soil stability in dry-landsiirc.narod.ru/4conference/section/sec2-2.pdf · 2013. 4. 6. · 2 in...

Proceedings of The Fourth International Iran & Russia Conference 559

Soil stability in dry-landsA. R. Mermut University of Saskatchewan, Department of Soil Science, Saskatoon SASK. S7N 5A8, Canada

Abstract Drylands are recognized with very low rainfall and high rates of evaporation and cover a significant area of the world. About half of the world's productive land is on drylands. Life in the drylands can be difficult. It is estimate that more than one billion people worldwide directly depend upon dryland products for their livelihoods. It is difficult to find a definition for soil stability and information about factors controlling soil stability. This term is even not included in published Soil Science glossaries. However, it is well established that the increased organic matter in soils increases the stability of land for sustainable agricultural use and management, including the arid regions. The objective of this paper was to identify dryland and main factors that influences their stability. Because of the comprehensiveness of the topic attention is given to the role of carbon balance, especially in dry regions. The possibility of large-scale carbon sequestration in drylands seems to be unlikely. The rate of sequestration is negligible. There is a need to test this idea in different arid zones of the world. However, any efforts to sequester atmospheric CO2 in drylands involve significant scientific and organizational challenges. Key Words: Dryland ecosytems, carbon sequestration, carbon balance and flow, soil stability

Introduction

Drylands are defined as areas with very low rainfall and high rates of evaporation. Dryland boundaries are neither static nor abrupt (Middleton and Thomas. 1997). Despite the moisture deficit and harsh temperature regimes with a high variability, there are normally plants of some form. There are pressures affecting the relationship between people and the plants.

UNDP records show that the drylands cover a significant area of the world: i) about half of the world's productive land is on drylands "ii) 61 % of Africa's productive land is classified as drylands" Because of this, in 2002 UNDP has established a Drylands Development Centre in Nairobi Kenya that incorporates the work of former The United Nations Sahelian Office (UNSO) to lead its work in supporting long-term development in the drylands. The Centre launches a new Integrated Drylands Development Program that is being piloted in 19 countries in Africa, the Arab States and West Asia. These international centers have sub centers in South Africa, West and Central Africa, Arabian States and West Asia. Life in the drylands can be difficult. It is estimate that more than one billion people worldwide directly depend upon dryland products for their livelihoods.

The soil stability concept is not well understood and there is hardy any source that provide an acceptable definition for this important topic of the soil science. Large number of studies confirms that organic matter in soil helps maintain an open pore structure and provides strength to soil resisting erosion and creating conditions for better plant growth. A number of agricultural and other land management practices such as reduced or conservation tillage or establishment of grass may be leading to increased levels of organic matter in the top 5 cm of the soil that interacts most strongly with rainfall.


Materials and Methods

Figure 1. Aridity Zones of the world (Middleton, and Thomas. 1997).

The aridity index: The aridity index (AI) was derived from climatic data and calculated on a monthly basis as the ratio P/PET , where P is the precipitation and PET is potential evapo transpiration. The map shown in Figure 1 was drawn by. Five main classes were recognized in addition to cold ecoregions: 1) Hyperarid: P/PET <0.05, 2) Arid P/PET <0.20 and >0.05, 3) Semiarid P/PET <0.50 > 0.20 4) Dry subhumid P/PET < 0.65 and >0.50 5) Humid P/PET > 0.65.

Climate change in drylands: Balling (1989) has shown that for the dryland areas of the North America, between 1890 and 1990, there were annual variations in temperature aanamolies around an overall warming trend of 0.86oC (Figure 2). The linear trend line reveals a statistically significant warming.

Semiarid

Dry subhumid Hyperarid

Cold climates

Approximate equatorial scale 1: 104 million Source:CRU/UEA, UNEP/GRID


Fig. 2 Temperature anaamolies for the drylands of Mexico, the United states, and Canada for the period of 1891 and 1990. (Balling 1994).

