812.0 1471 8825 4302 4523 2.1 384 Coriander 26.4 7.4 19.3 2743.0 3508 42973 3645 39328 11.8 1868 radish 19.8 13.6 310.3 1126.0 3619 16286 4903 11383 3.3 708 T9.Bt cotton 26.6 18.9 14.3 1315 2920 67164 29010 38154 2.3 2901 Beet root 23.1 6.3 74.6 1124.0 887 5323 3951 1372 1.3 231 Cluster bean 46.1 16.8 10.1 1854.0 4894 33770 19873 13897 1.7 1468 Radish 23.6 21.2 364.6 1324.0 4122 18551 5217 13334 3.6 807 T10.Bt cotton

28.1 18.2 14.6 2816 2590 59580 27588 31992 2.2 2593

Price: cotton (Rs23.0/kg), radish (Rs 4.5/kg), vegetable cowpea (Rs 6.90 /kg), beet root (Rs 6.0 /kg), cluster bean (Rs6.90 /kg), coriander (Rs 12.25/kg) (prevailing price of experimental periods)

Quality Parameters

The modification/ changes of management practices might have specific impact on Bt cotton quality parameters. Pooled analysis indicated that none of the Bt cotton quality parameters were influenced by intercropping system and nutrient levels to intercrops. Mean fibre quality values recorded under the inter-crops varied for 2.5 % span length (30.9 to 33.3 mm), maturity ratio (0.80 to 0.85), uniformity ratio (47 to 52 %), micronaire (4.5 to 4.9 µ in-1), fibre strength (21.0 to 22.1 g/tex), FQI (299 to 341), count (43 to 51 %) and CSP (2120 to 2193). Fibre quality is genetically inherited and therefore the response was not distinct (Bhuva et al., 1995). Similarly, Mohammad et al. (2001) noticed that fibre qualities indices such as ginning percentage (GOT), fibre length, fibre fineness, maturity co-efficient and fibre strength were not altered by intercropping.


Multi tier (Intercropping) system

The advantages of this are physically reflected from the differential growth characters. For example, mean plant height, root length and root volume observed at 45 DAS were 21.2, 17.9 cm and 334.9 cc with radish, 27.5, 15.6 cm and 23.5 cc with vegetable cowpea, 24.1, 6.9 cm and 76.6 cc with beet root, 45.0, 16.2 cm and 11.2 cc with cluster bean, 21.8, 7.2 cm and 21.0 cc with coriander and 31.7, 19.1 cm and 13.0 cc with base crop Bt cotton. These variations resulted in formation of a multi-tier (Table 2). LAI of the different system was also worked out at 45 DAS by including leaf area of base crop (Bt cotton) and multi-tier forming intercrops (vegetables). LAI was significantly influenced by the multi-tier systems as highest of LAI of 2.2 was with Bt cotton + coriander + v.cowpea + cluster bean with 50 % RDF (T1) which in fact was 4.6 times than that in sole Bt cotton (0.48). Pooled mean data on per cent of light interception showed that highest value of 78.6 was measured with Bt cotton + coriander + vegetable cowpea + cluster bean with 100 % RDF.

Production Efficiency TABLE 3: GROWTH CHARACTERS, WEED SMOTHERING, INPUT USE EFFICIENCY AND PRODUCTION EFFICIENCY OF MULTI TIER SYSTEMS

Treatments Root Volume(cc) at

45 DAS

LAI at 45 DAS

Weed dry

Weight (kg/ha)

WSE (%)

LI (%)

WUE (kg/ha-cm)

WPY (Rs/M3)

RPE (%)

Total Nutrients (kg/ha)

PFP (kg/kg)

ENUE (Kg/Rs)

T1.50%RDF(IC).+C+Co+V.C+C.b

65.4 2.21 482 41.6 74.0 77.2 17.8 100.3 219.1 23.7 1.77


74.0 2.08 502 39.2 76.8 86.7 19.9 124.8 238.6 24.4 1.80


64.7 2.09 464 43.8 78.6 90.8 20.9 135.5 258.1 23.6 1.72

T4.50% RDF(IC). +C+ R+ B+ Co

405.9 1.28 615 25.5 58.6 75.8 17.4 96.6 237.4 21.4 1.60


395.5 1.20 586 29.1 62.5 82.1 18.9 113.0 266.1 20.7 1.53

T6.100% RDF(IC)+ C+ R+ B+ Co

416.8 1.12 562 32.0 63.6 87.3 20.1 126.4 294.7 19.9 1.45

T7.50% RDF(IC)+ C+ R+ C.b+ B

491.8 1.17 582 29.5 64.2 69.1 15.9 79.2 249.3 18.6 1.38

T8.75% RDF(IC)+C +R + C.b+ B

470.0 1.08 526 36.3 66.3 78.7 18.1 104.2 284.0 18.6 1.37

T9.100% RDF(IC)+ C+ R+ C.b +B

463.6 1.04 512 38.0 68.1 80.8 18.6 109.5 318.6 17.0 1.24

T10.Sole Bt cotton 14.6 0.48 826 0.0 38.7 38.5 8.9 0.0 180.0 14.4 1.12 SEd 16.2 0.10 58.3 7.6 1.5 6.1 1.1 0.2 CD(5%) 32.7 0.20 118 15.4 3.1 12.3 2.3 0.40

Various intercropping with nutrient levels efficiency parameters measured through relative production (RPE) and economic efficiency (REE), land equivalent ratio (LER), diversity index (DI) and area time equivalent ratio (ATER) were also significantly improved with multi-tier intercropping system in comparison to sole Bt cotton. The enhanced efficiency for multi-tier system varied for relative production efficiency (79.2 to 135.5 percent), relative economic efficiency (69.3 to 191.0), land equivalent ratio (1.51 to 2.43), diversity index (2.46 to 3.03) and area time equivalent ratio (1.20 to 1.72). Amongst the intercropping systems evaluated, multi tier system applied 100 % RDF to Bt cotton + coriander + vegetable cowpea + cluster bean (T3) was having the highest RPE, LER, ATER and DI of 135.5%, 2.43, 1.72 and 3.03 respectively (Table 3). Thus, the increased system efficiency was due to additional yield and return realised from intercrops by application of 100% RDF to intercrops. It was also reported that effective land use efficiency was reflected by higher land equivalent ratio and was higher with intercropping systems (Chelliah and Gopalaswamy, 2000). Diversity index (DI) indicated the system was more diversified and sustainable one. Many times, sustaining yield from a farming system in totality may


be of prime consideration for farmers under resource scarce condition than maximizing yield or income from a single crop. The most interesting biological and economic aspect of multi-tier intercropping is the potential for compensation among the components of the system, often referred to as biological or economic “buffering” in the system that leads to greater stability in (total) yields of component crops. ATER provides more realistic comparison of the yield advantage of intercropping over monocropping since it takes into account both area and time taken by the component crops in an intercropping.

Input Use Efficiency

Water

Being a costly and scarce resource, irrigation water and its availability for agriculture is expected to go down further due to increased domestic and industrial demands. Water use efficiency (WUE, kg ha-1cm-1) and water productivity (WPY, Rs m-3) and weed smothering efficiency (WSE %) were influenced significantly by multi-tier cropping systems. The significantly highest water use efficiency of 90.8 kg ha-

1cm-1, water productivity of Rs 20.9 m-3 of water and 43.8 % weed smothering efficiency were calculated for 100 % RDF with Bt cotton + coriander + vegetable cowpea + cluster bean (T3) (Table 4). Sole Bt cotton system produced only 38.5 kg by using one hectare centimetre of water while gross return of Rs 8.9 per hectare was realized for m3 of water (1000 litres) used. Thus, the most efficient cropping pattern is one which is capable of giving maximum return per unit quantity of water on a long term sustainable basis (Jain, 2008).

TABLE 4: AVAILABLE MAJOR NUTRIENTS STATUS OF POST HARVEST SOIL AND NUTRIENT UPTAKE AS INFLUENCED BY MULTI TIER SYSTEMS AND NUTRIENT LEVELS

Treatments Nutrient Uptake (kg/ha) Available Nutrients (kg/ha) N P K N P2O5 K2O

T1.50%RDF(IC).+C+Co+V.C+C.b 156.7 30.7 156.1 187.4 17.1 728.4 T2.75%RDF(IC)+C+Co+V.C+C.b 178.0 35.3 181.0 184.8 17.5 725.6 T3.100%RDF(IC)+C+Co+V.C+C.b 183.4 36.3 187.2 192.4 16.9 720.8 T4.50% RDF(IC). +C+ R+ B+ Co 125.3 23.2 114.8 167.3 19.3 684.3 T5.75% RDF(IC)+ C+ R+ B+ Co 131.5 24.2 122.2 162.8 18.6 652.8 T6.100% RDF(IC)+ C+ R+ B+ Co 136.5 25.0 127.3 168.5 20.4 675.6 T7.50% RDF(IC)+ C+ R+ C.b+ B 141.8 29.0 148.5 176.2 17.4 712.6 T8.75% RDF(IC)+C +R + C.b+ B 158.1 32.3 168.7 178.4 17 714.6 T9.100% RDF(IC)+ C+ R+ C.b +B 163.0 33.3 172.5 172.9 17.9 736.8 T10.Sole Bt cotton 101.1 19.1 83.1 182.3 17.9 706.3 SEd 7.5 4.1 8.1 7.1 1.8 18.2 CD(5%) 15.2 8.2 16.4 14.3 NS 36.8

Nutrients

Nutrient use efficiency of multitier system and nutrient application was assessed by calculating the partial factor productivity (kg ha-1) and economics of nutrient use efficiency (kg Rs-1) and found that both the indices were significantly influenced by multi-tier cropping systems. Partial factor productivity (kg/ha) of multi- tier cropping systems with nutrient levels were higher and the degree of advantage varied from 118.0 to 169.4%. Multitier intercropping systems with levels of nutrients was increased the economics of nutrient use efficiency by 110.7 to 160.7% due to effective utilisation of resources by the components in the mixture which in turn produced higher yield and better economic nutrient use efficiency. Multi-tier intercropping of Bt cotton + coriander + vegetable cowpea + cluster bean with 75 % RDF (T2) also gave the highest partial factor productivity (PFP) of nutrient (24.4 kg per kg of nutrients) and economics of nutrient use efficiency of 1.80 kg seed Bt cotton per rupees invested on fertiliser were arrived (Table 4).


Light

Unlike rainfall and nutrients, use of solar energy is limited to be captured and stored for latter use in the way that other natural resources are managed as light is instantaneously available and needs to be instantaneously intercepted and used. In the current investigation, the highest percentage of light interception was observed at 45 DAS by 100 % RDF with multi-tier system of Bt cotton intercropped with coriander, v.cowpea and cluster bean (T3, 78.6 %), and was followed by that 75 % RDF with coriander, vegetable cowpea and cluster bean (T2, 76.8 %) (Table 3). High foliage producing capacity of coriander, vegetable cowpea and cluster bean resulted in high light interception in the above systems. Under multiple cropping situations, the component crops were grown in such a way that competition for light was minimized and total interception increased. Since the least light interception (38.7) was observed with sole Bt cotton, the efficiency was not much higher in sole Bt cotton mainly because of slow ground coverage by Bt cotton foliage.

Labour

Labour requirement of the multi-tier cropping with nutrient levels ranged from 329 to 543 man days in comparison to sole Bt cotton with 268 man days. Labour intensiveness associated with multi-tier systems was analysed and it was observed that labour use efficiency in term of gross return per labour was higher with systems. Amongst intercropping systems, the highest labour use efficiency of Rs 387.8 per labour (of 8 hours) was arrived at with Bt cotton + radish + beet root + coriander with 100 % RDF (T6) and sole Bt cotton system had value of Rs 222.4/ labour.

NUTRIENT AVAILABLE AND UPTAKE

Multi-tier intercropping system is highly intensive in nature and their impact on fertility of the soil needs to be assessed. In the present study, nutrient available in post harvest soil and uptake of nutrients by different systems varied significantly except for soil available P (Table 4). Distinctive feature of pulses is the capability to fix atmospheric N2 biologically by prime modulators. Significantly higher available N (192.4 kg /ha) in 100 % RDF with multi-tier system of Bt cotton intercropped with coriander, vegetable cowpea and cluster bean (Table 4) was recorded. The treatment combination (T3) had legumes (vegetable cowpea and cluster bean) in addition 100% RDF was applied to intercrops that resulted in native soil enrichment. Thus, growing of legumes, as an intercrop was beneficial to soil health and soil fertility (Basu, 1992). Significantly least soil available N (162.8 kg/ha) and K (652.8 kg/ha) were estimated with the combination consisting of multi-tier one viz., Bt cotton + radish + beet root + coriander with 75 % RDF (T5) which might be due to non legume intercrops and more uptake of nutrients, might have reduced soil available nitrogen considerably.

NPK uptake of multi-tier system with different levels of nutrient application in the present trial ranged from 125.3 to183.4, 23.2 to 36.3 and 114.8 to 187.2 kg per hectare for N, P and K respectively. Results revealed that 100 % RDF with multi-tier system of coriander, vegetable cowpea and cluster bean (T3) had significantly higher NPK than sole Bt cotton. The ability of intercropping systems to make more efficient use than sole crops was evident both for soluble and non-soluble nutrients. Because of different root growth pattern of component species, intercropping also explore entire soil mass in the rooting zone. This was also ascribed to the more availability and absorption of nutrient under intercropping situation. Similarly, maximum N, P and K uptake were under cotton + blackgram than sole cotton (Harisudan, 2004).

Economics

The multi-tier system with application of nutrient to intercrops significantly increased gross return, net return, BCR, per day profitability and seed cotton equivalent yield. This enhanced the gross return in the range of 179.1 to 235.4%, net return by 169.3 to 291.0%, BCR by 94.0 to 149.5%, and per day profitability by 169.0 to 291.0% and seed cotton equivalent yield by 179.1 to 235.4% in comparison to sole Bt cotton. Highest gross return and seed cotton equivalent yield were obtained with multi-tier system


of coriander, vegetable cowpea and cluster bean and 100% RDF (T3, Table 5). However, the system is labour intensive, resulted in enhanced cost of cultivation; hence multi tier planting of Bt cotton + radish + beet root + coriander with application of 100 % nutrient to intercrops registered the significantly highest net profit (Rs 93, 098/ha), benefit cost ratio (3.23), per day profitablity (620.7) and relative economic efficiency (191%). Intensification of crop on time and space dimension in the system (T6) (by selecting short duration, non competitive crops), application of 100 % of recommended levels of nutrient to intercrops (in addition to main crop) and method of planting adopted could not suppress growth of the base crop and produce statistically as much as equal seed cotton yield (2896 kg/ ha) as that of sole crop in addition to supplementing it by vegetable yield. On the similar lines, cropping system did not influence seed cotton yield but additional yield of intercrops makes a system more remunerative than sole cotton (Sepat and Ahlawat, 2010).

TABLE 5: ECONOMICS AND EFFICIENCY OF LAND, LABOUR, TIME AND DIVERSITY AS INFLUENCED BY MULTI TIER SYSTEMS AND NUTRIENT LEVELS

Treatments Gross Return (Rs/ha)

Net Return (Rs/ha)

B/Cratio SCEY(q/ha)

Labour (Man

Days/ha)

Per Day Profitablity

REE (%)

LER ATER DI

T1.50%RDF(IC).+C+Co+V.C+C.b

119334 69680 2.40 51.9 445 464.5 117.8 2.03 1.47 2.85


133931 79280 2.45 58.2 506 528.5 147.8 2.34 1.67 2.98


140270 84102 2.50 61.0 520 560.7 162.9 2.43 1.72 3.03

T4.50% RDF(IC). +C+ R+ B+ Co

117115 77723 2.97 50.9 329 518.2 142.9 1.51 1.20 2.48


126863 86154 3.12 55.2 341 574.4 169.3 1.61 1.25 2.65

T6.100% RDF(IC)+ C+ R+ B+ Co

134861 93098 3.23 58.6 348 620.7 191.0 1.68 1.28 2.74

T7.50% RDF(IC)+ C+ R+ C.b+ B

106725 54153 2.03 46.4 485 361.0 69.3 1.85 1.39 2.46

T8.75% RDF(IC)+C +R + C.b+ B

121661 65136 2.15 52.9 530 434.2 103.6 2.09 1.53 2.67

T9.100% RDF(IC)+ C+ R+ C.b +B

124808 66757 2.15 54.3 543 445.0 108.7 2.14 1.59 2.59

T10.Sole Bt cotton 59580 31992 2.16 25.9 268 213.3 0.0 1.00 1.00 1.00 SEd 8258 6729 0.10 3.31 46.4 14.10 0.19 0.16 0.25 CD(5%) 16718 13622 0.20 6.7 94.0 28.6 0.38 0.32 0.50

SCEY-Seed cotton equivalent yield, LER- land equivalent ratio, ATER- Area time equivalent ratio, DI- Diversity index

Multitier cropping systems are dynamic interactive practices aimed at better use of the production components such as soil, water, air space, solar radiation and all other inputs on sustainable basis. Bt cotton + radish + beet root + coriander with application of 100 % nutrient to intercrops registered the significantly highest net profit, benefit cost ratio, per day profitability and relative economic efficiency. Thus, higher yield and profit could be realized with the introduction of multi-tier cropping in a unique tier-arrangement in Bt cotton hybrids with application of recommended level of nutrient to intercrops in addition to cotton nutrient level under irrigated condition, the study suggested.

REFERENCES [1] Basu, A.K. (1992)-Integrated nutrient supply system in cotton based cropping system. Fert. News 37, 47–54. [2] Bhuva, K.S., Sukhadia N.M., Malavia D.P. (1995) - Intercropping in upland cotton (Gossypium hirsutum) with pulse and

oil seed crops under rainfed conditions. Indian Journal of Agronomy 40, 95–97. [3] Brouwer, C., Heibloem M. (1986) - Irrigation water needs, Irrigation water management training manual No.3, FAO,

Rome, Italy. [4] Chelliah, N., Gopalaswamy N. (2000) - Effect of intercropping and foliar nutrition on the productivity of summer irrigated

cotton. Madras Agriculture Journal 87, 267–270. [5] Chowdhury, R.K., Singh BP.(1983) - Intercropping of pulses – A good bet. Field Crop Abstracts 30, 900. [6] Harisudan, C. (2004) - Nutrient management in cotton + Black gram intercropping system. M. Sc thesis, Tamil Nadu

Agricultural University, Coimbatore, India.


[7] Heibsch, CK. (1980) - Effect of N fertilization and crop duration on equivalency ratios in intercrops versus monoculture comparisons. Ph.D Thesis, North Carolina State University, Raleigh, N.C., USA.

[8] Jain, T C. (2008) - New Paradigm in Agronomic Research and Development. Indian Journal of Agronomy 53: 241–244. [9] Mohammad Bismillah Khan, Mahboob Akhtar, Abdul Khaliq. (2001) - Effect of planting patterns and different

intercropping systems on the productivity of cotton (Gossypium hirsutum L.) under irrigated conditions of Faisalabad. International Journal of Agriculture & Biology 3: 432–435.

[10] Seema Sepat, Ahlawat I.P.S. (2010) - Effect of cropping system, phosphorus sources and level on phosphorus uptake and yield of cotton (Gossypium hirsutum). Extended summary In: XIX national symposium on “Resource Management approaches Towards Livelihood Security”2–4 Dec. 2010 Bengaluru, Karnataka, organised by Indian Society of Agronomy, Indian Council of Agricultural Research and University of Agricultural Sciences.

[11] Woodhead, T., Huke, R., Huke E. (1994) - Areas, location and on-going collaborative research for the rice-wheat system in Asia, Bangkok, Thailand. FAO Bullet. 68–97.

Effects of Prolonged and Integrated Use of Organics and Inorganics

on the Performance of Cotton

S.N. Upperi1 and V.B. Kuligoud2 1Extension Leader, College of Agriculture, Bheemaraynagudi, UAS, Raichur;

2Associate Professor, College of Agriculture, Bheemaraynagudi, UAS, Dharwad

Abstract—Experiment was conducted to evaluate the prolonged and integrated use of organic and inorganic nutrient sources on the performance of hybrid cotton and leaf reddening during growing seasons of 1999, 2000 and 2001 at ARS, Bheemarayanagudi, Karnataka. The experiment was laid out in Split plot design with four organics viz., incorporation of cotton stalk @ 5 t /ha, vermicompost @ 2.5 t/ ha and FYM @ 10 t /ha, along with graded levels of in organics application viz. 50 percent RDF ( 75:37.5:37.5 kg NPK/ha), 50 percent RDF + MgSO4 @ 20 kg/ ha and 100 percent RDF + MgSO4 @ 20 kg/ ha. The anthocyanin content ( mg/ g of FW) of the leaf samples were decreased from 0.27 with 50 per cent RDF to 0.06 with 100 per cent RDF + MgSO4 @ 20 kg/ ha) thereby revealed decrease in leaf reddening with increased nutrition and supply of Mg. This improvement in leaf greenness enhanced cotton yield significantly from supply of 50 % RDF (25.66 q /ha ) to the application of 50 percent RDF + MgSO4 @ 20 kg/ha ( 28.41 q/ ha) and 100 per cent RDF + MgSO4 @ 20 kg/ha ( 32.17 q/ ha). Results also revealed significant increase in organic carbon contents (from 0.42 to 0.63), available P2O5 ( 24.39 and 28.50 kg/ ha) and available potassium (from 522.22 to 561.889 kg /ha) with the application of vermicompost @ 2.5 t/ ha.

Keywords: Cotton, Anthocyanin, organics, kapas yield and available nutrients.

INTRODUCTION

Cotton an economic fiber crop of India has low average yields (526 kg/ha) compared to the world average (761 kg/ha) and that of Australia (1991 kg/ha). Lower yields of India, are attributed to cultivation of cotton under vagaries of monsoon, soil fertility stress and management factors. Supplying optimal quantities of mineral nutrients of macro and micronutrients to growing crops is one way to improve crop yields (Zubillage et al., 2002). The concentration of mineral nutrients in the soil solution varies over a wide range, depending on many factors such as pH, soil organic matter and fertilizer application (Marschner, 1986). Depleted soil nutrient status under intensive cultivation, will not sustain the productivity in the long run, unless they are replenished timely with both organics and inorganics. India presently uses 15 million tons of nutrients in the form of chemical fertilizers. Supplying the same through organic sources alone is impossible. The chemical fertilizers should be used judiciously and synchronized the demand of the crop uptake by supplying right doses of inorganics with organics (Bekunda et al., 1997). Green manures and FYM applied along with a reduced rate of NPK were able to reduce the mineral fertilizers used by 50%. When cotton stalks are chopped and incorporated into the soil, 48% of the N, 41% of the phosphorus and 74% of the potassium taken from the soil by the cotton plant was returned to the soil. Vermicomposts are finely divided peat-like materials with high porosity, aeration, drainage, water-holding capacity (Edwards and Burrows, 1988) and contained more mineral elements than commercial plant growth media, and many of these elements were changed to forms that could be readily taken up by the plants, such as nitrates, available phosphorus, and soluble potassium, calcium, and magnesium.

With these views, an experiment was conducted to study the impact of different organic sources and graded levels of fertilisers on the productivity of irrigated cotton.

60



A field experiment was conducted at ARS Bheemarayanagudi, (Karnataka) during 1999, 2000 and 2001 in a split plot design replicated thrice. The main plots comprised cotton stalk (5 t/ha), vermicompost (10 t/ha) and FYM (10 t/ha) along with a control. Sub-plots were 50%, 50% RDF + MgSO4.

Chopped small pieces of cotton stalks, vermicompost and FYM were weighed and incorporated into the soil, 15 days before sowing. Imidacloprid treated seeds were dibbled on the ridges of the furrow at a spacing of 30 x 20 cm. Irrigation were given as and when crop demanded usually once in 25 days. The recommended dose of fertilizers were applied around the plants and irrigated. Hand weeding and cultural operations like spraying were carried out as and when needed. The leaf samples were collected at 60 days for chlorophyll and at 90 days for anthocyanin estimation. Half of the N and full dose of P fertilizers were applied at the time of sowing and the rest N and K at 30 and 60 days after sowing. Seed cotton was picked treatment wise and yield was recorded.

Soil Analysis

Soil samples collected before and after the experiment were processed and analysed for pH, EC, organic carbon, N, P, K, S and micronutrients status. The initial soil samples and the FYM nutrient content is presented in Table 1. Available N was determined by alkaline potassium permanganate distillation method as described by Subbaiah and Asija (1959). Soil pH, electrical conductivity, available phosphorus content of the soil was determined by Olsen’s method, available potassium was determined by Flame photometer, after extracting the soil with neutral normal ammonium acetate, Calcium and Magnesium by EDTA method, available sulphur was determined by turbidometric method using 0.15% CaCl2 as an extractant, as outlined by Jackson (1973). Available micronutrients were determined by atomic absorption spectrophotometer after DTPA (diethylene triamine penta acetic acid) extraction as proposed by Lindsay and Norvell (1978).

Chlorophyll and Anthocyanin OD values were determined by the procedure outlined by spectrophotometer at 663 and 525 nm respectively.

TABLE 1: ANALYTICAL RESULTS OF SOIL AND FYM

Parameters Soil FYM 1. pH 8.54 7.10 2.Electrical Conductivity 0.19 dSm-1 0.58 3. Exchangeable Ca+Mg 56 (C mol (p+)/kg) - 4. Available K2O 540 kg/ha 0.64 % 5. Available P2O5 36.2 kg/ha 0.40% 6. Available N 240 kg/ha 0.90 % 7. Available sulfur 48 kg/ha 0.32 %

DTPA Extractable Micronutrients (ppm) Zn 0.6 101 Cu 0.36 82 Mn 2.98 152 Fe 5.9 266


The pooled data in Table 2 indicated that the application of organics and graded level of inorganic fertilizers increased the yields significantly over the years. The highest significant mean yields of 3114 and 3043 kg/ha, recorded with the application of FYM and vermicompost respectively over control (2497) and cotton stalk (2714 kg/ha), attributed to the combined effect of both organics and inorganics in supplement of nutrients, plant growth regulators and increased microbial activities associated. Similar results were reported by Nenthran et al. (1999) and he was of the opinion that the hormones and growth regulators of the vermicompost higher the yield. Where has the application of 100% RDF + 20 kg MgSO4 recorded significantly increased yields of cotton, over only 50% RDF and 50% RDF + 20 kg

Effects of Prolonged and Integrated Use of Organics and Inorganics 361

MgSO4. This may be due to the favorable effect of MgSO4 for chlorophyll synthesis and ATP formation. The increased yield of 50% RDF + 20 kg MgSO4 over only 50% RDF was attributed to the nutrients released from the cotton stalks. Similar result were reported earlier (Blaise and Ravindran, 2003) wherein they observed retention of cotton crop residues increased soil organic matter and also possible nutrient recycling. The interaction of organics and in-organics was not found significant.

TABLE 2: YIELD OF COTTON (Q/HA) AS INFLUENCED BY THE APPLICATION OF THE ORGANICS AND INORGANICS

Tre

atm

ents

Ist Year IInd Year IIIrd Year Pooled analysis

50%

RD

F

50%

RD

F+ 2

0kg

MgS

O4

100

% R

DF

+ 20

kg M

gSO

4

Mea

n

50%

RD

F

50%

RD

F+ 2

0kg

MgS

O4

100

% R

DF

+ 20

kg M

gSO

4

Mea

n

50%

RD

F

50%

RD

F+ 2

0kg

MgS

O4

100

% R

DF

+ 20

kg M

gSO

4

Mea

n

50%

RD

F

50%

RD

F+ 2

0kg

MgS

O4

100

% R

DF

+ 20

kg M

gSO

4

Mea

n

Inor

gani

c O

rgan

ics

Control 27.18 30.61 29.22 29.01 18.09 20.64 22.16 20.29 23.13 26.26 32.15 28.28 22.8 25.92 27.85 25.52Cotton Stalks @ 5t/ha

32.84 32.13 33.81 32.93 19.43 20.94 24.26 21.55 25.7 29.54 37.19 30.81 26 23.55 31.77 28.44

Vermi compost @2.5t/ha

29.42 31.05 34.14 31.53 20.91 23.8 27.62 24.11 30.18 33.61 39.91 34.57 26.76 29.55 33.71 30.31

FYM @ 10t/ha 30.75 32.61 35.99 33.12 21.28 23.76 25.48 28.49 29.17 35.31 44.54 36.34 27.06 30.65 35.34 31.02Mean 30.5 31.6 33.29 19.91 22.48 24.88 27.05 31.18 38.2 25.65 28.41 32.17 Comparison SEM CD (0.05) SEM CD (0.05) SEM CD (0.05) SEM CD (0.05) Organics 1.33 4.219 0.417 1.022 1.68 4.11 0.73 2.52 In-organics 1.15 1.5 0.361 0.882 1.45 3.56 1.38 3.91 Interaction 2.31 3.02 0.72 NS 2.91 NS 1.29 NS

The results in the Table 3 indicated that the application of graded doses of RDF with MgSO4 recorded lower significant anthocyanin (0.1 mg/g) content compared to 50% RDF. This may be due to higher contents of N, P, and Mg. Similar results were reported by Chakravorty (1980). However, significant differences were not observed with organics. This finding is in accordance with the work of Dhopte and Lall (1987) and they indicated that healthy leaves of cotton contained 2% N, 1.7 P (ppm), 155 K (ppm), 30.4 Ca (me/l.), 18.3 Mg (me/l.) and higher micronutrients compared to partly red.

TABLE 3: EFFECT OF APPLICATION OF ORGANICS AND INORGANICS ON THE ANTHOCYANIN AND CHLOROPHYLL CONTENT OF THE COTTON LEAVES, AVERAGED OVER YEARS

Anthocyanin (mg/g FW) Chlorophyll (mg/g FW) 50% RDF

50% RDF+ 20kg MgSO4

100 % RDF + 20kg MgSO4

Mean 50% RDF 50% RDF+ 20kg MgSO4

100 % RDF + 20kg MgSO4

Mean

0.24 0.17 0.11 0.18 3.49 3.55 4.01 3.70 0.27 0.15 0.09 0.17 3.48 3.50 3.94 3.64 0.26 0.17 0.10 0.18 3.27 3.46 4.36 3.70 0.22 0.12 0.06 0.13 4.29 3.85 5.16 4.44 0.25 0.17 0.10 0.16 3.68 3.60 4.37 3.87

CD 5 % Organics 0.107 0.612

Inorganics 0.089 0.327 Interaction 0.179 0.653


Application of 100% RDF+20 kg MgSO4 increased the chlorophyll content (4.37) significantly over 50% RDF (Table 3), which is due to increased chlorophyll content, where N and Mg being the components of chlorophyll with higher contents of N, P, K and Mg as revealed by Dhopte and Lall (1987). However the application of organics had no significant effect on chlorophyll content, which may be due to availability of both macro and micro nutrients in the plant systems.

The plant nutrient contents mentioned in Table 4 indicated significant differences in N content of the leaves with the treatments, which varied from 2.17 to 2.83%. This is due to synchronized nutrient uptake due to solubalising effect of organics. Similar results were reported by Mahavishnan et al. (2005). However the treatment differences were not observed in case of P and K content inspite of their higher contents with graded doses of nutrients. The soil sample collected after harvest of the crop was analysed. Organic carbon content varied from 0.48 to 0.63% and did not differ significantly among treatments. However the available P2O5 and K2O differed significantly due to organic sources. Application of FYM recorded (28.5 kg/ha) and vermicompost (24.4 kg/ha) recorded significantly higher available P compared to the cotton stalk (20.93 kg/ha) and control (15.45 kg/ha). Similarly singnificant available K contents were recorded with vermicompost (561.9 kg/ha) followed by FYM (551.6 kg/ha) than control (522.2 kg/ha) and cotton stalk (543.9 kg/ha). Application of organics sustained the availability of P2O5 and K2O compared to the soil values before the experiment.

TABLE 4: EFFECT OF APPLICATION OF ORGANICS AND INORGANICS ON THE NUTRIENT STATUS OF COTTON LEAVES AT 90 DAS

Treatments Per cent Nitrogen Per cent Phosphorous Per cent Potassium 50% RDF

50% RDF+ 20kg

MgSO4

100 % RDF + 20kg

MgSO4

Mean 50% RDF

50% RDF+ 20kg

Mgso4

100 % RDF + 20kg

MgSO4

Mean 50% RDF

50% RDF+ 20kg

MgSO4

100 % RDF + 20kg

MgSO4

Mean

Control 2.17 2.32 2.49 2.33 0.22 0.32 0.19 0.25 1.32 1.16 1.67 1.37 Cotton stalk @ 5t/ha

2.60 2.62 2.65 2.62 0.25 0.24 0.22 0.24 1.47 1.47 1.43 1.458

Vermi compost @2.5t/ha

2.74 2.73 2.71 2.76 0.19 0.26 0.17 0.21 1.24 1.39 1.23 1.26

FYM @ 10t/ha

2.66 2.72 2.82 2.74 0.22 0.22 0.19 0.21 1.51 1.45 0.96 1.31

Mean 2.55 2.59 2.69 2.61 0.22 0.26 0.19 1.39 1.35 1.313 Comparison SEM CD 5 % CD 5 % CD 5 % Organics 0.010 0.034 NS NS In organics 0.010 0.030 NS NS Interaction 0.019 0.057 NS NS

CONCLUSION

Application of organics not only reduced the cost of fertilizer (50% RDF) but also increased the nutrients availability and sustained higher production levels.

REFERENCES [1] Bekunda, M.A., Bationo A. and SSali H. (1997). Soil fertility management in Africa. Wisconsin, USA. [2] Blaise, D. and Ravindran, C.D. (2003) – Influence of tillage and residue management on growth and yield of cotton grown

on a Vertisol over 5 years in a semi-arid region of India. Soil Tillage Res., 70: 163–173. [3] Chakravorty, S.C. (1980). Chemical factors responsible for red leaf disease in G. hirutum cotton. Proc. 76th Indian, Sci.

cong. Part III PP 120 Ab no. 100 Agri.Sci. section. [4] Dhopte, A.M. and. Lall, S.B. (1987). Relative efficacy of antitranspirant, growth regulators and mineral nutrients in control

of leaf reddening in hirsutum cotton under dryland conditions. Ann. Plant Physiology. 1(1): 56–71. [5] Edwards, C.A. and Barrows, I. (1988). The potential of earth worm compost and plant growth media. In earth worm in

Environmental and waste management Ed. C.A, SPB Academic public. b.v. The Netherland 211–220. [6] Jackson M.L. (1973). Soil chemical analysis. Prentice Hall of India, Pvt. Ltd., New Delhi, India. [7] Lindsay, W.L. and Norvell, W.A. (1978). Development of a DTPA soil test for zinc, iron, manganese and copper. Soil. Sci.

Soc. America J., 42: 421–428

Effects of Prolonged and Integrated Use of Organics and Inorganics 363

[8] Marschner H. (1986). Functions of mineral nutrients: macronutrients. In: Haynes RJ, editor. Mineral Nutrition of Higher Plants. Academic Press, Orlando, FL., 195–267.

[9] Mahavishnan, K., Mangal Prasad and Bhanu Rekha, K. (2005). Integrated nutrient management in cotton-sunflower cropping system in the sandy loam soils of north India. Journal of Tropical Agriculture 43(1–2): 29–32.

[10] Nenthran, N.N., Jayaprasad, K.V. and Kale R.D. (1999). China aster cultivation using vermicompost as organic amendment. Crop Research Hissar. 17: 209–215.

[11] Subbaiah B.V. And G.L. Asija. (1959). A rapid procedure for the estimation of the available nitrogen in soils. Curr. Sci. 25: 259–260.

[12] Zubillaga MM, Aristi J.P, and Lavado R.S. (2002). Effect of phosphorus and nitrogen fertilization on sunflower (Helianthus annus L) nitrogen uptake and yield. J Agron Crop Sci. 188: 267–74.

Response of Cotton to Bio Boron and its Use Efficiency in Vertic Ustropept Soil

of Tamil Nadu, India

P. Janaki1 and S. Meena1

1Department of Soil Science & Agricultural Chemistry, Tamil Nadu Agricultural University, Coimbatore–641003, Tamil Nadu, India,

E-mail: [email protected]

Abstract—A field investigation was carried out to study the effect of different boron (B) sources on yield and its use efficiency by hybrid cotton on Vertic Ustropept of Tamil Nadu, India. The experiment with hybrid cotton (RCHB 708Bt XL) was conducted during the winter season (August 2007 to January 2008) Treatments included supply of B through different organic manures, borax and control besides including the foliar spray of B. Results showed that the supply of B through organic and inorganic sources produced significantly higher seed cotton yield (SCY) than B alone. Manure application had a positive influence on through slow and continued supply of B along with other nutrients to cotton. The agronomic efficiency was high for the organic B source treatments. This could be attributed to the higher absorption of N from soil and translocation to the fruiting organs from vegetative part by the applied B.

Keywords: Bio Boron, cotton, B use Efficiency, seed cotton yield

INTRODUCTION

In India, 33 % soils are deficient in B (Singh, 2006) Cotton requires B to support the processes of growth and development of cotton fibre in the boll (Stewart, 1986). Organic matter plays an important role in controlling B concentration in the soil solution and that it has a prominent effect on reducing B uptake by plants (Yermiyagu et al., 2001). Foliar applications sometimes needed due to heavy rain or the absence of irrigation facilities besides the toxicity of B application caused by the narrow range of B requirement between deficiency and sufficiency of B by the crops. Janaki et al. (2005) found that the application of recommended dose of NPK plus Zn @ 25 kg ha-1 and B foliar spray twice @ 0.3 kg ha-1 during early and peak boll formation stages is beneficial in increasing the seed cotton yield (SCY). Hence the supply of B through organic sources is beneficial for agricultural crops besides improving the efficiency. Therefore, a study was conducted to study the effect of different boron sources on yield and its use efficiency by hybrid cotton in Vertic Ustropept soils of Tamil Nadu, India.

METHODOLOGY

The experiment with hybrid cotton (RCHB 708Bt XL) was conducted during the winter season (August 2007 to January 2008) in Salem district of Tamil Nadu, India. The soil of the experimental fields belongs to Periyanaickenpalayam series with the available NPK status of 165:46:497 kg ha-1. The experimental field was clay loam in texture with hot water soluble B content of 0.78 mg kg-1 soil. The irrigation water used for the experiment had EC of 3.09 dS m-1and B content 0.29 meq/lit.

The treatments compromised T1: Absolute control; T2:FYM 5 t ha-1; T3: Poultry manure 5 t ha-1; T4: vermicompost 5 t ha-1; T5: cotton waste compost 5 t ha-1; T6: Ground nut husk compost 5 t ha-1; T7: T2+ borax @ 5 kg/ha as basal; T8: Borax @ 5 kg/ha as basal; T9: T8 + Foliar spray of B @ 0.3 % (2 times at 45 DAS and 75 DAS) and T10: Foliar spray of B @ 0.3 % (2 times at 45 DAS and 75 DAS) for cotton with three replications in a randomized block design. The cotton hybrid was sown at a spacing of 0.90 m between rows and 0.60 m between plants and the plot size is 20 m2. Nitrogen was applied in two splits, half at 25 days after sowing (DAS) along with entire amounts of P and K. Nutrient fertilizers were spot applied. The remaining N was applied 60 DAS. The growth and yield parameters were determined at boll bursting stage and combined yield of all six pickings were recorded from each treatment plots.

61

Response of Cotton to Bio Boron and its Use Efficiency in Vertic Ustropept Soil of Tamil Nadu, India 365

RESULTS

Seed Cotton Yield

Boron application significantly enhanced the SCY than the control treatment (Table 1). The per cent increase in yield due to the application of organic and inorganic B sources ranged from 5.8 to 16.5 and 21.9 to 27.8 respectively. Among organic sources, ground nut husk performed better than the other sources. Application of 5 kg borax as basal plus two foliar sprays of B produced higher SCY among inorganic sources. This showed a positive effect of B on influencing the SCY. Howard et al. (1998) reported that the efficiency of foliar applied B is higher than soil application when deficient condition of B is suspected. B applied through foliar spray enhanced the seed cotton yield by efficiently translocating the sugars from leaves to fruiting organs and by utilizing applied fertilizer N. Similar findings were reported by Anderson and Bosewell (1968). Supply of B through organic and inorganic sources produced significantly higher SCY than B alone. This showed the positive influence of manure application on increasing SCY probably due to slow and continued supply of B along with other nutrients to cotton. Similar findings were reported by Padole et al. (1998).

TABLE 1: INFLUENCE OF B SOURCES ON YIELD, YIELD ATTRIBUTES AND QUALITY PARAMETERS OF HYBRID COTTON

Treatments Seed Cotton Yield

(kg ha-1)

No. of Bolls/ Plant

Plant Height (cm)

Ginning Percentage

(kg ha-1)

Lint Index

Uniformity Ratio (%)

T1 2861 78 156 32.3 0.35 44 T2 3027 79 160 38.3 0.37 46 T3 3093 78 164 38.6 0.37 45 T4 3032 86 164 36.2 0.36 46 T5 3233 80 164 39.4 0.36 46 T6 3333 84 160 38.9 0.37 45 T7 3509 95 162 39.9 0.38 47 T8 3488 91 165 40.4 0.38 47 T9 3655 98 163 38.7 0.39 47 T10 3613 96 160 39.3 0.38 47

Mean 3284 87 161.8 38.2 0.37 46 CD (5%) 385** 6.2* NS 2.9* 0.02* NS

In cotton production, number of bolls per plant and its weight assume practical significance as they are directly related to productivity. In this study, boll weight was increased with the application of B. Basal borax plus foliar spray produced higher boll biomass per plant and SCY and was similar with the organic B sources treatments.

Seed –Lint Characters

Ginning percentage is a measure of lint yield from seed cotton. All the treatments except control performed well in increasing the ginning percentage. Roberts et al. (2000) reported that the application of B (0.22 kg ha-1) as foliar spray increased the lint yield by 8 %. Lint index is the weight of lint obtained from 100 g of seed cotton and similar results as that of ginning percentage was observed.

B concentration of Index Leaf

The B content was high in the soil application of borax plus B foliar spray at squaring and boll formation stages. This is in accordance with the earlier findings of Heitholt (1994) who reported the beneficial effect of B spray on leaf blade B concentration. B concentration in index leaf increased from squaring to boll formation stage suggesting high requirement at boll development phase to produce quality fibre.

Significant relationship of B content with N content was observed in this study (Fig. 1). The relationship was positive between B and N at squaring and boll formation stages whereas the relationship of B with P and B with K was significant only at squaring and boll formation stages, respectively. The

366

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TABLE 2: INFLUENC

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B UPTAKE BY DIFFEREN

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367

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TABLE 3: INFLUENCE OF B SOURCES ON B AVAILABILITY AND B BALANCE IN SOIL AT DIFFERENT GROWTH STAGES

Treatments B availability (mg kg-1) B balance in soil (g ha-1) at harvest Squaring Boll formation Harvest

T1 0.88 0.69 0.51 0.280 T2 1.15 0.70 0.62 0.241 T3 1.04 0.74 0.62 0.275 T4 1.10 0.74 0.64 0.261 T5 1.16 0.78 0.61 0.248 T6 0.46 0.78 0.64 0.196 T7 1.45 0.96 0.76 -0.148 T8 1.55 0.93 0.80 -0.148 T9 1.53 0.93 0.81 -0.122 T10 1.05 0.74 0.64 0.410

Mean 1.14 0.80 0.67 - CD (5%) 0.35* 0.17** 0.13** -

CONCLUSION

It is concluded from the study that the soil application of borax (5 kg ha-1) alongwith 0.3 kg B ha-1 as foliar spray twice at 45 and 75 DAS recorded high dry matter production and SCY. All other B applied treatments produced similar biomass and SCY. This showed that the supply of B to cotton can be met by the organic B sources by integrating B rich organic wastes in the compost production. This also paves a way for the organic cotton production where the frequent use of inorganic B sources is warranted. However, a detailed research is required in this division in future.

