Volume 48 Number 1 2014

Contents

Review Paper

Rainfed agriculture in Central India: strategies for combating climate change 1-13S.D. Upadhyaya and K.K. Agrawal

Emerging trends of nutrient management for sustainable agriculture in India-componentand tools 14-21Megha Dubey, K.K. Agrawal and Suchi Gangwar

Research Paper

Path analysis studies in indigenous and exotic germplasm lines of rice 22-25Pankaj Nagle, S. K. Rao, G. K. Koutu and Priya Nair

Genetic diversity analysis in indigenous and exotic germplasm lines of rice under climaticconditions of Kymore Plateau zone, Madhya Pradesh 26-32Pankaj Nagle, S. K. Rao, G. K. Koutu and Priya Nair

Effect of foliar sprays on seed yield and economics of niger 33-35G.K. Rai, S.K. Thakur, M.R. Deshmukh and A.K. Rai

Screening for resistance to Heterodera sacchari infection in upland rice cultivars 36-42L.I. Akpheokhai, A.O. Claudius-Cole, B. Fawole and A.A. Tanimola

Effect of Khamer (Gmelina arborea) + Lemongrass (Cymbopogon flexuosus) intercroppingon plant and soil characters under agro climatic conditions of Jabalpur Madhya Pradesh 43-46O.P. Dhurve, I.M. Khan, S.D.Upadhayay and Sharad Nema

Evaluation of maize genotypes for physiological efficiency and productivity underagroclimatic conditions of Kymore plateau zone, Madhya Pradesh 47-54Atole Avinash, A.S. Gontia, Amit Jha, Anubha Upadhyay and Preeti Sagar Nayak

Estimation of genetic variability and correlation for grain yield and its components in RILsderived population of rice 55-59Prabha Rani Dongre, D.K.Mishra, G.K. Koutu and S.K. Singh

Effect of weed management control practices on nutrient uptake and soil propertiesin fodder maize 60-63Pratik Sinodiya and A.K.Jha

Performance of dolichos bean (Lablab purpureus L.) genotypes in coastal Andhra Pradesh 64-67Ajay Kumar Verma, K. Uma Jyothi, A.V.D. Dorajee Rao and R.P. Singh

ISSN : 0021-3721 JNKVVVolume : 48 Research JournalNumber(1) : 2014 (January - April, 2014)

Influence of integrated nutrient management on morphology, phenology and yieldpotential of hybrid tomato under polyhouse condition, at Jabalpur, Madhya Pradesh 68-72Aradhana Singh, P.K. Jain, A.S. Gontia and Yashpal Singh

Variation in sensitivity in flowering behavior and heat unit requirement of soybean genotypesto varying dates of sowing during summer season under agro climatic conditions of Kymoreplateau zone of Madhya Pradesh 73-78K.K. Agrawal, A.P. Upadhyay, Sandip Silwat and S.K. Vishwakarma

Provocation of food imbalance in Ogbomosho Metropolis, Oyo State, Nigeria 79-84Olayiwola O.Olaniyi, P.K.Awasthi and N.K. Raghuwanshi

Supply riposte of major crops in Madhya Pradesh 85-89O.O. Olayiwola, P.K. Awasthi and N.K. Raghuwanshi

Evaluation of self-help development activities and patterns of participation in communitydevelopment projects among rural dwellers: case of Oguta Local GovernmentArea, Imo State, Nigeria 90-94Victor Chibuzor Umunnakwe

Determinants of cooking energy demand in the rural households of Osun State, Nigeria: Anapplication of Bivariate Probit Model 95-98Ayodeji Oluwaseun Ogunleke

Disposition of rural households toward women's international migration in Imo West (Orlu)Senatorial District, Nigeria 99-103Victor Chibuzor Umunnakwe and Ayodeji Oluwaseun Ogunleke

Identification of effective insecticides, miticides and fungicides and their combination forthe control of panicle mite in rice 104-105R.Bala Muralidhar Naik, D.Bhadru, Md.Latheef Pasha and P.Rajanikanth

Haematological profile of pigs affected with sarcoptic mange 106-111Sourabh Gupta, M.L.V. Rao, P.C. Shukla, Vandana Gupta and Bharat Sharma

Prevalence of canine pyometra - a retrospective study 112-113U.V. Bhaskar, M.L.V. Rao, Pooja Dixit, P.C. Shukla and O.P. Shrivastava

Incidence of subclinical ketosis in lactating buffaloes in Jabalpur, Madhya Pradesh 114-115Monika Gupta, M.L.V. Rao, D.K. Gupta, M.A. Quadri and Pooja Dixit

Monitoring fetal age and viability at different interval of gestation by ultra sonography inGerman Shepherd bitches 116-119B. L. Sharma, Swadesh Thapak, V. K. Bhardwaj, Sourabh Gupta and O. P. Shrivastava

Issued : 30 May 2014

Available on website (www.jnkvv.nic.in)

1

Rainfed agriculture in Central India: strategies for combatingclimate change

S.D. Upadhyaya and K.K. Agrawal*Department of Plant Physiology*Department of AgronomyJawaharlal Nehru Agricultural UniversityJabalpur 482004 (MP)Email : sdu1954@ gmail.com/kkagrawal59@gmail.com

JNKVV Res J 48(1): 1-13 (2014)

Abstract

All non irrigated area constitutes the rainfed agriculture. Interms of area and population dependant on it, rainfedagriculture has become far more extensive in Central Indiathan irrigated agriculture, Most of the cereals, pulses and oilseed crops are grown in rainfed areas, wich are important forthe agrarian economy of the country. A large area of landunder rainfed agriculture is expected to undergo changes inrainfall patterns, temperature and extreme events over thenext several decades due to climate change thus makingrainfed agriculture more risk prone. Climate related factorsthat would affect agricultural productivity in coming decadesare change in temperature, precipitation, short term weathervariability and surface water run off. The purpose ofadaptations and mitigation options to address the impact ofclimate change on agriculture is therefore to attempt a gradualreversal of the effects caused by climate change and sustaindevelopment. Infact, systematic studies on climate changein rainfed areas are scarce. Improved technologies and newpolicy initiative are needed to enable farmers to cope withclimate change impact. There are several mitigation andadaptation measures might be helpful to reduce the adverseimpact of climate change through crop/cropping system-based technologies, use of resource-conservation technologyand socio- economic and policy interventions etc. Some ofthese measures are discussed in this article to suggesteffective strategies to combat climate change with specialreference to rainfed agriculture in Central India. The gapbetween current and achievable crop productivity due toclimate change can be closed through crop improvement andnatural resources management strategies.

Keywords: Rainfed, climate change, greenhousegasses, global warming, climate variability, adaptations,mitigations, resource conservation

Climate change and rainfed agriculture are interrelatedprocesses. Variable climate and uncertain rainfall, highinter and intra seasonal variability, frequent occurrenceof mid season & terminal droughts, water scarcity, moreunstable yield linked with poverty and vulnerablelivelihoods are the characteristics of rainfed agriculture.Worldwide 80 percent of the arable land is rainfed whichgenerates 65 to 70 per cent staple foods but 70 percent of the population of these areas are poor due tolow and variable productivity. India ranks first amongthe rainfed agricultural countries of the world in termsof both extent and value of produce. Rainfed agriculturein India is practiced in two-thirds of the total croppedarea of 162 million hectares (66 percent), whichsupports 40 percent of the national food basket. Thesignificance of rainfed agriculture is obvious from thefact that 55 percent of rice. 91 percent coarse grains.90 percent pulses. 85 per cent oilseeds and 65 per centcotton are grown in rainfed areas. 'These areas receivean annual rainfall between 400 mm to 1000 mm whichis in adequate unevenly distributed, highly uncertainand erratic. In certain areas, the total annual rainfalldoes not exceed 500mm (Asha latha et al. 2012). As aresult of low and erratic rainfall, a significant fall in foodproduction is often noticed. Within agriculture, it is therainfed agriculture that will be most impacted by climatechange (Singh and Venkateshwarlu 2009). Temperatureis an important weather parameter that will affectproductivity of rainfed crops. The last three decadeswitnessed a sharp rise in all India mean annualtemperature. Though most rainfed crops tolerate hightemperature, rainfed crops grown during rabi arevulnerable to changes in minimum temperatures(Venkateswarlu and Shanker 2009).

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Climate is one of the main determinants ofagricultural production particularly rainfed area.Throughout the world there is significant concern aboutthe effects of climate change and its variability onagricultural production (Dagar et al. 2012, Gadgil 1995,Kang Khan Ma 2009). Researchers and administratorsare concerned with the potential damages and benefitsthat may arise in future from climate change impactson agriculture, since these will affect domestic andinternational policies, trading pattern, resource use andfood security. In fact, the climate change is defined asany change in climate over time that is attributed directlyor indirectly to human activity that alters the compositionof the global atmosphere in addition to natural climatevariability observed over comparable time periods(IPCC 2007a). Since climatic factors serve as directinputs to agriculture, any change in climatic factors isbound to have a significant impact on crop yields andproduction. Studies have shown a significant effect ofchange in climatic factors on the average crop yield(Mall et al. 2006). In developing countries, climatechange will cause yield declines for the most importantcrops and South Asia will be particularly hard hit. Manystudies in the past have shown that India is likely towitness one of the highest agricultural productivitylosses in the world in accordance with the climatechange pattern observed and projected scenarios. Incourse of time where the industrial revolution occurredin western countries and usage of the fossil fuelsincreased rapidly, on the other side the natural bufferingsystem for climate change i.e. forests, were destroyedindiscriminately for want of fuel, fodder and timbers inthe developing countries. These factors were intensifiedby the human activities in the past 250 years, whichhad tremendous impact on the climate system.According to the IPCC the green house gas emissioncould cause the mean global temperature to rise byanother 1.4 0C to 5.8 0C. Many studies (Lal et al. 2001,Parry et al. 1999, Darwin 2004) find that region-specificanalysis is required to evaluate the agronomic andeconomic impact of weather changes in more detail.

Agriculture and food security are amongcasualties of climate change in India. Strategies suchas adopting necessary mitigation measures andreducing greenhouse gas emission along withwidespread awareness on this issue, are needed(Agrawal 2007 2008, Sinha and Varshneya 2009). Mostimportant greenhouse gas i.e. CO2 which was in thesteady state at 280 ppm till the pre historic period (1850)is likely to be doubled by the end of 21st century (Keelinget al. 1995) Climate change is a complex alteration ofclimate, mysterious and continuous, yet extremelyimportant through its consequences on vegetation of

various types that thrived under constant or relativelyunchanged climate. The effects of climate change havereached such an extent that irreversible changes in thefunctioning of the planet are feared. Some of the maineffects of climate change with specific reference toagriculture and food production especially during thelast decade are: increased occurrence of storms andfloods; increased incidence and severity of drought andforest fires; steady spreading out of frost-free intervalsand potential growing season; increased frequency ofdiseases and insect pest attacks; and vanishinghabitats of plants and animals.

In addition agriculture is not only sensitive toclimate change but is also one of the major drivers ofclimate change. Scientif ic evidence about theseriousness of the climate threat to agriculture is nowunambiguous, but the exact magnitude is uncertainbecause of the complex interactions and feed backprocesses in the ecosystem and the economy. In India.Agriculture sector contributes 22 % of the totalgreenhouse gases (GHG) emissions (Sharma et al.2006). The emissions are primarily due to methaneemitted from paddy fields, enteric fermentation inruminant animals, and nitrous oxides from applicationof manures and fertilizers to agricultural soils.

Parts of western Rajasthan, Southern Gujarat,Madhya Pradesh, Maharashtra, Northern Karnataka,Northern Andhra Pradesh, and Southern Bihar are likelyto be more vulnerable in terms of extreme events (Mallet al. 2006). Significant negative impacts have beenimplied with medium-term (2010-2039) climate changepredicted to reduce yields by 4.5 to 9.0 percent,depending on the magnitude and distribution ofwarming. The impact of long-run climate change (2070-2099) is even more detrimental, with predicted yieldsfalling by 25 percent or more.

Climate change defined

Many definitions for climate change, vulnerability andadaptation to climate change are found in literature. In1992, United Nations Framework Convention on ClimateChange (UNFCCC) defined climate change as "achange in climate which is attributed directly or indirectlyto human activity that alters the composition of the globalatmosphere and which is in addition to natural climatevariability observed over comparable time periods." In2001, the Intergovernmental Panel on Climate Change(IPCC), which is the major scientific body associatedwith climate change at the international level, defined itas "any change in climate over time, whether due tonatural variability or as a result of human activity". The

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major concepts associated with climate | change issueare impacts, vulnerability, adaptation and resilience.

Climate-change trends

Simulation of future climate in India under A2scenario by Indian Institute of Tropical Meteorology(IITM), Pune, and Hadley Centre, UK, indicate that bythe last quarter of the present century the mean annualtemperature in the country will most likely increase by3-5 0C. the spatial average for the increase in annualrainfall during the period is 7-10% (Rupakumar et al.2006). Simulation results from (IPCC 2007b) indicatethat the average global surface temperature could riseby 0.6 to 2. 5 0C in the next 50 years, and by 1.4 to 5.80C in the next century. It is now widely accepted thatthe average mean temperature will increase by 1 to 20C during this century which may reduce the durationof crops like wheat (Swaminathan 2012).

There will be high disparity in the changes indistribution of rainfall and temperature. North India isexpected to be warmer than the south but moreimportantly, night temperature and winter temperaturewould register higher of 5 0C increases over the mostpart. It is also predicted that by 2071 the overall summermonsoon rainfall in India will increase by 20%, extremerainfall events would rise sharply especially in parts ofGujrat, Maharashtra, Madhya Pradesh, Karnataka andAndhra Pradesh. Incidence of tropical storms in theArabian sea coast is also likely to increase. The onsetof summer monsoon could become more variable.Overall the changes are likely to have more adverseeffects than benefits. Increase in temperature is likely

to be less during the rainy (kharif) season and moreduring the winter (rabi) season, whereas the rabi rainfallwill be more uncertain. The kharif rainfall is likely toincrease by 10%. The likely changes in temperatureand rainfall in the country from the present are presentedin Table1.

Climate Change Impact

IPCC (2007) defines climate change impacts as theeffects of climate change on natural and humansystems. It can be of two types: potential impacts andresidual impacts.

Potential impacts: These may occur given a projectedchange in climate without considering adaptation.

Residual impact: These are the impacts of climatechange that would occur after / adaptation measures.

Analysis of rainfall trend during the last 100 yearsby Indian Institute of Tropical Management (IITM)revealed that the summer monsoon rainfall, whichcontributes more that 85% of the total annual rainfall inthe region, has increased marginally(<10%) in thesouthern and eastern parts of Thar Desert, but hasalready declined by 10-15% in its north-western part.Rainfall trend in arid Kachchh and Banaskantha did notshow much change, but arid Saurashtra experienced10-25% increase. Earlier studies on changes in rainfalland air temperatures of north-west India showed thatthe rainfall increased marginally by 141 mm in the past100 years (Pant Hingane 1988), especially in theirrigated belt of Ganganagar region particularly during

Table 1. Expected changes in temperature and rainfall in India

Year Season Temperature Change (0C)Rainfall Change (%)Lowest Highest Lowest Highest

2020s Annual 1.00 1.41 2.16 5.97Rabi 1.08 1.54 -1.95 4.36

Kharif 0.87 1.12 1.81 5.102050s Annual 2.23 2.87 5.36 9.34

Rabi 2.54 3.18 -9.22 3.82Kharif 1.81 2.37 7.18 10.52

2080s Annual 3.53 5.55 7.48 9.90Rabi 4.14 6.31 -24.83 -4.50

Kharif 2.91 4.62 10.10 15.18Source Lal et al. 2001

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the past 3 decades (Rao 1996).

It is estimated that a rise of 0.5 0C in wintertemperature could cause a 0.45 tones per hectare fallin India's wheat production. Modeling studies project asignificant decrease in cereal production by the end ofthis century (Majumdar 2008). Simulation results offuture climate change in the arid western part ofRajasthan and adjoining parts of Punjab and Haryanaindicate that by the last quarter of this century, summermonsoon would decline notably in large parts of theseareas (up to 30%), especially during July-August. Winterrainfall may increase slightly, and the variability inrainfall will be more pronounced. Along with thesevariabilities, the rainy days are likely to decline by 10 innorth-western Rajasthan and adjoining Punjab plains,and by 5 in arid Gujarat. Consequently, there will behigher incidence of droughts and floods in arid parts ofwestern India. Based on the current understanding ofsimulation results of climate change, Kar (2007) broadlydivided the north-western hot arid zone into 3 majorsubzones: (i) the hotter and very dry north-westRajasthtan and adjoining Punjab; (ii) the warmer andmoderately wetter arid Gujarat and adjoining south

Rajasthan; and (iii) the hotter and slightly wetter easternfringe of arid Rajasthan and adjoining Haryana.

The predicted changes-when viewed in theperspective of current trends in human and animalpopulation growth-expansion of crop land, reduction infallow land system, fast-declining ground-water reservedue to over irrigation etc., indicate that agriculture willbe more challenging here, through direct and indirectimpacts on crops, livestock, pests and diseases, andsoils, thereby threatening the food security andlivelihood security of farming communities. Both kharifand rabi crops are likely to be affected by the changes,especially due to changes in temperature regime, butalso due to shifts in rainfall distribution. Highertemperatures and likely faster soil-moisture depletionmay offset the slight gains of rainfall increase in theeastern fringe of arid Rajasthan and adjoining semi-arid tract.

Rainfed crops are grown during rabi with higherinput cost to farmers, and since the crops are moretemperature-sensitive that the common kharif crops, thenegative impact on farmers' economy would possibly

Fig 1. Climatic characterizations of differnet environments (Temperature in 0C)

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be higher during rabi. A recent simulation study hasshown that a rise in temperature by 1 0C can led to adecline in wheat production by ~250 kg /ha in Rajasthanand by 400 kg/ha in Haryana. In Indian mustard thedecline was ~100 kg/ha/degree rise in temperature inRajasthan, and by 200 kg/ha in Haryana, whereas inchickpea (gram) the decline was ~200 kg/ha inRajasthan and 500 kg/ha in Haryana. There areconcomitant declines in biomass yield also. Thedeclines tended to taper off eastward in Uttar Pradesh(Kalra et al. 2008). Such a scenario for crop yieldswarrants careful crop planning and management, aswell as development of suitable varieties. Sinha,Swaminathan (1991), showed that an increase of 2 0Cin temperature could decrease rice yield by about 0.75tones/ha in the high yield areas and a 0.5 0C increasein winter temperature would reduce wheat yield by 0.45tones/ha. Agrawal et al. (2000) have shown that innorthern India rice yields during last three decades areshowing a decline trends and this is possibly related toincreasing temperatures. Similar trends have also beennoticed in the Philippines (Peng et al. 2004) yield declinehave also been reported in rice and mustard due totemperature increase in West Bengal. Another

uncertainty in crop yields is likely to be introduced bythe change in CO2 concentration. Usually a higherconcentration of CO2 leads to an increase in biomassyield due to better performance by the crop growthregulators and faster maturity, but adequate studies arelacking on the interrelationship between changes inrainfall, temperature and CO2 concentration to predictthe kind of yield changes expected. With increase inboth rainfall and temperature, it is necessary to ascertainwhether the impact of increased rainfall and CO2 willoverwhelm the impact of increased temperature andhence evapo-transpiration over specific regions forspecific crops.

Climatic Characterization of Madhya Pradesh

Changes in temperature and precipitation patternstogether with occurrence of extreme events are majorthreat to future food security due to climate change.After collection of long term data for more locations ofMadhya Pradesh, climatic characterization was donefor long-term trends and occurrence of extreme eventsfor precipitation and temperature in relation to crop

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growth phases as climate change related occurrenceof these extreme events can have seriousconsequences for agricultural production. A comparisonof these trends for different places reveals importanttrends that can be useful for making future changes incropping panems. For example, long term trends inweekly temperatures for Jabalpur and Gwalior reveal avery different trend; whereas in Gwalior there is acooling trend for most part of crop growth period butJabalpur has a warming trend during kharif and coolingtrends during rabi season. These places also varyremarkably in terms of occurrence of extreme events ofprecipitation and temperature (Fig 1). Based on thecharacteristics rainfall patterns, the crop planning of aparticular region can be made. Agrawal and Singh(2012) based on analysis of 31 years (1978-2008) dailyrainfall data of Jabalpur (MP) strategic & the cropplanning for differential behavior of monsoon. Further,Agrawal et al. (2008) comprehensively discussed theimpact of climate change on rainfed agriculture ofMadhya Pradesh which is predominate a rainfed state.

Climatic variability at different Places of MP

The trend analysis of different climatic variables suchas maximum temperature, minimum temperature andrainfall were carried out to assess the temporal; andspatial variation in the climate in three different altitudesof Pachmarhi Biosphere Reserve namely Pachrnarhi(Top), Chhindwara (middle) and Powerkheda (bottom).Remarkably significant variations were found in thetrend of maximum temperature, in kharif, rabi andsummer in these altitudes. Hence, such temporal andspatial variability behavior of these climatic variablesare going to influence the existing biodiversity in thesealtitudes which may be in terms of shift in the vegetationor in affecting the accumulation of active ingredients ofsome economically useful plants by differential geneexpression due to genotype and environmentinteraction, which needs further validation (Fig 2A, 2B& 2C).

Variability in Rainy days

Model predictions of future scenarios indicate that therewill be an overall decrease in number of rainy days(NRD) over a major part of country. The decline will begreatest i.e. more than 15 days in the western andcentral parts of the country. Looking to above, numberof rainy days of different district of Madhya Pradeshhas been analyzed.

• The analysis of data indicates that in Chhindwaradistrict number of rainy days were decreasingduring month of July and August, whereas shownincreasing trends in June and September monthof monsoon season. In winter season Octoberand January month indicated decreasing trendwhile increasing in October and Decembermonth.

• In Indore district numbers of rainy days weredecreasing in June and August where as wereincreasing in July and September month of rainyseason. During winter season trend wasdecreasing all most all the month exceptNovember.

• Gwalior shows the decreasing trends in the monthof July and August but in June and Septembertrend was increasing during S-W monsoonseason. The trends were decreasing in all themonths except November in winter month.

• Trend of rainy days during monsoon months inJabalpur district indicated decreasing trendduring June and July where as shows increasingtrend in August & September months. In winterseason numbers of rainy days were decreasingin all most all the months except October.

• Trend analysis shows that in Morena district ofMadhya Pradesh number of rainy days wereincreasing in all the monsoon months where asit was decreasing all most all the months exceptDecember during winter.

• Analysis of rainfall data shows that Chhatarpur& Tikamgarh received around 50 percent timebelow normal rainfall, while 14 and 43 percenttime below NRD, Respectively. Whereas,Jabalpur received 56 percent time below normalRF and NRD.

Block (micro) level analysis

• Block level rainfall of Chhindwara district showthat out of 11 blocks, 08 blocks shown decreasingtrend of rainfall and 03 shown increasing trend.Trend of rainy day was also in the same order ofrainfall.

• Season wise rainfall analysis of Chhindwaradistrict at block levels indicated decreasing trendsof rainfall and rainy days during Rabi season.

7

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• Analysis of rainfall and rainy days at block levelof Hoshangabad district of MP out of 05 blocks03 show increasing rainfall and rainy days trends.During Rabi season rainfall and rainy days wasshown decreasing trend in all the blocks ofHoshangabad district.

• Trends of rainy days in f ive district i.e.Chhindwara, Indore, Gwalior, Jabalpur andmorena district not shown any definite in June,July and August where as the trend wasincreasing in September month in all the fivedistrict.

Variability in temperature

Trend analysis of temperatures of five districts ofMadhya Pradesh viz; Gwalior, Morena, Powarkheda(Hoshangabad), Jabalpur and Chhindwara was doneto study of impact of climate change. For the analysistotal period had been divided in different part as peravailability of data.

• To study the temperatures variation in Gwalior totalperiod was divide in two part i.e. from year 1980-1991 and 1992-2005 and depicted in figure 40-43 Trend analysis indicated that both maximumand minimum temperature was shown significantdecreasing trend in both the season and period,except in during 1980-1991 where both thetemperature indicated increased trend.

• For analysis of temperature of Morena period hadbeen divided in two phase (1981 to 1993 and1994-2007) and two season i.e. kharif & Rabianalysis indicated that in both the period andseason maximum and minimum shownsignificantly increasing trend except minimum

temperature in Rabi season during the period1981 to 1993 where the trend was decreasing.

• Temperature of Pawar kheda had been divided intwo phase i.e. from 1983 to 1993 and 1994 to2005. Trend analysis reveals the decreasingtrend in both the season and period except during1994, 2005 where maximum temperature showsincreasing trend in Rabi season.

• In Jabalpur district maximum temperature shownsignificant increasing trend I during 1968 to 1989in both the season where as minimum showndecreasing trend. Similarly trend of minimumtemperature was also significantly increasingduring 1990-1999 in both the crop season.

• Chhindwara district shows decreasing trend inmaximum temperature during Rabi season while,it was increasing in Kharif season during 1975-1989. Whereas the trend of minimumtemperature was opposite of maximum during thesame period. In the second phase maximumtemperature of Chhindwara indicated that trendwas decreasing in both the crop season.

• Decadal temperature (s) variation in four district,Jabalpur, Indore, Gwalior and Chhindwada wasanalyzed. The above analysis indicatedtemperature variability has a decadal trend atJabalpur and Indore during the winter season,which may be consider in the crop management.

Trend analysis for crop productivity

Soybean

Trend analysis for major crops of the state was done inrelation to temperature and precipitation. The

Fig 3. Relationship between normalized yield and rainfall deviation in soybean at Chhindwara

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normalized yields were compared with normalizedrainfall and temperature to assess the sensitivity of cropproductivity with changes in these important weathervariables. In soybean for example, productivity atJabalpur is negatively associated with increasing rainfallwhereas at Indore there is an increase in yield withincreased precipitation. The relationship betweennormalized yield and rainfall deviation in soybean atChhindwara is shown in Fig 3.

Rainfall is the most important factor whichadversely affect the soil moisture stress and reduce thesoybean yield (Agrawal et al. 2009).

Wheat

Wheat is most important food grain crop of MadhyaPradesh grown during winter season. Total area of wheatin MP is 4045 thousand hectares with the totalproduction of 7237 thousand tones with averageproductivity of 1800 kg/ha. The productivity is belowthan its potential yield. Among different abiotic stressesnutritional, water and temperature stress are commonin rainfed agro ecosystem. High temperature duringreproductive phase of different winter crops in the majorfactor influencing the yield of rabi crops. The presentstudy was carried out to understand the effect oftemperature in wheat and mustard in different districtof state. Wheat being a important cereal crop of thestate, analysis of wheat productivity in relation totemperature was done in four district of MadhyaPradesh, analysis was done on the basis of long termavailable data of temperature and crop productivity. Thelong term analysis of crop productivity revealed the

increasing trends. Increase in yield of wheat in theJabalpur, Hoshangabad and Gwalior was significantwhile it was non-significant in Chhindwara. Normalizedyield of wheat at Gwalior and Chhindwara shows nochange in crop productivity. Whereas the trend was indecreasing order in Jabalpur and Hoshangabad.Relationship between normal temperature (maximum& minimum) and normal yield of wheat crop indicatedthat yield of wheat crop decreases with increase intemperature in different district except Chhindwara.

Climate change will have direct impact on theagricultural productivity because of alterations intemperature and rainfall. Long term trend analysis ofcrop productivity revealed that vulnerability of wheatproductivity due to increasing maximum temperature islocation specific and cannot be generalized as a whole.The declining wheat productivity in certain environmentsis attributed to the superimposition of criticalreproductive phase with the increased highertemperature stress and interference with the minimumvemalization effect which is a sine-quo-non for the floralbud initiation. Similarly, it was found that there is a directrelationship between the rainfall (winter) and yield ofwheat. However, impact of minimum temperature on theproductivity found to be negligible (Fig 4A, 4B, 4C &4D).

Chickpea

Chickpea crop is an important grain legume crop of Indiaand state of Madhya Pradesh is a major contributor tothe national chickpea productivity. Growth, phenologyand seed yield of chickpea is mainly controlled by

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temperature, day length and water availability. Thereproductive phase of the crop coincides with the risingtemperature often exposing the crop to sub-optimalthermal regimes. In climate change scenario, conditionswill become much more adverse due to abiotic factorlike rise temperature, occurrence of drought, changesin crop growing season climate and perhaps, increasein biotic stresses. Improved and novel agronomic cropproduction practices like adjustment of planting datesto minimize the effect of high temperature increase-induced spikelets sterility can be used to reduced yieldinstability, by avoiding flowering to coincide with thehottest field. Agro-meteorological research on crop-weather relation can help to improve the understandingof climate related constraints in achieving the potentialproductivity. The seed and biological yields was higher(20.56 and 50.00 q/ha) with November sown crop withDesi chickpea types. Lower seed and biological yieldwas recorded in January sown crop with Desi type. Yieldreduction was noted in all the chickpea types as cropwas planted earlier or later than November (Agrawal etal. 2002, 2010).

Trend analysis of Mustard Crop in Relation toTemperature

Long term trend analysis of crop productivity of mustardshows the increasing trend in Gwalior and Morenadistrict of MP. Analysis of normalized yield of mustardin two districts indicating the decreasing trend. Graphbetween normal yield and mean temperature of Gwaliorshows the decreasing order whereas Morena showsthe increasing trend.

Strategies to Mitigate Climate Change

• Development and adoption of water conserving/moisture retenting, soil erosion preventing, weeddepressing, energy efficient tillage practices.

• Biotechnological advances and breeding forclimate hardy, heat tolerant, drought resistant,shattering resistant varieties of crops that aretolerant to frequent and abrupt temperaturefluctuations and can sustain its yield potential tillmaturity.

• Improvement in nutrient use efficiency andfertilizer use efficiency of crops and developmentof composite fertilizers that are slower in leaching,as cost of product and labour is set to increasein times to come.

• Alerting local farmers and cultivators by meansof mass media and short message service (SMS)regarding new and improved cultivation methods/ practices.

• Establishment of local forewarning systems forweather and insect pest attack.

• Further strengthening up of agro-meteorologicalservices at local, regional and global level apartfrom publicizing agromet advisory service to thelast man.

• Protection, conservation, preservation,multiplication and propagation of local andregional biodiversity.

• Adoption of technologies to reduce green houseemissions at all levels and at all farm levels.

• Generation and adoption of technologies to utilizeenergy effectively, efficiently, profitably andsustainably.

• Commercial household and farm use of alternateenergy generation mechanisms like wind, solar,bioenergy alternatives etc.

• Treatment of un-cultivatable land and naturallyremediating; sodic, saline, acidic and alkalinesoils, reclaiming desert, barren and bringing themunder some kind of vegetation to mitigate effectof climate change.

• Prevention of ecological degradation and earlyrehabilitation of degraded lands.

• Development of location specific and need basedagro-forestry models, agri-horticultural systems,agri-silvicultural systems intercropping systems,alternate land use systems etc. All these shouldbe promoted and governmental subsidy shouldcontinue on them.

• Minimization of use of chemical, synthetic anddamage causing pesticides should be done; useof biosafe, ecosafe natural pesticides should bepromoted.

• Development and adoption of low resource inputeco-safe agriculture and agricultural practices

• Massive plantations in and around villages andcities and in forests should be done withrestriction to animal grazing at initial stage ofplantation.

• Green and clean development mechanism shouldbe adopted at all levels.

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• Small efforts at individual level should be donethis will further help in reducing the ill effects ofclimate change before irreversible damage isdone to our climate.

• Mass dissemination of information thoughinformation sharing, community education,knowledge sharing and integration should bedone with respect to climate change.

• Management of livestock, poultry, piggery,compost, night soils, manure and wastemanagements to reduce CH4, NO2, NO3 and SO2emissions.

• Promoting insect, disease and weed surveillanceand monitoring practices through remote sensingand GIS and interlinking it with climatic variablesand agro-meteorological data so that prophylacticprotection measures are taken to avoid damageto crops

• Energy-efficient less polluting environmentallysafe farm machinery should be produced toreduce emissions.

Research and development priorities

• To develop crops varieties that can perform wellunder increased temperature and change inrainfall pattern.

• Developed suitable agronomic practices,especially for pest and weed management.

• Development of improved methods for resourceconservation technologies under changingenvironment.

• Identification of suitable intercropping andfarming system modules.

• Development of efficient tree based farmingsystems to mitigate the impact of climate changeand improving carbon sequestration potentials.

• Development of contingent crop plan for adverseweather situations.

• Stakeholders consultation, trainings, workshop,and demonstration for farming communities toshare and disseminate agro-climatic zonespecific information.

• Preparation of database on climate change and

impact on agriculture with particular referencedagro climatic zone of Madhya Pradesh.

It is practically self-evident that deliberate effortsneed to be made to cope up changing climate, to sus-tain life for coming generations apart from increasingand sustaining agricultural yields. An integrated inter-disciplinary approach to reduce the impact of adverseclimate change is more likely to give desirable resultsfor long terms sustainable crop yield. It is high time totake prophylactic, preventive and adaptive measuresto avert this climate change phenomena otherwise ifwe do not still wake up agricultural productivity andwhole mankind will have to face serious repercussions.In fact predominant feature of the State is more than 60percent area is under and this often becomes a majorconstraints as far as climate change is concern.However, the environmental domain is not particularlyharsh and has a high productivity if the resources arewell managed for crop production. The constraints incrop production are sub-optimal soil moisture regimeswhich, in every agro-climatic zone affects the cropproductivity. The success of rainfed farming almostentirely depends on the south-west monsoon rainfall,which itself is very erratic and unevenly distributed-overthe crop growing-season. The, rainfall ranges from 800mm in the western part of the state to 1500 mm in theeast, which shows that its total value decreases fromsouth to north, and east to west. Quantum of rainfall,throughout the state should be sufficient to meet theentire water requirement in crop production. However,it requires better rainwater management strategies tomake best use of this important natural resource. Inorder to maintain food security in view of climate changescenario, a holistic approach that includes stresstolerant germplasm, sustainable crop and naturalresources management and sound policy interventionswill be needed. The crop growing season is limited bywater availability, as temperatures are generallyfavorable for crop growth except for a limited periodduring summer. In a nut shell, the effect of climate onagriculture is related to variabilities in local climate ratherthan in global climate patterns which can be mitigatedby adapting suitable agriculture practices as copingstrategies.

References

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Agrawal KK, Bhadauria UPS, Amit Jha, Sanjay Jain (2010)Crop weather relationship studies on chickpea forimproving crop adaption to climate change IJTA 28(1-2) : 239-242

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Agrawal KK, Upadhyaya AP, Sanjay Jain Bhadauria UPS(2009) Assessing the climate based productivitypotential of soybean in Madhya Pradesh. J Agromet11(2) : 132-134

Agrawal KK, Upadhyaya AP, Shankar U, Gupta VK (2002)Photothermal effects on growth, development andyield of gram (Cicer arietinum) genotypes. Ind J AgrilSci 72 (3) : 169-170

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Asha Latha KV, Muni Samy, Gopinath Bhatt APS (2012)Impact of climate change on rainfed agriculture inIndia : A case study of Dharwad. Int J Env Sci & Dev3(4) : 368-371

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IPCC (2007b) Assessing vulenerabilities and risk from climatechange, ML Canziani, OF Palutikof, JP Vanderlinden,PJ, Hanson CE(Eds.). In climate change. Impact,adaptation and vulnerability contribution of workinggroup II to the Fourth Assessment report of InterGovernmental Panel on Climate change Cambridgeuniversity press, Schneider SH, Seminor S;Patwardhan A; Burton, I, CHD Oppenheimes, MPittock, AB Rahman A, Smith JB, Surarez A, YaminF (2007) Cambridge at New York pp 779-810

Kalra N, Chakraborty D, Sharma A, Rai HK, Jolly M, ChanderS, Ramesh Kumar P, Bhadraray S, Barman D, MittalRB, Mohan Lal and Sehgal M (2008) Effect of

increasing temperature on yield of some winter cropsin north west. India Curr Sci 94:82-88

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Keeling, C. D., Whorf, T. P., Wahlen, M., M. and Van derPlicht, J. (1995). Interannual extremes in the rate ofrise of atmospheric carbon dioxide since 1980',Nature 375, 666-670.

Lal M, Nozawa T, Emori S, Harasawa H, Takahashi K, KimotoM, Abe-Ouchi A, Nakajima T, Takemura T, NumagutiA (2001) Future Climate Change: Implications forIndian Summer Monsoon and its variability. Curr Sci81: 1196-1207

Mall R, Singh Ranjeet, Gupta Akhilesh, Srinivasan G, RathoreL (2006) Impact of Climate Change on IndianAgriculture: A Review. Climatic Change 78:445-478

Mujumdar PP (2008) Implications of climate change forsustainable water resources management in IndiaPhysics and Chemistry of the Earth, Parts A/B/C33:354-358

Pant GB, Hingane LS (1988) climate change in and aroundthe Rajasthan desert during the 20th century, Jour.of Climatology 8: 391-401

Parry MC, Rosenzweing A, Inglesias G, Fisher livermarc M(1999) Climate change and world food security. Anew assessment, Global Envir. Change 9 : 551-567

Peng S, Huang J, Sheeby JE, Laza RC, Visperas RM, ZhongX, Centeno CS, Khush GS, Cassman KG (2004) Riceyield declines with higher night temperature fromglobal warming. In. Proc. National Acad on Sci 101(27) : 9971-9975

Rao AS (1996) Climatic changes in the irrigated tracts of IndiraGandhi Canal Region of arid western Rajasthan,India Annals of Arid Zone 38(2): 111-116

Rupakumar K, Sahai AK, Krishna Kumar K, Patwardhan SK,Mishra PK, Revadekar JV, Kamala K, Pant GB(2006) High- resolution change scenarios for Indiafor the 21st century. Curr Sci 90 : 334-345

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Singh AK, Venkateswarlu B (2009) Climate Change andRainfed Agriculture. Indian J Dryland Agric Res &Dev 24 (2): 1-9

Sinha SK, Varshneya M C (2009) Mitigation option for climatechange. Ind J Agro 54 (2) :231-236

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(Manuscript Receivd : 15.10.2013; Accepted 25.2.2014)

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Abstract

Nutrient management is one of the most important need forsustainable agriculture. Due to imbalance use of chemicalfertilizers it resulted in deterioration in soil health,environmentalpollution,deficiency of micro nutrients and destruction ofgrowth of micro-organisms. As the fertilizer consumption isincreasing day by day it resulted in nutrient mining,deficiencyof secondary and micro-nutrient,low nutrient useefficiency,decline in soil organic carbon etc. To maintain andenhance the availability of fertilizers the 4R strategies rightsource, right time, right place and right rate must beapplied.Emerging trends in nutrient management forsustainable agriculture are balance fertilizer application tocrops,real time nutrient management, fertigation, integratedplant nutrient management, site specific nutrient managementand soil test crop response are the major trends which arestudied in brief in this review. Some important instrumentslike LCC (Leaf color chart), Spad meter, chlorophyll meter,nutrient expert, nutrient manager, GIS and GPS based fertilitymaps are used.

Keyword: Nutrient mining, sustainable agriculture, slowrelease fertilizer, fortified fertilizer

Nutrient management is a system used by farmers tomanage the amount, form, placement and timing ofapplication of nutrients to plants. The purpose is tosupply plant nutrients for optimum forage and cropyields, to minimize pollution and contamination ofgroundwater and to maintain and improve the conditionof soil. Nutrient management is needed due toimbalance use of chemical fertilizers, deterioration ofsoil health, environmental pollution, deficiency ofsecondary and micro nutrients and destruction of growthof micro-organisms.

Emerging trends of nutrient management for sustainableagriculture in India - component and tools

Megha Dubey, K.K.Agrawal and Suchi GangwarDepartment of AgronomyJawaharlal Nehru Krishi Vishwa VidhyalayaJabalpur 482004 (MP)

Fertilizer use scenario

India is the second largest consumer, third largestproducer and the largest importer of fertilizers in theworld. Approximately 45 million tonns of nutrients NPKwill be required to produce 300 mt of food grains tofeed 1.4 billion people by 2025.The N:P:K ratio is6.5:2.9:1 in 2011-12 as reported in Indian Journal ofFertilizers 2012. Total fertilizer consumption is 58 milliontonns and total nutrient consumption is 28.1 milliontonns. Urea accounted for 78% of the total N, DAP (62%)and (8%) by SSP fertilizer consumption in 2010-11.Annual consumption of fertilizers in terms of N, P and Khas increased from 0.07 to 28 mt from 1951-52 to 2010-

Table 1. Trends in growth of fertilizer consumption since1950-55 to 2011-12

Year N P2O5 K2O Total kgha-1

Million tones

1950-55 0.055 0.009 0.006 0.070 0.5

1965-66 0.57 0.13 0.077 0.78 5.1

1980-81 3.7 1.2 0.6 5.5 32.0

1990-91 8.0 3.2 1.3 12.5 67.6

1992-93 8.4 2.8 0.9 12.1 65.5

1999-00 11.6 4.8 1.7 18.1 95.9

2000-01 10.9 4.2 1.6 16.6 90.1

2003-04 11.1 4.1 1.6 16.8 88.3

2008-09 15.1 6.5 3.3 24.9 128.6

2010-11 16.6 8.1 3.5 28.1 144.1

2011-12(Est.) 17.3 7.6 2.7 27.6 141.5

JNKVV Res J 48(1): 14-21 (2014)

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11. The per hectare consumption has gone up to 142kg. Presently, India is the 3rd largest producer and 2ndlargest consumer of fertilizer in the world. State likePunjab, Haryana, AP and Tamil Nadu shows highestwhereas Rajasthan, Assam and Orissa show lowestfertilizer consumption pattern.

Nutrient Management issues with respect to IndianAgriculture

Nutrient mining

The continuous mining of nutrients from soils coupledwith inadequate and imbalanced fertilizer use hasresulted in emergence of multinutrient deficiencies.Thedeficiencies of at least six nutrients (N, P, K, S, Zn andB) are quite widespread in Indian soils.The increasingdeficiencies of secondary and micronutrients havestarted limiting the response to primary nutrients(NPK).The problem of secondary and micronutrients ismore acute under high productive intensively cultivatedareas. The 89% deficiency of nitrogen is obtained insamples as given in (Table 2) reported by (Tewatia 2007).

Low and imbalance use of fertilizer, low nutrientuse eff iciency, use of high analysis fertilizers,unsatisfactory soil testing facilities, availability offertilizers to farmers, soil health and crop productivity,decline in soil organic carbon, fertilizer use andenvironment, nutrient management strategies.

The 4 R - the cornerstones of best managementpractices

Right Source

An example is given in Table 3 to explain the effect ofright source of nutrients on pearl millet.

Right Time

At proper stage of plants

Right Place

Depending on the plant split application and deepplacement of fertilizer is decided

Right Rate

Depending upon the soil test values the dose of fertilizerand organic manures is decided.

As the uptake of N and P increases then the yield ofpearl millet also increases as shown in table 4 andreported by (Gautam 1988).

Emerging Trends in Nutrient Management forSustainable Agriculture

1. Balanced fertilizer application to crops

2. Real time nutrient management by leaf colour chart

3. Fertigation

4. IPNS (Integrated Plant Nutrient Management System)

5. Site Specific Nutrient Management (SSNM)

6. Soil Test Crop Response (STCR)

Balanced fertilizer application to crops (right type, rightdose, right time, right method of fertilizer application)Right from the beginning efforts were made to improvethe balanced use of fertilizers. Most of the governmentprogrammes aimed at increasing fertilizer consumption

Table 2. The extent of nutrient deficiencies in Indiansoils

Nutrient % deficiency in samples

Nitrogen 89Phosphorus 80Potassium 50Sulphur 40Zinc 48Boron 33Iron 12Manganese 5

Increasing secondary and micro nutrient deficiencies

Out of secondary nutrients sulphur deficiency constitute46% in Indian soils. The percent increase in yield withapplication of sulphur varies from 14.36-55.35% incereals, 19.85-67.65% in pulses and 27.78-60.24% inoilseed crops.It is recorded that sulphur deficiency ismaximum in M.P. as given in Table 3 and reported byTewatia (2006).

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Table 3. Extent of sulphur deficiency in major regions/states

Region/state No. of samples No. of samples in % of samples incategory category

low medium high low medium highNorthern region 15323 6742 4615 3966 44 30 26

Uttar Pradesh 6250 3063 2375 812 49 38 13

Uttarakhand 1558 660 633 265 42 41 17

Haryana 1515 575 540 400 38 36 26

Punjab 3750 561 715 2474 15 19 66

Himachal Pradesh 2250 1883 352 15 84 16 0

Western region 12474 5591 3719 3164 45 30 25

Madhya Pradesh 2000 660 1100 240 33 55 12

Chhattisgarh 1492 343 567 582 23 38 39

Gujarat 3016 995 875 1146 33 29 38

Maharashtra 1045 408 282 355 39 27 34

Rajasthan 4921 3185 895 841 65 18 17

Eastern region 10108 3549 3301 3260 35 33 32

Bihar 600 156 180 264 26 30 44

Odisha 2261 469 552 1240 21 24 55

Jharkhand 809 413 251 146 51 31 18

West Bengal 6438 2511 2318 1610 39 36 25

Southern region 11289 7112 2976 1201 63 26 11

Andhra Pradesh 1880 1053 639 188 56 34 10

Karnataka 1703 732 545 426 43 32 25

Tamil Nadu 1716 446 704 566 26 41 33

Kerala 5990 4881 1088 21 81 18 1

All India 49194 22993 14610 11591 46 30 24

Table 4. Effect of Nutrient Management on yield and nutrient uptake of pearl millet

Treatment Grain yield Total N uptake Total P uptake(q/ha) (kg/ha) (kg/ha)

Control 21.3 65.2 5.860 kg N +40 kg P2O5 / ha (RDF) 32.1 100.1 9.830 kg N +20 kg P2O5 / ha (1/2 RDF) 25.9 77.4 7.130 kg N +20 kg P2O5 / ha + Azospirillum 29.7 87.5 8.330 kg N +20 kg P2O5 / ha + PSB 28.4 80.6 8.130 kg N +20 kg P2O5 / ha + Azospirillum+ PSB 31.7 97.2 9.5CD at 5 % 1.40 12.49 1.36

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in a balanced manner using 4:2:1 NPK consumptionratio as guideline. The NPK consumption ratio in Indiareached to 6.5:2.9:1 in 2011-12. Due to imbalance useof fertilizers the multi nutrient deficiency occur in soil.The low efficiency of fertilizer use has always been amatter of great concern. Nitrogen use efficiency in waterlogged rice is only 30-35 % whereas 40-50% in uplandcrops. Phosphorus use efficiency never exceeds 20 %for potassium is 65-70%, micro nutrients 2-4%.

Customized fertilizers : (area and crop specific)

Realizing the importance of role to be played insustaining soil health, encourage balanced fertilizationand improve crop productivity, Government of Indianotified a scheme in the form of guidelines for productionof area and crop specific customized fertilizers underclause 20 B of Fertilizer Control Order, 1985. Nutrientrequirement of the crop in a particular area is mixedphysically and steam granulated by using technologyknown as fusion blending. It ensures the product qualityas the emission norms are within permitted levels. Thefarmers get all the required nutrient in terms of NPKwith secondary and micro nutrients suitable for the soiland crop. Customized fertilizer is defined as multi-nutrients carriers designed to contain macro and / micronutrients forms both from inorganic and/or organicsources, manufactured through a systematic processof granulation, satisfying the crop nutritional needsspecific to its site, soil and stage, validated by a scientificcrop model capability developed by an accreditedfertilizer manufacturing/marketing company. The mainaim of customized fertilizers is to promote site-specificnutrient in order to achieve the maximum fertilizer useefficiency of applied nutrient in a cost effective manner.It is a key component of SSNM and precision agriculturewhich promote maximum fertilizer use efficiency ofapplied nutrient in a cost effective manner.It is a soil-crop-climate based fertilizer take care of all the nutrientsand supply these nutrient in a right proportion.Itincreases crop yield and quality and economic returns.Improves soil health as it is site and crop specificfertilizers.

Slow release fertilizers

These fertilizers are labour and time saving becausefertilizers need to be applied only once. Increasednutrient recovery resulting in a higher return oninvestment for applying fertilizers. Improve yield andreduced negative environmental impact exampleSulphur coated urea andU rea super granule.

Controlled release of fertilizers (crop and climatespecific)

Where release rates are predictable. Consumption ofcontrolled release fertilizers was estimated about 1 mtin 2005. This type of fertilizers use sophisticated polymercoating technologies to carefully manipulate nutrientrelease characteristics to meet the specific nutrientuptake needs of specific crop and the particular climaticconditions.

Fertilisers with nitrification and urease inhibitors

Nitrification inhibitors are compounds that delaybacterial oxidation of the ammonium ion and nitrite bydepressing over a certain period of time the activitiesof Nitrosomonas and Nitrobacter bacteria in thesoil.These bacteria are used to control leaching,denitrification and to increase the efficiency of nitrogenapplied fertilizer., ex. neem coated urea.Urease inhibitorprevent or depress over a certain period of time thetransformation of amide-N in urea to ammoniumhydroxide and ammonium.They slow down the rate atwhich urea hydrolyzes in soil thus avoid volatilizationlosses of ammonia to the air.