Factors affecting the soil stability: These factors are many. Here are a few mentioned by Odom (1989):• Disturbance frequency and intensity (e.g., how often and what kind of tillage) • Rate of nutrient or energy flux (e.g., how fast are nutrients and energy moving in and out of

the system or input:output efficiency) • Species diversity (e.g., intercropping or rotations), interactions (e.g., competition for water

and nutrients from weed species), and life history strategies (e.g., do the species grow fast and produce many seeds or slow with few seeds)

Organic Matter: It is beyond human capacity to directly monitor the C pool of any soil during its development. However there are two general methods to determine rates of soil C accumulation and cycling: 1) the cronosequence which measures soil organic carbon (SOC) stores in soils of different ages but similar environment and parent material. With this method it is possible to calculate empirical rates of carbon sequestration after geological or anthropogenic disturbances. 2) a ‘Mass Balance’, in which C cycling rates are infrared for soils near or at steady styate. This method provides data to help constrain soil responses to perturbation, such as climate change and cultivation (Amundson, 2001). Application of stable isotope geochemistry to measure isotopic composition of organic C is rather new. This way we are able to measure the rate of organic matter accumulation with changes in climatic and environmental conditions.

Results and Discussion


Carbon dynamics: The possibility of large-scale carbon sequestration in drylands seems to be unlikely. The rate of sequestration is negligible. However, according to Middleton and Thomas (1997) dryland store 60 times more than the CO2 added to the atmosphere annually by fossil fuel burning. A small addition to sequestration rate these soils can have a large impact on the atmospheric carbon budget. Increasing carbon storage in the dryland ecosystems would involve converting degraded lands into land-use systems that performs close to natural production level. The idea seems to be excellent. There is a need to test this idea in different arid zones of the world.Of the three major active reserviors, the oceans contain approximately 38,000 Pg (1015 g) of C, the terrestrial system about 2,500 Pg, and the atmosphere about 720 Pg C (Fig. 1). Geologic C, including C in fossil fuels contains 5,000 to 10,000 Pg C. Soil organic C together with inorganic C or pedogenic carbonates are estimated to be about 2,500 Pg C and both play important roles in the global C cycle. Although short-term C cycling (photosynthesis and soil respiration) has been documented for many ecosystems there are few reliable estimates of long-term net C fluxes from the atmosphere to the soil (Schlesinger, 1990; Harden et al., 1992). Most of the estimated values of the mass of organic C in soils fall in the range of 1,400-1,600 Pg C, regardless of whether the global pool is estimated from an aggregation of vegetation biomes

Figure 3 The present day carbon cycle (Modified from Schlesinger and Andrews, 2000)

(Schlesinger, 1997), or soil orders (Eswaran et al., 1993). Approximately 55 Pg C of the total resides in the fresh litter or detritus on the soil surface. Because this value is similar to the net primary production (NPP), the mean residence time of surface litter globally is considered to be about one year (Schlesinger, 2000). It is now clear that dryland also a prime candidate for major carbon sequestration (Glenn et al. 1993). However, any efforts to sequester atmospheric CO2 in drylands involve significant

Soil 1,500

92 90

Atmospheric Pool720

Net destruction ofVegetation

6

75

120

45

Rp

GPP

Rivers0.4 DOC0.4 DIC

38,000

0.9

Ocean

Land Plants 560

Global Carbon Cycle

Pedogenic carbonate700-1,700

Geological 5,000- 10,000

0.1 burial


scientific and organizational challenges. It is clear that significant incentives may be required to get land users and owners to participate in carbon sequestration program. Compared to other ecosystems dryland accumulates much less than temperate and tropical environment. Whether from a global or dryland perspective, it is imperative that they become sinks.

Soil fertility: Half of the earths 13 billion hectares of land is now used by human. We have entered an age in which soil and ecosystem research is indispensable for improving land management. In the last century land management greatly increased soil productivity. To achieve high production soil inputs of N, P, K increased 3 to 8 folds between 1960 and 1995.

References Amundson, R. 2001. The carbon budget in soils. Ann. Rev. Earth Planet Sci. 29: 535-562. Balling, R. C. 1994. Analysis of historical temperatures and participation trendin the dryland of

mexico, United States, and Canada. Report to UNIPED/INCD, Nairobi. Eswaran, H., E. N. Van den Bergh, P. Reich. 1993. Organic carbon in soils of the world . Soil Science. Ame. J. 57: 192-194. Glenn, E. P., V. R. Sequires, M. Olsen, R. Fry. 1993. Potential for carbon sequestration in the drylanda.. Water, Air, and Soil Pollution 70: 341-355. Harden, J. W., E. T. Sundquist, R. F. Stallard, and R. K. Mark. 1992. Dynamics od soil carbon during deglaciation of the laurentide Ice Sheet. Science 258: 1921-1924. Middleton, N. and D. Thomas. 1997. World atlas of Desertification. Second edition, Arnold a member of the Hodder Headline Group. London. Odum, E.P. 1989. Ecology and our endangered life support systems. Sinauer Associates, Inc.,