REFERENCES [1] Anderson, O.E. and Bosewell, F.E. (1968) - Boron and manganese effects on cotton yield, lint quality, and earliness of

harvest. Agron. J., 60: 488–493. [2] Heitholt, J.J. (1994) - Supplemental boron, boll retention percentage, ovary carbohydrates, and lint yield in modern cotton

genotypes. Agron. J., 86: 492–497. [3] Howard, D.C., Gwathmey, C.O., Roberts, R.K. and Lessman, G.M. (1998) - Potassium fertilization of cotton produced on a

low K soil with contrasting tillage system. J. Prod. Agric., 11: 74–79. [4] Janaki, P., Arivalagan, T., Malarkodi., M. and Meena, S. (2005) - Integrated nutrient management for cotton – Brinjal

cropping sequence. J. Agrl. Res. Mgt., 4(Suppl.): 87–89. [5] Miley, W.N., Hardy, G,W., Sturgis, M.B. and Sedberry, J.E. (1969) - Influence of boron, nitrogen and potassium on yield,

Nutrient uptake, and abnormalities of cotton. Agron. J., 61: 9–13. [6] Padole, V.R., Deshmukh, P.W., Nikesar, R.J. and Bansode, N.V. (1998) - Effect of organics and in organics on yield and

quality of cotton grown on vertisol. Pkv. Res. J, 22: 6–8 [7] Roberts, R.K., Ufersman, J.M. and Howard, D.D. (2000) - Soil and foliar applied boron in cotton production: An Economic

Analysis. The J. Cotton Sci. 4: 171–177. [8] Sharma K.R., Srivastava, P.C., Srivastava, P. and Singh, V.P. (2006) -. Effect of farmyard manure application on boron

adsorption-desorption characteristics of some soils. Chemosphere. 65(5): 769–777 [9] Singh, M.V. (2006) - Emerging Boron Deficiency in Soils and Crops in India and Its Management. In: Proceedings of

18th World Congress of Soil Science held at Philadelphia, Pennsylvania, USA during July 9–15, 2006, pp. 104. [10] Stewart, J. McD. (1986) - Integrated events in the flower and fruit. In: J.R. Mauney and J.McD. Stewart (ed). Cotton

Physiology, Cotton foundation, Cotton Council of America, Memphis, pp.261–297. [11] Yermiyahu, U., Keren, R. and Chen, Y. (2001) - Boron uptake by plants as affected by organic matter. In: Plant Nutrition -

Food security and sustainability of agro-ecosystems, Edited: W.J. Horst et al., 92: 852–853

A Thermal Optimum Approach to Irrigation Scheduling in Australian Drip Irrigated Cotton

W.C. Conaty1,4,5, J.E. Neilsen1,2, J.R. Mahan3, B.G. Sutton4 and D.K.Y. Tan4 1CSIRO Plant Industry and Cotton Catchment Communities CRC,

Locked Bag 59 Narrabri NSW 2390 Australia; 2Monsanto Australia, Level 12, 600 St Kilda Rd, Melbourne VIC 3004

3USDA/ARS Plant Stress and Water Conservation Laboratory, 3810 4th St, Lubbock, TX 79415, USA;

4Faculty of Food, Agriculture and Natural Resources, The University of Sydney, Sydney NSW 2006

e-mail: [email protected]

Abstract—To optimise irrigation water application there is a need for efficient and targeted irrigation scheduling systems. A thermal optimum approach to irrigation scheduling uses the principle that plant performance is optimised when a plant is maintained within its optimal temperature range through timely irrigation. The advantage of such an irrigation scheduling system is that it is based directly on plant stress physiology, and not on indirect measurements of plant stress such as soil water and/or environmental load. Researchers at the USDA/ARS have previously used the relationship between canopy temperature (Tc) and plant water status to schedule irrigations based on a temperature-time threshold system. These systems command irrigations when the crop’s Tc exceeds its optimal temperature threshold (TT) for a pre-determined period of stress time (ST), i.e. time the Tc > TT. The aim of this research was to assess the utility of this thermal optimum approach to irrigation scheduling in Australian drip irrigation systems. At the Australian Cotton Research Institute at Narrabri, Australia, field based Tc was continuously monitored with infra-red thermometers (Zytemp TN901, Hsinchu, Taiwan) in drip irrigated cotton with varying soil water deficits. The optimum temperature of the cotton cultivar Sicot 70BRF was assessed by both crop (lint yield) and leaf level (gas exchange) responses to Tc. Data showed a single relationship where yield reductions occurred beyond Tc of ~ 28 °C, validating previous work on the species-specific optimal temperature of cotton. In drip systems, which have the capacity to rapidly supply water at precise volumes, the ST threshold for peak lint yield was observed at 5.0 h daily ST. Future work should include further field validation, investigations in furrow irrigated systems and an integrated approach to stress detection, which should consider both the degree and duration of time Tc > 28 °C. The thermal optimum approach to irrigation scheduling offers potential for plant based precision irrigation scheduling. This study provides the TT and ST thresholds, as well as a research overview in irrigation scheduling using a thermal optimum approach in a high yield context.

INTRODUCTION

Irrigation scheduling serves to ensure that water is available in sufficient quantity at the time when it is needed by the plant. A thermal optimum approach to irrigation scheduling uses the principle that plant performance is optimised when a plant is maintained within its normative temperature range through irrigation water application (Mahan et al., 2005). This approach is based on the theory that as soil water availability declines, transpiration is reduced, and hence, canopy temperatures rise. The advantage of such an irrigation scheduling system is that it is based directly on plant stress physiology, and not on indirect measurements of plant stress: soil moisture conditions or environmental load. The use of canopy temperatures as an indicator of plant stress is well established (Idso, 1982; Jackson et al., 1981). Irrigation scheduling based on the measurement of plant temperatures is the focus of this paper. One such irrigation scheduling system is BIOTIC (Biologically Identified Optimum Temperature Interactive Console), described by Mahan et al. (2005).

In an effort to develop early indicators of plant water stress, the optimum plant temperature, estimated from the thermal dependence of the Michaelis constant of an enzyme (KM), and the range of temperatures conducive to optimal metabolic activity, the thermal kinetic window (TKW), of cotton was defined (Burke et al., 1988; Upchurch and Mahan, 1988). This was supported by the finding that, through evaporative cooling from transpiration, well watered cotton plants preferentially maintained their leaf

62


temperature (Tl) at about 28°C (Hatfield et al., 1987). Plants exhibit homoeothermic behaviour, where they will preferentially maintain their in vivo temperature at a specific temperature, and growth occurs at these plant temperatures (Burke and Upchurch, 1989; Mahan and Upchurch, 1988). This finding led USDA researchers to develop BIOTIC, an irrigation scheduling tool that manages the timing of irrigations using Tc measurements and a specific time threshold (Upchurch et al., 1996).

The BIOTIC continually measures the Tc of the target crop with an infra-red thermometer (IRT). After each measurement, the Tc is compared with a pre-determined threshold of water stress Tc, where if the Tc is greater than this threshold, the crop is considered thermally stressed. The crop can then be irrigated if relative humidity is not limiting transpirational cooling. Although this approach provided precise and rapid alleviation of water stress, the approach needed to be modified for use in systems with longer irrigation intervals (3-7 d) (Mahan et al., 2005). Under such circumstances, a time threshold is required in the decision making process. The time threshold defines the average amount of time that the canopy temperature of a well watered crop can exceed the temperature threshold, even in the absence of a water deficit. Irrigation is appropriate when the temperature and time thresholds have been reached, and relative humidity is not limiting transpirational cooling (Mahan et al., 2005).

The BIOTIC approach to water stress detection is limited by the availability of energy to raise the plant temperature above the optimum, water availability for transpirational cooling, and potential incorrect water stress detection during periods of high vapour pressure deficit that may not allow for transpirational cooling to the temperature optimum.

This paper aims to describe the implementation of BIOTIC irrigation scheduling in cotton (Gossypium hirsutum) in the semi-arid cotton growing region of Narrabri in New South Wales, Australia. The temperature threshold was assessed through a dual-level approach, where the optimum temperature of the Australian cotton cultivar Sicot 70BRF was considered in terms of leaf-level gas exchange and crop level yield- Tc response. The time threshold will also be determined through yield- Tc associations. This is the first study adapting the BIOTIC protocol to Australian conditions, and will provide a foundation for the development of the BIOTIC theory to irrigation scheduling in the Australian cotton industry.


Site Description

A field experiment was conducted at the Australian Cotton Research Institute (ACRI) in Narrabri (30° S, 150°E) NSW Australia. The climate of this region is characterised by mild winters, hot summers and summer-dominant rainfall patterns, with an annual average of 642mm (Bureau of Meteorology, 2009). The soil of the site was a uniform grey cracking clay (Australian soil taxonomy: Grey Vertosol; USDA soil taxonomy: Typic Haplustert). Weather data is shown in Table 1.

TABLE 1: WEATHER CONDITIONS OBSERVED FROM EMERGENCE TO MATURITY, MEASURED 0.4 KM FROM THE EXPERIMENTAL FIELD SITE

Weather variables Average Tmax (°C) 30.4 Average Tmin (°C) 15.6 Tmax >36 °C (d) 13 Accumulated day degrees 2115 Solar radiation (MJ m-2 d-1) 23.6 Precipitation (mm) 354 Average daily RHmax (%) 75.9

Experimental Design and Plot Management

The cotton cultivar Sicot 70BRF was planted on 5 October 2007 in a randomised complete block design with five replicates of surface drip irrigation treatments based on daily evapotranspiration (ETo) rates. Plots were six rows of surface drip-irrigated cotton with an additional 2 rows buffer around each plot. Plots were irrigated daily to every three days, depending on daily ETo and in-season rainfall. Five

A Thermal Optimum Approach to Irrigation Scheduling in Australian Drip Irrigated Cotton 371

irrigation treatments included a control or theoretical optimal (nominally 100% daily water requirement of control applied - Treatment 4), an excessive (nominally 125% of control daily water requirement of control applied - Treatment 5) and three deficit (nominally 65%, 75% and 90% of control daily water requirement applied - Treatments 3, 2 and 1) irrigation regimes. Daily irrigation rates were calculated according to Allen et al. (1998) where the daily water requirement (crop evapotranspiration) is equal to the product of a ETO and a locally calibrated crop factor (KC). ETO was calculated using on site weather station data and the Penman-Monteith equation (Allen et al., 1998). The KC used was based on recent experimental data (not published) conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) agronomic management program over a number of growing seasons. Management for all field experiments followed current high-input commercial practices outlined by Hearn and Fitt (1992). Each experiment was managed according to its individual requirements (e.g. with respect to pest control), with all plots receiving the same management regime.

Gas Exchange-leaf Temperature Relationships

Leaf carbon assimilation (A) and stomatal conductance (gs) were measured using an infra-red gas analyzer (IRGA), Portable Photosynthesis System; Li-COR® model 6400-XT (Li-COR Biosciences, Lincoln, Nebraska, USA). Measurements were taken on the youngest fully expanded leaf in all plots of the theoretical optimal (control) (Treatment 4), excessive (Treatment 5) and the largest soil moisture deficit (Treatment 1) irrigation treatments. A range of irrigation treatments was used, ensuring an array of studied Tl and corresponding gas exchange rates. The Tl were measured with a chromel-constantan thermocouple junction located within the sensor head of the Li-6400 (Li-COR, 2004a).

As gas exchange is affected by light intensity, humidity, temperature, carbon dioxide and time of day, the Li-6400 was matched to ambient conditions and held constant for the time period of measurements. This resulted in cuvette relative humidity controlled at 50 - 70%, carbon dioxide maintained at 380 μmol (CO2) mol-1 air, photosynthetic active radiation (PAR) set to 2000 μmol m-2 s-1 and air temperatures ranging from 23 to 42 °C (depending on daily ambient conditions). Equations for calculating A (in μmol (CO2) m-2 s-1) and gs (in mol (H2O) m-2 s-1) are given in the Li-COR Biosciences manual (Li-COR, 2004b).

Yield- canopy Temperature and Yield- stress Time Relationships

Mechanically-picked seed cotton weight data was recorded from one centre row of each plot. The gin turn-out (per cent lint of seed cotton) and subsequent lint yield was then calculated from a sub sample of the seed cotton.

Wireless, battery-operated Smart CropTM infrared thermometers (Smartfield Inc., Lubbock, TX, U.S.A.) were placed in four replicates of the experiments. The SmartCrop system uses a Zytemp model TN901 infrared thermometer (IRT) (Zytemp, Hsinchu, Taiwan). The remote IRTs measure average output temperature within the field of view at a one minute intervals, and transmit a 15 min average temperature to a base station via a low power radio link. Data were collected throughout the season from flowering through to crop maturity (80 DAS to 178 DAS). Sensors were positioned and maintained periodically at 15 cm above the canopy pointing south (to reduce the effects of specular reflectance) at an angle of 70° for the duration of the measurement period.

Stress time (ST) is defined as the daily period of time canopy temperatures exceed 28 °C. This was calculated daily from Tc data, and is represented as a daily seasonal average.

Statistical Analysis

All data were analysed in GenStat 11th edition. Differences in treatments were assessed at the 95% confidence level (P=0.05) on normally distributed data.


RESULTS

Temperature Threshold

A split-line regression was fitted to the lint yield- Tc (R2=0.86) (Fig. 1), where yield reductions were observed when seasonal average day-time Tc > 27.7 ± 0.9 °C. Although a split line regression was fitted to the data, it is not expected that lint yield will remain constant at canopy temperatures below about 25 °C. Although the data set was range limited, crops with average Tc below 25 °C may not accumulate the required day-degrees for crop maturity, and would be outside the range of seasonal canopy temperatures experienced in a commercial Australian production setting. Despite this, because of the range of data available, this regression provided the best fit to the data.

Fig. 1: Split-line Regression of Lint Yield- canopy Temperature (Tc) Response Showing Lint Yield Reductions when Tc > 27.7 °C (R2 = 0.86; when x ≤ 27.67, y = 3320.5; when x > 27.67, y = -413.4x +14757.21).

Fig. 2: (a) Polynomial Regression (P<0.001) of Leaf Net Carbon Assimilation (A) and Leaf Temperature (Tl) Peaking at Tl = 29.3°C (R2=0.41; y= -0.52x2 +30.50x -407.83); and (b) Polynomial Regression (P<0.001) of Stomatal Conductance (gs) and Tl Peaking at Tl = 29.1°C (R2=0.48; y= -0.019x2 +1.09x -

15.07). Vertical Bars Represent two Standard Errors of the Mean.

The measured gas exchange parameters exhibited a second order polynomial response to temperature. The model-calculated peak A occurred at 29.3 °C, with an observed standard error of 3.61 μmol (CO2) m2 s-1 (R2 = 0.41; P<0.001) (Fig. 2a). The model calculated peak gs at 29.1 °C, with an

Tc (°C)

24 26 28 30 32

Lint

yie

ld (k

g ha

-1)

1000

1500

2000

2500

3000

3500

4000

(a)

A (µ

mol

(CO

2) m

2 s-1

)

15

20

25

30

35

40

45

50

(b)

Tl (°C)

24 26 28 30 32 34 36

g s (m

ol (H

2O) m

2 s-1

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2


observed standard error of 0.124 mol (H2O) m2 s-1 (R2 = 0.50; P<0.001) (Fig. 2b). Although the fit of these regressions was not particularly strong, obvious trends in gas exchange were observed with peak A and gs occurring at ~ 29°C. These regressions are weakened by the limitation in the range of data as field-based data were collected over a relatively narrow (but representative of commercial irrigated Australian cotton systems) range of Tl (26 – 34 °C). Hence, there are no data providing leverage to the regression with low A and gs due to low or high Tl. The range of Tl that represent A equal to that of the calculated peak assimilation (29.3°C) occurred between 27.5 and 31.2°C, whilst the range for peak stomatal conductance (29.1°C) occurred between 26.8 and 30.5°C.

Time Threshold

Average Tc and ST displayed a positive linear relationship (R2 = 0.97; P<0.001), where average ST increased by approximately 1 h for every 1 °C increase in average Tc (Fig. 3). Using this relationship, a temperature threshold of 28 °C is expected to produce an average daily ST of 5.3 h. Because average daily ST is calculated from Tc, and associations are auto correlated, this value can only be considered a rough estimate of the ST threshold for irrigation, required to produce peak yields. The association between average daily ST and lint yield showed similar results to the approach used in Fig. 3. A quadratic relationship was fitted to average daily ST and lint yield (R2 = 0.89; P<0.001; y = -81.49x2 + 618.98x + 2293.06), where peak lint yields were observed between 2.6 and 5.0 ST h, with an average of 3.8 ST h. This suggests that in practice, lint yield reductions occurred when average daily ST > 5.0 h.

Fig. 3: Association between Canopy Temperature (Tc) and Average Daily Stress Time (ST) (P<0.001) (R2 = 0.97; Y = 1.05x -24.12). when Tc = 28 °C, Average Daily ST = 5.3 h.

DISCUSSION

In arid environments, plant temperatures can range from significantly less than air temperature under optimal water status to significantly higher than air temperature when plant water status is less than optimal (Mahan et al., 2010). Therefore, Tc can potentially be used to infer transpiration rates, and provide a basis for determining plant water stress. Average Tc in this study reflected this trend, where Tc increased with decreasing water supply. Using a split-line regression (Fig. 1), lint yield reductions occurred when Tc > 28 °C. On the semi-arid Texas High Plains, Wanjura et al. (1990; 1992; 1988) took a different approach to determining the temperature threshold. They hypothesised that maximising the amount of time Tc were at the optimum or within the TKW, lint yields should also be maximised. Wanjura et al. (1990; 1992; 1988) showed that when cotton irrigations were scheduled, if the average Tc during a 15 min period exceeded a pre-determined temperature threshold of 26, 28, 30 or 32 °C, lint yields were consistently highest in the 28 °C temperature threshold.

5.3 h ST

Tc (°C)

24 26 28 30 32

Stre

ss ti

me

(h)

0

2

4

6

8

10


Gas exchange provide a measure of the degree of drought stress imposed on a crop and the response of leaf gas exchange measurements have been used to detect and quantify water stress (Baker et al., 2007). Therefore, leaf A and gs were used as surrogates for plant performance at a given Tl. The peak in gas exchange parameters, both A and gs, occurred at Tl of ~ 29 °C (Fig. 2). This initially suggested that, when measured in the same cultivar, the optimum for gas exchange in field grown Australian cotton may be slightly higher than the historical temperature optimum (Burke, 1990), and the threshold for yield reductions due to excessively high Tc (Fig. 1). However, the range of Tl that produced optimum gas exchange rates equal to that of the peak at 29 °C occurred between 26.8 °C and 31.2 °C. This range in optimum temperatures was similar to the TKW for cotton (23.5 °C to 32 °C) and encompassed the optimum temperature for cotton metabolism (28 °C) as outlined by Burke et al. (1988). Therefore, we conclude that the thermal optimum for the Australian cotton cultivar Sicot 70BRF is about 28 °C, which is consistent with the optimum temperature of cotton growth and metabolism outlined in a recent review by Burke and Wanjura (2010).

The ST threshold was also evaluated in this study. The ST threshold represents the site-specific average daily period of time that Tc can be expected to exceed the thermal optimum, regardless of soil water availability (Mahan et al., 2005). Using a temperature threshold of 28 °C, Tc-ST associations showed a corresponding ST of 5.3 h (Fig. 3), while yield-ST associations showed a reduction in yield when ST > 5.0 h. This suggests that in practice, peak yields can be achieved in drip irrigated cotton grown at Narrabri with a ST threshold of up to 5.0 h. On the semi-arid Texas High Plains, Wanjura et al. (2006) observed a negative linear response of lint yield with ST where an increase in 1 h ST resulted in a decrease of 343 kg lint ha-1 (19% reduction ST h-1). In this environment, the highest lint yields were observed at about 5.5 h ST, and a curvilinear response was not observed. However, this response was observed over a smaller range of ST (6 – 9 h ST compared with 2 – 9 h ST in our study), and when our data are restricted to this range, a linear response is observed (R2 = 0.79) where an increase in 1 h ST resulted in a 513 kg lint ha-1 yield reduction (15% reduction ST h-1).

CONCLUSION

This paper represents the first step in developing the temperature and time thresholds required for efficient plant based irrigation scheduling in the semi-arid cotton growing region of Narrabri, Australia. It is recommended that a temperature threshold of 28 °C and a time threshold of 5 h are employed in the system, to produce peak lint yields with the most efficient use of water. This approach is limited to irrigation systems that have a high capacity to supply water at small time intervals (drip/sprinkler irrigation), and is site specific. Future work should further investigate the utility of the system in Australian production systems, such as furrow and large deficit irrigation systems, and may need to assess the environmental conditions where canopy temperatures are inefficient in detecting plant water stress, such as high vapour pressure deficit environments.

ACKNOWLEDGEMENT

This study was funded by the Cotton Catchment Communities Cooperative Research Centre and the Cooperative Research Centre for Irrigation Futures, with further support from the University of Sydney. Thanks to Merry Errington, Nicola Cottee, Jo Price, Jono Cuell, Mitch Cuell, Martyn Tann and Meghan Smith in Narrabri for technical assistance. We are grateful to Steven Yeates for agronomic advice and providing the crop factor. Further thanks to Michael Bange and Greg Constable, for advice and reviewing the manuscript.

REFERENCES [1] Allen R.G., Pereira L.S., Raes D. and Smith M. (1998)- Crop Evapotranspiration: Guidelines for computing Crop Water

Requirements -Irrigation and Drainage Paper no. 56, Food and Agricultural Organisation, Rome, Italy. [2] Baker J.T., Gitz D.C., Payton P., Wanjura D.F. and Upchurch D.R. (2007) - Using Leaf Gas Exchange to Quantify Drought

in Cotton Irrigated Based on Canopy Temperature Measurements - Agronomy Journal, 99: 637–644. DOI: 10.2134/agronj 2006. 0062.


[3] Bureau of Meteorology (2009) - Bureau of Meteorology: Climate Data Online, in: T. A. Government (Ed.). [4] Burke J.J. (1990) - Variation Among Species in the Temperature Dependence of the Reappearence of Variable

Fluorescence Following Illumination - Plant Physiology, 93: 652–656. [5] Burke J.J. and Upchurch D.R. (1989) - Leaf temperature and transpirational control in cotton - Environmental and

Experimental Botany, 29:487–492. [6] Burke J.J and, Wanjura D.E. (2010) - Plant Responses to Temperature Extremes- In: J. M. Stewart, et al. (Eds.), Physiology

of cotton, Springer Science+Business Media, New York. [7] Burke J.J., Mahan J.R. and Hatfield J.L. (1988) - Crop-specific Thermal Kinetic Windows in Relation to Wheat and Cotton

Biomass Production - Agronomy Journal, 80: 553–556. [8] Hatfield J.L., Burke J.J., Mahan J.R. and Wanjura D.E. (1987) - Foliage Temperature Measurements: A Link Between the

Biological and Physical Environments - in: R.J. Hanks (Ed.), Proceedings of international conference on measurement of soil plant and water status, Utah State University, Utah State University, Logan, Ut USA. pp. 99–102.

[9] Hearn A.B. and Fitt G.P. (1992) - Cotton Cropping Systems - In: C.J. Pearson (Ed.), Field crop ecosystems, Elsevier, Amsterdam. pp. 85–142.

[10] Idso S.B. (1982) - Non-water-stressed Baselines - a Key to Measuring and Interpreting Plant Water-stress - Agricultural Meteorology, 27: 59–70.

[11] Jackson R.D., Idso S.B., Reginato R.J. and Pinter P.J. (1981) - Canopy Temperature as a Crop Water-Stress Indicator- Water Resources Research, 17: 1133–1138.

[12] Li-COR. (2004a) - OPEN's system variables, Using the Li-6400 portable photosynthesis system, Li-COR Biosciences Inc., Lincoln, Nebraska, USA. pp. 14-7 to 14–8.

[13] Li-COR. (2004b) - System description, Using the Li-6400 portable photosynthesis system, Li-COR Biosciences Inc., Lincoln, Nebraska, USA. pp. 1–7 to 1–11.

[14] Mahan J.R. and Upchurch D.R. (1988) - Maintenance of Constant Leaf Temperature by Plants -1. Hypothesis- Limited Homeothermy - Environmental and Experimental Botany, 28: 351–357.

[15] Mahan J.R., Burke J.J., Wanjura D.F. and Upchurch D.R. (2005) - Determination of Temperature and Time Thresholds for BIOTIC Irrigation of Peanut on the Southern High Plains of Texas - Irrigation Science, 23: 145–152.

[16] Mahan J.R., Conaty W., Neilsen J., Payton P. and Cox S.B. (2010) - Field Performance in Agricultural Settings of a Wireless Temperature Monitoring System Based on a Low-Cost Infrared Sensor - Computers and Electronics in Agriculture, 71: 176–181. DOI: 10.1016/j.compag.2010.01.005.

[17] Upchurch D.R. and Mahan J.R. (1988) - Maintenance of Constant Leaf Temperature by Plants. 2. Experimental Observations in Cotton - Environmental and Experimental Botany, 28: 359–366.

[18] Upchurch D.R., Wanjura D.F., Burke J.J. and Mahan J.R. (1996) - Biologically-identified Optimal Temperature Interactive Console (BIOTIC) for Managing Irrigation -U.S Patent No. 5, 539, 637.

[19] Wanjura D.F., Upchurch D.R. and Mahan J.R. (1990) - Evaluating Decision Criteria for Irrigation Scheduling of Cotton - Transactions of the A.S.A.E., 33: 512–518.

[20] Wanjura D.F., Upchurch D.R. and Mahan J.R. (1992)- Automated Irrigation Based on Threshold Canopy Temperature - Transactions of the A.S.A.E., 35: 1411–1417.

[21] Wanjura D.F., Upchurch D.R. and Mahan J.R. (2006) - Behavior of Temperature-Based Water Stress Indicators in BIOTIC-controlled Irrigation- Irrigation Sci., 24: 223–232.

[22] Wanjura D.F., Upchurch D.R. and Hatfield J.L., Burke J.J., Mahan J.R. (1988) - Cotton irrigation Using the "Thermal Kinetic Window" Criteria -Beltwide Cotton Conference. pp. 183–185.

Efficient Water Management Technology for Sustainable Cotton Production in Central India

V. Kumar1, R.G. Patil2 and J.G. Patel3

1Research Scientist (Cotton) & Head, NAU, Surat 2Research Scientist (SWM), NAU, Navsari

3Associate Research Scientist, NAU, Bharuch

Abstract—Three research achievements are considered as milestones of Indian agriculture. The first was introduction of Norin 10- Brevor 14 dwarfing gene in wheat which lead to green revolution in mid sixties. The second was development of world’s first successful hybrid of cotton Hybrid-4 which lead to white revolution in early seventies and third is introduction of cry gene (Bt) in cotton which is considered the beginning of gene revolution in early years of current century.

To attain self sufficiency in agriculture, government paid utmost attention to the development of irrigation projects, fertilizer factories and pesticide industry and framed policies conducive to the overall development of agriculture in India. The outcome is that today, the country is self sufficient in most agricultural commodities. All this has been possible because of high yielding varieties/hybrids and input intensive production technologies. To harvest more and more, injudicious use of inputs especially water, fertilizer and pesticides became a fancy. Consequently, water resources have either dried up or become unusable due to salt ingress, soils have become sick/deficient in organic matter, and multiple nutrients and lower microbial activity, resulting in a declining/steady crop productivity. Declining water availability for agriculture, frequent droughts and unprecedented floods warrant long term and strategic research programmes. Therefore efficient use of water becomes of paramount importance.

Central zone of India, comprising the states of Madhya Pradesh, Gujarat and Maharashtra has cotton area which is mainly rain dependent (70-75%). The productivity is low in this zone vis-a-vis Northern zone which is 100 per cent irrigated and Southern zone which is about 40-45 per cent irrigated. The technology has over the years, been devised to (i) conserve rain water in rainfed tract through use of tillage, land configuration and mulch (ii) water management in irrigated tract through drainage and scheduling of irrigation based on soil moisture depletion, critical growth stages and climatological changes and (iii) other approaches for efficient water use through planting methods, tolerant variety, planting time, intercropping and use of (agro) chemicals. Efficient use of available water and fertilizers for better seed cotton yields and saving in irrigation water are discussed.

INTRODUCTION

Cotton has been a crop of importance from the days of yore. Excavations of Indus valley civilization are testimony of cotton use more than 5000 years ago. Much has changed over centuries, over decades and much over recent years. The first big change was in 1971 when the first hybrid cotton Hybrid 4 (H4) was released for commercial cultivation. This transformed the entire cotton scenario in the country whether it was research, cultivation practices, seed and pesticide industry or it was production and productivity. The second big change is more recent when cotton hybrids were released for cultivation in 2002.

Today, India produces about 30.0 m bales of cotton from nearly 10.0 m ha with an average productivity of around 500 kg lint/ha. Yet it is much below the world average of >700 kg (Anonymous, 2010). Even with best productivity of 560 kg/ha, it ranks 24th amongst cotton producing countries (Venugopalan et al., 2009). This calls for immediate attention why Indian cotton yields, despite Bts and hybrids, have not even touched world average, leave aside China and Israel. Is it production technology or is it water or both? Data presented in Table 1 would make it amply clear that water plays a predominant role in cotton productivity. Gujarat contributed 81 per cent production from 60 per cent irrigated area during 2006-09 against 19 per cent from 40 per cent unirrigated area during the same period. The average irrigated productivity was 743 kg against 262 kg of unirrigated being +34.8 per cent and -52.5 per cent, respectively, over average productivity (551 kg) of the state.

63

Efficient Water Management Technology for Sustainable Cotton Production in Central India 377

That does not undermine the importance of production technology. In fact, there are farmers harvesting as much as 2000 kg lint/ha in irrigated areas of Kutch, Rajkot and Narmada districts of Gujarat against 743 kg of state irrigated average. Hence, this gap is to be narrowed but the real challenge is for rainfed areas.

TABLE 1: AREA, PRODUCTION AND PRODUCTIVITY OF COTTON IN GUJARAT (AV. 2006-09)

Particulars Irrigated Rainfed Total Area (M ha) 1.431 60) 0.952 (40) 2.383 Production (M Bales) 6.257 81) 1.465 (19) 7.722 Productivity(kg/ha) 743 (+34.8) 262(-52.2) 551

Figures in parenthesis are per cent over state total. Source: Statistical Unit, Dept. of Agriculture, Gandhinagar

Since independence in 1947, the irrigated area under cotton in India has grown by little over five fold, so is the lint yield (Sivanappan, 2004). Currently, the country grows cotton on 11.0 m ha having wide range of soils and agroclimatic conditions. Nearly 65 per cent of cotton area is unirrigated, majority of it is in central zone (75%) followed by South zone (60%). The central zone comprises Maharashtra, Gujarat and Madhya Pradesh occupies 66 per cent of the cotton area in the country contributing 58.6 per cent production to the national kitty (Table 2).

TABLE 2: STATE WISE AREA, PRODUCTION AND PRODUCTIVITY OF COTTON (2008-09)

State Area (M ha) Production (M Bales) Productivity (Lint kg/ha) North Zone [100] 1.215 (13.0) 3.900 (13.4) 546 (+ 3.8) Central Zone [24] 6.205 (66.2) 1.700 (58.6) 466 (-11.4) South Zone [40] 1.855 (19.8) 6.700 (23.1) 614 (+ 16.7) Others 0.098 1.400 India [35] 9.373 29.00 526

Figures in parenthesis are percentage of the country. [ ]% irrigated area

The productivity in Maharashtra and Karnataka irrigated area which are predominantly unirrigated is abysmally low vis-à-vis Gujarat, Andhra Pradesh and Tamil Nadu. Central India is especially prone to the vagaries of weather where insufficient rainfall, uneven distribution and even failures are not uncommon. Therefore, the strategies have to be multipronged.

MOISTURE CONSERVATION IN RAINFED TRACT

Tillage TABLE 3: EFFECT OF DEPTH OF TILLAGE ON YIELD OF COTTON G.COT.HY.12 UNDER SOUTH GUJARAT CONDITIONS

Treatment Seed Cotton (kg/ha) Surat (Irri.) Bharuch (Rainfed)

Depth of Tillage 10 cm 2115 1514 20 cm 2417 1785 30 cm 2509 1648 S. Em. ± 51 54 C.D. at 5% 152 170

Anon., 2009

Tillage for moisture conservation is an age old practice traditionally done by bullock drawn plough. Development of mold board and disc plough lead to intensive use in cotton, as well (Kairon et al., 2002). In central and southern India, deep ploughing once in 2-3 years is common practice (Blaise and Ravindran, 2003). Preparatory criss-cross harrowing and planking, repeated interculturing (3-5) by bullock drawn hoe or mechanized cultivator is practiced by cotton farmers. However, due to variations in soil, climate, rainfall, weediness and type of cottons grown, tillage practices also vary. Reduced tillage was found to be better or equal to conventional tillage (Blaise et al., 2005) in rainfed central India. In sandy loam soils of North India, deep ploughing followed by conventional tillage was much better than no tillage or conventional tillage (Anon., 2003). Bt hybrids performed better with reduced tillage than


conventional tillage (Blaise, 2011 and Jalota et al., 2008). Tillage at 20 cm depth was found optimum in rainfed as well as irrigated Vertisols of South Gujarat (Table 3).

Land Configuration

Effective management of soil moisture is crucial for realizing potential productivity of hybrid cotton. Making beds (7.5 m) and furrows (20 cm) had been found effective in conserving moisture and enhancing yield in Gujarat (Table 4).

TABLE 4: EFFECT OF LAND CONFIGURATION ON RAINFED COTTON EQUIVALENT YIELD (POOLED OVER 3 YEARS)

Treatment Bed width Mean Depth of furrow 5.0 m 7.5 m 10.0 m 20 cm 1153 1284 1061 1166 30 cm 1169 1285 1095 1183 Mean 1161 1284 1077 CD at 5 %( Bed) 92

Achhalia (Anon., 2005).

Forming beds (120 to 180 cm wide) and furrows on a grade for in situ water harvesting was found to be efficient in deep black soils with 700-850 mm rainfall (Venkateshwarlu, 1980). Opening of furrows after every row of cotton between 30-45 days after sowing and mulching with crop residue were found promising in Maharashtra (Giri et al., 2008). Opening of alternate furrows in sole crop after last interculture enhanced yield of seed cotton, intercropping black gram further improved WUE and returns (Patel et al., 2008). Shinde et al., (2009) however, reported opening of furrows in every row after last interculture to be the most efficient.

Mulch Practice of mulching is in vogue for a long time. It is an important technique known to reduce evaporation, increase average soil moisture and yield. Besides biomulches, plastic mulches have become handy due to inherent advantage in weed suppression, maintaining soil temperature, faster mineralization in addition to efficient moisture conservation.

Use of black plastic mulch was found advantageous in increasing seed cotton yield to an extent of 22% in rainfed Vertisols and 92% in coastal salt affected soils (Table-5)

TABLE 5: ADVANTAGES OF MULCHING IN COTTON

Location Variety Soil Type Mulch Material Increase in Yield (%) Bharuch G.Cot.-10 Clay (Rainfed) Grass

Black plastic Soil 8 22 12

Danti G.Cot.Hy-8 Clay (Coastal salt affected) (Rainfed) Grass Black plastic

62 92

Source: Bharuch (Raman et al (1995). Danti Patel et al (1996).

Straw mulching and biomulching with sunnhemp were superior to opening furrows in alternate rows in improving WUE at Akola and Nagpur (TMC, 2008).

WATER MANAGEMENT IN IRRIGATED TRACT

Drainage

Cotton requires equal attention for drainage, as for irrigation. Because it is sensitive to root aeration, no water stagnation should be allowed to occur. In cotton growing areas of South Gujarat, water stagnation occurs during the monsoons. Opening 22.5 cm deep furrows after every 2 or 4 rows of cotton (120 cm apart) increased seed cotton yield by 53 or 42 per cent respectively, at Tanchha (Fig-1). In coastal salt affected rainfed soils at Danti providing drain of 0.6 m depth every 2.4 m significantly enhanced seed cotton yield. At Parbhani, providing drain at 75 m distance enhanced cotton equivalent yield by 70 per cent over normal planting without drain (Anon. 2007).


Source: (Anon., 2005)

Fig. 1: Effect of Drainage on Seed Cotton Yield at Tanchha

ASSESSING WATER REQUIREMENT

Cotton is mostly grown as kharif crop and needs 2 to 10 irrigations after cessation of rainfall under different soils and climate of western India. Thus, consumptive use varies around 650 to 1000 mm at different places (Bonde and Shanmungam, 1990). Mohan and Arumagan (1994) worked out crop coefficient (Kc) of cotton to be 0.46, 0.70, 1.01 and 0.39 at four different growth stages. The breakup of water use at different growth stages of crop under normal condition is 3.8 mm/day upto 1st flowering, 8.5 mm/day upto peak bloom and 5.1 mm/day thereafter, till harvest (Kairon et al., 2002).

Scheduling of Irrigation

Ever increasing demand of irrigation water coupled with depleting ground water sources, expanding domestic and industrial demand calls for efficient use of water. Normally cotton is grown by flood irrigation technique wherein efficiency of water is low, as also of other associated inputs. Different methods for scheduling of irrigation so as to achieve efficient use of water have been recommended.

Soil Moisture Depletion Approach

Under surface method, the crop is irrigated mainly through furrows, occasionally in border strip. The scheduling of irrigation at 75 per cent available moisture depletion was found to be optimum throughout the country (Table-6) except in sandy loam soil of Hisar (25% ASM) and loamy soils of Madurai (50% ASM) (Anon,. 1971-72 to 1977-79).


TABLE 6: SEED COTTON YIELD AS AFFECTED BY DIFFERENT SOIL MOISTURE REGIMES

Location Soil Type Seed Cotton (kg/ha) Available Soil-moisture Depletion (%)

25 50 75 100Coimbatore Sandy-clay loam 2340

(13) 2560 (9)

2930 (5)

2870 (4)

Siruguppa Medium black clay 2970 (14)

3270 (11)

3210 (8)

-

Hisar Sandy-loam 1180 (4)

1040 (2)

950 (1)

-

Rahuri Black clay 1910 (4)

1950 (3)

1890 (2)

-

Madurai Loam - 2000 (14)

1590 (9)

1140 (7)

Bhavanisagar Clay loam 975 (18)

965 (13)

975 (9)

900 (5)

Dharwad Medium black clay 2470 (12)

2585 (9)

2565 (7)

-

( ) = Numbers of irrigation Anon,. (1971-72 to 1977-79)

Critical Growth Stages Approach

Cultivation of cotton with protective irrigation is one of the mechanisms to enhance productivity and this should be based on critical growth stages (Grimes et al., 1968 and Buttar et al., 2005). In loamy-silty/clay loam soil of Sriganganagar, three irrigations at 45 DAS, flowering and boll development stage were found advantageous (Anon., 2005-06). Shinde et al. (2009) also reported similar findings from Parbhani. However, in high rainfall area of South Gujarat, two irrigation at flowering and boll development were sufficient with equal yield and WUE of surface irrigation at 0.8 IW/CPE (Table 7).

TABLE 7: YIELD AND IRRIGATION USE EFFICIENCY OF COTTON AS AFFECTED BY IRRIGATION AT CRITICAL GROWTH STAGES

Treatment Seed cotton (kg/ha)

Irrigation Use Efficiency (kg/ha/mm)

Pooled Avg. Rainfed control 1222 - Irrigation at flowering 1390 17.3 Irrigation at boll development stage 1487 18.6 Irrigation at 25 days after boll development 1354 16.9 Irrigation at flowering and boll development stage 1692 10.6 Irrigation at boll development and 25 days after boll development stage

1560 9.8

Irrigation at flowering, boll development and 25 days after boll development stage

1485 6.2

Irrigation at 0.8 IW/CPE 1660 10.3 S.Em± 50 - CD at 5% 142 -

(Patel et al., 2008)

Climatological Approach

The temperature requirement of cotton varies from 15°C (Doorenbos and Kassam, 1979) to 43°C (Kulandaivelu, 1980 and Thind, 2007) depending upto stage of the crop. Therefore, there cannot be a better approach than climatological data for surface, drip and sprinkler irrigation.

Surface Irrigation

Surface irrigation based on IW/CPE ratio 0.6 was found to be better for South Gujarat (Patel et al., 1995) and 0.75 – 0.80 for Rahuri and Sriganganagar (Anon., 1977-78 and 1981-83) (Table 8).


Sprinkler

Sprinklers have not got much acceptance amongst cotton farmers because of high sensitivity of pollen to water. Burke (2003) observed that single spray reduced seed set by 55 per cent. Additional sprays resulted in further losses and flower shedding. The findings of Sriganganagar also substantiate this (Anon., 2002-03 and 2003-04).

TABLE 8: EFFECT OF IW/ CPE RATIOS ON SEED COTTON YIELD

Location Soil type Mean Seed Cotton Yield (kg/ha) at IW/CPE ratio 0.3 0.4/ 0.45 0.6 0.75/ 0.8 0.9 C.D. @5%

Rahuri Black clay loam - -

2003 (48)

2475 (58)

2640 (64)

2518 (70)

- -

Sriganga nagar Silt laom

1800 (35)

2000 (49)

- -

2180 (63)

- -

121 -

Surat Black clayey - -

2267 (3)

2299 (5)

2021 (6)

- -

NS -

Achhalia Clay 752 (48)

1009 (56)

1112 (64)

1243 (72)

- -

- -

( ) = Depth of irrigation in cm Source: Anon. (1977-78, 1981-83) Patel et al. (1995), Anon.(2006-07)

Drip TABLE 9: ADVANTAGES OF DRIP TECHNOLOGIES OVER CONVENTIONAL METHOD OF IRRIGATION IN COTTON

Location Variety/ Hybrid

Soil type Technology

Water saving (%)

Fertilizer saving (%)

Yield increase

(%)

Source

Gujarat Khandha

G Cot Hy.8 Clay Drip 40 - 33 Anon. (1995)

Surat

G Cot Hy.6 Clay Drip 40 - - Anon. (1999)

Junagadh

G Cot Hy. 10 Clay Drip, fertigation

20 25 30 Malavia et al., (1999)

Danti

Hy G Cot.8 Clay (salt affected)

Drip + Mulch (Paired row)

- - 54 Anon. (2002)

Maharashtra Rahuri NHH-44 Clay Drip,

fertigation (liquid fertilizer)

- 25 12 Mane et al., (1996)

Parbhani NHH-44 Clay Drip, normal plain

10 - 22 Tekale et al., (1999)

Parbhani

NHH-44 Clay Drip, paired row

32 - 12 Bharambe et al., (2002)

Akola

AHM-68 Clay Drip, planting densities (0.8 ETC)

- - Higher yield with less density

Dahatonde and Deshmukh

(2003)

Rajasthan Sriganganagar

LH-144 Loam to silty clay loam

Drip, normal planting

10 - 4 Anon. (2002-03)

Raskar (2009) found distinct benefit of drip over furrow (paired row) irrigation in terms of yield (11.4%), WUE (61.6%) and saving in water (71.6%) at Rahuri.

Padmakumari and Sivanappan (1979) observed distinct advantage of drip over surface method in silty clay loam of Coimbatore as early as 1979. Extensive studies carried out in Gujarat found that water


saving due to drip ranged from 20 per cent in Saurashtra to as high as 76 per cent in middle Gujarat. Along with saving, drip irrigation also increased cotton yield by 30 per cent under normal soil condition of South Gujarat. However, this increase was 54 per cent in coastal salt affected soils (Anon., 1995; Anon., 1999; Malavia et al., 1999 and Anon., 2002). In Maharashtra, savings in irrigation water (Banke, 1996; Bharambe et al., 2002) upto 50 per cent and increase in seed cotton yield from 10 to 32 per cent Takale, et al., 1999; Mane et al., 1996) and savings in fertilizer (Banke, 1996; Mane et al., 1996) have been widely reported. Dahatonde and Deshmukh (2003) observed similar advantage of drip with less planting densities. (Table 9).

OTHER CROP/WATER MANAGEMENT PRACTICES

In addition to moisture conservation and irrigation water management practices – there are ways and means through which water requirement of cotton could be better managed for yield advantage.