Fortified fertilizer

All products covered in subsidy scheme are eligible tobe fortified/coated up to 20 per cent of their production.Eleven zinc and boron based fortified fertilizers havebeen included in FCO 1985. IFFCO has put up a plantfor production of zinc sulphate mono hydrate 33% zincat its Kandla plant with an annual capacity of 30,000tonnes.Presently,use of micronutrient in the country isabout 0.18 mt. Examples Boronated SSP, Zincatedurea, NPK Complex fortified with boron (10:26:26:0.3and 12:32:16:0.3), DAP fortif ied with boron(18:46:0:0.3), NPK Complex fortif ied with zinc(10:26:26:0.5 and 12:32:16:0.5), DAP fortified with zinc(18:46:0:0.5), Nitrophosphate with potash fortified boron(15:15:15:0.2).

100% water soluble complex fertilizers

At present 16 grades of 100% water soluble fertilizershave been notified in the FCO (1985) and are completelysoluble in water as the name denoted.These fertilizersare applied by drip irrigation or foliar application.About80,000 tonnes of such fertilizers are used in the country.

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Example are Urea phosphate (17:44:0) and Potassiumnitrate (13:0:45).

Real time nutrient management by leaf colour chart inrice

What is LCC

LCC stands for leaf colour chart. As we know thatnitrogen requirement for rice plant is not the samethroughout the growth period. Application of nitrogennot synchronizing with the demand of the plant oftenleads to increase N losses. The optimum use of N comesfrom matching supply with crop demand. Thus,application per demand of rice plant is called "crop needbased N management". The LCC is an innovative costeffective tool for real time or crop need based Nmanagement in rice, maize and wheat.LCC is a visualand subjective indicator of plant nitrogen deficiency andis in expensive, easy to use and simple alternative tochlorophyll meter/spade meter (Joshi et al. 2013).

Procedure to take reading by LCC

Take reading in the morning (8-10 a.m) or in the afternoon(2-4 p.m) preferably by the same person from randomlyselected fully expanded new leaves. Under the shade,measure the color of each leaf by holding the LCC andplacing the middle part of the leaf on the top of the colorstripe for comparison. If the color of the leaf falls betweenthe two shades, then take mean of the two values. Takereading at an interval of 7-10 days. Generally critical

value for semi dwarf high yielding varieties is 4.0. If theaverage value fall below 4.0, top dress N fertilizer (20-30 kg/ha) to correct N deficiency. LCC is also suitablefor maize and wheat providing farmers with a gooddiagnostic tool for detecting N deficiency. The LCC isrelevant to be use for Sugarcane, Potato, Cotton,Cassava, etc. are under Research and Development inorder to maximize the yield of these crops.

Four panel leaf colour chart

Six panel leaf colour chart

Research study on Leaf color chart in Rice

Tamil Nadu Agricultural University reported a saving of20-40kg N/ha (44 kg - 88 kg urea/ha) by using LeafColor Chart. Rice grain yield increase, because ofSSNM using LCC ranged from 0.34 to 1.07 tonns/hawith an average of 0.75 tonns/ha. Farmers realizedadditional profits of Rs 2250-6300 /ha per season.(TamilNadu Rice Research Institute (SSNM)Pamphlet 2008).

LCC an inexpensive alternative tool to SPAD METER

The SPAD - 502 Plus enables quick, easy measurementof the chlorophyll content of plant leaves withoutdamaging the leaf. Chlorophyll content is an indicatorof plant health and can be used to optimize the timingand quantity of applying additional fertilizer to providelarger crop yields of higher quality with lowerenvironmental load.

Difference between SPAD 502 and Field Scout CM 1000 chlorophyll meter

Spad 502 Field scout (CM 1000 ®)

Its reading is not affected by sunlight so reading can It is dependent on time of day, sunlight and sun angle.be taken in different weather condition.

It measures only one spot of one leaf on each It is easy and fast to use and give reliable readingsmeasurement.

Many measurements are taken to get reliable average. Measurements are made in standing or walking posi-tion by pull of trigger

It is time consuming and laborious It measure a circular area of 13 to 35 sq. inch (3-5 feet)so canopy measurement is taken rather than single leaf.

Best time to use is 10 am to 2 pm. Reading must betaken at 900 to the canopy surface

19

Chlorophyll meter

There are at two types of handheld chlorophyll meters

SPAD 502 - uses thumb pressure to close a chamberand measure light transmittance/absorbance (T/A) todetermine chlorophyll content.

Field Scout CM 1000® - ambient and reflected light isused to calculate the relative chlorophyll index with Rchlorophyll meter.

Fertigation

Achieves nutrient economy as the nutrients are suppliedto restrict wetted area of the field. Improve fertilizer andwater use efficiency, reduce weed growth. Improvequality and quantity of crop yield. Advantages ofFertigation are it helps in uniform application of fertilizernutrient, nutrient placement in the root zone will improveavailability of plant nutrient and there uptake, frequentapplication is possible, it provide an opportunity to applyfertilizers more frequently as per the stage of cropgrowth and need which ensures a balance nutrientsupply at all stages of crop growth, it is quick andconvenient method which saves cost, time, labour,equipment and energy, save fertilizers and improvefertilizer use efficiency by about 30 to 40 % and increasecrop yield up to 25%.It minimize wetting of soil andhence reduces soil compaction which occur duringcultural operations. Drip fertigation ensures supply ofboth nutrients and water in controlled and balancedmanner (Table 5).

On an average drip fertigated sugarcane fields hadregistered higher cane yield of 205t/ha which is about2.4 times more than Maharastra state average 84t/ha(Subrahmanyam et al. 2012).

Integrated Plant Nutrient Management System

Involve the combined use of fertilizers, organic manures,bio fertilizers and crop residue. IPNS is an approach,which adapts plant nutrition to specific farming systemsand particular yield targets, with consideration of theresource base, available plant nutrient source, and thesocioeconomic background. Further, since plantnutrients are transferred in cyclical processes, IPNSinvolves monitoring all pathways of flow of plantnutrients in agricultural production systems to maximizeprofit so that farming as a profession can be sustained,which is the only way to produce food. Thus IPNSdemands a holistic approach to nutrient managementfor crop production and it involves judicious combineduse of fertilizers, biofertilizers, organic manures (FYM,compost, vermicompost, biogas slurry, green manures,crop residues etc.), and growing of legumes in thecropping systems (Prasad, 2008). IPNS alsoencompasses balanced fertilization and SSNM.Considerable research on IPNS has been done in India(Rao et al. 2002; Katyal and Rattan 2003; Gupta et al.2006). Moreover, long-term fertilizer experiments haveshown that addition of organic manures in addition toNPK (add-on series) results in high yields over a longperiod of time as compared to a decline in yield overtime when only inorganic fertilizers were applied(Swarup 2002). Sarkar and Singh (2002) reported thatfor soybean-wheat cropping system in the acidic soilsof Ranchi (pH < 5.4), soybean yield (averaged over 28years) was 0.33 Mg·ha-1 and wheat yield, 0.43 Mg·ha-1

for plots receiving N alone as compared to 1.59 Mg·ha-1

in soybean and 2.65 Mg·ha-1 in wheat when NPK wasapplied. Application of FYM with NPK increased thesoybean yield to 1.86 Mg·ha-1 and that of wheat to 3.19Mg·ha-1. Further, the effects of NPK + FYM were at parwith NPK + lime, implying that in acid soils continuousapplication of FYM can also partially offset soil acidity.

Table 5. Representing the fertilizer saving in vegetables under drip fertigation

Vegetables Water saving over Fertiliser saving over traditionalsurface irrigation (%) system of application

Bulb crops 30-35 30-40

Cole crops 40-45 40-50

Solanaceous vegetables 45-50 40-50

Cucurbits 60-70 60-70

Sweet corn 50-60 50-60

20

Objectives of IPNS

• To reduce the use of inorganic fertilizers

• To restore organic matter in soil

• To enhance nutrient use efficiency

• To maintain soil health

Site specific nutrient management

The dynamic, field specific management of nutrients ina particular cropping season to optimize the supply anddemand of nutrients according to their difference incycling through soil-plant systems. Although growershave begun to adopt and implement precision farmingtechnologies, the profitability of this technology isuncertain in nutrient management. The profitabilitypotential for variable-rate management is significantlyenhanced if the initial means of forming applicationmaps are inexpensive( Khosla 2001). Recent researchin precision farming has focused on site-specificmanagement zones (SSMZ) as a means to generateapplication maps and improve nutrient management incropping systems (Khosla et al. 2002). Site-specificmanagement zones are defined as homogenous sub-regions of a field that have similar yield limiting factors(Khosla and Shaver 2001). These studies haveindicated SSMZ could be an effective alternative to gridsoil sampling for quantifying and managing spatial

variability. However, these studies did not address theeconomics of precision nutrient management onmanagement zones versus traditional uniformmanagement. No data currently exists in the WesternGreat Plains region demonstrating the economicfeasibility of precision farming technology in conjunctionwith nitrogen (N) management. A comprehensive, on-farm, enterprise-based field study is needed to quantifythe economic feasibility of variable-rate N application.The potential profitability of variable rate applicationdepends on isolating areas in the field where additionalinputs will increase revenue on a scale that is greaterthan the added cost, and in isolating areas wherereducing inputs will decrease costs on a scale that isgreater than potential revenue reduction correlated withlower grain yield. Therefore, the economic feasibility ofvariable-rate N application is focused on whetherincreases in gross revenue and/or decreases in N inputcosts outweigh the added cost of technologies and/orservices needed for precision N management(Thikrawala et al. 1999). One of the objectives of ourmulti-disciplinary, multi-agency, multi-location, multi-year, and multi-crop precision agriculture project is toassess the economics of uniform N fertilizer applicationversus variable-rate N fertilizer application and analyzethe variable-rate N applications under both a customand a farmer application scenario. The assessmentincludes various N management strategies (i.e., uniformN application, grid soil sampling based N application,and management zone based N application using avariable and constant yield goal) that are commerciallypracticed in the region.

Table 6. Representing the Soil test based fertilizer recommendation

Crop Treatment Nutrient dose kg/ha Yield kg/haN P2O5 K2O

Wheat STCR (soil test crop response) target 5 t/ha 126 41 49 4887

SR (state recommendation) 120 40 60 4567

FP(farmer's practices) 80 0 57 3662

Pearl millet STCR (soil test crop response) target 5 t/ha 100 43.5 42 2540

SR (state recommendation) 80 40 40 2020

FP( farmer's practices) 46 0 23 1360

Mustard STCR (soil test crop response) target 2.55 t/ha 97 35 75.5 2281

SR (state recommendation 100 40 40 1890

FP( farmer's practices) 60 0 57 1312

21

Tools of Site Specific Nutrient Management are

Nutrient Expert

Nutrient Manager

GIS and GPS based fertility maps

Nutrient Expert- developed for Wheat, Hybrid Maize andRice

It develop an optimal planting density for a location. Itevaluate current nutrient management practices andhelp to determine a meaningful yield goal based onattainable yield. It help us to estimate fertilizer NPK ratesrequired for the selected yield goal. It translate fertilizerNPK rates into fertilizer sources. It develop anapplication strategy for fertilizers (right rate, right source,right location, right time). It compare the expected oractual benefit of current and improved practices

Nutrient Manager

An easy-to-use, interactive computer-based decisiontool developed in 2008 by International Rice ResearchInstitute.This decision tool consists of 10-15 multiplechoice questions that can easily be answered within 15minutes by an extension worker or farmer. In 2010, IRRIdeveloped a computer-based version of NutrientManager for Rice in Philippines, which can be accessedby extension workers and farmers through either theinternet or a mobile phone. The website for releasedinternet applications of Nutrient Manager is:www.irri.org/nmrice.

GIS and GPS based fertility maps

These map help to locate the fertility level of soils froma wide distance. It also help in site specific weedmanagement, planting etc.

References

FAI (2011) Fertiliser Statistics. The Fertilizer Association ofIndia (FAI), New Delhi

Gupta RK, Singh Y, Singh V, Singh, B (2006) Integratednutrient management for improving soil and cropproductivity in rice-wheat crop rotation innorthwestern India. Indian Farming 56(4): 10-14

Hasan M, Singh Balraj, Singh M C, Singh A K, Kaore SU,Tarunendu Sabir N Tomar B (2010) S.Tech. Bull.

No.TB-ICN: IARI, New Delhi-12.pp: 1-44, 80Joshi Ekta, Gautam P, Lal B (2013) Leaf Color Chart:A simple

tool to optimize nitrogen application. Agrobiosnewsletter.11(11):15-16

Khosla R (2001) Zoning in on Precision Ag. Colorado StateUniversity Agronomy Newsletter 21(1):2-4

Khosla R, Alley MM, Griffith WK (2002) Soil-specific nitrogenmanagement on Mid-Atlantic Coastal Plain soils.Better Crops with Plant Food 83(3):6-7

Khosla R, Shaver T (2001) Zoning in on nitrogen needsColorado State University Agronomy Newsletter21(1):24-26

Prasad R (2008) Integrated plant nutrient supply system (IPNS)for sustainable agriculture. Indian J Fert 4(12): 71-90

Rao AS, Chand S, Srivastava S (2002) Opportunities forintegrated plant nutrient supply for crops/croppingsystems in different agro-ecosystems. FertilizerNews 47(12): 75-90

Katyal JC, Rattan RK (2003) Integrated plant nutrition systemsto meet the challenges of nutrient mining threateningsustainable agriculture. FAI Ann. Sem. Papers, 511-1/1/-8

Sarkar AK, Singh RP (2002) Importance of long-term fertilizeruse for sustainable agriculture in Jharkhand. FertilizerNews 47(11): 107-111

Swarup A 2002 Lessons from Long term fertilizer experimentsin improving fertilizer use efficiency and crop yields.Fertilizer News. 47(12): 59-73.

Subrahmanyam SVS, Bhaskaran MN (2012) Efficacy of watersoluble fertilizers through drip irrigation on sugarcaneproductivity. Indian Journal of Fertilizers 8(11):106-

113Tamil Nadu Rice Research Institute (SSNM) Pamphlet 2008Tewatia RK, Choudhary RS, Kalwe S P (2006) In Proceedings

of the TSI-FAI-IFA Workshop on Sulphur in BalancedFertilisation FAI, New Delhi p 158

Tewatia RK (2007) In Fertiliser Best Management Practices,IFA, Paris, France

Thrikawala S, Weersink A, Kachanoski G, Fox G (1999)Economic feasibility of variable-rate technology fornitrogen on corn. American J Economics 81:914-927

(Manuscript Receivd : 25.9.2013; Accepted 15.3.2014)

22

Abstract

Rice is the staple food crop of India, providing 43% of caloricrequirement for more than 70% Indian population. The successof any crop improvement programme depends on nature andmagnitude of genetic variability, heritability, genetic advance,characters association, direct and indirect effects on yield andits attributes and considerable genetic diversity present amongthe genotypes. Correlation and path coefficient analysisstudies provide better understanding of yield components andfurnishes information of influence of each contributing trait toyield directly as well as indirectly and also enables breedersto rank the genetic attributes according to their contribution.The path analysis study revealed that characters viz., numberof grains panicle-1, biological yield plant-1, harvest index andnumber of effective tillers plant-1 contributes maximum to grainyield plant-1. Direct effect of these characters on grain yieldrevealed that they can be utilized efficiently for yieldimprovement.

Keywords: Germplasm characterization, path analy-sis, Oryza sativa L.

Rice (Oryza sativa L.) is the staple food crop of India,providing 43% of caloric requirement for more than 70%Indian population. The success of any crop improvementprogramme depends on nature and magnitude ofgenetic variability, heritability, genetic advance,characters association, direct and indirect effects onyield and its attributing traits of the genptypes. Yield ofpaddy is a complex quantitative character controlledby many genes interacting with the environment and isthe product of many factors called yield components.Selection of parents based on yield alone is oftenmisleading. Hence, the knowledge about relationshipbetween yield and its contributing characters is needed

Path analysis studies in indigenous and exoticgermplasm lines of rice

Pankaj Nagle, S. K. Rao, G. K. Koutu and Priya NairDepartment of Plant Breeding and GeneticsJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)priya_nair_04@yahoo.co.in

for an efficient selection strategy for the plant breedersto evolve an economic variety. Path coefficient analysisfurnishes information about the influence of eachcontributing trait to yield directly as well as indirectlyand also enables breeders to rank the genetic attributesaccording to their contribution and thus, useful in indirectselection of elite genotypes from diverse geneticpopulations. Based on these important aspects, thepresent study was undertaken to study the direct andindirect effects of yield attributing traits on seed yield.

Material and methods

The material used in the study comprised of 71exogenous and 9 indigenous lines (Table 1) receivedfrom IRRI, Philippines under the project INGER. Theexperiment was carried at Seed Breeding Farm,Department of Plant Breeding and Genetics, Collegeof Agriculture, JNKVV, Jabalpur (MP) in randomizedcomplete block design with three replications. Theobservations was recorded on fifteen quantitative traitsviz., plant height (cm), panicle length (cm), panicleweight plant-1 (g), number of tillers plant-1, number ofeffective tillers plant-1, 1000 grain weight (g), number offilled grains panicle-1, number of unfilled grains panicle-1,number of grains panicle-1, spikelet density, days to 50% per cent flowering, biological yield plant-1 (g), grainyield plant-1 (g), harvest index (%) and panicle index.

The direct and indirect contribution of variouscharacters to yield were calculated through pathcoefficient analysis as suggested by Wright (1921) andelaborated by Dewey and Lu (1959).

JNKVV Res J 48(1): 22-25 (2014)

23

Tabl

e 1.

Ger

mpl

asm

s us

ed in

stu

dy

S.N

o.N

ame

of G

erm

plas

mS

.No.

Nam

e of

Ger

mpl

asm

S.N

o.N

ame

of G

erm

plas

mS

.No.

Nam

e of

Ger

mpl

asm

1O

M 5

936

21IR

774

98-1

27-3

-2-3

-241

IR 7

9253

-55-

1-4-

661

IR 7

3885

-1-4

-3-2

-1-6

(MA

TATA

G 9

)2

KO

NAW

E22

OM

563

642

IR 8

1350

-95-

2-1-

262

IR 8

1330

-19-

2-1-

33

IR 6

423

IR 8

2355

-9-1

-243

IR 7

7542

-167

-1-1

-1-1

-363

BO

ND

OYU

DO

4A

NG

KE

24IR

806

92-6

4-3-

2-1

44IR

809

14-6

-3-1

-264

IR 8

0694

-44-

1-2-

25

IR 7

7542

-127

-1-1

-1-1

-225

IR 8

1350

-9-3

-3-3

45IR

792

53-1

9-3-

3-5

65IR

780

91-1

20-3

-2-2

-36

IR 7

1677

-161

-2-3

26IR

809

22-3

-2-2

-346

IR 7

9193

-83-

1-1-

166

IR 7

9482

-106

-2-2

-17

IR-5

027

IR 7

9532

-21-

2-2-

147

IR 8

2355

-5-2

-367

CIM

ELA

TI8

IR 8

0914

-8-3

-2-1

28IR

729

06-3

2-1-

3-3

48IR

753

86-1

4-3-

2-2

68IR

792

47-1

07-1

-2-1

9IR

809

09-8

-2-2

-329

IR 8

1852

-120

-2-1

-349

IR 8

0397

-87-

1-2-

369

IR 8

1173

-33-

1-2-

310

IR 7

7504

-36-

3-3

30IR

818

90-2

6-3-

3-1

50IR

563

81-1

39-2

-2 (P

SB

RC

28)

70IR

769

39-9

8-1-

1-1

11P

SB

RC

68

31IR

785

45-4

9-2-

2-2

51IR

781

19-2

4-1-

2-2-

271

IR 7

1186

-122

-2-2

-3 (N

SIC

RC

158

)12

Loca

l Che

ck J

R 2

0132

SU

NG

GA

L52

IR 7

6993

-49-

1-1

72IR

CE

LEB

ES

13IR

811

71-4

2-1-

2-3

33IR

795

4-65

-1-3

-253

IR O

M 5

625

73O

M 5

900

14IR

785

85-9

8-2-

2-1

34IR

796

48-3

5-2-

1-1

54IR

742

84-1

0-1-

2-3-

274

Bal

agha

t15

IR 7

2176

-307

-4-2

-2-3

35IR

730

04-3

-1-2

-155

IR 8

1373

-119

-2-2

-175

Jeer

a S

hank

ar16

IR 7

9088

-36-

1-1-

3-2

36IR

752

88-3

8-3-

156

BA

TAN

G G

AD

IS76

Ram

ker

17IR

72

37IR

798

54-3

82-1

-457

IR 7

8555

-68-

3-3-

377

Pee

so18

IR 7

6928

-74-

3-2-

138

IR 7

9089

-149

-2-3

-3-3

58IR

811

74-1

25-2

-3-1

78K

aket

i19

IR 8

1166

-60-

3-1-

239

IR 7

9643

-39-

2-2-

359

IR O

M 5

935

79P

andr

i20

IR 7

7542

-234

-1-1

40IR

780

91-6

-2-3

-1-1

60IR

811

73-6

4-2-

1-2

80U

rai B

indu

Tabl

e 2.

Phe

noty

pic

path

coe

ffici

ent a

naly

sis

show

ing

dire

ct a

nd in

dire

ct e

ffect

s of

diff

eren

t cha

ract

ers

on s

eed

yiel

d of

ger

mpl

asm

line

s of

rice

.

DTF

PH

PL

NO

T/P

NO

ET/

PN

FG/P

NU

FG/P

NG

/PBY

/PH

I% P

I%SF

%S

S%

SD

GW

DTF

0.01

60-0

.000

50.

0040

0.00

060.

0007

-0.0

014

0.00

440.

0000

0.00

24-0

.003

00.

0002

-0.0

055

0.00

54-0

.002

3-0

.000

7P

H0.

0013

-0.0

431

-0.0

181

0.00

520.

0053

-0.0

088

-0.0

140

-0.0

118

-0.0

191

0.01

10-0

.004

40.

0094

-0.0

094

-0.0

027

-0.0

064

PL

-0.0

002

-0.0

003

-0.0

007

0.00

000.

0000

-0.0

003

-0.0

002

-0.0

003

-0.0

003

0.00

010.

0000

0.00

01-0

.000

010.

0000

-0.0

001

NO

T/P

-0.0

012

0.00

41-0

.000

2-0

.034

0-0

.032

90.

0024

-0.0

028

0.00

14-0

.008

7-0

.000

30.

0124

0.00

49-0

.005

00.

0009

0.01

10N

OE

T/P

0.00

13-0

.003

7-0

.000

60.

0294

0.03

03-0

.001

80.

0030

-0.0

009

0.00

720.

0009

-0.0

115

0.00

470.

0048

-0.0

005

-0.0

098

NFG

/P0.

0776

-0.1

848

-0.3

409

0.06

370.

0547

-0.9

024

-0.1

950

-0.8

699

-0.3

343

-0.0

333

-0.1

732

0.14

73-0

.145

8-0

.705

50.

0308

NU

FG/P

-0.0

857

-0.1

007

-0.1

054

-0.0

258

-0.0

304

-0.0

671

-0.3

106

-0.1

454

-0.1

054

0.01

370.

0179

0.26

30-0

.262

9-0

.078

00.

0691

NG

/P-0

.003

00.

3096

0.49

14-0

.046

6-0

.031

91.

0906

0.52

941.

1313

0.48

390.

0242

0.17

88-0

.093

80.

0954

0.87

79-0

.103

5BY

/P0.

1230

0.35

860.

3603

0.20

640.

1931

0.29

920.

2740

0.34

550.

8076

-0.3

267

-0.1

992

-0.1

015

0.16

960.

1469

0.04

38H

I%-0

.131

0-0

.178

8-0

.070

00.

0061

0.01

990.

0258

-0.0

309

0.01

50-0

.283

00.

6996

-0.3

076

0.03

26-0

.030

90.

0807

0.03

03PI

%-0

.001

5-0

.015

00.

0020

0.05

340.

0551

-0.0

280

0.00

84-0

.023

00.

0359

0.06

41-0

.145

7-0

.019

60.

0198

-0.0

287

-0.0

174

SF%

-0.0

002

-0.0

001

-0.0

001

-0.0

001

-0.0

001

0.00

01-0

.000

40.

0000

-0.0

001

0.00

000.

0001

0.00

05-0

.000

50.

0000

0.00

01S

S%

0.00

870.

0056

0.00

470.

0038

0.00

40-0

.004

20.

0218

0.00

220.

0054

-0.0

011

-0.0

035

- 0.0

257

-0.0

257

-0.0

010

-0.0

058

SD

-0.0

013

0.00

06-0

.000

6-0

.000

3-0

.000

10.

0072

0.00

230.

0072

0.00

170.

0011

0.00

180.

0004

-0.0

004

0.00

92-0

.001

1G

W-0

.004

20.

0138

0.00

77-0

.030

2-0

.030

2-0

.003

2-0

.020

7-0

.008

50.

0050

0.00

400.

0111

0.02

04-0

.020

8-0

.011

10.

0930

R2 =

0.9

51 R

esid

ual e

ffect

= 0

.221

24

Result and discussion

Path analysis is the standardized partial regressioncoefficient, which splits the correlation coefficient intothe measures of direct and indirect effects of a set ofindependent variables on the dependent variable. If thecorrelation between yield and character is due to thedirect effects of character, it reflects true relationshipbetween them, selection can be practiced for such acharacter in order to improve yield. However ifcorrelation is due to indirect effect of the characterthrough another component trait, the breeder has toselect for the latter trait through which indirect effect isexerted.

Path coefficient analysis revealed the charactersviz., number of grains panicle-1, biological yield plant-1

and harvest index had positive and high direct effecton grain yield which was in conformity with the reportsof Padmavathi et al. (1996), Choudhary and Das (1998),Samonte et al. (1998), Tomar et al. (2000), Mishra andVerma (2002), Surek and Beser (2003), Shanthala(2004), Vaithiyalingan and Nadarajan (2005), Gazafrodiet al.(2006), Agahi et al. (2007) and Khan et al. (2009).

While, 1000 grain weight , number of effectivetillers plant-1, spikelet sterility % , days to 50 % flowering,and spikelet fertility % and spikelet density had positiveand very low direct effects which was in accordancewith the results of Ibrahim et al. (1990), Gravois et al.(1992), Gravois et al. (1993), Satyavanthi (1994),Deosarkar and Nerkar (1996), Padmavathi et al. (1996),Choudhary and Das (1998), Samonte et al. (1998),Tomar et al. (2000), Babu et al. (2002), Sinha andBanerjee (2002), Mishra and Verma (2002), Shanthala(2004), Vaithiyalingan and Nadarajan (2005), Gazafrodiet al. (2006) and Agahi et al. (2007) whereas thecharacters number of tillers plant-1 , plant height ,paniclelength, panicle index and had very low negative directeffects which was correspondingly similar to the reportsof Ibrahim et al. (1990), Satyavanthi (1994), Deosarkarand Nerkar (1996), Padmavathi et al. (1996), Choudharyand Das (1998), Samonte et al. (1998), Babu et al.(2002), Mishra and Verma (2002), Babar et al. (2007).Number of filled grains panicle-1 and number of unfilledgrains panicle-1 recorded high negative direct effectswhich were complementary to the reports of Samonteet al. (1998), Surek and Beser (2003) and Chakrabortyet al. (2010).

/kku gekjs Hkkjr o"kZ dh ,d egRroiw.kZ Qly gS A gekjs ns'k dh70% vkcknh bl Qly ij fuHkZj gS ,oa ;g Qly gekjs 43%Å"ek vko';drk dh iwfrZ djrk gS A fdlh Hkh Qly ds mUufrdj.kdk;ZØe dh lQyrk ml Qly dh iztkfr;ksa esa ikbZ tkus okyhvkuqokaf'kd ifjofrZrk] vkuqokaf'kd fodkl ,oa mlds mRikndrk dksvlj djus okys vaxHkwr xq.kksa dh ijLij la/kuk ij fuHkZj djrk gSA

la/k fo'ys"k.k i)fr ls ge mit ij vlj Mkyus okys dkjdksa dkcsgrj v/;;u dj ldrs gS ,oa mit ij gksus okys izR;{k ,oavizR;{k vlj dks tkudj ikS/k iztud bu dkjdksa dh egRrk dks tkubudk oxhZdj.k dj ldrk gS A

bl fo'ys"k.k ls ;g Kkr gksrk gS lglaca/k xq.kkad ls ;g O;Dr gksrkgS dh mit ij lcls T;knk izHkko tSfod mit izfr ikS/kk] iSnkokjlwpdkad] nkus izfr iq"i xqPN] Hkjs nkus izfr iq"i&xqPN] iq"i&xqPN dhyEckbZ] iq"i xqPN lwpdkad ,oa ckyksa ds /kuRo dk iM+rk gS A vr%/kku dh Qly dh mUufrdj.k dk;ZØe esa bu dkjdksa dk egRroiw.kZLFkku gS A

References

Agahi K, Farshadfar E, Fotokian MH (2007) Correlation andpath coefficient analysis for some yield-related traitsin rice genotypes (Oryza sativa L.). Asian J PlantSci 6

Choudhary PKD, PK Das (1998) Genetic variability, correlationand path analysis in deep water rice. Ann Agric Res19 (2):120 - 124

Deosarkar DB, Nerkar YS (1996) Correlation and path analysisin F1 population of rice cultivars. PKV Res J 20 (1):82- 83

Dewey DR, Lu KH (1959) A correlation and path coefficientanalysis of components of crested wheat grass seedproduction. Agron J 51: 515 - 518

Gazafrodi A, Honarnegad AR, Fotokian MH, Alami A (2006)Study of correlations among agronomic traits andpath analysis in rice (Oryza sativa L.). J Sci andTechnol Agric Nature Resour 10 (2):107 - 110

Gravois KA, Helms RS (1992) Path analysis of rice yield andyield components as affected by seeding rate. AgronJ 84:1 - 4

Gravois KA, Mc New RW (1993) Combining ability andheterosis in U.S. southern long grain rice. Crop Sci33: 83 - 86

Ibrahim SM, Ramalingam A, Subramanian M (1990) Pathanalysis of rice grain yield under rainfed lowlandconditions. IRRN 15:11

Khan S, Imran AM, Ashfaq M (2009) Estimation of geneticvariability and correlation for grain yield componentsin rice (Oryza sativa L.). American-Eurasian J AgricEnviron Sci 6 (5):585 - 590

25

Mishra LK, Verma RK (2002). Genetic variability for qualityand yield traits in non-segregating populations of rice(Oryza sativa L.). Plant Archives 2(2):251 - 256

Padmavathi N, Mahadevappa M, Reddy OVK (1996)Association of various yield components in rice(Oryza sativa L.). Crop Res 12 (3):353 - 357

Samonte SOPB, Wilson LT, Mc Clung AM (1998). Pathanalysis of yield and yield related traits of fifteendiverse rice genotypes. Crop Sci 38: 1130 - 1136

Shanthala J (2004) Path coefficient analysis for grain yieldwith yield components in hybrid rice. Environmentand Ecology 22(4):734 - 736

Sinha MK, Banerjee SP (2002) Path analysis of yieldcomponents in rice. Kasetsart J (Nat Sci), 21:86 -92

Surek H, Beser N (2003). Correlation and path coefficientanalysis for some yield-related traits in rice (Oryzasativa L.) under Thrace conditions. Turk J Agric For27:77 - 83

Tomar JB, Dabas BS, PL Gautam (2000) Genetic variability,correlation coefficient and path analysis forquantitative characters under rainfed ecosystem inthe native land races of rice. Indian J Pl Genet Resour13 (3): 229 - 246

Vaithiyalingan M, Nadarajan N (2005) Correlation and pathanalysis in inter sub-specific rice hybrids. Res onCrops 6 (2):287 - 289

Wright S (1921) Correlation and Causation. J Agric Sci 20:557 - 587

(Manuscript Receivd : 10.9.2013; Accepted : 15.1.2014)

26

Genetic diversity analysis in indigenous and exotic germplasmlines of rice under climatic conditions of Kymore Plateauzone, Madhya Pradesh

Pankaj Nagle, S. K. Rao, G. K. Koutu and Priya NairDepartment of Plant Breeding and GeneticsJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : priya_nair_04@yahoo.co.in

Abstract

The estimation of genetic divergence in the availablegermplasm (71 exogeneous and 9 indigeneous lines) isimportant for successful selection of parents for hybridizationpurpose. The divergent lines belonging to different anddistantly located clusters exhibited higher probabilities of givingheterotic hybrids or superior progenies than those parentallines belonging to the same cluster or group possessing lowgenetical distance. The contribution of characters through D2

revealed that the Cluster VI and VII were most distantlysituated. The cluster mean value for spikelet density was thelowest (4.23) in Cluster VI and the highest mean value (247.67)for number of grain panicle-1 in Cluster V. It is thereforeconcluded that character number of grain plant-1 contributemaximum to genetic diversity. The estimates of D2 andcanonical analysis respectively revealed that characterspanicle index (73.92) and number of filled grains panicle-1

(8.92) contributed maximum to genetic divergence. As evidentfrom the canonical analysis, characters viz., days to 50 %flowering (5.65),biological yield plant -1 4.05), spikeletfertility(3.67) and plant height (3.42) contributed significantlyto genetic divergence. This substantiates that these traitscontribute maximum to genetic divergence and can be utilizedin further plant breeding/crop improvement programmes.

Keywords: Oryza sativa L., genetic diversity,germplasm, canonical root analysis, D2 analysis

Rice (Oryza sativa L.) is the staple food crop of India,providing 43% of caloric requirement for more than 70%Indian population. The success of any crop improvementprogramme depends on genetic variability, heritability,genetic advance, characters association, direct andindirect effects on yield and its attributes andconsiderable genetic diversity present among thegenotypes. Yield of paddy is a complex quantitativecharacter controlled by many genes interacting with the

environment and is the product of many factors calledyield components. Selection of parents based on yieldalone is often misleading (Rahman et al. 1997). Thediversity in crop varieties is essential for increasing foodproduction, poverty alleviation and promoting economicgrowth overall contributing to the development ofagriculture. It serves as an insurance against unknownfuture needs and conditions. The estimation of geneticdivergence in the available germplasm is important forsuccessful selection of parents for hybridizationpurpose. The divergent lines belonging to different anddistantly located clusters have higher probabilities ofgiving heterotic hybrids or superior progenies than thoseparental lines belonging to the same cluster or grouppossessing low genetical distance (Rao 1952).Grouping or classification of genotypes based onsuitable scale is quite imperative to understand theusable variability existing among them. Hence, D2

statistics and Anderson's canonical analysis are mostfrequently used for classificatory purposes.

Material and methods

The material used in the study comprised of 71exogenous and 9 indigenous lines (Table 1) receivedfrom IRRI, Philippines under the project INGER. Theexperiment was carried at Seed Breeding Farm, De-partment of Plant Breeding and Genetics, College ofAgriculture, JNKVV, Jabalpur (MP) in randomized com-plete block design with three replications during Kharif2010.

The data on different characters were analyzedthrough Mahalanobis' generalized distance D2 (1936).Grouping of the populations into various clusters wasdone by using Tocher's method as described by Rao(1952). The criterion used in clustering by this method

JNKVV Res J 48(1): 26-32 (2014)

27

is that any two variables belonging to the same clustershould at least on an average, show a smaller D2 valuethan those belonging to different clusters. For thispurpose, D2 values of the combinations of eachgenotype were arranged in ascending order of theirmagnitudes as described by Singh and Chaudhary(1979).

Result and discussion

Grain yield in rice is a complex character, quantitativein nature and an integrated function of a number ofcomponent traits. Therefore, selection for yield per semay not be much rewarding unless yield componentsare taken into consideration.

Genetic diversity analysis

Success of hybridization and subsequent selection ofdesirable segregants largely depends on the selectionof parents with high genetic variability for differentcharacters. The more diverse the parents, within overalllimits of fitness greater are the chances of obtaininghigher amount of heterotic expression in the F1's andbroad spectrum of variability in segregating generations.

The estimate of genetic divergence in theavailable germplasm is important for the selection ofdesirable donors for breeding programme. Severalworkers have emphasized the importance of geneticdivergence for the selection of desirable parents Murthyand Arunachalam (1966), Sinha et al. (1991) andRahman et al. (1997). The assessment of divergencefor a set of characters using multivariate analysis likeMahalanobis's D2 (distance analysis); Anderson'scanonical analysis has been attempted and effectivelyused in a number of crop species with diverse breedingsystems. The use of Mahalanobis's D2 statistic forestimating genetic divergence has been emphasizedby Shukla et al. (2006) and Sarawagi and Binse (2007).

The present investigation on genetic divergencewas carried using Mahalanobis's D2 statistics and wasconfirmed by canonical analysis. The results of D2

analysis revealed that all the genotypes exhibitedconsiderable diversity and were grouped into 7 clusters(Table 2). The mode of distribution of genotypes fromdifferent eco-geographical regions into various clusterswas at random indicating that geographical distributionand genetic diversity were not related. Further, thecharacters viz., panicle index and number of filled grainspanicle -1 contributed maximum towards geneticdivergence. While, days to 50 % flowering, biological

yield plant-1, spikelet fertility and plant height contributedmoderately. Similar results were reported by Naik et al.(2006) and Chandra et al. (2007) for biological yieldplant-1 and Shanthi and Singh (2001), Naik et al. (2006),Reddy et al. (2006) and Chandra et al. (2007) for 1000grain weight. Plant height contributed moderatelytowards genetic divergence, followed by panicle index,number of filled grains panicle-1 and days to 50 %flowering.

The average intra and inter-cluster distancevalues (Table 3 and 4) revealed that the highest intracluster distance of 74.44 was observed in cluster VIIfollowed by cluster VI (51.67). It is evident from inter-cluster distance that the most divergent clusters wereVI and VII (D2= 74.44) followed by clusters IV and VII(D2= 70.86) and clusters III and VII (D2= 66.28)suggesting wide diversity between them and thegenotypes in these clusters could be used as parentsin hybridization programme since hybridization betweendivergent parents is likely to produce wide variabilityand transgressive segregations with high heteroticeffects. Such recommendations were also made byMurthy and Arunachalam (1966), Qian and He (1991)and Rao and Gomanthinayagam (1997), Pradhan andRay (1990), Rahman et al. (1997) and Bose andPradhan (2005) and reported that selection of parentsfor hybridization should be done from two clustershaving wider inter-cluster distances to get maximumvariability in the segregating generations.

The maximum intra cluster D2 value was recordedfor Cluster II (D2=15.73) followed by Cluster I (D2=14.14).Cluster III, IV, V, VI and VII were mono-genotypicclusters consisting of one genotype each. Cluster IIIrecorded the highest cluster mean value for biologicalyield plant-1 and Cluster IV for spikelet fertility %. ClusterV exhibited the highest cluster mean value for numberof grain plant-1 and Cluster VI for number of tillers plant-1,number of effective tillers plant-1 and spikelet sterility %and Cluster VII for days to 50 % flowering and panicleindex.

The minimum inter-cluster distance (D2=12.38)was found between clusters IV and VI followed byclusters III and IV (D2= 14.50) and clusters III and V(D2=15.50) indicating the genotypes of these clusterswere genetically similar. Cluster I the largest clustercomprised of sixty six genotypes which indicated allthe sixty six genotypes were having some similarcharacteristics or common parent which led to theirgrouping followed by cluster II with nine genotypes. Theclusters III, IV, V, VI and VII were monogenotypicclusters. This indicated negligible genetic diversityamong the genotypes for each character. The clustering

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Table 1. Rice germplasm under study at JNKVV, Jabalpur during Kharif 2010

S.No. Name of Germplasm S.No. Name of Germplasm S.No. Name of Germplasm S.No. Name of Germplasm

1 OM 5936 21 IR 77498-127-3-2-3-2 41 IR 79253-55-1-4-6 61 IR 73885-1-4-3-2-1-6(MATATAG 9)

2 KONAWE 22 OM 5636 42 IR 81350-95-2-1-2 62 IR 81330-19-2-1-33 IR 64 23 IR 82355-9-1-2 43 IR 77542-167-1-1-1-1-3 63 BONDOYUDO4 ANGKE 24 IR 80692-64-3-2-1 44 IR 80914-6-3-1-2 64 IR 80694-44-1-2-25 IR 77542-127-1-1-1-1-2 25 IR 81350-9-3-3-3 45 IR 79253-19-3-3-5 65 IR 78091-120-3-2-2-36 IR 71677-161-2-3 26 IR 80922-3-2-2-3 46 IR 79193-83-1-1-1 66 IR 79482-106-2-2-17 IR-50 27 IR 79532-21-2-2-1 47 IR 82355-5-2-3 67 CIMELATI8 IR 80914-8-3-2-1 28 IR 72906-32-1-3-3 48 IR 75386-14-3-2-2 68 IR 79247-107-1-2-19 IR 80909-8-2-2-3 29 IR 81852-120-2-1-3 49 IR 80397-87-1-2-3 69 IR 81173-33-1-2-310 IR 77504-36-3-3 30 IR 81890-26-3-3-1 50 IR 56381-139-2-2 (PSB RC 28) 70 IR 76939-98-1-1-111 PSBRC 68 31 IR 78545-49-2-2-2 51 IR 78119-24-1-2-2-2 71 IR 71186-122-2-2-3

(NSICRC 158)12 Local Check JR 201 32 SUNGGAL 52 IR 76993-49-1-1 72 IR CELEBES13 IR 81171-42-1-2-3 33 IR 7954-65-1-3-2 53 IR OM 5625 73 OM 590014 IR 78585-98-2-2-1 34 IR 79648-35-2-1-1 54 IR 74284-10-1-2-3-2 74 Balaghat15 IR 72176-307-4-2-2-3 35 IR 73004-3-1-2-1 55 IR 81373-119-2-2-1 75 Jeera Shankar16 IR 79088-36-1-1-3-2 36 IR 75288-38-3-1 56 BATANG GADIS 76 Ramker17 IR 72 37 IR 79854-382-1-4 57 IR 78555-68-3-3-3 77 Peeso18 IR 76928-74-3-2-1 38 IR 79089-149-2-3-3-3 58 IR 81174-125-2-3-1 78 Kaketi19 IR 81166-60-3-1-2 39 IR 79643-39-2-2-3 59 IR OM 5935 79 Pandri20 IR 77542-234-1-1 40 IR 78091-6-2-3-1-1 60 IR 81173-64-2-1-2 80 Urai Bindu

Table 2. Distribution of rice genotypes in different clusters

Cluster Number of Genotypesgenotypes

1. 66 IR 7954-65-1-3-2, IR 80914-6-3-1-2, IR 77542-167-1-1-1-1-3, IR 74284-10-1-2-3-2, IR 73885-1-4-3-2-1-6(MATATAG 9), IR 80922-3-2-2-3, IR 79648-35-2-1-1, IR 78545-49-2-2-2, IR 77504-36-3-3, KONAWE, IR 79193-83-1-1-1, IR82355-9-1-2, IR81171-42-1-2-3, IR 77542-234-1-1,IR 76939-98-1-1-1, IR 81174-125-2-3-1, IR 79247-107-1-2-1, IR 80909-8-2-2-3, IR 72176-307-4-2-2-3, IR 73004-3-1-2-1, IR 77542-127-1-1-1-1-2, Kaketi, IR 81330-19-2-1-3, IR 81173-64-2-1-2, IR 78555-68-3-3-3, IR 80694-44-1-2-2, BONDOYUDO, ANGKE, IR 64, IR 81173-33-1-2-3, IR 76928-74-3-2-1, IR 79532-21-2-2-1, Peeso, BATANG GADIS, IR 79854-382-1-4, IR 76993-49-1-1, Pandri, IR79253-19-3-3-5, OM 5936, IR 75288-38-3-1, OM 5636, IR81350-9-3-3-3, IR 77498-127-3-2-3-2, IR 79643-39-2-2-3, IR 79089-149-2-3-3-3, IR 78119-24-1-2-2-2, IR 75386-14-3-2-2, IR 71186-122-2-2-3(NSICRC 158),IR 72906-32-1-3-3, IR78091-6-2-3-1-1, IR 81350-95-2-1-2, IR 80692-64-3-2-1, IR 82355-5-2-3, IR 80914-8-3-2-1,IR 56381-139-2-2 (PSB RC 28), SUNGGAL, IR 79088-36-1-1-3-2, IR 80397-87-1-2-3, Ramker,IR CELEBES, IR 81890-26-3-3-1, IR 72, IR 81852-120-2-1-3,Urai Bindu, IR 79253-55-1-4-6,IR-50

2. 09 IR 79482-106-2-2-1, CIMELATI, IR OM 5935, IR 78585-98-2-2-1, IR 78091-120-3-2-2-3,OM 5900, Balaghat, IR 81166-60-3-1-2, Local Check JR 201

3. 01 IR 71677-161-2-3

4. 01 IR 81373-119-2-2-1

5. 01 Jeera Shankar

6. 01 IR OM 5625

7. 01 PSBRC 68

29

Table 3. Cluster distance value of different characters

Cluster I Cluster II Cluster III Cluster IV Cluster V Cluster VI Cluster VII

Cluster I 14.14 29.10 20.91 23.35 21.26 27.94 51.26

Cluster II 29.10 15.73 43.44 46.93 37.67 51.67 27.95

Cluster III 20.91 43.44 0.00 14.50 15.50 16.13 66.28

Cluster IV 23.35 46.93 14.50 0.00 25.70 12.38 70.86

Cluster V 21.26 37.67 15.50 25.70 0.00 28.05 59.06

Cluster VI 27.94 51.67 16.13 12.38 28.05 0.00 74.44

Cluster VII 51.26 27.95 66.28 70.86 59.06 74.44 0.00

Table 4. Cluster means values of different characters

Clusters Days to 50% Plant Panicle Number of Number of Number of Number of Number offlowering height length tillers effective filled unfilled grains

plant-1 tillers grains grains panicle-1

plant-1 panicle-1 panicle-1

1 102.26 71.61 25.21 12.98 12.56 133.48 13.35 146.83

2 100.78 75.05 24.69 12.48 12.22 147.15 16.48 163.63

3 99.00 88.73 29.00 16.00 15.00 143.67 18.00 161.67

4 90.00 58.27 22.80 17.67 16.33 114.67 7.00 121.67

5 98.00 140.33 33.93 16.67 15.67 210.00 37.67 247.67

6 106.00 64.47 23.80 20.67 20.00 81.33 19.00 100.33

7 116.00 78.60 26.00 11.67 11.00 118.33 23.33 141.67

Table 5. Cluster means values of different characters

Clusters Biological Harvest Panicle Spikelet Spikelet Spikelet 1000 grain Grainyieldyield plant-1 index(%) index (%) fertility(%) sterility(%) density weight plant-1

1 90.18 41.78 10.15 91.05 8.97 5.89 24.99 36.85

2 78.18 35.04 15.67 89.85 10.15 6.69 25.17 26.45

3 168.00 35.57 7.17 88.56 11.44 5.54 25.77 58.00

4 78.00 57.21 5.71 94.60 5.40 5.37 19.20 43.80

5 151.00 29.39 9.34 85.26 14.74 7.34 16.47 44.13

6 101.33 42.96 4.60 81.56 18.48 4.23 20.35 43.13

7 98.67 19.55 21.05 84.27 15.73 5.56 19.51 18.73

30

Table 6. Values of the four canonical vectors and roots

Characters 1 vector 2 vector 3 vector

Days to 50 % flowering 0.00485 0.28990 0.53762Plant height (cm) 0.00911 0.29634 -0.06978Panicle length (cm) -0.00804 0.19868 -0.07367Number of tillers plant-1 -0.02632 -0.03746 0.07415Number of effective tillers plant-1 -0.01196 0.00170 0.04088Number of filled grains panicle-1 0.03960 0.48344 -0.57597Number of unfilled grains panicle-1 -0.01152 0.34055 0.27436Number of grains panicle-1 0.00000 0.00000 0.00000Biological yield plant-1(g) -0.04986 0.47913 -0.18434Harvest index (%) -0.09000 0.05174 -0.04268Panicle index 0.98889 0.02370 0.03733Spikelet fertility (%) 0.04463 -0.37476 -0.47021Spikelet sterility (%) 0.02783 0.07060 0.09911Spikelet density 0.02593 0.20460 -0.130561000 grain weight (g) 0.06600 -0.10269 -0.02476Grain yield plant-1 (g) -0.03187 -0.08327 0.02682Values of canonical roots 13434.38000 1220.50600 927.19900Percentage of variation expressed 78.91550 7.16943 5.44650Cumulative (%) of variation expressed 78.91550 86.08496 91.53146

Table 7. Contribution of different characters towards clustering in rice genotypes

Character Times ranked 1st Percentage of contribution towardsdivergence (%)

Panicle index 2336 73.92Number of filled grains panicle-1 262 8.29Days to 50 % flowering 179 5.65Biological yield plant-1 (g) 128 4.05Spikelet fertility (%) 116 3.67Plant height (cm) 108 3.42Panicle length (cm) 12 0.38Harvest index (%) 7 0.22Number of tillers plant-1 4 0.131000 grain weight (g) 2 0.06Number of unfilled grains panicle-1 2 0.06Spikelet density 2 0.06Grain yield plant-1 (g) 1 0.03Spikelet sterility (%) 1 0.03Number of effective tillers plant-1 0 0.00Number of grains panicle-1 0 0.00

31

pattern of the genotypes from different sources clusteredtogether indicated that there was no associationbetween eco-geographical distribution of genotypes andgenetic divergence. The reason for grouping ofgenotypes obtained from different places into onecluster could be the free exchange of germplasm amongthe breeders of different regions or unidirectionalselection practiced by breeder in tailoring promisingcultivars for different regions which was in accordancewith the reports of Verma and Mehta (1976), Chaturvediand Maurya (2005) and Sabesan and Saravan (2008).