Sunderland, MA. 283 pp. Richter, D. D., D. Markewitz. 2001. Understanding of soil change: soil sustainability over

millenia, centuries and decades. Cambridge University Press, England. Schlesinger, W. H. 1990. Evidence from cronosequence studies for low carbon stprage potential of soils. Nature 348: 232-234. Schlesinger, W. H. 1997. Biogeochemistry: An analysis of global change. Academic press. New York, USA. Schlesinger, W. H., J. F. Andrews. 2000. Soil respiration and the global carbon cycle. Biogeochemistry 48: 7-20.


Using Mycorrhiza to Reduce the Stressful Effects of Soil Compaction on Corn Growth

M. Miransari1, H.A. Bahrami2, M.J. Malakouti3, D. Smith4, Farhad Rejali5

1- Ph.D. student of soil science, Department of Agriculutre, Tarbiat Modarres University, Tehran, Iran E-mail: [email protected] 2- Associate professor of soil science, Department of Agriculutre, Tarbiat Modarres University, Tehran, Iran 3- Professor of soil science, Department of Agriculutre, Tarbiat Modarres University, Tehran, Iran4- Professor of crop physiology, Department of Plant Science, Macdonald Campus of McGill University, Montreal, Canada.5- Ph.D. in soil science, Soil and Water Research Institute, Tehran, Iran

Abstract Influencing the biological and hydrological balance of the root as well as the soil aeration, compaction affects plants growth and reduces root growth and nutrient uptake, resulting in yield reduction. Therefore, controlling soil compaction may improve root development and nutrient uptake. Mechanical methods of controlling soil compaction is laboriour and costly. Hence, evaluation of biological methods to control soil compaction can be of great importance. These methods may be very useful in terms of environmental, economical and social points of view. The objectives of this study are 1) to evaluate the stressful effects of soil compaction on nutrient uptake in corn and wheat, 2) use mycorrhiza to control compaction stress in corn and wheat, 3) increase corn and wheat yields, and 4) increasing phosphorus solubility and reducing its usage. Field and greenhouse experiments have been done, with two soil treatments (in the greenhouse experiment), three compaction treatments and four mycorrhiza treatments in four replicates. Soil resistance and bulk density as well as plant parameters were measured. Compaction treatments were statistically different. Some level of compaction may enhance plant growth. Mycorrhiza significantly increased yield. Hence, mycorrhiza may reduce the stressful effects of soil compaction on wheat and corn growth.

Key Words: mycorrhiza, plant growth, soil compaciton.

Introduction Intensive agricultural production of various crops have created unfavorable soil physical conditions of which soil compaction is one of the most important ones. For example, the excictence of compacted layers in the agricultural soils of Khuzestan province has reduced yield production up to 60%. By changing the soil structure, soil compaction causes unfavorable physical, chemical and biological properties. It may also affect the emission of greenhouse gases, the runoff of water and chemicals as well as the movement of nitrate and chemicals into the surface and drainage water, respectively. The amounts of fertilizers and energy required for crop production can also be influenced in compacted areas (Soane and vanOuwerkerk, 1995).

The adverse effects of soil compaction result in crop yield reduction (Soane, and vanOuwerkerk, 1994b), lower plant growth (Masle and Passioura,1987), higher rates of denitrification (Torbert and Wood,1992), and lower microorganisms activities due to reduction of soil available air (Jensen et al.,1996a). Soil compaction is caused mainly by excessive use of agricultural machinery in the field and can change the biological, hydrological and aeration balance of plant roots up to 50 cm depth (Blackwell et al.,1986).


Soil moisture content is an important parameter in soil resistance. Compaction reduces soil porosity, and also may significantly change the distribution of soil pores. In compaction situations first the larger pores are influenced, which become smaller (Marshall and Holmes, 1988), and results in higher bulk density and soil resistance. In noncompacted and compacted soils the biomass of corn (Zea mays L.) roots may be similar but in noncompacted soils roots are found in a deeper depth (Boone et al.,1978).