Right Variety TABLE 10: PERFORMANCE OF TOP TEN BT COTTON ENTRIES IN IRRIGATED AND UNIRRIGATED SITUATION IN GUJARAT

Irrigated (Surat & Junagadh) Rainfed (Bharuch, Dhanduka & Amreli) Average Across Locations Hybrids Seed Cotton

(kg/ha) Hybrids Seed Cotton (kg/ha) Hybrids Seed Cotton

(kg/ha) RCH-138 3023 KDCHH-9632 BGII 2429 RCH-377 2572 PRCH-31 2966 KCH-14 BGII 2425 RCH-138 2561 ABCH-1065 BGII 2925 GK-205 2378 KCH-14 BGII 2532 RCH-377 2894 RCH-377 2357 PRCH-31 2529 TULASI-4 BGII 2849 KDCHH-9821 2274 KDCHH-9821 2484 AKKA 2826 RCH-138 2253 GK-205 2463 AKKA BGII 2802 MRC7247 BGII 2244 AKKA 2462 KDCHH-9821 2800 PRCH-31 2237 AKKA BGII 2397 TULASI-9 2741 AKKA 2220 MRC-7351 2374 IT-923 2720 RCH-515 BGII 2185 MRC-7305 BGII 2371

Source: Patel et al. (2010)

Since about 90 per cent cotton area in the country is under Bt hybrids, it is indeed a difficult task for a farmer to make a choice out of 1340 Bt varieties. Water requirement of different varieties vary according to their stature, yielding ability, duration besides soils and climatic conditions. An attempt made under TMC MM I – 1.4 across five locations in Gujarat indicated that out of ninety hybrids tested, five of the top ten Bt hybrids were common in both rainfed and irrigated conditions indicating their wider adaptability (Patel et al., 2010). The difference in yield in the two situation was 4-7 q/ha (Table 10). The third best Bt hybrid in irrigated did not find a place in top ten in rainfed. Similarly, the best performing hybrid in rainfed did not come up in top ten in irrigated. Thus, selection of variety could prove to be very important for profitable yield in a given situation.

Date of Sowing TABLE 11: SEED COTTON YIELD AS INFLUENCED BY TIME OF SOWING

Sowing Time No. of Bolls/ plant Seed Cotton (kg/ha) 16th Met. Week (16-22 April) 73.92 2200 18th Met. Week (30-03 May) 75.79 2248 20th Met. Week(14-20 May) 84.23 2409 22th Met. Week (28-03 June) 76.54 2292 24th Met. Week(11-17 June) 70.00 1974 26th Met. Week (25-01 July) 66.61 2004 CD @ 5 % 1.75 47.0

Pachora, Maharashtra Source: Lalage et al.,(2004)

Date of sowing is crucial in managing water relation of cotton plant. The normal sowing time is after onset of monsoon in central zone. Withdrawal of monsoon in mid September causes serious problem at


peak flowering and boll development stages due to limited non availability of water. This results in either shedding and/or poor boll development. Such a situation can be avoided by planting at the right time. Planting cotton in third week of May was most advantageous for cotton in Vidarbha, Khandesh and Marathwada pockets of Maharashtra (Table-11).

Similarly in Gujarat, the best planting time for cotton was mid May, if irrigation is available to avoid terminal stress at flowering (75 DAS) and boll development stage (90 DAS) (Anon., 2002a).

Intercropping

Cotton is a long duration crop of 160-180 days and has slow initial growth. Given the spacing adopted, there remains scope for intercropping in irrigated as also in rainfed cotton. This ensures better returns and assurance and higher input use efficiency (Willey, 1979). Cotton + green gram, cotton + pigeonpea, cotton + black gram, cotton + soybean, cotton + maize, cotton + groundnut, cotton + sesamum are recommended and being practiced in central zone (Table 12).

TABLE 12: INTERCROPPING IN COTTON

Treatments Nanded Rahuri Seed Cotton

(kg/ha) 2007-09 Cotton Equivalent

(kg/ha) Seed Cotton

(kg/ha) 2007-09 Cotton Equivalent

(kg/ha) T1-Sole Bt cotton 2552 2552 1832 1832 T2- Cotton + Pigeon pea (4 :2) 1849 2893 1768 2381 T3- Cotton + Soya bean (1:1) 2156 2949 2202 2163 T4- Cotton + Green Gram (1:1) 2291 2720 2276 2412 T5- Paired row planting cotton (60:120 cm)

2493 2493 1952 1952

T6-Paired row cotton +Green Gram 2249 2909 1978 2117 T7- Cotton + Cluster Bean (1:1) 2215 2717 1715 2342 T8- Cotton + Black Gram(1:1) 2146 2593 2043 2066 T9- Cotton + Sesamum (1:1) -- -- -- -- T10- Cotton + Green Gram (1:3) in 150 cm rows spacing

-- -- -- --

SEm ± 57.12 67.00 26.8 26.8 CD at 5% 158.1 185.4 46.5 46.5

Source Anon., 2010

Use of Antitranspirants/ Osmoprotectants TABLE 13: EFFECT OF ANTITRANSPIRANTS ON YIELD OF BUDDED COTTON G. COT. 101

Year Kaoline 6% Kaoline 4% PMA 5 mg/l

PMA 2.5 mg/l

Film Forming

Water spray

Unsprayed control

CD @ 5%

1999-00 1242 1294 1399 1714 1600 1198 1106 146 2000-01 967 1008 1253 1284 1228 847 743 241

TABLE 14: STUDIES ON AMELIORATION OF WATER STRESS THROUGH USE OF OSMOPROTECTANTS

Treatments Seed Cotton Yield (kg/ha) Average Percent over Control Glycine betaine 0.3% 1619 12.5 Calcium Chloride 0.25% 1549 7.6 Potassium nitrate 0.5% 1511 5.0 Potassium nitrate 1.0% 1525 6.0 Calcium Chloride 0.25+ 0.5%KNO3 1522 5.8 Thioglycolic acid 100 mg/l 1528 6.2 Mercaptaethyl amine 100mg/l 1694 17.7 Thiourea 500mg/l 1575 9.5 Water 1506 4.6 Control 1439

Surat, Anon., 2008


Antitranspirants haven’t found wide spread use in agricultural crops. Yet, they continue to be researchable lot. Experiments carried out in rainfed area of middle Gujarat clearly showed benefit of using phenyl mercuric acetate (2.5 mg/l) and kaoline 4 per cent on perennial budded cotton (Anon., 2002) (Table 13). Use of osmoprotectants to ameliorate stress did not indicate consistent effect, nevertheless mercapataethyl amine (100 mg/l) brought about 17.7 per cent increase in yield over rainfed control in a three year trial (Table 14).

SUMMARY

India grows cotton on 10.0 m ha having wider range of soil and agroclimatic condition. Wide variability does exist in productivity in irrigated and unirrigated cotton. Whereas this gap is to be narrowed, the productivity of irrigated has to be increased to attain the targets of XII FYP (39.0 m bales?). Water management could play a major role in this. Making beds and furrows and opening of furrows was found effective in conserving moisture. Deep tillage at least once in 2-3 years followed by conventional tillage has been found better than conventional tillage.

Drainage in water logging prone areas is advised. The size of drain depends upon the soils and area. Mulching by crop residue and/or black plastic (25 micron) has proved very effective in conserving moisture, checking evaporation, suppressing weeds, therefore efficient utilization of resources. Irrigation based on ASM depletion IW/CPE ratio /critical growth stages saved water and enhanced water use efficiency. Drip irrigation with (out) fertigation extensively established their usefulness in saving 20-76 per cent water, increase 10-32 per cent yield with better WUE depending upon soils and climatic conditions. Other approaches for efficient water management are usually advanced date of sowing, adoption of intercrops and selection of right variety.

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(Gossypium sp.) in India and its agronomic requirement.-A review. Indian J. Agron., 54(4): 343–360. [45] Willey, R.W. (1979) - Intercropping: its Importance and research needs part I. Competition and yield advantages. Field

Crop Abstract 32: 1–10.

Biodegradable Polyethylene Mulching—A New Approach for Moisture Conservation, Weed Control

and Enhanced Productivity of Winter Irrigated Cotton-Maize System

P. Nalayini1, K. Sankaranarayanan1, K. Velmourougane2 and M. Suveetha1

1Central Institute for Cotton Research, Regional Station, Coimbatore–641003, India 2Central Institute for Cotton Research, Nagpur, Maharashtra–440010, India

Abstract—Field experiment was conducted during 2008-09 and 2009-10 cropping season under winter irrigated (August – February) cotton followed by maize (March-May) at Coimbatore, Tamilnadu. Eight mulch treatments including polyethylene mulching, biodegradable polyethylene, sugarcane trash, maize stover, gunny sheet, coir waste (subsoil), surface coir waste application were compared with no mulch control under three moisture regimes, 0.4 ETc through drip, 0.8 ETc through drip and conventional irrigation. The results indicated that the cotton (RCHB 708 Bt) responded significantly to moisture regimes and mulches. The crop responded up to 0.8 ETc under no mulch condition. However, under mulched situation, the seed cotton yield started decreasing beyond 0.4 ETc. Among the mulches, polyethylene and biodegradable polyethylene mulching were similar and better than the other mulches. Among the combinations, polyethylene mulching with drip irrigation at 0.4 ETc recorded 5641 kg /ha and was on par with biodegradable polyethylene (5241 kg/ha) at the same moisture level of 0.4 ETc. The water requirement for ELS cotton RCHB 708 Bt was 464, 630 and 800 mm at 0.4 ETc, 0.8 ETc and conventional irrigation, respectively. The zero tilled rotation maize grown after cotton was also significantly influenced due to moisture regime and mulch treatments.

INTRODUCTION

Use of polyethylene mulching for moisture conservation, weed control and enhancing the productivity of agricultural crops has been documented and gaining global significance now than ever before as it reduces the water loss, improves efficiency of water and thus helps in saving the precious resource. Drip irrigation used in combination with plastic mulch needs water to meet the crop water requirement as the other losses of water are kept under minimum thereby increasing the use efficiency of water. Yield of cotton increased by 1.9 and 2.1fold following zero tilled rotation maize due to poly ethylene mulching as compared to conventional method without mulching (Nalayini et al., 2009). However, polyethylene film cannot biodegrade naturally and it can be made biodegradable by adding a small quantity (4%) of pro- degradant additive (a patented product from UK) at the time of manufacturing the poly film. This technology produces plastic which degrades by a process of oxo- degradation. The very small quantity of added additive changes the behavior of the plastic products. The plastic after the desired usage will not be just a fragment, but will be consumed by bacteria and fungi after the additive has reduced the molecular structure to sub 40,000 daltons which permits living micro-organisms access to the carbon and hydrogen and the transformed plastic behaves in the same way as nature’s wastes. It is assimilated by the same bacteria and fungi and they convert the degraded plastic to cell biomass just like lignocelluloses such as straw, leaves and twigs (Michael Stephenson, 2009). Oxo-biodegradable plastics sheets can be programmed at manufacturing stage to degrade soon after the harvest or until the mission is accomplished. Field trial in Australia using biodegradable mulch film on Capsicum spp. has shown that biodegradable plastic performs as well as polyethylene film and offers advantage of being able to be ploughed into the ground after harvest which can reduce the disposal costs while enriching the soil with carbon on degradation (Anon, 2004). The use of biodegradable mulch films seems to be a promising alternative because the films can degrade right in the field and the amount of waste ending up in landfills can be avoided (Kijchavengkul et al., 2008).

64

Biodegradable Polyethylene Mulching—A New Approach for Moisture Conservation, Weed Control 387

Polyethylene mulching standardized for cotton (30 or 50 µ) poses environmental threats as it is non-degradable. Hence, if a substitute with biodegradable nature and with advantages of poly ethylene, it is the need of the hour and so this study was attempted.


Field experiments were conducted consecutively during 2008-09 and 2009-10 with cotton in winter (August-February) cotton followed by maize in the summer (February-April) season. Raised bed 1.5 m wide and 9 m long, were made using tractor drawn broad bed furrow (BBF) maker with 30 cm furrow for irrigation. The polyethylene sheet of 30 µ thickness with silver colour top layer and black colour bottom layer was used. The sheet was spread uniformly over the raised bed and the edges were sealed with soil, leaving 30 cm from both the edges. Two sowing lines were fixed using sowing rope having markings at 60 cm apart. The holes were made at 60 cm apart using a 5 cm diameter GI pipe (by gently pressing the GI pipe, holes were made easily as the thickness of the film was very less). Sowing was done carefully inside the sowing holes and the sowing irrigation was given immediately. For biodegradable polyethylene mulching, the same procedure was followed. In the polyethylene mulch sheet at the time of manufacturing, an additive (d2w, a patented product supplied by Symphony Environmental Technologies PIC. a global supplier of pro-degradant additive.) was added at the time of manufacturing at the rate of 4% concentration to make the biodegradable polyethylene used in the experiment. For sub soil coir treatment, the coir waste was applied before sowing using the tractor drawn subsoil coir pith applicator which could open the furrow at the centre of the raised bed and placed the coir wastes and covered it with soil. For sugarcane trash, maize stover and surface coir mulching, 50 % of the mulch materials were applied 15 DAS and the remainder applied 30 DAS above the entire raised bed. Sugarcane trash and maize stover were cut into 10-15 cm using chop cutter before application. The gunny sheet was spread in between the two sowing rows. Sowing (RCHB 708 Bt) was done on 29 August 2008 and 5 September 2009. The design used was split plot with eight mulch treatments, control (no mulch), sub soil coir pith (20 t/ha), maize stover (50 t/ha), sugar cane trash (50 t/ha), surface coir (20 t/ha), gunny sheet, biodegradable poly mulching and poly mulching in the main plots with three moisture regimes viz., conventional irrigation, drip irrigation at 0.4 ETc and drip irrigation at 0.8 ETc in the sub plot. The soil of the experimental sites was low in available nitrogen (168 to174 kg/ha), high in available P (27.6 to 28.6 kg/ha) and high in available K (625 to 654 kg/ha). The infiltration rate of the experimental soil was 1.88cm/hr and moisture content (%) at field capacity and permanent wilting point was 23.4 and 17.4 respectively. Fertilizers (120: 26.4: 50 kgs NPK/ha) were applied uniformly to all the treatments. Full dose of phosphatic fertilizer and ¼ of nitrogenous and potassic fertilizers were given as basal at sowing and the remaining dose of N and K were given in three equal splits at monthly interval. Drip lines were fitted with one lateral for two rows and one dripper for two crop hills.

Calculation of Water to be Applied through Drip System

The volume of water to be given on alternate days through drip was calculated using the following formula.

( ) AKKEV cpp ×××= 7.0 (1)

Where, V is Volume of water to be given (liters) / dripper Ep is Pan evaporation (mm)

Kp is Pan Co-efficient (0.7) and Kc is Crop Co-efficient (0.45, 0.75, 1.15 and 0.75 for initial (0-25 DAS), development stage (26-70 DAS), boll development (71-120 DAS) and maturity stage (121- harvest), respectively).

Based on the above formula, a ready reackoner was prepared and the irrigation was scheduled on alternate days based on the open pan evaporimeter reading and the exact quantity of water given was measured using water meter. For control plot, the water to be irrigated was measured through a ‘V’ notch. After the harvest of cotton, stalks were cut below the cotyledon leaves to avoid re growth of cotton. Holes were made 5 cm away from the cotton rows at 20 cm (plant to plant) and the maize hybrid, CORH M4 was sown.



Growth Attributes

Growth attributes like, plant height, leaves/plant and dry matter accumulation were influenced significantly due to mulches and moisture regimes (Table 1). All these growth attributes were higher under drip at 0.8 ETc and was similar to drip at 0.4 ETc and significantly higher than conventional irrigation. Except surface coir pith application and no mulch control all other mulches recorded significantly taller plants, more number of leaves and higher dry matter accumulation and were on par with each other.

TABLE 1: GROWTH AND YIELD ATTRIBUTES OF EXTRA LONG STAPLE BT COTTON CV. RCHB 708 BT AS INFLUENCED BY MOISTURE REGIMES AND MULCHES

Treatments Plant ht (cm) Leaves/ Plant

Sympodia/ Plant

Bolls/Plant Boll wt. (g/boll)

Mulches No mulch control 118.7 229.5 22.7 44.7 5.90 Sub soil coir pith (2 kg/ m2 ) 145.7 261.5 24.4 49.1 5.90 Maize stover (5 kg / m2 ) 146.2 263.0 24.6 51.7 6.13 Sugarcane trash (5 kg / m2 ) 147.2 264.0 24.2 50.7 6.00 Surface coir pith (2 kg/ m2 ) 120.5 245.0 23.1 45.2 5.83 Gunny sheet mulching 145.0 260.0 24.5 49.8 6.20 Biodegradable poly mulching 152.5 288.2 25.5 72.3 6.10 Poly ethylene mulching 154.2 295.4 26.2 79.4 6.30 CD(P = 0.05) 16.8 41.5 1.35 7.92 NS Moisture regimes 0.4 ETc (Drip) 152.0 285.2 24.9 59.0 6.25 0.8 ETc (Drip) 156.2 297.0 25.3 56.6 5.93 Conventional irrigation 115.5 207.8 23.0 50.5 5.98 CD(P = 0.05) 7.34 18.12 0.59 3.45 NS CD (p=0.05) for interaction NS NS NS 13.30 NS

Yield Attributes TABLE 2: BOLL NUMBERS IN EXTRA LONG STAPLE BT COTTON CV. RCHB 708 BT AS INFLUENCED BY INTERACTION BETWEEN MOISTURE REGIMES AND MULCHES

Mulches Moisture Regimes Drip

(0.4 ETc) Drip

(0.8 ETc) Conventional

Irrigation Mean

No mulch control 40.3 54.3 39.5 44.7 Sub soil coir pith (2 kg/ m2 ) 53.9 48.1 45.2 49.1 Maize stover (5 kg / m2 ) 54.7 52.9 47.5 51.7 Sugarcane trash (5 kg / m2 ) 52.2 49.0 50.8 50.7 Surface coir pith (2 kg/ m2 ) 49.1 46.0 40.5 45.2 Gunny sheet mulching 54.4 50.6 44.4 49.8 Biodegradable poly mulching 78.2 72.9 65.9 72.3 Poly ethylene mulching 89.3 78.7 70.0 79.4 Mean 59.0 56.6 50.5 CD (p=0.05) for mulches 7.92 CD (p=0.05) for moisture regimes 3.45 CD (P=0.05) for interaction 13.30

The ELS cotton RCHB 708 Bt responded significantly to the type of mulches, moisture regimes and their interaction (Table 2). The results indicated that under no mulch condition, the ELS Bt cotton responded up to 0.8 ETc through drip which recorded 54.3 bolls/plant as against 39.5 bolls/plant at conventional irrigation. In general wherever mulches were applied, the response was obtained up to 0.4 ETc and the bolls number started decreasing beyond 0.4 ETc. Polyethylene mulching and biodegradable polyethylene mulching recorded higher number of bolls/plant and was on par and significantly superior to the rest of the treatments. Boll weight was not significantly altered either due to mulches or due to moisture regimes.


Seed Cotton Yield (SCY)

SCY followed trend similar to that of boll numbers. The interaction effect between mulches and moisture regimes was significant (Table 3). Under no mulch condition, RCHB 708 Bt responded up to 0.8 ETc through drip and SCY was 44.5 % greater over conventional irrigation. However, under mulched condition, the response was seen only up to 0.4 ETc and the SCY started decreasing at 0.8 ETc. Among the mulches, polyethylene mulching and biodegradable poly ethylene mulching recorded significantly higher SCY than the other mulches. Among the combinations, irrigation through drip at 0.4 ETc with polyethylene mulching recorded the highest seed cotton yield of 5641kg/ha and was on par with biodegradable polyethylene mulching (5234 kg/ha) at the same moisture level and polyethylene mulching under conventional irrigation and biodegradable polyethylene mulching under conventional irrigation. Thus, we infer that when polyethylene mulching and biodegradable polyethylene mulching are used even the conventional irrigation is sufficient to get the desired yield. In terms of conserving moisture further we can combine the polyethylene or biodegradable polyethylene with drip irrigation. Higher yield and better growth under mulched condition at lower moisture regime were due to better moisture conservation and favourable condition for plant growth. Ramakrishna et al. (2006) reported 94.5% higher yield in groundnut due to polyethylene mulching. Elias and Goldhamer (1991) have reported 39% enhanced yield due to mulching in cotton crop in California, USA.

TABLE 3: SEED COTTON YIELD IN EXTRA LONG STAPLE BT COTTON CV. RCHB 708 BT AS INFLUENCED BY INTERACTION BETWEEN MOISTURE REGIMES AND MULCHES

Mulches Moisture regimes Drip

(0.4 ETc) Drip

(0.8 ETc) Conventional

Irrigation Mean

No mulch control 3175 4070 3421 3555 Sub soil coir pith (2 kg/ m2) 4314 4067 4092 4158 Maize stover (5 kg / m2 ) 4254 4215 3979 4149 Sugarcane trash (5 kg / m2) 4208 4077 4002 4096 Surface coir pith (2 kg/ m2) 4111 3809 3442 3787 Gunny sheet mulching 4354 4250 3994 4199 Biodegradable poly mulching 5234 4647 4679 4853 Poly ethylene mulching 5641 5418 5357 5472 Mean 4411 4319 4121 4284 CD (p=0.05) for mulches 620.0 CD (p=0.05) for moisture regimes 270.1 CD (p=0.05) for interaction 928.5

Water Use Efficiency TABLE 4: DRY MATTER PRODUCTION, WATER USE EFFICIENCY AND NUTRIENTS UP TAKE OF BT COTTON CV. RCHB 708 BT AS INFLUENCED BY MOISTURE REGIMES AND MULCHES

Treatments DMP(t/ha)

WUE* Nutrients Uptake (kg/ha) N P2O5 K2O

Mulches No mulch control 4.80 58.4 120.0 25.4 134.4Sub soil coir pith (2 kg/ m2) 6.30 70.2 158.1 34.0 217.9Maize stover (5 kg / m2 ) 6.39 68.9 160.4 35.5 215.5Sugarcane trash (5 kg / m2) 6.45 68.7 162.5 35.8 216.4Surface coir pith (2 kg/ m2) 5.15 63.3 127.2 26.3 164.8Gunny sheet mulching 6.42 69.2 161.8 34.0 215.4Biodegradable poly mulching 6.61 81.8 172.4 35.6 218.1Poly ethylene mulching 6.98 91.6 177.3 35.4 230.3 CD(P = 0.05) 1.68 19.35 1.41 15.38Moisture regimes 0.4 ETc (Drip) 6.59 95.2 165.4 35.9 210.30.8 ETc (Drip) 6.75 68.6 170.8 36.4 216.0Conventional irrigation 5.05 50.7 128.7 25.9 178.5 CD(P = 0.05) 0.73 8.44 0.62 6.72CD (p=0.05) for interaction NS NS NS 24.10

*WUE = water use efficiency (Kg of seed cotton/ha cm of water used)


The water use efficiency as measured by the ratio of seed cotton produced to the water consumed by the cotton crop ranged from 58.4 to 91.6 kg/ha cm, with the highest values recorded for the poly ethylene mulching followed by biodegradable poly mulching (Table 4) and the least with no mulch. Among the moisture regimes, drip at 0.4 ETc recorded the highest WUE which was probably due to better moisture conservation due to mulches at lower moisture regime of 0.4 ETc.

Uptake of Nutrients

The uptake of nutrients by ELS cotton, RCHB 708 was influenced significantly by mulches and moisture regimes (Table 4). Higher temperature and faster mineralization under mulching might have caused increased uptake of nutrients and the favourable rhizospheric and phyllospheric microbes such as Diazotrophs, Facultative methylotrophs, Azospirillum, Phosphorus Solubilizing Bacteria (PSB) and Vesicular Arbuscular Mycorrhizae (VAM) were higher by 2 to 3 fold due to mulching (Nalayini et al., 2009). The enhanced temperature under mulching coupled with higher available soil moisture regime was highly favourable for the rhizosphere and phyllosphere microorganism. The temperature increase due to mulching has been reported earlier (Chen and Yin, 1989 and Chakraborty and Sadhu, 1994). All other mulches except no mulching and surface coir recorded significantly higher uptake of N, P2O5 and K2O. The significant reduction in uptake of these nutrients in surface coir application might be due to higher C/N ratio of the coir material when it is applied on the surface of the soil which caused stunted growth and lesser dry matter accumulation by the cotton crop. However, when the coir was placed deep using coir pith applicator, it was as good as application of other plant wastes like sugarcane trash or maize stover mulching. Among the moisture regimes, drip at 0.4 ETc and 0.8 ETc were similar and significantly superior to conventional irrigation.

Fibre Quality

Fibre quality parameters (Span length, strength, micronaire, uniformity and maturity ratio) were not influenced significantly due to the mulches or moisture regimes.

Zero Tilled Rotation Maize TABLE 5: GRAIN YIELD OF ZERO TILLED ROTATION MAIZE AFTER THE HARVEST OF COTTON AS INFLUENCED BY MOISTURE REGIMES AND MULCHES

Mulches

Moisture regimes Drip

(0.4 Etc) Drip

(0.8 Etc) Conventional

Irrigation Mean

No mulch control 4.00 4.80 4.13 4.31 Sub soil coir pith 6.27 6.23 6.20 6.23 Maize stover 6.23 6.40 6.23 6.29 Sugarcane trash 6.30 6.40 6.27 6.32 Surface coir pith 2.70 2.63 2.70 2.68 Gunny sheet mulching 6.37 6.40 6.13 6.30 Biodegradable poly mulching 6.23 6.77 6.27 6.42 Poly ethylene mulching 6.57 6.40 6.50 6.49 Mean 5.58 5.75 5.55 CD (p=0.05) for mulches 0.19 CD (p=0.05) for moisture regimes 0.08 CD (p=0.05) for interaction 0.32

The treatment surface coir application resulted in the lowest maize grain yield (Table 5). This may be due to wider C/N ratio of the coir waste (how much) when applied on the surface. All the mulches except surface coir mulching recorded higher maize grain yield over no mulch control might be due to moisture conservation and also due to supply of nutrients due to degradation of crop waste applied as mulches over period of six to eight months. Among the moisture regimes, drip at 0.8 ETc recorded the highest maize grain yield followed by drip at 0.4 Etc and conventional method. The efficient moisture conservation achieved at lower moisture regime of 0.4 ETc for cotton crop could not be achieved for maize crop as the applied mulches started degradation over a period of time.


CONCLUSION

Our studies suggest biodegradable polyethylene was as good as polyethylene mulch for Bt cotton. Crop based mulches like sugarcane trash mulching, maize stover mulching and sub-soil application of coir waste could be considered as next only to polyethylene and biodegradable polyethylene mulching. Surface coir application was not useful. Under no mulch condition ELS cotton responded up to 0.8 ETc. while under mulched condition, the crop responded only up to 0.4 ETc and the yield level started decreasing at 0.8 ETc. Hence, whenever mulching is used we can reduce irrigation water requirement by half. Polyethylene mulching and biodegradable polyethylene mulching with irrigation at 0.4 ETc through drip system was similar to conventional irrigation with mulch. Hence, whenever polyethylene mulching and biodegradable polyethylene mulching were used, even the conventional irrigation is sufficient to get the desired yield.

REFERENCES [1] Anon, 2004. Degradable Plastics-Breaking news Down Under. Australian Government Department of the Environment and

Heritage, News letter, Issue 2, 1–5. [2] Chakraborty, R.C. and Sadhu, M.K.1994. Effect of mulch type and colour on growth and yield of tomato (Lycopersicum

esculentum) Indian Journal of Agricultural Sciences 64: 608–612. [3] Chen, Q.E. and Yin, J.S.1989. Effects of plastic mulch on soil properties and cotton growth in saline – alkali soils. Journal

of Soil Science China 20: 1–3. [4] Elias, Fereres and Goldhamer, David A. 1991. Plastic mulch increases cotton yield, reduces need for pre-season irrigation.

California Agriculture 45(1): 25–28 [5] Kijchavengkul,T.,Auras,R.,Rubino,M.,Ngouajic,M.,Fernendez,R.T.2008.Assessment of aliphatic – aromatic copolyester

biodegradable mulch films.Part I: field study. Chemosphere 71(5), 942–953. [6] Michael Stephenson.2009. Biodegradable plastics. In: Proceedings of the Plas Tec2008- International Conference on

Plastics and Environment held during 22nd-23rd February, 2008 at Chennai pp 127–144. Organized by Chennai Plastics Manufacturers and merchants Association.

[7] Nalayini, P., Raja, R and Anderson A. Kumar.2006.Evapotranspiration based scheduling of irrigation through drip for cotton (Gossypium hirsutum). Indian Journal of Agronomy 51(3): 232–235.

[8] Nalayini, P., Anandham, R., Sankaranarayanan. K and Rajendran, T.P. (2009) -Polyethylene mulching for enhancing crop productivity and water use efficiency in cotton (Gossypium hirsutum) and maize (zea mays) cropping system. Indian Journal of Agronomy 54(4): 409–419.

[9] Ramakrishna, A., Hoang Minh Tam., Suhas P. Wani, Tranh dinh Long. 2006. Effect of mulch on soil temperature, moisture, weeds infestation and yield of groundnut in northern Vietnam. Field Crops Research 95: 115–125.

Comparative Study of Different Weeding Methods on Cotton Crop under Drip Irrigation System

Dil Baugh Muhammad1, M.N. Afzal2, I. Raza2 and P.L. Dupont3 1Senior Scientific Officer/Head, Agronomy Section, Central Cotton Research Institute, Multan

2Scientific Officer, Agronomy Section, Central Cotton Research Institute, Multan 3Director, M/S. Jaffer Agro Services (Pvt.) Ltd.

Abstract—Weeds are the most efficient users of resources due to their different kinds, intensity, fast growth and soil coverage habits. It is important to keep weeds below the critical threshold level where the use of tractor drawn implements is not possible. Field experiment was conducted in two consecutive years (2009 & 2010) to evaluate the effect of different weed control measures on weed intensity, seed cotton yield, its components and fibre characteristics under drip irrigation. Treatments comprised pre-emergence application of Stomp 330E (Pendimethalin 33%) @ 2.5 litre/ha, Dual Gold 960EC (s-metolachlor) @ 2.0 litre/ha, and post emergence alone with Mera -71SG (Ammonium salt of Glyphosate) @ 2.0 kg /ha, or following the pre-emergence treatment, respectively. (Stomp 330E + Mera -71SG, Dual Gold 960EC + Mera-71S), plastic mulching, manual weeding and untreated check. Treatments were arranged in a randomized complete block design with four replications. Cotton cultivar CIM-573 was dibbled manually on top of the beds at the Experimental Research Area of Central Cotton Research Institute, Multan on silty loam soil. Results indicated that among chemical weeding treatments, the greatest yield gains were obtained by pre emergences followed by post emergence than pre emergence alone and post emergence alone. Furthermore, chemical weeding methods significantly broad and narrow leaved weeds than untreated. Dual Gold 960EC + Mera -71SG resulted in 98.5 and 98.6 % broad and narrow leaved weeds control, respectively at 60 days after sowing over untreated. Among treatments, manual weeding (3 times) produced the maximum seed cotton yield among the treatments which was 167.8% more than untreated. Whereas, plastic mulching resulted in the lowest (49.5%) increase in seed cotton yield over untreated. Fiber characteristics were not affected by any of the treatments.

Keywords: Cotton (Gossypium hirsutum L.), Pre- and post-emergence weedicides, Weed intensity, Seed cotton yield. Drip irrigation

INTRODUCTION TABLE 1: THE MAJOR WEED FLORA OF COTTON IN SOUTHERN PUNJAB, PAKISTAN

Botanical Name Common name Local Name Euphorbia prostrata Petty spurge Dhodak Convolvulus arvensis Field bindweed Lehli Cynodon dactylon pers Bermuda grass Khabbal ghass Cyperus rotundus Purple nutsedge Deela Portulaca oleracea Common purslane Kulfa Sorghum halepense Johnsongrass Baru Trianthema monogyna Horse purslane It-Sit Amaranthus viridis Green amaranth Chulai Echinochloa colonum Jungle rice Swanki ghass Setaria viridis Green foxtail Loomar ghass Euphorbia helioscopia Sun spurge Dhodak Corchorus tridens Wild jute Jangli Patsun Digeria arvensis Kundra Tandla Tribulus terrestris Pucturevine Bhakhra

Cotton (Gossypium hirsutum L.) is the leading fibre and cash crop of Pakistan and major source of foreign exchange earning. The crop always remains at risks and suffers production constraints due to many factors including weeds, insect pests and diseases. Weeds are responsible for losses in cotton yield to an extent of 34-61.4% (Ahmad, 1995) Therefore, to enhance productivity weeds should be controlled either by cultural, mechanical, biological and chemical methods. The major weed flora observed in cotton fields of southern Punjab of Pakistan are presented in Table-1.

65

Comparative Study of Different Weeding Methods 393

In developing countries, farmers are reluctant to adopt chemical weed control methods due to lack of comprehensive knowledge about its use. Weed management becomes complicated when mechanical weeding is not possible and manual weeding becomes very expensive. Furthermore, manual labourers are not available when needed. The highest weed control was achieved by the application of pre emergence weedicide (Pendimethalin) followed by post emergence weedicide (Glyphosate) application with two hand weedings and two hoeings at 20 and 40 days (Deshpande et al., 2006). Similarly, application of S-metolachlor and Glyphosate-TM AEPOST increased cotton lint yield (Clewis et al., 2008). Where as, others use plastic mulching to alter soil temperature, crop water use, reducing weed competition for other resources, improving crop quality and development and enhancing yields (Lamont, 2005). The present study was conducted to evaluate the effect of different weed control measures on yield, yield components and fibre characteristics of cotton crop under drip irrigation.

MATERIAL AND METHODS

Field experiments were conducted during 2009 and 2010 at the Agronomic Research Area of Central Cotton Research Institute, Multan. The experiment were laid out in a randomized complete block design with four replications on well prepared soil and bed furrows were made 75cm apart by tractor driven implements. Bed shaping was done having ditches 75cm apart from each other and from top 45cm on raised sides to support irrigation water by drip. Cotton cultivar CIM-573 was dibbled manually on inner side of raised edges at the top of the beds on 17 May 2009 and 12 May 2010 on silt loam soil. Thinning was done 22 days after sowing (DAS) by making single plant per hill. Stomp 330E @ 2.5 litre/ha-1 (Pendimethalin 0.825 kg a.i ha-1) and Dual Gold 960EC @ 2.0 litre/ha-1 (S-metolachlor 1.92 kg a.i/ ha-1) were sprayed soon after planting on moist soil with knapsack hand sprayer. The post emergence herbicide Mera -71SG (Ammonium salt of Glyphosate) @ 2.0 kg /ha was sprayed 35 DAS in specific treatments as protective spray by using shield with spray nozzle. Manual weeding was done at 24, 40 and 52 DAS during 2009 and 25, 41 and 55 DAS during 2010. Removal of weeds by manual weeding from all treatments including control plots was done 70 DAS. All kinds of uprooted weeds were removed from the field. The data were statistically analyzed by using Fisher’s analysis of variance techniques and least significance difference test at 5% probability level applied to compare the significance of the treatment means (Steel and Torrie, 1997).


Data presented in Table 2 showed that all the pre-emergence and mechanical (mulching & manual) weed control methods gave significant weed control over untreated. The dry weight of weeds 30 DAS with Stomp 330E and dual Gold 960EC was reduced by 75 to 86 than the untreated. Among pre-emergence weedicides, Dual Gold 960EC produced better weed control as compared to Stomp 330E. Mulching method resulted in 100.00 and 95.8% control of broad and narrow leaved weeds, respectively over untreated. Manual weeding resulted in 89.7 and 91.0% broad and narrow leave weeds control over untreated, respectively. In case of mechanical weed control methods; mulching gave 10.3% broad leave weeds and 4.8% narrow leave weeds control higher than manual weeding. Similar results were also found by earlier researchers (Ali et al., 2005).

TABLE 2: DRY WEIGHT OF WEEDS AND WEED CONTROL %AGE 30 DAYS AFTER SOWING

Dry weight (g m-2) %age Weed Control Broad Leaves

Narrow Leaves

Broad Leaves

Narrow Leaves

Stomp 330E @ 2.5 L ha-1 at sowing 15.6 24.4 81.6 75.1 Dual Gold 960EC @ 2.0 L ha-1 at sowing 11.6 14.4 86.3 85.3 Mulching (Plastic) at sowing 0.00 4.1 100.00 95.8 Manual weeding 8.7 8.8 89.7 91.0 Untreated check 84.6 97.9 - - C.D 5% 2.270 2.433 - -

Post-emergence weedicide application gave near total control of both broad and narrow leaved weeds over untreated, respectively at 60 DAS (Table-3). Interactive effect of Stomp 330E as pre-emergence and


Mera 71SG @ 2.0 kg ha-1 as post emergence also resulted in 98.0 and 98.3% broad and narrow leave weeds control over untreated, respectively. Similarly, Dual Gold 960EC pre-emergence along with Mera 71SG post-emergence gave effective weed control over untreated, respectively. Mulching and manual method of weeding reduced weed biomass significantly compared to untreated. Sheikh et al. (2006) reported similar results.

TABLE 3: DRY WEIGHT OF WEEDS AND WEED CONTROL %AGE 60 DAYS AFTER SOWING

Treatments Dry Weight (g m-2) % Age Weed Control Broad Leaves

Narrow Leaves

Broad Leaves

Narrow Leaves

Stomp 330E @ 2.5 L ha-1 at sowing 122.2 78.5 40.3 63.5 Dual Gold 960EC @ 2.0 L ha-1 at sowing 116.6 72.7 43.1 66.2 Mera 71SG @ 2.0 kg ha-1

37 days after sowing 3.2 3.2 98.4 98.5

Stomp 330E @ 2.5 L ha-1+ Mera 71SG @ 2.0 kg ha-1

4.1 3.6 98.0 98.3

Dual Gold 960EC @ 2.0 L ha-1+ Mera 71SG @ 2.0 kg ha-1

3.0 3.1 98.5 98.6

Mulching (Plastic) at sowing 1.7 21.4 99.2 90.1 Manual weeding 4.7 4.8 97.7 97.8 Untreated check 204.8 215.1 - - C.D 5% 2.588 1.792 - -

Plant growth and seed cotton yield were significantly better with pre-emergence, post-emergence, mulching and manual weed control methods alone or in combination than the untreated (Table-4). Stomp 330E, Dual Gold 960EC and Mera 71SG alone produced 91.7, 101.4 and 63.1% higher seed cotton yield over untreated, respectively. Among weedicides, Mera 71SG (post emergence) applied at day 35 after sowing gave less increase in seed cotton yield as compared to pre-emergence weedicides due to initial weed-crop competition. Pre-emergence weedicides (Stomp 330E and Dual Gold 960EC) alongwith post-emergence weedicide increased seed cotton yield by 116.2 and 141.5% over untreated, respectively. Among treatments, manual weeding showed maximum increase in seed cotton yield (167.8%) over untreated. Mulching (Plastic film) gave smallest (49.5%) increase in seed cotton yield over untreated. This was probably due to low evaporation and less aeration which may have affected root expansion and limiting plant growth. These results are in line with the findings of other scientists including Cheema et al. 2005 and Dilbaugh et al. 2009).

TABLE 4: SEED COTTON YIELD AND ITS COMPONENTS INFLUENCED BY DIFFERENT WEED CONTROL METHODS ON BED-FURROW PLANTING UNDER DRIP IRRIGATION

Treatments Plant height cm) Number of bolls m-2 Boll weight (g) Seed cotton yield (kg ha-1)Stomp 330E @ 2.5 L ha-1 83.8 68 2.81 1641 Dual Gold 960EC @ 2.0 L ha-1 87.5 73 2.80 1724 Mera 71SG @ 2.0 kg ha-1 68.2 56 2.76 1396 Stomp 330E @ 2.5 L ha-1+ Mera 71SG @ 2.0 kg ha-1

86.6 79 2.78 1851

Dual Gold 960EC @ 2.0 L ha-1+ Mera 71SG @ 2.0 kg ha-1

88.8 88 2.80 2067

Mulching (Plastic) 67.1 54 2.78 1280 Manual weeding 91.5 96 2.85 2292 Untreated check 61.3 35 2.68 856 C.D 5% 7.530 3.647 NS 256.418

The economics of all the weeding methods tested in this study are presented in Table 5 showed that under mechanical methods manual weeding (3 times) gave maximum net profit Rs.107549 ha-1 followed by chemical weeding control method (Dual Gold 960EC + Mera 71) produced maximum net profit of Rs.92777 ha-1. It is clear from the results that use of pre-emergence with post-emergence weedicide is necessary for profitable cotton production on bed-furrow under drip irrigation system. Ali et al (2005), Cheema et al. (2005) and Dilbaugh et al. (2009) reported similar results on cotton planted under bed-furrow planting technique.

Comparative Study of Different Weeding Methods 395

TABLE 5: ECONOMICS ANALYSIS OF DIFFERENT WEEDING METHODS

Weeding Method Input Cost of Variable Factor (Rs ha-1)

Value of Increased Seed Cotton Yield over no Weeding (Rs. ha-1)

Net Profit

Stomp 330E@ 2.5 L ha-1 1795 62564.5 60769.5 Dual Gold 960EC@ 2.0 L ha-1 1540 69179.6 67639.5 Mera 71SG@ 2.0 kg ha-1 2200 43038.0 40838.0 Stomp 330E @ 2.5 L ha-1 + Mera 71SG @ 2.0 kg ha-1

3995 79301.5 75306.5

Dual Gold 960EC @ 2.0 L ha-1 + Mera 71SG @ 2.0 kg ha-1

3740 9516.7 92776.7

Mulching (plastic) 3848 33792.8 29944.8 Manual weeding 6900 114449.2 107549.2Untreated check - - -

Stomp 330E = Rs.718/lit. Dual Gold 960EC = Rs.770/lit. Mera 71SG = Rs.1100/kg Manual weeding = Rs.115/day Price of Seed cotton = Rs.3188/40 kg

With regard to fiber characteristics, staple length (25.9 -27.1 mm) micronaire (5.2 to 5.4) and fiber strength (27.1 to 28.2 g/tex) of cotton lint was not influenced by different weeding management methods either by chemical, manual or mulching.

CONCLUSION

Weed management under drip irrigation requires special attention. Weeds can be effectively controlled by application of pre and post emergence weedicides. Mera 71SG (post-emergence) weedicide was effective in control of perennial weeds. Though plastic mulching controlled weeds effectively, it did not result in yield gains obtained with manual and chemical treatments. Net profit was the most with manual weeding followed by chemical weed control.

REFERENCES [1] Ahmad, Z. (1995) - Twenty-five years research activities at Central Cotton Research Institute, Multan from 1970–75,

pp. 6-7. [2] Ali, M.A., Sabir, S., YAr, K., Ali, M. and Saeed, M. (2005) - Cultural and chemical weed management in cotton for higher

profitability in Pakistan. J. Agric. Res. 43(1): 51-59. [3] Cheema, M.S., Akhtar, M. and Iqbal, M.S. (2005) - Evaluation of mechanical, chemical and manual weed control methods

in cotton. Pak. J. Weed Sci. Res. 11(3-4): 137-140. [4] Clewis, S.B., Millere, D.K., Koger, C.H., Baughman, T.A., Price, A.J., Portefield, D. and Wilcut, J.W. (2008) - Weed

management and crop response with glyphosate, S- metolachlor, Trifloxysulfuron, Prometryn and Msma in glyphosate resistant cotton. Weed Tech. 22(1): 160-167.

[5] Deshpande, R.M., Pawar, W.S., Mankar, P.S., Bobde, P.N. and Chimote, A.N. (2006) - Integrated weed management in rainfed cotton (Gossypium hirsutum L.). Ind. J. Agron. 51(1): 68-69.

[6] Dilbaugh, M., Afzal, M.N., Raza, I. and Mian, M.A. (2009) - Effect of mechanical and chemical weed control on the productivity of cotton. Pak. J. Weed Sci. Res. 15(2-3): 117-122.

[7] Lamont, W.J. (2005) - Plastics. Modifying microclimate for the production of vegetable crops. Hort Tech. 15: 477-481. [8] Sheikh, M.A., Saleem, A. and Malik, N.A. (2006) - Integrated weed management and its effects on seed cotton yield in

cotton (Gossypium hirsutum L.) crop. Pak. J. Weed Sci. Res. 12(1/2): 111-117. [9] Steel, R.G.D. and Torrie, J.H. (1997) - Principles and procedures of Statistics. A biometric approach, 2nd edition, McGraw

Hill Book Co. Inc. Tokyo.

Comparative Efficiency and Economic Viability of Herbicides for Controlling Weeds in Bt Cotton

(Gossypium hirsutum L.)