The inter-cluster D2 value was maximum betweenCluster VI(IR 81373-119-2-2-1) and VII(PSBRC 68)(D2=5541.31) attributable to diversity in cluster meansfor number of grains panicle-1, number of filled grainspanicle-1, biological yield plant-1, plant height and daysto 50% flowering (Table 5). The Cluster VI recorded thelowest cluster mean value for spikelet density (4.23),panicle index (4.60), number of grains panicle-1 (100.33)and number of filled grains panicle-1 (81.33). Thus, thegenotypes included in these clusters are geneticallymost divergent for number of grains panicle-1and hence;this character can be used for indirect selection forhigher yields.

The aggregate effect of all the seventeencharacters tested by Wilk's criterion indicated highlysignificant differences among the genotypes in thepresent study. The principal component analysisshowed that the first two canonical roots (Table 6 and 7)accounted for 86.07% of the total variation. With respectto the relative predominance of the characters viz.,panicle index, 1000 grain weight, spikelet fertility %,number of filled grains panicle-1, biological yield plant-1

and plant height and revealed that the above listed traitscontributed maximum towards genetic diversity amongstthe genotypes studied. Number of grains panicle-1 didnot contribute to the formation of canonical vectors.

Cluster VI (IR 81373-119-2-2-1) and VII (PSBRC68) were most divergent hence; genotype included inthese clusters may be incorporated in crop improvementthrough hybridization programmes. Cluster V (JeeraShankar) recorded the highest value of cluster meansfor maximum number of characters; therefore thisgenotype may be used as a superior parent/donor incrop genetic improvement. The cluster mean value forspikelet density was the lowest in Cluster VI and thehighest mean value for number of grain panicle-1 inCluster V. Therefore, thecharacter number of grain plant-1 contribute maximum to genetic diversity. The estimatesof D2 and canonical analysis respectively revealed thatcharacters panicle index and number of filled grainspanicle-1 contributed maximum to genetic divergence.

As evident from the canonical analysis, characters viz.,biological yield plant-1, 1000 grain weight, days to 50 %flowering, number of unfilled grains panicle -1 andspikelet sterility contribute maximum to geneticdivergence and can be utilized in further plant breeding/crop improvement programmes.

ikS/k iztUu ,oa vuqokf'kdh esa Mh LdoSj ,oa dsuksfudy fo'ys"k.kvkuqokaf'kdh fofo/krk ijh{k.k dh ,d ,slh i)fr gSa ftlds }kjk ladjcukus ds fy, lcls mi;qDr ik=ksa dk pquko fd;k tk ldrk gSA bli)fr }kjk ijLij o varj nwjh dks Kkr dj ykÃuksa dks >q.Mksa@ xqPNksaesa foHkkftr fd;k tkrk gSAA tks ykÃu ftrus i`Fkd o nwjh ds xqPNsa esaLFkkfir gksxk muls lQy o mUur lUrfr ikus dh laHkkouk mruhvf/kd jgsxh ,oa oks ykÃu ftudh vkuqokf'kad nwjh de gksrh gSA oksvfU=rj gksrs gSA izLrqr ijh{k.k ls ;g Kkr gksrk gS fd xqPN N% ,oalkr lcls T;knk nwjh ij fLFkr gSa fuEure vkSlr eku ckyh ds nkusds ?kuRo ¼4-23½ ds fy, xqPN N% ik;k x;k ,oa mPpre vkSlr ekunkus izfr ikS/ksa ¼247-67½ ds fy, xqPN i¡kp esa ik;k x;kA

Mh LdoSj ,oa dsuksfudy fo'ys"k.k ls ;g fu"d"kZ fudkyk tkldrk gSa fd iq"i xqPN lwpdakd ¼73-92½ ,oa Hkjs nkus izfr ckyh¼8-92½ vkuqokf'kd vykok vU; xq.k tSls 50 izfr'kr Qwyu fnol¼5-65½] tSfod mit izfr ikS/k ¼4-05½ LikÃdfyr QfVZfyVh ¼3-67½,oa ikS/ks dh yackà ¼3-42½ vkfn Hkh vkuqokaf'kd fofo/krk esa lg;ksxnsrs gSA vr% ;g izekf.kr gksrk gS fd mi;qDr xq.k vf/kdrevkuqokf'kd fofo/krk esa lg;ksx djrs gS ,oa budk mi;ksx Qlylq/kkj ,oa mUufrdj.k dk;ZØe esa lQyrkiwoZd fd;k tk ldrk gSA

References

Bose LK, Pradhan SK (2005) Genetic divergence in deepwater rice genotypes. J Central European Agri 64:635- 640

Chandra R, Pradhan SK, Singh S, Bose LK, Singh ON (2007)Multivariate analysis in upland rice genotypes. WorldJ Agri Sci 3(3):295 - 300

Chaturvedi HP, Maurya DM (2005) Genetic divergenceanalysis in rice (Oryza sativa L.), Adv Plant Sci 18(1):349 - 353

Mahalanobis PC (1936) On the generalized distance instatistics. Proc Nat Inst Sci (India) 2: 49 - 55

Murthy BR, Arunachalam V (1966) The nature of geneticdiversity in relation to breeding system in crop plant.Indian J Genet 26: 188 - 198

Naik D, Sao A, Sarawgi AK, Singh P (2006) Geneticdivergence studies in some indigenous scented rice(Oryza sativa L.) accessions of Central India. AsianJ Pl Sci 5 (2):197 - 200

32

Pradhan K, Ray A (1990) Genetic divergence in rice. Oryza27:415 - 418

Qian YW, He KM (1991) Utilization of exotic rice germplasmresources in Guang - Dong province. Crop GenetResour 2:36 - 37

Rahman M, Acharya B, Sukla SN, Pande K (1997) Geneticdivergence in low land rice genotypes. Oryza 34(3):209 - 212

RaoCR (1952) Advanced statistical methods in biometricalresearch. John Wiley and Sons Inc NY

Rao TP, Gomathinayagam P (1997) Genetic diversity in semidry rice under different environments. Madras AgricJ 84:314 - 317

Reddy YM, Lavanya GR, Babu GS (2006) Estimation ofgenetic divergence in irrigated early type ricegermplasm. Res on Crops 7 (2):433 - 436

Sabesan T, Saravan K (2008) Genetic divergence analysis inrice (Oryza sativa L.). Paper presented in GoldenJubliee Commemorative National seminar on "Fiftyyears of Indian Agriculture: Problems, Prospects andFuture thrust". Annamalai University, Tamil Nadu,India. 20-21 March pp 14

Sarawgi AK, Binse R (2007) Studies on genetic divergenceof aromatic rice germplasm for agro - morphologicaland quality characters. Oryza 44 (1) : 74 - 76

Shanthi P, Singh J (2001) Genetic divergence for yield andits components in induced mutants of Mahsuri rice(Oryza sativa L.). Res on Crops 2 (3):390 - 392

Shukla V, Singh S, Singh H, Pradhan SK (2006) Multivariateanalysis in tropical japonica "New plant type" rice(Oryza sativa L.). Oryza 43 (3): 203 - 207

Singh RK, Chaudhary BD (1979) Biometrical methods inquantitative genetic analysis. 210 - 214

Sinha PK, Chouhan US, Prasad K, Chouhan JS (1991)Genetic divergence in indigenous upland ricevarieties. Indian J Genet 51: 47 - 50

Verma VS, Mehta RK (1976) Genetic divergence in Lucerne.J Maharashtra Agri Univ 1:23-28

(Manuscript Receivd :11.9.2013; Accepted : 17.1.2014)

33

Abstract

Field experiments were conducted on niger cv. JNC 1 duringkharif seasons of 2010 and 2011 at research farm ZonalAgricultural Research Station, Chhindwara (MP) under rainfedconditions on sandy loam soils. Nine treatments consisting ofdifferent nutrient management [ soil application (SA) and SA+ foliar applications of 2% urea and DAP at different stages ]were tested in a randomized block design with threereplications. Results revealed that higher seed yield andmaximum NMR (526 kg/ha and Rs 12516/ha) was obtainedwith RDF ( N40+P20+K10 kg/ha) + foliar applications of 2 % DAPat flowering + capitula formation stage (T9). But the B:C ratio(2.37) was maximum with 100% RDF + foliar application of2% DAP at flowering stage (T7).

Keywords: Niger, RDF, foliar application, seed yield,economics

Niger [Guizotia abyssinica (L.f.) Cass] is an edibleoilseed crop which is extensively grown by the resourcepoor farmers mainly in tribal areas under rainfedcondition. Niger seeds contain a considerable quantityof edible oil (38 to 43 %), protein (20%) and mineralsessential for human and animal meals (Gentinet andTeklewold 1995). In India, 25 percent of the seed is usedfor oil. When extracted the oil is used in foods, for paintand soap making and for lighting, In India about 75percent of the harvested seeds are used for oil extractionwhile the rest is exported for bird feed. Roasted or friedseeds are eaten as a snack or used as condiment. Thepress cake from oil extraction contains 31 to 40 percentprotein and is used for feeding cattle. India ranks onthe second and forth position in the world for its acerageand annual production respectively. It is extensivelygrown in Madhya Pradesh, Chhattisgarh, Orissa,

Effect of foliar sprays on seed yield and economics of niger

G.K. Rai, S.K. Thakur, M.R. Deshmukh* and A.K. RaiAll India Coordinated Research Project on NigerZARS Chhindwara 480001 (MP)*Project Coordinating UnitSesame and Niger ICARJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

Maharashtra states and to a lesser extent in Karnataka,Bihar, Jharkhand and Andhra Pradesh in the country,It covers an area of nearly. 3.93 lakh hectare in thecountry with on annual production of about 1.2 lakhtonnes and productivity of 297 kg seeds/ ha. MadhyaPradesh contributes nearly 1.16 lakh hectare area underthis crop with annual production of 0.27 lakh tonnesand productivity of 229 kg seeds /ha (Damodaram andHegde 2010). It's cultivation is confined in tribal beltsof Chhindwara, Dindori, Mandla, Seoni, Jabalpur andShadol district on marginal, sub-marginal and erodedlands without or with use of negligible quantity offertilizers owing to very low productivity (Sharma 1993).This low productivity can be enhanced in sustainablemanner by manipulation of production technologies. Themajor plant nutrients applied through chemical fertilizersmay not be taken up properly by plant roots of cropplants due to organic carbon content and water holdingcapacity of soil. The applied little quantity of nitrogen isgenerally lost either by leaching or volatilization. Undersuch circumstances, nitrogen application can be donein the forms of foliar spray to avoid the aforesaid lossesof nitrogen (Jaiswal and Elamathi 2007). Appliedphosphorus is immobile and it remains fixed by the soilunless it is placed near the plants. Efficiency of thisapplied phosphorus can also be improved by applyingthrough foliar spray. Studies conducted at Jabalpuremphasized that application of nitrogen and /orphosphorus in legume crops resulted into considerableincrease in seed yields (Raghuwanshi et al. 1993). Butinformation pertaining to these aspects are not enoughon this crop for the region. Keeping above facts in view,the present investigation was undertaken with theobjectives to find out the suitable combination of soiland foliar application of nutrients for better growth andyield of niger.

JNKVV Res J 48(1): 33-35 (2014)

34

Material and methods

Field experiments were conducted during kharif seasonsof 2010 and 2011 at research farm of Zonal AgriculturalResearch Station, Chhindwara (Madhya Pradesh). Thesoil of the experimental field was sandy loam in textureand neutral in reaction (pH 7.1). The experimental fieldwas low in available N (225 Kg/ha), P (25 kg/ha) andhigh K (345 kg/ha) contents. The rainfall received duringthe crop growing seasons was 347 and 383 mm in 18and 20 rainy days respectively. Nine treatmentsconsisting of different nutrient management (Table 1)were tested in randomized block design by replicatingthrice on a well prepared seed bed. Niger cv. JNC 1was sown with 5 kg/seeds/ha in rows 30cm apart duringlast week of July in both year. An intra-row plant spacingof 10 cm was maintained by thinning, The recommendeddose of fertilizers (RDF) i.e. N40+P20+K10 kg/ha wasapplied through urea, single super phosphate andmuriate of potash respectively. Half quantity of N andfull quantity of phosphorus and potash was applied asbasal. Remaining ½ quantity of N was top dressed at30 days old crop. The foliar applications were done asper treatments strictly. The recommended agronomicpractices were adopted for raising the crop byperforming all operations uniformly under all treatments.Various observations viz., plant population, plant height,number of branches/plant, number of capitula/plant, testweight and finally seed yields were recorded at finalstage. The economic analysis of the treatments wasalso made on the basis of mean seed yields over theyears.

Results and discussion

Productivity (seed yield)

The seed yield was maximum with 100% RDF + foliarapplications of 2% DAP at flowering + capitula formationstages (T9) as observed on the basis of data for 2010,2011. The seed yield recorded in T9 (661 kg/ha ) wasbeing at par with the seed yields of than those recordedin T7- RDF + foliar application of 2% DAP at flowering[654 kg/ha], T8-RDF + foliar applications of 2% urea atflowering + capitula formation stages (640 kg/ha), T6-RDF+foliar application of 2% urea at flowering (635kg/ha) and T5-75% RDF+ foliar applications of 2% DAP atflowering + capitula formation stages ( 600 kg/ha)/ Thehighest seed yield of 661 kg/ha recorded in T9 wassignificantly higher with T1= RDF (577 kg/ha), T4 = 75% RDF + foliar application of 2% urea at flowering +capitula formation stages ( 567 kg/ha ) , T3-75% RDF +Ta

ble

1. E

ffect

of f

olia

r app

licat

ion

on s

eed

yiel

d an

d ec

onom

ics

of n

iger

dur

ing

2010

- 20

11 a

t Chh

indw

ara

(MP)

Trea

tmen

tSe

ed Y

ield

(kg

/ha)

Net

Mon

etar

y R

etur

nsB:

C r

atio

(Rs/

ha)

2010

2011

Mea

n20

1020

11M

ean

2010

2011

Mea

nT 1 =

RD

F (S

oil a

pplic

atio

n)27

857

742

786

7613

280

1097

81.

662.

922.

29T 2 =

75%

RD

F +

folia

r app

licat

ion

of u

rea

2% a

t flo

wer

ing

260

513

387

7869

1083

093

501.

522.

522.

02T 3 =

75%

RD

F +

folia

r app

licat

ion

of D

AP 2

% a

t flo

wer

ing

271

560

416

8274

9190

8732

1.60

2.71

2.15

T 4 = 7

5% R

DF

+ fo

liar a

pplic

atio

ns o

f ure

a 2%

at f

low

erin

g +

capi

tula

form

atio

n28

156

742

487

2912

210

1047

01.

462.

602.

03T 5 =

75%

RD

F +

folia

r app

licat

ions

of D

AP 2

% a

t flo

wer

ing

+cap

itula

form

atio

n28

560

044

388

7113

210

1104

01.

652.

692.

17T 6 =

T1 +

folia

r app

licat

ion

of u

rea

2% a

t flo

wer

ing

285

635

460

8842

1482

011

831

1.63

3.00

2.31

T 7 = T

1 + fo

liar a

pplic

atio

n of

DAP

2%

at f

low

erin

g29

265

447

391

7315

410

1229

11.

693.

062.

37T 8 =

T1 +

folia

r app

licat

ions

of u

rea

2% a

t flo

wer

ing

+ ca

pitu

la fo

rmat

ion

295

640

468

9181

1452

011

850

1.64

2.84

2.24

T 9 = T

1 +fo

liar a

pplic

atio

ns o

f DAP

2%

at f

low

erin

g +

capi

tula

form

atio

n39

066

152

698

5215

180

1251

61.

752.

912.

33SE

m±

9.17

21.6

9-

-1.

48-

-0.

10-

CD

(P =

0.0

5)27

.36

63.8

0-

-4.

37-

-0.

30-

CV%

5.82

6.25

--

19.4

--

6.28

-R

DF

= 40

:20:

10 -

N:P

:K (k

g/ha

)

35

foliar applications of 2% DAP at flowering (560 kg/ha),T2 - 75%RDF+ foliar application of 2% urea at flowering(513 kg/ha ) being the minimum, The mean maximumseed yield of 526 kg/ha in T9 was followed by 473 kg/hain T7 and 468 kg/ha in T 8. These results alsocorroborated the findings of (Gautam 2009 and Anon2011).

Economic viability

The maximum NMR obtained during 2011 in T7 (Rs15410 /ha) was significantly higher than recorded in T2(Rs 10830/ha) and in T3 (Rs 9190/ha) being minimum,The maximum NMR of Rs 15410 /ha obtained in T7 wasalso at par with T9 (Rs. 1580/ha) , T6 ( Rs 14820/ha) T8(Rs 14520 / ha), T1 ( Rs 13280/ha), T5 (Rs13210/ha )and T4 (Rs 12210/ha). The two years mean maximumNMR of Rs 12516/ha was in T9 followed by Rs 12291/ha in T7 and Rs 11850/ha in T8. The minimum NMR ofRs 8732/ha was noticed in T3. During 2011, the highestB:C ratio of 3.06 was in T7 which was significantly higherover T3 (2.71), T5 (2.69), T4 (2.60) and T2 ( 2.52 ) . Thetwo years mean highest B:C ratio (2.37) was noticed inT7 followed by 2.33 in T9 and 2.31 in T6 respectively(Table 1). Similar monetary returns due to application of2% DAP twice at flowering + capitula formation stagesare also obtained in other agro climatic conditions asadvocated by Gautam (2009) from his heir investigationand appended in Anon (2011).

On the basis of the results of the aboveinvestigations it is inferred that the foliar application inniger has resulted into increase in the seed yield. Thehighest seed yield was obtained with the use of RDF +foliar applications of 2% DAP at flowering + capitulaformation stages. The economics of the treatmentsvaried with the cost of fertilizers and the wages of labourengaged for spraying operations of the nutrients.

jkefry dh mRiknu {kerk ,oa vk;&O;; lac/kh iz{ks= v/;;ujkefry lq/kkj ifj;kstuk] vkapfyd d`f"k vuqla/kku dsUnz] fNUnokM+k¼eiz½ dh jsrhyh nqeV Hkwfe ij o"kkZ vk/kkfjr fLFkfr;ksa esa o"kZ 2010,oa 2011 ds [kjhQ eksSleksa esa iks"kd rRoks dk lh/ks Hkwfe esa ,oa [kMhQly esa iq"ikoLFkk rFkk ckSaMh cuus dh voLFkk ¼izkjafHkd voLFkk½ esafNMdko djrs gq;s ijh{k.k fd;s x;sA ijh{k.kksa ls izkIr ijh.kkeksa dsurhtks ls ;g fofnr gqvk gS fd txuh ¼jkefry½ dh Qly esavuq'kaflr jklk;fud moZjdks dh ek=k ds lkFk 2 izfr'kr Mk;veksfu;eQkLQsV dk nks ckj iq"ikoLFkk ,oa cksaMh cuus ds le; fNM+dko djusls vf/kdre vkSlr mRiknu (526 fdyksxke@gSDVs;j) ,oa 'kq) vk;:i; fLFkfr;kssa ¼12516 gsDVs;j½ izkIr gqbZ] fdUrq vf/kd vk; &O;;vuqikr 2-37 jkefry dh Qly ls vuq'kaflr jklk;fud moZjdksa dhek=k ds lkFk 2 izfr'kr Mk;veksfu;e QkWLQsV ds ?kksy dk iq"ikoLFkkes fNM+dko djus okys mipkj ls izkIr gqvkA

References

Anonymous (2011) Annual Progress Report, ProjectCoordinating unit (S&N). JNKVV Campus, Jabalpur332

Sharma SM (1993) Status and strategies of sesame and nigerresearch in India. National Seminar on OilseedsResearch and Development in India, 2-3 August,Directorate of Oilseeds Res Hyderabad 62-69

Jaiswal Akhilesh Kumar, Elamathi S (2007) Effect of nitrogenlevels and foliar spray of diammonium phosphate,on growth and yield of blackgram (Vigna mungo).Agromomy Digest (6&7) : 24-25

Gentinet A, Tekle Wold (1995) An agronomic and seed qualityevaluation of niger [Guizotia abyssinica (L.f.) Cass]germplasm grown in Ethiopia. Plant Breed 144: 375-376

Raguwanshi MS, Rathi GS, Sharma RS (1993) Effect of foliarspray of diammonium phosphate and urea on thegrowth and yield of chickpea. JNKVV Res J 27(1) :131-132

Damodaram T, Hegde DM (2010) Oilseeds situation: AStatistical Compendium. Directorate of OilseedsResearch Hyderabad 486

Gautam SP (2009) Effect of nutrient management on growthand yield of niger [Guizotia abyssinica(L.f.) Cass ].MSc. (Ag) Thesis JNKVV, Jabalpur (MP) 68

0

100

200

300

400

500

600

700

T1 T2 T3 T4 T5 T6 T7 T8 T9

Fig 1.Effect of foliar application on seed yield andeconomics of niger during 2010 – 2011

2010

2011

Mean

(Manuscript Receivd : 28.12.2012; Accepted :15.1.2014)

36

JNKVV Res J 48(1): 36-42 (2014)

Abstract

Upland rice species Oryza sativa, O. glaberrima andinterspecific rice progeny (New Rice for Africa - NERICA)derived from crosses between O. sativa and O. glaberrimawere screened in pot experiment for resistance to cystnematode, Heterodera sacchari. Three-week old seedlingsof rice were inoculated with 5,000 eggs and juveniles of H.sacchari. Plants were rated for cyst production at sixty daysafter inoculation and host resistant rating was also supportedwith reproductive factor (RF). Data collected were analyzedusing analysis of variance (ANOVA) and means separatedusing least significant difference (LSD) at P?0.05. The highestcyst production was recorded in O. sativa and NERICA ricecultivars (4.0) and the lowest on CG14 genotypes (0.3). TheO. glabberima (CG14) was rated resistant to H. sacchari withcyst production rating < 1. The O. sativa genotypes were allsusceptible (P 0.05) to H. sacchari. Fourteen of theinterspecific rice progeny (NERICA) were rated susceptible,while NERICA 6 and 8 were rated moderately resistant(P 0.05). There was less nematode reproduction in NERICA6 (RF=4.4) and NERICA 8 (RF=2.5) compared with O. sativaand other NERICA rice hybrids. The moderately resistanthybrids are potential cultivars for H. sacchari management infield production.

Keywords: Screening, Resistance rating, Heteroderasacchari, NERICA, Oryza sativa, Oryza glaberrima

Rice (Oryza spp.) is an important cereal crop in WestAfrica and remains essentially a grain for humanconsumption and a staple food for about half of the worldpopulation (Coyne et al. 2000, WARDA 2008a ; 2008b).Escalating human populations are currently leading torapidly escalating demand for rice in most urban markets(Becker and Assigbe 1995, Randolph and Gaye 1996).

Screening for resistance to Heterodera sacchari infection in uplandrice cultivars

L.I. Akpheokhai1, A.O. Claudius-Cole2, B. Fawole2 and A. A. Tanimola3

1Department of Crop ScienceFaculty of Agriculture, University of Uyo2Department of Crop Protection and Environmental BiologyFaculty of Agriculture and Forestry, University of Ibadan3Deparment of Crop and Soil ScienceFaculty of Agriculture, University of Port Harcourt, Nigeria

Rice production systems in West Africa includingNigeria are becoming more intensive and thusfacilitating the development of associated pests anddiseases. Intensive cropping of sites for rice productionin order to meet the increasing demand could rapidlylead to Increase in H. sacchari population densities andthis could lead to yield losses of up to 50% (Coyne andPlowwright 1998, Coyne 1999). The production of fertileNERICA (New Rice for Africa) rice offspring fromcrosses between O. glaberrima and O. sativa in 1994was a step to improve yield in rice in Africa. TheNERICAs are considered to be rice cultivars that caneither resist or tolerate most various stresses (includingpathogens), and can survive and produce high yieldswith minimal inputs (Jones et al. 1997). The cystnematode, Heterodera sacchari Luc and Merny, hasbeen reported in both upland and hydromorphicenvironments in West Africa (Coyne et al. 1998, Bridgeet al. 2005). In Nigeria, H. sacchari occurred in bothupland and lowland rice cultivation systems withdiscernible symptoms of infection in upland rice(Babatola 1983a and 1984). Jerath (1968) and Babatola(1983a) reported H. sacchari on rice in Nigeria, andthis nematode was shown to be highly pathogenic onrice. The selection and cultivation of resistant cultivarsof crops could be a valid control method for pests andpathogens (Adesiyan et al. 1990). Babatola (1983b)screened 50 rice cultivars in Nigeria; he observeddifferences in degrees of susceptibility, which did notexactly prove resistance. Plowright et al. (1999) in theirsearch for resistance to H. sacchari from Côte d' Ivoireon the parents and interspecific rice crosses, found onlytwo interspecific rice hybrids resistant to the nematode,

37

this is based on the final number of females thatdeveloped on the roots of rice. However, Plowright etal. (1999) reported high population densities of H.sacchari J2 in the roots of these rice hybrids in the field.Babatola (1984) reported the prevalence of H. sacchariand other plant parasitic nematodes in Nigeria's soils.The new rice for Africa (NERICA) is becomingwidespread among the rice growers in Nigeria,especially NERICAs 1 and 2, although, little is knownabout NERICA rice resistance to H. sacchari in Nigeria.Therefore, this paper intends to evaluate uplandNERICA rice cultivars for resistance to H. sacchari.

Materials and methods

Extraction of cyst nematodes from soil

Nematode cysts were extracted from soil collected froma naturally-infested plot with cyst nematode at AfricaRice Centre (ARC) field, Ikenne, Ogun State (Lat. 20

67´N and 70 97´N, Long. 60 32´E, and 40 40´E, at 60.8m above sea level, average annual rainfall: 1480 mm,monthly mean temp. range: 18-240C, soil type: Ultisol).The cyst nematode culture was initiated from single cystand multiplied on rice (cultivar OS6) (Salawu 1992) inthe Screenhouse at the Nematology ResearchLaboratory of International Institute of TropicalAgriculture (IITA) Ibadan. Cysts were isolated frominfected host roots and soil, and nematode identity wasconfirmed as Heterodera sacchari at Biosystematics,ARC-PPRI, Pretoria, Republic of South Africa. Inoculumwas collected from mature cysts of H. saccharirecovered by floating organic debris, extracted from soilusing a jet of water. This was decanted to a 250 µmaperture sieve and collected in fluted filter papers. Cystswere picked from the debris using a pair of forceps intoa glass petri dish containing 200 ml of distilled water.Cysts were individually pierced and crushed using adissection needle under a dissecting microscope.Broken cyst was washed into a measuring cylinder andthe suspension agitated for five minutes to free eggsand juveniles. The liberated eggs and juveniles weresubsequently collected on nested sieves viz: 60 µm,38 µm and 25 µm. Where aperture size 60 µm trappedthe cyst cuticle, 38 µm and 25 µm trapped the emergingjuveniles and eggs, respectively (Coyne 1999). Theeggs in distilled water were kept for 48 hours to hatchinto second-stage juveniles (J2) in an incubator set at28±1oC. The J2 suspension was standardised to finalconcentration of 5,000 J2 per ml of distilled water.

Screening of rice cultivars for resistance to Heteroderasacchari

The Experiment was carried out in the screenhouse atIITA, Ibadan (Lat. 70 3'N, 30 45'E at 232.5 m above sealevel). Twenty four cultivars of upland rice seeds wereselected on the basis of wide cultivation across Nigeria.The seeds were obtained from Africa Rice Centre (ARC)formerly known as West African Rice DevelopmentAssociation (WARDA), Ibadan. These includedseventeen cultivars of upland New Rice for Africa(NERICA) 1, 2 ...18 as well as LAC 23, Moroberekan,ITA 150, Suakoko, WAB 56-104, WAB 56-50. OS6reported to be susceptible to H. sacchari (Salawu 1992)and resistant CG 14 (Plowright et al. 1999) were alsoincluded as checks. Five litre plastic pots filled withsteam-sterilised soil were used for the experiments. Twoseeds of each rice cultivar were sown per pot and laterthinned to one stand per pot at two weeks after sowing(WAS). 100 ml of nutrient solution containing 1.8 g ofammonium phosphate + 6.1 g of potassium nitrate +2.8 g of calcium nitrate + 3.8 g of magnesium sulphate/litre of water (Coyne, pers. com.) was applied to riceplants in each pot when plants showed symptoms ofnutrient deficiency/loss of vigour at a day after thinning.Three weeks after sowing, each seedling was inoculatedwith 5,000 second-stage juveniles (J2) of H. sacchari.This was accomplished by making four holes aroundthe roots of each plant with a glass rod and dispensing5 ml of nematode suspension containing 5, 000 J2 of H.sacchari taken from homogenized egg suspension withthe aid of an Eppendorf pipette. Thereafter, the holeswere covered with soil. The uninoculated plants servedas the control, 5 ml of distilled water was added into theholes around the plants and the holes were coveredwith soil. The experiment was arranged in a CompleteRandomised Design with four replicates. The plantswere adequately watered throughout the period of thestudy. The experiment was terminated at sixty days afterinoculation which is the time required for the completionof at least two generations of H. sacchari. Theexperiment was repeated without any modification.Plants in each pot were carefully upturned and adheringsoil particles were gently removed from the roots priorto root examination and rating. Rice cultivars were rated'susceptible' or 'resistant' to H. sacchari on the basis ofnumber of cysts produced per plant as described byCook and Noel (2002) (Table 1) and reproductive factor.The RF was calculated to support cyst production ratingwhere RF between 0-0.9 (resistant), 1.0-9.9 (moderatelyresistant), 10-19.9 (moderately susceptible), 20-29.9(susceptible) and RF > 30 (highly susceptible). Fivegrams root sub-sample per pot was weighed and

38

observed under a dissecting microscope in order tocheck for any adhering white females or matured cystsbefore second-stage juvenile extraction. White femalesof H. sacchari on roots were extracted by dislodgingwith a jet of water and collected over nested sieves of 2mm and 250 µm. Eggs or second-stage juveniles (J2)per cysts of H. sacchari were extracted from thecollected cysts. Also, second-stage juveniles wereextracted from infected roots which were cut into 1-2 cmin water and also from 250 ml of infested soil using themodified Baermann funnel method (Hooper 1986).Matured cysts were extracted from 250 ml of soil usingthe method described by Coyne et al. (2007). Cyststrapped on 250 µm sieve were washed onto a filter paperheld in a funnel and air-dried. Dried cysts were removedfrom organic debris using a damp camel hair brushunder a Leica Wild M3C stereomicroscope. All sampleswere examined after 48h extraction period andnematodes were counted using a Leica Wild M3Cstereomicroscope. Data were collected on number ofJ2 (soil and root) in pot, number of eggs in soil/pot,number of white females in pot, number of matured cysts(soil and root), average number of eggs and J2/cyst.Final nematode population per pot was determined[summation of total number of white females in pot, totalnumber of J2 in soil and roots (total number of maturedcysts in soil and roots X average number of eggs andJ2 per cyst)]. Reproductive Factor (RF) per plant wasdetermined by Pf/Pi; where Pf = Final nematodepopulation and Pi = initial nematode population (5000J2).

Data analyses

Count data were transformed using Log10 (X+1) beforeanalysis (Gomez and Gomez 1984). Data weretransformed in order to follow normal distribution. Datawere analysed with Analysis of Variance (ANOVA) usingstatistical analysis system (SAS) 9.1 (2002) packageand means were separated using Least Significant

Difference (LSD) at 5 % level of probability

Results

Screening of rice cultivars for resistance to cystnematode (Heterodera sacchari)

The 24 rice cultivars evaluated for resistance to cystnematode exhibited differences in their host status toH. sacchari. The cyst production rating and reproductivefactor (RF) varied across rice cultivars. The cystproduction and RF were the criteria upon which ratingfor resistance to H. sacchari was based.

The total number of second-stage juveniles in soiland roots and total number of white females, totalnumber of cyst in soil recovered from pot of NERICArice and O. sativa rice cultivars were significantly(P 0.05) higher than the those obtained from NERICA6, 8 and CG14, respectively (Table 2). The numbers ofeggs and J2 per cyst were significantly (P 0.05) fewerin CG14, NERICA cultivars 6 and 8 than those obtainedfrom O. sativa and other NERICA rice cultivars (Table2).

The cyst production rating was significantly(P?0.05) higher in both O. sativa and NERICA ricecultivars (except NERICA cultivars 6 and 8) than CG14.The cyst production rating of 2.7 was obtained forNERICA 10, while 4.0 was obtained for NERICA 1, 7,14, 17, WAB 56-50, ITA 150 and OS6 rice cultivars,respectively. The least cyst production rating of 0.3 wasobtained in CG14, whereas NERICAs 8 and 6 recordedlow RF of 1.2 and 1.4, respectively (Table 3). The highestRF of 614.8 was observed in OS6 rice cultivar, and thiswas significantly higher (P 0.05) than the RF obtainedfor NERICA rice and O. sativa rice cultivars. The RF ofNERICA rice cultivars ranged from 118.4 in NERICA12 to 434.4 in NERICA 7, and the Oryza sativa cultivars(LAC 23, Moroberekan, ITA 150, WAB 56-50, WAB 56-

104, Suakoko 8) had RF between 75.3 and 481.3 andthese were significantly (P 0.05) higher than CG 14,NERICA 6 and 8 rice cultivars, respectively. The RF of0.3, 4.4 and 2.5 were obtained in CG14, NERICA 6 and8, respectively (Table 3).

On the basis of cyst production rating and alsoreinforced with reproductive factor, the 24 rice cultivarsscreened for resistance were appropriately assignedresistant host status. Oryza sativa and NERICA cultivars(except NERICA 6, 8 and CG14) were found to supportreproduction and multiplication of H. sacchari and thusclassified as susceptible (S) (Table 3). NERICA 6 and 8were classified as moderately resistant (MR) while

Table 1. Cyst production rating scale

Number of Cyst Rating

0 = no cyst (Resistant)1 = 1-2 cysts (Moderately Resistant)2 = 3-10 cysts (Moderately Susceptible)3= 11-30 cysts (Susceptible)4 = 31 cysts and above (Highly susceptible)Source: Cook and Noel (2002)

39

Tabl

e 2.

Pop

ulat

ion

dens

ity o

fHet

erod

era

sacc

hari

on ri

ce c

ultiv

ars

at 6

0 da

ys a

fter i

nfes

tatio

n in

pot

Cul

tivar

Tota

l J2 i

n po

tTo

tal e

ggs

inTo

tal w

hite

fem

ales

Tota

l cys

tE

ggs+

J 2/To

tal e

ggs

Tota

lH. s

acch

ari

(soi

l+ro

ot)

soil

(pot

)in

pot

in s

oil

Cys

t&

J2 p

opul

atio

npo

pula

tion/

pot

in c

ysts

NER

ICA1

(P)

2553

.2 (

3.4)

3381

2.5

(4.5

)87

997.

5 (5

.0)

3968

.8 (

3.6)

230.

694

3625

.0 (

6.0)

1067

988.

2 (6

.0)

NER

ICA2

(P)

2286

.9 (

3.4)

6875

.0 (

3.8)

8952

5.7

(5.0

)42

18.8

(3.

6)21

1.9

9520

00.0

(6.

0)10

5068

7.5

(6.0

)N

ERIC

A3 (P

)37

46.3

(3.

6)25

000.

0 (4

.4)

8129

3.8

(4.9

)98

43.8

(4.

0)20

1.8

2047

687.

5 (6

.3)

2157

727.

5 (6

.3)

NER

ICA4

(P)

4719

.4 (

3.7)

1500

0.0

(4.2

)10

2271

.9 (

5.0)

4734

.4 (

3.7)

207.

397

2062

.5 (

6.0)

1094

053.

8 (6

.0)

NER

ICA5

(P)

2967

.5 (

3.5)

5625

0.0

(4.8

)95

845.

0 (5

.0)

2671

.9 (

3.4)

184.

258

6718

.8 (

5.8)

7417

81.3

(5.

9)N

ERIC

A6 (P

)25

62.5

(3.

4)12

50.0

(3.

1)81

5.6

(2.9

)85

.0 (

1.9)

51.3

1742

5.0

(4.2

)22

053.

2 (4

.3)

NER

ICA7

(P)

7350

.0 (

3.9)

3218

7.5

(4.5

)17

4025

.0 (

5.2)

6562

.5 (

3.8)

282.

819

5807

8.1

(6.3

)21

7164

0.7

(6.3

)N

ERIC

A8 (P

)22

6.3

(2.4

)36

5.0

(2.6

)35

73.2

(3.

6)93

.8 (

2.0)

25.4

8375

.0 (

3.9)

1253

9.2

(4.1

)N

ERIC

A9 (P

)52

06.3

(3.

7)16

822.

8 (4

.2)

4836

5.7

(4.7

)83

12.5

(3.

9)24

7.1

1907

187.

5 (6

.3)

1978

207.

3 (6

.3)

NER

ICA1

0 (P

)42

65.1

(3.

6)35

365.

0 (4

.6)

7899

7.5

(4.9

)21

06.3

(3.

3)19

0.4

4805

52.5

(5.

7)59

9178

.8 (

5.8)

NER

ICA1

1 (P

)63

94.4

(3.

8)19

937.

5 (4

.3)

1336

41.9

(5.

1)53

75.0

(3.

7)22

2.0

1139

843.

8 (6

.1)

1299

817.

6 (6

.1)

NER

ICA1

2 (P

)69

00.0

(3.

8)34

062.

5 (4

.5)

2189

66.3

(5.

3)12

96.9

(3.

1)22

5.8

3318

28.2

(5.

5)59

1756

.9 (

5.8)

NER

ICA1

4 (P

)30

06.3

(3.

5)30

937.

5 (4

.5)

1325

18.8

(5.

1)60

00.0

(3.

8)25

1.2

1561

031.

3 (6

.2)

1727

493.

8 (6

.2)

NER

ICA1

6 (P

)86

93.8

(3.

9)24

062.

5 (4

.4)

1188

87.5

(5.

1)60

72.7

(3.

8)24

1.1

1537

750.

0 (6

.2)

1689

393.

8 (6

.2)

NER

ICA1

7 (P

)51

20.7

(3.

7)25

625.

0 (4

.4)

1910

03.0

(5.

3)38

12.5

(3.

6)20

6.7

9575

00.0

(6.

0)11

7924

8.8

(6.1

)N

ERIC

A18

(P)

2402

.5 (

3.4)

1062

5.0

(4.0

)16

4658

.2 (

5.2)

1828

.2 (

3.3)

245.

246

4437

.5 (

5.7)

6421

23.2

(5.

8)LA

C23

(OS)

6590

.7 (

3.8)

6187

5.0

(4.8

)24

9881

.3 (

5.4)

6171

.9 (

3.8)

262.

416

4059

3.8

(6.2

)19

5894

0.7

(6.3

)M

orob

erek

an (O

S)48

93.8

(3.

7)34

687.

5 (4

.5)

1570

71.9

(5.

2)77

81.3

(3.

9)28

0.9

2209

500.

0 (6

.3)

2406

153.

2 (6

.4)

WAB

56-5

0 (O

S)58

09.4

(3.

8)66

562.

5 (4

.8)

2249

28.2

(5.

4)43

90.7

(3.

6)28

2.8

1247

687.

5 (6

.1)

1544

987.

5 (6

.2)

ITA1

50 (O

S)45

87.5

(3.

7)22

812.

5 (4

.4)

2393

40.7

(5.

4)56

09.4

(3.

8)24

6.0

1449

171.

9 (6

.2)

1715

912.

5 (6

.2)

WAB

56-104

OS

(Par

ent)

5078

.2 (

3.7)

8947

.5 (

4.0)

1015

43.2

(5.

0)17

81.3

(3.

3)22

7.8

3784

37.5

(5.

6)49

5057

.5 (

5.7)

Suak

oko

8 (O

S)50

42.5

(3.

7)81

25.0

(3.

9)84

290.

7 (4

.9)

1421

.9 (

3.2)

190.

227

8750

.0 (

5.5)

3762

08.2

(5.

6)C

G14

Og

(Par

ent)

0.0

(0.0

)0.

0 (0

.0)

0.0

(0.0

)18

.3 (

1.3)

29.5

1472

.5 (

3.2)

1491

.5 (

3.2)

OS6

(OS)

1237

8.8

(4.1

)99

375.

0 (5

.0)

2071

94.4

(5.

3)10

000.

0 (4

.0)

272.

427

5500

0.0

(6.4

)30

7394

8.2

(6.5

)LS

D P

0.05

5452

.1 (

3.7)

5920

9 (4

.8)

1555

67.0

(5.

2)41

74.0

(3.

6)74

.71.

1 x

106 (

6.0)

1.2

x 10

6 (6.

1)

P= In

ters

peci

fic p

roge

ny (W

AB 5

6-104

X C

G14

), O

g =

Ory

za g

labe

rrim

a, O

S=O

ryza

sat

iva,

Val

ues

in p

aren

thes

is w

ere

trans

form

ed u

sing

log(

x+1)

,*

= m

eans

of 8

repl

icat

es L

SD fo

r com

parin

g m

eans

with

in th

e sa

me

colu

mn.

40

CG14 was classified as resistant (R) (Table 3). Themeans of the data collected in the two trials were notsignificantly different (P 0.05). Therefore, the data werecombined for analysis and their means presented.

Discussion

The activities of plant-parasitic nematodes including H.sacchari are often influenced by environmental factorssuch as temperature, moisture content of the soil andhost susceptibility which affect the survival, distribution,egg hatch, migration, penetration, development andsymptom expression in plants. The rate of multiplicationof H. sacchari varied considerably across rice cultivarsused in this study. Based on cyst production rating,reproductive factor (RF), Oryza sativa and 14 out of the16 upland NERICA rice cultivars were observed to besusceptible to H. sacchari infection while NERICA ricecultivars 6 and 8 were observed to be moderatelyresistant to H. sacchari infection, and CG 14 was ratedas resistant, since it did not support the reproduction ofH. sacchari. This corroborates the work of Babatola(1983b) who reported differences in the degrees ofsusceptibility among the 50 rice cultivars screened forresistance to H. sacchari.

Mature females of H. sacchari were also observedin large numbers along the root and root tips ofsusceptible rice cultivars. This was also observed byBabatola (1983b) who reported that the matured femalesof H. sacchari aggregated along the root systems ofsusceptible upland rice at 60 days after inoculating ricecultivars with 6 cysts of H. sacchari per litre of soil.

NERICA 6, 8 (rated moderately resistant) andCG14 identified to be resistant probably possess someresistant genes to H. sacchari which can be incorporatedalongside with broad range of desirable agronomiccharacteristics in widely accepted and cultivated ricecultivars with a view to improving their yield. Reversatand Destombes (1998), Plowright et al. (1999) reportedthat O. glaberrima provides a high level of resistanceto sedentary endoparasitic nematodes that can betransferred to improved hybrids. Plowright et al. (1999)also identified resistance to H. sacchari isolate from Côted'Ivoire in rice hybrid WAB 450-I-B-160 (NERICA 6).

The moderately resistant cultivars identified inthis study may produce satisfactory yield in H. sacchari-infested soils, they would however leave largepopulation of cysts in the soil since they permit somelevel of nematode reproduction and suffer little or nodamage. This could however be detrimental if asusceptible NERICA rice cultivar is planted after the

moderately resistant cultivar in a rotation schemes. Theyare therefore not suitable in crop rotation schemes withcyst nematode-susceptible crops. Planting ofsusceptible upland rice cultivars that support highreproduction of H. sacchari in infested soils wouldhowever lead to a considerable yield loss in addition toleaving large population of cysts in the soil for

Table 3. Reproduction of Heterodera sacchari onNERICA rice, O. sativa and O. glaberrima rice cultivarsat 60 days after infestation

Cultivar Cyst Reproductive Hostproduction

rating factor rating

NERICA1 (P) 4.0 213.6 SNERICA2 (P) 2.9 210.2 SNERICA3 (P) 3.9 431.6 SNERICA4 (P) 3.5 218.8 SNERICA5 (P) 3.0 148.4 SNERICA6 (P) 1.4 4.4 MRNERICA7 (P) 4.0 434.4 SNERICA8 (P) 1.2 2.5 MRNERICA9 (P) 2.8 395.7 SNERICA10 (P) 2.7 119.5 SNERICA11 (P) 3.5 260.0 SNERICA12 (P) 3.0 118.4 SNERICA14 (P) 4.0 345.5 SNERICA16 (P) 3.9 337.9 SNERICA17 (P) 4.0 235.9 SNERICA18 (P) 3.0 128.4 SLAC23 (OS) 3.9 391.8 SMoroberekan (OS) 3.6 481.3 SWAB56-50 (OS) 4.0 309.0 SITA150 (OS) 4.0 343.2 SWAB56-104 OS (Parent) 3.6 99.1 SSuakoko 8 (OS) 2.9 75.3 SCG14 Og (Parent) 0.3 0.3 ROS6 (OS) 4.0 614.8 SLSD P 0.05 1.1 236.9

P= Interspecific progeny (WAB 56-104 x CG14), Og =Oryza glaberrima, OS= Oryza sativa, Cysts productionrating: 0 = no cyst {Resistant(R)}; 1 = 1-2 cysts{moderately resistant (MR)}; 2 = 3-10 cysts {moderatelysusceptible (MS)}; 3 = 11-30 cysts {susceptible(S)}; 4= 31 cysts and above (Highly susceptible), ReproductiveFactor (RF) = Pf/Pi where Pf = final nematode, Pi =5,000 second-stage juvenile (J2) / plant, * = means of 8replicates LSD for comparing means within the samecolumn

41

subsequent cropping seasons. Resistant cultivars,because of their ability to suppress nematodemultiplication have been reported to increase andstabilize yield of crops (Osunlola 2011). They aretherefore useful in rotation scheme on H. sacchari-infested soils. If resistant cultivars are not acceptableto farmers and local markets, the resistance genes ofthese cultivars could be used in breeding program toimprove the popular/acceptable susceptible cultivars.In order to avoid crop failure, the susceptible cultivarsare not recommended for planting in soil infested withH. sacchari. However, where they are preferred forcultivation they could be planted in rotation with resistantcultivars, the use of resistant cultivar is compatible withthe traditional cultural practices. The use of resistantcultivars is simple, non-toxic, cheap and requires nospecialized equipment or use of skilled workers.

Finally, breeders should work in collaborationwith phyto-nematologists during their breedingprogrammes so that cultivars/landraces with usefulnematode resistant genes can be identified andincorporated with other desirable agronomic qualitiesthat are generally acceptable to the end users. Thedevelopment of NERICA rice genotypes with H.sacchari-resistance would be valuable in managing cystnematode population in intensive cropping systemswithout the use of chemicals. Use of resistant cropcultivars would improve crop yield, reduce nematodereproduction and limit damage on the crops grown afterit in a rotation scheme. The use of resistant cultivarsand integration of other compatible pest managementoptions may be required for the control of cyst nematodedue to their survival mechanism which enables them toprotect eggs and juveniles within cysts. This survivalmechanism enable H. sacchari to perpetuate itself andincrease in population when conditions becomefavourable in the next cropping season or even manyyears after fallow.

Acknowledgements

The senior author is grateful to Dr. Danny L. Coyne foroffering him the use of the Nematology ResearchLaboratory, IITA, Ibadan; Dr. Francis Nwilene of AfricaRice Centre for providing NERICA rice seeds requiredfor the experiments.

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Reversat G, Destombes D (1998) Screening for resistance toHeterodera sacchari in the two cultivated ricespecies, O. sativa and O. glaberrima. Fundamentaland Appl Nematol 21: 307-317

Salawu E O (1992) Reaction of sugarcane hybrids to the sugarcyst nematode, Heterodera sacchari: In Proceedingsof the first regional symposium on the biology andcontrol of nematode pest of food crops in Africa.(eds.) Fawole B, Egunjobi O A, Adesiyan S O, IdowuA A African Society of Nematologists: 209-216p

SAS Institute (2002). SAS User's Guide: Statistics, version9.1e SAS Institute, Cary. NC. USA

WARDA (2008a) Africa Rice Trends 2007. Cotonou, Benin:Africa Rice Center (WARDA). Fifth edition; (eds)Aliou Diagne, Ibrahima Bamba, Ali A. Touré andAchille Medagbe: 84

WARDA (2008b) NERICA: The New Rice for Africa- acompendium. E A Somado, R G Guei S O Keya(eds.). Cotonou, Benin: 195

(Manuscript Receivd :5.1.2014; Accepted :20.3.2014)

43

Abstract

Studies on the effect of Khamer + Lemon grass inter croppingon plant and soil characters in two planting geometry revealedthat 5.0m x 2.5m planting of Khamer with lemon grass wassuperior than that of 2.5 , 2.5m planting of Khamer + Lemongrass for all plant growth characteristics. Plant height (1.51m),diameter (22.8cm), number of tillers/plant (54.0) and averageleaf area ( 445.0 cm2) were significantly superior in 5.0 x 2.5mplanting. The content of chlorophyll-a, chlorophyll-b andchlorophyll a+b (1.01, 0.326 and 1.34 mg/g fresh weight ofleaf respectively) was also superior in wider planting. Themean light density below the canopy was higher (721 lux) in5.0 x 2.5m planting than 2.5 x 2.5 m planting (642 lux). Themaximum mean monthly soil temperature below the canopywas in the month of April- May and minimum in January in allthree depth of soil (5.10 and 20 cm). Significantly superiorsoil moisture content (15.8%), soil organic carbon (0.59%)and pH (6.90) was associated with Khamer + Lemon grassintercropping in comparison to Khamer and Lemon grassalone.