Soil compaciton may decrease the root growth resulting in the reduction of water and nutrient uptake. Altered soil structure in compacted soils can influence the amount of soil organic matter and also increase soil erosion, which may affect the nutrient cycles. Higher soil resistance in compacted soils can limit the growth and development of roots and thus less amount of soil is available to the plant root and the uptake of water and elements reduces. For example, the reasons for lower N uptake in compacted soils are less available water due to lower infiltration rates, reduced root growth, reduction in macropores and soil N (Motavalli et al., 2003).

In dry years, some level of soil compaction may enhance plant growth but higher levels of compaction limit root growth. In wet years soil compaction decrease N availability due to higher rates of denitrification and runoff (Motavalli et al., 2003).

To reduce soil compaction mechanical methods may not be economical and also can be difficult. Hence, use of biological methods is an interesting option. Using mycorrhiza is one of these methods. Enhancing the availability of nutrients required by plants (specially phosphorous) (Abbott et al., 1983 ; Querejeta et al., 1998), improving soil structure (Tisdall,1991), and reducing water stress (Boyle and Hellenbrand,1991) are some advantages of mycorrhiza that may help plants grow better in dry areas (Nelson and Safir, 1982). The cycle of macro and microelements in the soil is an ecosystem on which different parameters such as soil microorganisms are effective (Barea and Jeffries, 1995). In this cycle, the mycorrhiza microbial activity and its nutrient uptake is very important (Barea, 1991). Mycorrhiza mycelium is able to attach the plant roots to the surrounding soil in which microbial population are present, hence increasing the soil volume for the roots, and making plants survive in situations which water and nutrients are deficient (Marschener and Dell, 1994).

Compared with plant roots mycorrhiza hyphae are able to transfer elements from more distant areas in which elements are deficient (Jakobsen, 1995). Therefore, in situations with low concentration of elements mycorrhizal roots may absorb higher amounts of macro and microelements which have relatively low mobility (Faber et al., 1990; Subramanian and Charest, 1999). Since, there is not much information about whether arbusclular mycorrhiza is able to reduce soil compaction stress, the hypothesis of this research is “using mycorrhiza to reduce soil compaction stress on root growth partially or completely resulting in root development and increasing plant uptake and yield”.

Materials and methods The experiment was done in the experimental farm of Soil and Water Research Institute in Meshkin-Dasht, Karaj. The soil physical and chemical characteristics of the field (0-30cm) were determined. Also, soil bulk density was measured by using a cylinder (251.2 cm3) which was


1.20, 1.27, and 1.46 g/cm3 for compaction treatments respectively. To impose the compaction treatments passes of a John Deere tractor (model 3140) have been used in a certain soil moisture (7.2%) and treatments of control, 4 passes and 12 passes were created. After compacting the soil, using a penetrometer (model 20063 cernusco Italy), soil resistance for different treatments at 3 measurements for each plot (3 times during the growing season) was measured. Soil moisture for all measurements were determined which were 13.08, 8.89 and 8.17%, respectively.

Using the Most Probable Number (MPN) test the ability of mycorrhiza for inoculation was determined. On the 16th of July the seeds of corn (v. 704) in plots measuring 2.4 x 4 m2 were planted. Seeds were planted 20 cm apart from each other on each row and the spacing between rows was 60 cm. To avoid any interaction between adjacent plots there was enough spacing between the plots. At planting mycorrhiza treatments, that had already been produced (Feldmann and Idczak, 1992), including control (M1), Glomous mossea (Iranian) (M2), Glomous etunicatum (M3), Glomous mossea (Canadian) (M4) combined with the compaction treatments were applied. The field was irrigated when required. After planting, the N, P, K fertilizers were applied at 260, 160, and 40 kg/ha respectively.

On the 18th of September 2003, plant heights were measured and leaf samples were collected. Fresh and dry weights of leaves were measured. Plants were harvested on the 15th and 17th of October 2003 and fresh and dry weights of yield were calculated.

The experimental design was a split plot on the basis of randomized complete block design, with 4 replicates. The compaction treatments were the main plots and the mycorrhiza treatments were the subplots. Using SAS (SAS Institute Inc. 1988) data were analyzed and the differences between treatments were determined. The means were compared using the GLM method and the LSD test (Steel and Torrie, 1980).