J.G. Patel, V.C. Raj, V.P. Usadadiya, R.R. Parmar, C.M. Sutaria, R.L. Leva and V. Kumar

Main Cotton Research Station, Navsari Agricultural University, Surat–395007 India

INTRODUCTION

Cotton is the most important commercial crop of India, with an important role in the Indian economy. Being a long duration and widely spaced crop, loss of seed cotton yield in India due to weeds ranged from 50 to 85 per cent (Singh and Madhukar, 1981). Besides initial slow growth, continuous rains make the soil wet and sticky and trafficability is poor. As a result, weeds grow profusely. Thus, weed management plays a crucial role. Weeds being naturally hardy compete well with crop for moisture, nutrients, light and space resulting in poor yield of crop. Looking to the variety of weed flora, feasibility and conveniences, it is difficult to adopt any single method of weed management, thereby emphasizing integrated approach in this regard. Keeping this in view, studies were conducted to compare efficiency and feasibility of herbicides for weed control in Bt cotton.


A field experiment was conducted at Main Cotton Research Station, Navsari Agricultural University, Surat under irrigated condition during 2007-08 to 2009-10. The soil of experimental site is deep black, low in organic carbon (0.36), medium in available P (32.7 kg/ha) and high in exchangeable K (536 kg/ha). The experiment comprised ten treatments of weed management viz., weedy check (control), farmers’ practices (Hand weeding at 20, 40 and 60 DAS and interculture at 45 and 90 DAS) and different rates of herbicides (pendimethalin and fluchloralin as pre-emergence and Quizalofop ethyl as post emergence). These treatments were replicated thrice in a randomized block design. Cotton (RCH-2 Bt) was sown in the last week of June with a spacing of 120 cm between rows and 45 cm between plants. Nitrogen was applied through urea in four equal split doses (60 kg/ha) at an interval of 30 days. The crop was irrigated twice at 20 days interval after cessation of monsoon. Weed population was counted from 1 m2 area at 30 and 60 DAS. Weed dry weight from the same fixed area in each plot was also recorded at 30 and 60 DAS and at harvest. Observations related to cotton growth and yields were also recorded.


Weed Density, Dry Weed Weight and Weed Control Efficiency

The maximum dry weight of weed was recorded at harvest in the weedy check (Table 1). All the herbicides and cultural operation treatments decreased significantly weed density than weedy check. Low weed density as well as dry weed weight was recorded in the pendimethalin (1.0 kg a.i./ha pre emergence) along with hand weeding at 30 and 60 DAS treatment followed by fluchloralin (0.75 and 1.0 kg a.i./ha pre-emergence) with hand weeding 30 & 60 DAS and farmers’ practices. Weed control efficiency was the highest under treatment of pendimethalin (1.0 kg/ha) + hand weeding at 30 and 60 DAS followed by fluchloralin (0.75 or 1.0 kg/ha) + hand weeding at 30 and 60 DAS and local practices. The higher weed control efficiency with the herbicide treatments was attributed due to lower weed dry bio-mass when compared to the non-herbicide treatments.

66

Comparative Efficiency and Economic Viability of Herbicides for Controlling Weeds 397

TABLE 1: WEED DENSITY, WEED DRY WEIGHT AND WEED CONTROL EFFICIENCY AS AFFECTED BY DIFFERENT WEED MANAGEMENT TREATMENTS

Treatments

30 DAS 60 DAS Dry weed Weight (kg/ha)

Weed Control Efficiency (%)

Weed Density (no/m2)

Dry Weed Weight (g/m2)

Weed Density (no/m2)

Dry Weed Weight (g/m2)

T1:Unweeded control 48.3 27.6 75.7 133.8 1478 -- T2:Farmers practices (HW at 20,40 & 60 DAS + IC at 45 & 90 DAS) 27.3 16.9 20.5 45.0 449 69.62

T3:Pendimethalin @ 0.75 kg a.i. /ha pre-em.+ HW at 30 & 60 DAS 23.8 16.7 21.8 46.6 470 68.20

T4:Fluchloralin @ 0.75 kg a.i./ha pre-em. + HW at 30 & 60 DAS 24.2 14.7 22.1 43.0 431 70.84

T5:Pendimethalin @ 1.00 kg a.i./ha pre-em. + HW at 30 & 60 DAS 20.5 14.1 21.0 40.2 401 72.87

T6:Fluchloralin @ 1.00 kg a.i./ha pre-em. + HW at 30 & 60 DAS 21.3 15.8 20.5 44.1 442 70.09

T7:Pendimethalin @ 0.75 kg a.i./ha pre-em.+ HW at 30 & 60 DAS 25.0 18.5 26.3 54.0 545 63.13

T8: Fluchloralin @ 0.75 kg a.i./ha pre-em. + Quizalofop-ethyl @ 0.04 kg a.i./ha at 30 & 60 DAS

22.5 15.6 28.0 64.2 641 56.63

T9 :Pendimethalin @ 1.00 kg a.i./ha pre-em. + Quizalofop-ethyl @ 0.05 kg a.i./ha at 30 & 60 DAS

20.8 13.2 25.7 65.3 656 55.62

T10:Fluchloralin @ 1.00 kg a.i./ha pre-em. Quizalofop-ethyl @ 0.05 kg a.i./ha at 30 & 60 DAS

17.8 14.5 23.0 52.3 525 64.48

S. Em. + 2.42 1.77 2.90 5.77 62.6 -- C D (P=0.05) 7.77 5.67 9.27 18.46 186.1 --

Growth, Yield and Yield Attributes TABLE 2: GROWTH, YIELD AND ECONOMICS OF BT COTTON (RCH-2) AS INFLUENCED BY DIFFERENT WEED MANAGEMENT PRACTICES

Treatments Plant Height at Harvest

(cm)

No. of Sympodial

Branches/ Plant

No. of Bolls/ Plant

Boll Weight

(g)

Seed Cotton Yield

(kg/ha)

Net Return (Rs/ha)

BCR

T1:Unweeded control 85.6 13.6 25.2 3.7 1780 29091 2.60T2:Farmers practices (HW at 20,40 & 60 DAS+ IC at 45 & 90 DAS) 96.3 16.1 31.9 3.9 2353 41262 3.00

T3:Pendimethalin @ 0.75 kg a.i. /ha pre-em.+ HW at 30 & 60 DAS 94.4 15.7 35.1 4.0 2575 47623 3.30

T4:Fluchloralin @ 0.75 kg a.i./ha pre-em. + HW at 30 & 60 DAS 92.4 15.6 32.0 3.9 2315 40738 3.00

T5:Pendimethalin @ 1.00 kg a.i./ha pre-em. + HW at 30 & 60 DAS 97.7 17.6 38.9 4.0 2753 51667 3.50

T6:Fluchloralin @ 1.00 kg a.i./ha pre-em. + HW at 30 & 60 DAS 96.4 16.1 35.8 3.8 2545 46184 3.30

T7:Pendimethalin @ 0.75 kg a.i./ha pre-em.+ HW at 30 & 60 DAS 93.5 14.7 31.0 3.9 2152 36768 2.80


90.3 15.1 30.1 3.9 2128 36212 2.80

T9 : Pendimethalin @ 1.00 kg a.i./ha pre-em. + Quizalofop-ethyl @ 0.05 kg a.i./ha at 30 & 60 DAS

92.2 15.1 34.3 3.9 2504 45745 3.20


95.1 16.5 36.3 4.0 2629 48788 3.40

S. Em. + 2.9 0.63 1.55 0.11 79.4 - - C D (P=0.05) NS 1.79 4.62 NS 224 - -


Most of the growth and yield attributes were affected remarkably due to different weed management treatments (Table 2). All herbicidal treatments as well as hand weeding at 20, 40 and 60 DAS and inter-culturing at 45 and 90 DAS resulted in more number of sympodial branches, bolls per plant and larger bolls than the weedy check. The highest seed cotton yield was recorded under the treatment of pendimethalin (1.0 kg/ha) pre emergence + hand weeding at 30 and 60 DAS and the lowest in the weedy check treatment. The higher yield under the integrated weed management practices may be attributed to better weed control and reduced crop weed competition in early growth stage, ultimately increase the cotton yield.

Economics

In terms of economics, the maximum net monetary returns (Rs.51667/ha) and BCR (3.50) was accrued under the treatment of pendimethalin @ 1.0 kg/ha as pre-emergence + HW at 30 and 60 DAS.

CONCLUSION

It is concluded that among various weed management practices the ‘treatment with application of pendimethalin (1.0 kg/ha) pre emergence alongwith two hand weeding dose at 30 and 60 DAS was the best with respect to growth and yield of cotton crop. The treatment also had low weed infestation. Thus, an integrated weed management practice integrating chemical and manual control is effective, efficient and economical to control weeds in Bt cotton.

REFERENCES [1] Singh, C. and Madhukar, M.J. (1981) - Review on loss of cotton due to weeds. In: Proceeding of Weed Science

Conference, Bangalore, 1981.

Agronomic Management and Benefits of Glyphosate Tolerant Transgenic

Cotton Hybrids

C. Chinnusamy, C. Nithya, P. Muthukrishnan and S. Jeyaraman

DWSRC, Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore–641003, India


Abstract—Crops made resistant to herbicides by biotechnology are being widely adopted in North America and entering other parts of the world. Those containing transgenes that impart resistance to post-emergence, non-selective herbicides such as glyphosate and glufosinate will have a major impact. These products allow the farmer to more effectively use reduced or no-tillage, eliminate use of some environmentally suspect herbicides and use fewer herbicides to manage the entire spectrum of weed species. Introduction of herbicide-resistant crops has dramatically changed weed management in crop production systems. Particularly, glyphosate-resistant cotton technology has been readily adopted by producers. Further, wide scale introduction of glyphosate-resistant (Round up Ready) cotton cultivars in addition to the herbicides flumioxazin, pyrithiobac and trifloxysulfuron-sodium has increased options for weed management in cotton. Farmers have a powerful new tool that, if used wisely, can be incorporated into an integrated pest management strategy to economically and effectively manage weeds.

Keywords: Agronomic management, herbicide tolerant cotton, weed density, weed dry weight, weed control efficiency, seed cotton yield

INTRODUCTION

Cotton hybrids are cultivated under wider plant spacing and heavy fertilizer nutrients, which inturn invite multiple weed species infestation. Due to increased scarcity of labourers, manual weeding is seldom economical and the currently available pre - emergence herbicides have lesser weed control efficiency in controlling major problematic weeds like Cyperus rotundus and Cynodon dactylon for a longer initial growth phase of cotton hybrids. Many pre-emergence herbicides presently used in cotton for weed control take care of weeds for a limited period. Hence, late emerging weeds escape killing. The available post-emergence herbicides are mostly non-selective and even directed spray of some herbicides cause considerable crop damage. So, there is an ample scope for controlling weeds by the application of early post-emergence herbicide.

Glyphosate resistant cotton was introduced in 1997 and revolutionized weed control in cotton. Round up Ready cotton provided producers with greater flexibility in the timing of herbicide application and also offered a broad spectrum of weed control than other systems. Glyphosate and glufosinate have almost no soil residual activity because they are tightly bound to the organic particles in the soil. This trait facilitates crop rotation by providing flexibility in selection of potential rotation crops. Glyphosate tolerant crops will not cause any residual effect on succeeding crops.

Enhanced glyphosate resistant (Round up Ready Flex) cotton was introduced in 2006. RR Flex cotton exhibits both vegetative and reproductive tolerance to glyphosate to be applied POST over the top at any growth stage, without risk of boll abortion. The technology has allowed growers to reduce or eliminate soil-applied herbicides and to abandon cultivation. It also has allowed a shift to conservation tillage. Glyphosate-resistant technology has gained tremendous acceptance in cotton producing states since commercialization, with the majority of acreage planted to cultivars containing this herbicide-resistant trait. Increased dosages and an extended application time are beneficial since glyphosate provides broad-spectrum control of many annual and perennial grasses, sedges, and broadleaf weeds. RRF cotton has potential benefits, including an expanded window for POE glyphosate applications, enhanced application flexibility and

67


convenience, increased production efficiencies, less dependence on selective spray equipment and the ability to tailor herbicide applications to the intensity of weed infestation instead of cotton growth stage. Therefore, studies were conducted to test selective early post emergence herbicides for weed control in cotton.


Field experiments were conducted at Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu during winter seasons of 2009-10 and 2010-11 with the objectives, to evaluate the weed control efficiency of glyphosate on herbicide tolerant transgenic stacked cotton and to study the agronomic management and benefits of herbicide tolerant transgenic cotton. The experiments were laid out in randomized block design with treatments replicated thrice. In which, the treatments consisted of glyphosate at 900 (T1), 1350 (T2), 1800 (T3), 2700 (T4) and 3600 (T5) and 5400 (T6) g a.e ha-1 and hand weeding twice at 15 and 30 days after sowing DAS (T7) along with unweeded check (T8) were imposed. Glyphosate spraying was done twice (25 and 65 DAS) in case of herbicide tolerant transgenic cotton.


Agronomic Benefits of Herbicide Tolerant Cotton

Broader Spectrum of Weed Control TABLE 1: EFFECT OF GLYPHOSATE APPLICATION ON TOTAL WEED DENSITY (NO. M-2), DRY WEIGHT (G M-2) AND WEED CONTROL EFFICIENCY (WCE %)

IN HERBICIDE TOLERANT TRANSGENIC COTTON

Treatments Winter, 2009-10 Winter, 2010-11 25 DAFHS 25 DASHS 25 DAFHS 25 DASHS

Density Dry Weight

WCE Density Dry Weight



WCE

Gly. 900 g a.e ha-1 3.31 (9.00)

2.57 (4.58)

93.36 3.26 (8.67)

3.05 (7.33)

92.79 3.99 (14.0)

2.80 (5.28)

91.27 4.13 (13.0)

2.91 (6.47)

92.33

Gly. 1350 g a.e ha1

2.94 (6.67)

2.33 (3.43)

95.02 2.81 (6.00)

2.45 (4.13)

95.95 3.74 (12.0)

2.65 (5.08)

92.44 3.77 (10.0)

2.35 (3.57)

95.96

Gly. 1800 g a.e ha-1

2.43 (4.00)

1.84 (1.26)

97.97 2.58 (4.67)

1.97 (1.90)

98.16 2.94 (6.67)

2.29 (2.92)

95.12 3.22 (8.33)

1.98 (1.93)

98.00


2.00 (2.00)

1.77 (1.03)

98.37 2.23 (3.00)

1.87 (1.50)

98.55 2.23 (3.00)

2.11 (2.21)

96.29 2.66 (7.00)

1.90 (1.60)

98.42


1.80 (1.33)

1.76 (1.00)

98.41 1.80 (1.33)

1.82 (1.02)

98.75 1.80 (1.33)

1.92 (1.58)

97.34 2.55 (7.00)

1.81 (1.27)

98.83


1.41 (0.00)

1.41 (0.00)

100.0 1.61 (0.00)

1.41 (0.00)

99.70 1.41 (0.00)

1.85 (0.22)

97.81 2.08 (2.67)

1.64 (0.52)

99.54

HW on 15 & 30 DAS

5.46 (28.00)

2.26 (3.12)

95.48 7.22 (50.3)

5.56 (29.2)

71.24 6.53 (40.7)

4.34 (17.0)

74.91 7. 36 (50.0)

6.08 (35.0)

56.67

Unweeded check 10.60 (110.7)

8.42 (69.0)

- 9.98 (97.7)

10.16 (101.3)

- 11.01 (119.3)

8.36 (67.9)

- 11.04 (109.7)

9.07 (80.3)

-

SEd 0.32 0.25 - 0.29 0.29 - 0.30 0.21 - 0.35 0.16 - CD (P=0.05) 0.68 0.45 - 0.63 0.65 - 0.62 0.44 - 0.75 0.34 -

Figures in parenthesis are original values; DAFHS - Days after first herbicide spray; DASHS - Days after second herbicide spray

Non-selective herbicides such as glyphosate and glufosinate aid in broadening the spectrum of weeds controlled, which is particularly important in no-till (NT) systems, and those “weedy” fields. Weed flora of the experimental field predominantly consisted of twelve species of broad-leaved weeds, five species of grasses and a sedge weed. Dominant among grassy weeds were Dactyloctenium aegyptium Beauv. and Cynodon dactylon (L.) Pers. Trianthema portulacastrum (L.), Cleome gynandra (L.), Digera arvensis (Forsk) and Parthenium hysterophorus (L.) were the dominant ones among the broad-leaved weeds. Cyperus rotundus (L.) was the only sedge present in the experimental fields. Experimental results revealed that application of glyphosate at 2700 g a.e ha-1 recorded lower weed density, dry weight and higher weed control efficiency when compared to other doses of glyphosate and hand weeding method (Table 1). According to

Agronomic Management and Benefits of Glyphosate Tolerant Transgenic Cotton Hybrids 401

France et al., (1997) in addition, the systemic activity of glyphosate also helped with the control of perennial weeds and their perennial vegetative structures such as stolons and rhizomes. It was especially true for control of perennial grassy species such as quackgrass (Elytrigia repens (L.) Beauv.), foxtail barley (Hordeum jubatum) and johnsongrass (Sorghum halepense (L.) Pers.). Koger and Reddy (2005) also found that glyphosate provided marginal or no control of weeds such as Bermuda grass (Cynodon dactylon (L.) Pers.), hemp dogbane (Apocynum cannabinum L.), hemp sesbania, Ipomoea species, horse nettle (Solanum carolinense L.) and tropical spiderwort (Commelina benghalensis L.).

Less Carry-over Effect of Herbicides

Glyphosate and glufosinate have almost no soil residual activity because they are tightly bound to the organic particles in the soil. Hence, there are few restrictions for planting or replanting intervals or injuries to the subsequent crops. This trait facilitates crop rotation by providing flexibility in selection of potential rotation crops. Herbicide tolerant crops (HTC) will not cause any residual effect on succeeding crops (Chinnusamy, 2010).

TABLE 2: RESIDUAL EFFECT OF HERBICIDES ON PLANT HEIGHT (CM) AND DRY MATTER PRODUCTION (DMP) (KG HA-1) OF SUCCEEDING CROPS AT 60 DAS

Treatments Winter, 2009-10 Winter, 2010-11 Sunflower Soybean Pearl millet Sunflower Soybean Pearl Millet

Plant Height

DMP Plant Height

DMP Plant Height

DMP Plant Height

DMP Plant Height

DMP Plant Height

DMP

Gly. 900 g a.e ha-1 154.8 4382 75.8 2080 190.4 3178 138.7 4281 75.4 2287 200.7 2888Gly. 1350 g a.e ha1 153.4 4536 76.6 2210 195.5 3385 142.2 4684 78.8 2504 220.0 3317Gly. 1800 g a.e ha-1 157.6 4459 77.5 2566 192.3 3283 148.3 4563 76.4 2412 212.8 3460Gly. 2700 g a.e ha-1 151.6 4596 79.8 2185 185.0 3290 144.9 4652 75.8 2582 208.0 3458Gly. 3600 g a.e ha-1 150.6 4410 78.0 2347 189.7 3369 140.3 4368 77.8 2664 218.9 3124Gly. 5400 g a.e ha-1 149.8 4659 76.3 2750 188.1 3477 143.2 4487 77.0 2475 202.6 2915HW on 15 & 30 DAS 148.9 4350 75.8 2166 179.5 2992 136.8 4276 76.3 2364 198.4 2892Unweeded check 148.0 4253 74.8 2078 177.4 2871 135.8 4186 75.2 2154 194.7 2774SEd 12.5 178 4.6 135 15.8 142 11.6 184 4.2 136 16.8 139 CD (P=0.05) NS NS NS NS NS NS NS NS NS NS NS NS

Succeeding crops like sunflower, soybean and pearl millet were sown after cotton crop in the treatment blocks to assess the carry over effect of Potassium salt of Glyphosate. Observations were recorded on germination percentage and vigour for all the treatments. Treatments difference found to be non-significant for both the parameters hence there was normal growth and development of succeeding crops (Table 2). The results are in line with the findings of Nadanassababady et al. (2000) who had reported that bioassay of herbicide residues indicated that none of the herbicides evaluated for the chemical control of weeds in cotton persisted in the soil to the level of affecting the germination and growth of succeeding crops like finger millet and cucumber.

Reduced Crop Injury TABLE 3: PER CENT RATING OF PHYTOTOXIC EFFECTS IN HERBICIDE TOLERANT TRANSGENIC COTTON

Treatments Winter 2009-10 Winter 2010-11 7 DAHS 14 DAHS 21 DAHS 7 DAHS 14 DAHS 21 DAHS

Glyphosate 900 g a.e ha-1 0.0 0.0 0.0 0.0 0.0 0.0 Glyphosate 1350 g a.e ha-1 0.0 0.0 0.0 0.0 0.0 0.0 Glyphosate 1800 g a.e ha-1 0.0 0.0 0.0 0.0 0.0 0.0 Glyphosate 2700 g a.e ha-1 0.0 0.0 0.0 0.0 0.0 0.0 Glyphosate 3600 g a.e ha-1 3.0 3.0 2.0 3.0 3.0 2.0 Glyphosate 5400 g a.e ha-1 4.0 4.0 3.0 4.0 4.0 3.0 HW on 15 and 30 DAS 0.0 0.0 0.0 0.0 0.0 0.0 Unweeded check 0.0 0.0 0.0 0.0 0.0 0.0

Data not statistically analysed

Various post-emergence type herbicides used for weed control in soybean, canola, or corn can cause crop injury and ultimately yield loss. Crop injury is more severe when the crop is under stress or unfavorable


environmental conditions occur. In contrast, crop injury is reduced with the use of herbicide tolerant crops. The phytotoxicity symptoms were not observed in cotton with glyphosate at lower doses viz., 900, 1350, 1800 and 2700 g a.e ha-1. Higher doses viz. 3600 and 5400 g a.e ha-1 were noticed with phytotoxicity symptoms at early stages of herbicide application (Table 3). Glyphosate cause almost no crop injury, compared to some traditional herbicides (e.g., lactofen, chlorimuron), especially when applied to cotton. The greatest benefit to growers is the broad-spectrum weed control with post emergence application of glyphosate to cotton without crop injury as earlier reported by Wilcut et al. (1996).

Use of Environmentally Safe Herbicides TABLE 4: EFFECT ON WEED MANAGEMENT METHODS ON SOIL BACTERIA (X 10-6 CFU G-1), FUNGI (X 10-4 CFU G-1) AND ACTINOMYCETES (X 10-2 CFU G-1)

IN HERBICIDE TOLERANT TRANSGENIC COTTON

Treatments Winter, 2009-10 Winter, 2010-11 Bacteria Fungi Actinomycetes Bacteria Fungi Actinomycetes1

DASS45

DASS 1

DASS 45

DASS 1

DASS45

DASS1

DASS45

DASS1

DASS 45

DASS 1

DASS 45

DASSGly. 900 g a.e ha-1 21.60 24.45 21.62 22.42 10.54 12.32 23.61 25.24 19.74 22.34 11.05 12.02Gly. 1350 g a.e ha1 22.10 24.21 20.52 22.24 10.74 12.14 23.24 25.14 21.25 23.40 10.42 11.72Gly. 1800 g a.e ha-1 23.00 23.83 21.71 21.64 10.82 12.32 23.64 24.86 20.54 24.60 10.78 11.56Gly. 2700 g a.e ha-1 22.62 24.34 22.54 21.20 10.95 11.53 24.29 25.64 20.62 23.38 10.57 11.62Gly. 3600 g a.e ha-1 21.12 20.47 20.68 20.04 10.47 10.58 22.44 21.63 19.42 19.93 10.70 10.47Gly. 5400 g a.e ha-1 20.70 19.64 20.84 19.45 10.34 9.24 20.71 20.27 18.54 18.70 10.26 10.06HW on 15 & 30 DAS 24.61 25.55 23.74 25.24 10.65 12.05 25.56 25.92 21.24 22.64 11.24 12.05Unweeded check 25.22 26.82 25.24 25.35 11.05 12.75 24.39 26.24 22.05 23.35 11.33 12.57SEd 2.06 2.09 1.92 2.00 0.82 1.07 1.92 2.21 2.15 2.08 1.20 1.04 CD (P=0.05) 4.10 4.15 3.87 3.92 1.58 2.05 3.78 4.34 4.20 4.15 2.14 2.00

DASS–Days after second spray

In general, glyphosate and glufosinate have lower toxicity to humans and animals compared to some other herbicides. Since they are absorbed by the organic particles in the soil and decompose rapidly, they pose little danger for leaching and contamination of ground water or toxicity to wildlife (Knezevic and Cassman, 2003). Glyphosate applied at doses like 900, 1350, 1800 and 2700 g a.e ha-1 recorded more number of bacteria, fungi and actinomycetes (Table 4). This might be due to glyphosate applied directly on the weeds that added organic material to the soil. Microbial population increases during decomposition of organic material. Haney et al. (2000) reported that glyphosate was available to soil and rhizosphere microbial communities as a substrate for direct metabolism leading to increased microbial biomass activity. Higher doses of glyphosate with 3600 and 5400 g a.e ha-1 led to slight reduction in microbial population as observed at initial stages and recovered within 45 days. These results corroborate the observations of Weaver et al. (2007). Glyphosate had only small and transient effects on the soil microbial community, even when applied at greater than field rates.

Mode of Action for Resistance Management

Since the discovery and report of triazine resistance almost 40 years ago, weed resistance to herbicides has been well documented. For example, there are 40 dicot and 15 monocot species known to have biotypes resistant to triazine herbicides. Also, at least 44 weed species have been reported to have biotypes resistant to one or more of 15 other herbicides or herbicide families (Heap, 2001). The list of herbicide-resistant weeds will continue to grow, especially with repeated use of herbicides with the same mode of action. Many of the selective herbicides in corn and soybean have similar or identical mechanisms of action such as the inhibition of enzyme acetolactate synthase (ALS) or the inhibition of acetyl-co-enzyme-A-carboxylase (ACCase). Therefore, HTC particularly cotton (e.g., glyphosate and glufosinate) can provide a new mode of action when used in an IWM program as an aid in resistance management.


Crop Management Flexibility

The HT technology is simple to use. It requires neither special skills nor training. The technology does not have major restrictions and is flexible, which is probably one of the reasons for such wide adoption by producers. In particular, crops that are tolerant to broad-spectrum herbicides such as glyphosate extend the period of herbicide application for effective weed control, which is helpful in dealing with rainy and windy days during the optimal periods for weed control measures. In contrast, poor weather during the critical period for weed control can greatly limit the effectiveness of more selective herbicides (Peterson et al., 2002). According to Chinnusamy (2010), total weed density was significantly lowered with post-emergence application of Glyphosate at 2700, 3600 and 5400 g a.e/ha in transgenic cotton hybrids when compared to hand weeding (15 and 25 DAS). Keeling et al. (1998) also observed that, weed control is often excellent (95 %) with the application glyphosate as post emergence in cotton.

Increased Yield and Income TABLE 5: EFFECT OF GLYPHOSATE APPLICATION ON YIELD ATTRIBUTES AND SEED COTTON YIELD OF HERBICIDE TOLERANT TRANSGENIC COTTON

Treatments Winter, 2009-10 Winter, 2010-11 Sympodial

Branches Plant-1 No. ofBolls

Plant-1

Boll Weight

(g Boll-1)

Seed cottonYield

(kg ha-1)

Sympodial Branches Plant-1

No. of Bolls

Plant-1

Boll Weight (g boll-1)

Seed Cotton Yield

(kg ha-1)Gly. 900 g a.e ha-1 18.05 57.3 5.10 2607 17.54 50.6 4.92 2470 Gly. 1350 g a.e ha1 18.64 58.3 5.23 2841 17.82 52.5 5.02 2575 Gly. 1800 g a.e ha-1 19.84 59.7 5.72 2984 20.12 55.6 5.31 2846 Gly. 2700 g a.e ha-1 22.51 63.3 5.86 3195 21.46 60.3 5.74 3092 Gly. 3600 g a.e ha-1 21.37 58.3 5.70 3114 19.64 57.1 5.34 3023 Gly. 5400 g a.e ha-1 20.58 59.0 5.32 2849 18.28 54.9 4.98 2753 HW on 15 & 30 DAS 17.88 53.7 4.85 2504 15.84 49.2 4.82 2323 Unweeded check 10.35 35.7 4.06 839 9.47 30.5 4.27 713 SEd 1.36 2.9 0.24 157 1.19 3.3 0.25 144 CD (P=0.05) 2.68 5.7 0.42 322 2.34 6.6 0.48 286

Cotton crop being slow in its initial growth and is grown with wider spacing, is always encountered with severe weed competition during early stage, which results in low yield. Broad spectrums of weeds with wider adaptability to extremities of climatic, edaphic and biotic stresses are observed in the cotton fields. High persistence nature of weeds is attributed to their ability of high seed production and seed viability. Hand weeding or hoeing twice is the most commonly adopted method of weed control in cotton. However, complete weed control could not be achieved by using any single method alone. Herbicidal weed control seems to be a competitive and promising way to control weeds at initial stages of crop growth.

Higher yield of HT transgenic cotton recorded with glyphosate at 2700 g a.e ha-1 over hand weeding twice during both the seasons during winter 2009-10 and winter 2010-11 (Table 5). It could be attributed to efficient control of weeds during the cropping period. The findings are in accordance with observation of Main et al. (2007) who had earlier reported that Roundup Ready Flex cotton could provide producers with acceptable weed control without compromising cotton yield. Glyphosate at 2700 g a.e ha-1 applied twice (25 and 65 DAS) with lower weed density, weed dry weight and higher weed control efficiency, might be the reason for higher productivity. Cotton productivity is mainly decided by the weed control efficiency of weed management methods as earlier observed by Grichar et al. (2004) who have noted that, trends in cotton yield reflected through weed control, which was further proved through glyphosate application system provided 96 per cent control of weeds, producing greater than 950 kg ha-1 of seed cotton.


Glyphosate at 2700 g a.e ha-1 recorded higher gross and net returns and B:C ratio in HT transgenic cotton. In transgenic cotton, higher gross return was obtained with hand weeding twice, whereas higher net return and return per rupee invested were recorded with pendimethalin at 1.0 kg ha-1 + hand weeding (Fig.1). PoE spray of Glyphosate (2700 g a.e ha-1 ) twice (25 and 45 DAS) resulted in complete control of broad spectrum weeds with higher seed cotton yield and net income in HT transgenic cotton during winter season. Treatments, hand weeding twice (25 and 45 DAS) or pre-emergence pendimethalin ( 1.0 kg a.i. ha-1) 3 DAS alongwith one hand weeding ( 45 DAS) have higher weed control efficiency and seed cotton yield of transgenic cotton with better economic returns. Power weeder weeding on 25 DAS + one hand weeding 45 DAS is a promising alternative weed management method for winter irrigated transgenic cotton with higher seed cotton yield and better economic returns.

Fig. 1: Weed Management Methods on Economics in Cotton

CONCLUSION

HTC are strongly impacting weed management choices. In many crops their use will decrease the cost of effective weed management in the short to medium term. Their use will speed adoption of conservation tillage, greatly reduce the environmental damage of farming by reducing soil erosion by both wind and water and reduce herbicide use. Herbicide resistance and new weed species problems that arise as a result of this technology will be dealt with by traditional methods, such as rotating herbicides, mixing herbicides, and rotating crops. However, they offer the farmer a powerful new tool that, if used wisely, can be incorporated into an integrated pest management strategy that can be used for many years to more economically and effectively manage weeds.


REFERENCES [1] Chinnusamy, C. (2010) - Evaluation of bio-efficacy, residue, phytotoxicity and carryover of potassium salt of glyphosate

formulation MON 76366 on transgenic stacked cotton hybrids (Mon 15985 x Mon 88913). Interim report, DWSRC, Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore.

[2] Franz, J.E., Mao, M.K. and Sikorski, J.A. (1997) - Glyphosate: A Unique Global Hebricide. Monograph 189. Washington, D.C. American Chemical Society.

[3] Grichar, W.J, Besler, B.A., Brewer, K.D. and Minton, B.W. (2004) - Using soil-applied herbicides in combination with glyphosate in a glyphosate-resistant cotton herbicide program. Crop Prot., 23: 1007–1010.

[4] Haney, R.L., Senseman, S.A., Hons, F.M. and Zuberer, D.A. (2000) - Effect of glyphosate on soil microbial activity and biomass. Weed Sci., 48: 89–93.

[5] Heap, I. (2001) - International survey of herbicide resistant weeds. Online. HRAC, NAHRAC and WSSA. [6] Keeling, J.W., Dotray, P.A., Osborne, T.S. and Asher, B.S. (1998) - Annual and perennial weed management strategies in

Roundup Ready cotton with Roundup Ultra. Proceedings of the Southern Weed Science Society, Champaign, IL, Vol. 51, pp.49.

[7] Knezevic, S.Z. and Cassman, K.G. (2003) - Use of herbicide-tolerant crops as a component of an integrated weed management program. Online. Crop Management doi: 10.1094/CM-2003-0317–01–MG.

[8] Koger, C.H. and R.N. Reddy. (2005) - Glyphosate efficacy, absorption and translocation in pitted moringglory (Ipomoea lacunose). Weed Sci. 53: 277–283.

[9] Main, C.L., A.J. Michael and E.C. Murdock. (2007) - Weed response and tolerance of enhanced glyphosate-resistant cotton to glyphosate. J. Cotton Sci., 11: 104–109.

[10] Nadanassababady, T. Kandasamy, O.S. and Ramesh, G. (2000) -Integration of pre and non-selective post-emergence herbicides and cultural method for weed control in cotton and its effect on succeeding crops. Trop. Agric. Res., 12: 217–225.

[11] Peterson, J.M, K.G., Cassman and Cantrell, R. (2002). Changes in cultural practices of farmers in southeast Nebraska as a result of their adoption of transgenic crops. J. Ext. 40: 1.

[12] Weaver, M.A, Krutz, L.J., Zablotowicz, R.M. and Reddy, K.N. (2007) - Effects of glyphosate on soil microbial communities and its mineralization in a Mississippi soil. Pest Manag Sci. 63(4): 388–93.

[13] Wilcut, J.W., Coble, H.D., York, A.C. and Monks, D.W. (1996) - The niche for herbicide-resistant crops in U.S. agriculture. In: Herbicide-Resistant Crops: Agricultural, Environmental, Economic, Regulatory, and Technical Aspects. S.O. Duke (ed.) CRC Press, Boca Raton, FL. pp. 213–230.

Evaluation of Pyrithiobac Alone and in Combination with Grassy Herbicides

on Weed Control in Cotton

A.S. Rao

Integrated Weed Management Unit, Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Lam, Guntur–522034, A.P.

Abstract—A field experiment was conducted during kharif 2009-10 and 2010-11 at Regional Agricultural Research Station, Lam, Guntur (A.P.) to evaluate pyrithiobac alone and in combination with grassy herbicides on weed control in cotton and also to characterize their nature of interaction. The experiment was conducted in a randomized block design with three replications. Results indicated that all the weed control treatments significantly reduced weed dry weight over unweeded check. Tank mixing of pyrithiobac with grassy herbicides was superior to application of post emergence herbicides alone. Among the treatments, post emergence application of pyrithiobac 32 g + quizalofop ethyl 25g/ha recorded the lowest weed dry weight and highest seed cotton yield (2270 kg/ha) and net monetary returns (Rs.44,245/-) and benefit cost ratio (0.95)and was on par with other herbicide mixtures. All the herbicide combinations were found to be synergistic.

INTRODUCTION

Cotton being a wide row spaced and relatively slow growing crop in initial stages is subjected to severe weed competition and reduces yield to an extent of 60% (Sadangi and Barik, 2007). Present recommendation of pre emergence herbicide (pendimethalin) application followed by two or three inter-cultivations is a common practice (Prabhau et al., 2010). However, most often due to incessant rains during kharif season and inter-cultivation is not possible due to excessive moisture in black cotton soils. Though, paraquat is used as directed spray (Srinivasulu and Rao, 2000; Srinivasulu et al., 2004), it causes plant injury. Therefore, farmers seek for selective post emergence broad spectrum herbicide/herbicide mixtures in order to control all weeds in a single spray. Pyrithiobac and grassy herbicides (clodinafop propargyl, fenoxaprop ethyl, quizalofop ethyl) are systemic herbicides with a specific target site of action. Pyrithiobac an ALS herbicide inhibits Acetolactate synthase, a key enzyme in the biosynthesis of branched chain amino acids. Whereas the grassy herbicides, clodinafop propargyl, fenoxaprop ethyl, quizalofop ethyl are Acc ase inhibitors, which inhibit the enzyme Acetyl CoA Carboxylase (Accase), a key enzyme in the biosynthesis of fatty acid synthesis. Thus, tank mixing of herbicides with two distinctly different target sites of action has a potential for effective weed control. Since the type of interaction between pyrithiobac and grassy herbicides vary considerably depending on herbicide chemistry and weed species, it was attempted to characterize the nature of interaction between pyrithiobac and grassy herbicides. Further, information pertaining to tank mixing of selective post emergence herbicides on cotton is scanty (Snipes and Allen, 1996). With this in view, the present experiment was conducted to evaluate the post emergence herbicide pyrithiobac alone and in combination with grassy herbicides.


A field experiment was conducted at Acharya N. G. Ranga Agricultural University, Regional Agricultural Research Station, Lam, Guntur (A.P.) during kharif 2009-10 and 2010-11. The soil of the experimental field was clay loam in texture, medium in available N and P but high in available K with a pH 7.7. The experiment consisting of seven treatments arranged in a randomized block design with three replications. Cotton (RCH-2 Bt) was sown with a spacing of 90x60 cm. The post emergence herbicides, 20 DAS (days after sowing), were sprayed with a knap sack sprayer fitted with flat fan nozzle using a spray volume of 500 l/ha. All the recommended package of practices other than weed control was

68

Evaluation of Pyrithiobac Alone and in Combination with Grassy Herbicides on Weed Control in Cotton 407

adopted to raise the experimental crop. The data on density was recorded at 30, 60 DAS and final picking and dry weight of weeds was recorded at 60 DAS and data were subjected to square root ( ) transformation before statistical analysis to normalise their distribution (Panse and Sukhatme, 1978). The method described by Colby (1967) was used to calculate the expected response of herbicide combinations. Expected and observed values were compared using the CD values calculated for the observed data (Rao and Reddy, 1999). If the observed response of an herbicide combination was either significantly lower or greater than the expected value, then combination was declared antagonistic or synergistic, respectively. Combinations were considered to be additive (no interaction) when observed and expected responses were not significant.

RESULTS AND DISCUSSIONS

Effect on Weeds

The dominant weed flora of the experimental plots consisted of grasses (Echinochloa colona, Dactyloctenium aegyptium, Panicum repense, Dinebra retroflexa) Sedges (Cyperus rotundus), and broad leaf weeds (Trianthema portulacastrum, Cleome viscosa, Phyllanthus niruri, Digera arvensis, Physalis minima, Abutilon indicum)

All the weed control treatments significantly reduced the density and dry weight of weeds as compared to weedy check at all stages of observations (Table 1). Among the treatments, pyrithiobac alone or in combination with grassy herbicides was found to be superior to application of grassy herbicides alone at 60 DAS in reducing the weed dry weight. Among the herbicide combinations, lowest weed growth was observed with treatment pyrithiobac (32 g) + quizalofop ethyl (25g/ha) and was on par with other combinations. The reduced weed growth in these treatments might be due to broad spectrum control of weeds due to combined effect of pyrithiobac and grassy herbicides.

TABLE 1: EFFECT OF DIFFERENT TREATMENTS ON DENSITY AND DRY WEIGHT OF WEEDS IN COTTON (POOLED DATA OF TWO YEARS)

Treatments Dose (g/ha)

Time of Application

(DAS)

Total Weed Density (no./m2) at

Total Weed Dry Weight

(g/m2) at 60 DAS

Weed Dry Weight Percent Reduction

30 DAS 60 DAS Final Picking

Observed (%)

Expected (%)

T1 - Unweeded check

- - 16.7 (298.7)

14.2 (215.7)

9.3 (86.5) 20.2 (423.3) 0 -

T2 - Pyrithiobac 63 20 10.3 (108.7)

10.1 (115.8)

7.6 (58.5) 14.5 (228.3) 28.2 -

T3 - Clodinafop propargyl

60 20 12.2 (158.2)

11.2 (131.2)

8.1 (67.0) 16.7 (295.0) 17.3 -

T4 - Fenoxaprop ethyl

65 20 12.8 (167.8)

10.5 (112.3)

8.2 (69.5) 16.9 (294.2) 16.3 -

T5 - Quizalofop ethyl

50 20 12.1 (151.7)

10.1 (105.7)

7.5 (56.5) 17.8 (325.8) 11.9 -

T6 - Pyrithiobac + Clodinafop propargyl

32+30 20 9.3 (91.7)

08.5 (76.7)

5.9 (36.5) 15.6 (260.0) 22.6 40.6

T7 - Pyrithiobac + Fenoxaprop ethyl

32+28 20 9.2 (90.2)

08.7 (78.7)

05.6 (32.5)

14.5 (223.3) 28.2 39.9

T8 - Pyrithiobac + quizalofop ethyl

32+25 20 8.5 (76.3)

08.1 (68.2)

04.9 (25.7)

14.5 (220.8) 28.2 39.9

SED 0.68 0.57 0.45 0.75 0.75 CD (0.05%) 1.95 1.65 1.36 2.14 2.14

Note: DAS: Days after sowing. Data transformed to transformation. Figures in parentheses are original values. Interactions were considered significant if the difference between the observed and expected values exceeded the appropriate CD value


Herbicide interactions were evaluated by comparing the observed weed dry weight recorded at 60 DAS with that of Colby’s expected values. The herbicide combinations consisting of pyrithiobac with all the grassy herbicides (clodinafop propargyl, fenoxaprop ethyl, quizalofop ethyl) exhibited synergistic response (Table 1). The observed synergism could be due to differential mechanism of action of these herbicides resulting in increased phytotoxicity on weeds when applied as tank mixtures.

Effect on Crop

All the herbicides either applied alone or tank mixtures did not injure cotton crop. All the weed control treatments significantly influenced crop dry weight, boll number, boll weight and seed cotton yield (Table 2). Among the treatments, the highest seed cotton yield was obtained with post emergence tank mix application of pyrithiobac and quizalofop ethyl over alone herbicide application but was on par with other tank mixtures of pyrithiobac with fenoxaprop ethyl and clodinafop propargyl. This treatment recorded 98per cent higher seed cotton yield than weedy check. The increase in seed cotton yield was due to effective broad spectrum control of weeds by tank mixing of grassy and broad leaf herbicide, which resulted in higher crop dry matter, yield components and yield. The results are similar to those reported by Grichar et al. (2003) on interaction of graminicides applied in combination with pyrithiobac.

TABLE 2: EFFECT OF DIFFERENT TREATMENTS ON YIELD AND YIELD COMPONENTS IN COTTON (POOLED DATA OF TWO YEARS)

Treatments Boll Number/ Plant

Boll Weight (g) Seed Cotton Yield

(kg/ha)

Net Returns (Rs/ha)

BCR (Rs/R)

T1 - Unweeded check 19.2 03.9 1148 920 0.02 T2 - Pyrithiobac 24.3 04.1 1723 22360 0.48 T3 - Clodinafop propargyl 22.9 04.1 1627 19030 0.41 T4 - Fenoxaprop ethyl 24.9 04.3 1807 26230 0.57 T5 - Quizalofop ethyl 25.3 04.3 1876 28490 0.61 T6 - Pyrithiobac + Clodinafop propargyl

26.5 04.4 2057 36055 0.78

T7 - Pyrithiobac + Fenoxaprop ethyl

27.7 04.5 2209 42054 0.71

T8 - Pyrithiobac + Quizalofop ethyl

28.5 04.6 2270 44245 0.95

SED 0.8 0.1 103.3 CD (0.05%) 2.4 0.4 296.6

ECONOMICS

The highest net monetary returns (Rs 44,245/-) and benefit cost ratio (0.95) was obtained with the treatment pyrithiobac and quizalofop ethyl applied. This was closely followed by pyrithiobac and fenoxa prop ethyl with net monetary return of Rs. 42,054/- and benefit cost ratio of 0.91.

From this study, it can be concluded that in cotton crop, post emergence tank mix application of pyrithiobac and quizalofop ethyl applied at 20 DAS was effective and economical without any phytotoxicity to cotton crop, whenever inter-cultivation is not possible due to incessant rains during kharif season. The next best treatment is pyrithiobac and fenoxa prop ethyl tank mixture.