Khamer (Gmelina arborea) is an indigenous mediumsized timber tree species of tropical dry deciduous forestnext to teak (Tictona grandis). Grown well on stress sites,fruits are edible and leaves have fodder values. It ismedicinally important woody perennial used for makingwrapping, writing and printing papers.

Lemongrass (Cymbopogon flexuosus) is aperennial aromatic grass used in perfumery and

Effect of Khamer (Gmelina arborea) + Lemongrass (Cymbopogonflexuosus) intercropping on plant and soil characters under agroclimatic conditions of Jabalpur Madhya Pradesh

O.P. Dhurve, I.M. Khan, S.D.Upadhayay* and Sharad Nema **Department of Plant PhysiologyCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaRewa 486 001 (MP)*Department of Plant PhysiologyCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)**SOS Forestry-WildlifeBastar Vishwa Vidyalaya Jagdalpur CGEmail : sdupadhyaya@yahoo.co.in

pharmaceutical industries, leaves contain lemon likeodor containing 75-80% citrol. Lemon grass is a veryhardy drought tolerant crop, adopted to a wide varietyof soil characters condition. Intercropping of annual orperennial crop in between tree space is not a newpractice its an old age practices known as agroforestry(Bean et al. 1977).

Hence present study was designed to determinethe effect of Khamar + lemon grass intercropping onplant and soil characters in two different plantinggeometry to find out the effect of Khamer + lemon grassintercropping on plant characters and to see the effectof Khamer + lemon grass intercropping on soilcharacters.

Material and method

A field experiment was conducted in the medicinalgarden of department of plant physiology, college ofAgriculture, JNKVV Jabalpur during the year 2004-05to know the effect of planting geometry of Khamer tree+ Lemon grass intercropping on soil physio-chemicalcharacteristics, growth and production parameters.Experiment design was RBD with four replications.Intercropping of lemon grass was planted in 60 x 60 cmspacing in the month of July in between two plantinggeometry of Khamer tree ie, 5.0 x 2.5m and 2.5 x 2.5m.

JNKVV Res J 48(1): 43-46 (2014)

44

Observations were taken on plant height, diameter,number of tillers, leaf area by leaser area meter (CI-203, inc. CID make) chlorophyll content (a & b, totalchlorophyll) by Anderson and Boardman (1964) method,light intensity was measured by digital lux meter at 1 mheight for ground level soil characters viz, soil moisturecontent, soil organic carbon, and soil pH were also takenfrom the soil samples of experimental area.

Plant height (cm) was recorded form the base ofthe plant up to the leaf tip for which quadrants of size 1x1 m were laid in intercrop and sole crop. Number oftillers per plant was counted in 1 x 1 m quadrates underboth tree planting geometry and mean were calculated.The total number tillers per plant in lemon grass wereestimated per clump or plant by counting the tillers.

The laser area meter of CI-203 inc. CID makewas used to record the individual leaf area. The leafarea of lemon grass was studied at an interval of threemonths. The plant canopy analyzer (LAI-2000 plantcanopy analyzer) was used for the measurement ofKhamer tree canopy (LAI).

Now 10g of the soil sample was taken in a beakerand kept in the oven for 24 hours at 105 0C after 24hours the soil samples were weighed again. Soilmoisture was determined gravimetrically.

Representative soil - samples were collected fromexperiment site composite soil sample from surface soil(0-15cm) and subsurface (15-30cm) soil were collectedrandomly with the help of soil- sampling tube beforesowing and after harvesting of each crop from each plot.The samples were mixed thoroughly and dried in aircrushed with wooden hammer sieved through 2 mmsieves. The samples so prepared were analyzed forfertility status.

The chlorophylls 'a' and 'b' are the best knownand most abundant and are found in all autotrophicorganisms. The estimation of chlorophyll 'a' andchlorophyll 'b' was don by Anderson and Boardman(1964).

Ten plant samples were collected from the fieldhaving Khamer in 5x2.5 m spacing. Similarly ten moreplant sample of lemongrass was also collected growingas intercrop under Khamer 2.5 x 2.5 m spacing. Theplant samples of lemongrass were also collected formthe plants growing outside of Khamer tree canopy forthe comparison. The optical density was determinedthrough spectrophotometer model spectronic 20 at 645& 663 nm respectively for chlorophyll 'a' and 'b'.

The light intensity was measured by using thedigital Lux meter for each treatment light intensity wasrecorded at a height of 1 m above from the ground leveland below the tree and between two plants of Khamerand out side of the Khamer plantation (Open). Allmeasurement were taken on the clear sky day andalmost at the same time of the day for each plot for allthe treatments. The timing of the study starting from10.00 am to 11.00 am. One additional measurementwas taken after each measurement in full (open)opening located out side of plantation.

Result and discussion

Results of the growth behavior of lemon grassintercropped with Khamer (Table 1) indicated thatmaximum of 445 cm2 leaf area were observed undertree canopy where Khamer tree raised at 5 x 2.5 mspacing compared with lemon grass (439.4 cm2) leafarea under tree spacing 2.5 x2.5 m and in openenvironment (371.2 cm2).

45

Similar pattern of growth performance of lemongrass under 5x 2.5 m tree planning geometry wasobserved with maximum mean values of plant height(1.51 m) based diameter (22.8cm) number of tillers/plant(54) and leaf area (445 cm2) under study, which isstatistically superior than the growth of lemon grassunder 2.5 x 2.5 m tree planting geometry (Fig. 1).

Mean chlorophyll 'a' content of lemon grass underwider tree spacing (i.e. 5 x 2.5 m) revealed thesignificantly higher content of average chlorophyll 'a'(1.010) under 5.0x2.5m planting as compared tochlorophyll content of 0.752 mg.g.fresh weight of lemongrass growing under closer tree spacing (i.e. 2.5 x 2.5m) Khamer (Gmelina arborea) and the chlorophyll 'a'(0.722 and 0.724) lemon grass growing out side thetree canopy of Khamer at 5 x 2.5 and 2.5 x 2.5 m tree

spacing respectively (Fig 2).

In case of chlorophyll 'b' the maximum mean of(0.326 mg.g.fresh weight) was observed in lemon grassgrowing under wider tree spacing (i.e. 5 x 2.5 m) ascompared to chlorophyll 'b' (0.23 mg.g.fresh weight) oflemon grass growing under closer (i.e. 2.5 x 2.5 m)spacing of Khamer and the chlorophyll 'b' content of0.224 and 0.222 mg.g.fresh weight of lemon grassgrowing out side the canopy of Khamer at 5 x 2.5 and2.5 x2.5 m spacing, respectively. similar trend was alsoobserved in case of mean chlorophyll 'a+b' content withmaximum (1.34 mg.g.fresh weight) below canopy ofKhamer planted at 5 x 2.5 spacing as compared to lemongrass growing out side the tree canopy chlorophyll 'a+b' content was 0.93 mg.g.fresh weight which was lower

Table 1. Growth behavior of lemon gross intercropped with Khamer

Growth parameter Spacing mean Open SEm± CD (0.05)5 x 2.5 2.5 x 2.5

Average height (m) 1.51 1.26 0.93 0.106 0.295Average diameter (cm) 22.8 16.2 11.5 1.090 3.027Average number of tillers/plant 54.00 44.00 39.0 1.092 3.033Average leaf area (cm2) 445.00 439.40 371.2 1.090 3.027

Table 2. Soil moisture variation in Khamer + lemon grass agro forestry system

Observation Khamer alone Lemon grass alone Khamer + lemon grass Open

1 14.3 13.77 15.45 11.542 14.66 14.87 15.96 11.213 14.89 15.31 16.05 12.70Mean 14.526 14.560 15.820 01.816SEm± 0.319CD (0.05) 0.782Soil organic carbon (%)

Below tree canopy Out side tree canopy5 x 2.5 2.5 x 2.5 5 x 2.5 2.5 x 2.5

Mean 0.594 0.560 0.390 0.375SEm± 0.0083 0.010CD (0.05) 0.026 0.031Soil pHMean 6.84 6.90 7.112 7.102SEm± 0.031 0.059CD (0.05) 0.086 0.164

46

than chlorophyll content of lemon grass raised open.

Soil moisture in different depths revealed thatmore moisture was in Khamer + lemon grasscombination as compared to Khamer and lemon grassalone (Table 2). Soil moisture percent revealed that inKhamer + lemon grass system at wider tree spacingi.e. 5 x 2.5 m, the soil moisture was more ( 15.82%) ascompared to close tree spacing i.e. 2.5 x 2.5 m andopen ( 11.82) Buttery and Buzzel (1977) noticed highersoil moisture with low density plantation in all season .

The soil organic carbon (%) was notice maximumof 0.594 % under 5 x 2.5 m planting geometry which isstatistically significant at 5 percent level of significanceas compared to 0.560 % organic carbon under 2.5 x2.5 m tree planting geometry. The soil organic carbonwas recorded more under tree canopy as compared tooutside canopy of Khamer tree (Table 2) The soil pHwas recorded minimum of 6.84 under tree canopy at 5x 2.5 m tree planting as compared to pH at 2.5 x 2.5 mtree density which was less than the pH value of outsideof tree canopy .

The light intensity in Khamer + lemon grass basedagroforestry system varied significantly between outside canopy and below the canopy of Khamer however,the mean light intensities out side canopy were ranged1048 to 1001 and 1109 to 1033 lux during eachmeasurement outside canopy at 5 x 2.5 m and 2.5 x.2.5m planting geometry respectively. Whereas, in belowcanopy under 5 x 2.5 m tree planting geometry maximumlight intensity of 721 lux was observed the minimum of509 lux observed under 2.5 x2.5 m tree plantinggeometry (Fig 3).

Hence, it is concluded that Khamer + lemonggrass intercropping on planting geometry 5 x 2.5m wassuperior than that of 2.5 x 2.5 m planting for plant growthcharacters. Plant height 1.51m); diameter (22.8 cm),number of tiller/plant (54.0) and average leaf area (445.0cm2) were significantly superior in 5 x 2.5 m planting.Chlorophyll content a, chlorophyll -b and totalchlorophyll a + b (1.01- 0.326 and 1.34mg/g fresh weightof leaf respectively) was superior in wider planting. Themain light intensity below the canopy was higher in 5 x2.5 m planting then 2.5 x 2.5 m planting. Significantlysoil moisture content (15.8%) organic carbon (0.59%)and pH (6.90) was also superior with Khamer + Lemongrass intercropping in comparison to Khamer alone andlemon grass alone.

[kekj $ uhcw ?kkl vUrorhZ; Qly dk ikS/kksa ,oa e`nk ds xq.kksa ijizHkko dk v/;;u nks jksi.k f=T;kfefr ds fd;k x;k ifj.kke n"kkZrsgS fd 5-0 eh- X 2.5 eh- jksi.k 2-5 X 2-5 eh- jksi.k i)fr lsikS/kksa ds leLr xq.kksa esa Js"B ikbZ xbZA ikS/kksa dh ÅpkbZ ¼1-51eh-½]O;kl ¼22-8 ls-eh-½] dYyksa dh la[;k@ikS/k ¼54-0½ rFkk vkSlr i.kZ{ks=QYk ¼445-0 ls-eh-½ 5-0 eh X 2-5 eh- jksi.k esa Js"B ik;s x;sAblds vfrfjDr gfjr yod &v] gfjr yod &c ,oa gfjr yod v$ c ¼1-01] 0-326 ,oa 1-34 fe- xzke@xzke rkth iRrh dk Hkkj½dk eku Hkh mDr ikS/k jksi.k f=T;kfefr esa Js"B ik;k x;kA QlyvkPNknu ds uhps vkSlr izdk'k ?kuRo ¼721 yDl½ Hkh jksi.k esaldjsa jksi.k ¼642 yDl½ ls vf/kd FkkA vkSlr vf/kdre ekfldrkiØe Qly vkPNknu ds uhps ekg vizSy&ebZ esa rFkk y?kqrerkiØe tuojh ekg esa lHkh rhuksa ¼5-0] 10-0 ,oa 20 ls- eh-½ e`nkxgjkb;ksa esa ik;k x;kA Js"B e`nk ty ek=k ¼15-87 izfr"kr½] e`nktSfod dkcZu ¼0-59 izfr"kr½ ,oa ih ,p eku ¼6-90½ [kekj $ uhcw?kkl vUrohZ; Qly esa [kekj vdsys ,oa uhcw ?kkl vdsys dh rqyukesa FkkA

Reference

Anderson JM, Boardman NK (1964) Studies on greening ofdark grown bean plants VI Development ofPhotochemical activity Aust Biol Sci 17: 93- 101

Buttery BT, Buzzel RT (1977) The relationship betweenchlorophyll content and rate of photosynthesis insoybeans Candian J Plant Sci 57 (1): 1-6

Bean JC, Beall HW, Cole A (1977) Trees food and peopleIDR Ottawa

(Manuscript Receivd : 26.9.2013; Accepted : 20.2.2014)

47

JNKVV Res J 48(1): 47-54 (2014)

Abstract

The present research experiment was conducted during Kharifseason of 2012-13 at the Research Farm, Adhartal area,Department of Agronomy, JNKVV, Jabalpur (MP). Thetreatments comprised of eight maize genotypes. Theinvestigations revealed that the genotype KAVERI SUPER244 out yielded other genotypes (48.39 g plant-1 and 6452 kgha-1) owing to its highest chlorophyll index (21.40), dry matterproduction (101.87 g plant -1), a comparatively highercarboxylation efficiency (0.049µmolm-2s-1 (µmolmmol-1)-1 andwater use efficiency (3.57µmolmmol-1) reflected in its highestplant height (153.98 cm), no. of cobs plant-1 (2.0), no. of grainscob-1 (227.84), cob length (25.19 cm), cob girth (39.10 mm),biological yield (127.65 g plant-1 and 17019 kg ha-1) andultimately grain yield. KAVERI 25-K60-for quantum efficiency(0.0125) and photosynthetic rate (14.96 µmolm-2s-1), KAVERI25-K45- carboxylation efficiency (0.057 µmolm-2s-1(µmol mmol-1)-1), HPQM - water use efficiency (4.74 µmol mmol-1),mesophyll efficiency (1523.60 mol mol-1 ( mol m-2s-1)-1) andHI (40.15%) and lowest stomatal coductance (0.19 molm-2s-1), transpiration rate (3.15 mmol m -2s-1) for droughtresistance may also be utilized in a breeding programme.

Keywords: Maize, genotypes, photosynthetic rate

Maize (Zea mays L.) is the third most important cerealcrop in the world after wheat and rice. Most researchersbelieved that the progenitor of cultivated maize isteosinte. Maize belongs to Family Poaceae and GenusZea. Wild species of maize are Zeamexicana,Zeaperennis, Zealuxurians, Zeadiploperennis. Maize ishighly cross pollinated species. It was also one of thefirst plant species identified to photosynthesize by C4pathway with high yield potential. In India it is grown in8.49 million hectares area with production of 21.28

Evaluation of maize genotypes for physiological efficiency andproductivity under agroclimatic conditions of Kymore plateau zone,Madhya Pradesh

Atole Avinash, A.S. Gontia, Amit Jha*, Anubha Upadhyay and Preeti Sagar NayakDepartment of Plant Physiology*Department of AgronomyJawaharlal Nehru Krishi VishwavidyalayaJabalpur 482 004 (MP)Email: ajaygontia@gmail.com

million tones and average yield of 2507 kg/ha(Anonymous 2011). In Madhya Pradesh it is cultivatedin 0.83 million hectares with production of 1.05 milliontones and contributes 10.07 % and 6.25 % of the totalarea and production in the country. The average yieldof maize in the state is 1256 kg/ha (Anonymous 2010).Its cultivation is confined to Chhindwara, Jhabua, Dhar,Ratlam and Rajgarh districts of Madhya Pradesh. Theplant grows quickly and gives palatable and nutritiousfodder for livestock. Green cobs of the maize and drygrains are considered as nutritious food for humanbeings.

There is a direct correlation between variety andgrowth parameters. Grain yield is highly correlated withleaf area index, plant height, photosynthetic rate,chloroplast, photochemical activity, specific leaf weightand nitrate reductase activity. Gering and Mitcenkova(1961) suggested that genetic difference inphysiological traits should be considered in breedingprogramme based on yield components in maize. It isalso stated that selection of genotypes based on thephysiological traits should be an effective method forimproving grain yield in maize (Camussi and Ottaviano1987). In order to screen out the maize genotypeshaving physiological superiority a physiologicalapproach is needed. Efforts are also being made toidentify constraints of productivity and ways toameliorate them. Though few studies have beenconducted on growth analysis in Maize crop, scantyinformation is available with regards to influence ofvarious physiological mechanisms viz., photosyntheticrate, transpiration rate, stomatal conductance, water useefficiency, carboxylation efficiency and quantumefficiency etc. on economic productivity in maize.

48

Though maize is an important crop occupies ahuge area in India physiological characteristicscontributing to yield are not clearly understood.Therefore, the present investigations were undertakento evaluate Maize genotypes for physiological efficiencyand growth traits influencing productivity.

Materials and method

A field experiment was conducted at the Research Farm,Adhartal, Department of Agronomy, College ofAgriculture, JNKVV, Jabalpur (MP) during Kharif seasonof 2011-2012 in a randomized block design replicatedthrice and treatments comprised of eight maizegenotypes viz ; G1 - 3110, G2 - KAVERI SUPER 244, G3- KMH 3696, G4 - KMH 2589, G5 - KMH 3712, G6 -KAVERI 25K-60, G7 - KAVERI 25K-45 and G8 - HPQMwhich were sown in the field adopting recommendedcultural practices. The date of sowing and harvestingwere 6th July 20012 and 26th October 2012,respectively. The chlorophyll index was worked out byusing chlorophyll meter (Model CCM 200), whereas,dry matter production was recorded by drying the plantsin an electric oven at 80 0C temperature for 36 hrs. tillconstant weight. The physiological mechanisms viz ;photosynthetic rate, stomatal conductance, transpirationrate, canopy temperature, PAR (photosyntheticallyactive radiation) absorption, intercellular CO 2concentration etc. were quantified by using Infra - redgas analyser (IRGA) Li-Cor-6400 (LiCor InstrumentsUSA ). The quantum efficiency was determined as perspecifications of Pandey et al. (2001), whereas, wateruse, carboxylation and mesophyll efficiencies werecalculated as per methods suggested by Kannan andVankataraman (2010), respectively.

Results and discussion

Chlorophyll index

In fifteen maize composites the chlorophyll content inleaves was estimated high (4.7 and 4.46 mg/g) in YMC9905 at the initial stages of growth (45 and 60 daysafter sowing (DAS), respectively); while it was high (4.0and 3.80 mg/g) in NAC 6004 in the later stages (75 and90 DAS, respectively) (Sorte et al. 2005). The rapidavailable chlorophyll had increased kernel yield(Changyu et al. 2002). The chlorophyll content hadsignificant decrease due to water stress than itscorresponding control. The maximum leaf chlorophyllcontent was achieved in young fully expended leaf andgradually started to decline with the advancement of

age and senescence (Nair et al. 2006).

The results revealed (Table 1) that genotypesKAVERI SUPER 244 (21.4), KAVERI 25-K60 (19.82)and (18.75) KAVERI 25-K45 recorded significantlyhigher chlorophyll index over rest of the genotypes. Traitcan be utilized in a breeding program for enhancingphotosynthetic efficiency of crop as the photosyntheticcapability of plant increased with chlorophyllconcentration (Bonner 1952).

Quantum efficiency

The Chlorophyll a fluorescence as represented byquantum yield of electron transport at PS II andefficiency of electron transfer at PS II (Fv/Fm) waslowered due to high temperature (Karim et al. 1999).The quantum yield increased maximum with IAA underfield capacity. It decreased with increasing duration ofwater logging (Pandey et al. 2001). The photosyntheticcapability and status of a photosynthetic organ and AQYreflects the potential photochemical activity of PSII(Zhang and Qiang 2010).

The quantum efficiency represents the efficiencyof crop plants in converting solar energy absorbed bythe plant to the chemical energy. The genotypesKAVERI 25-K60 (0.0125), HPQM (0.120) and KMH3696 (0.0118) possessed significantly more quantumefficiencies over rest of the genotypes (Table 1). Thegenotypes may be used for this trait in a breedingprogram. On the other hand genotype 3110 (0.110) wasassociated with the lowest magnitude for this character.

Carboxylation efficiency

The ratio of net photosynthesis rate to intercellular CO2concentration is termed as intrinsic carboxylationefficiency, higher ratio better the efficiency forcarboxylation (Hamerlynck et al. 2000). Thecarboxylation efficiency differed significantly among theclones such as K37, T6, T21 and K4 which had high CEalso showed relatively higher WUE (Natraja and Jacob1999). The leaf carboxylation efficiency decreased atelevated CO2 (Kim et al. 2006). The inhibition ofphotosynthesis during water stress would be that thestoma closed and the internal CO2 concentration (Ci)decreased (Schulze et al. 1986).

The present study showed (Table 1) thatgenotypes KAVERI 25-K45 (0.0570) and KAVERI 25-K60 (0.0564) indicated higher carboxylation efficienciesindicating better utilization of intercellular CO2 for

49

converting it in to photoassimilates. The higher CE maybe attributed to the higher magnitudes and efficiency ofPhosphoenol pyruvate carboxylase enzyme in thesegenotypes. The lowest value was found in 3110 (0.0439)and KMH 2589 (0.0446).

Water use efficiency

The water use efficiency has been defined as theamount of DM produced/ unit of water lost from the plant.The water use efficiency for irrigation after 70, 100 and130 mm evaporation from evaporation pan was 1.30%,1.16% and 1.03%, respectively. Hybrid KSC700 hadthe highest water use efficiency (Hajibabaei and Azizi2012).

The present investigations revealed (Table 1) thatthe genotype HPQM (4.74) possessed the maximumwater use efficiency at par with KAVERI 25-K45 (4.37)and KAVERI 25-K60 (4.10) suggesting their suitabilityfor drought prone areas. On the other hand genotypes3110 (3.36) and KAVERI KMH 3696(3.44) were foundto be associated with the lowest magnitude for thischaracter.

Mesophyll efficiency

At a given stomatal conductance, lower Ci indicatedbetter mesophyll efficiency and better draw down rateof the substrate CO2 (Ramanjulu et al. 1968).

Table 1. Physiological parameters as influenced by various treatments

Treatment/Genotypes Chlorophyll Quantum Carboxylation Water use Mesophyllindex (gm-2) efficiency efficiency efficiency efficiency

( molm-2s-1 ( molmol-1)-1) ( molmmol-1) ( mol mol-1 ( mol m-2s-1)-1)

G1- 3110 14.25 0.0111 0.0439 3.36 1085.46G2- KAVERI SUPER 244 21.40 0.0112 0.0492 3.57 1130.90G3 KMH 3696 16.69 0.0118 0.0483 3.44 1259.35G4- KMH 2589 13.59 0.0111 0.0446 3.46 1206.28G5- KMH 3712 13.80 0.0114 0.0514 3.55 1287.31G6- KAVERI 25-K60 19.82 0.0125 0.0564 4.10 1126.09G7- KAVERI 25-K45 18.75 0.0116 0.0570 4.37 1194.90G8- HPQM 12.64 0.0121 0.0494 4.74 1523.60SEm ± 0.18 0.0003 0.0020 0.28 79.36CD @ 5% 0.55 0.0009 0.0061 0.86 240.17

Table 2. Physiological parameters as influenced by various treatments

Treatment/Genotypes Canopy Photosynthetic Stomatal Conductance Transpirationtemperature (0C) rate ( molm-2s-1) (molm-2s-1) rate (m molm-2s-1)

G1- 3110 33.30 13.25 0.28 4.12G2- KAVERI SUPER 244 33.31 13.42 0.24 3.78G3 KMH 3696 33.64 14.19 0.23 3.95G4- KMH 2589 33.76 13.30 0.25 3.96G5- KMH 3712 34.19 13.64 0.21 3.85G6- KAVERI 25-K60 33.27 14.96 0.23 3.64G7- KAVERI 25-K45 33.42 13.92 0.20 3.19G8- HPQM 33.38 14.46 0.19 3.15SEm ± 0.16 0.35 0.017 0.20CD @ 5% 0.50 1.05 0.051 0.62

50

The present study revealed (Table 1) thatgenotypes HPQM (1523.6) and KMH 3712 (1287.31)were found to be associated with higher mesophyllefficiencies, a beneficial trait for breeding purposes. Theminimum was recorded in 3110 (1085.46).

Canopy temperature

The sorghum hybrids and their parents with hightranspiration displayed lower leaf resistance values aswell as cooler leaf temperature (Hoffman et al. 1981).The higher productivity of semi dwarf wheat cultivarshas been attributed to their greater canopy temperaturedepression (Fischer et al. 1998).

The results revealed (Table 2) that genotypesKMH 3712 (34.19) and KMH 2589 (33.76) wereassociated with higher canopy temperature. The highercanopy temperature is negatively correlated with thetranspiration rate as the transpiration causes the coolingof canopy. On the other hand these genotypes seem tohave drought tolerance as also indicated by lowesttranspiration rate.

Photosynthetic rate

The crop yields depend upon both the rate and durationof photosynthesis and increased photosynthesispotential is considered to be a possible approach inimproving yield (Ojma et al. 1969). The rate ofphotosynthesis assessed as carbon exchange rate wasthe important component which has direct relevancewith yield components (Camussi and Attaviano 1987).

The net photosynthesis rate (pn) per plant is theimportant factor that determines the biomass productionand water use efficiency of a species. Variation in Pnhas been reported as determinant of plant productivity(Natraja and Jacob 1999). In most of the speciesphotosynthesis rate increases when stomatalconductance increases (Heber et al. 1986). The yieldtraits (including 100 kernel weight, cob length, Kernelsrow-1, row cob-1, kernels cob-1 and kernel weight cob-1)and kernal yield enhanced with increasingphotosynthetic rate in maize varieties (Changyu et al.2002).

The present study revealed the varietal differencein photosynthetic rate in maize genotypes (Table 2). Thevarietal difference in dry matter production is to be moredependent on the difference in leaf growth than onphotosynthetic rate in maize (Duncan and Hesketh1968). The genotypes KAVERI 25-K60 (14.96), HPQM(14.46) and KMH 3696 (14.19) indicated higherphotosynthetic rate as compared to other genotypes.These genotypes may be used in a breeding programmefor increasing photosynthetic productivity. On the otherhand genotypes 3110 (13.25) and KMH 2589(13.30)were found to be associated with the lowestphotosynthetic rate.

Stomatal conductance

The photosynthesis is largely depends upon stomatalregulation (Hsiao 1973). The non significant differencewas noted in stomatal conductance when leaf waterpotential was high (Ackerson et al. 1980). The stomatalconductance is of utmost importance when

Table 3. Yield and yield contributing attributes in maize genotypes

Treatment/Genotypes Plant height Number of Number of Cob length Cob girth 100 grain(cm) Cobs/plant grains/cob (cm) (mm) weight (g)

G-1 3110 147.88 1.00 152.66 23.66 32.10 20.51G2- KAVERI SUPER 244 153.98 2.00 227.84 25.19 39.10 21.44G3- KMH 3696 149.47 1.33 132.82 24.96 33.52 29.39G4- KMH 2589 146.86 1.00 122.40 22.9 33.43 20.45G5- KMH 3712 146.5 1.00 162.28 24.72 33.20 25.86G6-KAVERI 25-K60 147.7 1.67 157.60 23.67 33.26 24.38G7-KAVERI 25-K45 153.5 1.00 162.86 25.01 33.97 21.69G8- HPQM 138.9 1.00 142.26 23.03 31.90 18.81SEm ± 0.3508 0.1606 0.7408 0.2190 0.1338 0.5763CD @ 5% 1.0617 0.4861 2.2421 0.6629 0.4049 1.7442

51

photosynthesis is concerned. Stomata play a pivotalrole in controlling the balance between assimilation andtranspiration (Beadle et al. 1981). The stomatalconductance of genotypes declined with decrease insoil water availability. The variation in rate ofphotosynthesis was observed with reference to stomatalconductance (Balsimha et al. 1993).

The results indicated that (Table 2) genotypes3110 (0.280), KMH2589 (0.253) and KAVERI SUPER244 (0.243) possessed higher magnitudes for stomatalconductance. The higher stomatal conductance isassociated with the higher transpiration rate. Theminimum was registered in HPQM (0.192) at par withKAVERI 25-K45 (0.205).

Transpiration rate

It is necessary to have higher plant conductance toachieve higher canopy photosynthesis which not onlyenhance the CO2 exchange rate but also results inhigher transpiration rate (Farquhar and Sharkey 1982).The rate of transpiration decreased with theadvancement of maturity. It might have occurred onaccount of the cumulative effect of decreased soilmoisture content (Gupta et al. 2012). The transpirationis one of the major gas exchange parameters related/associated with plant growth and productivity (Taiz andZeiger 2002).

The present study indicate that genotypes3110(4.12), KMH 2589(3.96) and KMH 3696 (3.95)recorded the higher transpiration rates, respectivelyindicating their unsuitability for drought sensitive areas

(Table 2). However, the higher transpiration rate is abeneficial trait as long as water availability is inabundance. It causes stomatal opening which facilitatesCO2 entry in the plants required for production ofphotoassimilates. On the other hand if transpiration rateexceeds absorption rate which is common in soils havingwater scarcity this will cause stomatal closure retardingCO2 uptake in plants subsequently reducingphotosynthesis. The lowest transpiration rate wasrecorded in HPQM (3.15) and KAVERI 25-K45 (3.64)indicating suitability of these genotypes for cultivationin drought sensitive areas.

Total dry matter production

Sayer (1948) proved that the accumulation of dry matterin maize plant follows the typical sigmoid shape curve.The maximum rate of dry matter production occursduring the period when the plants are at tasseling andsilking stages. Duncan and Hesketh (1968) noted thatthe rate of increase in leaf area is directly related to dryweight growth which may be more dependent on rateof leaf expansion than on the net rate of photosynthesisper unit leaf area.

The present study (Table 4) indicated that thegenotypes KAVERI SUPER 244 (101.87) and KAVERI25-K45 (98.35) recorded higher dry matter productionover the remaining genotypes. Tollenaar (1991) whilestudying with the various maize hybrids for theirphysiological performance noted the differences in rateof dry matter accumulation between the hybrids variedamong 3 phases of development. The dry matter

Table 4. Total dry matter production (g/plant), Yield and yield contributing attributes in maize genotypes

Treatment/Genotypes Total dry matter Grain yield Biological yield Harvestproduction Grain yield Grain yield Biological Biological index(g/plant) (g/plant) (kg/ha) yield (g/plant) yield (kg/ha) (%)

G-1 3110 93.31 31.13 4278 81.73 11564 36.99G2- KAVERI SUPER 244 101.87 48.39 6452 127.65 17019 37.91G3- KMH 3696 91.58 38.25 5355 105.45 14410 37.17G4- KMH 2589 89.71 31.30 4871 84.84 12610 38.62G5- KMH 3712 91.73 33.21 4876 86.00 12805 38.09G6-KAVERI 25-K60 90.73 39.19 5481 105.61 15137 36.21G7-KAVERI 25-K45 98.35 35.43 5197 93.60 13726 37.86G8- HPQM 78.49 26.37 4133 65.69 10291 40.15SEm ± 0.5823 0.4307 1285 0.699 353 0.1954CD @ 5% 1.7624 1.3036 3890 2.115 1070 0.5913

52

accumulation rate was higher by 10, 30 and 48% inimproved hybrids as compared to old hybrids duringthe 3 phases of growth from planting to maturity.

Grain yield

The present study revealed (Table 3-4) that the genotypeKAVERI SUPER 244 outyielded other genotypes (48.39g/plant and 6452 kg/ha) owing to its maximum plantheight (153.98cm), cob number/plant (2.00), number ofgrains/cob (227.84), cob length (25.19cm), cob girth(39.10 mm) and biological yield (127.65 g/plant and17019 kg/ha) which had reflected in its highest yield.Genotype KAVERI 25-K60 (39.19g/plant and 5481kg/ha) was ranked second in yield performance due to itscomparatively higher number of cobs/plant (1.67),number of grains/cob (157.6), 100 grain weight (24.38g)and biological yield (105.61g/plant and 15137kg/ha).Genotype HPQM produced lowest grain yield which wasattributed to a low performance of all the yieldcomponents.

Biological yield

Biological yield refers to the total biomass of the plantincluding economic yield. Biological yield had directeffect on grain yield/plant (Singh et al. 2003). Thebiological yield was the main determinant of seed yieldand possessed strong and positive correlation with grainyield.

The present study revealed (Table 4) that thegenotype KAVERI SUPER 244 (127.65g/plant and17019 kg/ha) indicated the highest biological yield overother genotypes which was attributed to higher DMproduction by the genotype. On the other hand genotypeHPQM (65.96g/plant and 10291 kg/ha) recorded thelowest biological yield (Choudhary et al. 1991).

Harvest index

In the present investigations (Table 4) it has beenobserved that the genotype HPQM was associated withthe highest harvest index (40.15) over the remaininggenotypes a trait which can be utilized in a breedingprogram for enhancing the mobilization efficiency ofgenotype. KAVERI 25-K60 registered the minimum HI(36.21) which indicated that the translocation efficiencyof the genotype is required to be improved.

Conclusion

Thus the investigations revealed that the genotypeKAVERI SUPER 244 yielded out other genotypes (48.39g plant -1 and 6452 kg ha -1) owing to its highestchlorophyll index (21.40), dry matter production (101.87g plant-1), comparatively higher carboxylation efficiency(0.049µmolm -2s -1(µmolmmol -1) -1) and water useefficiency (3.57µmolmmol-1) reflected in its highest plantheight (153.98 cm), no. of cobs plant-1 (2.0), no. of grainscob-1 (227.84), cob length (25.19 cm ), cob girth (39.10mm), biological yield (127.65 g plant-1 and 17019 kgha-1) and ultimately grain yield. Genotypes KAVERI 25-K60 for quantum efficiency (0.0125) and photosyntheticrate (14.96 µmolm -2s -1), KAVERI 25-K45 forcarboxylation efficiency (0.057 µmolm -2s-1(µmolmmol-1)-1), HPQM for water use efficiency (4.74 µmolmmol-1), mesophyll efficiency (1523.60 ( mol mol-1 ( molm -2s -1) -1) and HI (40.15%) and lowest stomatalconductance (0.19 mol m-2s-1), transpiration rate (3.15mmol m-2s-1) for drought resistance may also be utilizedin a breeding programme.

izLrqr vUos"k.k tokgjyky usg: d`f"k fo'ofo|ky;] tcyiqj ds 'kL;foHkkx ds vuqla/kku iz{ks= esa [kjhQ 2012&13 esa fd;s x;sA thuksVkbidkosjh lqij 244 us lcls vf/kdre iSnkokj ¼48-39 xzk- ikS/k&1 ,oa6452 fd-xzk- gs-&1 ½ dh tks fd lcls vf/kd DyksjksfQy lwP;kad¼21-4½] 'kq"d Hkkj iSnkokj ¼101-87 xzk- ikS/k&1½] rqYukRed vf/kddkcZu Mkb vkWDlkbM Lokaxhdj.k {kerk ¼0-049 ekbØkseksy eh-&2

ls-&1 ¼ekbØkseksy fe-eksy&1½&1½,oa ty mi;ksx {kerk ¼3-57 ekbØkseksyfe-eksy&1½ tks fd vf/kdre ÅWpkbZ ¼153-98 ls-eh-½] HkqV~Vk la[;kikS/k&1 ¼2-0½] izfr HkqV~Vk nkuksa dh la[;k ¼227-84½] HkqV~Vk yackbZ¼25-19 ls-eh-½] HkqV~Vk eksVkbZ ¼39-10 fe-eh-½] tSfod iSnkokj¼127-65 xzk- ikS/k&1 ,oa 17019 fd-xzk- gs-&1½ ,oa iSnkokj esaifjofrZr gqbZ A dkosjh 25&ds 60 & vf/kd DokaVe iSnkokj¼0-0125½,oa izdk'k laLys"k.k nj ¼14-96 ekbØkseksy eh-&2 ls-&1½ds fy;s] dkosjh 25 & ds 45 & dkcZu Mkb vkWDlkbM Lokaxhdj.k{kerk ¼0-057½] ,p-ih-D;w-,e-&ty mi;ksx {kerk ¼4-74½] ehtksfQy{kerk ¼1523-60 ekbØkseksy eksy&1 ¼eksy eh-&2 ls-&1½&1½,oa,p-vkbZ- ¼40-15%½] ,oa fuEure okrja/kz pkydRo & ¼0-19 eksy eh-&2 ls-&1½] mLosnu nj ¼3-15 fe-eksy eh-&2 ls-&1½ ds fy;s ikniiztuu dk;ZØe esa lw[kk izfrjks/kd thuksVkbi mRiUu djus esa mi;ksxfd;k tk ldrk gSA

53

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54

dates. Indian J Plant Physiol 10: 254-259Sorte NV, Phad KM, Balachandran Sripriya, More MB, Titare

PS (2005) Chemical and bio-chemical traits in maizecomposites. J Soils and Crops 15 (2): 424-427

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(Manuscript Receivd : 10.1.2014; Accepted :25.3.2014)

55

Abstract

One hundred twenty one Japonica x Indica derived F10Recombinant Inbred Lines (RILs) were planted in Alpha latticedesign with two replications during kharif 2011 at SeedBreeding Farm, Department of Plant Breeding and Genetics,JNKVV Jabalpur. Significant variation due to genotypes forall the characters revealed that the genotypes differsignificantly for all the characters under study. In presentstudy high heritability coupled with high genetic advance wasexhibited for characters viz., number of tillers per plant, numberof filled grains per panicle, number of unfilled spikelets perpanicle, number of spikelets per panicle, grain yield per plant,panicle index and harvest index. It was also observed thatnumber of tiller/plant, culm height, plant height, biological yield/plant, panicle index and harvest index were positivelycorrelated with grain yield and also had positive direct effectand therefore these traits should be given due importancewhile practicing selection, aimed for improvement of grainyield. These RILs can be used as parents for hybrid productionin rice.

Keywords: Rice, genetic variability, correlation andpath analysis

Rice (Oryza sativa L.) is one of the important cerealcrops and is central to the lives of billions of peoplearound the world. Rice is predominant staple food for17 countries in Asia and the pacific, nine countries inNorth and South America and eight countries in Africa.India rank 1st in area (43.92 million ha) and 2nd inproduction (91.61 million tonnes) after China (Patil andPatil 2009). The current world population of 6.1 billionis expected to reach 8.0 billion by 2030 and riceproduction must increase by 50 percent in order to meetthe growing demand. If this goal is to be met, it isnecessary to use rice varieties with higher yieldpotential, durable resistance to diseases insects and

tolerance to abiotic stresses.

Yield of rice is a complex quantitative characterscontrolled by many genes interacting with environment.Population of Recombinant Inbred Lines can serve asa powerful tool to cover the complexity of yield relatedtraits. They are the recombinant output from whichsuperior stabilized segregants can be directly used asbreeding lines. Considering the above facts the RILspopulation were used to assess the variability of yieldand its component traits and there association.

Material and methods

Japonica x Indica derived F10, one hundred twenty oneRecombinant Inbred lines along with two parents (JNPT89 & IR 64) were planted in an Alpha lattice design withtwo replications during Kharif 2011-12 at Seed BreedingFarm, Department of Plant Breeding and Genetics,J.N.K.V.V., Jabalpur. Twenty one days seedling of eachgenotype was planted in five rows of three meter lengthwith 20 cm row spacing, keeping single seedling perhill. Recommended package of practices were followedto raise a good crop.

The observations on yield and yield attributingcharacters were recorded as per standard procedure.The data were subjected to statistical analysis toworkout GCV, PCV, heritability, correlation and pathcoefficient analysis as per standard methods.

Result and discussion

The mean, range, phenotypic and genotypic co-efficientof variation, heritability estimates and genetic advancepercentage of mean are presented (Table 1). Significantvariation due to genotypes for all the characters

Estimation of genetic variability and correlation for grain yield andits components in RILs derived population of rice

Prabha Rani Dongre, D.K.Mishra, G.K. Koutu and S.K. SinghDepartment of Plant Breeding & GeneticsJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : gk_koutu@yahoo.co.in

JNKVV Res J 48(1): 55-59 (2014)

56

Tabl

e 1.

Para

met

ers

of g

enet

ic v

aria

bilit

y an

d its

attr

ibut

es in

reco

mbi

nant

inbr

ed li

nes

of ri

ce

Cha

ract

ers

Mea

nR

ange

GC

V (%

)PC

V (%

)H

2(bs

) (%

)G

A as

%M

in.

Max

.of

mea

nD

ays

to 5

0 pe

rcen

t flo

wer

ing

(X1)

95.0

570

.50

113.

5010

.30

10.3

399

.35

21.1

5C

ulm

hei

ght (

cm)

(X2)

76.0

957

.70

129.

4510

.59

10.7

297

.56

21.5

5Pa

nicl

e le

ngth

(cm

)(X

3)25

.31

21.2

029

.55

6.51

7.36

78.3

611

.88

Pant

hei

ght (

cm)

(X4)

101.

4980

.80

155.

408.

488.

6296

.63

17.1

6N

umbe

r of t

illers

/pla

nt(X

5)6.

642.

8513

.25

32.2

533

.53

92.4

963

.88

No.

of f

illed

grai

ns /

pani

cle

(X6)

173.

1694

.05

301.

5023

.62

26.1

481

.64

43.9

6N

o. o

f unf

illed

spik

elet

s /p

anic

le(X

7)53

.69

6.25

239.

5962

.94

64.6

794

.71

126.

19N

o. o

f spi

kele

ts /p

anic

le(X

8)22

7.59

128.

6141

3.39

28.7

629

.46

95.3

257

.85

Pani

cle

wei

ght /

plan

t (g)

(X9)

28.1

417

.60

49.7

521

.09

22.0

491

.56

41.5

7Te

st w

eigh

t(g)

(X10

)25

.01

19.0

431

.70

9.32

9.57

94.9

018

.70

Biol

ogic

al y

ield

/ pl

ant (

g)(X

11)

57.6

734

.45

80.0

516

.57

17.7

986

.80

31.8

0G

rain

yie

ld /p

lant

(g

)(X

12)

25.8

79.

5047

.00

25.4

426

.98

88.9

549

.43

Pani

cle

inde

x(X

13)

95.4

795

39.4

019

5.21

31.9

833

.95

88.7

262

.05

Har

vest

inde

x(X

14)

45.8

975

16.1

790

.06

29.7

530

.00

98.3

660

.79

revealed that the genotypes differ significantly for allthe characters under study. Considering the magnitudeof phenotypic and genotypic coefficient of variation itrevealed that number of unfilled spikelets per panicle,number of tillers per plant, panicle index, harvest index,number of spikelets per panicle, number of filled grainsper panicle panicle weight per plant had relatively largeamount of genetic variability. In present study highheritability coupled with high genetic advance wasexhibited for characters viz., number of tillers per plant,number of filled grains per panicle, number of unfilledspikelets per panicle, number of spikelets per panicle,grain yield per plant, panicle index and harvest index.Such values indicated, predominantly the presence ofadditive gene action in the expression of these traitsand consequently, greater chance of improving thesetraits through simple selection. High heritability coupledwith low genetic advance was also observed for daysto 50% flowering, culm height, plant height and testweight. Such values indicated non-additive gene actionand influenced by the favourable environment ratherthan genotypes. These results are in agreement withPadmaja et al. (2008), Chandra et al. (2009), Jayasudhaand Sharma (2010) and Selvaraj et al. (2011).

It was observed that grain yield per plant waspositively associated with number of tillers per plant,culm height, plant height, biological yield per plant,panicle index and harvest index (Table 2). The pathcoefficient analysis (Table 3) revealed that harvest index,panicle index, biological yield per plant, number ofspikelets per panicle, panicle weight per plant, culmlength, panicle length, test weight and days to maturityhad positive direct effect on grain yield per plant andtherefore, these traits must be given due importancewhile practicing selection, aimed for improvement ofgrain yield in rice. These results agree with the findingsreported by Gazafrodi et al. 2006; Agahi et al. 2007;Chakraborty et al. 2010 and Nandan et al. 2010.

On the basis of finding, the selection of RILs forhigher yield may be given due importance on the basisof good number of tillers per plant, moderate plantheight, high biological yield per plant, high panicle indexand high harvest index. These RILs can be used asparents for hybrid production in rice.

/kku esa tiksfudk x bafMdk iztkfr;ksa ds ladj.k ls izkIr 121jhdkEchusUV bucszM ykbZu ¼jhy½ dks vYQk ysfVl fMtkbu ds nksjsIyhds'ku esa lu~ 2011 ds [kjhQ ekSle esa ikS/k iztuu ,oavuqokaf'kdh foHkkx ds cht iztuu iz{ks= ij yxk;k x;k A lekfo"Vksaesa mit ,oa mit ls lacaf/kr vU; xq.kksa dk vkdyu ekud fof/k;ksa

57

Tabl

e 2.

Phen

otyp

ic c

orre

latio

n be

twee

n yi

eld

and

its a

ttrib

utes

in re

com

bina

nt in

bred

line

s of

rice

X1

X2

X3

X4

X5

X6

X7

X8

X9

X10

X11

X12

X13

X14

X1

1-0

.254

**0.

0305

**-0

.250

5**

-0.4

311*

*0.

3994

**0.

4083

**0.

4849

**0.

4003

**-0

.004

9-0

.102

4-0

.402

1**

-0.1

234*

-0.2

017*

*

X2

10.

1825

**0.

9678

**0.

1512

**0.

1749

**-0

.162

8**

0.03

640.

0861

0.06

330.

0508

0.12

83**

0.17

47**

0.21

48**

X3

10.

3776

**0.

069

0.20

49**

0.05

550.

1693

**0.

0804

0.08

54-0

.021

9-0

.048

10.

0356

0.00

98

X4

10.

1639

**0.

1931

**-0

.165

6**

0.04

750.

0745

0.07

260.

0472

0.12

61*

0.16

86**

0.20

19**

X5

1-0

.581

2**

-0.4

941*

*-0

.636

1**

-0.2

009*

*-0

.120

9*0.

1759

**0.

3615

**0.

1535

**0.

2894

**

X6

10.

3756

**0.

8544

**0.

4793

**0.

1179

*-0

.066

8-0

.351

0**

-0.0

217

-0.0

691

X7

10.

7634

**0.

4689

**0.

0259

-0.0

731

-0.4

361*

*-0

.188

0**

-0.2

278*

*

X8

10.

5968

**0.

0905

-0.0

814

-0.4

813*

*-0

.114

4*-0

.166

1**

X9

10.

0259

-0.0

136

-0.5

033*

*0.

0625

0.07

2

X10

1-0

.015

90.

0534

0.08

990.

0876

X11

10.

1490

**-0

.428

2**

0.20

55**

X12

10.

6427

**0.

7969

**

X13

10.

7770

**

X14

1.00

00

X1

Day

s to

50

perc

ent f

low

erin

gX

8N

o. o

f spi

kele

ts /p

anic

leX

2C

ulm

hei

ght (

cm)

X9

Pan

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wei

ght /

plan

t (g)

X3

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leng

th (c

m)

X10

Test

wei

ght(g

)X

4P

ant h

eigh

t (cm

)X1

1B

iolo

gica

l yie

ld /

plan

t (g)

X5

Num

ber o

f tille

rs /p

lant

X12

Gra

in y

ield

/pla

nt (

g)X

6N

o. o

f fill

ed g

rain

s / p

anic

leX1

3Pa

nicl

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dex

X7

No.

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X14

Har

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inde

x* S

igni

fican

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% le

vel

;** S

igni

fican

t at 1

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vel

58

Tabl

e 3.

Path

coe

ffici

ent b

ased

on

geno

typi

c co

rrel

atio

n co

effic

ient

(sho

win

g di

rect

and

indi

rect

effe

ct o

f cha

ract

ers

cont

ribut

ing

to g

rain

yie

ld in

rice

X1

X2

X3

X4

X5

X6

X7

X8

X9

X10

X11

X12

X13

X1

0.00

19-0

.000

50.

0001

-0.0

005

-0.0

009

0.00

080.

0008

0.00

090.

0008

0.00

00-0

.000

2-0

.000

8-0

.000

2X

2-0

.058

30.

2231

0.04

700.

2174

0.03

430.

0421

-0.0

379

0.00

730.

0181

0.01

390.

0102

0.03

000.

0390

X3

0.00

130.

0077

0.03

640.

0141

0.00

260.

0085

0.00

250.

0069

0.00

320.

0037

-0.0

013

-0.0

015

0.00

17X

40.

0597

-0.2

250

-0.0

896

-0.2

308

-0.0

382

-0.0

481

0.03

99-0

.010

4-0

.016

0-0

.016

9-0

.008

6-0

.031

1-0

.039

5X

50.