ResultsMeasuring soil resistance showed that the soil had been compacted because of tractor passing (Table 1). The differences between soil resistance values for different compaction treatments were statistically significant (α = 0.05) (Table 1).

Soil compaction (α=0.05) and mycorrhiza treatments (α=0.1) significantly affected plant height. At all levels of compaction, mycorrhiza treatments increased plant height compared with the control treatment. In comparison with the M1 treatment M2, M3 and M4 treatments were significantly effective on plant height (α=0.05). There is no significant differences between the effects of mycorrhiza treatments on plant height (Table 1). Compaction increased corn height (Table 1).

The effects of soil compaction on leaves fresh and dry weights were significant (α=0.05). At the levels of C2 and C3 mycorrhiza increased leaves fresh and dry weights, and this increase was significant for C2 (α=0.1). M3 leaves fresh weights were significantly higher than M1 (α=0.1). For the fresh and dry weights of leaves in C1 mycorrhiza treatments were not effective whereas in the C2 level M3 and in the C3 level M2 and M3 were effective, although these differences were not significant. Leaves fresh and dry weights were increased because of compaction (Table 1).


The effects of soil compaction (α=0.05) and mycorrhiza (α=0.1) on corn yield were significant and mycorrhiza increased corn yield at all levels of compaction. The amounts of yield at M2 (α=0.1) and M4 (α=0.05) were significantly higher than that of M1. For corn yield in the C1 level M3 and in the C2 and C3 levels M4 were more effective and these differences were not significant. Higher amounts of yields were obtained as a result of soil compaction (Table 1).

DiscussionCompaction using the tractor increased soil resistance and hence increased soil bulk density which is in agreement with Bouwman and Arts (2000) and Jensen et al. (1996). Measuring soil moisture at the time of measuring soil resistance is very important, because it differs at different moistures. Increasing soil moisture up to a level may decrease soil resistance, the lowest and highest levels of soil resistance are related to the highest (13.08%) and lowest (8.17%) levels of soil moisture. These are similar to the results of Whalley et al. (1995) who mentioned that soil compaction level does not change, a soil receiving higher amounts of rain is less resistant than a soil receiving lower amount of rain. Therefore, this may result in a more developed root system in a more moist soil resulting in higher yield. Different comparisons show the differences between different treatments. If the level of compaction is high, then root growth is adversely affected resulting in lower water and nutrients uptake as well as less oxygen absorbence, and plant yield decreases as it is clear from comparing C2 and C3 and is in agreement with Soane and van Ouwerkerk (1995).

Adding mycorrhiza made the corn plants grow better in the compacted soil and hence increased the plant height at all compaciton levels. This increase may be because of the higher density of the root in the presence of mycorrhiza hyphae and mycelium which enable the plant root to absorb more water, nutrients, and oxygen, thus reducing the stressful effects of compaction. The ability of corn roots to penetrate in the soil increases with the presence of mycorrhiza, because compared to corn roots, mycorrhiza hyphae and mycelium are thinner, enabling them to grow into smaller soil pores (Sylvia, 2003).

For the same reasons mentioned above, in the presence of mycorrhiza leaves growth also increased. Mycorrhiza increased leaves growth in C2 and C3 levels indicating that the enhancing effects of mycorrhiza on plant growth may become more clear in compacted situations. In other words, there may be interaction effects between mycorrhiza and soil compactoin although these effects were not siginificnt (P>0.27). Mycorrhiza increased corn yield because of providing better growth situation, resulting in higher rates of photosynthesis and photosynthates translocation to the grains.

Some level of compaction may have positive effects on soil properties and hence on the yield (Bouwman and Arts, 2000) This is because of improving soil properties as an environment for seed generation and growth and also decreasing soil erosion (O'Sullivan and Simota, 1995). When soil moisture is steady, soils with higher resistance may tolerate higher levels of soil compaction stress. The relative significance of soil moisture and the imposed pressure in a compaction situation are related to the soil texture. In comparison with sand, higher rates of clay or a loam texture (the soil texture of the field) in the soil increases the soil resistance against imposed pressures, when soil moisture is higher, and soil compaction does not reach an


unfavorable level (Sanches-Giron et al., 1998). These reasons may explain the observed differences between compaction treatments (C1, C2, C3) in this experiment. Some level of compaction has enhanced plant growth, as it is clear from comparing C1 with C2 and C3. For example higher plant heights, higher leaves fresh and dry weights (except for the nonmycorrhizal treatments) show that in a compaction situation mycorrhiza is more effective, and also increases yield.