REFERENCES [1] Colby, S.R. (1967) – Calculating synergistic and antagonistic response of herbicide combinations. Weeds 15: 20–22. [2] Grichar W. James, Brew, A. Besler, Kevish, D. Brewer and Reobert, G. Lemon (2003) - Interaction of pyrithiobac and

graminicide for weed control in cotton. Weed Technology 17(3): 461–466. [3] Panse, V.G. and Sukatme P.V. (1978) – Stastical Methods for Agricultural Workers, ICAR, New Delhi, pp: 152. [4] Prabhu, G., Halepyati, A.S. and Pujari, B.T. (2010) – Weed Management studies in Bt cotton (Gossypium hirsutum) under

irrigated conditions. Proc. of XIX National Symposium on Resourse Management Approach towards livelihood security, 2–4 December, 2010, UAS, Bangalore, pp: 263.

[5] Rao, S.A. and Reddy, K.N. (1999) – Purple nutsedge (Cyperus rotundus) and sickle pod (Senna obtusifolia) response to glyphosate mixtures with ALS-inhibiting herbicides. Weed Technol., 13(2): 361–366.

Evaluation of Pyrithiobac Alone and in Combination with Grassy Herbicides on Weed Control in Cotton 409

[6] Sadangi, P.K. and Barik, K.C. (2007) – Effect of weed management practices on nutrient depletion by weeds, yield and economics winter irrigated cotton (Gossypium hirsutum). Indian J. Agron., 52(2): 172–175.

[7] Snipes Charles, E. and Allen Ralph, L. (1996) - Interaction of graminicides applied in combination with pyrithiobac. Weed Technol., 10(4): 889–892.

[8] Srinivasulu, G. and Rao, A.S. (2000) – Effect of sequential application of herbicides on weed management in cotton. Proc. of symposium on Challenges in Agronomic crop management in early 21st century held by Society of Agronomists, ANGRAU, R’nagar, Hyderabad, 24–25. May, 2000, pp: 33–35

[9] Srinivasulu, K., Hema, K., Rao, A.S. and Rao, K.V. (2004) – Evaluation of pre and post emergence herbicides in rain fed cotton. Intl symposium on Strategies for sustainable cotton production a global vision 2-crop production, 23–25. November, 2004, University of Agricultural Sciences, Dharwad, pp: 244–245.

Defining Optimal Application Rate and Timing of Mepiquat Chloride for Cotton Grown in Conditions that Promote Excessive

Vegetative Growth

G.D. Collins1, R. Wells2, R. Riar2 and K.L. Edmisten2

1University of Georgia, 2North Carolina State University, USA

Abstract—Mepiquat chloride (MC) is commonly used to control vegetative growth and promote early maturity of cotton. Responses to MC are dependent upon the prevailing environmental conditions, with favorable responses often observed in conditions that promote vegetative growth or delayed maturity. There have been recent claims of growers in the Southeastern U.S. using extremely high MC rates on cotton grown in fertile soils and high moisture environments while failing to adequately control growth. These results raise the question as to whether these high rates are actually necessary, or should growers be utilizing more aggressive application strategies in order to achieve optimal growth suppression. Experiments were conducted during 2007 and 2008 to investigate the effects of various MC application strategies and rates on cotton grown in high-moisture, high-fertility conditions. Treatments consisted of three MC application strategies (low-rate-multiple, modified early bloom, and early bloom) using normal (1x), moderate (1.5x), and high (2x) rates, which were compared to a non-treated control (NTC). Results suggested that normal rates (1x) are equally effective in controlling plant growth or modifying maturity characteristics as the high rates (1.5x and 2x), therefore the high rates may not be necessary. The less aggressive early bloom strategy controlled growth similarly to the more aggressive application strategies. However, the modified early bloom strategy may provide growers with some flexibility in adequately controlling growth in situations where timely MC application to all cotton hectares at early bloom is challenging. This strategy also requires less equipment trips through the field compared to the low rate multiple strategy, and it is equally effective in controlling plant growth.

INTRODUCTION Plant growth regulators (PGRs) are commonly used in Southeastern U.S. cotton production to manage cotton growth for optimal productivity. The PGR products utilized most in this region contain either MC (N,N-dimethyl piperidinium chloride) or mepiquat pentaborate (N,N-dimethyl piperidinium pentaborate), which, if used properly, can positively influence cotton growth and maturity traits in some environments. Mepiquat-containing PGRs influence cotton growth by obstructing cell elongation (Cordell et al., 2005) and expansion resulting from inhibiting the biosynthesis of gibberellins (Hake et al., 1991). Plant growth regulators can have several impacts on the structure of cotton plants. Researchers have found that PGRs reduced internode length (Reddy et al., 1992), plant height, the number of nodes (Jost and Dollar, 2004; Nichols et al., 2003; Reddy et al., 1992; Siebert and Stewart, 2006; Wilson et al., 2007), height-to-node ratio (Johnson and Pettigrew, 2006; Nichols et al., 2003; O’berry et al., 2009), and nodes above white flower (NAWF) (Johnson and Pettigrew, 2006; O’berry et al., 2009; Pettigrew and Johnson, 2005). Others have noticed increased boll retention on lower branches (Prince et al., 2000) which can promote early maturity of the crop (Hake et al., 1991). In addition to early maturity, the effects of PGRs on cotton growth could potentially improve harvest efficiency (Jost and Dollar, 2004) and insect management (Edmisten, 2008), while potentially decreasing boll rot (Edmisten, 2008; Jost and Dollar, 2004). The effect of PGRs on lint yield is inconsistent and varies depending upon other management practices and the prevailing environmental conditions. Researchers have reported no yield response to mepiquat-containing PGRs (Edmisten, 1994; Pettigrew and Johnson, 2005), yield losses (O’berry et al., 2009) or yield increases (Coccaro et al., 2004; Elbehar et al., 1996; Elbehar and Welch, 1996; Johnson and Pettigrew, 2006; Nichols et al., 2003; Siebert and Stewart, 2006; Wilson et al.,, 2007) in some situations.

Determining the optimal MC application rate or strategy, in terms of achieving optimal growth suppression or yield, can be difficult as the best rate and application strategy often varies depending upon the potential for excessive vegetative growth. Conditions that could delay maturity or promote vigorous

69

Defining Optimal Application Rate and Timing of Mepiquat Chloride for Cotton Grown 411

and excessive growth, such as late-planted cotton, high plant populations, and excessive nitrogen and soil moisture, would likely result in a positive response to MC treatment (Edmisten, 2008; O’berry et al., 2009). In North Carolina, the currently recommended MC application strategies include the low-rate-multiple strategy (LRM), the modified early bloom strategy (MEB), and the early bloom strategy (EB) (Edmisten, 2008). The LRM strategy is the most aggressive strategy, and consists of three to four applications of low rates of MC beginning at the match-head square (MHS, 7- to 8-leaf cotton with squares ranging from 3.2 to 6.4 mm in size) growth stage and continuing every seven to 14 days until early bloom (EB, five to six white blooms per 7.6 m of row) or EB plus seven days. The MEB strategy consists of at least two, and potentially three, applications of moderate rates of MC at 10 to 14 days prior to EB, again at EB, and seldomly 10 to 14 days after EB. The EB strategy is the least aggressive strategy, consisting of at least one, and potentially two, applications of MC at higher rates beginning at EB. In a study involving narrow-row cotton in North Carolina, Wilson et al., (2007) observed that the LRM and MEB strategies reduced plant height more than did the EB strategy, and they suggested that these strategies may be more appropriate for narrow-row systems. Edmisten (1994) reported that a plant monitoring point system used in conjunction with a LRM strategy required lower MC rates than a standard LRM strategy. Similar application strategies were implemented across the cotton belt. In Louisiana, Siebert and Stewart (2006) reported that their MEB strategy increased yield when compared to cotton receiving no mepiquat during one year. In Tennessee, Craig and Gwathmey (2005) reported that multiple-application strategies, of both low and high rates, had more impact on plant height than did the near-bloom applications, although all strategies reduced height when compared to non-treated cotton. Jost and Dollar (2004) in Georgia, compared multiple-application strategies using different mepiquat-containing products and rates and found no difference in terms of plant height reduction among treatments. In a study conducted in Mississippi, Coccaro et al., (2004) found that mepiquat-containing PGRs applied beginning at the 7- to 8- nodes stage did not control plant height as well as when applications were initiated at 14 nodes. O’berry et al., (2009) suggested that the yield losses they noticed in Virginia and South Carolina, may be a result of early applications and/or high seasonal rates, which may be too aggressive.

Recently, in the Southeastern U.S., there have been some claims or reports of growers utilizing excessively high rates (0.22 to 0.31 kg a.i. ha-1) while failing to adequately control growth. This raises the question whether these rates were actually necessary, or should growers be utilizing more aggressive application strategies (multiple applications and/or initiating applications sooner) in order to achieve optimal growth control. The objective of this experiment was to define the optimal MC application rates and strategies for cotton grown in conditions that promote extremely excessive vegetative growth, such as soils with high fertility and moisture capacity or in coastal areas that typically experience frequent rainfall.


Experiments were conducted during 2007 and 2008 on a Norfolk fine sandy loam soil (fine-loamy, kaolinitic, thermic, Typic Kandiudult) at an on-farm site in Duplin County near Beulaville, NC. Late-maturing cultivars, ST 6611B2RF® and DP 164 B2RF®, were planted at a rate of 13.1 seeds row meter-1 on 2 May 2007 and 6 May 2008, respectively, using a two-row vacuum planter. Plots contained four 12.2 m long rows spaced 0.97 m apart. Treatments consisted of three MC application strategies {LRM, MEB, and EB} along with a non-treated control (NTC), and three MC rate schematics {0.05 (1x), 0.07 (1.5x) and 0.1 (2x) kg a.i. ha-1} to determine the optimal application strategy and rate for cotton grown in conditions conducive to excessive vegetative growth. Treatments were replicated four times in a randomized complete block design. Dry poultry litter was applied at a rate of 13.5 tons ha-1 prior to planting to ensure high nitrogen fertility. All other production and pest management practices were conducted according to the North Carolina Cooperative Extension recommendations for that region (Bacheler, 2008; Crozier, 2008; Edmisten, 2008; Koenning, 2008; York and Culpepper, 2008).

The LRM treatments received MC {N,N-dimethyl piperidinium chloride (Mepex®, Nufarm Americas Inc., Burr Ridge IL.)} at the respective total rates divided into four applications beginning at the MHS


growth stage and continuing every seven to 14 days until EB or EB plus seven days. The MEB treatments received MC at the respective total rates divided into two applications beginning at the MHS growth stage or MHS plus seven days and again at the EB growth stage. The EB treatments received MC at the respective total rates in a single application at the EB growth stage. The NTC received no MC at any time throughout the season.

Treatments were applied using a CO2-pressurized backpack sprayer calibrated to deliver 140 L ha-1 using TeeJet® XR110-02 flat-fan nozzles (TeeJet Technologies, Wheaton, IL). Plant heights, nodes, and uppermost fully expanded internode length (distance between fourth and fifth true leaf from the top of the plant) were recorded for six plants in the center two rows of each plot at each MC application, and plant height, nodes, and NAWF were recorded for six plants in the center two rows of each plot during early August of each year. Prior to defoliation, the percentage of open bolls was recorded in a randomly chosen 1-m section of row within each plot. Nodes above cracked boll (nodes between highest first position cracked boll and highest harvestable boll) and angle measurements of the main stalk relative to the ground were recorded for six plants from the center two rows of each plot. Prior to harvest, plant mapping data, including boll distribution and growth characteristics, were collected for six plants from the center two rows of plot.

The center two rows of each experimental unit were mechanically harvested using a two-row spindle picker on 12 October 2007 and 17 October 2008. Seedcotton weights for each plot were recorded and sub-samples were collected for high volume instrumentation analysis and lint percentage. Harvest data included lint yield, micronaire, fiber length, length uniformity, and fiber strength.

Data for growth characteristics, maturity parameters, lint yield, and fiber quality parameters were subjected to analysis of variance using the general linear model in SAS version 9.1.3 (SAS Institute, Cary NC). Means of significant main effects and interactions were separated using Fisher’s Protected LSD at p<0.05 or 0.1.

RESULTS AND DISCUSSION TABLE 1: EFFECTS OF MEPIQUAT CHLORIDE (MC) APPLICATION STRATEGIES AND RATES ON PLANT HEIGHT, TOTAL NODES,

NODES ABOVE WHITE FLOWER, AND TOTAL BOLLS PER PLANT IN 2007 AND 2008

MC Application Strategy

MC Rate at Each Application

Plant Height

Total Nodes Per Plant

Nodes Above White Flower

Total Bolls Per Plant

kg ha-1 ____ cm ____ ________________________ No. _________________________ Low Rate Multipley 0.01 113.23 bc 18.46 4.17 bc 9.58

0.02 108.99 bc 19.93 4.57 ab 10.1 0.025 108 bc 17.5 3.65 c 11.44

Modified Early Bloomx

0.025 113.85 bc 18.58 4.58 ab 10.69 0.04 108.07 bc 20.21 4.04 bc 10.67 0.05 101.51 c 19.53 4.03 bc 11.31

Early Bloomw 0.05 121.85 b 18.89 4.21 bc 11.9 0.07 112.79 bc 19.72 4.32 bc 11.19 0.1 117.85 b 19.38 4.28 bc 12.52

Non-treated Control _____ 139.5 a 21 5.31 a 10.38 P-value _____ 0.0030 0.3391 0.0402 0.3008 LSD _____ 14.505 NS 0.8118 NS zData are pooled over years. Means within a column followed by dissimilar letters are significantly different based on Fisher’s Protected LSD test at α = 0.1 or α = 0.05. yThe Low Rate Multiple Strategy consisted of four applications beginning at match-head square, and every 7 to 14 days until early bloom or early bloom plus 7 days. xThe Modified Early Bloom Strategy consisted of two applications beginning at match-head square or match-head square plus 7 days, and again at early bloom. wThe Early Bloom Strategy consisted of a single application at early bloom

There were no interactions between MC treatments and years, therefore all data are reported combined over years. All MC application strategies and rates within each treatment significantly reduced plant height compared to the NTC (Table 1) similar to the findings of Jost and Dollar (2004), Nichols et al.,


(2003), Reddy et al., (1992), Siebert and Stewart (2006), and Wilson et al., (2007). Within each application strategy, the higher MC rates (1.5x and 2x) did not significantly reduce plant height more than the normal-use (1x) rates. The 2x rate applied according to the MEB strategy significantly reduced plant height more than both the 1x and 2x rates applied according to the EB strategy. In contrast to the findings of Nichols et al., (2003), height-to-node ratio (data not shown), and the number of mainstem nodes were not affected by MC treatment (Table 1). Neither MC application strategy nor rate affected the total number of bolls per plant, and there was also no effect on the number of sympodial nodes, the node of first sympodia, the number of vegetative or sympodial bolls, or sympodial boll retention (data not shown). Similar to the findings of Johnson and Pettigrew (2006), O’berry et al., (2009), and Pettigrew and Johnson (2005), most MC treatments significantly reduced NAWF when compared to the NTC indicating that MC may promote earlier maturity. Higher rates applied according to both the MEB and EB strategies did not reduce NAWF any more than the lower rates.

Regarding boll distribution, the 1.5x and 2x rates applied according to the LRM strategy, and the 1x and 1.5x rates applied according to the MEB strategy increased the number of bolls on nodes four through seven compared to the NTC (Table 2). Mepiquat chloride applied according to the EB strategy, regardless of rate, had no effect on the number of bolls in this node zone, as well as the LRM using the 1x rate and the MEB using the 2x rate. This indicates that the more aggressive application strategies may enhance maturity more than the less aggressive EB strategy. All MC application strategies and rates increased the number of bolls on nodes eight through 10. These results are similar to those of Prince et al., (2000). The number of bolls in this node zone increased as MC rate increased when applied according to the LRM strategy. Neither MC application strategy nor rate had any effect on the number of bolls on nodes 11 through 13 and nodes 14 through 16.

TABLE 2: EFFECTS OF MEPIQUAT CHLORIDE (MC) APPLICATION STRATEGIES AND RATES ON BOLL DISTRIBUTION IN 2007 AND 2008.


MC Rate at Each Application

Number of Bolls on Nodes 4–7




kg ha-1 ___________________________________ No. ___________________________________ Low Rate Multipley 0.01 2.08 a-d 2.75 d 1.83 1.17

0.02 2.42 ab 2.77 cd 2.13 1.56 0.025 2.29 abc 3.13 ab 2.48 2

Modified Early Bloomx

0.025 2.4 ab 3.06 abc 2.23 1.54 0.04 2.48 a 2.98 bcd 2.08 1.31 0.05 2 bcd 3.1 ab 2.67 1.73

Early Bloomw 0.05 1.88 cd 3.31 a 2.71 1.96 0.07 2.1 a-d 2.92 bcd 2.67 1.92 0.1 2.17 a-d 3.19 ab 2.92 1.85

Non-treated Control _____ 1.7 d 2.35 e 2.42 2 P-value _____ 0.0698 0.0016 0.1165 0.1562 LSD _____ 0.4759 0.2938 NS NS zData are pooled over years. Means within a column followed by dissimilar letters are significantly different based on Fisher’s Protected LSD test at α = 0.1 or α = 0.05. yThe Low Rate Multiple Strategy consisted of four applications beginning at match-head square, and every 7 to 14 days until early bloom or early bloom plus 7 days. xThe Modified Early Bloom Strategy consisted of two applications beginning at match-head square or match-head square plus 7 days, and again at early bloom. wThe Early Bloom Strategy consisted of a single application at early bloom.

Most MC application strategies and rates increased the angle of the main stalk relative to the soil surface compared to the NTC (Table 3), indicating that MC may prevent or reduce the potential for lodging by reducing plant height and increasing the proportion of bolls retained on lower nodes, resulting in plants that are less top-heavy. For the LRM and the MEB strategies, the higher MC rates had no additional effect on stalk angles. Mepiquat chloride at all rates according to the MEB and EB strategies increased percent open bolls, suggesting that MC may promote an earlier maturity thus earlier harvest. Within all application strategies, the higher rates did not improve boll opening. Neither MC application strategy nor rate had any effect on lint yield, lint percentage, or any fiber quality parameter (data not shown).


TABLE 3: EFFECTS OF MEPIQUAT CHLORIDE (MC) APPLICATION STRATEGIES AND RATES ON STALK ANGLE AND PERCENT OPEN BOLLS IN 2007 AND 2008


MC Rateat Each Application

(kg ha-1)

Angle of Main Stalk Relative to Soil Surface

(______ degrees ______)

Percent Open Bolls

(______ % ______) Low Rate Multipley 0.01 74.38 ab 48.9 ab

0.02 74.88 ab 52.87 a 0.025 74.25 ab 45.97 ab

Modified Early Bloomx 0.025 72.83 ab 52.85 a 0.04 78.5 a 55.66 a 0.05 80.25 a 54.91 a

Early Bloomw 0.05 72.63 ab 50.8 a 0.07 65.13 bc 52.78 a 0.1 78 a 50.16 a

Non-treated Control _____ 61.13 c 39.24 b P-value _____ 0.0586 0.0964 LSD _____ 10.969 9.87 zData are pooled over years. Means within a column followed by dissimilar letters are significantly different based on Fisher’s Protected LSD test at α = 0.1 or α = 0.05. yThe Low Rate Multiple Strategy consisted of four applications beginning at match-head square, and every 7 to 14 days until early bloom or early bloom plus 7 days. xThe Modified Early Bloom Strategy consisted of two applications beginning at match-head square or match-head square plus 7 days, and again at early bloom. wThe Early Bloom Strategy consisted of a single application at early bloom.

These results are consistent with previous research in that MC can reduce plant height and promote earlier maturity as seen with the effects on boll distribution in lower node zones, NAWF, and percent open bolls. In most instances, the normal-use rates, or the 1x rates, had the same effects on plant growth and maturity as did the higher rates. The higher rates, regardless of the application strategy used, seldom resulted in any plant modifications that would be advantageous to growers, therefore the normal-use (1x) rates should suffice in environmental conditions that promote excessive vegetative growth, similar to the conditions in which this experiment was conducted as supported by the plant height of the NTC. Although these results suggested that the EB strategy was effective in controlling growth, in these environmental conditions, the MEB strategy may be more appropriate than the EB strategy because it could allow growers to control growth with more flexibility due to time constraints of applying MC to all cotton hectares at the EB stage. This strategy may be especially effective in years when excessive rainfall limits the ability of equipment to travel through the field. The MEB strategy also requires fewer equipment passes through the field compared to the LRM strategy, allowing for optimal growth control while avoiding unnecessary equipment operation costs.

These results are inconsistent with the claims from growers that excessive MC rates controlled plant height poorly in similar environmental conditions, likely because the growers making these claims applied MC later than recommended in terms of growth stage. Siebert and Stewart (2006) reported similar findings, suggesting that excessive rates may not be necessary to adequately control plant growth. Delaying MC application beyond the appropriate growth stage could potentially result in poor plant height suppression. Another potential reason for this discrepancy could be that growers likely used glyphosate-resistant (Round-up Ready®) varieties which have the potential for poor pollination and fruit abortion when glyphosate is used (Pline et al., 2002). Fruit abortion, especially when it occurs on the lower nodes (occasionally noted with Round-up Ready® cotton), can cause terminal growth to be slightly more aggressive, possibly making plant height more difficult to control. This experiment was conducted using Roundup Ready Flex® varieties, which are tolerant to glyphosate at any growth stage, potentially allowing more fruit to be retained on lower nodes, which could restrain terminal growth. This characteristic can often result in less aggressive terminal growth potential, therefore allowing lower MC rates to suffice in terms of controlling plant growth.


REFERENCES [1] Bacheler, J.S. (2008) - Managing insects in cotton. In K.L. Edmisten (ed.). North Carolina Cotton Information. Publ.

AG–417. North Carolina Cooperative Ext. Serv., Raleigh, NC. pp. 138–161. [2] Coccaro, J.C., McCarty, H.W., Rhodes, A. and Smith, H.R. (2004) - Response of PGR’s on DP 555 BG/RR by soil type. In

Proc. Beltwide Cotton Conf. San Antonio, TX. Jan 5–9. Natl. Cotton Counc. Am., Memphis, TN. pp. 2082–2083. [3] Cordell, M., Robertson, B. and Groves, F. (2005)-Evaluation of mepiquat chloride treatments at cutout or the latest possible

cutout date. In Proc. Beltwide Cotton Conf. New Orleans, LA. Jan 4–7. Natl. Cotton Counc. Am., Memphis, TN. [4] Craig, C.C. and Gwathmey, O. (2005)-Variety response to mepiquat chloride applications. In Proc. Beltwide Cotton Conf.

New Orleans, LA. Jan 4–7. Natl. Cotton Counc. Am., Memphis, TN. [5] Crozier, C.R. (2008)-Fertilization. In K.L. Edmisten (ed.). North Carolina Cotton Information. Publ. AG–417. North

Carolina Cooperative Ext. Serv., Raleigh, NC. p. 43–57. [6] Edmisten, K.L. (2008) - Suggestions for growth regulator use. p. 58–64. In K.L. Edmisten (ed.). North Carolina Cotton

Information. Publ. AG–417. North Carolina Cooperative Ext. Serv., Raleigh, NC. [7] Edmisten, K.L. (1994) - The use of plant monitoring techniques as an aid in determining mepiquat chloride rates in rain-fed

cotton. In Challenging the Future: Proceedings of the World Cotton Research Conference – 1. G.A. Constable and N.W. Forrester (eds.), Brisbane Australia, February 14–17. pp. 25–28.

[8] Elbehar, M.W., and Welch, R.A. (1996)-Evaluation of plant growth regulators with varying nitrogen management. In Proc. Beltwide Cotton Conf. Natl. Cotton Counc. Am., Memphis,TN. 2: 1415–1418.

[9] Elbehar, M.W., Welch, R.A. and Meredith, W.R. Jr. (1996) - Nitrogen rates and mepiquat chloride effects on cotton lint yield and quality. In Proc. Beltwide Cotton Conf. Natl. Cotton Counc. Am., Memphis, TN. 2:1373-1378.

[10] Hake, K., Kerby, T.W., McCarty, D.O’ Neal and Supak, J. (1991)-Physiology of PIX. Physiology Today newsletter. May 2: no. 6.

[11] Johnson, J.T. and Pettigrew, W.T. ( 2006) - Effects of mepiquat pentaborate on cotton cultivars with different maturities. J. Cotton Sci. 10: 128–135.

[12] Jost, P., and Dollar, M. (2004) - Comparison of mepiquat pentaborate and mepiquat choride effects on DP555BR. In Proc. Beltwide Cotton Conf. San Antonio, TX. Jan. 5–9. Natl. Cotton Counc. Am., Memphis, TN.

[13] Koenning, S. (2008) - Disease management in cotton. In K.L. Edmisten (ed.). North Carolina Cotton Information. Publ. AG–417. North Carolina Cooperative Ext. Serv., Raleigh, NC. p. 65–75.

[14] Nichols, S.P., Snipes, C.E. and Jones, M.A. (2003)-Evaluation of row spacing and mepiquat chloride in cotton. J. Cotton Sci. 7: 148–155.

[15] O’berry, N.B., Faircloth, J.C., Jones, M.A., Herbert, D.A. Jr., Abaye, A.O., McKemie, T.E. and Brownie, C. (2009) - Differential responses of cotton cultivars when applying mepiquat pentaborate. Agron. J. 101: 25–31.

[16] Pettigrew, W.T., and Johnson, J.T. (2005) - Effects of different seeding rates and plant growth regulators on early-planted cotton. J. Cotton Sci. 9: 189–198.

[17] Pline, W.A., Viator, R.J., Wilcut, W., Edmisten, K.L., Thomas, J. and Wells, R. (2002) - Reproductive abnormalities in glyphosate-resistant cotton caused by lower CP4-EPSPS levels in the male reproductive tissue. Weed Science 50: 438–447.

[18] Prince, W.B., Livingston, C.W. and Fernandez, C.J. (2000) - Effects of planting date and mepiquat chloride on cotton growth, lint yield, and fiber quality in the south Texas coastal plains. In Proc. Beltwide Cotton Conf. Natl. Cotton Counc. Am., Memphis, TN. 1: 681–681.

[19] Reddy, V.R., Trent, A. and Acock, B. (1992) - Mepiquat chloride and irrigation versus cotton growth and development. Agron. J. 84: 930–933.

[20] Siebert, J.D. and Stewart, A.M. (2006) - Influence of plant density on cotton response to mepiquat chloride application. Agron. J. 98: 1634–1639.

[21] Wilson, D.G. Jr., York, A.C. and Edmisten, K.L. (2007) - Narrow-row cotton response to mepiquat chloride. J. Cotton Sci. 11: 177–185.

[22] York, A.C., and Culpepper, S. (2008)-Weed management in cotton. In K.L. Edmisten (ed.). North Carolina Cotton Information. Publ. AG–417. North Carolina Cooperative Ext. Serv., Raleigh, NC. pp. 77–137.

Effect of Cool Conditions on Cotton Seedlings

D.K.Y. Tan1, S. Ormiston1, M.P. Bange2 and J.S. Amthor1

1Faculty of Agriculture, Food and Natural Resources, University of Sydney 2CSIRO Division of Plant Industry and Cotton, Australia

Abstract—Suboptimal temperatures reduce cotton germination, seedling emergence, and early vigour, all of which can lead to poor or delayed stand establishment. Strategies that provide early season low-temperature tolerance might improve cotton growth when cooler than expected temperatures are experienced early. It could also provide growers with increased flexibility for planting times especially in cooler regions. Ensuring better establishment under cool conditions should reduce costs associated with replanting and contribute to greater stand uniformity which will assist in attaining optimum yield and fibre quality. Plant growth regulators (PGRs) are a potential option of increasing cotton seedling hardiness against cool temperatures, and we tested whether the PGRs ‘paclobutrazol’ and ‘trinexapac-ethyl’ can improve cold tolerance in cotton seedlings. In this preliminary investigation, we found no evidence that these PGRs improved cotton germination under cool conditions in the laboratory, whereas they reduced seedling length under warm conditions.

Keywords: Chilling injury, cotton, germination, establishment, screening techniques, plant growth regulators, paclobutrazol, trinexapac-ethyl

INTRODUCTION

Cotton (Gossypium hirsutum) is a cold sensitive plant. Chilling injury occurs in cotton seedlings whenever the temperature drops below 15 °C for a few hours during the first several days after germination (Lauterbach et al., 1999) and temperatures below 20 °C slow the germination of cotton seed and of seedling emergence (Cole and Wheeler, 1974). This emphasises the opportunity to develop cultivars or management practices that would allow better crop establishment, reducing the costly need to replant following post-emergence cold shocks, aiding uniformity of crop establishment. Improved establishment with a reduction in gappy stands leads to improvement in yield and fibre quality. It will also provide growers with increased flexibility regarding early sowing date options (Constable et al., 1998).

This work extends previous assessments of genotypic variation of cotton germination and establishment in response to cold conditions (Duesterhaus, 2000; Duesterhaus et al., 2000; Tuck et al. 2010). Their research suggested that there was genotypic variability in field-grown seedling establishment in response to cold temperatures, and they developed methods to distinguish differences in cold tolerance of cultivars in the laboratory using germination tests in both warm and cool conditions and validated under field conditions. Tuck et al. (2010) successfully used seedling length as an indicator of cultivar variation however; the approach needs to be validated with a wider range of genotypes and conditions.

Plant growth regulators (PGRs) and retardants have been suggested for improving plant stress tolerance by Rademacher (2000). Seed coatings containing PGRs may have the potential to improve cold tolerance by improving seedling vigour in stressed conditions (Rademacher, 2000). It is hypothesised that two novel plant growth regulators; ‘paclobutrazol’ [PBZ (chemical name (2RS, 3RS)-1-(4-chlorophenyl)-4,4-dimethyl-2-(1H-1,2,3-triazol-1-yl)pentan-3-ol)] and ‘trinexepac-ethyl’ [TXP (chemical name 4-(cyclopropyl-alpha-hydroxy-methylene)-3,5-dioxocyclohexanecarboxylic acid ethyl ester)] may be used as a seed coating to assist in early crop vigour in cold conditions at establishment (Rademacher, 2000). PBZ is a potent inhibitor of gibberellins biosynthesis and triazole fungicide, and has been shown to reduce desiccation in seedlings by reducing shoot growth (Gavidia et al., 1997). TXP is an acylcyclohexanedione that influences the electron transfer chain in plant mitochondria (Heckman et al., 2002), and interferes with the later stages of gibberellins synthesis reducing plant shoot growth (Rademacher, 2000). The use of TXP in perennial ryegrass in New Zealand increased harvest index

70

Effect of Cool Conditions on Cotton Seedlings 417

(increased partitioning to the harvestable plant parts), increased seed head density, increased seeds per spike, improved seed retention and reduced lodging (Chynoweth et al., 2010). No research has been conducted, however, on the effect of PBZ and TXP on the cold tolerance of cotton seedlings.

This paper reports preliminary investigations to further develop cold tolerance screening methods and assesses the use of the plant growth regulators PBZ and TXP on cotton germination in warm and cool laboratory conditions.


Experiments were conducted using seed germination facilities at the Australian Cotton Research Institute (ACRI) in Narrabri, NSW and the Biomedical Building, University of Sydney, NSW.

To assess the impacts of PBZ and TXP on cotton germination, untreated seeds of the cultivar Sicot 71BRF (CSIRO, Australia) were treated with various rates of the PGRs and then germinated in warm and cold conditions. Seeds were treated with 0, 4, 20, 40, 80 and 160 g/100 kg of TXP and 0, 4, 8, 16, 32 and 64 ml/100 kg of PBZ.

Four replications of 50 seeds were subjected to both warm and cool germination tests for each seed treatment. Seedlings with a radicle length of 3 mm or longer were counted as germinated (Wanjura and Buxton, 1972). Ten seedlings were selected randomly and the length from the hook of the hypocotyl to the tip of the radicle was measured (hereafter referred to as seedling length). A combination of the warm and cool germination tests was reported to be a reliable indicator of field performance (Bird and Reyes, 1967). For the warm germination tests seeds are germinated at 300 and seed germination percentage and seedling length recorded after 4 d. For the cold germination tests seeds are germinated at 14 oC and seedling lengths are measured after 7 d. A cool-warm vigour index was calculated as the average of the warm (300 C) seedling length on day 4 divided by the cool (140 C) seedling length on day 7 (Tuck et al., 2010). The cool-warm vigour index provided the best prediction of field emergence under cool conditions in Tuck et al. (2010).


The Sicot 71BRF seed supplied had low germination percentages below 50 % (Tables 1 and 2). PBZ did not affect the germination percentage at 4 d, but seedling length in the warm germination test at 4 d was significantly different (Table 1). Seedling length was reduced by up to 20 % in the PBZ treatments compared with the untreated control and there was a second order polynomial response of seedling length with PBZ concentration (Figure 1). There were no significant differences in seedling length in the cold germination treatment on 7 d at 14 ˚C. While there were significant differences in the cool-warm vigour index, there was no improvement in the index with the application of PBZ over the control.

TABLE 1: EFFECT OF PACLOBUTRAZOLE (PBZ) CONCENTRATION (G/100 KG) ON GERMINATION AND SEEDLING LENGTH AT 30 ˚C ON DAY 4, AT 14 ˚C ON DAY 7 AND COOL-WARM SEEDLING

LENGTH (AVERAGE OF WARM SEEDLING LENGTH (MM) AT 30 ˚C ON DAY 4 AND 14˚C ON DAY7

PBZ treatment (mL/100 kg)

Germination Probability on at 30 ˚C on Day 4

Seedling length (mm) at 30 ˚C on Day 4

Seedling length (mm) at 14 ˚C on Day 7

Cool-Warm Seedling Length (mm)

0 (Control) 0.48 100 13 57 4 0.47 81 11 46 8 0.40 83 11 47 16 0.49 85 11 48 32 0.48 83 13 48 64 0.55 76 11 43

P value 0.071 <0.001 0.5 <0.001 LSD (5%) - 7.8 3.3 4.5

418

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Effect of Cool Conditions on Cotton Seedlings 419

CONCLUSION

The PGRs PBZ and TXP did not improve cold tolerance in cotton seeds and seedlings, but they reduced cotton seedling lengths in warm conditions. Thus, while the treatments did not improve performance under cool conditions, they had the potential to reduce performance under warm conditions.

ACKNOWLEDGEMENT

We thank the BION fund (Cotton Seed Distributors and Syngenta) for financial support and Ken Mckee and Robert Battaglia for seed coating with the plant growth regulators.

REFERENCES [1] Bange MP, Milroy SP. (2004). Impact of short-term exposure to cold night temperatures on early development of cotton

(Gossypium hirsutum L.). Australian J. Agril. Res. 55: 655–664. [2] Bird LS, Reyes AA. (1967). Effects of cottonseed quality on seed and seedling characteristics. Proceedings of the Beltwide

Cotton Production Research Conference, pp. 199–206. [3] Chynoweth R, Rolston M, BL M. (2010). Plant growth regulators: a success story in perennial ryegrass seed crops. In 'Seed

symposium: Seeds for the Future. Proceedings of a Joint Symposium between the Agronomy Society of New Zealand and the New Zealand Grassland Association, pp. 47–57. (Agronomy Society of New Zealand: Massey University, Palmerston North, New Zealand).

[4] Cimen I, Basbag S, Temiz M, Sagir A. (2003). Effect of paclobutrazol on earliness of cotton (Gossypium hirsutum). Indian J. of Agril. Sci. 73: 298–300.

[5] Cole DF, Wheeler JE. (1974). Effect of pregermination treatments on germination and growth of cottonseed at suboptimal temperatures. J. Crop Sci. 14: 451–454.

[6] Constable GA, Eveleigh R, Kay A, Marshall J. (1998). Replanting Guide - Cotton Seed Distributors Grower Information. (Cotton Seed Distributors: Narrabri).

[7] Constable GA, Shaw A.J. (1988). Temperature requirements for cotton. New South Wales Department of Agriculture and Fisheries P5.3.5.

[8] Davis TD, Curry EA. (1991). Chemical regulation of vegetative growth. Critical Rev. Plant Sci. 10: 151–188. [9] Duesterhaus B. (2000). Laboratory screening test for the evaluation of cold tolerance in cotton. Texas Tech University. [10] Duesterhaus B, Hopper NW, Gannaway JR, Valco TD. (2000). A screening test for the evaluation of cold tolerance in

cottonseed germination and emergence. Proceedings of the Beltwide Cotton Conference. Memphis, Tennessee pp. 596–599. (National Cotton Council).

[11] Gavidia I, Zaragoza C, Segura J, Perez-Bermudez P. (1997). Plant regeneration from juvenile and adult Anthyllis cytisoides, a multipurpose leguminous shrub. J. Plant Physiol., 150: 714–718.

[12] Haughan PA, Lenton JR, Goad LJ. (1988). Sterol requirements and paclobutrazol inhibition of a celery cell-culture. Phytochemistry 27, 2491–2500.

[13] Heckman NL, Elthon TE, Horst GL, Gaussoin RE. (2002). Influence of trinexapac-ethyl on respiration of isolated wheat mitochondria. J. Crop Sci., 42: 423–427.

[14] Lauterbach B, Krieg DR, Jividen GM. (1999). Fatty acid composition of lipid fractions in germinating cotton as affected by temperature. Proceedings of the beltwide cotton conference. Memphis, Tennessee pp. 564–565. (National Cotton Council of America).

[15] Rademacher W. (2000). Growth retardants: effects on gibberellin biosynthesis and other metabolic pathways. Ann. Rev. Plant Physiol. Plant Mol. Biol., 51: 501–531.

[16] Tuck CA, Tan DKY, Bange MP, Stiller WN. (2010). Cold-tolerance screening for cotton cultivars using germination chill protocols. Food Security from Sustainable Agriculture. Proceedings of 15th Agronomy Conference 2010. (Eds H Dove, RA Culvenor). (Australian Society for Agronomy: Lincoln, New Zealand).

[17] Wanjura DF, Buxton DR. (1972). Water uptake and radicle emergence of cottonseed as affected by soil moisture and temperature. Agronomy J., 46: 427–431.

Increased Nutrient Uptake and Salinity Tolerance in AhCMO Tansgenic Cotton

Huijun Zhang1, Jianlong Dai2 and Hezhong Dong2

1Cotton Research Institute, Shanxi Academy of Agricultural Sciences, Yuncheng–044000 China 2Cotton Research Center, Shandong Key Lab for Cotton Culture and Physiology, Shandong

Academy of Agricultural Sciences, Jinan–250100 China E-mail: [email protected], [email protected]

Abstract—Many previous studies have shown that salinity stress limits nutrient uptake in cotton (Gossypium hirsutum L.), but the relationships between salt tolerance and nutrients uptake was not fully studied. The objective of this study was to determine if improved nutrient uptake increases salinity tolerance of cotton. Two experiments with either transgenic salt-tolerant cotton or a split-root system were conducted in the greenhouse. In the first experiment, a transgenic AhCMO cotton line (CMO4) with increased salt tolerance and its wild line (SM3) were grown under salinity stress in pots containing substrate mixture (seedling substrate: vermiculite = 1:1, v/v). In the second experiment, cotton plants were cultured in hydroponics with a split-root system, in which one part of the root system was non-stressed (C) and the other part was salt-stressed (S). Both sides of the root system were also fed with either low (LN) or moderate level of nutrient solution (MN). Contents of essential nutrient elements (N, P, K, Ca, Mg, Fe, Mn, Cu, Zn) and Na+ in plant tissues, leaf photosynthesis (Pn) and plant biomass were determined after salinity (NaCl) treatment in both experiments. In the first experiment, salinity stress with 150 mM NaCl reduced plant biomass, leaf Chl, and Pn of both SM3 and CMO4 compared with their non-stressed controls. However, the CMO4 suffered significantly lower reductions than the wild line, SM3, suggesting an increased salinity tolerance of CMO4 relative to SM3. Total uptake and contents of main nutrient elements (N, P, K, Ca, Mg, Fe, Mn, Cu, Zn) in CMO4 were higher than those in SM3. Also, less Na+ accumulation and lower extreme ratios of Na/N, Na/P, Na/K, Na/Ca, Na/Mg, Na/Fe, Na/Mn, Na/Cu and Na/Zn were observed in CMO4 than in SM3 30 days after salt stress (DAS). Increased salt tolerance in transgenic AhCMO cotton may be attributed to its superior nutrient uptake compared with SM3. In the second experiment, the non-stressed root half fed with moderate nutrient solution and salt-stressed half fed with low nutrient solution (CMN/SLN) in the split-root system experienced increased dry weight (22.3%), leaf area per plant (15.5%), leaf Pn (33.3%) and Chl content (35.3%), relative to those with salt-stressed root-part fed with moderate level of nutrient solution and non-stressed root-part fed with low nutrient solution (CLN/SMN). Plants absorbed more N, P, K, Ca, Mg, Fe, Mn, Cu and Zn, but less Na+ under CMN/SLN than CMN/SMN. Moreover, ratios of Na/N, Na/P, Na/K, Na/Ca, Na/Mg, Na/Fe, Na/Mn, Na/Cu and Na/Zn in CMN/SLN were lower than those in CLN/SMN. Increased plant growth with high concentrations of nutrients in lower saline root part was also attributed to enhanced uptake of nutrients. The overall results suggest that improved nutrient uptake increases salinity tolerance of cotton.

Keywords: Cotton, Salt tolerance, Nutrient uptake, Split-root

INTRODUCTION

Soil salinity is one of the major abiotic stresses affecting plant productivity (Yildirim et al. 2009). Although cotton (Gossypium hirsutum L.) is classified as a salt tolerant crop, it is negatively affected by excessive salt in the soil, particularly in arid and semiarid regions. It is generally believed that soil salinity affects plant growth and development by ways of osmotic stress, injurious effects of toxic ions and the resulting nutrient imbalance (Sairam and Tyagi, 2004). Therefore, searching for ways or techniques to reduce osmotic stress and ion toxicity under salinity has attracted great research attention world-wide (Gorham et al., 2009; Munns and Tester, 2008).

Ways or techniques to reduce osmotic stress and ion toxicity have been explored by improving agronomic practices. Seed-bed preparation (El-Swaify, 2000; Meiri and Plaut, 1985), plastic mulching (Dong et al., 2008, 2009), and foliar application of plant growth regulators (Hoque et al., 2007) were reported to reduce either osmotic stress or ion toxicity, and significantly decreased salt injury in the greenhouse or saline field conditions. Glycine betaine is an osmoprotectant that is correlated with salt tolerance (Sulpice et al., 2003; Sakamoto and Murata, 2001). Foliar application of glycine betaine

71

Increased Nutrient Uptake and Salinity Tolerance in AhCMO Tansgenic Cotton 421

improved salt tolerance of rice (Harinasut et al., 1996). Glycine-betaine is synthesized from choline through two steps: choline→betaine aldehyde→glycine betaine, in which choline monooxygenase (CMO) catalyzes the first step and betaine aldehyde dehydrogenase (BADH) catalyzes the second step (Rhodes and Hanson, 1993). Transgenic cotton lines carrying CMO gene cloned from Atriplex hortensis accumulated significantly more glycine betaine and less Na+ than their wild line, and showed increased salt tolerance in either greenhouse or field conditions (Zhang et al., 2007, 2009). Increased salt tolerance of the AhCMO transgenic cotton lines was attributed to reduced osmotic stress and ion toxicity (Zhang et al., 2009). However, it is still not clear if the increased salt tolerance is also related to nutrients uptake under salinity stress.

Salinity levels are seldom uniform in saline fields. Hence, a part of the root system would be under lower salinity than the other root part (Kaman et al., 2006; Zekri and Parsons, 1990). Such unequal salt distribution can be accurately simulated with a split-root system, in which the root systems are divided into two or more equal portions and each portion irrigated with varied concentrations of NaCl solution (Shani et al., 1993; Zhu and Ito, 2000; Messedi et al., 2004; Lycoskoufis et al., 2005). Under the split-root system, many studies indicated that unequal salt distribution improved plant growth and yield in tomato (Tabatabai et al., 2004), cucumber (Sonneveld and De Kreij, 1999), halophytic shrub (Bazihizina et al., 2009) and cotton (Dong et al., 2010) compared to equal salt distribution. However, the underlying mechanism of plant growth enhancement under unequal salt distribution, particularly the relationship between salt tolerance and nutrient uptake is not fully understood.