0013

-0.0

004

-0.0

002

-0.0

005

-0.0

028

0.00

180.

0015

0.00

190.

0006

0.00

04-0

.000

5-0

.001

1-0

.000

4X

6-0

.117

6-0

.050

0-0

.062

0-0

.055

40.

1768

-0.2

654

-0.1

245

-0.2

419

-0.1

336

-0.0

383

0.01

930.

0977

0.00

46X

7-0

.079

20.

0321

-0.0

128

0.03

260.

0998

-0.0

886

-0.1

888

-0.1

501

-0.0

931

-0.0

056

0.01

730.

0899

0.03

73X

80.

1968

0.01

290.

0747

0.01

79-0

.268

70.

3608

0.31

470.

3959

0.23

440.

0377

-0.0

371

-0.1

946

-0.0

468

X9

0.10

230.

0198

0.02

150.

0170

-0.0

543

0.12

320.

1206

0.14

490.

2446

0.00

60-0

.006

3-0

.123

50.

0162

X10

-0.0

001

0.00

070.

0012

0.00

09-0

.001

50.

0017

0.00

040.

0011

0.00

030.

0118

0.00

000.

0008

0.00

11X

11-0

.046

50.

0193

-0.0

151

0.01

570.

0777

-0.0

307

-0.0

387

-0.0

395

-0.0

108

-0.0

005

0.42

170.

0325

-0.2

045

X12

-0.1

896

0.05

95-0

.018

30.

0597

0.17

32-0

.163

1-0

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9-0

.217

8-0

.223

60.

0300

0.03

410.

4431

0.29

13X

13-0

.086

80.

1221

0.03

220.

1196

0.10

94-0

.012

0-0

.138

0-0

.082

50.

0463

0.06

65-0

.338

60.

4588

0.69

81

X1

Day

s to

50

perc

ent f

low

erin

gX

8N

o. o

f spi

kele

ts /p

anic

leX

2C

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ght (

cm)

X9

Pani

cle

wei

ght /

plan

t (g)

X3

Pani

cle

leng

th (c

m)

X10

Test

wei

ght(g

)X

4Pa

nt h

eigh

t (cm

)X

11Bi

olog

ical

yie

ld /

plan

t (g)

X5

Num

ber o

f tille

rs /p

lant

X12

Gra

in y

ield

/pla

nt

(g)

X6

No.

of f

illed

grai

ns /

pani

cle

X13

Pani

cle

inde

xX

7N

o.of

unf

illed

spik

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s/pa

nicl

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14H

arve

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dex

* Si

gnifi

cant

at 5

% le

vel;

** S

igni

fican

t at 1

% le

vel

59

ls fd;k x;k A jhdkEchusUV buczsM ykbZu ds dkj.k cnyko ifjyf{krgq;s] blls ;g fu"d"kZ fudyrk gS fd lHkh thu :i mit ,oa vU;lHkh xq.kksa dks cnyko ds lkFk iznf'kZr djrs gSaA ifj.kkesa ds foospukds ckn ;g Hkh fu"d"kZ fudyrk gS fd /kku esa dYyksa dh la[;k] rusdh mapkbZ ck;ksfyftdy mit] isfudy bUMsDl vkSj gkosZLV bUMsDlbR;kfn xq.k vf/kd mit ds fy;s mRrjnk;h gSaA vr% mRre jhy dspquko djrs le; mijksDr xq.kksa dk /;ku j[kuk vko';d gSA pqus gq;sjhyksa dk mi;ksx ladj /kku ds fi=ksa ds :i esa fd;k tk ldrk gSA

References

Agahi K, Fotokian MH, Farshadfar E (2007) Correlation andpath analysis of some yield related traits in ricegenotypes (Oryza sativa L.). Asian J Plant Sci 6(3):513-517

Chakraborty S, Das PK, Guha B, Sarmah KK, Barman B(2010) Quantitative genetic analysis for yield andyield components in boro rice (Oryza sativa L.). NatSci Bio 2(1): 117-120

Chandra, Satish B, Reddy TD, Kumar SS (2009) Variabilityparameters for yield, its components and quality traitsin rice (Oryza sativa L.). Crop Res 38 (3):144-146

Gazafrodi A, Honarnegad AR, Fotokian MH, Alami A (2006)Study of correlations among agronomic traits andpath analysis in rice (Oryza sativa L.). J Sci & TechnolAgric & Natur Res 10(2): 107-110

Jayasudha S, Sharma D (2010) Genetic parameters ofvariability, correlation and path-coefficient for grainyield and physiological traits in rice (Oryza sativa L.)under shallow lowland situation. Electronic J PlantBreeding 1(5): 1332-1338

Padmaja D, Kumar R, Subba LV, Padma V (2008) Studies onvariability, heritability and genetic advance forquantitative characters in rice (Oryza sativa L.).Indian J Plant Genet Res 21(3): 71-84

Patil HE, Patil SD (2009) Genetic engineering for droughtresistance in Rice. Agrobios Newsl 7(8): 6-7.

Nandan R, Sweta, Singh SK (2010) Character associationand path analysis in rice (Oryza sativa L.) genotypes.World J Agri Sci 6(2): 201-206

Selvaraj CI, Nagarajan P, Thiyagarajan K, Bharathi M,Rabindran R (2011) Genetic parameters of variability,correlation and path coefficient studies for grain yieldand other yield attributes among rice blast diseaseresistant genotypes of rice (Oryza sativa L.). AfricanJ Bio 10(17): 3322-3334

(Manuscript Receivd :22.1.2013; Accepted : 19.2.2014)

60

Abstract

Investigations were carried out during Kharif 2012 at Jabalpur,Madhya Pradesh to evaluate the weed management practiceson maize. Ten treatments consisted with pre emergenceapplication of atrazine 1.0 kg/ha, pendimethalin 1.0 kg/ha andalachlor 2.5 kg/ha alone and with hand weeding at 30 DAS,combined application of atrazine 0.75 kg/ha + pendimethalin0.75 kg/ha and atrazine 0.75 kg/ha + alachlor 2.25 kg/ha,hand weeding twice at 20 and 40 DAS and weedy check weretested. The hand weeding twice at 20 and 40 DAS wasmaximum uptake of N (210.3 kg/ha), P (69.6 kg/ha) and K(293.6 kg/ha) followed by pre emergence application ofatrazine 1.0 kg/ha + hand weeding at 30 DAS was maximumuptake of N (204.9 kg/ha), P (68.3 kg/ha) and K (289.4 kg/ha)among all the treatments .

Keywords: Fodder Maize, Nutrient uptake, Soilproperties

Maize (Zea mays L.) is a dual purpose crop cultivatedby farmers for human consumption as cornflakes,popcorn and other industrial purposes (mainly starch,dextrose, corn syrup etc.) besides animal feed. In Indiait is grown in 8.49 million hectares area with productionof 21.28 million tonnes and average yield of 2507 kg/ha (Anonymous 2011). Weeds reduce crop yield bycompeting for light, water, nutrients and carbon dioxide,interfere with harvesting and increase the cost involvedin crop production (Oerke 2005). The predominant weedflora were Echinochloa crusgalli L. and Cynodondactylon L. among monocots; Cyperus rotundus L.among sedges; and Amaranthus viridis L., Digeraarvensis L., Portulaca oleracea L., Alternenthara sessiliL. and Trianthema spp. among dicots (Arvadiya et al.2012). The infestation of these weeds is increasing dayby day in the maize growing belt of the state especiallywhere the farmers are using atrazine year after year.So, in order to widen the weed control spectrum, it is

Effect of weed management control practices on nutrientuptake and soil properties in fodder maize

Pratik Sinodiya and A.K.JhaDepartment of AgronomyJawaharlal Nehru Krishi Vishwa VidalayaJabalpur 482004 (MP)Email : amitagcrewa@rediffmail.com

desirable to use tank mix combinations of two herbicideshaving different mode of action and integrated weedmanagement practices for better weed control. Hence,systematic investigations on these aspects areimportant.

Material and methods

Field experiment was conducted at Research Farm,AICRP on Forage Crops, Department of Agronomy,Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur(Madhya Pradesh) during Kharif season, 2012. The soilof the experimental field was sandy clay loam in texture,neutral in reaction (pH 7.2), and low in organic carbon(0.53%) as well as with low available nitrogen (232 kg/ha), medium available phosphorus (17.18 kg/ha) andmedium available potassium (315 kg/ha) contents withnormal electrical conductivity (0.32). Ten treatmentsconsisted with pre emergence application of atrazine1.0 kg/ha, pendimethalin 1.0 kg/ha and alachlor 2.5 kg/ha alone and with hand weeding at 30 DAS, combinedapplication of atrazine 0.75 kg/ha + pendimethalin 0.75kg/ha and atrazine 0.75 kg/ha + alachlor 2.25 kg/ha,hand weeding twice at 20 and 40 DAS and weedy checkwere tested in a randomized block design with threereplications. Sowing of maize cv. African Tall was doneon 13th July, 2012 by using the seed rate 40 kg/ha asper treatments in the rows 60 cm apart. A uniform doseof 80 kg/ha N + 40 kg P2O5 + 20 kg K2O/ha was appliedin all plots. Half quantity of N as per treatment alongwith full quantity of P and K fertilizers were given asbasal application at the time of sowing and remainingN was top-dressed at 25 DAS and 45 DAS.The sampleof seeds and stover were taken at the time of harvest ofcrop and then they were allowed to dry in an oven till toreach the constant weight. After this, these samples

JNKVV Res J 48(1): 60-63 (2014)

61

were grinded into fine powder with the help of mortarand pistal. After this, N, P and K contents of thesesamples were analysed by microkjeldahl digestionmethod (AOAC, 1966) for nitrogen, Vanadomolybdophosphoric acid yellow colour method (AOAC, 1966)for phosphorus and flame photometer method (Khannaet al. 1971) for potassium, respectively. The values ofNPK contents for seeds and stover were recordedtreatment wise and then N, P and K uptakes weredetermined for seed and stover yields of each treatment.The total uptake of these nutrients was recorded bysummation of uptake. The soil samples were taken fromeach plot after harvesting maize crop to assess the effectof various treatments on the changes in soil chemicalproperties over their parental status. These sampleswere taken with the help of screw type soil auger fromeach plot and tagged with luggage labels. The samplesfrom 3 replications were mixed treatment wise and thendried well. After this, treatment wise composite sampleswere powdered with the help of morter and pistal. Then,the grinded soil samples passed through 2 mm sizedsieve. The treatment wise samples were subjected tovarious analysis for chemical properties as per standardprocedure given by Organic carbon %) Walkey andBlack rapid titration method (Walkley and Black 1934),available N alkaline Permanganate method (Subbiahand Asija 1956), available P Calorimetric method (Olsenet al. 1954), available K Flame photometric method(Hanway and Heidal 1953), Electrical conductivitySolubridge method (Black 1965), Soil pH GlassElectrode pH meter (Piper 1967). The data obtained

from the analysis were used for interpretation of theresults.

Result and discussion

Uptake of nutrients

Uptake of nutrients viz., nitrogen, phosphorus andpotassium by fodder maize was significantly influencedby weed control treatments are given in Table 15 andthe details of nutrient content in seed and stover givenin Table 1.

Uptake of nitrogen

The maximum uptake of nitrogen by crop was recordedunder hand weeding at 20 and 40 DAS (210.3 kg/ha)closely followed by atrazine 1.0 kg/ha + hand weedingat 30 DAS (204.9 6 kg/ha), pendimethalin 1.0 kg/ha +hand weeding at 30 DAS (199.6 kg/ha) and alachlor2.5 kg/ha + hand weeding at 30 DAS (198.9 kg/ha) Allthese three are statistically similar and had higheruptake of N than rest of the treatments. However, theyremoved significantly higher nitrogen than atrazine 0.75kg/ha + alachlor 2.25 (196.8 kg/ha) and atrazine 0.75kg/ha + pendimethalin 0.75 kg/ha (195.7 kg/ha) whichwere at par. The nitrogen uptake by maize wasstatistically at par in herbicides used alone as pre

Table 1. Effect of different treatments on total uptake of nutrients in fodder maize

Treatments Total uptake (kg/ha)N P K

T1 Atrazine 1.0 kg/ha PE 193.8 66.4 271.0T2 Pendimethalin 1.0 kg/ha PE 188.7 59.6 261.2T3 Alachlor 2.5 kg/ha PE 194.2 61.1 270.3T4 T1 + Hand weeding at 30 DAS 204.9 68.6 289.4T5 T2+ Hand weeding at 30 DAS 199.6 65.1 278.9T6 T3+ Hand weeding at 30 DAS 198.9 68.3 281.4T7 Atrazine 0 .75 kg/ha + Pendimethalin 0 .75 kg/ha tank mixed PE 195.7 63.8 272.9T8 Atrazine 0 .75 kg/ha + Alachlor 2.25 kg/ha tank mixed PE 196.8 63.1 279.1T9 Hand weeding at 20 and 40 DAS 210.3 69.6 293.6T10 Weedy check 161.8 52.0 235.1

SEm± 2.03 0.44 1.61CD at 5% 6.07 1.33 4.82

62

emergence atrazine 1.0 kg/ha (193.8 kg/ha), alachlor2.5 kg/ha (194.2 kg/ha) but lower in pendimethalin 1.0kg/ha (188.7 kg/ha). The lowest uptake of nitrogen(161.8 kg/ha) is found in weedy check plots. Satao andNalamwar (1993) reported that nutrient uptake by cropswas lowest under weedy check, which might possiblybe due to more crop weed competition for N, P and Kby weeds (Table 1).

Uptake of phosphorus

The maximum total uptake of phosphorus by crop wasrecorded under hand weeding (69.6 kg/ha) closelyfollowed by atrazine 1.0 kg/ha + hand weeding at 30DAS (68.6 kg/ha) and alachlor 2.5 kg/ha + hand weedingat 30 DAS (68.3 kg/ha) pendimethalin 1.0 kg/ha + handweeding at 30 DAS (65.1 kg/ha). All these three arestatistically similar and had higher uptake of P than restof the treatments. However, they removed significantlyhigher nitrogen than atrazine 0.75 kg/ha + pendimethalin0.75 kg/ha (63.8 kg/ha) and atrazine 0.75 kg/ha +alachlor 2.25 (63.1 kg/ha) which were at par. Thephosphorus uptake by maize was statistically at par inall the herbicides used alone as pre emergence atrazine1.0 kg/ha (66.4 kg/ha), alachlor 2.5 kg/ha (61.1 kg/ha)and pendimethalin 1.0 kg/ha (59.6 kg/ha). The lowestuptake of phosphorus (52.0 kg/ha) is found in weedycheck plots (Table 1).

Uptake of potassium

The maximum total uptake of potassium by crop wasrecorded under hand weeding (293.6 kg/ha) closelyfollowed by atrazine 1.0 kg/ha + hand weeding at 30DAS (289.4 kg/ha), alachlor 2.5 kg/ha + hand weedingat 30 DAS (281.4 kg/ha) and pendimethalin 1.0 kg/ha +hand weeding at 30 DAS (278.9 kg/ha) atrazine 0.75kg/ha + alachlor 2.25 kg/ha (279.1 kg/ha) which is similarto atrazine 0.75 kg/ha + pendimethalin 0.75 kg/ha (272.9kg/ha). The phosphorus uptake by maize wasstatistically at par in all the herbicides used alone aspre emergence atrazine 1.0 kg/ha (271.0 kg/ha),alachlor 2.5 kg/ha (270.3 kg/ha) and pendimethalin 1.0kg/ha (261.2 kg/ha). The lowest uptake of phosphorus(235.1 kg/ha) is found in weedy check plots (Table 1).

Changes in chemical properties of soil

Chemical properties of soil viz., soil pH, EC, OC andavailable N, P and K contents did not show remarkablevariation in the soil after harvesting of kharif crops inany of the treatment over their initial status (Table2).

vf[ky Hkkjrh; pkjk vuqla/kku] ifj;kstuk t-us-d`-fo-fo esa pkjkeDds ij [kjirokj fu;a=.k ds nl mipkj iz;ksx fd;s x;sA nlmipkj es ,Vªkthu 1 fd-xzk-@gS- isUMhesFkyhu 1 fd-xzk-@gS- ,oa,ykDyksj 2-5 fd-xzk-@gS- dks vdsys ,oa 30 fnu ij gkFk }kjk

Table 2. Effect of different treatments on soil properties

Treatments pH EC Organic N P K(dS/mol) carbon(%) (kg/ha) (kg/ha) (kg/ha)

T1 Atrazine 1.0 kg/ha PE 7.41 0..42 0.63 235.24 17.10 335.21T2 Pendimethalin 1.0 kg/ha PE 7.42 0.41 0.61 232.45 17.30 333.25T3 Alachlor 2.5 kg/ha PE 7.43 0.42 0.62 234.32 17.20 332.65T4 T1 + Hand weeding at 30 DAS 7.44 0.43 0.63 235.63 17.20 332.58T5 T2 + Hand weeding at 30 DAS 7.43 0.42 0.61 233.17 17.30 334.62T6 T3 + Hand weeding at 30 DAS 7.45 0.42 0.62 233.52 17.20 335.62T7 Atrazine 0.75 kg/ha + Pendimethalin 7.44 0.41 0.64 236.22 17.23 334.25

0 .75 kg/ha tank mixed PET8 Atrazine 0.75 kg/ha + Alachlor 2.25 7.44 0.43 0.63 235.46 17.22 333.21

kg/ha tank mixed PET9 Hand weeding at 20 and 40 DAS 7.43 0.42 0.61 235.55 17.21 331.56T10 Weedy check 7.41 0.40 0.60 234.22 17.20 330.25

Initial 7.20 0.32 0.53 232.00 17.18 315

63

funkbZ ds lkFk] 20 ,oa 40 fnu ij gkFk }kjk funkbZ ,oa ,Vªkthu 0-75 fd-xzk-@gS-+++ isUMhesFkyhu 1 fd-xzk-@gS- vkSj ,Vªkthu 0-75 fd-xzk-@gS- + ,yDyksj 2-25 fd-xzk-@gS- dk VsUd feDl Mkyk x;kA izkIrifj.kkeksa ds vk/kkj 20 ,oa 40 fnu ij gkFk }kjk funkbZ djus ijvf/kd N¼2010-3 fd-xzk-@gS-½ P ¼69-6 fd-xzke@gsDVs;j ½ ,oaK ¼293-6 fd-xzk-@gS-½ dk vo'kks"k.k gqvkA blds i'pkr ,Vªkthu1-0 fd-xzk-@gsDVs;j + 30 fnu ij gkFk }kjk funkbZ ls N ¼204-9fd-xzk-@gS- ½ P ¼68-3 fd-xzk-@gS-½ ,oa ¼289-4 fd-xzk-@gS-½ dkvo'kks"k.k nwljs mipkj fd rqyuk esa vf/kd Fkk A

References

Anonymous (2011) Agricultural statistical at a glance,Directorate of Economics and statistics, Ministry ofAgriculture and cooperation, Govt. of India New Delhi

Arvadiya LK, Raj VC, Patel TU Arvadiya MK (2012) Influencesof plant population and weed management on weedflora and productivity of sweet corn (Zea mays).Indian J Agron 57(2): 162-167

A.O.A.C. (1966) Official Methods of Analysis of the Associationof Official Agricultural Chemists, 8th edition,Washington DC

Khanna SS, Gupta, SK Pal AR (1971) Potassiumdetermination. Laboratory manual for chemicalmethods of plant analysis, published by departmentof Soil Science and Agricultural Chemistry. JNKVV,Jabalpur (MP): pp 24.

Walkley A Black CA (1934) Methods of soil analysis soilsciences, 37:28

Subbiah BV Asija GH (1956) A rapid procedure for theestimation of nitrogen in soils. Curr. Sci 25:259-260.

Piper CS (1967) Soil and plant analysis. Asia PublishingHouse, Bombay and New Delhi.

Olsen SR, Cole CV, Frank S, Watanabe Dean, LA (1954)Estimation of available phosphorus in soil byextraction with sodium bicarbonate. USDA Cic.939:1-19.

Hanway JJ Heidal H (1953) Soil Analysis Method Used inIowa State College Soil Testing laboratory. IowaAgriculture.

Black CA (1965) Method of Soil Analysis. Amer Soc AgronInc, Washington by grain and straw for eachtreatment separately

Satao RN Nalamwar RV (1993) Studies on uptake of nitrogen,phosphorus and potassium by weeds and sorghumas influenced by integrated weed control. Proc. Int.Symp ISWS Hissar November 18-20 Vol.IIT:103-107

(Manuscript Receivd : 22.8.2014; Accepted :10.3.2013)

64

Abstract

Present investigation was carried out at Horticultural College& Research Institute, Venkataramannagudem, West GodavariDist. (Andhra Pradesh). The experimental material comprisedof twelve genotypes of Lablab purpureus collected from IIPRKanpur and AICRP on pegeionpea Bangalore. The objectiveof the experiment was to identify suitable genotypes to beused as cultivar in coastal Anhdra Pradesh. The analysis ofvariance revealed significant differences between genotypesindicating presence of sufficient amount of variability in allthe characters studied. Wide range of variability was observedfor plant height, days to first flowering, days to 50% flowering,number of inflorescences per plant, number of pods perinflorescence, pod yield per plant, number of pods per plantand days to last pod harvest indicating the scope for selectionof suitable initial breeding material for further improvement.On the basis of the mean performance of the genotypesamong traits studied, the following were identified as promisinglines for further crop improvement in dolichos bean viz., GL243, Culture 47 and GL 671. Among all the genotypes studied,genotypes GL 243 recorded the highest pod yield per plantand found suitable to the local agro-climatic conditions.

Keywords: Dolichos bean, Lablab purpureus, meanperformance, genotypes, variability

A most preferred food legume consumed in several partsof Asia, including in India is Dolichos Bean (Lablabpurpureus L. (Sweet) or Dolichos lablab L.). It is memberof the family Fabaceae, sub-family Faboideae and oneof the most ancient crop known for its food and foddervalue and having the various synonyms viz., HyacinthBean, Country Bean, Bonevist Bean, Tonga Bean,Lablab Bean, Indian Bean, Butter Bean, Field Bean,Poor's man Bean and in Hindi Sem or Semi (Peter andKumar 2008).

Performance of dolichos bean (Lablab purpureus L.) genotypes incoastal Andhra Pradesh

Ajay Kumar Verma, K. Uma Jyothi, A.V.D. Dorajee Rao and R.P. SinghDepartment of HorticultureDr.Y.S.R. Horticultural UniversityVenkataramannagudem, West Godavari Dist. Andhra PradeshEmail: ajayhorti19@gmail.com

In any crop improvement program, basicinformation with respect to variability present in the cropis essential. There is no single variety/cultivar whichhas occupied a large area in Andhra Pradesh. Only localtypes, traditional farmer collections and cultivars arebeing cultivated. The consumer preference also varieswith respect to pod size, shape, colour and aroma. Theefforts of improving the crop by utilizing indigenous andexotic germplasm have been useful in breaking the yieldbarriers (Shivashankar et al. 1993) resulting indeveloping compact plant type with reduced durationand photo-insensitivity. Hence, comprehensivegermplasm evaluation by identification of suitablegenotypes for pure crop is essential.

Materials and methods

The present investigation was carried at College ofHorticulture and Research Institute,Venkataramannagudem, West Godavari Dist. AndhraPradesh during 2012-13. The experimental materialconsisted of 12 field bean genotypes, collected fromdifferent parts of India through IIPR Kanpur, AICRP onLegume crops, GKVK, Bangalore and IIHR, Bangalore.

The experiment was laid out in Randomized BlockDesign with three replications during September 2012-13 and various characters were studied. The meanreplicated data collected on twelve genotypes ofdolichos bean were subjected to the appropriatestatistical analysis.

Results and discussion

During the investigation, out of the eighteen charactersunder consideration, plant height, number of primary

JNKVV Res J 48(1): 64-67 (2014)

65

branches and number of secondary branches per plantare considered as growth attributes. Days to firstflowering, days to 50 per cent flowering, days to firstpod harvest and days to last pod harvest are theearliness attributes. Number of inflorescences per plantand number of pods per inflorescence are consideredas flower attributes. Pod length, pod width, number ofpods per plant and number of seeds per pod areregarded as pod attributes, while hundred seed weightand protein content are the seed attributes.

Among the growth attributes, greater plant heightand more number of secondary branches are preferable.Similarly among the flower attributes, more number ofinflorescences per plant and more number of pods perinflorescence are desirable. For earliness, lessernumber of days to first flowering, days to 50 per centflowering and days to first pod harvest are desirable,whereas more number of days to last pod harvest ispreferable in dolichos bean for getting high yield. Amongthe pod attributes, lengthy pods with medium pod width,more number of seeds per pod and more number ofpods per plant are preferable, while other attributes like

more hundred seed weight and higher protein contentare desirable for the consumer acceptance in dolichosbean.

Highly signif icant differences among thegenotypes were observed for all the characters studiedindicating presence of sufficient amount of variability inall the characters studied (Table 1). A wide range ofvariations existing for various quantitative traits has alsobeen reported in doilchos bean by Ganesh (2005), Lalet al. (2005), Rai et al. (2008), Patil and Lad (2007),Chattopadyay and Dutta (2010) and Upadhyay andMehta (2010).

Out of 12 genotypes, GL 243, Culture 47 and GL671 recorded significantly high marketable pod yieldper plant than the commercial checks Arka Jay and ArkaVijay. GL 243 was superior to the checks for marketablepod yield due to more number of pods per plant. Otherattributes like number of inflorescence per plant, numberof pods per inflorescence, days to last pod harvest, podwidth and hundred seed weight were also higher in thegenotype. Hence this can be selected for further

Table 1. Analysis of variance for eighteen quantitative traits in 12 genotypes of dolichos bean

Character Mean sum of squaresReplications Treatments Error

(df=2) (df=11) (df=22)

Plant height (cm) 0.9880 1105.3408** 1.2003Number of primary branches per plant 0.0064 0.9436** 0.0089Number of secondary branches 0.0808 23.1869** 0.3609Days to first flowering 1.3448 217.8703** 0.5844Days to 50% flowering 0.1315 236.3669** 0.7749Number of inflorescences per plant 0.0513 24.8617** 0.3211Number of pods per inflorescence 0.1115 6.2589** 0.1228Number of pods per plant 0.1702 1640.2615** 0.4551Number of seeds per pod 0.0030 0.5706** 0.0056Mean pod weight (g) 0.0090 1.5729** 0.0046Pod length (cm) 0.1516 4.1666** 0.1182Pod width (cm) 0.0057 0.1077** 0.0083Days to first pod harvest 0.7308 176.8363** 1.1199Days to last pod harvest 0.7600 892.4038** 1.7545Marketable pod yield per plant (g) 36.3124 16255.1076** 23.8341100 seed weight (g) 3.4290 504.8041** 1.3363Protein content (%) 0.0574 39.1166** 0.0868Pod yield/ha (q) 0.2537 5030.1911** 0.1181

*, ** = significant at 5% and 1% levels of significance respectively

66

Tabl

e 2.

Mea

n pe

rform

ance

s of

the

eigh

teen

qua

ntita

tive

char

acte

rs in

twel

ve g

enot

ypes

of d

olic

hos

bean

S. N

o.PH

NP

BN

SB

DFF

DF5

0%N

IPN

PIN

PP

NS

PM

PW

PL

PWD

FHD

LHP

YP

PH

SWPC

PYP

H

Cul

ture

-497

.20

4.26

8.05

66.8

868

.90

5.87

3.85

20.5

03.

904.

306.

651.

6490

.16

115.

3986

.30

29.8

427

.65

47

Cul

ture

-711

0.13

4.30

9.05

57.4

960

.42

11.2

92.

9130

.16

4.08

3.82

7.66

1.57

83.3

914

8.85

116.

6523

.62

26.0

464

Cul

ture

-47

109.

262.

6014

.56

46.8

448

.85

12.8

17.

3478

.34

4.65

3.57

6.52

1.80

73.7

517

0.26

265.

8454

.42

19.6

014

7

Cul

ture

-62

121.

533.

3012

.73

63.2

367

.16

12.2

56.

1870

.27

3.54

2.86

4.92

1.98

89.3

413

1.75

203.

2719

.68

27.6

611

2

GL-2

4385

.43

2.50

15.2

844

.76

46.7

613

.53

7.93

93.1

23.

773.

415.

572.

0670

.35

175.

5130

0.83

61.6

317

.74

167

GL-

388

79.2

33.

2513

.50

49.4

052

.40

11.3

96.

4571

.14

3.40

2.98

5.31

1.84

76.4

815

7.42

212.

5043

.24

24.6

511

8

GL-

411

75.6

32.

9212

.26

55.3

958

.42

10.4

66.

8668

.38

3.63

2.42

4.73

1.52

83.1

314

6.29

166.

1133

.83

25.6

492

GL-

671

74.7

02.

8913

.85

48.6

550

.65

12.5

77.

1575

.64

3.67

3.43

5.73

1.71

75.4

916

7.31

252.

6153

.85

20.5

714

0

GL-

416

73.6

73.

289.

9360

.71

63.7

28.

486.

3853

.13

3.58

2.13

5.00

2.02

86.5

814

1.42

113.

6933

.80

26.8

263

HA-

468

.33

3.46

6.36

48.4

851

.44

4.07

5.73

20.5

33.

354.

445.

491.

8372

.82

137.

6791

.38

30.3

619

.25

50

Ark

a Ja

y(C

)68

.40

3.50

10.1

842

.69

44.6

810

.88

5.32

57.7

94.

104.

048.

551.

7671

.49

139.

8423

4.37

32.5

022

.80

130

Ark

aVija

y(C

)63

.91

3.47

10.2

640

.26

42.3

811

.50

5.39

62.2

04.

643.

906.

792.

0868

.46

151.

9224

2.40

41.4

720

.19

134

Gra

nd m

ean

85.6

23.

3111

.33

52.0

654

.65

10.4

25.

9558

.43

3.86

3.44

6.07

1.82

78.4

514

8.63

190.

4938

.19

23.2

110

5

SE

m ±

0.63

0.05

0.34

0.44

0.50

0.32

0.20

0.38

0.04

0.03

0.19

0.05

0.61

0.76

2.81

0.66

0.17

0.19

CV

(%)

1.27

2.86

5.30

1.46

1.61

5.43

5.88

1.15

1.94

1.98

5.65

5.02

1.34

0.89

2.56

3.02

1.26

0.32

CD

(P=0

.05)

1.85

0.16

1.01

1.29

1.49

0.95

0.59

1.14

0.12

0.11

0.58

0.15

1.79

2.24

8.26

1.95

0.49

0.58

PH

= P

lant

hei

ght (

cm);

NP

B=

Num

ber o

f prim

ary

bran

ches

per

pla

nt; N

SB

= N

umbe

r of s

econ

dary

bra

nche

s pe

r pla

nt, D

FF=

Day

s to

firs

t flo

wer

ing;

DF5

0% =

Day

sto

50%

flow

erin

g; N

IP=N

umbe

r of i

nflo

resc

ence

s pe

r pla

nt; N

PI=

Num

ber o

f pod

s pe

r inf

lore

scen

ce; N

PP

=Num

ber o

f pod

s pe

r pla

nt, N

SP

=Num

ber o

f see

ds p

er p

od;

MP

W=M

ean

pod

wei

ght (

g), P

L= P

od le

ngth

(cm

); P

W=P

od w

idth

(cm

); D

FH=

Day

s to

firs

t pod

har

vest

; DLH

= D

ays

to la

st p

od h

arve

st; P

YPP

= P

od y

ield

per

pla

nt(g

), H

SW

= hu

ndre

d se

ed w

eigh

t (g)

; P

C=

Pro

tein

con

tent

(%);

PYP

H=P

od y

ield

per

hec

tare

(q).

67

evaluation and directly released as a variety. Meanperformances of the eighteen quantitative charactersin twelve genotypes of dolichos bean are presented inthe Table 2.

Culture 47 recorded second place in respect ofmarketable pod yield per plant along with number ofpods per plant, number of inflorescences per plant,number of pods per inflorescence, days to last podharvest, pod width and hundred seed weight. GL 671was significantly superior to both the checks with regardto number of pods per plant, number of pods perinflorescence, pod width and other attributes likehundred seed weight. Genotypes GL 243, Culture 47and GL 671 recorded significantly better performancefor marketable pod yield per plant than the checks andrecorded the highest number of pods per plant, hundredseed weight. Hence, the genotypes GL 243, Culture 47and GL 671 can be selected for further improvement.

Among the other genotypes, GL 388 and Culture62 produced significantly more number of inflorescencesper plant, pods per inflorescence, protein content andmoderate marketable pod yield per plant. Culture 7recorded significantly lengthy pods and high proteincontent. Hence, these genotypes can be used as donorparents for the respective characters.

Three genotypes viz., GL 243, Culture 47 andGL 671 showed significantly higher yield over thechecks. Based on the findings of the presentinvestigation, the conclusion drawn for furtherimprovement of dolichos bean genotypes is that thereis a need to evaluate these high yielding genotypes inlarge plots and over locations in coastal Andhra Pradeshfor their commercial utilization. A large number of pestsand diseases affect the crop. There is a need tosystematically test the genotypes for pest and diseasereaction. They can be directly selected for generalcultivation after confirming their performance in largeplots across environments.

orZeku iz;ksx ckxokuh dkyst ,.M fjlpZ baLVhV~;wV osdVjeUukxqMseif'pe xksnkojh ftyk vka/kzizns'k esa fd;k x;k A iz;ksxkRed lkexzh esaHkkjrh; nygu vuqal/kku laLFkku dkuiqj vkSj ,vkbZlfvkjih fituihcsZxaykj ls ycyc dk mn~ns'; rVh; vka/kzizns'k esa Qly ds :i esabLrseky fd;s tk ldus ;ksX; mi;qDr thuksVkbi dh igpku djuk FkkA fopj.k ds fo'ys"k.k ls v/;;u fd;s x;s lHkh y{k.kkas esa ifjorZu'khyrkds fy, i;kZIr jkf'k dk irk pyk tks fd thuksVkbi ds chp egRoiq.kZvarj dk ladsr gS A ikS/k Å¡pkbZ igyk Qwy yxus ds fnukssa] izfrizfr'kr Qwy yxus ds fnuksa] izfr ikS/k iqLiØeksa dh la[;k] izfr iq"iØe

Qfy;ksa fd la[;k] izfr ikS/k Qyh mit ,oa vafre rqjkbZ ds fnuksa tSlsy{.kksa ds fy, O;kid fofHkUurk ikbZ xbZ tks fd vkxs lq/kkj ds fy,mi;qDr izkjafHkd iztUu lkexzh ds p;u dh laHkkouk dks n'kkrsZ gS AthuksVkbi ds vkSlr izn'kZu ds vk/kkj ij th-,y- 243] dYpj 47,oa th-,y- 671 thuksVkbi lse esa Øe'k% Qly lq/kkj ds fy,loZJs"B ik, x, A v/;;u fd;s x, lHkh thuksVkbi ds chp] th-,y- 243 thuksVkbi esa mPpre Qyh mit ntZ dh xbZ vkSj LFkkuh;—f"k tyok;q ifjfLFkfr;ksa ds fy, mi;qDr ik;k x;k A

References

Chattopadyay A, Dutta S (2010) Characterization andidentification of selection indices of pole type dolichosbean. Vegetable crops research bulletin 73: 33-45

Ganesh BN (2005) Genetic variability and divergence studiesby D2 statistics and RAPD analysis in field bean(Lablab purpureus L. Sweet). M Sc (Agri) Thesis,Acharya N G Ranga Agricultural University (AP)

Lal H, Rai M, Verma A, Vishwanathm P (2005) Analysis ofgenetic divergence of dolichos bean (Lablabpurpureus) genotypes. Veg Sci 32 (2): 129-132

Patil SB, Lad DB (2007) Variability studies in Wal (Lablabpurpureus (L.) Sweet). J Maharashtra Agri Univ32(2): 296-297

Peter KV, Kumar PT (2008) Genetics and breeding ofvegetable crops. Indian Council of AgriculturalResearch, New Delhi, pp-231-234

Rai N, Singh PK, Verma A, Lal H, Yadav DS, Rai M (2008)Multivariate characterization of Indian Bean (Lablabpurpureus (L.) Sweet.) genotypes. J Plant GenResources 21(1): 42-45

Shivashankar G, Kulkarni RS, Shashidhar HE, Mahishi DM(1993) Improvement of field bean. Adv in Horti 5:277-286

Upadhyay D, Mehta N (2010) Biometrical studies in DolichosBean (Dolichos lablab L.) for Chattisgarh Plains. ResJ Agri Sci 1 (4): 441-447

(Manuscript Receivd :6.1.2014; Accepted :20.3.2014)

68

JNKVV Res J 48(1): 68-72 (2014)

Abstract

An investigation was carried to determine the effect ofintegrated nutrient management in respect of growthand yield attributes of tomato hybrid under poly-housecondition. Indeterminate hybrid tomato Katrina waschosen for the study. Results revealed that integratedapplication of 50% RDF +10 t/ha FYM + 5 t/ha Poultrymanure + biofertilizers (Azospirillum+PSB@5kg/ha),recorded significantly highest number of leaves / plant(36.82), number of fruits per cluster (8.11), fruit length(7.21 cm), number of fruits / plant (76.26), yield / plot(39.67 kg) over other treatments.

Keywords: Integrated nutrient management,polyhouse, tomato, growth, and yield

Tomato (Solanum lycopersicon Mill.) is one of the mostimportant vegetable cultivated under natural fieldconditions as well as protected condition throughoutthe world. In India area under tomato cultivation is 8.80lakh ha. with a total production of 182.26 MT andproductivity of 20.7 mt./ha (Annonymous 2013). Tomatois a thermo-sensitive crop and fruit set is usuallyadversely affected when night temperature reachesbelow 13oC or day temperature exceeds 30oC. Theoptimum temperature required for its cultivation is 15 -27oC. Prevailing low temperature and frost injury duringwinter are the limiting factors in India. To make itscultivation profitable during winter and spring summerseason, poly-house offers a vital solution. Green houseshave tremendous potential in increasing productivity oftomato (Chandra et al. 2000). Tomato being a heavy

Influence of integrated nutrient management on morphology,phenology and yield potential of hybrid tomato under polyhousecondition, at Jabalpur, Madhya Pradesh

Aradhana Singh, P.K. Jain, A.S. Gontia and Yashpal SinghCollege of AgricultureDepartment of HorticultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : aradhanar_285@rediffmail.com

feeder and exhaustive crop responds well to nutrientapplication. Among factors responsible for lowproduction, nutrition is of prime importance. Applicationof chemical fertilizers alone increase the crop yield inthe initial year however adversely affected thesustainability subsequently. The cost of chemicalfertilizers is increasing. Therefore, to reducedependence on chemical fertilizers along withsustainable production seems to be issues in modernagriculture that can be achieved possible throughintegrated plant nutrient supply systems (IPNS). On theother hand, organic manures including FYM, poultrymanure and vermicompost are cheap and easilyavailable in local condition and can be efficiently utilizedfor tomato production in a particular region. Integratednutrient sources increase the nutrient use efficiently andsoil fertility thus enhance the productivity of tomato.Farmers are attracted towards the organic farming indeveloped countries in order to reduce the harmfulchemical reduces, but it has not yet taken roots inmajority of developing countries, including IndiaHowever, the information about cultivation of tomatocrop under poly-house cultivation in align with integratednutrient management is very limited in Madhya Pradesh.Therefore, the present investigation was made to studythe effect of integrated nutrient management onmorphology, phenology and yield potential of tomatounder poly-house condition.

Materials and methods

An experiment to evaluate effect of INM on tomato wasconducted under polyhouse at the vegetable research

69

farm, Maharajpur. Department of Horticulture,Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur,India during the year of 2012-13.Jabalpur is situated ata latitude of 23.910 N and longitude of 79.50 E. Thealtitude of the place is 411.78 meters above the meansea level. The climate of the region is typically semi-arid and sub-tropical having extreme winter andsummer. The average temperature and relative humidityinside the poly-house was kept 15-30oC and 65-70 %respectively. The hybrid seeds of tomato were procuredfrom Rashi Seed Company, Bangalore, India. Theexperiment was laid out in completely randomizeddesign with three replications. The treatmentscomprised of fertilizer levels consisting of organic,inorganic and bio fertilizers, i.e. T 1-100% RDF(Recommended dose of fertilizer) + 10 t/ha FYM + 3 t/ha Vermicompost + BF0 (without biofertilizer),T2-100%RDF + 10 t/ha FYM + 3 t/ha Vermicompost + BF1 (PSB& Azotobactor @ 5 kg/ha ),T3-100% RDF + 10 t/ha FYM+ 2.5 t/ha Poultry manure + BF0, T4-100% RDF + 10 t/ha FYM + 2.5 t/ha Poultry manure + BF1, T5-75% RDF+ 10 t/ha FYM + 4.5 t/ha Vermicompost + BF0, T6-75%RDF + 10 t/ha FYM + 4.5 t/ha Vermicompost + BF1, T7-75% RDF + 10 t/ha FYM + 3.75 t/ha Poultry manure +

BF0, T8-75% RDF +10 t/ha FYM + 3.75 t/ha Poultrymanure + BF1, T9-50% RDF + 10 t/ha FYM + 6 t/haVermicompost + BF0, T10-50% RDF + 10 t/ha FYM + 6t/ha Vermicompost + BF1, T11-50% RDF + 10 t/ha FYM+5 t/ha Poultry manure + BF0, T12-50% RDF +10 t/haFYM + 5 t/ha Poultry manure + BF1 and T13

-100% RDF(200:100:100) kg/ha.

Full dose of organic manures were applied beforeone week of transplanting. NPK was supplied throughUrea, SSP and MOP. Full dose of P, K and half dose ofN was applied at the time of transplanting and remaininghalf of N was applied 30 days after transplanting. Biofertilizers (Azospirillum and PSB) were inoculated beforetransplanting as seedling root dip for 30 minutes @ 5kgha-1 each. One month old seedlings were transplantedin plots that measured 2.4x 1.0 m at the rate of 60x60cm. After transplanting seedlings from the nursery tothe plots, the following operations were carried out topromote their early establishment, quick growth anddevelopment. The crop was irrigated as requireddepending on the moisture status of the soil andrequirement of plants. Plots with transplanted seedlingswere regularly observed to identify damaged or deadseedlings for replacement. Weeding was done as

Field view of tomato cultivationGeneral view of tomato cultivation inside polyhouse

70

Table 1. Effect of integrated nutrient management on morphology and phenology of tomato hybrid

Treatments Plant height Number of Days to first Days to 50% Flowers/ Days to first Days to first(cm) leaves/plant flowering flowering cluster fruit maturity picking

T1 247.55 27.69 32.44 38.47 8.92 76.81 98.33T2 244.27 27.38 33.89 39.16 9.06 77.50 99.02T3 272.96 30.62 33.31 38.67 8.52 77.01 98.53T4 240.83 32.91 34.64 39.64 9.21 77.99 99.51T5 246.89 33.38 32.58 38.00 9.41 76.34 97.87T6 250.98 33.91 34.11 39.78 10.07 78.12 99.64T7 252.60 31.73 33.02 38.82 9.89 77.17 98.69T8 257.66 32.20 33.49 39.18 9.37 77.52 99.04T9 261.40 33.02 38.64 44.31 8.95 82.66 104.18T10 262.47 31.53 38.29 43.87 7.98 82.21 103.73T11 265.45 32.67 40.29 45.91 9.72 84.26 105.78T12 266.15 36.82 41.11 46.73 9.73 85.08 106.60T13 219.70 31.84 32.40 38.07 7.50 76.41 97.93SEM ± 2.62 0.55 0.44 0.44 0.45 0.60 0.64CD @ 5% 7.38 1.55 1.23 1.25 1.26 1.69 1.81

Table 2. Effect of integrated nutrient management on yield parameters of tomato hybrid

Treatments Fruit set % Fruits/cluster Number of Yield/plot Length of Girth offruits/plant (kg) fruit (cm) fruit (cm)

T1 77.83 6.96 71.11 30.30 6.75 5.97T2 69.67 6.33 57.96 30.53 6.78 5.77T3 76.18 6.44 55.15 33.53 6.85 5.68T4 77.01 6.96 55.93 33.90 6.72 5.64T5 81.72 7.70 63.04 33.11 6.99 5.90T6 80.64 8.00 62.26 34.14 6.87 5.72T7 75.32 7.41 66.19 34.66 6.69 5.74T8 83.64 7.85 67.63 34.95 6.91 5.68T9 73.46 6.48 62.07 37.11 6.95 5.73T10 83.13 6.63 61.04 38.66 6.89 5.76T11 81.48 7.89 74.89 38.93 7.10 5.97T12 83.01 8.11 76.26 39.67 7.21 5.87T13 76.20 5.67 29.70 28.13 6.11 5.24SEM ± 2.41 0.32 5.77 1.46 0.13 0.11CD @ 5% 6.78 0.91 16.23 4.12 0.36 0.30

71

required and also plant protection measures werecarried out against insects and diseases throughappropriate application of pesticides.

Results and discussion

Growth characters

Perusal of data presented in Table 1 revealed thatgrowth. NPK fertilizers with different organic manuresalong with bio fertilizers alone or in combination werefound to have significant positive effect on growthcharacters as compared to RDF alone (Table 1).Application of 100% RDF + 10 t/ha FYM + 2.5 t/haPoultry manure (T3) recorded maximum plant height(272.96 cm) at par with treatment T12 (266.15 cm) whilethe lowest values of Plant height (219.70 cm) wasrecorded with RDF alone (T13). The maximum numberof leaves per plant (36.82) was recorded in treatmentT12 (50% RDF +10 t/ha FYM + 5 t/ha Poultry manure +Biofertilizers) and the minimum value was recorded intreatment T2 (27.38).Combination of organic andinorganic fertilizers significantly increased the numberof leaves in cabbage (Kabir 1998, Azad 2000).Itindicates the importance of adding organic manures andbio fertilizers to the soil in conjunction with inorganicfertilizer, which increases the availability of nutrientsconsiderably resulting in positive effect on growthparameters. Poultry manure which was readily availableto the plant, more C:N ratio, abundant supply ofavailable nutrients to the soil with comparatively lesserretention in roots and more translocation to the aerialparts for protoplasmic proteins and synthesis of othercompounds. The added organic manures in terms ofpoultry manure would have improved the soil physical

conditions and increased nutrient availability resultingin better plant growth. The increased growth charactersmay be attributed to higher availability of nitrogen thatimproved the plant growth due to the fact that nitrogenafter being taken by the plant is converted into amino-acids which are the building blocks of protein whichmight have led to increase in the rate of meristematicactivity resulting in better growth characters.This mightbe attributed to certain growth promoting substancessecreted by the bio fertilizers which in turn in might haveled to better root development, better transportation ofwater, uptake and deposition of nutrients. These resultsare in conformity with the finding of Deepika et al. (2010)in Capsicum, Vimera et al. (2010) in king chilli andSentiyangla et al. (2010) in radish. They recordedmaximum growth characters with 50% NPK + 50% FYM+ bio fertilizers.

The least number of days (32.40 days) for firstflowering was recorded in treatment T13 though did notdiffer significantly with treatment T1,T3,T5,T7 and T8

respectively moreover treatment T5 was significantlyearliest in number of days (38 days) for 50% flowering,(48.20 days) for first fruiting and first picking (97.87days) at par with treatment T13.Whereas delay in numberof days taken for these traits was observed in treatmentT12 which is at par with treatment T11.These results arein close conformity of the findings of Renuka and RaviShankar (1998). the maximum of flowers per cluster(10.07) was produced in treatment having 75% RDF +10 t/ha FYM + 4.5 t/ha vermicompost + bio fertilizers(T6) showing singnificant difference with treatment T3

(8.52) over the rest. The minimum numbers of flowersper cluster (7.50) was produced in treatment havingRDF alone. These results are in conformity with thefindings of Chaurasia et al. (2001) and Gajbhiye et al.(2003).

72

Yield and yield attributing characters

The treatment having 75% RDF +10 t/ha FYM + 3.75 t/ha Poultry manure + biofertilizer (T8) produced maximumfruit set percent (83.64%) and was found to be at parwith treatments T10 (83.13%),T12 (83.01%), T5 (81.72%),T11 (81.48%), T6 (80.64%), T1 (77.83%) and T4 (77.01%)(Table 2).Treatment with 50% RDF +10 t/ha FYM + 5 t/ha Poultry manure + biofertilizers (T12) produced thehighest number of fruits per cluster (8.11). Besides, thistreatment also recorded maximum number of fruits perplant (76.26),fruit yield per plot (39.67 kg) and fruit length(7.21 cm) over all other treatments. Similar findings wereobtained by Sendur et al. (1998).Treatment T1 (5.97 cm)had highest magnitude for fruit girth exhibiting non-significant differences with all other treatments exceptT4 (5.64 cm). Treatment having RDF alone laggedbehind other treatments for fruit set percentage (76.20),fruits per cluster (5.67), fruit length (6.11 cm) and fruitgirth (5.24 cm),number of fruits per plant (29.70) andfruit yield per plot (28.13 kg).The increased yield andyield attributes might be due to increased nitrogen andphosphorus as well as accumulation of plant hormonessynthesized by biofertilizers which in turn might haveincreased carbohydrate supply and nutrient mobility byimproving better root activity, leaf area, chlorophyllcontent and photosynthetic rate in addition to increasinghormonal levels in the active sinks viz., flowers, fruitsetc. The results indicates positive effects of integratingNPK with manures as well as bio fertilizers. The efficacyof inorganic fertilizer is much pronounced when theyare combined with organic manure.