Based on the results, we conclude that mycorrhiza can help the plant both when soil compaciton has possitive effects and when it has adveres effects, and hence reduces the unfavorable effects of compaction.

References Abbott LK, Robson AD, and Hall IR. (1983). Introduction of vesicular–arbuscular-mycorrhizal

fungi into agricultural soils. Australian Journal of Agricultural Resources 34: 741–749. Barea JM. (1991). Vesicular-arbuscular mycorrhiza as modifiers of soil fertility. In B. A. Stewart

(Ed.), Advances in Soil Science, Springer, New York, pp. 1-40. Barea JM, Jeffries P. (1995). Arbuscular mycorrhiza in sustainable soil plant systems. In A.

Varma and B. Hock (Eds.), Mycorrhiza: Structure, Function, Molecular Biology and Biotechnology, Springer, Heidelberg, pp. 521-559.

Blackwell PS, Graham JP, Armstrong JV, Ward MA, Howse KR, Dawson CJ, Butler AR. (1986). Compaction of a silt loam by wheeled agricultural vehicles. I. Effects upon soil conditions. Soil and Tillage Research 7: 97-116.

Boone FR, Bouma J, de Smet LA. (1978). A case study on the effect of compaction on potaito growth in a loany sand soil. I. Physical measurements and rooting patterns. Netherland Journal of Agricultural Sciences 26: 405-420.

Bouwman LA, Arts WBM. (2000). Effects of soil compaction on the relationships between nematodes, grass production and soil physical properties. Applied Soil Ecology 14: 213–222.

Boyle CD, Hellenbrand KE. (1991). Assessment of the effect of mycorrhizal fungi on drought tolerance of conifer seedlings. Canadian Journal of Botany 69: 1764–1771.

Faber BA, Zasoski RJ, Burau RG, Uriu K. (1990). Zinc uptake by corn as affected by vesicular arbuscular mycorrhizae. Plant and Soil 129: 121-130.

Feldmann F, Idczak E. (1992). Inoculum production of vesicular arbuscular mycorrhizal fungi for use in tropical nurseries. Methods in Microbiology, pp. 339-357. Volume 24. Academic Press Limited.

Jakobsen I. (1995). Transport of phosphorus and carbon in VA mycorrhizas. In A. Varma, and B. Hock (Eds.), Mycorrhizas: Structure, Function, Molecular Biology and Biotechnology, Springer, Berlin, Germanny, pp. 297-324.

Jensen LS, McQueen DJ, Ross DJ, Tate KR. (1996a). Effects of soil compaction on N-mineralization and microbial C and N. II. Laboratory simulation. Soil and Tillage Research38: 189-202.

Jensen, LS, McQueen DJ, Shepherd TG. (1996). Effects of soil compaction on N-mineralization and microbial-C and –N. I. Filed measurements. Soil and Tillage Research 38: 175-188.

Marschener H, Dell B. (1994). Nutrient uptake in mycorrhizal symbiosis. Plant and Soil 159: 89-102.

Marshall TJ Holmes JW. (1988). Soil Physics. 2nd edn. Cambridge University Press. Cambridge, 374 pp. England.


Masle J, Passioura JB. (1987). The effect of soil strength on the growth of young wheat plants. Australian Journal of Plant Physiology 14: 643-653.

Motavalli PP, Stevens WE, Hartwig G. (2003). Remediation of subsoil compaction and compaction effects on corn N availability by deep tillage and application of poultry manure in a sandy-textured soil. Soil & Tillage Research 71: 121–131.

Nelson CE, Safir GR. (1982). Increased drought tolerance of mycorrhizal onion plants caused by improved phosphorus nutrition. Planta 154: 407–413.

O’Sullivan MF, Simota C. (1995). Modelling the environmental impacts of soil compaction: a review. Soil and Tillage Research 35: 69-84.

Querejeta JI, Roldn A, Albaladejo J, Castillo V. (1998). The role of mycorrhizae site preparation and organic amendment in the afforestation of a semi-arid Mediterranean site with Pinushalepensis. For. Science. 43: 203–211.