The relation between salt tolerance and nutrient uptake is an important basis for cotton fertilization in saline soils. It was usually studied by using various fertilizer rates or forms (Elgharably et al., 2010; Gimeno et al., 2009; Irshad et al., 2008). Previous studies with the traditional system have shown that soil salinity inhibits uptake of nutrients in cotton, and proper fertilization alleviates the detrimental effects of salinity under certain conditions (Rathert, 1983; Martinez and Lauchli, 1991; Chen et al., 2010). Adequate N fertilization increased N uptake and plant growth at both low and medium soil salinities, but had little effect at higher salinity even at an elevated N rate (Chen et al., 2010). As the amount of nutrient uptake does not always change with fertilizer rate under moderate and high salinity, it is very difficult to determine the relation between salt tolerance and nutrient uptake using the traditional system. In contrast, varying nutrient uptake rates in cotton plants can be realized with unequal salt distribution under the same salinity level in a split-root system, this helps to accurately study the tolerance-uptake relations.

In this paper, one experiment was conducted with an AhCMO transgenic cotton line to determine if the improved salt tolerance of the transgenic cotton was related to increased nutrient uptake. The other experiment was conducted in a split-root system to determine if plants with non-stressed root half in high nutrient solution could uptake more nutrients and grow better than those with salt-stressed root half in high nutrient solution. Our main objective was to determine if improved nutrient uptake increases salinity tolerance of cotton.


Plant Culture and Treatment in Experiment 1

An elite Chinese cotton (Gossypium hirsutum L.) cv. Simian 3 (SM3, wild type) was transformed with the AhCMO gene (gb: AF270651, patent no.: ZL00109 164.6) from Atriplex hortensis, mediated by Agrobacterium tumefaciens in our previous study (Zhang et al., 2009). A transgenic line CMO4 carrying AhCMO gene at the T4 generation and wild line (non-transgenic) SM3 were used in this experiment. Seeds of CMO4 and SM3 were sown in 5 L plastic pots containing substrate mixture (seedling substrate: vermiculite=1:1, v/v) and allowed to grow in the greenhouse at 32/24oC and relative humidity of 35–50%. Seedlings were thinned to five of uniform size per pot when the first true-leaf of most seedlings fully expanded. Salinity treatment was imposed to seedlings at the 2 true-leaf stage by irrigating with NaCl solution. This was added at 50 mM increments every 12h, until the final concentration of NaCl reached 150 mM. The experiment was arranged into a completely randomized design with four

422

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distilled water daily. Seedling plants were sampled and analyzed 42 days after treatment. The experiment was arranged into a completely randomized design with four replicates. Each replicate had 3 plants.

Data Collection in Both Experiments

Data collected were fresh and dry weight of a whole plant and its organs (leaf, stem and root), concentrations of mineral elements (N, P, K, Ca, Mg, Fe, Mn, Cu, Zn and Na+) in plant tissues, Pn rate and Chl content in the 4th leaf on the main stem from growth terminal at 30 DAS in the 1st experiment and 42 DAS in the 2nd experiment.

Determination of Growth Parameters

In the first experiment, plants were removed from pots carefully at 30 DAS. The remaining roots in the substrate were also collected by washing the potted substrate through a 4 mm sieve. In both experiments, plants were divided into leaves, stems and roots, and their respective fresh weight (FW) was weighed. Leaf area was measured by passing the leaves through a LI-3000 area meter (LI-COR, Lincoln, NE, USA). Dry weight (DW) was determined after oven drying at 75℃ until a constant weight was reached.

Determination of Physiological Parameters

Net photosynthetic (Pn) rate of the 4th fully expanded young leaf on the main-stem from terminal was taken between 09:00 and 11:00 h on cloudless days when ambient photosynthetic photon flux density exceeded 1500 μmol m−2 s−1, using a LI-6400 portable photosynthesis system (Li-Cor, Lincoln, NE, USA). Leaf chlorophyll (Chl) contents were determined as described in He et al. (2002). Briefly, 0.20 g fresh leaves were placed in a 100 mL test tube. The tissues were homogenized with a polytron after adding 10-15 ml pure methanol. The homogenate was then filtered and made up to 100 mL with pure methanol. The Chl concentration in the supernatant was spectrophotometrically determined by measuring the absorbances at 652 and 665 nm for Chl a and Chl b, respectively.

Determination of Mineral Concentrations

Ashing was carried out by means of incineration in a muffle oven at 450±25℃ until an ash was obtained. The ash was acid digested (HCl) for the determination of micro and macro elements. Nitrogen was determined by Kjeldahl procedure. Phosphorus was determined spectro-photometrically (TU-1901, Beijing, China). The elements potassium (K), sodium (Na), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), cuprum (Cu) and zinc (Zn) were measured by an atomic absorption spectrophotometer (TAS-990, Beijing, China) equipped with hollow cathode lamps. Recovery of added known amounts of standards to samples gave 98–105% of expected values for all minerals.

Statistical Analysis

Data were statistically analyzed with DPS Data Processing System (Tang and Feng, 1997). Means were separated using a t-test.

RESULTS

The 1st Experiment

Plant biomass

Salinity stress with 150 mM NaCl significantly reduced plant growth of both SM3 and CMO4 in terms of their fresh weight and dry weight of root, stem and leaves (Table 1). Root, stem, leaf and total plant of CMO4 were decreased by 33.9, 45.8, 37.7 and 36.8% in fresh weight and 20.0, 28.4, 30.0 and 24.1% in dry weight, respectively, while those of SM3 were decreased by 58.8, 61.3, 65.5 and 62.7% in fresh weight and 50.0, 56.6, 60.0 and 53.8% in dry weight, compared with their respective NaCl-free controls. It was noted that the salt-induced decrement in both fresh and dry weights in CMO4 was significantly lower than that in SM3.


TABLE 1: FRESH AND DRY WEIGHTS OF TRANSGENIC AHCMO COTTON CMO4 AND ITS WILD LINE SM3 30 DAYS AFTER SALT (150 MM NACL) STRESS

Cultivar Treatment Fresh Weight (g. Plant-1) Dry Weight (g. Plant-1) Root Stem Leaf Total Root Stem Leaf Total

SM3 CK 6.86a 8.05a 11.09a 26.0a 1.23a 1.10a 1.63a 3.96a Salt 2.80d 3.11c 3.80d 9.7d 0.64d 0.49c 0.68c 1.81d

CMO4 CK 5.59b 5.91b 7.60b 19.1b 1.01b 0.70b 1.21b 2.92b Salt 3.78c 3.27c 4.85c 12.0c 0.83c 0.53c 0.84c 2.20c

*Different letters within a column indicate significant differences at p=0.05.

Photosynthesis

Salt stress significantly reduced net photosynthetic (Pn) rate and chlorophyll (Chl) content in the 4th main stem leaf from terminal for both SM3 and CMO4 at 30 DAS compared with their respective control (Table 2). However, the decrement in Pn (26.9%) and Chl content (17.5%) in CMO4 was lower than that of Pn (36.3%) and Chl (24.4%) in SM3. Moreover, Na+ concentration in roots, stems and leaves of CMO4 was 6.1%, 11.6% and 26.4% lower than that of SM3 under saline stress, respectively (Table 2). The result indicated less accumulation of Na+ in CMO4 than in SM3 tissues under salt stress. TABLE 2: CHLOROPHYLL (CHL) CONTENT, PHOTOSYNTHETIC (PN) RATE OF MAIN-STEM LEAVES AND NA CONTENTS IN TRANSGENIC AHCMO COTTON CMO4 AND ITS WILD LINE SM3 30 DAYS

AFTER SALT (150 MM NACL) STRESS.

Cultivar Treatment Na content (mg.g-1) Chl (mg.g-1FW)

Pn (μmolCO2.m-2.s-1) Root Stem Leaf

SM3 CK 7.53c 3.33b 4.00c 17.2a 21.5a Salt 11.50a 13.80a 13.41a 13.0c 13.7c

CMO4 CK 6.59d 3.03b 3.25c 17.7a 23.4a Salt 10.81b 12.21a 9.86b 14.6b 17.1b

* Different letters within a column indicate significant differences at p=0.05.

Uptake of some mineral elements Differences in mineral nutrient content (uptake) and the ratios of Na to other mineral nutrient elements were observed between SM3 and CMO4 under salt stress. NaCl stress increased the content of N, P, K, Ca, Mg, Fe, Mn, Cu and Zn in the leaves of CMO4, compared with the non-stressed control. Whereas in the leaves of SM3, content of P, K, Ca, Fe, Mn and Zn decreased and only N, Mg and Cu concentrations increased . Similar results were obtained in the stem and root. Moreover, the content of N, P, K, Ca, Mg, Fe, Mn, Cu and Zn in leaf, stem and root in CMO4 were higher than that in SM3 under NaCl stress (Table 3).

TABLE 3: NUTRIENT ELEMENTS CONTENTS OF TRANSGENIC AHCMO COTTON CMO4 AND ITS WILD LINE SM3 AT 30 DAYS AFTER SALT (150 MM NACL) STRESS

Cultivar Treatments Macronutrients (mg.g-1 DW) Micronutrients (μg.g-1 DW)N P K Ca Mg Fe Mn Cu Zn

-----------------------------------------------------------Root----------------------------------------------------- SM3 CK 7.4c 0.94c 5.56c 0.33a 0.22b 366a 40.0b 28.2c 49.0c

Salt 8.5b 0.97c 5.32d 0.26c 0.17c 174d 28.1c 32.1b 36.0dCMO4 CK 7.5c 1.37b 5.95b 0.34a 0.21b 323b 44.1b 23.3d 70.3b

Salt 10.4a 1.75a 7.21a 0.29b 0.25a 258c 53.0a 35.0a 125.0a-----------------------------------------------------------Stem-----------------------------------------------------

SM3 CK 8.5c 1.54b 8.60b 0.43d 0.19c 301b 24.2d 26.1c 18.2dSalt 11.0b 1.24c 8.41b 0.46c 0.22b 225c 31.4c 30.0b 26.3c

CMO4 CK 7.9c 1.01d 8.40b 0.48b 0.17c 289b 36.1b 23.0d 50.1bSalt 13.1a 2.07a 9.80a 0.51a 0.26a 379a 43.8a 34.1a 81.0a

-----------------------------------------------------------Leaf------------------------------------------------------ SM3 CK 15.5c 2.56a 5.50b 1.26c 0.56c 404b 136c 44.0b 83c

Salt 16.8b 1.66c 5.01c 1.22d 0.60b 328d 97d 46.1a 56dCMO4 CK 15.3c 2.28b 4.60d 1.31b 0.57bc 375c 213b 36.0d 109b

Salt 18.2a 2.48a 5.80a 1.46a 0.65a 537a 266a 40.0c 139a*Different letters within a column about root, stem and leaf indicate significant differences at p=0.05.

Salt stress decreased total amount of mineral nutrients per plant in both SM3 and CMO4 compared with their respective control (Fig 2). However, the amount of N, P, K, Ca, Mg, Fe, Mn, Cu and Zn in CMO4 was reduced by 4.4, 6.4, 7.6, 24.9, 13.8, 16.1, 12.6, 2.3 and 2.3%, while the amount of N, P, K, Ca, Mg, Fe, Mn, Cu and Zn in SM3 was decreased by 49.5, 65.7, 56.8, 58.2, 55.6, 68.9, 67.1, 50.9 and


65.3%, respectively. The results indicated that the decrement in CMO4 was lower than in SM3. Moreover, total amount of mineral nutrients per plant in transgenic AhCMO cotton were significantly higher than in SM3 under NaCl stress.

Fig. 2: Total Accumulation of Nutrient Elements Per Plant in Transgenic AhCMO Cotton CMO4 and its Wild Line SM3 30 Days after Salt (150 mM NaCl) Stress. Means Followed by Different Letters Differ Significantly (p=0.05). Vertical Bars Show ±SD of Four Replicates with Five Plants Per Replicate

Salinity stress significantly increased the ratios of Na to main nutrient elements in different tissues for both cultivars relative to their respective control, but the increment of Na/N, Na/P, Na/K, Na/Ca, Na/Mg, Na/Fe, Na/Mn, Na/Cu and Na/Zn in CMO4 was significantly lower than that in SM3. The ratios of Na to main nutrient elements in leaf, stem and root of CMO4 were also lower than that in SM3 under NaCl stress (Table 4).

TABLE 4: RATIOS OF NA TO NUTRIENT ELEMENTS IN TRANSGENIC AHCMO COTTON CMO4 AND ITS WILD LINE SM3 30 DAYS AFTER SALT (150 MM NACL) STRESS.

Cultivar Treatments Na/N Na/P Na/K Na/Ca Na/Mg Na/Fe Na/Mn Na/Cu Na/Zn -----------------------------------------------------------Root--------------------------------------------------------

SM3 CK 1.03b 8.0b 1.37c 23.0c 34.2c 21.1c 188c 271c 154b Salt 1.16a 10.0a 1.85a 35.1b 59.0a 56.2a 356a 311b 275a

CMO4 CK 0.88c 4.7c 1.11d 19.1c 32.2c 20.0c 151c 267c 99c Salt 1.03b 7.3b 1.78b 47.0a 52.1b 50.1b 242b 370a 102c

-----------------------------------------------------------Stem------------------------------------------------------- SM3 CK 0.39c 2.27d 0.39c 7.8c 18.4c 11.3c 128c 128c 188b

Salt 1.28a 11.0a 1.64a 30.0a 62.0a 62.2a 442a 456a 537a CMO4 CK 0.38c 3.0c 0.36c 6.3d 17.1c 11.2c 84d 130c 61d

Salt 0.72b 6.3b 1.33b 26.1b 51.0b 35.0b 299b 389b 161c -----------------------------------------------------------Leaf--------------------------------------------------------

SM3 CK 0.26c 1.6c 0.72c 3.2c 7.1c 9.9c 29b 91c 48c Salt 0.86a 8.7a 2.88a 12.1a 24.0a 44.1a 151a 316a 262a

CMO4 CK 0.22c 1.4c 0.71c 2.5d 5.7d 8.1d 15c 91c 29d Salt 0.47b 4.0b 1.69b 6.7b 15.3b 19.0b 37b 246b 75b

* Different letters within a column about root, stem and leaf indicate significant differences at p=0.05.

0

10

20

30

40

50

N K

Acc

umul

atio

n (m

g. P

lant-1

) . SM3-CK SM3-Salt

CMO4-CK CMO4-Salt

0

2

4

6

8

P Ca Mg Fe

Acc

umul

atio

n (m

g P

lant-1

) .

0

100

200

300

400

Mn Cu Zn

Acc

umul

atio

n (μ

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lant-1

)

a

a

a

a

a

a

a

a

a

b

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cc

c

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426

THE 2ND EX

Plant Biom

Treat

CMN

CLN

CMN

CLN

* Different lette

In the splplant. Thconcentraplus mod(CMN/SL(22.3%), cand salinit

Photosynth

The uptakin CMN/S

Treatm

CMN/S

CLN/S

* Differ

Fi

XPERIMENT

mass TABLE 5

tments

-------------N/SLN

N/SMN

-----------N/SLN

N/SMN

ers within a column a

lit-root systehe root-half ated (1/2-conderate level LN) significacompared wty stress plus

hesis and Na+

ke and partitiSLN than in C

TABLE 6: LEAF AR

ments Sp

SLN CMNSLN

SMN CLNSMN

rent letters within

g. 3: Cotton Plants

World Cot

5: FRESH AND DRY WEI

Split Roo-------------------

CMN 2SLN 1CLN 1SMN 1

------------------CMN 4SLN 3CLN 3SMN 2

about fresh weight a

m, one root-was also fe

ncentrated) (Mof nutrition antly increasith the treatms moderate n

Accumulation

ioning of Na+

CLN/SMN, REA, CHLOROPHYLL (CH

UNDER

Na Content plit Root SN 1.3d 5N 8.1a N 1.6c 5N 7.7b n a column indica

s (a) and the 4th M

tton Research C

IGHTS OF COTTON UNDE

Root ot ------------Fresh21.3a 13.3b 18.1a 10.9b --------------Dry4.13a 3.02b 3.41b 2.26c

and dry weight indica

-half was saed in eitherMN) nutrien

in one sidesed plant grment combinnutrient in the

n + varied betwespecially in

HL) CONTENT, PHOTOS

R UNEQUAL SALT AND N

(mg.g-1) Stem Leaf5.32b 11.8b

5.77a 13.5a

ate significant diff

Main-stem Leaves

Conference on T

ER UNEQUAL SALT AND

Total Weighth Weight(g. pla

34.6a

29.0b

y weight (g. pla7.17a

5.64b

ate significant differ

lt stressed (Sr low concent solution. Te and salinirowth in ternation with ne other side (

ween CMN/Sn leaf (12.6%SYNTHETIC (PN) RATE O

NUTRIENT DISTRIBUTIO

Chl(mg.g-1Ff

b 9.2a

a 6.8b

ferences at p=0.0

(b) under Unequa

Technologies fo

D NUTRIENT DISTRIBUTI

Stem

ant-1)------------49.1a

38.6b

ant-1)-------------16.3a

13.3b

ences at p=0.05. S) and the oentrated (1/8Treatment coity stress plurms of freshnon-salinity s(CLN/SMN)

SLN and CLN%) and stem (

OF ON MAIN-STEM LEA

N 42 DAYS AFTER TRE

FW) (μm

5.

l Salt and Nutrient

or Prosperity

ION 42 DAYS AFTER TR

Leaf

-------------------31.3a

26.3b

-------------------6.2a

5.3b

other was not8-concentrateombination wus low nutr

h weight (22stress plus lo) (Table 5).

N/SMN. Les7.8%) (Tabl

AVES AND NA CONTENTS

EATMENT

Pn molCO2.m-2.s-1

12.8a

9.6b

t Distribution 42 D

REATMENT

Total w

-------------- 115.

93.9

------------- 29.6

24.2

t-stressed (Ced) (LN) or with non-salirient in the 2.7%) and dow nutrient i

ss Na was ace 6). S IN PLANT TISSUES

1) Lea

(cm2

1

1

Days after Treatm

weight

.0a

9b

6a

2b

C) for each r moderate inity stress other side

dry weight in one side

ccumulated

af area .plant-1) 345a

164b

ent


The leaf area, plant height, Chl content and Pn rate were higher in CMN/SLN than in CLN/SMN (Table 6 and Fig. 3. a). Clear symptoms of nutrient deficiency were observed in the 4th main-stem leaf of CLN/SMN, compared with that of CMN/SLN (Fig. 3. b). CMN/SLN had great advantage in photosynthesis and plant growth over CLN/SMN, although both treatments were imposed with the same level of salinity to their root systems.

Uptake of Main Mineral Nutrients

CMN/SLN treatment increased the concentration of N, P, K, Ca, Mg, Fe, Cu and Zn in leaf relative to CLN/SMN (Table 7). Similar trends were also observed in their roots and stems. There was a significant difference in concentrations of main mineral nutrients between the two sides of a split root for both treatments. More N (139%), P (40.9%), K (13.0%), Ca (20.9%), Mg (9.2%), Fe (29.9%), Mn (20.5%), Cu (7.7%) and Zn (25.6%) accumulated in CMN side than in CLN. Compared with SLN, concentrations of N, P, K, Mn and Cu in SMN was only increased by 18.6, 9.1, 3.7, 16.5, 10.3%, respectively, and Ca, Mg, Fe and Zn concentration even decreased.

TABLE 7: NUTRIENT CONTENTS IN DIFFERENT PLANT TISSUES UNDER UNEQUAL SALINE AND NUTRIENT DISTRIBUTION 42 DAYS AFTER TREATMENT

Elements Root Stem Leaf CMN/SLN CLN/SMN CMN/SLN CLN/SMN CMN/SLN CLN/SMN

CMN SLN CLN SMN

Mac

ronu

trien

ts

(mg.

g-1 D

W) N 16.0a 9.7c 6.7d 11.5b 3.5a 2.1b 13.3a 10.8b

P 3.1a 2.2c 2.2c 2.4b 1.5a 1.3b 1.5a 1.3b K 7.8a 5.4b 6.9a 5.6b 5.3a 3.9b 7.6a 5.9b Ca 5.2b 5.9a 4.3c 4.1c 4.8b 3.4a 8.5a 7.8b Mg 3.8a 3.6b 3.48c 3.3d 3.1a 2.8b 4.6a 4.3b

Mic

ronu

trien

ts

(μg.

g-1 D

W) Fe 291a 263b 224c 256b 104b 139a 290a 263b

Mn 40.0a 31.5d 33.2c 36.7b 30.4a 29.2b 57.3a 58.9a Cu 33.7a 30.2c 31.3b 33.3a 25.2a 24.5a 29.5a 28.7a

Zn 108b 119a 86d 96c 85a 78b 110a 104b

* Different letters within a row indicate significant differences at p=0.05.

Total amounts of main nutrient elements per plant in CMN/SLN were significantly increased relative to CLN/SMN (Fig. 4). The amounts of N, P, K, Ca, Mg, Fe, Mn, Cu and Zn per plant in CMN/SLN were 75.3, 43.4, 52.6, 54.1, 33.1, 19.7, 23.9, 24.9 and 63.2% more than in CLN/SMN. CMC/SLC significantly decreased the ratios of Na+ to most nutrient elements in different tissues, especially in leaf, compared to CLN/SMN (Table 8).

TABLE 8: RATIOS OF NA TO NUTRIENT ELEMENTS IN ROOT, STEM AND LEAF OF COTTON PLANT UNDER UNEQUAL SALT AND NUTRIENT DISTRIBUTION 42 DAYS AFTER TREATMENT

Ratios Root Stem Leaf CMN/SLN CLN/SMN CMN/SLN CLN/SMN CMN/SLN CLN/SMN

CMN SLN CLN SMN Na/N 0.08d 0.83a 0.23c 0.66b 1.52b 2.75a 0.89b 1.25a Na/P 0.43d 3.64a 0.70c 3.15b 3.55b 4.44a 7.89b 10.35a Na/K 0.17d 1.48a 0.22c 1.35b 1.01b 1.48a 1.56b 2.28a Na/Ca 0.25d 1.36b 0.36c 1.84a 1.11a 1.70b 1.39b 1.73a Na/Mg 0.35b 2.22a 0.44b 2.29a 1.72b 2.06a 2.57b 3.13a Na/Fe 4.53c 30.45a 6.84b 29.52a 51.16a 41.52b 40.83b 51.16a Na/Mn 33d 254a 46c 206b 175b 198a 207b 228a Na/Cu 39d 265a 49c 227b 211b 236a 401b 469a Na/Zn 12.2d 67.3b 17.8c 78.7a 62.6b 74.0a 107.6b 129.4a

* Different letters within a row indicate significant differences at p=0.05.


Fig. 4: Total Accumulation of Nutrient Elements Per Plant under Unequal Salt and Nutrient Distribution in Cotton at 42 Days after Treatment. Means Followed by Different Letters Differ Significantly (p=0.05). Vertical Bars Show ±SD of Four Replicates with Three Plants Per Replicate

DISCUSSION

Even as a salt-tolerant crop, cotton will be adversely affected in plant growth and development as well as yield and fiber quality if soil salinity level beyond a threshold of 7.7 dS m-1 (Chinnusamy, 2005; Maas, 1990). In the first experiment, we found a marked inhibition of fresh weight, dry weight, Pn and Chl content in both CMO4 and its wild line SM3 under salinity stress, but the inhibition effect of salinity on CMO4 was significantly lower than SM3. The result was in agreement with our previous reports that salt tolerance of CMO4 was greatly improved due to the introduction of AhCMO gene relative to its wild line SM3 (Zhang et al., 2009). In the second experiment, a split-root system was established by grafting of cotton seedlings, and unequal salt distribution in the root zone was thus constructed by irrigating one root side with water and the other side with NaCl solution. It was shown that the fresh and dry weight, leaf area, Pn and Chl content in CMN/SLN treatment were significantly higher than that in CLN/SMN. Since salinity stress imposed to only half root also greatly reduced plant growth as we previously reported (Dong et al., 2010), and both CMN/SLN and CLN/SMN were treated with same level of salinity stress (150 mM NaCl) in the present experiment, CMN/SLN treatment improved salinity tolerance of cotton compared to CLN/SMN.

0

50

100

150

200

250

N P K Ca Mg

Acc

umul

atio

n (m

g. P

lant-1

) . CMN/SLN CLN/SMNa

a

aa

ab

b

b b

b

0

1

2

3

4

5

6

Fe Mn Cu Zn

Acc

umul

atio

n (m

g. P

lant-1

) . a

aa

a

b

b b

b


Plant growth is affected by interactions of salt ions and availability or uptake of many essential mineral nutrients (Greenway and Munns, 1980; Grattan and Grieve, 1992; Romero et al., 1994). Soil salinity usually reduces uptake of both macronutrients like N (Dluzniewska et al., 2007), P (Rochester, 2010), K (Marschner, 1995), Ca (Yan et al., 2007) and Mg (Grattan and Grieve, 1999), and micronutrients like Fe, Mn, Cu, Zn (Grattan and Grieve, 1999; Hirpara et al., 2005; Kholová et al., 2009), and leads to nutrient imbalance in many plant species including cotton (Grattan and Grieve, 1999). The ratios of Na to nutrient elements (Na/N, Na/K, Na/Ca and Na/Mg) have been used as an indication of nutrient imbalance (Grattan and Grieve, 1999; Dluzniewska et al., 2007). In the present study, NaCl stress significantly reduced nutrients (N, P, K, Ca, Mg, Fe, Mn, Cu and Zn) uptake in SM3 and CMO4 relative to their respective non-saline treatment. However, the salt-induced decrements in nutrient uptake in CMO4 was significantly lower than that in its wild line SM3, and the absolute amounts of main nutrient elements (N, P, K, Ca, Mg, Fe, Mn, Cu and Zn) in salt-stressed CMO4 were significantly higher than in salt-stressed SM3. Also less Na+ accumulated in CMO4 than in SM3 as we reported previously (Zhang et al., 2009). As a result, the ratios of Na/N, Na/P, Na/K, Na/Ca, Na/Mg, Na/Fe, Na/Mn, Na/Cu and Na/Zn in CMO4 was significantly reduced relative to its wild line SM4, and thus nutrient imbalance was alleviated (Table 4). Although previous studies have shown that improved salinity tolerance of AhCMO transgenic cotton was attributed to enhanced glycine betaine synthesis (Zhang et al., 2009), our results in the present study further indicated that the increased salt tolerance was also associated with improved nutrient uptake.

Split-root system provides an accurate system to simulate unequal salt distribution in the root-zone (Bazihizina et al., 2009; Lycoskoufis et al., 2005). Many previous studies have shown that unequal salt distribution improves plant growth or salt tolerance relative to equal salt distribution with the same level of external salinity stress (Zekri and Parsons, 1990; Tabatabai et al., 2003; Bazihizina et al., 2009). In the present study, treatment combination with non-salinity stress plus moderate nutrient level in one side and salinity stress plus low nutrient level in the other side (CMN/SLN) significantly increased concentrations of main nutrient elements (N, P, K, Ca, Mg, Fe, Mn, Cu, Zn) in different tissues, especially in leaf, compared with the treatment combination with non-salinity stress plus low nutrient in one side and salinity stress plus moderate nutrient in the other side (CLN/SMN). Similar trends were also observed in total amount of nutrient elements per plant. The results affirmatively indicated that CMN/SLN significantly improved nutrient uptake relative to CLN/SMN. Since both treatments were imposed the same level of salinity stress, we suggest that the improved salt tolerance and plant growth in CMN/SLN was attributed to the increased uptake of main nutrients. The results further showed that higher nutrients uptake in CMN/SLN than in CLN/SMN was mainly attributed to much more nutrients uptake from the CMN side than CLN side (Table 7). The content of Na+ in CMN/SLN treatment is lower than that in CLN/SMN, especially in leaf. The result was consistent with Sonneveld and Voogt (1990) who suggested that unequal distribution of nutrients (EC=0.75/2.5) in the root environment increased N, P and K and reduced Na+ accumulation. More nutrients and less Na+ uptake in CMN/SLN resulted in lower ratios of Na+ to the main nutrient elements (Table 8), which might alleviate nutrient imbalance of cotton.

In conclusion, introduction of AhCMO gene to cotton significantly increased salt tolerance of the transgenic line CMO4. Such an improvement in salt tolerance was also associated with increased nutrient uptake in CMO4. There was a clear interaction between unequal salt distribution and unequal nutrient distribution on plant growth and salt tolerance of cotton plants in a split-root system. Plant growth and salt tolerance as well as nutrient uptake were significantly improved in CMN/SLN relative to CLN/SMN. Since levels of external salinity stress and nutrient supply were the same for both treatments, we suggest that the improved salt tolerance and plant growth were attributed to increased nutrient uptake. It was thus concluded that improved nutrient uptake can increase salinity tolerance of cotton under salinity.


ACKNOWLEDGEMENT

This work was supported by the earmarked fund for China Agricultural Research System (Cotton 2007–2011), the Major Project for Applied Agricultural Research (Cotton 2009–2011) and Seed Industry (Cotton 2010–2012) of Shandong Province, and the National Natural Science Foundation of China (30971720).

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Improvement of Partial Root-Zone Soil Environment Increases Salinity Tolerance of Cotton

Hezhong Dong, L.I. Weijiang and L.I. Zhenhuai

Cotton Research Center/Shandong Provincial Key Lab for Cotton Culture and Physiology, Shandong Academy of Agricultural Sciences, Jinan–250100, P.R. China


Abstract—Soil salinity is a major threat to cotton production worldwide. The objective of this study was to test whether improvement at least in a part of the root-zone environment would enhance tolerance salinity and alleviate salinity injury. Five experiments were conducted using a split-root system in the greenhouse or furrow seeding with or without plastic mulching under salt-affected field conditions. The results showed that plastic mulching, furrow seeding and early mulching or late-planting with short-season cotton either resulted in unequal salt distribution, or increased moisture and temperature in the root-zone, and effectively reduced salt-injury to cotton. It is concluded that improved partial root-zone environment increases salinity tolerance of cotton.

Keywords: Cotton, Salinity stress, Root-zone environment, Salinity tolerance, Plastic mulching

INTRODUCTION

Soil salinity has been a major concern to global agriculture throughout human history (Lobell et al.,, 2007). In recent times, it has become even more prevalent with intensification of land use (Meloni et al., 2003, Egamberdieva et al., 2010). Cotton, though classified as one of the most salt-tolerant major crops, its growth and development as well as yield and fiber quality are negatively affected by excessive salts in the soil (Maas and Hoffman 1977, Qadir and Shams 1997, Higbie et al.,, 2010). Cotton is a pioneer crop in reclamation of saline soils. But potential depends largely on ways and means to improve salt tolerance of cotton. Although, some progress was made in salt-tolerance improvement, the development of salt-tolerant cotton is not an easy job due to the complexity of the tolerance mechanisms and narrow germplasm resource. There are a number of agronomic practices which can effectively control salt damage through improvement in root-zone soil environment. Soil salinity expressed by ECe (electrical conductivity of a saturated-paste extract), with values of 7.7, 12.5, and 17.1 dS m−1 are referred to as low, moderate and high salinity level, respectively (Chen et al.,, 2009, Maas and Grattan, 1999). In general, soil salinity delays and reduces germination and emergence, decreases cotton shoot growth, and finally leads to reduced seed cotton yield and fiber quality characteristics at moderate to high salinity levels (Khorsandi and Anagholi, 2009). Since soil salinity and the related stress originate from the root-zone soil environment, it is hypothesized that improving at least part of the root-zone environment would alleviate salt injury. The hypothesis was tested in greenhouse and field experiments.

MATERIALS AND MEDHODS

In a split-root experiment (EXP 1), potted cotton plants were grown in a split-root system in the greenhouse and each root half was irrigated with either the same or two concentrations of NaCl (Fig. 1).

In a field experiment (EXP 2), we compared cotton grown on furrow-beds in saline fields with those grown on flat beds as controls. A separate field experiment was conducted to evaluate the effect of mulching with polyethylene film. Integration of plastic film with irrigation methods was also studied in a separate set of experiment. Details of treatments provided in Dong et al. (2009, 2010 a, 2010 b).

72

RESULTS A

When theleaf chlorcontrol. Hsalinity osignifican

Furrowearliness unequal d

Row water condistributioFurther stestablishmseeding al

Fig. 2(a), C

Normestablishmcotton in acotton sigreduced N

a

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Fig. 1: Effec

AND DISCUSSI

e entire root rophyll (Chl

However, whon growth anntly increased

w-bed seediwere signifi

distribution o

covering winservation. Ion in the saltudy showed

ment, earlinelone (Fig. 2)

2: Effects of SeConventional Se

mal planting ment, late ma saline fieldgnificantly imNa+ concentr

ment of Partial R

cts of Unequal Salin cth

ON

system was l) and photo

hen only half nd yield wad compared t

ing induced cantly improf salts in the

ith polyethyIn our field line soil, in d that the iss, yield, and(Dong et al.

eding Modes oneeding under M

of full-seasomaturity, and d of the Yellomproved seeration in cott

Root-Zone Soil E

t Distribution (100he Root-Zone on Co

exposed to osynthesis (Pf of the root sas significanto equal salt

unequal saoved. Such aroot zone (D

lene film enexperiment which part ontegration od yield comp., 2008).

n Stand EstabliMulching (b) and

and Full Stand

on cotton in increasing

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b

Environment Inc

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the same coPn) were sigsystem was ently reduceddistribution

alt distributian improvemDong et al., 2

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of the root sof plastic muponents of co

shment in a Sad Furrowing Sed Establishment

saline fieldcost of inpu

elta (EXP 4),ce and seedllative to norm

creases Salinity

and Equal Salt Distowth in the Green

oncentration gnificantly rexposed to lo

d. Plant biom(Dong et al.,

on in salinement in yield2010b).

nt growth byplastic mulchsystem develulching withotton more th

aline Field. Conveeding under Mt, Respectively

s in temperauts. After try, we observeling growth mal planting

c

y Tolerance of C

ribution (300/30House.

of NaCl, leareduced relaow-salinity, mass and se, 2010b).

e fields; plad and earline

y increasinghing also reloped in relah furrow sehan effective

ventional SeediMulching Resultey

ate areas is ying late pla

ed that late pdue to incre

g ( Dong et a

Cotton

0 mM NaCl)

af area, planative to the the inhibitio

eed cotton y

ant growth, ess was mai

g soil tempeesulted in unatively low-seeding enhanely mulching

ing without Mued in Poor, Mod

faced with panting of shlanting of sheased tempel.,, 2010a).

433

nt biomass, NaCl-free

on effect of yield were

yield and nly due to

rature and nequal salt saline soil. nced stand g or furrow

lching derate

poor stand hort-season hort-season erature and


Row mulching is conventionally applied after sowing, but pre-sowing evaporation in spring would cause accumulation of salts and moisture loss in the surface layer of saline soils. Row mulching with plastic film can be done 30 d before sowing (early mulching) in saline fields (EXP 5). Although both conventional and early mulching could effectively improve stand establishment, plant growth, earliness and lint yield of cotton, early mulching was more beneficial to stand establishment, plant growth and yield. The increased benefits of early mulching were due mainly to the better control of root-zone soil salinity, elevation of soil temperature and reduction of moisture loss (Dong et al., 2009).

CONCLUSION

About 23% of the world’s cultivated lands are saline. Plastic mulching, furrow seeding, late-planting of short season-cotton, irrigation and fertilization either reduced salinity, increased temperature and moisture, or increased supply of nutrients in the soil root-zone, thus reducing the salt damage in saline fields. We conclude that improvement in a part of the root-zone environment increases salinity tolerance of cotton.

REFERENCES [1] Chen, W., Hou, Z., Wu, L., Liang, Y. and Wei, C. (2010) - Effects of salinity and nitrogen on cotton growth in arid

environment. Plant Soil 326, 61–73. [2] Dong, H., Kong, X., Luo, Z., Li, W. and Xin, C. (2010b) - Unequal salt distribution in the root zone increases growth and

yield of cotton. Eur. J. Agron. 33, 285–292. [3] Dong, H., Li, W., Tang, W. and Zhang, D. (2008) - Furrow seeding with plastic mulching increases stand establishment and

lint yield of cotton in a saline field. Agron. J. 100, 1640–1646. [4] Dong, H., Li, W., Tang, W. and Zhang, D. (2009) - Early plastic mulching increases stand establishment and lint yield of

cotton in saline fields. Field Crop Res. 111, 269–275. [5] Dong, H., Li, W., Xin, C., Tang, W. and Zhang, D. (2010) - a: Late-planting of short-season cotton in saline fields of the

Yellow River Delta. Crop Sci. 50, 292–300. [6] Egamberdieva, D., Renella, G., Wirth, S. and Islam, R. (2010) - Secondary salinity effects on soil microbial biomass. Biol.

Fert. Soils 46, 445–449. [7] Higbie, S.M., Wang, F., Stewart, J. McD., Sterling, T.M., Lindemann, W.C. , Hughs, E., Zhang, J. (2010) - Physiological

response to salt (NaCl) stress in selected cultivated tetraploid cottons. Int. J. Agron. 1, 1–12. [8] Khorsandi, F. and Anagholi, A. (2009) - Reproductive compensation of cotton after salt stress relief at different growth

stages. J. Agron. Crop Sci. 195, 278–283 [9] Lobell, D.B., Ortiz-Monsterio, J.I., Gurrola, F.C. and Valenzuuela, L. (2007) - Identification of saline soils with multiyear

remote sensing of crop yields. Soil Sci. Soc. Am. J. 71, 777–783. [10] Maas, E.V., Hoffman, G.J. (1977) - Crop salt tolerance—current assessment. J. Irrig. Drain. Div. Am. Soc. Civ. Eng. 103,

115–134. [11] Maas, E.V., and Grattan, S.R. (1999) - Crop yields as affected by salinity. In: Skaggs RW, van Schilfgaarde J (eds)

Agricultural Drainage, Agron Monogr 38. ASA, CSSA, SSA, Madison, WI, pp 55–108. [12] Meloni, D.A., Oliva, M.A., Martinez, C.A., Cambraia, J. (2003) - Photosynthesis and activity of superoxide dismutase,

peroxidase and glutathione reductase in cotton under salt stress. Environ. Exp. Bot. 49, 69–76. [13] Qadir, M. and Shams, M. (1997) - Some agronomic and physiological aspects of salt tolerance in cotton (Gossypium

hirsutum L.). J. Agron. Crop Sci. 179, 101–106.

Do Female-Led Farms Perform Less Well in Cotton Production? Insight from Hebei Province (China)

Michel Fok1 and Guiyan Wang2

1CIRAD, UR SCA, Montpellier, FRANCE 2Hebei Agricultural University, Baoding, Hebei Province, CHINA

Abstract—Most of the emerging countries witnessed outmigration of farming community away from agriculture to cities. This was mainly due to rapid industrialisation, fast rising service sectors growth and low profitability of farming being agriculture is a gamble of rainfall. Generally, it is the men folk who leave their wives behind and migrate to cities in search of better alternative jobs. This phenomenon gives rise to the feminization of agriculture and raises the concern of lower productivity. A few studies in developing countries have indicated that female-led farms lack access to production factors such as land, labour and capital.

In China, the migration of farmers to cities and the feminization of agriculture were well documented, but studies on the impact of feminization on agricultural performance and productivity are rare. The impacts of feminization on agriculture have been assessed on a regional or cropping system level, but not on a specific crop.

In light of the above, the present paper deals with the status and performance of the feminization on cotton cropping in Hebei Province (Northern China). As cotton cropping is widely acknowledged to be both labour and capital intensive it is most appropriate to assess not only the impacts of feminization on its productivity but also to check the assumption whether female-led farms lack factors of production. Therefore, the study was conducted to analyse the cotton cropping systems on identified farms run by women and compare their yield and gross income with the alternative farms. The study stems its base on primary data collected through survey method for the period 2006 to 2009. Farms were considered to be female-led, when husbands or their spouse was away for more than five months per year.

INTRODUCTION

Most of the emerging countries witnessed outmigration of farming community away from agriculture to cities. This was mainly due to rapid industrialisation, fast rising service sectors growth and low profitability of farming being agriculture is a gamble of rainfall. Generally, it is the men folk who leave their wives behind and migrate to cities in search of better alternative jobs (Lastarria-Cornhiel, 2006). This phenomenon gives rise to the feminization of agriculture in which three definitions are generalised: women are taking over field tasks from men; Female participation rate in field work as compared to man is increasing; women accounts for more than half of the labour force in fields.

In China, the feminization phenomenon has been particularly analysed and debated because of the large extent of rural men migrating to cities. Several publications by Chinese scholars implicitly attribute feminization of agriculture as the share of female in agricultural labour has increased from less than half to more than half. Gao (1994) was among the first to point out the issue of feminization and to link it to the migration of rural men to cities. Cheng (1998) pointed out outnumbering of females over males in as the labour force in fields through the Fourth National Census, 1992 and that the trend was increasing (Zhou and Sun, 2006). Chen and Xu (2008) reported that, at national level, women amounted to more than 80% of the labour force in rural areas in 2000. Zhou et al. (2007) opined that this figure could reach up to 90% in some provinces with the vibrant economic development there. Referring to the definition of the female participation rate in field work, de Brauw et al. (2008) debated the reality of the phenomenon of feminization in Chinese agriculture which is contrary to the findings of Zhou and Sun (2006). He et al. (2010) found that in Jiangsu province, females were the major decision makers on farms.

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Few studies in developing countries indicates that the feminization phenomenon raises the concern of lower productivity in agriculture, as female-led farms lack access to factors of production such as land, labour and capital (Chikwama, 2010). Sun and Zhou (2008) reported that most of the Chinese scholars regarded feminization as a threat to agricultural productivity. Zhao and Zhao (2009) considered this phenomenon as an impediment to further development of the rural economy in China. The pessimistic view on productivity may arise from the association between feminization and aging that some scholars highlighted in China (Zhou and Song, 2008).

Nevertheless, studies on the impact of feminization on agricultural performance remain rare. In China, the impact of feminization have been assessed on a regional or cropping system level (de Brauw et al., 2008), without confirming decline in the farming productivity. No study was undertaken to assess the impact of feminization on productivity of a specific crop level.

The present paper deals with the status and performance of the feminization of cotton production in China. As cotton cropping is acknowledged to be labour- and capital-intensive, it is appropriate to assess the impacts of feminization on its productivity and to check the assumption that female-led farms lack production factors.


This paper deals with cotton production in Hebei Province, located in the Yellow River Valley, northern China. This province is one of the major cotton production locations in China. It was the first place where Bt cotton was commercially released in China, both with Monsanto varieties incorporating its Cry1Ac gene and Chinese varieties integrating the synthesized Cry1A gene (Guo and Cui, 2000; Guo and Cui, 2004). Favourable conditions for licensing1 Chinese Bt gene to Chinese breeding organizations were set to encourage the supply of new Chinese Bt cotton varieties and create market competition. This was in line with the institutional shift through the application2 of new laws on variety intellectual protection and seed marketing whereby only authorized and registered varieties could be legally marketed (Xu and Fok, 2010).

Our study was based on primary data from surveys covering four successive years from 2006 to 2009. Data from farmers growing cotton in 38 villages of five major districts in Hebei Province (Handan, Xingtai, Hengshui, Cangzhou and Shijiajiang) were collected.

To carry out the surveys, it was decided not to pass through local extension services, whose presence might have influenced the farmers' answers. Enumerators were selected from students of Agricultural University of Hebei whose families were doing farming in the cotton areas of Hebei province. Therefore, all the enumerators were familiar with agriculture and cotton growing. As they belonged to the same province, they gained confidence of native cotton farmers. Student-enumerators were trained on carrying out the survey and the survey was done during the Spring Festival (end of January or beginning of February). This period coincided with off-season when most of the farmers have already sold off their products from the previous calendar year.

Enumerators were asked to survey 20-30 farmers randomly using recall enumeration techniques through semi-directed questionnaires. The survey was conducted with the aim of determining farmers' cultivation practices and income in connection with the structures of their farms. Farmers were asked to answer on the basis of their memories. Villages and farmers surveyed were not the same from one year to the next.

The institutional framework in China does not force farmers to buy seeds through merchants every year (Fok and Xu, 2010b). Farmers can hold back seeds from one season to another, or mutually

1Up to 2007, royalties due for using the Chinese Bt gene were paid once and for all at the time of the official registration of a

new Bt cotton variety and not related to the amount of seeds sold. The fee has fluctuated from US$35,000 to 40,000. 2The laws were first issued in the first half of the 1990s, amended several times, but the decrees of application came into force in late 1999 or early 2000.