The present investigation established that theintegrated application of 50% RDF +10 t/ha FYM + 5 t/ha Poultry manure + biofertilizers( Azospirillum+PSB)in tomato is the most promising treatment in terms ofplant growth and yield. The treatment having 50% RDF+ 10 t/ha FYM+ 5 t/ha Poultry manure withoutbiofertilizers was found second best treatment in termsof growth and yield. Thus integrated use of chemicalfertilizers with organic manure and biofertilizers istherefore recommended over their lone application foroptimum growth, yield, productivity and profitability intomato cultivation under polyhouse condition which isalso in aligned with sustainable use of soil.

orZeku v/;;u iksyh gkml ds rgr ladj VekVj ds fodkl vkSjmit fo'ks"krkvksa ds laca/k esa ,dh—r iks"kd rRo izca/ku ds izHkko dksfu/kkZfjr djus ds fy, fd; x;k Fkk A vfufJr ladj VekVj dSVjhukv/;;u ds fy, pquk x;k Fkk A ifj.kke ls irk pyk gS fd,dh—r vuqiz;ksx 50% RDF + 10 Vu@gsDVj FYM + 5 Vu@gsDVj

dqDdqV [kkn + tSomoZjd (Azospirillum + ih,lch @ 5fdyks@gsDVj) esa] vf/kdre ifRr;k¡@ikS/k (36.82)] Qy dh la[;kizfr leqg (8.11)] Qy dh yackbZ (76.26)] mit izfr IykV(39.67 fdyks½ vU; mipkj dh rqyuk esa dkQh T;knk ntZ dh xbZ A

References

Anonymous, (2013) Indian Horticulture database, Publishedby Ministry of Agriculture, GOI: 177-182

Azad AK (2000) Effects of plant spacing, source of nutrientsand mulching on growth and yield of cabbage. M ScThesis. Department of Horticulture, BangladeshAgriculture University Mymensingh: 15-40

Chandra PP, Sirohi TK Behera, Singh AK (2000) Cultivationof vegetables in poly house. Indian Hort 9(2): 17-18

Deepika A, Singh AK, Kanaujia SP, Singh VB (2010). Effectof integrated nutrient management on growth, yieldand economics of capsicum (Capsicum annum L.)cv. California Wonder. J. Soils and Crops 20:33-38

Gosavi PU, Kamble AB, Pandure BS (2010) Effect of organicmanures with biofertilizer on yield contributingcharacters of tomato. Asian Sciences. 5:39-41

Kabir HT (1998) Effect of sources of nutrients on yield andquality of cabbage. M Sc Thesis. Department ofHorticulture. Bangladesh AgricultureUniversity.Mymensingh 13-39

Renuka B, Ravi Shankar C (1998) Effect of organic manureson growth and yield of tomato. South Indian Horti49:216-217

Sendur KS, Natarajan S,Thamburaj S (1998) Effect of organicand inorganic fertilizers on growth, yield and qualityof tomato. South Indian Horti 46 (3 & 4) : 203-205

Sentiyangla, Kanaujia SP, Singh VB, Singh AK (2010) INMfor quality production of radish in acid alfisol. J Soilsand Crops 20:1-9

Vimera K, Kanaujia SP, Singh VB, Singh PK (2010) Effect ofintegrated nutrient management on growth and yieldof King Chilli under foothill condition of Nagaland.National Seminar on Sustainable Natural Resourcesand its utilization for enhancing the Agriculturalproductivity in India, NU: SASRD, Nagaland, 17-19Nov

(Manuscript Receivd : 15.1.2014; Accepted :20.3.2014)

73

JNKVV Res J 48(1): 73-78 (2014)

Abstract

Flowering behavior of fourteen soybean genotypes undersowing dates during summer season was studied at J.N.Agricultural University, Jabalpur, Madhya Pradesh. Differentsoybean genotypes responded differently to photoperiod andtemperature. Critical day length of soybean genotypes, JS95-54, JS-2 and Bragg was higher a compared to other varietiesstudied. Day-length, temperature and accumulation of heatunit indices during February suitable for the flowering ofsoybean genotypes tried; indicating the possibility of growingof successful soybean crop in summer season (February toJune) if a photo and thermo in-sensitive genotypes areidentified.

Keywords: Soybean, genotypes, flowering, day-length

During the last two decades, fifty varieties of soybeanhave been identified/ released in India for cultivation(Bhatnagar and Karmakar 1995). The range of adoptionof these varieties is reported to be limited (Taware et al.1991, Bhatnagar and Karmakar 1993). Soybean is shortday plant, (Garner and Allard 1920) first recognized thesignificance of day length in the flowering behavior. MostSoybean genotypes responded to photoperiod but theyvary widely with respect to critical day length at whichflower formation is initiated.

Flowering in soybean occurs when day lengthbecomes shorter than the critical photoperiod for thevariety. It takes about 3 weeks between induction andopening of flowers in Biloxi (Borthwick and Parker 1993).Garner (1933), showed that responses to day lengthare modified by temperature. The primary effect of

Variation in sensitivity in flowering behavior and heat unitrequirement of soybean genotypes to varying dates of sowingduring summer season under agro climatic conditions of Kymoreplateau zone of Madhya Pradesh

K.K. Agrawal, A.P. Upadhyay, Sandip Silwat and S.K. VishwakarmaDepartment of Physics & AgrometeorologyFaculty of Agricultural EngineeringJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : kkagrawal59@gmail.com

temperature is on the photoperiod reaction in the leafblade (Parker and Borthwick 1943). Roberts (1943)showed that temperature during the dark period is moreimportant than that during light. Whigham et al. (1978);found that minimum temperatures were associated withdelays in flowering under tropical conditions. Optimumtemperatures, however, varies with the life of the plant.At certain periods, night temperatures has significantinfluence on development. Good flowering occurred witha leaf temperature of 650 F (18 0C) in the dark, but nonewith temperature below 55 0F (13 0C).Shanmugasundaram and Tsou (1978) observed that inthe tropics and subtropics, flowering behavior ofcultivars may differ with planting season even thoughphotoperiod at planting are similar. The variation ofphotoperiod among soybean genotypes allows the cropto grow successfully across a wide range of latitudes.Photoperiod influence the rate of development in pre-and post flowering stages. Change in photoperiod andtemperature are reported to alter the hampering ofgrowth stages, the growth and partitioning of dry matterof this photoperiod and thermosenstive short day C3crop (Lawn 1989). Hence, present study was undertaken to screen the soybean varieties with respect toflowering and other reproductive stage during nontradinal season at Jabalpur condition of MadhyaPradesh.

Materials and methods

The present experiment was conducted at JawaharlalNehru Krishi Vishwa Vidyalaya, Jabalpur, MadhyaPradesh; which is located at 230 09' North and 790 58'

74

and altitude of 411 m above msl. Fourteen genotypeswere planted in three different dates (January 10 & 30and February 22) during winter/ summer season. Theaverage maximum and minimum temperature rangedfrom to 16.2 to 44.7 0C and 29.0 to 30.6 0C respectivelyfrom January to June. The average sunshine hours (daylength) are from 10 hours 42 minutes and 11 hours 22minutes in the month of January and February. Date offlowering initiation, 50% flowering, physiological maturityand harvest maturity were recorded for each varietiesfor three dates of sowing. Fig 1 shows that day lengthand temperature(s) fluctuation from January to June atJabalpur. It is clear from the figure that soybean sownin January were exposed to increasing day length fromsowing to maturity while crop sown during Februaryexposed to decreasing temperature during the monthof June. The response of the soybean genotypesmeasured in term of days to 50% flowering was acombined effect of day length and temperature. Thevariation in days to flowering and physiological maturityof different soybean genotypes, for three sowing datesis given in Table 1. Growing degree days (GDD) wasworked out by using a base temperature of 10 oC. Thesum of heliothermal unit (HTU) and photothermal unit(PTU) for particular growth stage were determined bymultiplying degree days with actual bright sunshinehours respectively.

flowering was noted in the varieties JS 95-54, Braggand JS-2, however, the lowest variation was shown bythe cv. NRC-37 and JS 75-46, JS 72-280, PK- 472, JS80-21 while the response of different genotypes to threesowing dates. It was observed that sowing dates causedthe least variation in days to 50% flowering among thedifferent genotypes. However differences for floweringinitiation was less under sowing of first week of Januaryand it was more in February sowing. Number of days toattain physiological maturity was highest in the Januarysowing as compared to February sowing. All thegenotypes generally flowered late and tooks morenumber of days to reaches physiological maturity, whichmay due primarily to day length being shorter thancritical day length requirement. Low night and daytemperature may also be responsible for delayedflowering and physiological maturity in January sowncrop as compared to February sown. Varieties NRC-37, JS 75-46, PK 472, JS 72-28 and JS 80-21 showedless variation in days of flowering initiation over threesowing dates while the varieties JS 95-61, JS 95-46,JS 90-41, JS 335 and JS 71-05, exhibited moderatevariability in days to flowering initiation. The varietiesJS 95-54, JS -02 and Bragg showed higher variationover three sowing dates. Responsiveness of differentgenotypes to three dates of planting to days to 50%flowering was similar to flower initiation. However toattain physiological maturity varieties PK 472, JS 72-44, JS 71-05, JS 95-61, and JS 72-88 showed lessvariation to different planting times; while highervariation was noted in JS 80-21 and JS 95-54genotypes. This indicates that different varietiesresponded differentially to the prevailing day length andtemperature at different sowing dates. All the genotypesflowered late and taken higher number of days tophysiological maturity in the January first week sowingthan other sowing dates; possibly due to high criticalday length requirement and low temperatures.

Agrometeorological indices

The agrometeorological indices to reach different growthstages showed appreciable variation among the dateof sowing and genotypes (Table 2). The crop sown inlast week of February accumulated highest growingdegree days (2779), heliothermal unit (22948) and photothermal unit (35525) followed by crop sown in end ofJanuary and first week of January. This was due toavailability of higher temperature and longer day lengthto February sown crop. Among the different genotypeshighest growing degree day (2791) heliothermal unit(23600) and photo thermal unit accumulated by

Monthly sunshine duration during the crop period atJabalpur

Results and discussion

Fluctuations in day length and temperature resulted invariation in days to flowering and physiological maturityof particular genotypes over different sowing dates. Thehighest variation for flowering initiation and 50%

75

Tabl

e 1.

Day

s to

Flo

wer

ing

initi

atio

n, 5

0% fl

ower

ing

and

phys

iolo

gica

l Mat

urity

of F

ourte

en s

oybe

an g

enot

ypes

as

influ

ence

d by

sow

ing

date

ddu

ring

sum

mer

sea

son

Var

ietie

sFl

ower

ing

Initi

atio

n50

% F

low

erin

gPh

ysio

logi

cal M

atur

itySo

win

g D

ate

D1

D2

D3

Ran

geD

iff.

D1

D2

D3

Ran

geD

iff.

D1

D2

D3

Ran

geD

iff.

NR

C-3

764

5852

52-6

212

7460

5555

-74

1911

810

710

810

7-118

11

PK-4

7268

6051

51-6

817

7269

5555

-72

1711

211

211

011

0-112

2

JS-3

3565

5543

43-6

522

6860

4949

-68

1910

699

9595

-106

11

Brag

g65

5240

40-6

525

6860

4646

-68

2211

810

710

810

7-118

11

JS-2

6552

4040

-65

2568

5446

46-6

822

107

9811

198

-111

13

JS-9

0-41

6855

4646

-68

2270

5849

49-7

021

112

101

102

101-1

1211

JS 7

2-44

6552

4545

-65

2069

6052

52-6

917

112

107

105

105-1

127

JS 9

5-56

6952

4949

-69

2072

6053

53-7

219

118

107

103

103-1

1815

JS- 7

1-05

6352

4040

-63

2366

5346

46-6

620

9892

9592

-98

6

JS 9

5-61

6958

4949

-69

2073

6654

54-7

319

112

107

108

107-1

125

JS 7

5-46

6957

57-6

912

7360

60-7

313

127

127

127

0

JS 7

2-280

7768

6060

-77

1786

7664

64-8

622

112

101

119

101-1

1918

JS 8

0-21

7763

6060

-77

1785

6964

64-8

521

124

110

113

110-1

2414

JS 9

5-54

6857

4646

-68

2272

6052

52-7

220

124

107

111

107-1

2417

Mea

n68

56.5

47.7

69-

-72

.571

61.8

52.6

92-

-11

4.29

105.

910

6.77

--

Min

.63

5240

--

6653

46-

-98

9295

--

Max

.77

6860

--

8676

64-

-12

712

711

9-

-

Diff

.14

1620

--

2023

18-

-29

3524

--

SD4.

3145

54.

784

6.76

--

5.97

066.

1915

5.99

14-

-7.

8684

8.12

276.

8087

--

76

Tabl

e 2

(A).

Hea

t uni

t ind

ices

for d

iffer

ent s

oybe

an g

enot

ypes

in d

iffer

ent g

row

th s

tage

und

er d

iffer

ent g

row

ing

envi

ronm

ent S

tage

- Flo

wer

initi

atio

n

Gro

win

g D

egre

e D

ays

Hel

ioth

erm

al u

nit

Phot

othe

rmal

uni

tD

1D

2D

3D

1D

2D

3D

1D

2D

3V

arie

ties

10.1

.01

31.1

.01

22.2

.01

Mea

n10

.1.0

131

.1.0

122

.2.0

1M

ean

10.1

.01

31.1

.01

22.2

.01

Mea

nN

RC

-37

856

913

1037

935.

370

6974

9885

0176

89.3

395

9410

668

1246

310

908.

33PK

-472

937

956

1013

968.

777

1778

7482

9479

61.6

710

593

1119

912

167

1131

9.67

JS-3

3587

786

082

285

3.0

7198

7051

6639

6962

.67

9846

1004

098

1098

98.6

7Br

agg

877

798

752

809.

071

9865

2660

2465

82.6

798

4692

7989

4793

57.3

3JS

-287

779

875

280

9.0

7198

6526

6024

6582

.67

9846

9279

8947

9357

.33

JS-9

0-41

937

860

891

896.

077

1670

5172

6873

45.0

010

593

1004

010

650

1042

7.67

JS 7

2-44

877

798

867

847.

371

9865

2670

7269

32.0

098

4692

7910

365

9830

.00

JS 9

5-56

960

798

966

908.

079

0965

2680

2474

86.3

310

872

9279

1158

610

579.

00JS

- 71-

0583

679

875

279

5.3

6973

6526

6024

6507

.67

9362

9279

8947

9196

.00

JS 9

5-61

960

913

966

946.

379

0974

9880

2478

10.3

310

872

1066

811

586

1104

2.00

JS 7

5-46

960

889

-92

4.5

7909

7293

-76

01.0

010

872

1038

0-

1062

6.00

JS 7

2-28

1118

1132

1215

1155

.091

4594

5710

053

9551

.67

1282

213

352

1468

013

618.

00JS

80-2

111

1810

1612

1511

16.3

9145

8409

1005

392

02.3

312

822

1193

414

680

1314

5.33

JS 9

5-54

937

889

891

905.

777

1672

9372

6874

25.6

710

593

1038

010

650

1054

1.00

Mea

n93

7.64

887.

0093

3.77

7714

.29

7289

.57

7636

.00

7545

.79

1059

8.50

1036

1.14

1119

0.62

Tabl

e 2

(B).

Hea

t uni

t ind

ices

for d

iffer

ent s

oybe

an g

enot

ypes

in d

iffer

ent g

row

th s

tage

und

er d

iffer

ent g

row

ing

envi

ronm

ent S

tage

- 50%

Flow

er

Gro

win

g D

egre

e D

ays

Hel

ioth

erm

al u

nit

Phot

othe

rmal

uni

tD

1D

2D

3D

1D

2D

3D

1D

2D

3V

arie

ties

10.1

.01

31.1

.01

22.2

.01

Mea

n10

.1.0

131

.1.0

122

.2.0

1M

ean

10.1

.01

31.1

.01

22.2

.01

Mea

nN

RC

-37

1068

956

1102

1042

.087

2078

7489

5485

16.0

012

219

1120

013

268

1222

9.00

PK-4

7210

2711

5611

0210

95.0

8378

9635

8954

8989

.00

1172

013

637

1326

812

875.

00JS

-335

937

956

966

953.

077

1778

7480

2378

71.3

310

593

1120

011

586

1112

6.33

Brag

g93

795

689

192

8.0

7717

7717

7268

7567

.33

1059

311

200

1065

010

814.

33JS

-293

783

989

188

9.0

7717

6867

7268

7284

.00

1059

397

7810

650

1034

0.33

JS-9

0-41

982

913

966

953.

780

3674

9880

2378

52.3

311

163

1066

811

586

1113

9.00

JS 7

2-44

960

956

1037

984.

379

0978

7485

0180

94.6

710

872

1120

012

463

1151

1.67

JS 9

5-56

1027

956

1060

1014

.383

7878

7486

6983

07.0

011

720

1120

012

741

1188

7.00

JS- 7

1-05

897

819

891

869.

073

5866

8472

6871

03.3

310

098

9525

1065

010

091.

00JS

95-

6110

4710

8610

8110

71.3

8536

9024

8842

8800

.67

1196

612

797

1301

412

592.

33JS

75-

4610

4795

6-

1001

.585

3678

74-

8205

.00

1196

611

200

-11

583.

00JS

72-2

813

2513

2413

2013

23.0

1087

611

054

1110

911

013.

0015

239

1572

815

990

1565

2.33

JS 8

0-21

1291

1156

1320

1255

.710

635

9635

1110

910

459.

6714

940

1363

715

990

1485

5.67

JS 9

5-54

1027

956

1037

1006

.783

7878

7485

0182

51.0

011

720

1120

012

463

1179

4.33

Mea

n10

36.3

699

8.93

1051

.08

8492

.21

8239

.86

8653

.00

1181

4.43

1172

6.43

1263

9.92

77

Tabl

e 2

(C).

Hea

t uni

t ind

ices

for

diffe

rent

soy

bean

gen

otyp

es in

diff

eren

t gro

wth

sta

ge u

nder

diff

eren

t gro

win

g en

viro

nmen

t Sta

ge-

Phys

iolo

gica

lm

atur

ity

Gro

win

g D

egre

e D

ays

Hel

ioth

erm

al u

nit

Phot

othe

rmal

uni

tD

1D

2D

3D

1D

2D

3D

1D

2D

3V

arie

ties

10.1

.01

31.1

.01

22.2

.01

Mea

n10

.1.0

131

.1.0

122

.2.0

1M

ean

10.1

.01

31.1

.01

22.2

.01

Mea

nN

RC

-37

2141

2188

2553

2294

.018

453

1912

121

891

1982

1.67

2575

726

955

3231

428

342.

00PK

-472

1957

2317

2607

2293

.716

768

2026

622

417

1981

7.00

2326

928

661

3302

728

319.

00JS

-335

1785

1943

2081

1936

.315

082

1694

018

170

1673

0.67

2107

623

745

2604

923

623.

33Br

agg

2141

2188

2553

2294

.018

453

1912

121

891

1982

1.67

2575

726

955

3231

428

342.

00JS

-218

1219

1226

3321

19.0

1534

716

601

2255

818

168.

6721

419

2331

633

374

2603

6.33

JS-9

0-41

1957

2005

2405

2122

.316

768

1759

020

626

1832

8.00

2326

924

556

3034

526

056.

67JS

72-

4419

5721

8824

7722

07.3

1676

819

121

2117

519

021.

3323

269

2695

531

294

2717

2.67

JS 9

5-56

2141

2188

2429

2252

.718

453

1912

120

753

1944

2.33

2575

726

955

3066

327

791.

67JS

- 71-

0515

9517

2822

2818

50.3

1319

214

916

1950

715

871.

6718

697

2082

727

991

2250

5.00

JS 9

5-61

1957

2188

2553

2232

.716

768

1912

121

891

1926

0.00

2326

926

955

3231

427

512.

67JS

75-

4619

5720

05-

1981

.016

768

1759

0-

1717

9.00

2326

924

557

-23

913.

00JS

72-2

824

1618

5028

0823

58.0

2097

315

974

2312

620

024.

3329

397

2245

035

712

2918

6.33

JS 8

0-21

2602

2264

2681

2515

.722

716

1980

322

879

2179

9.33

3184

227

958

3402

731

275.

67JS

95-

5423

2821

8826

3323

83.0

2018

119

121

2255

820

620.

0028

239

2695

533

374

2952

2.67

Mea

n20

53.2

920

82.2

925

10.8

517

620.

7118

171.

8621

495.

5424

591.

8625

557.

1431

753.

69

Tabl

e 2

(D).

Hea

t uni

t ind

ices

for d

iffer

ent s

oybe

an g

enot

ypes

in d

iffer

ent g

row

th s

tage

und

er d

iffer

ent g

row

ing

envi

ronm

ent S

tage

- Har

vest

mat

urity

Gro

win

g D

egre

e D

ays

Hel

ioth

erm

al u

nit

Phot

othe

rmal

uni

tD

1D

2D

3D

1D

2D

3D

1D

2D

3V

arie

ties

10.1

.01

31.1

.01

22.2

.01

Mea

n10

.1.0

131

.1.0

122

.2.0

1M

ean

10.1

.01

31.1

.01

22.2

.01

Mea

nN

RC

-37

2202

2317

2786

2435

.019

124

2026

623

053

2081

4.33

2658

828

661

3541

630

221.

67PK

-472

2444

2520

2831

2598

.321

218

2210

223

226

2218

2.00

2975

531

348

3602

032

374.

33JS

-335

1812

2005

2553

2123

.315

347

1759

021

891

1827

6.00

2141

924

556

3231

426

096.

33Br

agg

2202

2345

2786

2444

.319

124

2055

523

053

2091

0.67

2658

829

036

3541

630

346.

67JS

-219

2719

4328

7922

49.7

1648

316

941

2332

318

915.

6722

872

2374

536

653

2775

6.67

JS-9

0-41

2202

2264

2701

2389

.019

124

1980

322

879

2060

2.00

2658

827

958

3427

529

607.

00JS

72-

4422

0223

4527

8624

44.3

1912

420

555

2305

320

910.

6726

588

2903

635

416

3034

6.67

JS 9

5-56

2444

2345

2701

2496

.721

218

2055

522

879

2155

0.67

2975

529

036

3429

531

028.

67JS

- 71-

0517

0719

4325

2820

59.3

1429

016

941

2166

917

633.

3320

109

2374

531

971

2527

5.00

JS 9

5-61

2202

2345

2786

2444

.319

124

2055

523

053

2091

0.67

2658

829

036

3541

630

346.

67JS

75-

4622

3422

64-

2249

.019

442

1980

3-

1962

2.50

2699

827

958

-27

478.

00JS

72-2

824

4628

7130

1127

76.0

2211

824

802

2351

223

477.

3331

102

3601

438

415

3517

7.00

JS 8

0-21

2898

2572

2902

2790

.724

863

2252

923

410

2360

0.67

3577

432

043

3696

734

928.

00JS

95-

5424

4423

4528

7925

56.0

2121

720

555

2332

321

698.

3329

755

2903

636

653

3181

4.67

Mea

n22

40.4

323

16.0

027

79.1

519

415.

4320

253.

7122

948.

0027

177.

0728

657.

7135

325.

15

78

genotype JS 80-21 followed by JS 72-28. Whereaslowest heat indices was accumulated by the cv. JS -335. Similar results were also reported by Singh et al.(2007).

From the experiment, it can be concluded thatdifferent genotypes differed in their response tophotoperiod and temperature. Critical day lengthrequirement for JS -95-54, JS-02 and Bragg was higherthan other varieties. However day length andtemperature during February to be suitable for theflowering of all the genotypes studied. Heat unitsaccumulation were also higher in February sown crop.Results indicating the possibility for growingsuccessfully crop of soybean in summer (February toJune) if a photo and thermo insensitive genotypes areidentified.

Acknowledgement

Authors are thankful to the Department of Science andTechnology, G O I, New Delhi, for providing financialassistance.

lks;kchu dh pkSng iztkfr;ksa dk xzh"e_rq esa fofHkUu frfFk;ksa ijcqvkbZ djus ij muds Qwy vkus ds O;ogkj dk v?;;u togkjykyusg: d`f"k fo'ofo|ky; tcyiqj esa fd;k x;kA lks;kchu dh fofHkUuiztkfr;ksa ij lw;Zdky (photo period) ,oa rkieku dk vyx&vyxizHkko ns[kk x;kA Økafrd fnu vof/k lks;kchu dh iztkfr;ksa ts-,l-95&54] ts-,l-&2 ,oa czsx dk vU; iztkfr;kssa dh rqyuk ij vf/kdik;k x;kA eksleh rRo tSls fnu dh vof/k] rkieku]Å"eh; lawpdkadvkfn Qjojh ds eghus esa lks;kchu dh cqvkbZ ds fy;s vuqdqy gSaAvr% mijksDr v/;;u ds vk/kkj ij ;g ladsr feyrs gSa dhxzh"edkyhu lks;kchu dks Qjojh ds eghus esa cqvkbZ djus ij Qlydks lQyrkiwoZd mxk;k tk ldrk gSA

References

Bhatnagar P S, Karmakar P G (1993) Stability of soybean(Glycine max) varieties diverse location of Indiasoybean. Gen Newsl 20: 59-62

Bhatnagar P S, Karmakar P G (1995) Achievements andprospects of breeding research on soybean (Glycinemax) in India. Indian J Agric Sci 65 : 1-9

Borthwick H A, Parker M W (1938 C) Photoperiodic perceptionin Biloxi Soybeans. Bot Gaz 100: 374-387

Garner W W, Allard H A (1920) Photoperiod responses ofsoybean in relation to temperature and otherenvironmental factors. J Agric Res 41: 719-735

Garner W W (1933) Plant physiology 8: 349-358Parker M W, Borthwick H A (1943) Influence of temperature

on photoperiodic reactions in leaf blades of Biloxisoybean. Bot Gaz 104: 612-619

Shanmugasundarm S, Tsou S C S (1978) Photoperiod andcritical duration for flower induction in soybeans. CropSci 18: 598-601

Singh T P, Madan P S, Phul P S, Ghai T R (1993) Floweringbehavior of soybean genotypes soybean. Gene Newl20:130-134

Taware S P, Haibankar G B, Raout V M, Patil V P (1991)Analysis of phenotypic Stability in Soybean (Glycinemax). Indian J Agric Sci 61 : 933-936

Whigham D K, Minor H C, Carmer S G (1978) Effect ofenvironment and management on soybeanperformance in the tropics. Agron J 70: 587-92

Lawn R J (1989) Agronomic and physiological constraint toproductivity of tropical grain legume and prospectsfor improvement. Exptl Agric 25: 509-528

Singh Ajit, Rao VUM, Singh Diwan, Singh Raj (2007) Studyon agrometeorological indices for soybean cropunder different growing environments. JAgrometeorology 9(1): 81-85

(Manuscript Receivd : 27.7.2012; Accepted : 5.1.2014)

79

Abstract

This paper focused on urban food security and insecurity withattention paid to the nature of the urban food system, askingthe questions of what food insecure is, and why urban foodinsecurity exists. The analysis is based on survey gatheredfrom 120 randomly selected households in the study area. Abinary logit model was used to identify factors influencinghousehold food insecurity. A total of twelve explanatoryvariables were included in the empirical model. Amongvariables considered, family size, annual income, amount ofcredit received, age of household head, farm size and livestockowned showed theoretically consistent and statisticallysignificant effect on probability of household to be foodinsecure. Estimated coefficients of number of livestock ownedand dependency ratio were inconsistent and statisticallyinsignificant in determining the household food insecurity.Estimated coefficients of sex of household head, total off-farm income, education of household head and amount offood aid received were not significant in determining householdfood insecurity in the study area. The study concludes thatthere is need for a policy that provides adequately trainedand equipped extension workers for disseminating improvedagricultural technologies that has the potential of raisingefficiency in food crop production, which enhances foodsecurity under present situation in Nigeria.

Key words: Food imbalance, Provocation and FoodSecurity

Food security is a concept that cuts across manydimensions. It means access to adequate food for ahealthy life. This definition points to at least two partsof this complex concept: access to available food andadequate nutrient intake for sustainable health. It is acomplex and tricky task to formulate a one-size-fits-all

Provocation of food imbalance in Ogbomosho Metropolis, OyoState, Nigeria

Olayiwola O.Olaniyi, P.K.Awasthi* and N.K. Raghuwanshi*Development Policy Centre, Ibadan, Oyo State, Nigeria*Department of Agricultural Economics & Farm ManagementJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 48004 (MP)Email : olubodun_2008@yahoo.com

JNKVV Res J 48(1): 79-84 (2014)

set of food security targets. This is clear from the multipledeterminants of the food security status of a householdor its members. The most salient determinantssummarised as:

Household composition: Households vary in terms ofsize (number of household members), age structure(adults and children) and gender (females and males).Nord and Hopwood (2007) examined the importance ofhousehold composition insofar as it aids inunderstanding the food security status of children inthe household.

Wealth and livelihood strategy: This consists of variousincomes (wages, social grants, etc) and assets (land,livestock, etc.)

Geographic location: This refers to the rural and urbanlocations, whether the settlement is largely formal orinformal, and distance from the nearest or fromfrequently-used food markets, as for the sake of thisstudy, a city (urban) is being considered.

Institutions: markets, the state, social capital/networks.

Time: the food security condition could be transitory orchronic.

Risk: shocks that are related to the weather, health aswell as commodity price movements. Household foodsecurity depends substantially on household incomeand asset (or wealth) status. A low-income householdis more likely to suffer food shortages than a wealthierhousehold. Food expenditure comprises a large shareof the spending of poor households, making themrelatively more vulnerable to the impacts of food priceinflation. This relationship between a household's foodsecurity status and its purchasing power is far fromstatic; it changes over time (Amaze 2006). All otherfactors remaining constant, changes in income alter the

80

quantity and quality of foods purchased and consumed.Price movements of food and non-food items also affectthe ability to buy food. For example to cope with rapidfood inflation, a household could cut its food purchasesand adjust its consumption patterns. Typical copingstrategies include: buy a smaller quantity of food, switchto different types of food, reduce dietary diversity andskip meals (Ajiber et al 2006). He also pointed out thatthose high dependency ratios mean that losing anincome-earning opportunity can make a household thatmight have been food-secure into one that is not. Inorder to combat threats of famine and pervasive povertythereby ensuring food security for its population, theGovernment strategy has rested on increasing theavailability of food grains through significant investmentsin agricultural technologies (high yielding varieties ofseeds, fertilizer), services (extension, credit, inputs), andrural infrastructure (roads, markets).

The phenomenon of urban food insecurity hasbeen attributed to the following interconnected factors:Income insecurity making an individual or householdunable to purchase sufficient food or food with adequatenutrient content to assure food security; spatial factorssuch as living in a neighbourhood without an affordablegrocery store, markets or other outlets; disproportionateincome allocation to other areas, such as rent, leavingan insufficient budget for food; and isolation, loss ofautonomy or a lack of a social network. The focus forthis paper will be on urban food security and insecuritywith attention paid to the nature of the urban foodsystem, asking the questions of who food insecure is,and why urban food insecurity exists. Community FoodSecurity is an alternative approach at the local levelempowering those most at risk to act to alleviate foodsecurity themselves. One of the means to do so is urbanagriculture, and its potential to provide increased urbanfood security will be assessed through theoretical andpractical analysis.

Methodology

The requisite primary data were collected from theselected households through well structured and pre-tested with interview schedule. Two Local GovernmentAreas (LGAs) - Ogbomosho South and OgbomoshoNorth with an area of 68 km2 and population of 100,816at 2006 census were selected and fifteen communitieswere purposively selected from each LGA. Systematicrandom sampling was employed to select a respondentfrom every third household in the communities. Eight(8) respondents were therefore chosen from each

community making a total sample size of one hundredand twenty (120). Cost-of calories (COC) and logitmodel are the analytical techniques used for the study.This method has been applied to several studies, whosemain focus was on food security (Greer and Thorbecke,1986; Amaze et al, 2006; Olayiwola 2012). Therefore,following their approach, the food insecurity line is givenas:

Ln X= a + bC ...(1)

Where X is the adult equivalent food expenditure (inNaira) and C is the actual calorie consumption per adultequivalent of a household (in kilo cal). The caloriecontent of the recommended minimum daily nutrientslevel (L) (FAO, 1982; Food Basket, 1995) was used todetermine the food insecurity line Z using the equation:

S = e (a + bL) ...(2)

Where

S= the cost of buying the minimum calorie intake (foodinsecurity line); a and b = parameter estimates fromequation 1; L= recommended minimum daily energy(calorie) level (The FAO recommended minimum dailyenergy requirement per adult equivalent is 2250kcal).Based on the S calculated, households were classifiedas food secured or food Insecure, depending on whichside of the line they fell. Due to differences in householdcompositions in terms of age and sex, there was a needto calculate the levels of expenditure required byhouseholds with different compositions. One of theeasiest ways to achieve this was to divide the household

Map of Nigeria showing study area

81

expenditure by household size to get the per capitaexpenditure as used by the World Bank (1996) andseveral other studies. The household expenditure wasdecomposed on per adult equivalent.

Empirical Model for the Determinants of Food Insecurity

A Logit model was used to examine the determinantsof household food insecurity, this is specified as:

Y1 = g (I1) ...(3)

Ii = b0 = = b X1 ...(4)

Where,

Yi is the observed response for the ith observation (i.e.the binary variable, Yi = 0 for food secure householdand Yi=1 for a food insecure household). I is anunderlying and unobserved stimulus index for the i thobservation (conceptually, there is a critical threshold(Ii* ) for each household; if Ii < Ii * the household isobserved to be food secure, if Ii ? Ii* the household isobserved to be food insecure). g is the functionalrelationship between the field observations (Yi) and thestimulus index (Ii ) which determines the probability ofbeing food secure. The logit model assumes that theunderlying stimulus index (I i *) is a random variable,which predicts the probability of being food insecure.The relative effect of each explanatory variable (X ji )on the probability of being food insecure is measuredby differentiating with respect to Xji, using the quotientrule (Amaza et al 2006):

Where Pi is the probability of an ith household beingfood insecure; Xi = vector of explanatory variables whichare defined below:

AGE - Age of head of Household (Years): Age ofhousehold head also matters for household foodsecurity. Urban households mostly devote their lifetimeor base their livelihoods on both farm and non-farmenterprises. The older the household head, the moreexperience she/he has in both enterprises. Moreover,older persons are more risk averters, and mostly theytend to diversify their production activities. As a result,the chance for such a household to be food insecure isless. Moreover, in a household where productive age

groups are higher than the non-productive age groups,the probability of a household to be in shortage of foodwould be less, provided that the area provides goodworking atmosphere and production potential.

FARMINC- Farm income of a household per annum (#).As income determines the household's ability to securefood, it remains to be an important variable whichexplains the characteristics of food secure and foodinsecure households. Income earned from any sourceimproves the food security status of the household.

NONFARMINC - Non Farm income (#)1 Householdswhich manage to secure larger income from any sourcehas better access to the food they need than thosehouseholds which do not.

HHSZE- Household size (no); As household sizeincreases, obviously the number of mouths to feed fromthe available food increases. Hence, it is hypothesizedthat family size and food insecurity are positively related.

EDUC - Educational level of household Head. (D=1, ifthe household head is literate; 0, if otherwise). Educationequips individuals with the necessary knowledge of howto make a living. Literate individuals are keen to getinformation and use it. Hence, it is supposed thathouseholds who have had at least primary educationor informal education are the ones to be more likely tobenefit from agricultural technologies and thus becomefood secure.

ASSETS - Total value of household disposable assets(N). Ownership of assets such as cultivated land andlivestock decreases the likelihood that the householdwill be food insecure.

GENDER- Sex of the head of household (D=1 if Male;D=0 if Female). Since male headed households are ina better position to pull more labor force than the femaleheaded ones, sex of the household head is an importantdeterminant of food insecurity in the study area.

CREDIT-Household head access to credit facilities D=1 if yes, D=0 if otherwise). Credit may also serve as animportant source of income. Those households whichreceive the credit they requested have better possibilityto spend on activities they wish. Either they purchaseagricultural input (improved seed and/or fertilizer) orthey purchase livestock for resale after they fattenedthem.

82

DPDCY- Child dependency ratio; EXTAG-Householdhead's access to extension agents (D= 1 if yes, D=0 ifotherwise); FASZ- farm size of a household (ha);FRMEXP-Farm experience (years); FLAB Family labour(man days); HLAB- Hired labour (man days); COOP-Cooperative membership (D=1 if yes; D=0 if otherwise);HPE- Household production Enterprise(D=1 if farmenterprise alone; D=0 if otherwise).Note that 1 Onedollar equivalent to 150 naira.

Results and discussion

The results revealed that food insecure and food securehousehold groups have statistically signif icantdifference with respect to mean of the variables suchas age of household head, household size, annual farmincome, non farm income, cooperative membership,household production enterprise and amount of creditreceived.

Categorical variables such as education of thehousehold head and assets were also found to bestatistically different for the two groups of households.Based on the recommended daily energy levels of 2250Kcal, the food insecurity line for the households is foundto be N67.82 per day per adult equivalent (N2102.42

per month per adult equivalent). On an annual basis,this is equivalent to N25229.04 per adult equivalent.From the food insecurity line, it was shown that 60% ofthe sampled households are food insecure byheadcount. Furthermore, the aggregate income gap of-463.49 indicates the amount (N463.49) by which thefood insecure households are away from meeting theirmonthly basic food requirements. In order to identifythe most important factors which determine householdfood insecurity from the hypothesized potentialvariables, binary logit model was estimated.

Provocation of food imbalance

Since the likelihood ratio test statistics exceeds the chi-square critical value by 18 degrees of freedom, thehypothesis that all coefficients of the model except theintercept are equal to zero is rejected. Out of theeighteen variables hypothesized to influence householdfood insecurity, fourteen were found to be statisticallysignificant. The maximum likelihood estimates of thelogistic regression model showed that household size,annual household income, amount of credit received,age of the household head, assets, cooperativemembership and so on were important factors identifiedto influence household food insecurity in the study area.

Table1. Descriptive Statistics of Indicators included inlogit model

Indicators Food Food t-valuesInsecure Secure

(72) (48)

Age 43.7 36.4 2.412*** Farm income 351 572 -1.47*Off farm income 263 863 -5.65***household 8.08 5.4 8.42***Education 38 62.5 1.47***Asset 702,631 980,441 2.04**Gender 64 43 0.25Credit 62,200 84,420 -1.7*Dependency Ratio 1.6 0.9 3.44***Extension 48 38 0.9Farm Experience 7.3 7.8 0.9Farm labour 6.2 4.1 0.6Hired Labour 6.3 6.2 0.7Cooperative 35 42 3.4***House protection 38 40 2.0*

Table 2. Maximum likelihood function of Logit Model

Indicators Coefficient of t-ValuesIndicators

Constant 2.45 1.48Age -0.03*** -1.9Farm income 0.64 0.53Off farm income 1.48* 1.94household 0.022** 2.04Education 0.06** 2.70Asset -0.72** -3.01Gender 0.29* 1.88Credit -0.03** -2.90DependencyRatio -0.07 -0.06Extension -0.21** -2.43Farm Experience 1.04* 1.54Farm labour -0.53 -0.43Hired Labour 0.03 0.07*indicate 10 percent, **indicate 5 percent and***indicate 1 percent

83

The results of the Logit regression are presented Table2. The sign of the coefficient of age of the householdhead shows a negative relationship with food insecuritywhich is statistically significant at 5%.

This means that an increase in the age of thehousehold head decreases the likelihood for thehousehold to become food insecure. This is possiblebecause as urban households acquire more experiencein farming operations, nonfarm businesses, accumulatewealth and use better planning, they have betterchances to become food secure. The result agrees withthe prior expectation. The coefficient of the variableeducation is significant and carries a positive sign,suggesting that the higher a household's expenditureis on education, the higher the probability of foodinsecurity and vice versa. This is plausible as educationof children is a priority area, for which the householdcould deny itself some comfort in the short-run.Households sometimes sell out of their food reserve toprovide for this need and as such expose themselvesto food shortages. Household size is found to be highlysignificant to determine household food insecurity inthe study area. This household factor revealed a positiverelationship with food insecurity indicating that theprobability of being food insecure increases with anincrease in the family size. The likely explanation is thatin an area where households depend on less productiveagricultural land, increasing household size results inincreased demand for food.

This demand, however, cannot be matched withthe existing food supply so ultimately end up with foodinsecurity. The coefficient of the sex of the head ofhousehold is significant at 10% and shows a positiverelationship with household's food insecurity status.Households headed by female have higher probabilityof being food insecure in the project area. Cultivatedfarm size was hypothesized to influence food insecuritynegatively. The results of the logit model indicated thatsampled households which had larger farm size hadless risk of being food insecure. This is confirmed bystatistically significant negative coefficient of thevariable. The possible justification is that farmhouseholds which had larger farm size had betterchance to produce more, to diversify the crop theyproduce and also have got larger volume of cropresidues, thus becoming less food insecure. The amountof off- farm income was hypothesized to have positiveinfluence on food insecurity.

In agreement with the hypothesis, its coefficientcame out to be positive and statistically significant.Households that have access to better off- farm incomeopportunities are less likely to become food insecure

than those households who had no or little access. Theresult of the logit model showed that amount of creditreceived has a significant and negative influence onfood insecurity in the study area. This result iscompletely in agreement with the prior expectation. Thismight be due to the fact that households which havethe opportunity to receive credit would build theircapacity to produce more through purchase and use ofagricultural inputs. It would also be possible for thehouseholds to spend the credit on some other incomegenerating activities so that the income from theseactivities position households on a better status toescape vulnerability to food insecurity. The result of thelogit model showed that the coefficient of householdproduction enterprises is significant at 10%. Urbanhouseholds who are into farming alone had higherprobability of food insecurity than those that havediversified from farming into some other non-farmenterprises and vice versa.

This is because households that have othersources of income in addition to farming are moreresilient in times of food crisis that those that are intofarming alone. The coefficient of the variablehousehold's access to extension agent is significant at5% and has a negative relationship with the foodinsecurity status of households. This implies thathouseholds that had access to extension agents havehigher probability of being foods secure than those thatdid not have access to extension agent and vice versa.This is because access to extension agents enhancesthe chances of households having access to better cropproduction techniques, improved input as well as otherproduction incentives and these go to affect their outputvis-à-vis their food security status.

Conclusion and recommendations

Socio-economic variables such as asset holding (mainlycultivated land, farm income, non farm income andhousehold production enterprises) and access toservices like credit are found to be important correlateswhich affect household food security favourably. Whilecontrolling for all other variables, households with betteraccess to credit, education, extension agents andcooperative membership are found to have significantlyhigher food security and so more likely to be foodsecure. However, among demographic variablesconsidered in this study, only age was found to have anegative and statistically significant effect on householdfood security. Contrary to usual expectation, thecoefficient of farm income, dependency ratio, family andhired labour were not statistically significant.

84

These may imply that household headship hasnot yet enhanced households' capabilities to adoptbetter production technologies accept technical advicefrom extension workers and diversify their source ofincome which would have reduced the risk of foodinsecurity among household. The following policyimplications and recommendations are suggested forreduction in food insecurity. Since food insecurityincreases with increase in household size, efforts shouldbe made at improving programmes and policies thatwill ensure a proper family planning which will reducethe number of children to that which the household canadequately cater for. Therefore, policy measuresdirected towards the provision of better family planningto reduce household size should be given adequateattention and priority by the government.

Education that encompasses all aspects oftraining and which brings about attitudinal changes isimportant for households in the project area. Also,strategies for an effective community participation inthe design of concepts and messages aimed atimparting knowledge about family planning to thehouseholds are recommended. A policy which providesadequately trained and equipped extension workers fordisseminating improved agricultural technologies hasthe potential of raising efficiency in food crop production,which enhances food security. Lastly, there is the needfor policy, which shall promote formal education as ameans of enhancing efficiency in food crop productionover the long-term period. Nutrition orientedprogrammes should be organized in an attempt toimprove the food substitution knowledge of householdsas educational status affect food security. In the short-term, informal education could be effective, especiallywhen targeted at household heads who have had limitedformal educational opportunities.

References

Allen Patricia (1993) Food For the Future. New York: JohnWiley & Sons food

Ajiber (2006) Food Basket Foundation International (1995).Nutrient composition of Commonly Eaten foods inNigeria-Raw, Processed, and Prepared 131

Amaze et al. (2006) A comparative Analysis of UrbanAgriculture Enterprises in Lagos and Port Harcourt,Nigeria. Environment and Urbanization 11(2)

Greer J, Thorbecke E (1986) A methodology for measuringpoverty applied to Kenya. Journal of DevelopmentEconomics 24 (1) : 59-74

Olayiwola OO (2012) Concept Note on Food Security inNigeria. Research in science and technology 1(7) :20-27

Olayiwola OO (2012) Determinants and strategies policiesfor resolving problem of Food Security in Nigeria.Research in Commerce and Management 1(10) :95-105

Nord and Hopwood (2007) An Introduction to Efficiency andProductivity Analysis Boston, Kluwer AcademicPress

Rural people view in Ogbomosho area of Oyo State, Nigeria

(Manuscript Receivd : 8.12.2013; Accepted :17.3.2014)

85

Abstract

The production of crops is an important economic activity forfarmers and economics transformation in both developed anddeveloping countries. To facilitate better understanding of thesynthetic parameter influencing supply responses, the studyused annual data of the Madhya Pradesh covering 1990-91to 2012-13 years to estimate the coefficient of the selectedprincipal crops through elasticity and ordinal least estimatetechniques (OLE).The responses of selected principal cropsto area, production and yield is weak and appropriate policyreform that can reduce sabotage faced by farmers in the studyarea is suggested.

Keyword: Principal crops, supply riposte and MadhyaPradesh

Madhya Pradesh is primarily an agrarian economy.Agriculture is the single largest producing sector ofeconomy since it comprises about twenty per cent ofthe country gross domestic product and employs aroundfifty per cent of the total labour forces. The performanceof this sector has an overwhelming impact onemployment generation, poverty alleviation and foodsecurity. According to most researchers, farmersanticipate price from the knowledge of current and pastprice. The pioneering work of Nerlove (1958) on supplyresponse enables to determine short run and long runelasticities; it is commonly used by many scientists dueto f lexibility in nature. Some of the importantcontributions on the methodology of panel data dynamicmodels are Nerlove (1958), Nowshirvani (1971),Arrellano and Bond (1991) and Olayiwola (2013). Also,various studies attributed problems in measurement ofvariables and the methodologies used for estimationas reasons for highly varying elasticities even within aregion. If we peruse the literature starting from Nerlovian(1958) model of supply response, improvement in thespecification was attempted by introducing competing

Supply riposte of major crops in Madhya Pradesh

O.O. Olayiwola, P.K. Awasthi and N.K. RaghuwanshiDepartment of Agricultural Economics & Farm ManagementJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 48004 (MP)Email : olubodun_2008@yahoo.com

crops concept where relative prices were introducedinstead of absolute prices. The next stage ofdevelopment was the introduction of risk and uncertaintyin the model. Behrman (1968) introduced standarddeviation of price and yield measured from previousthree years' data. This was criticized for the fact thatthe Nerlovian price expectation model is not consistentwith changing variance of the subjective probabilitydistributions. Nowshirvani (1971) modeled farmers' landallocation decision that accounted for uncertainties inprices and yields. Incorporating risk, Nowshirvani hasfound that area-price response turned out to benegative, implying stabilization schemes maysometimes be more effective policy instrument thanprice in bringing about area shifts among crops. Manyscholars use relative profitability rather than relativeprice, the reason being that it explains farmers' choicebehaviour in a better way. However profit calculationhas its own measurement problem such as identifyingproper imputation methods for own inputs andappropriate type of costs to compute profits andproblems related to common costs. Moreover price is adirect policy instrument and hence the results are handyfor policy purpose. During the last five decades, a largevolume of literature on supply response indicated thatthe response is much weaker. Non-price factors seemto dominate over price factors in farmers' decisionmaking, despite the introduction of reform process inearly nineties in India; some of the constraints thatIndian farmers are facing in responding to the marketincentives are more than expectation. However, thereis no firm evidence so far, which supports thishypothesis. Is it because the policies are still not ableto identify and target the proper constraints or is it dueto the nature of specification and methodology used inthe literature? Or is it that the time lag of the responseto liberalization is still longer so that the impacts areyet to be seen fully? Keeping this in the background,this paper will attempt to study the supply response of

JNKVV Res J 48(1): 85-89 (2014)

86

principal crops in Madhya Pradesh of India. There is adearth of studies on supply response at both pre andpost reform era. This study aims to fill up this gap relatedto variability on supply responses of selected principalcrops in the study area.