Do Female-Led Farms Perform Less Well in Cotton Production? Insight from Hebei Province (China) 437

exchange seeds with or without monetary compensation. Farmers' practices in using seeds were addressed in our surveys by asking whether they renewed seeds (instead of using seeds they held back from the former season), how frequently and from whom (notably merchants).

Production costs were recorded for all the surveyed farms. As the valuation of family labour used is debatable in rural areas, we did not integrate the family labour cost in total production costs. Cotton production costs and yields were only obtained at farm level and not at plot level. This was not a limitation when farmers used only one variety. Collected data were processed to analyse the structures of farms and farm families, with emphasis on the share of female members and on the involvement of family members in farm and off-farm activities, respectively.

The present study adopted a criterion that the farms were led by females when the farm heads (the males in China) were engaged in off-farm activities for at least five months a year. Two groups of farms namely male-led and female-led were compared with regard to the land areas cultivated, areas under cotton production, cost of cultivation, and two performance indicators, namely cotton yield and gross income.

A multi-regression analysis was done to check the extent to which yield was dependent on three groups of factors. Group 1 was composed of farm structure factors regarding the gender of the farm managers and the assistance of children in field work. A study on the same set of data revealed that farm structural factors such as family size, total cultivated area, etc had no impact on yield. Group 2 consisted of factors related to cotton areas and farmers' behaviour in using varieties and seeds (number of varieties used, the extent of annual seed renewal, and the frequency of seed purchases from merchants). Group 3 pertained to cost of production (corresponding to seeds, plastic mulching, irrigation, fertilization, pest control and disease control). Weed control by herbicide, soil preparation, growth regulators, etc were integrated in "other costs".

RESULTS

For most of the variables related to farm families (Table 1), the data were consistent over the four years, although the samples varied between years, in terms of both composition and size. In total, survey covered 2492 family members living in farming families. Feminization, if any, was not due to females outnumbering males in families. Female accounted for 45.4% of the total population, as against the sex ratio of 120 (120 males for 100 females), which was well above the common sex ratio of 105-106 worldwide. The sex ratio was quite similar for family members at least 16 years old who could be considered as potential active labour.

Farm heads were 47 years old in 2009, 0.8 years older than their wives. Globally, their children were of working age, the sons being almost two years older than the daughters. In 2009, they were 21.5 and 18.6 years old, respectively.

The feminization of Chinese agriculture may be sustained by the higher frequency of females involved in field work, even though the rate of all permanently involved family members was low. Among all farming family members with at least 16 year old, only 56.7% were permanently involved in field work. This is an indication of the specificity of China where agriculture is barely the main occupation of farming families. As already pointed out, females were less numerous than males, but they amounted to 55.8% of the permanent field workers. The wives of farm heads were the most permanent workers in the fields. Over the four years, 91.1% of them were involved permanently in field work, much more than their husbands (66.2%). The impression of feminization of agriculture was reinforced by the low participation rate of farm children. Farmers' sons or daughters with at least 16 years old took part in field work at a rate of 9.0% and 5.2%, respectively.


The observed feminization was the result of greater involvement of male in off-farm activities. Over the four year period, 94.3% of farm families had at least one member involved in off-farm activities on a long duration basis (at least five months a year). Males were more involved than females in off-farm activities (40.8 vs 14.4%) but the large gap was due to the very marginal involvement of farm heads' wives. Between boys and girls, the gap was far less (56.7 vs 38.3%). In the study, we assumed that farms were female-led when their husbands were involved in off-farm activities on a long term basis. Over the four-year period, for the 819 farms from which we obtained detailed product costs, 249 farms (30.4%) were female-led (Table 2). Cotton farms were tiny in size (0.75 ha of cultivated land) and they were smaller for female-led farms. 29.4% of farms managed to rent some land. This possibility was more limited for female-led farms. Considering all farms per capita cotton area was 0.44 ha, but this area was smaller for female-led farms and also for the cotton area per family member over the age of 16.

TABLE 1: CHARACTERISTICS OF FARM FAMILIES AND THEIR INVOLVEMENT IN OFF-FARM ACTIVITIES

Note: Figures in brackets are total numbers; figures in square brackets are standard deviations.

With regard to variety and seed use practices, there were no differences between farms led by males or females. In case of all farms 31.1 % use non-Bt cotton varieties, 69.6% farms goes for annual seed renewal (69.6%) and about 83% of farms source seeds through merchants (83.0%).

There were differences in performance between the two types of farms, but not in the expected direction. Compared to male-led farms, female-led farms performed better in yield with a similar level of total production costs. Female-led farms had higher costs to control diseases, but lower costs in a few cases (irrigation and other costs encompassing, for example, herbicides, growth regulators, etc.). These differences might be related to higher labour constraints (case of irrigation) or more routine practices (spraying against cotton plant diseases).

The better performance of female-led farms was more balanced in economic terms. This better performance was observed in case of cotton production value per area and gross income per area. Since female-led farms had a smaller area, their total cotton production value and total gross income were lower compared to male-led farms. This is the reason for lower labour productivity in terms of gross income per family member over 16 years old in female-led farms.

Features of farm families 2006 2007 2008 2009 AllNumber farms surveyed 119 207 360 175 861Farm demography

% females among all family members 49.7 [366] 44.0 [621] 44.1 [1008] 46.7 [497] 45.4 [2492]% females among family members ³ 16 years old 49.7 [340] 44.1 [580] 44.1 [966] 45.9 [468] 45.2 [2345]Number family members 3.1 (1.0) 3.0 (0.9) 3.0 (1.0) 3.1 (1.0) 3.0 (1.0)Number of children living in farm families 1,0 1,0 1,0 1.2 1,0Farm family members' ages

average age of farm holder 44.5 (9.2) 45.5 (9.2) 46.9 (8.7) 47.0 (8.9)average age of farm holder's wife 44.5 (9.3) 44.6 (9.0) 45.8 (8.3) 46.2 (8.9)average age of farmers' sons 20.8 (6.0) 20.8 (6.5) 22.2 (6.8) 21.5 (6.3)average age of farmers' daughters 18.3 (5.7) 18.9 (5.7) 20.1 (6.6) 18.6 (6.5)

Permanent involvement in farming% family members concerned, at least 16 years old 60.6 [340] 60.7 [580] 56.4 [966] 49.6 [468] 56.7 [2354]% females among total permanently involved members 56.3 [206] 55.7 [352] 53.4 [545] 61.2 [232] 55.8 [1335]% farm heads concerned 75.7 [115] 68.3 [205] 67.1 [343] 55.0 [160] 66.2 [823]% farm heads' wives concerned 95.6 [113] 94.4 [197] 87.2 [313] 91.4 [151] 91.1 [774]% farm children concerned

% heads' sons concerned, over 16 years old 4.4 [67] 10.1 [139] 12.6 [222] 2.8 [106] 9.0 [534]% heads' daughters concerned, over 16 years old 1.8 [56] 7.4 [68] 65 [107] 3.8 [78] 5.2 [309]

% farms with permanent participation of children in field works

3.4 [119] 7.2 [207] 8.8 [340] 3.8 [159] 6.7 [825]

Involvement in off-farm activities% farms with at least one member off-farming 97.5 [119] 95.7 [207] 93.8 [340] 91.2 [159] 94.3 [825]% males concerned, over 16 year old 38.0 [171] 38.3 [324] 40.6 [540] 44.7 [253] 40.5 [1288]% females concerned, over 16 year old 19.5 [169] 10.2 [256] 14.6 [426] 14.9 [215] 14.4 [1066]% farm heads concerned 22.6 [115] 25.4 [205] 27.4 [343] 33.8 [160] 27.5 [823]% farm heads' wives concerned 4.4 [113] 2.5 [197] 6.7 [313] 2.6 [151] 4.5 [774]% holders' sons concerned, over 16 years old 69.6 [56] 61.5 [117] 61.9 [202] 61.7 [94] 62.7 [469]% holders' daughters concerned, over 16 years old 58.1 [43] 40.4 [52] 42.9 [91] 44.3 [61] 45.3 [247]


TABLE 2: COTTON CROPPING, COSTS AND RETURNS, COMPARISON BETWEEN MALE- AND FEMALE-LED FARMS

Note: Figures in brackets are total numbers; figures in square brackets are standard deviations. Comparisons are made between male- and

female-led farms * significant at 5%, ** significant at 1%, *** significant at less than 0.1%; Other costs pertain to soil preparation, sowing, weed control by herbicides, growth regulation of cotton plants and occasional labour hiring.

TABLE 3: FACTORS AFFECTING YIELDS ON COTTON FARMS

Cotton cropping, costs, and returns All Farms Male-led farms Female-led farmsNumber of farms 819 570 249Total cultivated land (ha) 0.75 (0.54) 0.78 (0.62) 0.62 (0.39)**

Cultivated land per capita (ha) 0.26 (0.20) 0.28 (0.21) 0.23 (0.17)**Cultivated land per capita, at least 16 years old (ha) 0.29 (0.23) 0.31 (0.24) 0.25 (0.19)**

Own land (ha) 0.61 (0.31) 0.62 (0.29) 0.51 (0.25)**Own land per family member, at least 16 years old (ha) 0.24 (0.16) 0.25 (0.15) 0.21 (0.17)**

Land renting% of farms concerned 29.4 [819] 29.2 [570] 29.6 [249]average rented land (ha) 0.46 (0.71) 0.51 (0.81) 0.35 (0.44)*

Cotton area (ha) 0.44 (0.46) 0.49 (0.53) 0.36 (0.33)**Cotton area per family member, of at least 16 year old (ha) 0.17 (0.17) 0.19 (0.19) 0.13 (0.12)**

Number of varieties cultivated 1.6 1.6 1.5Percentage of farms with at least one non-Bt variety 31.1 [819] 33.2 [570] 26.5 [249]Seed renewal, % of farms with systematic annual renewal 69.6 [806] 67.4 [530] 74.3 [276]Seed source, % of farms with systematic purchase 83.0 [806] 81.8 [560] 85.8 [246]Cotton yield, seedcotton (kg/ha) 3795 (840) 3735 (765) 3945 (945)**Total cash-expenses production cost (US$/ha) 817 (181) 821 (190) 806 (152)

seed cost (US$/ha) 87 (58) 87 (59) 85 (52)mulching plastic cost (US$/ha) 58 (23) 58 (23) 59 (21)Irrigation cost (US$/ha) 54 (27) 56 (27) 50 (27)*Fertilisation cost (US$/ha) 294 (106) 294 (110) 294 (92)Pest control cost (US$/ha) 171 (79) 173 (79) 165 (75)Disease control cost (US$/ha) 25 (33) 21 (25) 35 (42)**Other costs (US$/ha) 127 (75) 131 (85) 121 (48)*

Total cotton production value (US$) 1178 (1120) 1270 (1214) 967 (832)**production value per area (US$/ha) 2661 (754) 2596 (685) 2831 (875)**production value per family member, at least 16 years old (US$)

439 (417) 476 (451) 357 (312)**

Total cotton gross income (US$) 808 (774) 865 (836) 679 (592)**Gross income per area (US$/ha) 1850 (792) 1175 (733) 2023 (886)**Gross income per family member at least 16 years old (US$) 300 (287) 323 (311) 250 (217)**

Independent variales Value Std deviation t value Pr > tConstant 3441,715 186,260 18,478 < 0,0001Female-led farms 191,419 63,406 3,019 0,003Help from children 331,142 114,373 2,895 0,004Cotton area -12,528 4,432 -2,827 0,005Number of varieties 96,063 40,142 2,393 0,017Annual seed renewal 26,618 67,094 0,397 0,692Seed sourcing from merchants -229,545 93,222 -2,462 0,014Perception that diseases need to be controlled -7,179 105,288 -0,068 0,946Seed cost -2,681 1,155 -2,321 0,021Plastic mulching cost 3,714 2,610 1,423 0,155Irrigation cost 8,590 2,062 4,166 < 0,0001Fertilization cost -0,074 0,532 -0,139 0,890Pest control cost -0,491 0,758 -0,647 0,518Disease control cost 8,004 1,808 4,427 < 0,0001Other costs 1,949 2,271 0,858 0,391


The better yield performance of female-led farms was confirmed by the multivariate regression analysis (Table 3). The female-led farms have shown a positive effect on seed cotton yield. The positive effect of assistance from the farm children was significant cotton being a labour intensive crop. The number of varieties that farmers used had a positive impact on yield, contrary to seed costs and seed provision from merchants. With regard to the costs of cultivation practices, only irrigation and disease control exhibited positive effects.

DISCUSSION

The study covered cotton production in Hebei province a major cotton belt of China. Cotton is produced on small farms where the average land holding is less than half a hectare (Fok et al., 2005; Pray et al., 2001). China is probably the country where cotton farms are smallest. The results were obtained from samples of farms whose representativeness was derived from the characteristics of the farm families. Feminization was observed in cotton production where, females amounted for more than half of the labour force in field work (55.8%) as females were essentially involved in farming (91.1%). These figures are consistent with the empirical data analysed by Mao and Liu (2009) but lower than those of Deng (2008), who carried out a large-scale survey and found that females accounted for 74.7% of the field labour force in 2006. The differences observed might be due to variations between provinces. Mainly wives of farm heads, who remained involved in farming were permanent workers in the fields, apart from those who were engaged in off-farm activities on a long-term basis (about 30% of them).

It was found that about 31% of farms being led by females as their husbands were involved in off-farm activities for more than five months a year. Our result was consistent with Deng (2008), who observed in 2006 that 20.7% of farms were led by females alone, and even up to 31 to 33% in some provinces.

The study confirmed the high frequency of farming family members involved in off-farm activities. We found that 94.3% of farm families had at least one member engaged in off-farm activities. The figure was higher than that found by Chen et al. (2010) where it was 74.3%. Further, it was found that youngsters engaged in off-farm activities compare to farm heads.

Because of their lower involvement in off-farm activities, females were the permanent workers in the fields, mainly wives of farm heads, but their husbands remained involved apart from those who were engaged in off-farm activities on a long-term basis (about 30% of them). The smaller the size of the farms, the higher was the frequency of farm heads involved in off-farm activities on a long-term basis, and consequently the frequency of farms being run by females. The results confirm the relevance of addressing the issue of the performance of female-led farms because these farms accounted for 30% of all cotton farms in Hebei province. The relevance also derives from their structural differences with reference to farms run by males. The female-led farms were smaller in size, indicating the probable causality between farm size and long term migration of the farm heads. The smaller the farms are, the more difficult it is to make ends meet without additional income from off-farm activities, thereby pushing farms heads to engage for many months in off-farm activities, leaving their wives alone to run their farms. This mechanism is indeed found by most of the studies addressing the feminization of agriculture in China (Gao, 1994; He et al., 2010; Li and Fang, 1999; Zhou and Sun, 2006).

From a technical perspective, female-led farms did not perform less well because they achieved higher yield. This result is contrary to what used to be claimed for developing countries (Lastarria-Cornhiel, 2006) and feared in China (Li and Fang, 1999; Wu and Zhang, 2008) although they were seldom based on empirical analyses. Our result is nevertheless close to the rare empirical studies in China (de Brauw et al., 2008; Zhang et al., 2004), which did not find confirmation that female-led farms performed less well. Li (2001) found in 1996 that the return on women's agricultural activities was higher than that of men, probably a direct consequence of more involvement in field work by women. In the study, female-led farms performed well because they entailed a similar level of total production costs, and also because they probably took better care of their smaller cotton plots. Their capacity to invest in


production costs was opposite to what used to be reported in other countries. We believe that this difference arises from the involvement of their husbands in off-farm activities and the resulting income. We found no evidence that women were less technically skilled, which was quite consistent with the results of Song (1999). Our result was contrary to the productivity decrease that was feared to result from the fact that women were less educated or was less familiar with techniques and farm management according to Li and Fang (1999) but that was at the early stage of feminization and may no longer hold now. Nevertheless, in economic terms, female-led farms performed less well because they were smaller. Their gross income per unit area was better, as a consequence of higher yield and similar production costs, but at farm level, this performance was mitigated by the smaller size of their cotton plots.

Female-led farms accounted for about 30% of cotton production in Hebei province. They were smaller but technically they performed well. The higher yield they achieved was due to their capacity to invest in production costs similarly to other farms –probably the income earned from the off-farming activities by the farm heads was also invested in the farming which resulted in better care of smaller cotton farms.

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Debunking the Myths

J. Reed, E. Barnes and P. O'Leary

Cotton Incorporated, USA

Abstract—Cotton and cotton textile industries are important to the economic well-being of more than 100 countries, developed and developing, alike. In spite of its benefits, cotton production in particular, has been mischaracterized as having negative impacts on the environment. Among those voicing claims against cotton are businesses seeking a competitive advantage and non-governmental organizations. The most common of these allegations are that cotton production requires excessive amounts of pesticides and water. This paper examines those and other claims against cotton and presents data to negate these assertions.

INTRODUCTION

Claims based on outdated information continue to be perpetuated despite repeated efforts to set the record straight. Although cotton’s past environmental history does provide some basis for the claims, significant improvements have occurred in recent years yet these gains are rarely acknowledged as part of cotton’s environmental story, focusing instead on cotton’s outdated history. This paper will address common misconceptions about cotton production in hopes of enabling others to counter misrepresentations of cotton production’s environmental impact.

PESTICIDES

Synthetic pesticides are widely used in agriculture to control crop losses caused primarily by insect, weed and disease pests. Cotton is no exception. According to Cropnosis (personal communication. 2010), plant protection chemicals worth US$40 billion were used globally in agriculture in 2009. Almost half of these were herbicides to control weeds and to facilitate conservation tillage. Plant protection chemicals worth US$2.5 billion were used in cotton. It has been estimated that over 80% of the global cotton crop would be lost in the absence of crop protection products. Arthropod and weed pests would be the primary contributors to this loss (Oerke & Dehne, 2003 ).

Claim

Cotton uses 25% of the world’s pesticides

The Facts

In the 1990s, the use of pesticides on cotton peaked; accounting for one quarter of the global annual value, equivalent to US$ 2 to 3 billion (Murray, 1994 as cited in de Blécourt, 2010). The global insecticides share used on cotton had declined from 19% in 2000 to 14%. In the same year, cotton’s pesticide consumption accounted for 6.2% of global use (Cropnosis, personal communication. 2010). A comparison of pesticide usage globally on the major crop groups is given in Table 1.

TABLE 1: COMPARISON OF THE GLOBAL USAGE OF PESTICIDES ON MAJOR CROP GROUPS. 2007 (CROPNOSIS, PERSONAL COMMUNICATION.2008)

Crop % Global Pesticides Used Cotton 6.8 Fruits & Vegetables 29.7 Cereals 17.0 Soy 9.6 Maize 9.3

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mammals. In a comparison of a conservation tillage system and a conventional tillage system the number and species of birds were higher in conservation tillage fields than in the conventional fields, regardless of the season. But in the winter the number and species of birds in conservation tillage fields far exceeded the numbers observed in conventional fields (Cederbaum, 2004). In addition, arthropods that are an important food source for birds are more abundant in conservation fields than conventional fields. Research in North Carolina showed that quail chicks required more than five times as much time to obtain their minimum daily requirement of insects in conventional fields as in conservation fields or natural fallow areas believed to be ideal quail habitat (Palmer, 1995 as cited in Fawcett & Towery, 2003).

Studies in the USA and in Australia show that Bt cotton has a positive impact on beneficial insects in cotton fields, thus promoting biodiversity (Naranjo, 2005; Head et al., 2005; Whitehouse et al., 2005; Torres & Ruberson, 2005). Thus, with the expanding acres planted to Bt varieties worldwide and corresponding reduction in pesticide usage we can expect arthropod biodiversity in cotton fields to increase.

Claim

Pesticide residue left on cotton products could be harmful to one’s health.

The Facts

Assumptions are often made that because pesticides are applied to the cotton plant then there must be pesticides on the fiber and subsequently on the finished textiles. Since 1991, the Bremen Cotton Exchange has tested raw cotton on an annual basis from a minimum of 16 countries for over 200 toxic substances, including heavy metals and pesticides according to Eco-Tex Standard 100. In 18 years there have been only four instances where substances were detected in quantities exceeding the most stringent level of the standard: two by only 0.01 mg/kg, another by 0.25 mg/kg and another by 0.7 mg/kg (Baumwollbörse, 2010). Therefore, one would expect the likelihood that cotton products are contaminated with pesticides to be almost nil.

BIOTECH CROPS

Biotech cotton varieties were first introduced in 1996. Two types of biotech cotton traits are currently available for commercial production. The first provides tolerance to herbicides and the second provides resistance to bollworms. One or both of these traits can be found in a cotton variety. Global adoption of biotech cotton has risen dramatically from 800,000 hectares in 1996 to 21 million hectares representing 14% of cotton area in 2010 (James, 1997; ISAAA, 2011). Indeed, this year marks the tenth year of Bt cotton production in India. During this time, India has seen an unprecedented 188-fold increase in Bt cotton acreage, from 50,000 hectares in 2002 to 9.4 million hectares in 2010. The increased yields, reduction in insecticide applications and profitability of resource poor farmers attributed to Bt cotton has transformed cotton production in India into the highly viable industry that it is today (James, 2010)

Claim

Bt cotton poses an environmental safety risk.

The Facts

Prior to commercialization, Bt cotton, like all genetically-enhanced products, are subjected to a rigorous regulatory evaluation by the EPA, USDA and the FDA (EPA, 2011). This includes comprehensive environmental and human safety studies to rule out the possibility of harm to existing plants, non-target organisms and humans. Strict refugia requirements for growers using Bt cotton ensure the durability of the technology and maintain ecological balance.

Debunking the Myths 445

Claim

Herbicide tolerant crops increase the use of pesticides and cause super weeds.

The Facts

While use of broad spectrum herbicides, notably glyphosate, has increased since the introduction of herbicide tolerant crops, the use of less environmentally benign products has fallen substantially, leading to net benefits to the environment (USDA, 1991 – 2011). Furthermore, herbicide-tolerant crops together with the environmentally benign herbicide glyphosate, have enabled widespread adoption of conservation tillage. This has led to significant enhancements in soil health and has contributed greatly to the reduction of agriculture’s environmental footprint by reducing soil erosion, greenhouse gas emissions and water and energy inputs (Fawcett & Towery, 2002).

One concern with biotech crops is the exchange of genes from the biotech plants to wild relatives potentially producing new biota that might disrupt the ecological balances. If this should occur, the resultant plants can be safely and easily controlled with herbicides other than glyphosate.

Herbicide resistance is a recurring agronomic problem, not just with cropping systems that include biotech crop plants but with all cropping systems where herbicides are used. However, without herbicides, every weed is a super weed. Therefore, herbicides in conjunction with established IPM and other good management practices play an important role in sustainable crop production.

WATER

Water consumption for cotton production is often misunderstood. Cotton is a highly drought-tolerant plant and can prosper in high salt soils. These attributes enable cotton to be grown in areas where it is often not feasible to grow any other crop. And while a harvestable crop of cotton is possible from an annual supply of only 250 millimeters of water, supplemental irrigation as for most crops ensures a consistently high yielding crop. Irrigation water is used in cotton production throughout the world but the amount and distribution varies widely depending on region, climate, available technologies and governmental policies.

Claim

Cotton is a water intensive crop.

The facts

Cotton’s global water footprint is about 2.6% of the world’s agricultural water use, lower than many other commodities and is proportional to cotton’s land use (Hoekstra & Chapagain, 2007). In the USA only 11% of the cotton crop is fully irrigated, 25% receives supplemental irrigation; the remaining 64% of U.S. acres receive no irrigation water at all, instead, relying totally on rainfall (USDA, 2009). Furthermore, compared to 25 years ago, US cotton growers today are producing over twice the amount of cotton for the same amount of irrigation water (USDA, 2004). According to a survey of USA growers in 2008, 72% of U.S. cotton growers list an adequate water supply as one of their top five concerns. This helps explain why 81% of the cotton growers who do use supplemental irrigation upgraded their irrigation systems within the last 10 years (Reed et al., 2009). And although abundant yields of high quality cotton are a primary goal of U.S. cotton growers, water-use efficiency −growing more using less water − is a common sense priority for them, as well. The same survey showed that US growers are moving away from surface irrigation systems (where leaching and non-uniformity of water application are a greater challenge) to the more uniform sprinkler systems. These include highly efficient technologies such as subsurface drip irrigation (SDI), and Low Energy Precision Application (LEPA). SDI is a system which as the name describes delivers water via underground tubes where it is not subject to evaporation. LEPA is also energy efficient since it less pressurization is required and the water is delivered precisely to the base of the plant where it’s needed. These and other irrigation systems are


made further efficient by various kinds of sensing or application technologies such as water-saving nozzles, soil moisture probes, or infrared leaf thermometers that detect when a plant is becoming water-stressed, in which case, the plants are only watered on an as needed basis. Remarkably, since 2002, the number of cotton acres being grown under the more efficient drip irrigation methods increased by 400% (Reed et al., 2009).

SOIL

Top soil depth and soil quality are integral to cotton producers’ livelihoods. U.S. growers recognize that preserving soil resources is paramount. One key technique in maintaining soil quality is conservation tillage.

Claim

Cotton harms the soil

The facts

Tillage is the primary source of soil quality loss and is the primary reason most conventional operations now use erosion control measures like conservation tillage. The advantages of conservation tillage are many. Included are: reduced erosion, increase populations of micro and macro flora and fauna, reduced runoff, increased soil moisture and reduction in fuel usage (Fawcett & Towery, 2003), In the US 2/3 of surveyed growers use some form of conservation tillage which decreases soil loss and improves the quality of the soil (Reed et al., 2009). Cotton can tolerate poor, infertile soils and its nutrient requirements are less than those for most crops. Because of its deep tap root, cotton makes efficient use of soil nutrients and fertilizers. Chemical fertilizers and pesticides do not reduce soil quality or soil organic matter, nor does their use preclude the use of animal manures.

ENERGY

Claim

Cotton production requires a large amount of energy

The Facts

There are 1.5 pounds of cottonseed in every pound of cotton lint. When accounting for the embedded energy contained in the seed it takes less energy to produce a cotton crop than the energy produced by the cotton crop because of the energy embedded in this cottonseed (Matlock, 2009). There is no doubt that energy resources will be limited in the future, and optimizing cotton’s energy use will continue to be an important priority for agricultural research in the US.

GREENHOUSE GASES

Claim

Large amounts of greenhouse gases are released by the production of cotton.

The facts

Cotton, like all green plants, take carbon from the atmosphere and stores it in the leaves, stems, roots and surrounding soil. One key technique to ensure maximum carbon storage is conservation tillage, a practice that leaves plant residues on the soil surface for erosion control and moisture conservation. Growing a hectare of reduced tillage cotton removes 3.4 MT of CO2 from the atmosphere every year (Causarano et al., 2006). In addition, the amount of CO2 removed by cotton plants worldwide is equivalent to taking over 7 million cars off the highways (Andy Jordan, personal communication, 2007) and the 5.1 MT of

Debunking the Myths 447

cotton consumed in the U.S. sequesters 7.7 billion kg of CO2 into textile products per year (Reed et al, 2009). In the US, more than two-thirds of growers have adopted some form of conservation tillage with the greatest adoption occurring over the past decade (Reed et al., 2009).

COMPETITON WITH FOOD

Claim

Cotton production competes with food production.

The Facts

At the beginning of each crop season, the cotton grower need not make a choice between growing food or growing fiber because one crop cycle produces both. The cotton plant produces fiber, cottonseed for oil and feed, and gin by-products which have multiple industrial uses. The same inputs of energy, water and nutrients used to produce a kg of cotton fiber also produce 1.5 kg of cotton seed. In the future, this protein-rich cotton seed will become an even more economically important feed stock but will also be edible by people, thanks to ongoing biotech research aimed at removing gossypol, a naturally-occurring but inedible pigment (Sunilkumar et.al. 2006).

SUMMARY

It is often claimed that Cotton production has a very large environmental footprint. The fact is, in the United States the environmental impact of producing a pound of cotton has fallen substantially over the last 20 years (Figure 1). There has been a 25% reduction in land requirement, a 34% drop in soil loss, a 49% decline in irrigation water use, a 66% reduction in energy, and a 33% decrease in GHG emissions (Field to Market, 2009). Cotton Incorporated continuously works to improve cotton’s environmental footprint through its funding of agricultural research programs throughout the U.S. and by assisting growers in adopting technologies and practices that conserve natural resources and enhance grower efficiency. These programs are highly valued by growers and have contributed significantly to cotton’s environmental gains over the past 40 years. It is hoped that by providing facts to dispel myths in venues such as these that others will be empowered to communicate cotton’s positive environmental story as well.

REFERENCES [1] Technical Information Section. 2008. US Cotton Growers Respond to Natural Resource Survey. International Cotton

Advisory Committee, THE ICAC RECORDER, Vol. XXVII. No. 2, June 2009. [2] http://www.epa.gov/pesticides/biopesticides/regtools/biotech-reg-prod.htm [3] Brookes, G. & Barfoot, P. (2008) - Global Impact of Biotech crops: Socio-economic and Environmental Effects,

1996–2006. AgBioForum, 11 (1):21–38. [4] Bremer Baumwollbörse, B. (2010) - Analysis of Chemical Residues on Raw Cotton.

http://www.baumwollboerse.de/index.php?l=2&n=16,0,0 [5] Brookes, G. & Barfoot, P. (2009) - GM Crops: Global Socio-economic and Environmental Impacts 1996-2007. PG

Economics 2009. [6] Causarano, H.J., Franzluebbers, A.J., Reeves, D.W. and Shaw, J.N. (2006) - Soil organic carbon sequestration in cotton

production systems of the Southeastern United States: A review. J. Env. Qual. 35: 1374–1383. [7] Cederbaum, S.B., Carroll J.P. & Cooper, R.J. (2004) - Effects of alternative cotton agriculture on avian and arthropod

populations. Conservation Biology 18 (2): 1272–1282. [8] Crumby, T.I., Mastrangelo, P., Sloan, C., Finlayson, B., Hosea, R., Trostle, M., Mitchell, T., Wells, S. and Karner, M.

(1996) - Results of wildlife monitoring as required under Furadan 4F insecticide/nematicide section 18 exemptions. Proceedings Beltwide Cotton Conference. National Cotton Council, Memphis, TN. 2: 894–896.

[9] De Blécourt, M., Lahr, J. &. van den Brink, P.J. (2010) - Pesticide use in cotton in Australia, Brazil, India, Turkey and USA. http://www.icac.org/seep/documents/english.html.

[10] (EPA) U.S. Enviromental Protection Agency. 2011. Introduction to Biotechnology Regulation for Pesticides. http://www.epa.gov/pesticides/biopesticides/regtools/biotech-reg-prod.htm.

[11] Fawcett, R. & Towery, D. (2003) - Conservation tillage and plant biotechnology: How new technologies can improve the environment by reducing the need to plow. http://www.ctic.org/media/pdf/Biotech2003.pdf


[12] Field to Market: The Keystone Alliance for Sustainable Agriculture Environmental Resource Indicators for Measuring Outcomes of On-Farm Agricultural Production in the United States, First Report, January 2009.

[13] Head, G., Moar, W., Eubanks, M., Freeman, B., Ruberson, J., Hagerty, A. & Turnipseed, S. (2005) - A multi-year, large-scale comparison of arthropod populations on commercially manged Bt and non-Bt cotton fields. Environ. Entomol. 34(5): 1257–1266.

[14] Hoekstra, A.Y. & Chapagain, A.K. (2007) - Water footprints of nations: water use by people as a function of their consumption pattern. Water Resource Management. 21(1): 35–48.

[15] (ISAAA) International Service for Acquisition of Agri-biotech Applications. 2011. Global Status of Commercialized Biotech/GM Crops: 2010. Brief 42-2010: Executive Summary. http://www.isaaa.org/resources/publications/briefs/42/executivesummary/default.asp.

[16] James, C. (1997) - Global Status of Transgenic Crops in 1997. ISAAA Briefs No. 5. ISAAA: Ithaca, NY. pp. 31. [17] James, Clive. (2010) - Global status of commercialized biotech/GN crops: 2010. ISAAA: Ithaca, NY. ISAAA Brief No.42. [18] Jordan, A. (2007) - Cotton carbon footprint. Cotton Incorporated [19] Kovach, J., Petzoldt, C., Degni, J. & Tette, J. (1992) - A method to measure the environmental impact of pesticides. New

York Food and Life Sciences Bulletin. NYS Agricul. Exp. Sta. Cornell University, Geneva, NY, 139. 8 pp. [20] Matlock, M., Nalley, L. and Clayton-Niederman, Z. (2009) - Carbon life cycle assessment of United States cotton: A view

of cotton production practices and their associated carbon emissions for counties in 16 cotton producing states. Feb. 5, 2009 Final report to Cotton Incorporated by the Center for Agricultural and Rural Sustainability, University of Arkansas Division of Agriculture, Fayetteville, AR.

[21] Murray, L. (1994) - Cultivating Crisis: The human cost of pesticides in Latin America. University of Texas Press. [22] Naranjo, S.E. (2005) - Long-term assessment of the effects of transgenic Bt cotton on the abundance of non-target

arthropod natural enemies.. Environ. Entomol. 34(5): 1193–1210. [23] Oerke, E.C., & Dehne, H.-W. (2003) - Safeguarding production – losses in major crops and the role of crop protection.

Crop Protection 23: 275–285. [24] Palmer, W. (1995) - Effects of modern pesticides and farming systems on northern bobwhite quail brood ecology. Ph.D.

Dissertation, North Carolina State University. pp.131 [25] Reed, J.N., Barnes, E.M., & Hake, K.D. (2009) - US cotton growers respond to natural resource survey. THE ICAC

RECORDER. http://cottontoday.cottoninc.com/2008-Cotton-Grower-Survey-Results/2008-Cotton-Grower-Survey-Results.pdf.

[26] Sunilkumar, G., Campbell, L.M., Puckhaber†, L., Stipanovic†, R.D. and Rathore, K.S. (2006) - Engineering cottonseed for use in human nutrition by tissue-specific reduction of toxic gossypol. PNAS: 103: 18054.

[27] Torres, J.B. & Ruberson, J.R. (2005) - Canopy- and ground-dwelling predatory arthropods in commercial Bt and non-Bt cotton fields: Patterns and Mechanisms. Environ. Entomol. 34(5): 1242–1256.

[28] (USDA) U.S. Department of Agriculture. 2011. Agricultural Chemical Usage 2010 Field Crops Summary. http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1560

[29] (USDA) U.S. Department of Agriculture. 2004. 2003 Farm and ranch irrigation survey). http://www.agcensus.usda.gov/Publications/2002/FRIS/index.asp.

[30] (USDA) U.S. Department of Agriculture. 2009. 2007 Census of Agriculture. http://www.agcensus.usda.gov/Publications/2007/Full_Report/index.asp.

[31] Whitehouse, M.E.A., Wilson, L.J. & Pitt, G.P. (2005) - A comparison of Arthropod communities in transgenic Bt and conventional cotton in Australia.. Environ. Entomol. 34(5): 1224–1241.

[32] Williams, M. (1997 – 2011). Insect losses report. Proceeding Beltwide Cotton Conference. National Cotton Council, Memphis, TN.

[33] Field to Market: The Keystone Alliance for Sustainable Agriculture Environmental Resource Indicators for Measuring Outcomes of On-Farm Agricultural Production in the United States, First Report, January 2009

Analysis of Growth and Instability of Cotton Production in India

Anuradha Narala1 and A.R. Reddy2

1Scientist, 2Senior Scientist Central Institute for Cotton Research, Nagpur

Abstract—Policy decisions are often made based on the growth rates which depend on the nature and structure of the data and instability in farm production. The present paper analyzes the growth and instability in cotton area, production and productivity during the period 1951-52 to 2010-11. Growth and instability of cotton during pre-introduction (1993-94-2001-02) and post- introduction of Bt cotton periods (2002-03-2010-11) was also analyzed. For this purpose compound growth rates were estimated by fitting the exponential function and coefficient of variation was worked out to find out instability associated. It was found that growth of cotton area and production was significant during 1950s, 1990s and 2001-10. Growth of cotton production was highest during the period 2001-10. Growth rate of productivity was also high during 2001-10. Instability analysis indicated that cotton area was more stable than production and productivity. Thus, policies should be made to reduce the risk in cotton production and to make it profitable so as to sustain the high growth rate experienced during the past few years.

INTRODUCTION

Cotton is an important commercial crop of India and plays a key role in the national economy. About 60 million people get employment either directly or indirectly in the agricultural and industrial sectors of cotton production, processing, textiles and related activities and by way exports, the foreign exchange earnings of cotton amounts Rs.3837.33 crores (CAB, 2008). With economic liberalization and globalization sweeping the world there is a scope for our country to play a leading role in the cotton production and export. It is encouraging to note that over the last few years the cotton production had shown a significant increase. In 2010-11 season, it touched a record production of 325 lakh bales with an average productivity of 496 kg/ha (Cotton Corporation of India, 2010). Majority of cotton produced in India is consumed domestically and hence, export of cotton from India is only 5.5 million bales. If this increasing production trend continues in years to come, India can become a major exporter of cotton. Researchers have shown that with the adoption of new technologies on farmer's fields, it is possible to increase the average productivity beyond 600 kg lint per ha to meet the increasing cotton demand. Growth rates are the measures of past performance of economic variables. They are commonly used as summaries of trends in time series data. They are developed to describe the trends in a variable over time. Policy decisions are often made based on such growth rates which depend on the nature and structure of the data. Instability in farm production is causing serious shocks to supply and farm income and there is a growing concern about increased volatility in farm production, prices and farm income (Ramesh Chand and Raju, 2008). Indian cotton production has undergone a metaphoric changes from 2002-03, after Bt cotton was introduced in the country, since then significant increase in area, production and yield was witnessed. Bt cotton now occupies 95% of the total cotton area in the country. These dynamic changes underline the importance of studying the growth performance and instability of cotton before and after Bt cotton introduction. Therefore the present study was undertaken to analyze the growth and instability in cotton area, production and productivity during 1951-2010.

METHODOLOGY

The present analysis is based on the secondary data sourced from Cotton Advisory Board, Ministry of Agriculture, and Government of India. The period of analysis is 1951-52 to 2010-11. The entire period was sub divided into six sub periods of ten years each. Further to study the growth before and after Bt cotton introduction the period from 1993-94 to 2001-02 was taken as pre-introduction of Bt cotton period and from 2002-03 to 2010-11 as post- introduction of Bt cotton period. Compound growth rates (CGR)

75


of area, production and productivity of cotton were worked for different periods as well as for entire period of analysis by fitting exponential function as given below:

Xt = abt Log Xt = Log a + t log b b = (1 + r) / 100 Where, Xt = Area/production/productivity of pulse crops in the year 't' t = time element which takes the value 1, 2, 3, ……. n a = intercept b = regression coefficient. CGR was worked out as follows: CGR (r) = (antilog b - 1) x 100 Student 't' test was used to test the significance of the CGR. Instability was analyzed by estimating coefficient of variation (CV) using the formula. CV= (σ / μ) * 100 Where σ = Standard deviation μ = Mean

COTTON PRODUCTION SCENARIO OVER THE YEARS TABLE 1: AREA, PRODUCTION AND YIELD OF COTTON IN INDIA

Year Area (m ha) Production (m bales) Yield (kg/ha) 1951-52 6.56 3.28 85 1955-56 8.09 4.18 88 1960-61 7.61 5.6 125 1965-66 7.96 4.85 104 1970-71 7.61 4.76 106 1975-76 7.35 5.95 138 1980-81 7.82 7.01 152 1985-86 7.53 8.73 197 1990-91 7.44 9.84 225 1995-96 9.04 12.86 242 2000-01 8.58 14.00 278 2005-06 8.68 24.4 478 2010-11 11.14 32.5 496

Source: CAB, Ministry of Agriculture, GOI.

Over the years, country has achieved significant quantitative increase in cotton production (Table 1). Till 1970s, country used to import massive quantities of cotton in the range of 0.8 to 0.9 m bales per annum. However, after the Government launched special schemes like intensive cotton production programmes through successive five-year plans, that cotton production received the necessary impetus. As a result of initiation of systematic cotton improvement programmes and the launch of AICCIP on 1st April 1967 for cotton improvement involving genetic, production and protection technologies has resulted in increasing country’s productivity by 7-8 folds from 88 kg to 553 kg lint ha-1. The realized raw cotton production of nation during 2010-11 was 32.5 m bales. It is expected that this trend is going to remain more or less the same.

Analysis of Growth and Instability of Cotton Production in India 451

If we examine cotton area over the years it is clear that area increase is less than that of production and productivity. Cotton area increased from 6.56 m ha in 1951-52 to 11.14 m. ha in 2010-11. But cotton production increased nearly 10 folds during the period of analysis. Cotton productivity increased to nearly 7-8 folds during the same period. Cotton productivity was only 85 kg/ha in the year 1951-52 and increased to 496 kg/ha in the year 2010-11. Increase in productivity and production was more prominent after 2000-01.

GROWTH OF AREA, PRODUCTION AND PRODUCTIVITY

Decadal growth rates of cotton area, production and productivity were worked out and are presented in Table 2. Growth rate of cotton was negative during two decades only. But t - values indicated that this negative growth was statistically not significant. Growth rate was positive in other periods as well as overall period. Cotton area increased significantly during 1951-60, 1991-00 and 2001-10, the growth rates were 1.91, 2.21 and 3.43 percent respectively. During other periods, growth rate of cotton area was not significant. Growth rate of cotton area was positive and significant during overall period.

Cotton production during 1960-1970 witnessed significant negative growth rate whereas in 1951-60, 1991-00 and 2001-10 cotton production increased significantly. Growth of cotton production was highest during 2001-10. During overall period of analysis also cotton production increased significantly at an annual rate of 3.29 %.

Cotton productivity also recorded positive growth during all the periods except during 1961-70 which has shown a negative growth rate. Among the decades showing positive growth rate all were statistically significant except the 1971-80 period. Productivity growth was highest during the last period i.e. 2001-10. During this period cotton productivity increased at a rate of 5.97% per annum, this may be due to introduction of Bt cotton. The growth rate for overall period was also positive and significant. The productivity increased at a rate of 2.94% per annum during the overall period.

INSTABILITY

CV for different periods was worked out for area, production and productivity and is given in Table 3. It is clear from the analysis that instability in cotton area was less when compared to production and productivity. Coefficient of variation for area was only 10.72 % where as it was 72.70% and 60.68% in case of production and productivity, respectively. Instability in cotton area was the least during 1961-70 followed by 1971-80. It was highest during 2001-10 followed by 1991-00 and 1951-60. Instability of cotton production was highest during 2001-10 and lowest during 1961-70. Similarly, CV of yield was also highest during 2001-10 followed by 1981-90.

TABLE 2: TREND IN GROWTH RATE (%) OF AREA, PRODUCTION AND YIELD OF COTTON IN INDIA

Period Area Production Yield 1951-52 to 1960-61 1.91** 4.22** 2.30* (2.4747) (2.7554) (1.9673) 1961-62 to 1970-71 -0.58 -0.27* -0.30 (-1.9056) (-0.2826) (0.3557) 1971-72 to 1980-81 0.50 1.10 1.50 0.9215 (1.7045) (1.8230) 1981-82 to 1990-91 -0.97 3.32 4.31*** (-1.4736) (1.7611) (3.1234) 1991-92 to 2000-01 2.21*** 4.63*** 2.37* (3.1864) (3.5850) (1.9493) 2001-02 to 2010-11 3.43*** 9.63*** 5.97*** (5.0203) (6.3209) (3.8722) Overall 0.34*** 3.29*** 2.94*** (5.4220) (21.4523) (25.2330)

***1% Significant level; ** 5% Significant level; * 10% Significant level Figures in the parenthesis indicates t-value


TABLE 3: DECADE-WISE INSTABILITY (CV IN %) OF AREA, PRODUCTION AND PRODUCTIVITY OF COTTON IN INDIA

Period Area Production Yield 1951-52 to 1960-61 8.45 17.56 12.41 1961-62 to 1970-71 3.20 8.11 7.17 1971-72 to 1980-81 4.81 11.49 8.14 1981-82 to 1990-91 6.22 19.21 17.64 1991-92 to 2000-01 8.71 17.58 12.34 2001-02 to 2010-11 11.84 27.10 19.45 Overall 10.72 72.70 60.69

PRE AND POST- BT COTTON INTRODUCTION

Growth & Instability

Bt cotton in India was introduced in the year 2002. Since then the area, production and yield of cotton in India has undergone dynamic changes. A comparative analysis of growth before and after the introduction of Bt cotton in India (Table 4) suggests significant increase in the growth in area, production and yield during the post-Bt introduction than the pre-Bt period. The growth in area, production and yield during post-Bt introduction were 4.45, 9.72 and 5.02 % respectively, as against the pre-Bt introduction of 1.53, 4.37 and 2.83 %.