Methodology

Madhya Pradesh is situated at the heart of India andlies between latitude 21o6'and 26o54'N and longitude74o and 82o47'E. It covers a geographical area of 308,245 sq.km which is about 9.38% of the total area ofIndia. The State is land - locked and at no point is thesea less than 300 km away. Uttar Pradesh,Chhattisgarh, Andhra Pradesh, Maharashtra, Gujaratand Rajasthan are surrounding states. There are manyimportant rivers which cross through Madhya Pradesh.Narmada River is the most important of all and is knownas the lifeline of Madhya Pradesh. The hills give rise tothe main river systems - the Narmada and the Tapti,running from east to west, and the Chambal, Sone,Betwa, Mahanadi, and the Indravati west to east. Thesecondary data collected from different sources of databank were used for the study. The data were mainlycollected from the published documents of Governmentof India and Madhya Pradesh and web site of thedepartment of farmer welfare and agriculturedevelopment, government of Madhya Pradesh. The datasources are: (a) Area, Production and Yield of majorcrops in Madhya Pradesh published yearly byDirectorate of Agriculture, Madhya Pradesh, Bhopal. (b)Tables of Agricultural Statistics of Madhya Pradeshpublished yearly by Commissioner of land Records &Settlement, Gwalior, and Madhya Pradesh. (c)Compendium of Agricultural Statistics published yearlyby Agricultural Department of Madhya Pradesh Stateagricultural Marketing Board, Bhopal. (d) Cost ofCultivation Principal Crops in India published yearly byMinistry of Agriculture. (e) Directorate of Economics &Statistics, Department of Agriculture and Cooperation,GOI, New Delhi.

Model Specification

Area response model for principal crops is given as:

At* = 0 + 1At-1 + 2MSP + 3MSPt

-1 + 4C1 + 5Ct-1 +

6C2 + 7Ct-1 + 8 t

-1 + t

At - At-1 = g(At* - At

-1) 0< <1 where,

At* = Desired Acreage

At = Actual Acreage of Crop in the Current Year

MSPt = Maximum Support Price at Current Year

MSPt-1 = Maximum Support Price at Current Year,

Lagged by One year

Ct = Cost C1 at Current Year

C1t-1 = Cost C1, lagged by One year

C2t-1 = Cost C2, lagged by one year

C2 = Cost C2 at Current Year

Qt-1 = Desired Quantity, lagged by One year

= Coefficient of Adjustment

Production responses model for selected principalcrops:

t* = 0 1At-1 + 2MSPt

-1 + 3MSPt-1 + 4C1 + 5Ct

-1 +6C2 + 7C2t

-1 + 8 t-1 + t

t - t-1 = (Q1 - t-1) 0< <1

Where,

t* = Desired Quantity

Qt-1 = Actual Production in the current year

= Coefficient of Adjustment

t = Error Term

Yield/ Productivity Response function for selectedprincipal crops:

Yt* = 0 + 1Yt-1 + 2At

-1 + 3MSPt-1 + 4MSPt + 5C1 +

6Ct-1 + 7Ct

-1 + 7C2 + 8 2t-1 + t

Yt* - Yt-1 = (Y1 - Yt-1) 0< <1where,Yt = Actual Yield in the Current YearYt* = Desired Yield

Coefficient of Adjustment

t Error Term

Results and discussion

Supply response functions provide us with usefulinformation on the extent of farmer's response to price

87

and other economic factors. An attempt has been madehere to study the supply response of principal crops inMadhya Pradesh during the period 1990/91 to 2012/13.From Table 1, it is seen that the coefficients of thecurrent year acreage of principal crops (At-1) and lastyear production cost of principal crops (C2 t-1) are allpositive and non significant except lentil crops that ispositive and significant at 1 per cent level.

There is a positive influence of these twovariables on the yield estimate of principal crop inMadhya Pradesh. There is no significant effect ofMaximum Support Price (MSPt) for all the regions ofMadhya Pradesh. The desired quantity of the selectedprincipal crop lagged by one year (Qt-1) is significant at5 per cent level in Madhya Pradesh. The current C2cost (Ct) has no impact on the acreage, production andyield of principal crops in the study area. The coefficientof the determination indicates that the variables areexplained more than 60 per cent variation to the currentyear acreage of the principal crops. As per the resultsshown in table-B the coefficient of the determinationindicates that more than 50 per cent variation isexplained by the independent variables, suggesting apositive influence of the independent variables on thecurrent year principal crops production in MadhyaPradesh.

The independent variable of cost C2 (C2t) hasnot shown any significant influence on the productionof principal crop in Madhya Pradesh. It is also clearfrom the table-3, the independent variables i.e., currentyear acreage (At), quantity of principal crop lagged byone year (Qt-1) and cost C2 lagged by one year (C2t)are having significant influence on the production ofthe principal crop in the current year.

It is evident from the table-C that the coefficientsof the variables, last year production of the principalcrop (Qt-1), and one year lagged (C2t-1) are havingsignificant influence on the current year productivity ofprincipal crop. But, the Coefficient of maximum supportprice lagged by one year (MSPt-1) is not having anysignificant influence on the current year productivity ofthe selected crop in Madhya Pradesh. The studysupports the results of available literature that farmers'response to price is very low in the short run and theiradjustment mechanism towards reaching the desiredlevel is slow for principal crops in Madhya Pradesh India.

However we found evidence that farmers alsorespond by intensive application of other inputs as theflexibility to shift acreage could be restricted in farming.Using ordinary least estimate models, the study hasrejected the hypothesis that economic liberalization hasimproved the acreage response. But one could not

simply dismiss that, efforts to improve agricultural growththrough price incentives is a futile exercise. Variousdiscussions on the supply response theme in theacademic literature and in the policy arena clearlypointed out that turning attention to removing some ofthe physical infrastructural constraints as well as creditconstraints will go a long way in increasing the supplyresponse. In the last decade lots of reforms took placein the agricultural credit market.

Easy access to credit was expected to strengthenthe supply response. Price risk is a crucial adversefactor that influences acreage response. Agriculturaldiversification requires risk management, and privatesector involvement in agro-processing opens upopportunities for sharing risk with the farmers throughcontract farming. Production risk due to adverse climaticcondition is an equally important factor, but we werenot able to find significant effect of risk variables. Hencewe could not add this variable in the final analysis.Pandey et al. (2005) has conducted an analysis ofsupply response of principal crops for Madhya Pradeshstate to see if domestic price fluctuation is havingnegative impact on selected principal crops production.The period of analysis is 1986-87 to 2001-02.

It was found that the supply elasticity for principalcrops in Madhya Pradesh is low beyond expectationdespite the maximum support price from theGovernment of Madhya Pradesh. It is evident thatfarmers produce different principal crops in MadhyaPradesh of India mostly depends on previous year'smaximum support price. Therefore, the maximumsupport price (MSP) prices of principal crops were takeninto consideration in this study because wholesale andretail price may not some time reflect the actual pricethat received by the farmers. On the other hand, therisks due to variations in yield and price are expectedto act as deterrent factors on acreage under variousselected principal crops during a particular year.

The magnitude of coefficient for tomato is closeto 1, indicating that the farmers had considerably highadjustment response (Table 1). The research findings isin line with Nahatkar et al. (2006) that studied the issuesconcerning growth in area, production, productivity andsupply responses of soyabean in different districts inMalwa plateau of Madhya Pradesh using time seriesdata of 1990-91 to 2002-03 and observed that coefficientof lagged area under soyabean was positive and havehigh significant impact on current area under soybeanin Indore, Ujjain, Dewas and Rajgarh districts.

Based on the above discussion, it may be inferredthat the area, production and yield of Principal crops inMadhya Pradesh of India increased to some extent

88

Table 3. Ordinal least estimate of yield of principal crops in Madhya Pradesh

Crops Const. At Qt-1 MSPt MSPt-1 C1 C1t-1 C2 C2t-1 R2

Paddy 5.443 o.6522* 0.0167** 0.6831 0.0923 0.0578* 0.1383 0.2371 0.1168** 0.7332**

Wheat 7.789 0.5772** 0.0189** 0.3255* 0.0534 0.0853 0.0127* 0.2264 0.1845** 0.6233**

Soybean 5.221 0.7721** 0.0282** 0.2861 0.0064 0.0221 0.0058 0.0136 0.3122** 0.6544**

Grams 8.553 0.6225** 0.0358** 0.1158 0.0863 0.2734 0.0064 0.1883 0.0923** 0.6991**

Cotton 6.332 0.6614** 0.0319** 0.426 0.0197 0.0321 0.0863 0.1675 0.0234** 0.6452**

Mustard 5.991 0.6833** 0.0432** 0.2351 0.0086 0.0079 0.0013 0.0064 0.0663** 0.7011**

Lentil 4.289 0.6023** 0.0092** 0.2334 0.0017 0.0034 0.0064 0.0863 0.0512** 0.6442**

Significant Level: 1 per cent indicate *, 5 per cent ** and 10 per cent *** respectively

Table 1. Ordinal least estimate of production of principal crops in Madhya Pradesh

Crops Const. At Qt-1 MSPt MSPt-1 C1 C1t-1 C2 C2t-1 R2

Paddy 5.86 0.6421** 0.7910** 0.0465* 0.0013 0.132 0.0456 0.009 0.047 0.6732**

Wheat 2.7421 0.4352** 0.8956** 0.0197 0.0064 0.224 0.358 0.038 0.1675 0.7503**

Soybean 5.2249 0.5380** 0.4092** 0.8956* 0.0197 0.689 0.0942* 0.055 0.0064 0.6642**

Grams 6.1189 0.5380** 0.8615** 0.0114 0.0086 0.039 0.532 0.028 0.0863 0.7531**

Cotton 4.7438 0.5472** 0.8692** 0.0124 0.0017 0.067 0.092 0.017 0.0839 0.7324**

Mustard 3.7948 0.4391** 0.8884** 0.0189* 0.015 0.334 0.0445 0.029 0.0612 0.7089**

Lentil 5.3455 0.3642** 0.7699** 0.0162* 0.1028* 0.345 0.0692 0.0678 0.0092* 0.6711**

Significant Level: 1 per cent indicate *, 5 per cent ** and 10 per cent *** respectively

Table 2. Ordinal least estimate of area of principal crops in Madhya Pradesh

Crops Const. At Qt-1 MSPt MSPt-1 C1 C1t-1 C2 C2t-1 R2

Paddy -3.6521 0.6344** 0.63.56** 0.0321 o.1036* 0.4511 0.6791** 0.0562 0.0789 0.5925**

Wheat -2.3478 0.5584** 0.5584** 0.0079 0.3247 0.3021 0.6026** 0.0723 0.0562 0.6899**

Soybean -3.2689 0.5911** 0.5911** 0.0016 0.0853 0.4453 0.7023** 0.0556 0.0923 0.7433**

Grams -7.0583 0.4978** 0.4978** 0.0486** 0.0221 0.6128 0.6833** 0.0623 0.0534 0.6511**

Cotton -4.2311 0.5123** 0.5123** 0.0034 0.2734 0.3277 0.6553** 0.0553 0.0623 0.6923**

Mustard -6.4529 0.6542** 0.6542** 0.0063 0.0379 0.5532 0.7325** 0.1773 0.0523 0.7232**

Lentil -5.5782 0.5231** 0.5231** 0.0174 0.0035 0.2268 0.6992** 0.2356 0.0893* 0.6322**

Significant Level: 1 per cent indicate *, 5 per cent ** and 10 per cent *** respectively

89

during study period due to adoption of improvedvarieties. Hence, to accelerate the existing growth ratesof principal crops production the following policyguidelines are taken into consideration.

• Before taking any maximum support price policy,price response level and price flexibility and crossprice flexibility effects must be considered furtherresearch.

• Cost of Cultivation and yield risk factors will needto be taken care of by appropriate measures ofprincipal crops in Madhya Pradesh of India.

• Non-price factors such as temperature, rainfall,humidity may be considered for future

• Adequate farm inputs must be made available atcheap price to the farmers and government mustfollow a clear channel supply systems of inputs.

• Frequency of extension visits should beincreased to encourage wider spread andadoption of improved farm technology.

References

Arellano M, Stephen Bond (1991) Some Tests of Specificationfor Panel Data: Monte Carlo Evidence and anApplication to Employment Equations. Rev EconStudies 58(2) : 277-97

Brauw Alan De, J Huang, Roselle S (2003) Sequencing andthe Success of Gradualism: Empirical Evidence fromChina's Agricultural Reform. Presented at JUNU-IFPRI workshop on The Dragon and the Elephant: AComparative Study of Economic and AgriculturalReforms in China and India p 112-116

Behrman JR (1968) Supply Response in UnderdevelopedAgriculture: A Case Study of Four Major AnnualCrops in Thailand 1937-63 Amsterdam: NorthHolland Pub Co

Eckstein Zvi (2010) A Rational Expectations Model ofAgricultural Supply. Political Econ 92(1):1-19

Epstein L (2012) Duality Theory and Functional Forms forDynamic Factor Demands. Rev Econ 48 / 81-95

Gallant A (2011) Three stage Least Square Estimation for aSystem of Simultaneous Non linear ImplicitEquations. Econometrics 5 : 71-88

Nerlove M (1958) The Dynamics of Supply: Estimation ofFarmers' Response to Price, John Hopkins Press.Further evidence on the Estimation of DynamicEconomic Relations from a time series of Crosssections. Econometrica 39 : 359-382

Nahatkar S B (2006): Growth and Supply Response ofSoyabean in Malwa Plateau of Madhya Pradesh.Soybean Res 4 : 49-53

Nowshirvani V F (1971) Land Allocation under Uncertainty inSubsistence Agriculture. Oxford Econ Papers 23 (3): 445

Pandey L M, Sant Kumar, Mruthyunjaya (2005) Instability,Supply Response and Insurance in OilseedsProduction in India. Agricult Econ Res Revi 18 : 102-112

Rao C H H (2003) Reform Agenda for Agriculture. EPWPerspectives, Economic and Political Weekly 14(56): 45-60

Surekha K (2005): Modeling Nonlinear AutoregressiveDistributed Lag Models: A New Approach.Quantitative Econo 4 : 101-114

Olayiwola O O (2013) Review on Methodology for SupplyResponse in Agriculture. Research in Manag &Techno 2(11) : 1-10

(Manuscript Receivd : 8.12.2013; Accepted :19.3.2014)

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Abstract

Despite the bulk of human and natural resources that arefound in the rural areas, there exist inequitable distributionsof development infrastructures between urban and rural areas.The dearth of social amenities from government has increasedthe involvement of marginal communities in the developmentof their areas and the extent of provision of theseinfrastructures has not been fully ascertained in Nigeria. Thisstudy was therefore conducted to identify the areas and extentof involvement of self help associations in rural communitiesas well as patterns of rural participation in communitydevelopment. The study was conducted in Oguta LocalGovernment Area of Imo State, Nigeria and 135 respondentswere sampled using multi stage sampling technique. Theresults revealed that most of the respondents were married,between 60 - 69 years old, Christians and had one child. Itwas also found that there were adequate involvement in selfhelp development in the basic social amenities and in particularroad rehabilitation, rural electrification and provision oftransformers as well as building of post offices. Majorityparticipated through contribution of money while poormanagement of fund; scarce resource and poor assistancefrom government were identified to be severe constraints tocommunity development. Therefore, leadership in the ruralcommunities should be strengthened and government shouldbe actively assist and encourage local planning andparticipation in order to enhance rural communitydevelopment.

__________________________________________________________________________________Keywords: Rural, self-help, community development,Imo West

Development can be defined as man's capacity toexpand his own consciousness and therefore his powerover himself, his environment and his society (Anyanwu

JNKVV Res J 48(1): 90-94 (2014)

Evaluation of self-help development activities and patterns ofparticipation in community development projects among ruraldwellers: case of Oguta Local Government Area,Imo State, Nigeria

Victor Chibuzor UmunnakweDepartment of Agricultural Extension and Rural DevelopmentUniversity of Ibadan, Ibadan NigeriaEmail: chivicumunna@yahoo.com

1999). According to Ohiorhenuam (1990), it is atransition of the society towards the desired state. In allcases, the emphasis of development is on people,because it is they who are stimulated, motivated, helpedor encouraged to adopt new methods and learn newskills, for their well being. This was corroborated by theformer Tanzanian President, Julius Nyerere in a keynoteaddress of the proceedings of the internationalconference on Adult Education and Development heldat Dares Salaam in 1976 who said that: "Developmentis for man, by man and of man."

Local communities have been over shadowed bylarger societies as a result of long isolation, colonialdomination and the consequent decline in communityspirit ( Ekong 1988). This has led to increased poverty,underdevelopment of available resources and individualpotentialities in these local communities.

About 75 % of Nigerian population resides in therural areas (Olayide 1981) and about 90% of the foodand fibre consumed in Nigeria comes from the ruralareas (Ekong 1988). Despite the bulk of human andnatural resources that are found in the rural areas, thereexist inequitable distributions of developmentinfrastructures between urban and rural areas. Thisinequitable distribution contributed immensely toreduction in agricultural production and increased rural-urban migration. Therefore developing agriculturalsector in the rural areas is important to achieve foodsecurity in Nigeria. However, this development shouldbe conceived under the concept of integrated ruraldevelopment in which a combination of factors whichinclude technology, education and access toinfrastructural facilities are essential to the improvementof rural population.

91

The dearth of social amenities from governmenthas increased the involvement of marginal communitiesin the development of their areas. Local associationsparticipate actively in the development of theircommunities through mobilizing people for self help(Fakoya et al. 2000). There are various groups andassociations within Nigeria communities that engagein the development of their communities. This spirit ofassociation is popular in Igbo ethnic group Nigeria.According to Emeh et al. (2012), the modern Igbo societyhas witnessed invigorated form of associational life;hence there are elements of dynamism in self-helpgroup relations. In Southeastern Nigeria, self helpgroups include: professional association; age gradeassociation; town unions, social clubs; town womenassociation and individual efforts. The principle of selfhelp enables people to exploit their resources whichwould otherwise be dormant and promotes widespreadcitizen participation. Deeply inherent in communitydevelopment is participation. According to Levine(1996), participation enables development to be builton the strength, tradition, beliefs and values ofcommunity. It is therefore imperative to understanddevelopment realities in rural communities from theperspectives of the dwellers for appropriate ruralcommunity development.

Methodology

The study area is Oguta Local Government Area of ImoState, Nigeria. It lies in the tropical rainforest vegetation.Farming, animal husbandry and fishing are the majorsources of livelihood for the people. Major crops grownare: yam, cassava, maize, plantain and oil palm. Whilethe major natural and mineral resources are: timber,Crude oil and natural gas.

Table 1. Selected the personal characteristics ofrespondents

Sex Frequency Percent

Male 62 46.3Female 72 53.7Total 134 100Age group Frequency Percent20-29 yrs 41 30.630-39 yrs 6 4.540-49 yrs 11 8.250-59 yrs 27 20.160-69 yrs 47 35.170 yrs above 2 1.5Total 134 100Marital status Frequency PercentSingle 41 30.6Married 85 63.4Divorced 3 2.3Widow 5 3.7Total 134 100Number of children Frequency Percent1 43 32.12 3 2.23 1 0.74 3 2.25 11 8.26 31 23.27 25 18.78 12 9.09 4 3.010 1 0.7Total 134 100Religion Frequency PercentChristianity 134 100

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Multi stage random sampling technique was usedto sample the respondents. There are 16 electoral wardsin the study area. Purposive sampling technique wasused to select 3 wards based on their rurality andintensity of farming. Afterward, random samplingtechnique was then used to select 45 respondents fromeach of the 3 wards, making the total sample size to be135 respondents.

Result and discussion

Data reveals that 53.7% of the respondents are femalewhile 46.3% are male (Table 1). The significant numberof female shows increasing participation of women onissues concerning community development. This iscorroborated by Odebode and Akinsorotan (2002) whoasserted that with the rapid socio-economic growthexperienced all over the world, women are found to beplaying significant roles wherever they are found. Theylow number of respondents between ages of 30-39 and40-49 could be attributed to rural-urban migration. Thisfinding supports the finding by Nwajiuba (2005) who

opined that significant rural out migration is taking placein Sub-Saharan Africa where the majority of ruralresidents are poor. Majority of the respondents weremarried (63.4%) which shows that strong family ties existin the study area. From the table, close to 70% of therespondents had children not more than six indicatingreduced number of children born by a single womanresulting from education and increased awareness andknowledge of family planning. Though traditional religionis prevalent in the study area, however none of therespondent was identified to hold traditional faith. Thisfinding is in conformity with that of Olawoye (1994) whichreported that Christians or Muslims may hold traditionalbeliefs.

Majority of the respondents evaluated noninvolvement of self help community development ineducation (except renovation of schools); health;lending of loans and farm inputs; provision ofemployment to indigenes; empowerment of youth;provision of agricultural extension services; massliteracy campaign and training of village leaders. Thiscould be due to the fact that the self help groups inthese rural communities do not have the wherewithal

Table 2. Self-Help community development activities

Development activities Regular Occasional No involvementProvision of processing machines - 33(24.6) 101(75.4)Building of cottage industries - 31(23.1) 103(76.9)EducationBuilding of schools - 29(21.6) 105(78.4)Renovation of schools - 134(100.0) -Provision of teachers and teachning materials - 91(15.7) 113(84.3)Provision of scholarship and bursaries 2(1.5) 48(35.8) 84(62.7)HealthBuilding of hospitals and dispensaries 5(3.7) 28(20.9) 101(75.4)Provision of drugs 3(2.2) 9(6.7) 122(91.0)Provision medical personnel 6(4.5) 5(1.5) 126(94.0)Basic social amenitiesRoad construction 6(4.5) 5(3.7) 123(91.8)Road rehabilitation 127(94.8) 5(3.7) 2(1.5)Rural electrification 123(91.8) 8(6.0) 3(2.2)Provision of transformers 130(97.0) 4(3.0)Drilling of boreholes 5(3.7) 29(21.6) 100(74.6)Distribution of water pipes 2(1.5) 47(35.1) 85(63.4)Lending of loans and from inputs through cooperatives 6(4.5) 3(2.2) 125(93.3)Building of post offices 2(1.5) 103(76.9) 29(21.6)Provision of employment to indigenes 5(3.7) 3(2.2) 126(94.0)Empowerment of youth 4(3.0) 27(20.1) 103(76.9)Provision of agric extension services - 2(1.5) 132(98.5)Mass literacy compaign 6(4.5) 8(6.0) 120(89.6)Training of village leaders 2(1.5) 31(23.1) 101(75.4)

93

to embark on the aforementioned activities or deemedit not critical to the wellbeing of their host communitiesat that moment. Considering the basic social amenitiesand in particular road rehabilitation, rural electrificationand provision of transformers as well as building of postoffices, majority of the respondents evaluated self helpassociations as considerably involved. This finding isin conformity with that of Okali et al. (2001) whichreported that town unions or age-grades in thesoutheastern Nigeria have contributed to the provisionelectricity, roads and transport systems, markets andpostal facilities.

Majority of the rural respondents (78.4%) and51.5% participated in church building and provision ofpipe-borne water respectively through contribution ofmoney. while majority (61.1%) and 78.4% did notcontribute to the road rehabilitation and communitysecurity respectively. This could be partly due to thefact that these projects (road rehabilitation andcommunity security) required more of execution andtherefore made demands on the young, energetic and

Table 3. Patterns of participation of rural dwellers in selected community development projects

Patterns of participation Contribution Contribution Contribution Planning Execution Execution NoCommunity of money of money and of money and of task of task and contributionDev. Projects materials planning planning

Building of churches 105(78.4) 15(11.2) 14(14.5) - - - -Road rehabilitation - - - 4(3.0) 31(23.1) 17(12.7) 82(61.1)Electrification 46(34.3) - 40(29.9) - 23(17.2) - 25(18.7)Provision pipe borne water 69(51.5) - 30(22.4) - 12(9.0) - 25(17.2)Community Security - - - - 29(21.6) - 105(78.4)

The percentages in parentheses

Table 4. Constraints to community development

Constraints Severe Moderate Low Not a constraint

Poor management of fund 48(35.8) 38(28.4) 31(23.1) 17(12.7)Poor leadership 52(38.8) 36(26.9) 33(24.6) 13(9.7)Personalities leadership clash - 46(34.3) 44(32.8) 44(32.8)Inefficient community development agent - - 58(43.3) 76(56.7)Lack of maintenance culture - 9(6.7) 33(24.6) 92(68.7)Scarce resources 101(75.4) 27(20.1) 6(4.6) -Inability to identify felt-need - 4(3.0) 34(25.4) 96(71.6)Lack of cooperation of the People - 8(6.0) 15(11.2) 111(82.8)Poor assistance from government 120(89.6) 14(10.4) - -The percentages in parentheses

Patterns of participation of rural dwellers in selected community development project s

0

10

20

30

40

50

60

70

80

90

Bu ilding of church ers roa d reha bil i tation Elec trification Prov is ion pipe bornewa ter

Comm unity securityP

erc

en

tag

e o

f p

art

icip

ati

on

Contr ibutio n of mon ey Contr ibu tion money and m aterials contr ib ution money and planningPlan ning Execution of t ast Executin of task and planningNo contr ivution

c

male rural dwellers.

It was noted that, 35.8% of respondents identifiedpoor management of fund as a severe constraint tocommunity development. This finding corroborated theobservation of Akande (1998) that many communityleaders betrayed the trust people placed on them by

94

misappropriating community fund which aggravated theproblem of abandonment of community projects. 75.4%of the respondents declared that scarce resource wasa severe constraint while 89.6% admitted that poorassistance by government was a severe constraint tocommunity development. This finding was supportedby that of Ladele (2005) who opined that goodgovernance cum commitment on the part of governmentis a sine qua non to meaningful rural development.

Conclusion

Adequacy of community facilities and services,opportunity for employment and the quality of communityenvironment are essential for the development of anycommunity. This study concludes that rural communitiesin the study area have been involved in self helpdevelopment activities in provision of social amenities.However their involvement in other areas leaves muchto be desired. Therefore, leadership in the ruralcommunities should be strengthened and governmentshould be actively assist and encourage local planningand participation in order to enhance rural communitydevelopment.

References

Akande J O (1998) Non Governmental OrganisationsInventions in the Promotion of CommunityDevelopment Programmes in Oyo State 1986 1996.Ph.D Thesis, Department of Adult Education,University of Ibadan

Anyanwu C N (1999) Introduction to Community DevelopmentGabesther educational publishers Ibadan: 4

Ekong E E (1988) An Introduction to Rural Sociology JumakPublishers Limited Ibadan 373-374

Emeh I E J, Eluwa I J Ukah F O (2012) Rural CommunityDevelopment in Nigeria; A Group DynamicsPerspective. Interdisciplinary J Contemporary ResBusiness 4(5): 1090-1107

Fakoya E O Apantaku S O and Oyesola O B (2000) Potentialof Local Associations Participation in CommunityDevelopment Projects in Ifedore Local GovernmentArea of Ondo State Nigeria. In Jibiwo A A, A A Ladeleand A B Ayanwale (Eds): Community LevelParticipation in Rural Development. The NigeriaRural Sociological Association: 11-16

Ladele A A (2005) Rural Development Process and Practice.In Agriculture Extension in Nigeria, S F Adedoyin(eds): 139-144

Levine C J (1996) From Oaxaca to Washington DC:Community Museums as Cultural and EconomicResources. Grassroots Development 20(1): 5-14

Nwajiuba C (2005) International Migration and Livelihoods inSoutheastern Nigeria, Global Migration Perspectives,no 50 Switzerland: 4

Nyerere, J (1976) Keynote Address, in the Proceedings ofthe International Conference on Adult Education andDevelopment, 21- 25 June Dares Salaam

Odebode S O, and Akinsorotan, A O (2002) Determinants ofRural Women's Participation in CommunityDevelopment projects in Iddo L.G.A of Oyo State,Nigerian J Rural Sociol 3 (1&2): 43-49

Ohiorhenuam, J E F (1990) A Primer on Development in theProceedings of the National Center for EconomicManagement and Administration TrainingProgramme on Project Analysis and Evaluation, Nov25- Dec. 14, Kano Nigeria

Okali D, Okpara E, Olawoye, J.E (2001) Rural-urbanInteractions and Livelihood Strategies Series: theCase of Aba and its Region, Southeastern Nigeria,Working Paper 4, International Institute forEnvironment and Development London

Olawoye J E (1994) Rural Sociology. In Sociology Theory andApplication, Otite, O (eds), Lagos Mult- house PressLtd

Olayide J O (1981) Scientific Research and the NigeriaEconomy, University of Ibadan Press: 19-23

(Manuscript Receivd : 20.12.2013; Accepted :20.3.2014)

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JNKVV Res J 48(1): 95-98 (2014)

Abstract

Energy consumption is of great importance to the societybecause of the strong dependent of socio-economic variablesand even to some extent cultural activities on it. Therefore, itis very important to know the factors responsible for demandof cooking energy in the rural areas. The results of the studyshow that 35.1% of the respondents were farmers, while64.9% were paid workers and artisans, 55.2% and 17.2%had tertiary and secondary education respectively, and 21.6%didn't go to school, while 6.0% attended primary school.Majority of the respondents have income category of N11,000 and N30, 000.00 with the highest percentage of 53.7.The family size range of majority of the respondents wasbetween 3 and 6. Overall goodness of fit as reflected by Prob> Chi were good (0.0004 for fuelwood and 0.0379 forkerosene). In comparison with income and occupation, theprobability that a person uses fuel wood was significant andnegatively related to income and occupation at P = 0.01 andP = 0.05 respectively. In the case of kerosene demand, incomparison with income and occupation, the probabilities thata person uses kerosene was positively relate to income andoccupation at P = 0.01 and P = 0.05 respectively.

Keywords: Energy, Demand, Households, Osun

A critical look at cooking energy demand and usageshows that fuelwood is the most used fuel globally.Although its usage has declined in urban areas as aresult of more rapid technological innovation associatedwith urbanization but the contrary is in rural communitiesdue to poor and slow technological innovation spread.The type of energy used by a family depends to a largeextent on their income and other social factor that relateto their welfare. Hertberg and Bacon (2003), viewedaccess to infrastructure as the major determinant ofenergy consumption pattern of households. Amonghouseholds that are electrified, and those that are not,there are significant differences regarding energy

Determinants of cooking energy demand in the rural householdsof Osun State, Nigeria: An application of Bivariate Probit Model

Ayodeji Oluwaseun OgunlekeDepartment of Agricultural EconomicsUniversity of Ibadan, Ibadan, Oyo State, NigeriaEmail : ayoogunleke@yahoo.com

consumption pattern among households. Whenswitched to electricity or kerosene stove for instance, itis generally another fuel source that is used for cooking.This holds true at the household and community levelsas electrification positively affects modern fuel use andnegatively affects fuel wood consumption per capitaexpenditure, education and urbanization are alsoassociated with fuel switching with larger householdsmore likely to use multiple fuels. The family averagesbudget differs according to the type of fuel use and thecity. The major determinants of per capita energydemand and consumption are household size,technology, consumption level, household population,household structure, employment level, householdincome, environmental factors and energy cost (Olabisi1999).

Material and method

The study was carried out in Egbedore LocalGovernment Area of Osun State in February 2009. Astratified sampling technique was used to select the ruralhouseholds. Egbedore Local Government has ten

Map of Osum State, Nigeria

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wards. The wards were stratified into three out of whichone village was randomly selected from each of stratifiedwards. The three selected villages are Awo, Ara, andIdo-osun. Further, simple random sampling was usedto select one hundred and thirty four (134) ruralhouseholds in proportion to the population of the villagesbased on the preliminary results from 2006 Census.Data required for the study were collected usingquestionnaires with the help of trained enumerators.

Model Specification

Bivariate Probit Model

The model was used to identify the socioeconomiccharacteristics affecting household use or non-use ofalternative cooking energy sources in the rural areassince there were only two energy sources (keroseneand fuelwood) identified.

The general specification of the bivariate probit modelis as:

Y * = x +, Y = 1 if Y * >0, 0 otherwise

Y* =X +, Y = 1 if Y * >0, 0 otherwise, (Green 2003)

The dependent variables in this estimation are definedto have two possible values: 1, denoting the use of fuelwood; and 2, denoting the use of kerosene.

The energy type used by households may be related tohousehold size, (Hs); level of education, (Ed);occupation, (Oc); income, (In); and area of residence,(Rs).

Hs = Household size (number)

Ed = Educational level of household head (years)

Oc = Occupation (1 = civil servant, 2 = Farming, 3 =Artisan)

In = Income

Results and discussion

Only 35.1% of the respondents in the study area whichhappens to be rural area were farmers (Table1). Whileothers that made up 64.9% were paid workers and

artisans. The reason is that most people in the ruralareas have other occupations apart from their farmingactivities. They devote more time to artisan work andonly go to farm when they do not have much to do or atthe peak of farming season and return to continue withother occupations. So they prefer to be recognized bytheir other occupations than be called farmers whichthey consider derogatory. It was also noted that majorityof the respondents had tertiary education (55.2%),followed by those that attended secondary school,17.2%. The result implies that majority of therespondents are literates only residing in the rural area.Also, about 53.7% of the respondents fall within theincome category of N11,000 and N30,000.00, showingthat they are medium income earners and therefore maynot be able to afford buying some of the expensiveenergy sources. The family size of majority of therespondents in the study area fall within the range of 3and 6, with percentage of 32.8. That implies that there

Table 1. Frequency distribution of socio economiccharacteristics of the households

Variables Frequencies Percentage

Family Size1-2 23 8.63-4 44 16.45-6 44 16.47-8 13 4.89-10 6 2.211-12 1 0.4Educational level of household headNone 29 21.6Primary 8 6.0Secondary 23 17.2Tertiary 74 55.2OccupationCivil servant 63 47.0Farming 47 35.1Artisan 24 17.9Income10,000 and below 49 35.311,000-20,000 32 24.221,000-30,000 34 29.531,000-40,000 16 11.240,000 and above 1 0.8Source: Field Survey 2009

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is likelihood of increased pressure on energy demandon the household due to large family sizes.

The overall goodness of fit as reflected byProb>Chi value were good (0.0004) and (0.0379) forfuel wood and kerosene respectively (Table 2). In termsof consistency with expectations on the relationshipbetween the dependent variables and the explanatoryvariables, the model appears to have performed well.The demand for fuel wood shows that in comparisonwith occupation and education, the probability that aperson uses fuel wood was negatively related tooccupation and education, and both were significantlyat P < 0.05. This means that the educated and those

that have better jobs other than farming are likely touse less of fuel wood. In comparisons with family sizeand income, the probability that a person uses fuel woodwas positively related to family size but negative toincome, though only income was significant at 0.01 < P< 0.05. That shows that households with higher familysizes are more likely to use more fuel wood while higherincome households are likely to use less of fuel woodin the rural area. It was found that both occupation andincome were also significant in the use of kerosene butall the signs were the reverse as in Fuel wood. That isin agreements with a priori expectations. In the case ofthe demand for kerosene, in comparison with income

Table 2. Result of Bivariate Regression Analysis of theuse or non use of Fuelwood and Kerosene

Variables Fuelwood Kerosene

Occupation -2.61 2.61(1.76)** (1.76)**

Education -2.69 2.69(2.09)** (2.09)**

Family size 0.013 -0.013(0.18) (0.18)

Income -2.42 2.42(2.82)*** (2.82)***

Constant 1.40 -1.40(0.76) (0.76)

Statistics: 2 54.61 37.62Prob.> 2 0.0004 0.0379No of Obs. 130Source: Field Survey, 2009. Figures in parentheses arethe t-values, **Significant @ 5%, ***Significant @ 1%

and occupation, the probability that a person useskerosene was positively relate to income and occupationand they were statistically significant at P= 0.01 and0.01 < P < 0.05 respectively. This result implies thatthose that can afford to use kerosene for cooking in therural areas are those that have better jobs other thanfarming and consequently those that have higherincome.

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Conclusion

Energy sources are used for cooking, heating and otherhousehold activities in Nigeria. The forgoing depicts thetrue situation of the rural households who are deniedof the use of modern cooking fuels, even kerosene,either as a result of scarcity or due to very high price.This makes it almost impossible for them to afford theenergy sources. In the case of liquefied petroleum gas,most of them don't even know what it is and there wasno encounter of its usage during the study. The onlycase of the use of kerosene by most of the rural dwellerswas for the lighting of the lanterns. This trend is nothealthy for our rural dwellers, especially for a countrylike Nigeria that is very wealthy with crude oil.

This is because of what the consequence portendfor their health, especially women and children, as aresult of air pollution in the use of fuel wood, the dangersand inconveniences involved in the search for fuel wood,its dirty nature, and forest depletion as a result of thefact that the people resort to the felling of life trees andwait for them to dry, in the time of serious scarcity. Inaddition, the psychological torture they pass throughas a result of this is immense, as they see themselvesas second class citizens who cannot even afford theuse of kerosene stove, due to the need for kerosene inits use. Thus the use of kerosene stove is a statussymbol for affluence. There is therefore a need for aproactive and pragmatic approach to the issue ofcooking energy supply and pricing in our rural areas, inorder to help alleviate the sufferings of the people andhelp lift them out of poverty.

References

Green WH (2003) Econometric Analysis (5 Ed). PearsonEducational International New Jersey, USA

Hertberg R and Bacon R (2003) Household Energy and thepoor; Result from a multi country study accessed 24February 2014 from http://www.worldbank.org/energy/

Olabisi IA (1999) Domestic Energy Situation in Nigeria:Technological Implication and Policy AlternativeSeminar Paper, Department of Sociology. ObafemiAwolowo University, Ile Ife

(Manuscript Receivd : 8.12.2013; Accepted : 19.3.2014)

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Abstract

In rural communities, gender affects how men and womenaccess livelihood and can impose constraints on livelihoodstrategies available to women especially migration. However,in recent time, there has been increasing number of womenin migration across the boarders and therefore it is imperativeto assess the disposition of rural households toward thisdevelopment. This study was conducted in Imo State, Nigeriaand 119 rural households were sampled using multi stagerandom sampling technique. The results revealed that majorityof rural households were male headed, 50 years and above,married, had education up to secondary school level,Christians and employed. It was also found that theirdisposition toward women's international migration tendedfrom less favourable to favourable disposition with majorityshowing favourable disposition. The demographiccharacteristics of respondents could explain 30.8% (R2 =0.308)of disposition toward women's' international migration.However, sex (t= -4.229; p=0.000), level of education (t=-1.762; p=0.081) and age (t=-1.429; p=0.156) were significantpredictors at 1%, 5% and 10% levels of significancerespectively. The study recommends that policies andprogrammes aimed at enhancing women livelihood strategiesshould take into consideration gender perspective in relationto migration as a livelihood strategy.

___________________________________________________________________________Keywords: Rural household, disposition, women,attitude, Imo West

In developing countries, agriculture providesemployment and livelihood for the majority of thepopulation. However, according to Vargas-Lundius andLanly (2007) in most developing countries employmentin agricultural sector is decreasing with younger workers

Disposition of rural households toward women's internationalmigration in Imo West (Orlu) Senatorial District, Nigeria

Victor Chibuzor Umunnakwe1 and Ayodeji Oluwaseun Ogunleke2

1Department of Agricultural ExtensionFaculty of AgricultureUniversity of Ibadan, Ibadan Nigeria2Department of Agricultural EconomicsFaculty of Agriculture and ForestryUniversity of Ibadan, Ibadan Nigeria

seeking to move out of agriculture because of severalsocio-economic reasons including low income, pooraccess to land, incentives and agro-climatic constraints.This has resulted in many rural areas undergoing aprocess of "de-agrarianization," with younger workers,seeking to move out of agriculture (Reardon et al. 1998)and has induced rural out-migration. In many ruralareas, out-migration of young men and women has ledto significant ageing of rural labour force with negativeeffect on farm production and farm income (FAO 1995).Conversely, according to Khan (2007) apart fromreversing the diminishing returns, migration can makefurther contributions to agriculture, most importantlythrough the remittances that migrants make. Stark andBloom (1985) observed that individual migration enablesthe household to maximize its chances for survival bydiversifying sources of income and spreading its risk.While Vargas-Lundius and Lanly (2007) noted that ruralout-migration has become an important income andemployment diversification strategy among ruralhouseholds. Andersson (2001) viewed migration inAfrica as part of the livelihood and survival strategy forthe rural family during 1980s.

In recent years, there has been unprecedentedrate of rural-urban migration and emigration intocountries of Europe and America. Adedokun (2003)reported that there has been a remarkable increase inemigration of Nigerians to Europe, North America, theMiddle East and South Africa from 1980's followingeconomic downturn and introduction of liberalizationmeasures. Scattered evidence on the origin of Nigerianimmigrants in Europe and United States stronglysuggest that the majority originates from the relativelydeveloped and densely populated Southern province.The Ibo from the southeast and the Yoruba from the

JNKVV Res J 48(1): 99-103 (2014)

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southwest and to a lesser extent the Edo and the Ogoniethnic groups seem to constitute the majority of Nigerianmigrants in United Kingdom (Hernandez-Coss et al.2006). Another trend in international migration is theincreasing number of women in the migration flow.According to Spaan and van Moopes (2006)landlessness and commercial agriculture have pushedwomen out of subsistence agriculture and fostered chainmigration. Several studies on international migrationhave been undertaken, predominantly on the receivingdeveloped countries. This situation made Sandron(2007) to conclude that the breadth of the rural exoduswarrants an interrogation on its impact on the places ofdeparture. Therefore present study was carried out toassess disposition of rural households toward womeninternational migration.

Methodology

Study area

The study area is Imo West (Orlu) Senatorial district,Nigeria. It is located in Imo State of Nigeria andcomprises 12 out 27 local government areas (LGAs) ofthe state. The LGAs under this district are: Orlu, Orsu,Njaba, Ohaji/Egbema, Nkwerre, Isu, Nwangele, Oguta,Ideato South, Ideato North, Oru East and Oru West.

Sampling Procedure

Multi stage random sampling was adopted in this study.Out of the 12 LGAs that make up the study area, OrluLGA was not considered for this study due to its urbanstatus going by its strategic position as the districtheadquarters. The remaining 11 LGAs were subjectedto random sampling in which 2 LGAs namely Oguta andNjaba were selected. Two electoral wards each were

also randomly selected from the 16 and 11 electoralwards that make up Oguta and Njaba LGAs respectively.One hundred and nineteen rural household heads wererandomly selected to constitute the sample size of thestudy.

Table 1. Sampled areas and Sampled frame

Selected LGAs Selected electoral Number ofwards households

selected

Oguta Izombe 30Akabo 30

Njaba Umuaka 30Ugbelle 29

Total 119

Data collection and measurement of dependent variable

Data was collected using structured interview schedulewhile dependent variable (disposition of ruralhouseholds) was measured with the aid of Likert's typeattitudinal scale. Mean and standard deviation (SD)were used to group disposition into less favourable,favourable and more favourable disposition.

Less favourable disposition: less than (mean - SD)

Favourable disposition: between (mean ±SD)

More favourable disposition: More than (mean + SD)

Result and discussion

Majority (62.2%) of the rural households were maleheaded while the remaining were female headed (Table2). This finding is in line with that of ADB (2001) andMOWCA (2008) which reported that lesser proportionof all households in Bangladesh were headed by womenwho were either widows, divorced or had a disabledhusband. 62.2% of the rural households were 50 yearsand above indicating more ageing rural population. Inmany rural areas, out-migration of young men andwomen has led to significant ageing of rural labour forcewith negative effect on farm production and farm income(FAO 1995). This finding was also corroborated byNwajiuba (2005) who reported that significant rural outmigration was taking place in Sub-Saharan Africa wherethe majority of people were rural residents and poor.

101

Data also revealed that majority of the rural populationwere married, educated up to secondary school level,Christians and employed.

It was observed that 63.9% of the respondentsdisagreed that sole migration of married woman is notculturally acceptable (Table 3). This implies that culturalrestriction to women's migration has been relaxed inrecent time. This finding supports that of Deshingkarand Grimm (2005) which proclaimed that a growingsocial acceptance of women's independence andmobility were some of the main factors behind increasingnumber of women migrants.

Whereas, 84.9% and 84.8% of rural householdsin the study area agreed that sole migration of marriedwomen could likely lead to abandonment of theirhusband for another and adultery respectively. Thisfinding is in conformity with the report of UNFPA (2007)which stated that due to cash dependency, poverty andgender discrimination, migrant women may resort totransactional sex for survival with little power tonegotiate safe sex practices.

It was noted that, 93.3% of the respondentsstrongly disagreed that children were likely to bewayward when mothers migrate than fathers. Thisrevelation did not conform with the report of Selah (2008)which asserted that though children left behind bymigrant parents tended to be less socially well adjustedand emotionally developed, mothers absence played agreater role and affected children more. This could beattributed to the fact that women were perceived as lessenergetic and assertive when compared to men andhence could not control their wards effectively like men.

Table 3. Likert's type disposition scale

Statements SA A U D SDSole migration of married women is not culturally acceptable 21 6 16 44 32

(17.0) (5.0) (13.4) (37.0) (26.4)Sole migration of married women is most likely to lead to abandonment 67 34 - 6 12of their husbands for another (56.3) (28.6) (5.0) (10.1)Sole migration of married women is most likely to lead in adultery 63 38 - 11 7

(52.9) (31.9) (9.2) (5.9)Children are most likely to be wayward when mothers migrate - 8 - - 111than fathers (6.7) (93.3)Unmarried migrant women are less likely to marry from their 10 40 - 56 13home town (8.4) (33.6) (47.1) (10.9)Women migrants are less likely to succeed than men migrants - 14 14 14 77

(11.8) (11.8) (11.8) (64.7)Women migrants are not better in sending remittances than men - 15 6 24 74migrants (12.6) (5.0) (20.2) (64.2)Percentages in parenthesesNote: SA = strongly agree, A = agree, U = undecided, D = disagree, SD = strongly disagree

Table 2. Demographic characteristics of respondents

Demographic variable Frequency PercentageSexMale 74 62.2Female 45 37.8Total 119 100Age15-29 24 20.230-49 21 17.650 and above 74 62.2Total 119100Marital statusUnmarried 22 18.5Married 75 63.0Separated/ widowed 22 18.5Total 119.0100Educational levelUp to primary level 31 26.1Up to secondary level 77 64.7Up to tertiary level 11 9.2Total 119100ReligionChristianity 119 100Total 119 100Employment statusSchooling 7 5.9Receiving training/apprenticeship 5 4.2Looking for employment 12 10.1Employed 95 79.8Total 119 100

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However, disposition of majority (58%) ofrespondents was at variance with apriori expectationthat unmarried women migrants were more likely tomarry from outside from thier home town. This findingis not unconnected with that of Nwajiuba (2005) whichreported that there is continued link between migrantsand their ancestral homes. This could be as a result ofcommunality and great desire for cultural identity.

Data revealed that, 76.5% and 82.4% of ruralhouseholds in the study area disagreed that womenmigrants were less likely to succeed and were not betterin sending remittances than men migrants respectively.This finding is in agreement with that of Skeldon (2003)which submitted that migrant women tend to send higherremittances to source areas.

It was recorded that, 26.05% of rural householdswere of less favourable disposition toward women'sinternational migration while majority (63.03) was offavourable disposition. Therefore rural householdstended from less favourable to favourable dispositionwith majority of favourable disposition toward women'sinternational migration (Table 4).

The multiple regression analysis showed thatdemographic characteristics of respondents couldexplain 30.8% of attitude towards women's' internationalmigration. However, sex (t = -4.229; p = 0.000), level ofeducation ( t= -1.762; p = 0.081) and age (t = -1.429; p

= 0.156) were significant predictors at 1%, 5% and 10%levels of significance respectively (Table 5).

Conclusion

This study has been able to ascertain the disposition ofrural households toward women's internationalmigration and conclude that majority were of favourabledisposition to women's international migration. It is aknown fact that migration is gendered and challengesand opportunities faced by women before and aftermigration differ greatly from those of men. Furthermore,the challenges and opportunities faced by rural womenalso differ from those of urban women due theirprecarious social economic condition. Therefore, itfollows that rural women should be prepared throughcapacity building in other to ensure gainful migration.Policies and programmes aimed at enhancing womenlivelihood strategies should take into considerationgender perspective in relation to migration as alivelihood strategy.