TABLE 4: GROWTH RATE (%) OF AREA, PRODUCTION AND YIELD OF COTTON IN INDIA – BEFORE AND AFTER INTRODUCTION OF BT COTTON

Period Area Production Yield Pre-Bt Introduction 1.53 4.3738** 2.83* (1993-94 to 2001-02) (1.7088) (2.7953) (1.9519) Post-Bt Introduction 4.45*** 9.72*** 5.02** (2002-03 to 2010-11) (9.5795) (5.0940) (2.7834) Overall 1.18*** 6.10*** 5.75*** (1993-94 to 2010-11 (3.0459) (10.6514) (9.3339)

***1% Significant level; ** 5% Significant level; *10% Significant level Figures in the parenthesis indicates t-value

Instability in area, production and yield of cotton before and after introduction of Bt-cotton in India is given in Table 5. CV which is the indicator of instability, for area production and yield of cotton was higher in post-Bt cotton introduction than pre-Bt introduction. During post-Bt period, the instability in the production of cotton was maximum (24.47%) followed by yield (16.28%) and area (12.41%).

TABLE 5: INSTABILITY IN AREA, PRODUCTION AND PRODUCTIVITY OF COTTON (CV IN %) IN INDIA- PRE AND POST BT INTRODUCTION

Particulars Area Production Yield Pre-Bt Introduction (1993-94 to 2001-02) 7.36 15.81 12.45 Post-Bt Introduction (2002-03 to 2010-11) 12.41 24.47 16.28 Overall (1993-94 to 2010-11) 10.47 38.10 32.14

CONCLUSION

From the above analysis it is clear that during the last period and in the post- Bt period there was a significant increase in area, production and productivity and registered high growth rate. This may be due to introduction of Bt cotton in India. Instability was also high during this period indicating that cotton production increased over the periods. Although the cotton production and productivity is following an increasing trend, it is associated with many problems. Cost of production is escalating due to the rise in the prices of inputs. The prices of cotton are fluctuating from place to place and year to year making the production risky. Most of the cotton area is sown with Bt hybrids which have very high seed cost. The nutrient requirement is also high. Similarly the labour cost for cotton picking is also increasing exorbitantly. Since the Bt cotton matures early and because of synchronized bole bursting at one time the labour demand for cotton-picking increases abruptly. Considering these facts policies should be made to

Analysis of Growth and Instability of Cotton Production in India 453

reduce the risk in cotton production and to make it profitable. Programs and policies such as rehabilitation of irrigation systems, adoption of improved technologies, strengthening of extension, reducing risks in cotton production can play a vital role in achieving stability and to sustain the high growth rate experienced during the past few years.

REFERENCES [1] Cotton Advisory Board (2008) - Ministry of Agriculture, Government of India. [2] Cotton Corporation of India. www.cotcorp.gov.in.

Ramesh Chand and S.S.Raju (2008) - Instability in Andhra Pradesh Agriculture – A Disaggregate Analysis – Agri. Economic Res. Rev., 21: 283–288.

Total Factor Productivity of Cotton in Gujarat (India)

A.R. Reddy1, S.M. Yelekar2, R.B. Petkar2 and N. Anuradha3

1Senior Scientist, 2Senior Research Fellow, 3Scientist, Central Institute for Cotton Research, Nagpur


Abstract—Gujarat is one of the important cotton producing states of India, which ranks first in production and second in area in the country. An analysis was carried out to study the total factor productivity (TFP) of cotton in the state for the period 1981-82 to 2007-08. Tornqvist-Theil indexing procedure was adapted to workout total input, total output and total factor productivity indices. These indices were worked out for first period (1981-1990), second period (1990-2000) and last period (2000-2008) separately. Input factors considered to workout TFP includes human labour (days/ha), bullock labour (days/ha), machine labour (days/ha), seeds (kg/ha), manure (q/ha), NPK (kg/ha), and insecticides (l/ha). The results indicated that the growth of TFP was positive and significant during the period of analysis. TFP increased at the rate of 5.71% per a. % per annum during the period of analysis. This indicates the sustainability of cotton production in the state. The growth of total input index was not significant during the period of analysis. But total output index showed a significant growth of 5.44 % per annum. This was due to the increase in the TFP during this period. TFP growth was positive during all the three periods. It was highest (10.75 %) during 2000-07 while it was lowest (1.05 %) during 1981-1990. Growth of input index was negative during first two periods while it was positive during the last period. Similarly growth of total output index was negative during the first period and positive during last two periods. This analysis indicates that the output growth of cotton in this state is sustainable as it is driven by the positive TFP growth. It also indicates that the growth of output can further be strengthened by improving the input growth.

INTRODUCTION

In agriculture, generally, productivity is measured in terms quantity produced per unit area of land. This was mainly because land is one of the scarce input and producer used to maximize production per unit area of land. But productivity will not entirely depend upon land. Other inputs like seeds, fertilizers, manure, labour, plant protection chemicals etc., play an important role. Inadequacies in the measurement of single factor productivity led economists to devise methods to measure total factor productivity (TFP). TFP measures the increase in total output which is not accounted for by increases in total inputs. The total factor productivity index is computed as the ratio of an index of aggregate output to an index of aggregate inputs. Therefore growth in TFP is equal to the growth rate in total output less the growth rate in total inputs. Earlier Laspyeres arithmetic indices were used most commonly to measure TFP (Pandya and Shiyani, 2002). But most recent literature on TFP (Kumar and Mruthyunjay, 1992; Kumar and Rosegrant, 1994; Desai and Nambudri, 1997; Mittal and Lal, 2001) has advocated and employed Tornqvist Theil or translog index in their study for drawing meaningful inferences.

Many of the studies conducted in the past considered agricultural sector as a whole and only a few studies were done on specific crops. No study was conducted to workout TFP of cotton in India especially in Gujarat. Gujarat is an important cotton growing state in the country. It ranked first in cotton production and second in cotton area. In Gujarat, cotton is grown on 26.3 lakh ha producing 102 lakh bales of lint with an average productivity of 658 kg/ha (2010-11). In this study, a critical analysis is done on the partial as well as total productivity of cotton for the period 1981 - 2008.

DATA AND METHODOLOGY

Data on cost of cultivation of cotton in Gujarat was collected from the Commission for Agricultural Costs and Prices, New Delhi. The data were collected for the period 1981-82 to 2007-08. The period was divided into three sub periods, period I (1981-82 to 1989-90), period II (1990-91 to 1999-2000) and period III (2000-01 to 2007-08).

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Total Factor Productivity of Cotton in Gujarat (India) 455

COMPOUND GROWTH RATES

Compound growth rates were worked out for area, production and productivity of cotton, inputs used and partial input productivities for the period of study. Compound growth rates were worked out by fitting exponential function as given below:

Xt = abt

Log Xt = Log a + t log b

Compound growth rate (r) = (antilog b - 1) x 100

Where,

Xt = Value of the variable in the year 't'

t = time element which takes the value 1, 2, 3, ……. n

a = constant

b = regression coefficient.

Student 't' test was used to test the significance of the compound growth rate.

PRODUCTIVITY OF INDIVIDUAL INPUTS

Productivity of individual inputs was worked out as the quantity of seed cotton produced per unit of each input

Partial productivity = quantity of seed cotton produced / quantity of input used

TOTAL FACTOR PRODUCTIVITY

The Tornqvist-Theil index is a superlative index which is exact for the linear homogeneous translog production function (Diewert, 1976). A further advantage of the Tornqvist-Theil index is that it accounts for changes in quality of inputs. Because current factor prices are used in constructing the weights, quality improvements in inputs are incorporated, to the extent that these are reflected in higher prices (Antle and Capalbo, 1988). The Tornqvist-Theil index provides consistent aggregation of inputs and outputs under the assumptions of competitive behaviour, constant returns to scale, Hicks-neutral technical change, and input-output separability. However, Caves et al. (1982) showed that Tornqvist-Theil indices are also superlative under very general production structures, i.e., non-homogeneous and non-constant returns to scale, so they provide consistent aggregation across a range of production structures. TFP indices were worked out by using Tornqvist-Theil indexing procedure expressed in logarithemic form, which is given by the following equation:

Ln (TFPt / TFPt-1) = ln (Qt/Qt-1) - ½ Σ (Cit+ Cit-1) ln (Xit / X it-1 )

Where

Qt = Output of cotton in year ‘t’

Cit = Share of input ‘i' in total input cost

Xit = Input ‘i' in year ‘t’

Specifying the index equal to 100 in a particular year and accumulating the measure based on above equation provides the TFP index.

Input variables considered for constructing the indices were human labour (days/ha), bullock labour (days/ha), machine labour (hours/ha), seeds (kg/ha), manure (q/ha), NPK (kg/ha) and insecticides (lit /ha).



Area, Production and Productivity

Cotton area in Gujarat increased from 15.7 lakh ha in 1980-81 to 26.3 lakh ha in 2010-11 while production increased from 17.1 lakh bales to 102 lakh bales in the same period (Table 1). This spectacular increase in production was mainly due to an increase in cotton productivity from a mere 185.36 kg lint/ha to 658 kg lint per ha. The cotton acreage increased at the rate of 2.34 % per annum while cotton production and productivity increased at the rate of 7.77 % and 5.30 % per annum.

TABLE 1: AREA, PRODUCTION AND PRODUCTIVITY OF COTTON IN GUJARAT

Year Area (lakh ha) Production (lakh bales) Yield (kg/ha)

1980-81 15.7 17.1 185 1985-86 14.0 19.9 240 1990-91 9.2 15.0 277 1995-96 14.1 31.3 377 2000-01 16.2 23.8 251 2001-02 16.9 32.5 327 2002-03 16.3 30.5 317 2003-04 16.5 50.0 515 2004-05 19.1 73.0 651 2005-06 19.1 89.0 794 2006-07 23.9 103.0 733 2007-08 24.2 110.0 772 2008-09 23.5 90.0 650 2009-10 26.3 98.0 635 2010-11 26.3 102.0 659

CGR

2.34** (5.944)

7.77** (8.394)

5.3** (8.910)

Note: *1 bale = 170 kg ** Significant at 1 % level Figures in the parentheses indicate t values

Input Utilization in Cotton Production

Human labour, animal labour, machine labour, seeds, fertilizers, and plant protection chemicals are important inputs used in cotton production. Human labour accounts for nearly 40 % of working cost. Human labour utilization fluctuated between 66.93 man days to 146.75 man days/ha during the period of analysis (Table 2). These fluctuations in human labour utilization were mainly due to variations in labour requirement for picking and weeding operations. Significant growth was not observed in human labour utilization during the period of analysis. Animal labour utilization showed a negative growth of 3.30 % per annum and reduced from 9.7 animal pair days per ha in 1981-82 to 5.32 animal pair days per ha in 2007-08. This may be due to the mechanization of some of the farm operations. This is evident as machine labour showed a positive growth of 6.80 % per annum. Machine power is mainly used for tillage and land preparation. Still many field operations are done using animal power.

Quantity of seeds used per ha showed a sharp decrease during the period of analysis. It reduced from 12.5 kg/ha in 1981-82 to 3.0 kg/ha in 2007-08. There was a negative growth of 3.17 %per annum in quantity of seed used. This was mainly due to an increase in area under hybrids especially Bt hybrids. Seed cost is not only high but seed requirement reduced as hybrids are grown at a wider spacing. Fertilizers (NPK) are another important input in cotton production, which registered a positive growth of 0.28 % annum. Fertilizers (NPK) consumption increased from 98.01 kg/ha in 1981-82 to 149.48 kg/ha in 2010-11. Significant change was not observed in the quantity of manures used in cotton production. Cotton is prone to many pests and diseases, which necessitates the use of chemicals. As the quantitative


data is not available an index was developed by using price index. Quantity of pesticides used in cotton production increased at the rate of 3.44 % per annum.

TABLE 2: QUANTITIES OF INPUTS USED IN COTTON CULTIVATION

(per ha) Year Human Labour

(Man days) Animal Labour

(Pair days) Machine labour

(hrs) Seeds (Kgs)

NPK (kgs)

Manures (Qtl)

Insecticides (Index)

1981-82 123 9.7 1.42 12.52 98.01 27.00 602.29 1982-83 144 14.1 2.82 7.25 162.63 37.39 970.73 1983-84 121 10.5 1.67 10.20 79.58 23.94 517.90 1984-85 107 9.9 0.70 9.14 78.19 23.30 481.71 1985-86 93 9.4 0.69 8.09 76.81 22.66 445.53 1986-87 80 8.9 0.67 7.03 75.42 22.02 409.34 1987-88 122 12.5 2.33 5.07 103.09 83.80 328.40 1988-89 113 9.8 0.73 6.76 88.24 30.10 840.45 1989-90 105 8.6 0.78 5.89 97.29 31.32 680.45 1990-91 115 9.2 1.35 5.61 111.18 38.87 715.19 1991-92 125 9.9 1.29 5.34 125.06 46.41 749.94 1992-93 134 10.6 1.02 5.06 138.95 53.96 784.68 1993-94 94 8.3 1.36 9.58 83.61 20.43 579.36 1994-95 96 7.4 0.54 8.83 83.12 25.56 754.64 1995-96 97 6.3 1.41 7.98 88.88 32.40 843.22 1996-97 99 5.6 2.74 7.34 82.14 35.81 1105.21 1997-98 105 6.2 3.37 7.55 89.69 39.05 1356.91 1998-99 108 6.0 3.27 6.98 90.62 41.08 1058.62 1999-00 92 6.3 2.85 6.08 81.41 39.26 1287.03 2000-01 67 5.4 3.72 6.14 59.19 41.70 473.47 2001-02 105 6.7 3.54 4.35 78.97 41.41 1196.80 2002-03 100 5.2 3.79 4.97 68.03 34.27 492.95 2003-04 140 5.8 4.85 4.61 94.85 26.59 963.04 2004-05 134 5.3 4.34 3.49 102.86 33.52 981.86 2005-06 147 6.0 4.38 4.41 142.46 36.81 1424.54 2006-07 121 5.2 3.81 3.54 124.93 33.31 1304.03 2007-08 124 5.3 4.29 3.02 149.48 36.75 1505.26

CGR

0.11 (0.229)

-3.30** (-9.967)

6.80** (5.184)

-3.17** (-5.461)

0.28 (0.446)

0.83 (1.094)

3.44** (4.067)

** Significant at 1 % level,* = Significant at 5 % level

Partial Productivities of Inputs Used in Cotton Production

Analysis revealed that productivity of land, human labour, animal labour and seeds increased while the productivity of machine labour decreased during the period of analysis (Table 3). Productivity of land showed a spectacular increase from 699 kg/ ha in 1981-82 to 1932 kg/ha in 2007-08. Productivity of land increased at the rate of 8.66% per annum. Similarly, productivity of human labour increased from 5.6 kg/man day in 1981-82 to 15.6 kg/man day recording a positive growth of 5.11% per annum. Similarly productivity of animal labour manures and seed showed a positive growth. Productivity of seeds, an important input, increased from 55.8 kg/kg seed in 1981-82 to 639.7 kg/kg seed in 2007-08. Significant growth of 4.92% was observed in the case of fertilizers whereas machine labour showed a non-significant growth.

Analysis of individual productivity has many limitations, as an increase in the productivity of individual input is not the effect of that input only. Other inputs which are applied in the production process have also contributed to it. Thus, attributing this to any single factor is not appropriate necessitating TFP analysis.


TABLE 3: PARTIAL PRODUCTIVITY OF INDIVIDUAL INPUTS

Year Human Labour(kg Seed Cotton/

Manday)

Animal labour (kg Seed Cotton/

pairday)

Machine Labour

(kg Seed Cotton/Hour)

NPK (kg Seed

Cotton/kg NPK)

Manures (kg Seed

Cotton/Q)

Seeds (kg Seed

Cotton/kg)

Land (kg Seed

Cotton/ha)

1981-82 5.67 72.36 493.85 7.13 25.89 55.83 699 1982-83 3.68 37.42 187.28 3.25 14.12 72.83 528 1983-84 4.38 50.39 316.54 6.63 22.06 51.76 528 1984-85 7.13 76.68 1086.96 9.75 32.70 83.34 762 1985-86 7.76 76.94 1042.38 9.41 31.91 89.41 723 1986-87 5.13 46.07 612.34 5.41 18.53 58.04 408 1987-88 1.73 16.74 90.21 2.04 2.51 41.42 210 1988-89 6.10 70.13 945.94 7.79 22.82 101.63 687 1989-90 7.14 87.67 963.87 7.74 24.04 127.84 753 1990-91 7.23 89.88 617.20 7.47 21.38 148.04 831 1991-92 5.47 68.77 527.19 5.45 14.67 127.61 681 1992-93 6.88 87.50 909.41 6.65 17.12 182.61 924 1993-94 11.22 126.51 773.84 12.59 51.54 109.92 1053 1994-95 10.68 137.33 1898.65 12.27 39.91 115.47 1020 1995-96 11.68 179.88 802.70 12.73 34.91 141.73 1131 1996-97 11.61 203.37 417.88 13.95 32.00 156.13 1146 1997-98 13.99 236.10 436.45 16.39 37.64 194.70 1470 1998-99 12.53 226.68 413.98 14.93 32.94 193.84 1353 1999-00 12.79 185.56 413.34 14.45 29.95 193.42 1176 2000-01 13.00 160.78 233.69 14.70 20.86 141.69 870 2001-02 9.78 152.11 289.70 12.99 24.78 235.86 1026 2002-03 10.56 204.37 278.12 15.48 30.73 211.87 1053 2003-04 10.16 246.81 293.65 15.02 53.59 309.11 1425 2004-05 11.52 291.30 355.75 15.02 46.09 442.69 1545 2005-06 13.17 320.80 441.18 13.56 52.49 438.10 1932 2006-07 15.98 374.60 507.73 15.46 58.00 545.76 1932 2007-08 15.59 363.33 450.79 12.92 52.57 639.74 1932

CGR 5.11** 8.81** -1.37 4.92** 4.36** 8.66** 5.22** T value 8.1341 10.940 -1.3042 7.0421 4.4908 7.6325 8.5700

** Significant at 1 % level * = Significant at 5 % level

Total Factor Productivity

Indices of total input, total output and TFP of cotton in Gujarat are given in Table 4. TFP index showed a positive growth rate of 5.81% per annum during the period 1981-2008. The growth in the total input index was negative but not significant. Output index showed a positive growth of 5.59% per annum which was contributed by TFP growth only. As the growth in output is backed by the growth in TFP, the output growth is sustainable. The output growth can further be accelerated by the enhanced input growth.

If we examine these indices in different time periods it is clear that TFP growth was highest during the period III. During this period TFP increased at the rate of 10.79% per annum. This period coincides with the introduction of Bt cotton, new insecticides as well as improved crop production technologies. All these contributed to the growth of TFP in this state. Input index also showed positive growth during this period. All these resulted in 12.2% output growth during this period which was comparatively higher than other two periods.

During period I, though total factor productivity growth was positive, output index showed negative growth. This was due to the negative growth in input index. Positive growth of TFP was offset by the negative growth in input index. In period II, output recorded a positive growth of 6.43% though there was a negative growth in the input index. This was made possible due to the positive growth of total factor productivity during this period.


TABLE 4: TOTAL FACTOR PRODUCTIVITY OF COTTON IN GUJARAT

Year Total Input Index Total Output Index Total Factor Productivity Index 1982-83 100 100 100 1983-84 81.20 100.00 123.15 1984-85 75.61 144.32 190.87 1985-86 70.50 136.93 194.24 1986-87 63.74 77.27 121.24 1987-88 91.24 39.77 43.59 1988-89 84.06 130.11 154.79 1989-90 73.51 142.61 194.02 1990-91 82.59 157.39 190.55 1991-92 86.55 128.98 149.02 1992-93 89.66 175.00 195.19 1993-94 70.27 199.43 283.81 1994-95 68.53 193.18 281.88 1995-96 70.32 214.20 304.61 1996-97 73.43 217.05 295.58 1997-98 76.89 278.41 362.10 1998-99 74.67 256.25 343.17 1999-00 72.50 222.73 307.23 2000-01 57.51 164.77 286.52 2001-02 76.83 194.32 252.91 2002-03 69.77 199.43 285.83 2003-04 83.00 269.89 325.15 2004-05 78.13 292.61 374.52 2005-06 86.67 365.91 422.20 2006-07 76.31 365.91 479.47 2007-08 87.97 365.91 415.94

Compound Growth Rate

Period I (1982-90) -1.81

(-0.1726) -0.76

(0.5865) 1.05

(0.5762)

Period I (1990-00) -1.71

(-1.1493) 6.43

(0.3602) 8.14

(1.1120)

Period I (2000-08) 1.41

(0.9752) 12.20** (4.5627)

10.79** (6.2287)

Overall period -0.27

(-0.8774) 5.44**

(6.9653) 5.71**

(6.7866) ** Significant at 1 % level; * Significant at 5 % level; Figures in the parentheses indicate t values

CONCLUSION

Our results indicate that the growth of TFP was positive and significant during the period of analysis, and increased at the rate of 5.7% per annum. Total output index showed a significant growth of 5.44 % per annum. This entire growth was was contributed by the TFP growth only as the growth in the input index was insignificant. This analysis indicates that the output growth of cotton in this state is sustainable as it is driven by the positive TFP growth. It also indicates that the growth of output can be strengthened further by improving input growth.

REFERENCES [1] Antle, J.M. and Capalbo, S.M. (1988) - An Introduction to Recent Developments in Production Theory and

Productivity Measurement. In: S.M. Capalbo and J.M. Antle (eds)- Agricultural Productivity Measurement and Explanation, Resource for the Future Washington D.C.

[2] Caves, D.W., Christensen, L.R. and Diewert, W.E. (1982) - Multilateral Comparisons of Output, Input and Productivity Using Superlative Index Numbers- Economic Journal, 92: 73–86.

[3] Diewert, W.E. (1976) - Exact and Superlative Index Numbers- Journal of Econometrics, 4 (2): 115–146. [4] Desai, B.M. and Namboodiri N.V. (1997) - Determinants of Total Factor Productivity in Indian Agriculture- Economic and

Political Weekly, 32: 165–171.


[5] Kumar, P. and Rosegrant, M.W. (1994) - Productivity and Sources of Growth for Rice in India- Economic and Political Weekly, 29: 183–188.

[6] Kumar, P. and Mruthyunjaya, (1992) - Measurement and Analysis of Total Factor Productivity Growth in Wheat- Indian Journal of Agricultural Economics, 47(3): 451–458.

[7] Pandya, M. and Shiyani, R. L. (2002) - Analysis of total Factor Productivity Growth in Food Crops of Gujarat-Artha Vijnana, 44(3-4): 367–374.

[8] Mittal, S. and Lal, R.C. (2001) - Productivity and Sources of Growth for Wheat in India- Agricultural Economics Research Review, 14(2): 109–120.

Transfer of Technology Initiatives for Profitable and Sustainable Cotton Farming in India—

An Empirical Analysis

S. Usha Rani1 and S.M. Wasnik2 1Scientist (Agrl. Extension), Central Institute for Cotton Research, Regional Station,

Coimbatore -641003, Tamil Nadu, India, 2Principal Scientist (Agrl. Extension), Central Institute for Cotton Research,

P.B. No. 2, Shankarnagar, Nagpur–10, Maharashtra, India E-mail: [email protected],[email protected]

Abstract—Transfer of Technology (TOT) programs implemented in cotton have underlined the importance of problem solving, creating effective scientists and farmers linkage and transferring the latest cotton production technologies. Front Line Demonstrations, Farmers Field Schools, Contract Farming Approach are some of the TOT programs which created remarkable impact on cotton production. Analysis on the laurels and let downs of those initiatives revealed that they have high farmers’ acceptability due to its focus on problem solving and the practical application of knowledge. They were effective in increasing the yields, sharing the knowledge but handicapped due to lack of professional execution and non-availability of latest technological dissemination tools for ready transfer. Exclusion of novel extension methods viz., cyber, market led, Farmer-led and environmental extension was observed in many of the programs. Market intelligence surveys for commercializing our technologies and institutional arrangements for freeing indebtedness and risk coverage had never found a significant place in those programs. Media utilization and efforts to organize the cotton growers were the other areas where our cotton extension programs created a meager impact. On the other side, Indian cotton sector is facing serious challenges posed by the changes viz., changing technology “Bt cotton”, changing demands of the textile industries and non woven sectors and changing scenario of retaining the top position in acreage and second position in production at world level. All these changes could not impact much on productivity which is a major setback. Reforms made in the technology delivery system in other cotton growing countries raised the productivity of cotton. Therefore, criticisms about the present Indian cotton extension system and the emerging challenges compel to review and restructure the existing cotton extension system. A contemporary synergetic extension model with novel extension concepts and facility to forecast technology considering the pros and cons of past methods and changing perspectives are suggested in this paper.

INTRODUCTION

Cotton is one of the major fibre crops of global significance, which is, cultivated by 20 million farmers worldwide. The global cotton production in 2010 was 25.1 M tons (1476 lakh bales of 170 kg/bale) from 34.0 M hectares. India has the largest acreage accounting for 33.0% and contributes 21.1% of global production currently ranking next to China. The production increased from a meager 2.3 M bales (170 kg lint/bale) in 1947-48 to a record production of 32.50 M bales during 2010-11. Both the accelerated transfer of technology due to the sustained and joint research and developmental efforts by the state/central institutions and the private sector agencies and the phenomenal spread of Bt cotton hybrids seem to be the main contributory factors for the break through achieved in cotton production. In our country, various Transfer of Technology (TOT) programs in cotton have underlined the importance of problem solving, creating effective scientists and farmers linkage and transferring the latest cotton production technologies. Front Line Demonstrations (FLD), Farmers Field Schools (FFS), Contract Farming Approach are some of the TOT programs which created remarkable impact on cotton production. The introduction of Bt cotton is a milestone in the history of cotton improvement, changing scenario of Indian cotton in terms of acreage, production, productivity, quality, species composition and export importance. As a result, new challenges for cotton extension in India emerged. In order to survive in this era of economic liberalization and gene revolution, attaining excellent extension strategies is inevitable for profitable and sustainable cotton farming in the country. This paper makes an attempt to review the laurels and letdowns of past cotton extension programmes and suggest innovative strategies for sustainable cotton production in the country.

77


TRANSFER OF TECHNOLOGY INITIATIVES FOR COTTON CROP IN THE COUNTRY

It is more often criticized that the results of cotton research do not reach the farmers in time. Often, it is observed that the extension efforts are handicapped due to inadequate interaction with the research efforts and non-availability of latest technological information for ready transfer. It is essential that the developments that benefit the producer and consumer is translated into action, quick enough to meet the growing challenges of the future. It is also vital to understand the socio-economic implications of new technologies and understand the constraints limiting the transfer of technology. The Indian Council of Agricultural Research (ICAR) has always underlined the importance of Scientist- Farmer linkage for an effective transfer of latest agricultural technologies. Towards this goal, several programmes such as, Lab to Land Programme, Operational Research Project (ORP), Front Line Demonstrations (FLD), Integrated Pest Management (IPM), Integrated Resistance Management (IRM), Institute Village Linkage Programme (IVLP), Intensive Cotton Development Programme (ICDP), Farmers Field Schools (FFS) etc., were launched and implemented with active cooperation of the ICAR Institutes, State Agricultural Universities and Extension personnel of the State Department of Agriculture. These programmes ensured not only the quick dispersal of technologies by linking the Scientists, extension personnel and the farmers but also helped scientists to get a feed back on the response of farmers to the latest technologies. Among them, the three major cotton extension programmes which are currently prevailing in the cotton sector are explained below.

Front Line Demonstrations on Cotton TABLE 1: NUMBER OF COTTON FRONT LINE DEMONSTRATIONS CONDUCTED FROM 1996-97 TO 2010-11 BY AICCIP CENTRES WITH BUDGET PROVISIONS

Year Number of Demonstrations Total amount Sanctioned (Rs in Lakhs) North Central South Total

1996-97 200 170 181 551 15.00 1997-98 275 240 245 760 20.00 1998-99 270 235 255 760 20.00 1999-00 290 265 510 1065 30.50 2000-01 475 725 550 1750 87.50 2001-02 185 375 240 800 39.00 2002-03 115 215 150 480 25.00 2003-04 100 160 90 350 20.00 2004-05 217 495 143 855+55* 100.00 2005-06 277 588 285 1150+16*+20** 60.00 2006-07 250 750 350 1350+21*+23** 72.00 2007-08 255 795 350 1400+24*+22** 75.00 2008-09 500 1250 550 2300+30*+24** 100.00 2009-10 325 850 425 1600+19*+19** 70.00 2010-11 200 325 175 700+16*+16** 30.00

Total 3934 7438 4499 15871+126*+124** 764.00 * - Number of unit demonstrations on farm implements ** - Number of unit demonstrations on Integrated Pest Management

The programme “Front Line Demonstration on cotton” has been performing better than all other cotton extension programmes prevailing in the country. Since 1996, the All India Coordinated Cotton Improvement Project (AICCIP), as a nodal agency conducted FLDs. Funding for FLDs was from ICDP that was later changed to the Technology Mission on Cotton (TMC) under Mini Mission II. The Project Coordinator (Cotton) coordinates and monitors the implementation of the FLD programme with headquarters at the Central Institute for Cotton Research, Regional Station, Coimbatore. The FLDs are organized through AICCIP centres besides CICR, Nagpur and its regional stations. So far 15871 demonstrations on cotton production technology, 126 unit demonstrations on cotton IPM and 124 unit demonstrations on cotton farm implements were conducted all over the country (Table 1). The FLDs are conducted for transfer of improved cotton production technologies, IPM and demonstration of farm implements. The main emphasis of the demonstrations includes enhancing the production of cotton in low productivity areas / problematic areas. A list of beneficiaries and their plot numbers were selected in consultation with local Agricultural Officers. A Bench mark survey was conducted before taking up the

Transfer of Technology Initiatives for Profitable and Sustainable Cotton Farming in India—An Empirical Analysis 463

trial including information on the crops and cropping system of the area, inter cropping, the average yields of cotton and the local practices adopted in terms of irrigation, use of fertilizer, plant protection and information on the cost of production for the area as a whole. An impact analysis after the harvest was done out in the light of reduction in insecticide use, reduction in cost of cultivation and awareness of modern technology.

Impact of Front line Demonstration in Cotton

The demonstration of high yielding varieties and hybrids suited for various agro-climatic conditions, Integrated Nutrient Management (INM) practices, IPM strategies, use of bio-fertilizers and bio-pesticides, efficient water management techniques like drip irrigation and use of compatible intercrops conducted in various FLDs through the cotton growing tracts of the country have helped the farmers reduce pesticide input significantly and make cotton production more profitable. Over years yield increased to the tune of 15 to 37 per cent over the check (Table 2) and cost benefit ratio was around 1:1.99 to 1:3.30. In total, FLDs were effective in increasing the yields, sharing the knowledge but handicapped due to lack of professional execution and non-availability of latest technological dissemination tools for ready transfer.

TABLE 2: IMPACT OF FRONT LINE DEMONSTRATIONS (FLD) CONDUCTED BY AICCIP SEED YIELD (KG/HA)

Period FLD Farmers’ Practices Percentage Yield Increase Due to FLDs 1997-98 1326 965 37.40 1998-99 to 2001-02 1344 1071 25.60 2002-03 to 2005-06 1487 1265 17.57 2006-07 to 2009-10 2002 1737 15.28 Average 1540 1260 23.96

Farmers Field Schools (FFS) on Cotton

FFS is a participatory approach to adult education adopted by the Indian Government since the 1990s, towards the achievement of an ecologically sound, profitable and socially sustainable small scale farming (Mancini, 2006). In cotton, FFS was introduced as a component of TMC-MM-II in 2005-06 to provide season long training to farmers, enable them to grow a healthy crop by adopting best management practices. FFS is a non formal education method for cotton farmers with 20 sessions during the crop season. Personnel from State Department of Agriculture who participated in the Season long Training programme (ToF) on cotton were facilitators for the program. One FFS has a maximum of 30 farmers. The main aim of the programme is to enable farmers to take quality decisions based on regular eco system analysis. FFS also has a social goal to promote the empowerment of farmers by building human and social capital (Gallagher, 2000). In FFS, farmers are no longer positioned as receivers of already developed technological packages, but as field experts, who collaborate with the extension staff to find solutions relevant to the local realities. FFS programmes emphasize farmers’ ownership, partnership and group collaboration. So far more than 5000 farmers were trained under this programme.

Impact of FFS on Cotton Farming

Studies showed that the adoption of IPM by FFS had significantly reduced pesticide usage. It is expected to mitigate the adverse effects of pesticides overuse on people’s health (Mancini et al., 2005), biodiversity (Walter-Echols et al., 2005) and water quality (CSE, 2003). The strong correlation between knowledge level and reduction in pesticide use proved that a skill-oriented, knowledge intensive and hands-on education approach, as used during FFSs, is an efficient system to deliver the complex IPM principles to farmers. The FFS approach focuses on the importance to judge the necessity for plant protection interventions on the basis of actual field needs, which is essential to achieve a more sustainable agriculture. Substituting pesticides with bio-control agents or other technologies such as biotech cotton is unlikely to become a definitive solution to sustain agricultural productivity, if these new technologies are not paired with educational programmes (Yang et al., 2005). FFSs are also effective in disseminating the technical knowledge but market intelligence surveys for commercializing the technologies and institutional arrangements for freeing indebtedness and risk coverage had never found a significant place in these programs.


Contract Farming

Contract farming is another extension program implemented in this country for considerable close cooperation between agricultural production and agribusiness and for vertical way of coordination. Contract farming was introduced in cotton to reduce the load on central and state level procurement system, to increase private sector investment and to promote processing and value addition. In this approach, the farmer is contracted to plant the contractor’s cotton variety /hybrid on his land. He has to harvest and deliver to the contractor, a quantum of kapas, based upon anticipated yield and contracted average at a pre-agreed price. Towards these ends the contractor supplies the farmers selected inputs at full cost. The Super Spinning mills, Tamil Nadu, the Appachi Cotton Mills, Tamil Nadu, CITI-Cotton Development Research Association (CITI-CDRA) and Royal Classic Mills, Tamil Nadu are some of the leading contract agencies in cotton contract farming.

Impact of Contract Farming

Cotton researchers are of the firm opinion that contract farming is the answer to the problems faced by the cotton mill sector and the farming community. Studies showed that the contract farming system effectively established a link between the consuming industry and the supplier of the produce. They added that through contract farming, the integrated cotton crop management practices were propagated much faster and spread of spurious seeds and other agro inputs were checked as the concerned industry supplied them. Farmers also got a reasonable price for their produce. But in some cases either the sponsor broke the contract by not giving the pre agreed price or the farmer sold the produce to others. This program was also effective in increasing the yields and knowledge sharing. However, lack of professional execution and non-availability of guidelines are bottlenecks for sustaining the contract.

LACUNAE OBSERVED IN THE CURRENT COTTON EXTENSION PROGRAMS

Analysis on all the cotton extension programs that are functioning in the country revealed that they were effective in some aspects but handicapped due to inadequate interaction with the research efforts. Although they were effective in increasing with yields, but handicapped due to lack of professional execution and non-availability of latest technological dissemination tools for ready transfer. Many of them excluded the novel extension methods viz., cyber extension, market led extension, Farmer-led extension and environmental extension for a wider reach. Technology forecasting is another major area where our cotton extension programs attempted very less initiatives. Many of the programs neglected the inclusive growth and development. Market intelligence surveys for commercializing our technologies and institutional arrangements for freeing indebtedness had never found a significant place in those programs. Media utilization and efforts to organize the cotton growers were the other areas where our cotton extension programs created a meager impact. This is one side of the country’s cotton sector in terms of TOT initiatives.

CHANGING PERSPECTIVES AND EMERGING ISSUES IN COTTON

The country’s agricultural sector is facing serious challenges posed by the degradation of natural resources viz., deforestation, salinity, soil erosion, water contamination and depletion. The agricultural extension service in the country was ineffective at reversing these negative trends. Presently, extension services are reorienting their development strategies towards supporting farmers’ empowerment. Among the various changes in cotton sector, three major issues are discussed below in this article.

Changing Scenario

Combined efforts of millions of cotton farmers, talented scientists and technology providers, dynamic seed production industry in the private sector and field extension agencies and favourable weather conditions made India to be accountable for about 32% of the global cotton area and 21% of the global cotton production, currently ranking second after China. India’s contribution to global cotton production increased from 14% in 2002 to 20.5% in 2007. The production increased from a meager 2.3 M bales (170


kg lint/bale) in 1947-48 to a record production of 32.5 M bales during 2010-11. Though, India ranks second in the world in cotton production after China, even its best productivity of 566 kg/ha, places it at 24th rank in the list of 80 cotton producing countries. Despite the good progress made by public and private sector research and development, it is a matter of concern that productivity started to decline from 566 kg/ha in 2007 to 522 kg/ha in 2008, 486 kg/ha in 2009 and 475 kg/ha in 2010.

Changing Need

All along with the area, production and productivity of Indian cotton, the quality profile of Indian cotton has also changed. Long staple cotton which constituted 38% prior to 2002, increased to an estimated 85% of the total cotton produced in 2010, primarily because of the Bt cotton hybrids, most of which are of the long staple category. However, the Confederation of Indian Textile Industries (CITI) estimates that in the 25.8 M bales utilization capacity, the current requirement of the Indian textile Industry is 37% long and extra-long staple cotton, 53% medium staple and 10% short staple.

Changing Technology

Bt cotton is an attractive alternative and novel technology for controlling the bollworms in cotton. The area under this Bt cotton increased phenomenally from a few thousand acres in 2002 to more than 1.01 million hectares in 2005 and it is estimated to be more than 9.5 million hectares in 2011. This tremendous adoption rate of the technology could be due to the relative advantage of the technology and marketing efforts of seed companies. Simultaneously, the area under public research bred varieties and hybrids reduced significantly to less than 8% of the total cultivable area. The area under hybrid cotton increased from 40%in 2002 to 92% in 2010. The area under G. hirsutum varieties was 33% in 2000 and now less than 3%. Similarly, area under G. barbadense, G. arboreum and G. herbaceum which was 6.6%, 25%and 13% during 1995, declined to less than 7% in 2010 for all the three species combined. With intensive selection pressure of Bt toxins used in more than 90% area of Bt crops, and less adoption of refugia and declining area of inter crops in cotton cropping systems, development of bollworm resistance to the Bt toxins is an emerging concern.

EXTENSION STRATEGIES TO FOSTER COTTON PRODUCTION IN THE COUNTRY

Fig. 1: Contemporary Synergetic Extension Model with Novel Extension Concepts for Profitable and Sustainable Cotton Farming in India


In order to survive in the current changing scenario of cotton sector, attaining excellent relevant and effective extension strategies is inevitable for sustainable cotton production in the country. On the basis of the analysis of issues, problems, opportunities of threats, a relevant and feasible model (Figure 1) has been worked out for carrying out the extension activities. The future cotton extension programmes in the country must include innovations like, cyber extension, market led extension, environmental extension and farmer led extension. Along with improving the productivity and income of the existing cotton growers, it must organize the clients, strengthen the linkage between various stakeholders in cotton and provide genuine information for forthcoming policies and programmes.

Extension Innovations for Future Cotton TOT Programmes

Access to knowledge is the major factor most commonly cited by farmers as their major limitations in cotton farming. The widespread availability and convergence of Information and Communication Technologies (ICTs) in India in recent years have led to unprecedented capacity for dissemination of knowledge and information to the rural population. Utilizing the ICTs for dissemination of knowledge is inevitable for the future cotton extension programmes. The present cotton extension must address the cotton market information along with the production technologies since price is another major constraint in cotton production as cited by many of the farmers. Evolving a cotton extension programme that plays a catalytic role for ushering market led agricultural extension, highly scalable, planned through bottom-up process, and implemented through active involvement and collaboration of agricultural market committees in India is the need of the hour.

Consequent indiscriminate use of inputs like fertilizers, plant protection chemicals, growth promoters, herbicides and the recently introduced transgenics in cotton production system have started causing alarms to natural resources viz., soil, water and environment. So, cotton extension has to reorient itself to address these emerging concerns of natural resource degradation and environmental safety. In totality, the changing scenario in cotton sector forces us to have a clear and more locally controlled and managed extension approach. The approach should promote farmers and other rural people as the principal agents of change in their communities. Such an approach is farmer led extension approach. In this approach, farmers are not only key agents to access services provided by professional extension specialists and researchers, but also make many of the management decisions and do much of the extension works.

Concerns of Future Cotton Extension Programs

Even though India crowns the credits of owning first in cotton acreage and second in cotton production at world level, its productivity level is very low. Disseminating novel cotton production technologies evolved by cotton researchers through innovative extension approaches should be the top most agenda of our future cotton extension programmes. With globalization there is a major change in the trade in cotton. Selling to multinational companies needs larger quantities of seed cotton of a standardized quality. This can be done by contract farming and by farmers’ organizations. Cotton extension has to play a catalytic role in mobilization and organization of various cotton farmers’ groups depending upon the local need and nature of enterprise. Building effective linkage among the cotton research (technology development) system, extension (technology transfer) system and client (Technology user) system is a strategic issue in management of cotton sector. Worldwide a great deal of attention is being given to explore the intricacies involved in forging strong functional linkages among the various stakeholders of cotton. The recently experimented approaches in cotton extension viz., IVLP, Strategic Research and Extension Plan (SREP) in Agricultural Technology Management Agency (ATMA) are fairly successful in addressing the issue of R-E-F linkage. However, given the diversity of conditions and contexts prevailing in our cotton sector, linkage issue needs to be looked more as a major issue in cotton extension programmes. In the context of rapidly changing global cotton scenario, cotton extension must emerge as a major policy instrument to address the growing challenges in cotton sector. In addition it should explore and provide information on the researchable problems to the cotton research system and adoptable research solutions to various cotton stakeholders.


CONCLUSION

Worldwide, it is now widely recognized that agricultural extension needs to reform in ways that allow it to fulfill a diverse set of objectives. The emerging paradigm of extension-plus ranges from better linking of farmers to input and output markets, to reducing the vulnerability and enhancing voice of the rural poor, development of micro-enterprises, poverty reduction and environmental conservation and strengthening the farmers’ organizations (Sulaiman and Hall, 2004). Similarly, cotton extension needs to embrace a broadened mandate as cotton farmers find themselves in an even more complex production and market environment, with an expanding need for information and services. Hence, including the modern extension innovations viz., cyber, market led, environmental and farmer-led extension approaches in our future cotton extension programmes is inevitable.

REFERENCES [1] CSE Report (2003) - Pesticide residues in bottled water, Centre for Science and Environment, New Delhi, India. [2] Gallagher, K.D. (2000) - Community study programmes for integrated production and pest management: farmer field

schools. In: Human resources in agricultural and rural development. Food and Agriculture Organization (FAO, Rome, Italy, pp. 60–67.

[3] Mancini F., Van Bruggen A.H.C., Jiggins L.S.J., Ambatipudi A., Murphy, H., (2005) - Acute pesticide poisoning of cotton growers in Andhra Pradesh, International journal of Occupational and Environmental Health; 11: 221–232.

[4] Mancini, F. (2006) - Impact of Integrated Pest Management Farmer Field Schools on health, farming systems, the environment, and livelihoods of cotton growers in Southern India. Doctoral theis, Biological Farming Systems Group, Wageningen University, The Netherlands

[5] Sulaiman, R.V. and Hall Andy, (2004) - Towards Extension -Plus: Opportunities and Challenges. Policy Brief 17, NCAP, ICAR, New Delhi.

[6] Walter-Echols G., Soomro M.H., (2005) - Impact of FFS training of the FAO-EU IPM Programme for cotton in Asia on the Environment. In: Ooi P.A.C., Praneetvatakul S., Waibel H., Walter-Echols G., The impact of the FAO EU IPM Programme for cotton in Asia, Pesticide Policy Project Publication Series No. 9, Hannover University.

[7] Yang P, Li K, Shi S, Xia J, Guo R, Li S, Wang L, (2005) - Impacts of transgenic Bt cotton and integrated pest management education on smallholder cotton farmers, International J. Pest Management 51: 231–244.

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