References

ADB (2001) Women in Bangladesh: Country Briefing Paper,Programme Department West and Office ofEnvironment and Social Development, AsianDevelopment Bank. Available:http://www.adb.org/Documents/Books/Country_Briefing_Paper/Women_in_Bangladesh/chap2.pdf

Adedokun O A (2003) The Right of Migrant workers andMembers of their families: Nigeria UNESCO seriesof Country Reports on the Ratification of the UNConvention on Migrant

Andersson J A (2001) Mobile Worker, Urban Employment andRural identities: Rural -Urban networks of Buheramigrants, Zimbabwe in: M.Dedruijn, R.van Dijk and

Table 5. Coefficient of the multiple regression analysis

Model Unstandardized coefficients Standardized coefficients t- value Sig.B Std Error Beta

Constant 29.675 1.456 20.386 0.000Gender -2.227*** 0.527 -429 -4.229 0.000Age of respondents -0.491* 0.343 -157 -1.429 0.156Marital status 0.090 0.343 0.022 0.263 0.793Education -0.996** 0.566 -0.225 -1.762 0.081R2= 0.308*** Significance @ 1%, ** significant@ 5%, *significant @ 10%

Table 4. General disposition of rural households towardWomen's International Migration

Less favourable Favourable More favourabledisposition disposition disposition

31 (26.05) 75 (63.03) 13 (10.9)

Percentages in parentheses

103

Dick Foeken (Eds), Mobile Africa: Changing patternsof movement in Africa and Beyond. Lieden, TheNetherlands

Deshingkar P , Grimm S (2005) Internal Migration andDevelopment: A Global Perspective, IOM MigrationResearch series no 19, IOM :79

FAO (1995) Modules sur le genre, la population et ledevelopment rural avec. Un interet particulier pourles regimes fonciers et les systemes exploitationagricole, module prepare Daphne Topouzis, sous lasupervision de Jackques du Guerny, SDWP Rome

Hernandez-Coss R Eguagu C and Josefssom M (2006) TheUK - Nigeria Remittance Corridor. DFID. Forthcoming

Khan A (2007) Importance of managing remittances: In RoundTable on Migration and Rural Employment inconjunction with the thirtieth session of FAO'sGoverning Council. February 2007: 6

MOWCA (2008) Ministry of Women and Children Affairs:Government of the People's Republic of Bangladesh.Available at http://www.mowca.gov.bd

Nwajuiba C (2005) International migration and livelihoods insoutheastern Nigeria. Global migration perspectivesNo 50, Switzerland: 4

Reardon T, Stamoulis K, Gruz M.E, Balisacan A Berdegue J,Banks B (1998) Rural non-farm income in developingcountries. In FAO: The State of Food and Agriculture(FAO Agriculture No. 31) Rome

Sandron F (2007) Internal versus international migration,Round Table on Migration and Rural Employment inconjunction with the thirtieth session of InternationalFund for Agricultural Development GoverningCouncil. February 2007: 7

Selah M A (2008) The Impacts of Migration on Children inMoldova: Working Paper UNICEF, pp20, extractedfrom www.unicef.org/The Impact of migration onhildren in moldova (1)

Skeldon R (2003) Migration and Poverty, Asia-PacificPopulation Journal: 67-82

Spaan E, Van Moppes P (2006) African Exodus. Trends andPatterns of International Migration in Sub-SaharanAfrica. Working papers Migration and DevelopmentSeries Report No. 4: 1-26

Stark O and Bloom D E (1985) The new economics of labourmigration. American Economic Review

UNFPA (2007) State of World Population: Unleashing thePotential of Urban Growth www.unfpa.org/

Vargas-Lundius R, Lanly G (2007) Migration and RuralEmployment: Paper prepared for the Round TableOrganised by the Policy Division during the ThirtiethSession of the Governing Council of InternationalFund for Agricultural Development, 14 February 2007

(Manuscript Receivd : 10.12.2013; Accepted : 5.2.2014)

104

JNKVV Res J 48(1): 104-105 (2014)

Abstract

An observational trial was carried out for two years i.e. forkharif 2008 & 09 for the control of panicle mite in NagarjunaSagar Project command area of Nalgonda district of AndhraPradesh. Two chemical combination plots and one untreatedplot were taken for the purpose. The combination ofMilbemectin 1EC @ 1ml/lt + Propicanozole 25 EC @ 1ml/ltsprayed plot gave best result where the highest number ofhealthy grains/panicle of 76.96 percent noticed and lowestnumber of discoloured grains/panicle of 7.91 percent noticedand with a highest yield of 7564kg/ha noticed when comparedto other treatments.

Keywords: Paddy, Panicle mite, Control

Rice is most important crop providing food security toAsian countries, where more than 90 percent of theglobal rice production is done and consumed. (Srinivasaet.al.2004). Traditionally insect pests, diseases andweeds are the triple evils responsible for lower yieldsof rice in India. Of all insect pests associated with ricecrop mites are also assuming major pest status in India.Among different species of mites associated with ricecrop panicle mite and leaf mite are important. The yieldlosses due to sheath mite Steneotarsonemus spinkiranged from 4.9 to 23.7 percent (Rao and Prakash1996). In India S. spinki infestation has been reportedfrom Orissa (Rao and Das 1977, Rao and Prakash 1992)and from East and West Godavari districts of AndhraPradesh. (Rao et al. 2000 and Anonymous 2001).Present studies carried out since this pest which wasearlier minor is making its presence rapidly more in thecommand area of Nagarjuna Sagar Project area ofNalgonda district. Also there are several complaints byfarmers during the diagnostic visits made by scientistsand department of agriculture staff, where there arereports of partial panicle infertility, rice plant sterility and

Identification of effective insecticides, miticides and fungicides andtheir combination for the control of panicle mite in rice

R.Bala Muralidhar Naik, D.Bhadru, Md.Latheef Pasha and P.RajanikanthAgricultural Research Station, KampasagarAcharya N.G. Ranga Agricultural UniversityNalgonda district 508207 (AP)Email :

deformity grains. Often grain discoloration was confusedwhether it is due to biotic factors like mites, fungi, ordue to bugs or it may be due to abiotic factors likerelative humidity, temperature or any other factor.

Hence an observational trial was carried for twoyears i.e. for kharif 2008 and 2009 by using BPT - 5204as a test variety. Plot size of 50sq.mt per treatment witha spacing of 20x15cm taken and crop was transplantedon August 27th for both the years. Three treatmentalsprayings were imposed where in first plot combinationof milbemectin 1 EC @ 1ml/lt + propiconazole 25 EC@ 1ml/lt taken in second plot combination ofdiafenthiuron 50 w.p @ 1g/lt + propiconzole 25 EC @1ml/lt taken and third plot is untreated control. Sprayingsof these combinations were carried out panicle initiationstage i.e. on 27th October, panicle development i.e. on16th November and 3rd application on need basisapplication i.e. on 28th November which was carriedout for both the years. Data from these three plots weretaken by taking 10 panicles from each plot wherenumber of healthy grains/panicle, number of discoloredgrains/panicle, number of normal sterile spikelets/panicle and number of discolored sterile spikelets/panicle were taken from these panicles. Treat mentalresults showed that combination of milbemectin 1 EC@ 1ml/lt + propiconazole 25 EC @ 1ml/lt is best wherehealthy grains of 76.96 percent and discolored grainsof 7.91 percent were noticed per panicle, where ascombination of diafenthiuron 50 w.p @ 1g/lt +propiconazole 25 EC @ 1ml/lt recorded healthy grainsof 66.47 and discoloured grains of 11.92 percent noticedwhere as untreated control with number of healthygrains of 55.13 percent and number of discolouredgrains of 14.25 percent per panicle was noticed. Numberof normal sterile spiklets and discoloured sterile spikletsare less in milbemectin + propiconazole combinationi.e. (8.71 and 4.47 percent) when compared to

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diafenthiuron + propicanazole combination of (13.62and 6.47 percent) and untreated control of (13.76 and13.52 percent) respectively. Grain yields are better inmilbemectin + propiconzole combination with 7564kg/ha when compared to diafenthuron + propiconazolecombination with 7040kg/ha and untreated control with6895kg/ha yield respectively.

References

Anonymous (2001) Highlights of Research (2000-2001), PCunit, ACIRP (Agri. Acarology) University ofAgricultural Sciences, Bangalore

Rao Y S, Das P K (1977) A New mite of rice in India.International Rice Research News letter 2 : 8

Rao J, Prakash A (1992) Infestation of tarsonemid mite,Steneotarsonemus spinki, smiley, in rice in orissa. JAppl Zool Res 3 :103

Rao J, Prakash A (1996) Cynodon dactylon (Linn) Pers.: analternate host of rice trasonemid mite. J Appl ZoolRes 7:103

Rao P R M, Bhavani B, Rao T R M, Reddy P R (2000) Spikeletsterility/ grain discolouration in rice in AndhraPradesh, India. International Rice Research Notes25 : 40

Srinivasa N, Prabhakara H, Mallik B (2004) The sheath mite,Steneotarsonemus Spinki smiley (Acari:Tarsonemidae). Status Paper. AINP on AgriculturalAcarology, UAS Bangalore

Table 1. Effect of certain chemical combinations on incidence of panicle mite and yield

Treatments NHG NDG NNSS NDSS Grain yield(Kg/ha)

T1 Milbemectin 1 EC @ 1ml/lt + Propiconazole 25 EC @ 1ml/lt 76.96 7.91 8.71 4.47 7564

T2 Diafenthiuron 50 w.p @ 1g/lt + Propiconazole 25 EC @ 1ml/lt 66.47 11.92 13.62 6.47 7040

T3 Untreated control 55.13 14.25 13.76 13.52 6895

NHG: No. of healthy grains NNSS: No.of normal sterile spikeletsNDG: No. of discoloured grains NDSS: No.of discoloured sterile spikelets

(Manuscript Receivd : 16.1.2014; Accepted : 20.2.2014)

106

JNKVV Res J 47(2): 106-111 (2013)

Abstract

The haematological parameters like TEC, TLC, Hb, PCV &DLC were included in the study to assess the initialpathological changes and to monitor post therapeutic efficacyof treatment deployed with sets of therapeutic regimen thatwould furnish comparative basis for evaluating efficacy. Bloodwas collected for haematological studies on 0, 7 14, 28 and42 days during the course of treatment. The TEC, PCV, Hb,& Lymphocyte count were decreased in all mange-infestedgroups as compared to normal healthy group on day '0'. TheTLC, neutrophil & eosinophil count were increased in allmange-infested animals in pretherapeutic stage. The posttreatment observation indicated the return of all thesehaematological values towards normalcy. Monocyte count wasfound to be more or less in normal range.

Keywords: Pigs, Sarcoptic Mange, Haematologicalalterations

Sarcoptic mange in pigs, caused by a minute, burrowingtype acarine pest, Sarcoptes scabiei-var-suis, is themost common but serious contagious dermatologicaldisease (Dobson and Davies 1993). Mange in pigs isan expensive disease because it requires repeatedtreatments. The disease causes intense itching,thickening and wrinkling of skin (Malm 1983). It causeshigh morbidity and wide spread lesion can result inemaciation, weakness and secondary bacterial andfungal infection which leads to other skin disorders(Bornstein 2004). Growth rate can be reduced up to12% concurrent with feed conversion. A significant effectof mange infestation on average daily weight gain wasreported by (Wooten et al. 1987). Chronic mange inbreeding pigs affects the body condition due to

Haematological profile of pigs affected with sarcoptic mange

Sourabh Gupta, M.L.V. Rao, P.C. Shukla, *Vandana Gupta and **Bharat SharmaDepartment of Veterinary Medicine**Department of Animal ReproductionGynaecology and ObstetricsCollege of Veterinary Science & Animal Husbandry Jabalpur*Department of Veterinary MicrobiologyCollege of Veterinary Science & Animal Husbandry RewaNanaji Deshmukh Pashu Chikitsa Vigyan Vishwa VidyalayaJabalpur 482004 (MP)

continuous skin irritation, which adversely affects thereproductive efficiency. The haematological parameterslike TEC, TLC, Hb, PCV & DLC were included in thestudy to assess the initial pathological changes and tomonitor post therapeutic efficacy of treatment deployedwith sets of therapeutic regimen that would furnishcomparative basis for evaluating efficacy.

Material and methods

Three ml of blood was collected aseptically in a vialfrom ear vein using EDTA (Ethylenediamine tetraaceticacid) at the dose of 1mg/ml as an anticoagulant, bloodwas then used for haematological studies. It was doneon 0, 7 14, 28 and 42 days during the course oftreatment. All haematological studies were done as perstandard procedure (Jain 1986).

The TEC was done using Red cell countingpipette after drawing blood upto 0.5 mark followed bydiluting fluid (Gower's solution) up to the mark 101. Highdry objective (40x) was used for counting erythrocytesafter charging the Neubauer's haemocytometer. Thenumber of RBC counted in the 4 corner and the centralsecondary square and calculated the number of RBCin per ml. of blood (Number of cells counted x 10, 000).The number is expressed in millions per ml of blood.

The TLC was done after drawing the blood inwhite cell counting pipette upto 0.5 mark and then thediluting fluid i.e. Turk's fluid was drawn upto the 11 mark.Leukocytes were counted in four corner primary squaresand the count multiplied by 50 to obtained the numberof cell in per l of blood. It is expressed in thousands

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per l of blood.

Hb in the blood was estimated by Sahli'shaemoglobinometer. The blood was drawn in the pipetteup to the mark and added in N/10 hydrochloric acid(HCL) present in the tube up to two mark. This wasmixed with a glass rod and kept for 20 minutes. Thendistilled water was added drop-by drop in the tube andstirred constantly with a glass rod till the colour matchedwith the standard tubes. Final reading was taken andat is expressed in gm/dl.

PCV percent was estimated by standard wintrobehaematocrit method and reading was taken aftercentrifugation of the tube at 3000 rpm for 30 minutes.

For the estimation of DLC blood smears wereprepared and stained by Leishman's stain and examinedunder oil immersion objective. One hundred leukocyteswere counted as per standard procedure and theirpercentage was recorded.

Results and discussion

The TEC was decreased in all mange-infested groupas compared to normal healthy group on day '0'. GroupI showed significantly increased TEC on day 28. A slightincrease was also observed on day 42, but improvementwas non-significant. Group II showed significantly(P<0.05) increase on day 42, however, it wassignificantly lower than normal healthy pigs. Group IIIand IV showed significantly (P<0.05) increase in TECon day 14 and day 28 and values were comparable tohealthy control group (Table 1).

The TLC was increased in all mange-infestedgroup as compared to normal healthy group. Asignif icant reduction was observed in group III(Doramectin treated) and in group IV (injectionIvermectin treated) on day 14 and on day 28. However,group I and II showed significant reduction on day 42(Table 2).

Table 1. Mean values of total erythrocyte count (106/ l)at different time intervals after administration of differ-ent miticidal drugs

Groups Days0 14 28 42

I 5.0A 5.4A 6.1C 6.5C

II 5.0A 5.4A 5.3A 5.9B

III 5.1A 6.1B 7.1C 7.2C

IV 5.0A 5.5B 6.3C 6.7C

V (control) 6.9 6.9 7.1 7.1

CD = 0.392Note: Means having same superscript are non-signifi-cant

Table 2. Mean values of total leukocyte count (103/ l)at different time intervals after administration of differ-ent miticidal drugs

0 day 14 days 28 days 42 days

Group I 18.0A 17.2A 16.6A 16.1B

Group II 17.7A 17.4A 16.8A 16.1B

Group III 17.7A 16.8B 15.3C 15.2C

Group IV 17.6A 16.6B 15.4C 15.4C

Group V 16.7 16.7 16.7 16.7

C.D. = 0.5426Note: Means having same superscript are non-signifi-cant

Table 3. Mean values of packed cell volume (%) at dif-ferent time intervals after administration of differentmiticidal drugs

0 day 14 days 28 days 42 days

Group I 29.66% 29.66% 31.33% 33.83%(31.4)A (33.3)A (34.3)B (35.9)C

Group II 27.16% 29.16% 31.83% 34.16%(31.7)A (32.9)A (34.7)B (36.1)C

Group III 26.83% 32.33% 37.83% 38%(31.5)A (34.9)B (38.2)C (38.2)C

Group IV 26.83% 31.33% 37.66% 37.83%(31.5)A (34.3)B (38.2)C (38.3)C

Group V 43% 43% 43.16% 43.16%(41.0) (41.3) (41.6) (41.4)

C.D. = 1.187Note: *Figure in paranthesis denote angular trans-formed value**Means having same superscript are non-significant

The PCV values were reduced significantly in allmange-infested groups in pretherapeutic stage. In groupIII and IV PCV values were quite comparable to normal

108

values on day 28, but it was significantly lower in groupI and group II. In group I and II PCV values weresignificantly increased on day 42, however values werenot comparable to normal healthy group (Table 3).

Neutrophil count was increased in all mange-infested animals as compared to normal healthy pigs.A marked change towards normalcy was observed onday 14, in group III and IV, and these values almostbecame normal on day 42. Group I showed significantreduction on day 14 and 28, while group II showedsignificant reduction on day 28 (Table5).Table 4. Mean values of haemoglobin (g/dl) at different

time intervals after administration of different miticidaldrugs

0 day 14 days 28 days 42 days

Group I 8.8A 9.5A 10.5B 11.5C

Group II 9.0A 9.6A 10.4B 11.3C

Group III 8.9A 10.6B 12.4C 13.3D

Group IV 8.9A 10.3B 12.4C 13.0D

Group V 14.3 14.3 14.3 14.3

C.D. = 0.688Note: Means having same superscript are non-signifi-cant

The level of Hb was significantly reduced in allmange- infested group. A definite increasing trend ofHb values were recorded in group III and group IV onday 14 onwards. Group I and group II showedsignificantly increased Hb on day 28 and on day 42but, values were not comparable to normal healthygroup. Overall, improvement in Hb was significantly highin group III and group IV (Table 4).

Table 5. Mean values of neutrophil count (%) at differ-ent time intervals after administration of differentmiticidal drugs

0 day 14 days 28 days 42 daysGroup I 51.33% 49.33% 46.33% 46.33%

(46.1)A (44.9)B (43.2)C (43.2)C

Group II 51.5% 49.33% 46.5% 46.5%(44.9)A (44.9)A (43.3)B (43.3)B

Group III 52% 50% 48% 46%(46.4)A (45.3)B (44.7)B (43.0)C

Group IV 51.33% 49.66% 48.16% 46.33%(46.1)A (45.1)B (44.3)B (43.2)C

Group V 35.33% 35.33% 35.33% 35.33%(36.8) (36.8) (36.8) (36.8)

C.D. = 0.543Note: *Figure in paranthesis denote angular transformedvalue**Means having same superscript are non-significant

Table 6. Mean values of lymphocyte count (%) at differ-ent time intervals after administration of differentmiticidal drugs

0 day 14 days 28 days 42 days

Group I 34% 36.66% 39.5% 42%(36.0)A (37.6)B (39.2)C (40.7)D

Group II 33.66% 36.83% 39.66% 42.83%(36.4)A (37.7)B (39.3)C (41.2)D

Group III 33.83% 37% 40.83% 40.83%(36.0)A (37.8)B (40.0)C (40.0)C

Group IV 34% 37.16% 41% 41%(36.1)A (37.9)B (40.1)C (40.1)C

Group V 54.66% 54.66% 54.66% 54.66%(48.0) (48.0) (48.0) (48.0)

C.D. = 0.34Note: *Figure in paranthesis denote angular transformedvalue**Means having same superscript are non-significant

Lymphocyte count was decreased significantlyin all mange-infested pigs as compared to normalhealthy pigs. A marked change towards normalcy wasobserved in all treated groups. In group III and IV valuescomparatively increased on day 28, while in group Iand II values came up to that level on day 42 (Table 6).

There was no significant change in monocytecount in mange-infested pigs and in normal healthy pigsobserved (Table 7).

Increased eosinophil was observed in all group(except control). Values were significantly reduced onday 14 and day 28 in group I, III and IV, while group IIshowed significantly reduction on day 28. Improvementwas earlier and maximum in group III as compared toother groups (Table 8).

TEC was found to be decreased in the initial stageof the experiment. During the study, it was observedthat TEC got lowered in mange-infested pigs. Group III(Doramectin treated) and IV (Inj Ivermectin treated)

109

showed good response and values were comparableto normal healthy pigs on day 28. It was difficult todeduce the actual cause of reduction in TEC values inthe mange-mite infested pigs as the parasites do notdirectly feed on those cells. Anorexia was observed inmange mite infested pigs might have resulted inreduction in production of erythrocytes. The posttreatment observation indicated the return of TEC valuestowards normalcy.

The present findings are in agreement with thosereported by Gorakh et al. (2000) in camel, Bhosale et

al. (2000) and Biswas and Roy (2005) in dogs,Shobamani et al. (1994) in rabbit and Dalpati et al.(1996) in goats.

The value of TLC was increased in all mangeinfested groups. After treatment with the differentacaricidal drugs in different groups, the value returnedtowards normalcy. Improvement was much better ingroup III (Doramectin treated) and group IV (injIvermectin treated) as compared to group II (Zerokeettreated). The result also confirmed that the actual causewas mange-mite infestation in pigs.

The leukocytosis may be due to allergic reactioncaused by the mite product or inflammatory reactionmay be a vital cause to produce leukocytosis in mangeinfested pigs. Leukocyte function as a first line ofdefence when foreign proteins enter the body.Moreover, leukocyte-promoting factor produced duringdisease may be responsible for leukocytosis. Thepresent observations also confirm the findings ofShobamani et al. (1994) in rabbits, Dalpati et al. (1996)in goats, Lowenstein et al. (1996) in cattle, Gorakh etal. (2000) in camels and Biswas and Roy (2005) in dogs.

A reduction in the concentration of Hb wasrecorded in various groups at the primary stage. Aftertreatment, elevation in concentration of Hb was noticedin all the treated groups. However, in group III(Doramectin treated) and IV (Inj Ivermectin treated)improvement was earlier than other groups. It might bedue to killing of mites by miticidal drugs, which preventedfurther damage of epithelium and loss of blood.

The above findings were in agreement with thefinding of Shobamani et al. (1994) in rabbits, Dalpati etal. (1996) in goats, Gorakh et al. (2000) in camels andSinha et al. (2004) in pigs.

The PCV of all the mange-affected groups werenoticed to be significantly low. There was a markedincrease in PCV values after treatment. In group III andIV maximum increase was noticed as compared to othergroups. The cause of the reduction of PCV values wasprobably due to reduction in production of erythrocytein the blood of mange-affected pigs. Similar finding wererecorded by Shobamani et al. (1994) in rabbits, Dalpatiet al. (1996) in goats and Sinha et al. (2004) and Talabiet al. (2004) in pigs.

The increase in neutrophilic count was observedin mange infested pigs and the values were restored tonormal in treated pigs. The probable cause ofneutrophilia was dermatitis and allergic reaction on theskin that lead to their higher number in circulation(Sheahan 1975). Lowenstein et al. (1996) in cattle and

Table 7. Mean values Monocyte count (%) at differenttime intervals after administration of different miticidaldrugs

0 day 14 days 28 days 42 daysGroup I 5.66% 5.66% 5.66% 5.66%

(14.4) (14.4) (14.4) (14.4)Group II 5.5% 5.5% 5.5% 5.5%

(14.2) (14.2) (14.2) (14.2)Group III 4.83% 4.83% 4.83% 4.83%

(13.3) (13.3) (13.3) (13.3)Group IV 5.5% 5.5% 5.5% 5.5%

(14.2) (14.2) (14.2) (14.2)Group V 5.66% 5.66% 5.66% 5.66%

(14.4) (14.4) (14.4) (14.4)

Result - Non-significant

Table 8. Mean values of eosinophil count (%) at differ-ent time intervals after administration of differentmiticidal drugs

0 day 14 days 28 days 42 daysGroup I 9.5% 8.5% 8.0% 8.0%

(18.4)A (17.5)B (16.9)C (16.9)C

Group II 8.66% 7.5% 6.66% 6.66%(17.6)A (16.4)A (15.4)B (15.4)B

Group III 8.16% 7.16% 6.16% 5%(17.1)A (16.0)B (14.9)C (13.5)C

Group IV 8.5% 7.5% 6.5% 5.5%(17.4)A (16.4)B (15.3)C (14.1)C

Group V 4% 4% 4% 4%(12.3) (12.3) (12.3) (12.3)

C.D. = 1.334Note: *Figure in paranthesis denote angular averagetransformed value **Means having same letters as superscript are non-significant

110

Biswas and Roy (2005) in dogs also reported similarfindings, while report of Gorakh et al. (2000) in camel iscontrary to this finding.

From the present study, it is evident that thelymphocytes count in mange-infested dogs decreasedin comparison to normal healthy group of animals. Therewas improvement in lymphocyte count after treatment.Improvement was maximum in group III and group IVas compared to group I and II. Chandy et al. (2000)and Wadhwa et al. (2002) also found reduction inlymphocyte count in mange infestation. This reductionmay be due to the accumulation of lymphocyte at theaffected site. While other workers Dalapati et al. (1996)in goats, Lowenstein et al. (1996) in cattle and Gorakhet al. (2000) in camel reported increased lymphocytecount in mange infestation, which is contrary to thisstudy.

There was higher eosinophilic count noticed inmange-infested animals in this study. The increase ineosinophil count might be due to hypersensitivitydeveloped due to mites or their secretion or irritationproduced by them during cavation. Eosinophilia isassociated with parasitism when the host is sensitizedto the protein of parasites (Jain 1986). Stress conditionproduced by pruritus in dermatitis also increases thenumber of neutrophilis, and eosinophils. Shobamani etal. (1994) in rabbits, Dalapati et al. (1996) in goats andBhosale et al. (2000) and Biswas and Roy (2005) indogs also reported similar f indings. However,Lowenstein et al. (1996) observed decreasedeosinophils in cattle.

Monocyte count was more or less in normal range.

The level of TEC, Hb and PCV were reducedsignificantly (P < 0.05) in the all groups in the initialstage. However, overall improvement in TEC, Hb andPCV was significantly (P < 0.05) high in group III andIV and these values were towards normal on day 42.The TLC significantly (P < 0.05) increased in mangeinfested pigs. On day 14, values of treated groupreturned towards normal in group III and IV. Differentialleukocyte count (DLC), revealed a marked increase inpercentage of neutrophils and eosinophils, while therewas decrease in lymphocyte and no change was foundin monocyte count on day 42. However, all the valuebecame towards normal.

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izR;sd lewgksa esa N% Ik'qkvksa dks fy;k x;kA lewg V esa LoF; tcfdlewg I, II, III o IV esa jksxxzLr Ik'kqvksa dks fy;k x;kA lewg I o II esaIk'kqvksa dks nokvksa Øe'k% vkbojesfDVu rjy ¼eq[k }kjk½ rFkk t+hjksdhVyks'ku ¼Ropk ij½ }kjk mipkj fd;k x;k] tcfd lewg III o IV dsIk'kqvksa dks nokvksa Øe'k% vkbojesfDVu rFkk MksjkesfDVu ¼ekalisf'k;ksaesa½ }kjk fuf'pr le; vUrjky ij mipkj fd;k x;kA jDr ijh{k.kdjus ij jksxxzLr Ik'kqvksa esa Hb o PCV dh ek=k,a rFkk TEC ofyEQkslkbV dh x.kuk lkekU; ls de ntZ dh xbZ] tcfd TLC,U;wVªksfQy o bvksfluksfQy dh x.kuk lkekU; ls vf/kd ikbZ xbZAeksuksLkkbV dh x.kuk yxHkx lkekU; ikbZ xbZA bu lHkh Ik'kqvksa esamipkj ds mijkar mijksDr lHkh jDr iSjkehVj dh ek=k,a@x.kuk,alkekU; gks xbZaA

References

Bhosale VR, Dakshinkar NP, Sapre VA, Bhamburkar VR,Sarode DB (2000) Haematobiochemical investigationin canine demodicosis. Indian Vet J 77:257-258

Biswas L, Roy S (2005) Haematological changes ofdemodectic mange infectiation in dog. Indian J AnimHlth 44(1):83-85

Bornstein, S (2004) Sarcoptic mange in pigs. Available at:http://www.animalscience.com/pdfs/reviews/PNIra194.Pdf.Accessed 23 Niow

Chandy J, Nambi AP, Jeyaraja B, Gowri B (2000) Clinico-pathological and biochemical studies in scabies indog. Indian Vet J 77:755-757

Dalapati MR, Bhowmik MK, Sarkar S (1996) Clinico-haematological, biochemical andpathomorphological changes of scabies in goats.India J Anim Sci 66(4):351-354

Dobson K, Davies P (1993) External parasites. In: Leman Aet al., Diseases of swine 7th Ed. Ames, Iowa: IowaState University Press 668-679

Gorakh M, Sena DS, Kumar R, Sahani MS, Mal G, Kumar R(2000) A study on the clinical, haemato-biochemicaland histopathological aspects of mange in camels.J Vet Para 14(1):27-30

Jain NC (1986) Schalm's Veterinary Haematology. Publ WBSaunders and Co Philadelphia 20-25

Lowenstein M, Loutal G, Baumgartner W and Kutzer E (1996)Histology of the skin and detrimintation of blood andserum parameters during the recovery phase ofsarcoptic mange in cattle after avermectin (ivomac)treatment. Applied Para 37(2):77-86

Malm A (1983) Sarcoptic mange in pigs. Ibid 1:110Sheahan J (1975) Pathology of Sarcoptes scabiei infection in

pigs: histopathological, histochemical andultrastructural changes at skin test sites J Comp Path85:97-109

111

Shobhamani B, Rao R, Gaffar AA (1994) Haemato-biochemical findings in mange in rabbits. Indian VetJ 71:946-947

Sinha S, Kumar A, Prasad KD (2004) Haematobiochemicalvariations during mange mite infestation in pigs andits therapeutic management. J Para Disease28(2):127-129

Talabi AO, Oye kunle MA, Onasanya AS, Tijani LA, SosanyaOS, Ettu RO (2004) Comparison of the efficacies ofDiazinon + Albendazole, Ivomec and Ivojec on thecontrol of gastro-intestinal nematodes andectoparasites of pigs. African J Livestock Extension3:55-58

Wadhwa DR, Mahajan A, Prasad B (2002) Demodicosis incanine and its treatment. Indian J Vet Med 22:118-119

Wooten SE, Broce AB, Stevenson JS (1987) Effect ofsarcoptic mange on growth performance in pigs. JEcom Entomol 80:625

(Manuscript Receivd : 25.2.2013; Accepted : 27.2.2014)

112

Abstract

Pyometra is characterized by uterine bacterial infection withpus accumulating in the uterus and systemic illness. In thepresent study, a retrospective study was done from January2009 to December 2011 for collection of data to know theprevalence of canine pyometra - thus systemic inflammatoryresponse syndrome. The prevalence was 2.18% (126/5783)with average age of 10.50 ±0.65 years (range 4-13 years).Out of 126 bitches different breeds i.e. GSD, Labrador, Spitz,Pomeranian, Mongrel, Greatdane, Doberman, Cocker spanialcross and Boxer breeds constituted the data. More cases werefound in winter season (54) than that of summer (40) andmonsoon (32).

Keywords: Canine, prevalence, pyometra

Pyometra is a common metoestral disease of intactbitches (Dow 1958). It is characterized by uterinebacterial infection with pus accumulating in the uterusand systemic illness (Borreson 1984). The disease iscaused by bacterial infection within the uterus thatresults in mild to severe and life threatening bacteraemiaand toxaemia. A retrospective study was performed toknow the epidemiological pattern of canine pyometra.

Material and methods

The retrospective study for determining the prevalenceof canine pyometra was done through collection andanalysis of data recorded in TVCSC, Jabalpur fromJanuary 2009 to December 2011.

Prevalence of canine pyometra - a retrospective study

U.V. Bhaskar, M.L.V. Rao, Pooja Dixit, P.C. Shukla and O.P. Shrivastava*Department of Veterinary Medicine*Department of Veterinary Gynaecology & ObstetricsCollege of Veterinary Science & Animal husbandryJabalpur 482001 (M P)Email : mlvrao@gmail.com

Results and discussion

Out of 5783 bitches brought for medical treatment, 126bitches were having pyometra so the overall prevalencewas 2.18 % (126/5783). The average age of pyometrabitches was 10.50±0.65 years with a range of 4-13 yearswhich is in accordance with the findings of Stone et al.(1988) and Okubo et al. (1995). The reason might bedue to feebler cycle with increasing age (Jones andJoshua 1982).

Regarding breedwise prevalence of the disease,Spitz (30), Pomeranian (23), German shepherd (22),Labrador (19), Doberman (13), Greatdane (7), Boxer(5), Cocker spanial (4) and Mongrel (3) werepredisposed in decreasing order. Only Pomeranian andSpitz breeds were found more predisposed. Preferredliking of these breeds as pets in the area is attributedfor the purpose.

No specific seasonal predisposition was noticedfor pyometra that might be due to bitches normallycoming into heat at every six months and the pyometrais usually diagnosed from 4 weeks to 4 months afteroestrus.

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18% vkSj vk;q 10-50 ± 0-65 lky ¼4&13 lky ½ ns[kh x;hA dqy126 'okuksa essa teZu 'ksQMZ] ySczkMksj] fLiV~Tk] ikejsfu;u] ekSaxjsy]xzsVMsu] MkscjeSu] dkWdj Lisfu;y ØkWl vkSj ckWDlj iztkfr;k FkhA ;gchekjh lcls T;knk B.Mksa esa] mlds ckn lnhZ esa vkSj lcls deckfj'k ds ekSle esa ns[kh x;hA

JNKVV Res J 48(1): 112-113 (2014)

113

References

Borreson B (1984) Pyometra in the dog - a pathophysiologialinvestigation. VI Acid base status and serumelectrolytes. Nordisk veterinaermrdicin 36: 11-12

Dow C (1958) The cystic endometrial hyperplasia - pyometracomplex in the bitch. Vet Rec 70: 1102-1108

Jones D E, Joshua J O (1982) Reproductive clinical problemsin the Dog. Publ., John Wright and Sons

Okubo T, Tsutsui T, Terada A (1995) Pyometra in the dog.Bull Nippon Vet Anim Sc University 44: 28-33

Stone E A, Littman M P, Robertson J L, Bovee K C (1988)Renal dysfunction in dogs with pyometra. J Am VetMed Assoc 193: 457-464

(Manuscript Receivd : 5.1.2013; Accepted : 10.2.2014)

114

Abstract

Subclinical ketosis is defined as elevated concentrations ofcirculating ketone bodies in the absence of clinical signs. Dueto heavy losses of milk production caused by the disease inbuffaloes the study was taken up to know the incidence of thedisease. In the study the overall incidence of subclinical ketosiswas found 24%. It was highest on 20th day post partumfollowed by that of 10th day and 30th day. Parity wise it washighest in 5th parity followed by 4th, 3rd, 6th and 7th parity.

Keywords: Buffalo, Incidence, subclinical ketosis

India has 105.34 million buffaloes (Bubalus bubalis),out of which 9.13 million are found in Madhya Pradesh.In dairy buffaloes, the incidence of metabolic diseaseis the highest at calving that extend until the peak oflactation is reached and thereafter the susceptibilityappears to be related to high turnover of fluids, mineralsand organic materials during the early part of lactation.Subclinical ketosis is defined as elevated concentrationsof circulating ketone bodies in the absence of clinicalsigns (Radostits et al. 2010).

In India, buffaloes are the major source of milk.Subclinical ketosis can cause economic losses due todecreased milk production, impaired reproductiveperformance, increased risk of displaced abomasumsand higher risk of clinical ketosis. Keeping the ill-effectsthe investigation was performed to determine theincidence of subclinical ketosis in lactating buffaloesunder Jabalpur condition.

Incidence of subclinical ketosis in lactating buffaloes in Jabalpur,Madhya Pradesh

Monika Gupta, M.L.V. Rao, D.K. Gupta, M.A. Quadri* and Pooja DixitDepartment of Veterinary Medicine*Department of Veterinary BiochemistryCollege of Veterinary Science & Animal husbandryJabalpur 482001 (M P)Email : mlvrao@gmail.com

Material and methods

A total of 200 post partum (within one month) buffaloesbelonging to University and private dairy farm wereincluded in the study. Information regarding age, stageof lactation, managemental practices, feeding standard,anorexia/normal eating and milk yield were recordedas per standard methods. These animals were within3-7th parity. Blood urine and milk samples werecollected from the animals and blood glucose wasestimated using glucometer, blood ketone bodies bythe method described by Werk et al. (1955) and urine,milk pH and ketone bodies were estimated as perKaneko (1997).

Results and discussion

The overall incidence of subclinical ketosis in buffaloeswas 24% (48/200) whereas incidence on the basis ofqualitative Rothera's test in milk and urine was 16.5%.Duffield (2001) had reported the incidence of ketosisapproximately 41%. The incidence was found higheston 20th day post partum (56.25%) followed by that of10th (25%) and 30th (18.75%) day post partum. On thebasis of parity the incidence of subclinical ketosis inbuffaloes followed the sequence of highest at 5th parity(33.3%) followed by 4th parity (25%), 3rd parity(20.83%), 6th parity (12.5%) and least in 7th parity(8.33%). Several workers have reported the incidenceof subclinical ketosis between 2nd to 4th or 6th week oflactation (Simensen et al. 1990, Geishauser et al. 1998).Similar trend was observed by Enjalbert et al. (2001).The data on systematic study on subclinical ketosis inbuffaloes is scarce in Jabalpur.

JNKVV Res J 48(1): 114-115 (2014)

115

lc Dyhfudy dhVksfll vFkkZr dhVksu ckWMht dh jDr esa vf/kdek=k gksuk ,oa mlds y{k.kksa dk u fn[kuk gSA bl chekjh dh otg lsnq/kk: HkSlksa dk nqX/k mRiknu dkQh de gks tkrk gSA blh dkj.k ls blchekjh dk vk;ru Hkkj izfr'kr tkuus ds mn~ns'; ls ;g v/;;ufd;k x;kA lc Dyhfudy dhVksfll yxHkx 24 izfr'kr nq/kk: HkSlksaesa feykA bldk izfr'kr C;kWr ds 20 fnu ckn lcls T;knk FkkA ikapohC;kWr esa lcls T;knk vkSj blds ckn pkSFkh] rhljh] NBoh ,oa lkrohC;kWr esa bldk izfr'kr Øe'k% de ns[kk x;kA

References

Duffield T (2001) Importance of Sub clinical ketosis in LactatingDairy Cattle. Proc Michigan Vet Conf

Enjalbert F, Nicot M C, Bayourthe C, Moncoulon R (2001).Ketone bodies in milk and blood of dairy cows:Relationship between concentrations and utilizationfor detection of subclinical ketosis. J Dairy Sci 84:583-589

Geishauser T, Lestile K, Kelton D, Bashiri A (1998) Evaluationof five cowside tests for use with milk to detect subclinical ketosis in dairy cows. J Dairy Sci 81: 438-443

Kaneko JJ (1997) Clinical Biochemistry of domestic animals.5thEdn Academic Press , New York. pp 79-120

Radostits OM, Gay CC, Blood DC, Hinchcliff KW (2010)Veterinary Medicine. A textbook of the diseases ofcattle, horses, sheep, pigs and goats. 10th Edn W BSaunders, U.S.A. pp 1452-1462

Simensen E, Halse K, Gillard P, Lutnaes B (1990) Ketosistreatment and milk yield in dairy cows related to milkacetoacetate levels. Acta Vet Scand 31: 433-440

Werk EE, McPhurson HT, Hemrick LW, Myers JD, Engel PL(1955) A method for quantitative estimation of splenicketone production. J Clin Invest 34: 1256

(Manuscript Receivd : 5.1.2013; Accepted : 10.2.2014)

116

Abstract

Dog breeding has now become a flourishing and quite lucrativeenterprise. Hence, an early and accurate diagnosis ofpregnancy in bitches is essential for commercial success ofany dog breeding programme. The corresponding calculatedgestational ages were 24.2, 26.87, 34.6, and 35.48 days, usingformula I. whereas the estimated gestational ages were 25.0,26.4, 34.45, and 35.58 days, respectively using formula II forbitch 1 to 4 in group I. In group II, the corresponding calculatedgestational ages were 39.965, 42.5, 43.71, and 51.33 days,respectively with formula II. The respective estimatedgestational ages were 39.75, 42.1, 44.5 and 52.175 days,when formula III was applied. With formula IV, the estimatedgestational ages were 43.65, 46.15, 46.8land 54.1 days, forbitch 5 to 8, respectively. By using formula V, thecorresponding values for gestational ages were 44.175, 46.2,47.415, and 53.11 days. In group III, The correspondingcalculated gestational ages were 50.9, 52.1, 59.15 and 60.5days, using formula III. The estimated gestational ages forbitch 9 to 12 were 54.13, 57, 57.9 and 62.3 days, respectivelyusing formula IV. The corresponding values were 55.149,54.867, 58.3 and 58.95 days, using formula V.

Keywords: Dogs, German Shepherd, Gestation, Fetalage, Ultra Sonography

Dog breeding has now become a flourishing and quitelucrative enterprise. Hence, an early and accuratediagnosis of pregnancy in bitches is essential forcommercial success of any dog breeding programme,as it reliably identifies between live and dead fetus, andother fetal abnormality and detection of the number ofthe fetus and their expected date of whelping.Ultrasound being non-invasive and with almost no sideeffects on mother and fetuses, it can be used repeatedlyto study fetal growth during gestation period. In dogs,

Monitoring fetal age and viability at different interval of gestationby ultra sonography in German Shepherd bitches

B. L. Sharma, Swadesh Thapak, V. K. Bhardwaj, *Sourabh Gupta and O. P. ShrivastavaDepartment of Animal ReproductionGynaecology and Obstetrics*Department of Veterinary MedicineCollege of Veterinary Science & Animal Husbandry JabalpurNanaji Deshmukh Pashu Chikitsa Vigyan Vishwa VidyalayaJabalpur 482001 (MP)

USG can be used to diagnose pregnancy and study itsnormal progress throughout the gestation, to monitoras well as to access the fetal viability in overdue bitches.Fetal age, which can be used to predict whelping dates,can be estimated with reasonable degree of precisionfrom fetal measurements made by scanning with modemultrasound machine. The images on the screen can befrozen and measurements of the fetuses can be takenfor predicting fetal age (Goddard, 1995).

Material and methods

The present investigation was carried out in the TeachingVeterinary Clinical Service Complex, Department ofAnimal Reproduction, Gynaecology and Obstetrics,College of Veterinary Science and Animal Husbandry,Jabalpur (MP). Ultrasonographic examination was doneby using ultrasound machine of Famio 5 (SSA-510A),with the help of microconvex transducer (3.7/5 MHz)and linear transducer (8/10 MHz). The females werescanned with 3/3.7 MHz transducer between 20 to 59days after last mating in 12 clinically normal bitches ofGerman shepherd breed with weight ranging from 20to 45 kg. The selected bitches were prepared forsonography by clipping hairs on the ventral abdomen.These animals were kept full bladder as it can be usedas an acoustic imaging window to locate the graviduterus. All the bitches were examined in left and rightlateral as well as dorsal recumbency. Each horn wasscanned separately in dorsal and lateral recumbency.An ultrasound coupling gel was applied to abdomen byprobe to increase the conductivity of ultrasoundbeginning from caudal end and moving the probecranially to scan entire abdomen transducer. The fetalstructures were identified and hard copies of the image

JNKVV Res J 47(2): 116-119 (2013)

117

were made using the printers attached to ultrasoundmachines. From each bitch measurements of all thefetuses that could be satisfactorily imaged were takenusing the electronic calipers of the ultrasound machine.Coupling media was applied on the skin surface toensure an intimate contact between the transducer andbody surface. The images on the monitor were freezed,different measurements were recorded and the imageswere stored on the computer.

In all the three groups of bitches, averages ofdifferent parameters (measurements) of the fetuseswere calculated.

Results and discussion

Estimation of fetal age

The calculated gestational ages were in accordancewith the findings of England (1998) and Nyland andMattoon (2002). In group I, the observed fetal age fromlast mating was 20, 23, 32 and 33 days, for bitch 1 to 4respectively. The corresponding calculated gestationalages were 24.2, 26.87, 34.6, and 35.48 days, usingformula I. whereas the estimated gestational ages were25.0, 26.4, 34.45, and 35.58 days, respectively usingformula II for bitch 1 to 4. When the fetal age on day ofsonography was calculated from day of calculated LHsurge, then the estimated ages were 26, 27, 35 and 36days, for bitch 1 to 4 respectively. Similar gestationallengths were reported by Concannon (1986),Concannon and Lein (1989).

In group II, the observed gestational ages fromlast mating were 37, 40, 46 and 50, days for bitch 5 to8, respectively. By using formula II, the correspondingcalculated gestational ages were 39.965, 42.5, 43.71,and 51.33 days, respectively. The respective estimatedgestational ages were 39.75, 42.1, 44.5 and 52.175days, when formula III was applied. With formula IV,the estimated gestational ages were 43.65, 46.15,46.81and 54.1 days, for bitch 5 to 8, respectively. Byusing formula V, the corresponding values forgestational ages were 44.175, 46.2, 47.415, and 53.11days. The fetal ages from day of calculated LH surgewere 41, 42, 47 and 56 days, respectively for bitch 5 of8. Similar gestational lengths were reported by Wright(1990) and England (1998).

In group III, the observed fetal ages for bitch 9 to12 were 51, 52, 55 and 58 days, respectively. Thecorresponding calculated gestational ages were 50.9,52.1, 59.15 and 60.5 days, using formula III. Theestimated gestational ages for bitch 9 to 12 were 54.13, Ta

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118

57, 57.9 and 62.3 days, respectively using formula IV.The corresponding values were 55.149, 54.867, 58.3and 58.95 days, using formula V and 53, 55, 57, and63 days, using formula of calculated LH surge. Similargestational lengths were reported by Kang et al. (1997),and Bang-Sil and Son (2007).

The above data were subjected to statisticalanalysis and the results are presented in Table 2. It wasfound that actual fetal ages were almost similar to thatcalculated by using various formulae and statistically,there was no significant difference among them.

Fetal anomaly/anomalies

There was no fetal anomaly detected during sonographyand after whelping, all the fetuses were live with noanatomical and physiological defect.

From the present study on biometry of pregnantuteri in vivo and estimation of fetal age and predictionof day of whelping, it is concluded that: Pregnancy canbe diagnosed as early as 20 days and embryos withcardiac activity can be visualized from day 22 postmating. Formula I and II are the good indicators to

predict fetal age from LH surge than from last matingand days for whelping between days 20 to 35 post-mating. Formula III is the most accurate to calculatethe fetal age then after formulae IV, V, and II, from LHsurge than from last mating in 36-50 days of gestation.

eknk 'oku esa vYVªk lksuksxzkQh }kjk Hkzw.k vfu;ferrkvksa dk ijh{k.k]xHkZ dh vk;q Kkr djus rFkk tuus ds fnu dk iwokZuqeku gsrqvuqla/kku fd;k x;kA bl gsrq p;fur eknk 'okuksa dks rhu lewgksa esaizfr lewg pkj 'okuksa ds vk/kkj ij ck¡Vk x;kA fuf'pr le; varjkyds Ik'pkr~ vYVªklksuksxzkQh dh xbZ rFkk fofHkUu ekin.Mksa tSls& lh-vkj- ,y-] th- ,l- Mh-] ch- ih- Mh-] th- ,- rFkk ch- Mh- dhx.kuk fofHkUu lw=ksa I, II, III, IV ,oa V ds mi;ksx }kjk dh xbZA blvuqla/kku ds QyLo:Ik ;g Kkr gqvk fd Hkwz.k dh vk;q dk vuqekula;qxeu ls de ls de 20 fnu ds Ik'pkr~] gn; dh fØ;k'khyrk dkvuqeku la;qxeu ls de ls de 22 fnuksa ds Ik'pkr~ rFkk ,y- ,p-ltZ dks la;qxeu ds Ik'pkr~ de ls de 35 fnukas ij Kkr fd;k tkldrk gSA bl vuqla/kku ls ;g Kkr gqvk fd mi;ksx esa yk;s x;s lw=ksaesa ls lw= III vR;f/kd mi;qDRk gSA

Table 2. Average measurement (Mean +SD) in centimeters of German Shepherd gestational sacs and fetusesduring gestation

Avg. CRL Avg. GSD Avg. GSD Avg. GSD Avg. BPD Avg. BDvertical horizontal combine

Group 1 Mean 2.687 1.88 1.547 1.80SD 0.243 1.20 1.080 1.03

Group 2 Mean 19.56 37.42SD 0.364 1.036

Group 3 Mean 24.02 41.3475SD 0.256 1.02

Table 3. Different calculation of gestational age on the day of sonography

Sr. No. Fetal age in daysObserved Calculated From LH surge

By I By II By III By IV By V

Group 1 Mean 27 30.287 35.015 31SD 6.48 5.60 0.799 5.22

Group 2 Mean 43.25 44.384 45.6 47.67 47.72 46.5SD 5.852 4.88 5.943 4.492 3.83 6.855

Group 3 Mean 54 55.66 57.83 56.81 57SD 3.16 4.86 3.38 2.10 4.32

119

References

Bang-Sil K, Son CH (2007) Time of initial detection of fetaland extra-fetal structures by ultrasonographicexamination in Miniature Schnauzer bitches. J VetSci 8(3): 289-293

Concannon P (1986) Canine physiology of reproduction. InBurke TJ edn Small Animal Reproduction andInfertility- a clinical approach to diagnosis andtreatment. Lea & Febiger Malvern PA USA pp79-83

Concannon P, Lein DH (1989) Hormonal and clinical correlatesof ovarian cycle, ovulation,/pseudopregnancy andpregnancy in dog. In: kirk RW Current VeterinaryTherapy, 5th Ed. Publ WB Saunders Co,Philadelphia, Penn USA pp 1269-1281

England GCW (1998) Pregnancy diagnosis. In: England GCWedn Allen's fertility and obstetrics in the dog. Blackwellscience Ltd, Oxford, UK pp80-88

Goddard PJ (1995) Veterinary Ultrasonography. 2ndEd. PublCraigie Buckler Aberdeen, UK pp 275-287

Kang BK, Choi HS, Son CH, Shin CR, Seo DH, IC Park (1997)Ultrasonographic appearance of the gestationalstructure throughout pregnancy in pet dogs. Time ofinitiation of the fetal and extrafetal structures. KoreanJ Vet Clin Med 14:287- 296

Nyland TG, Mattoon JS (2002) Small animal diagnosticultrasound. 2ndEd. Publ WB Saunders Co,Phildelphia, p:231

Wright PJ (1990) Application of vaginal cytology and plasmaprogesterone determination to the management ofreproduction in the bitch. J Small Anim Pract 31:335-340

(Manuscript Receivd : 25.2.2013; Accepted :27.2.2014)