Sanchez-Giron, V, Andreu E, Hernanz JL. (1998). Response of five types of soil to simulated compaction in the form of confined uniaxial compression tests. Soil and Tillage Research 48:37-50.

Soane BD, van Ouwerkerk C. (1994b). Soil compaction problems in world agriculture. In: B.D. Soane and C. van Ouwerkerk (Editors), Soil Copaction in Crop Production. Developments in Agricultural Engineering II. Elsevier. Amsterdam. 662 pp. The Netherlands.

Soane BD, van Ouwerkerk C. (1995). Implications of soil compaction in crop production for the quality of the environment. Soil and Tillage Research 35: 5-22.

Steel RGD, Torrie JH. (1980). Principles and procedures of statistics: A biometrical approach. Second edition, McGraw-Hill Book Company.

Subramanian, KS, Charest C. (1999). Acquisition of N by external hyphae of an arbuscular mycorrhizal fungus and its impact on physiological responses in maize under drought-stressed and well-watered conditions. Mycorrhiza 9: 69-75.

Sylvia D. (2003). http://dmsylvia.ifas.ufl.edu/mycorrhiza.htm. University of Florida, U.S.A. Tisdall JM. (1991). Fungal hyphae and structural stability of soil. Australian Journal of Soil

Resources 29: 729–743.Torbert HA, Wood CW. (1992). Effects of soil compaction and water-filled porespace on soil

microbial activity and N losses. Communications in Soil Science and Plant Analysis 23: 1321-1331.

Whalley WR, Dumitru E, Dexter AR. (1995). Bilogical effects of soil compaction. Soil & Tillage research 35: 53-68


Table 1.The effects of soil compaction (C) and mycorrhiza on soil resistance, plant height, leaves fresh and dry weights, and yield of corn. Level of

compaction (Number of

tractor passing)

Mycorrhiza treatment

Soil resistance(first time) (Mpa)

Soil resistance(second time) (Mpa)

Soil resistance(third time) (Mpa)

Plant height (cm)

Leaves fresh weight (g)

Leaves dry weight (g)

Amount of yield (t/ha)

M1 0.36de 0.94c 0.67e 198e 106.56bc 36.22ab 8.1d M2 0.38cde 0.88cd 0.80de 212de 98.86bc 33.27b 9.7cd M3 0.34e 0.79cd 0.80de 211de 85.11c 29.31b 10.3cd

C1

M4 0.36de 0.84cd 0.69e 210de 93.26bc 31.07b 9.2cd Mean 0.36 0.86 0.74 208 95.95 32.47 9.3

M1 0.49bc 0.86cd 1.09bc 231abc 90.89bc 32.48b 12.6abc M2 0.46cde 0.73d 1.09bc 244a 111.32abc 37.08ab 14.7ab M3 0.47bcd 0.79cd 0.95cd 241ab 139.50a 46.92a 12.1bc

C2

M4 0.47bcd 0.82cd 1.14ab 243a 120.07ab 39.33ab 16.0a Mean 0.47 0.80 1.07 240 115.44 38.95 13.8 C3 M1 0.6ab 1.31ab 1.81ab 214cde 97.32bc 32.10b 9.3cd M2 0.7a 1.26b 1.25ab 222cd 119.89ab 37.18ab 11.7bcd M3 0.67a 1.18b 1.36ab 223bcd 119.60ab 40.06ab 11.8bcd M4 0.63a 1.41a 1.30a 226abcd 111.05abc 38.73ab 12.6abc Mean 0.65 1.30 1.43 221 111.96 37.02 11.3 C ** ** ** ** ** ** ** M n.s. ** n.s. * n.s. n.s. * C*M n.s. n.s. n.s. n.s. n.s. n.s. n.s. M1 vs M2 n.s. * n.s. ** n.s. n.s. * M1 vs M3 n.s. ** n.s. ** * n.s. n.s. M1 vs M4 n.s. n.s. n.s. ** n.s. n.s. **

LSD (α=0.1) 0.1298 0.1545 0.1723 18.281 32.55 11.086 3.8962 C.V. 14 11 11 5 19 20 20 n.s.: Not significant *: Significant at α=0.1 **: Significant at α=0.05 C.V.: Coefficient of variation M1: control, M2: Glomous mossea (Iranian), M3: Glomous etanicatum, M4: Glomous mossea (Canadian) C1: control, C2: 4 passes of tractor, C3: 12 passes of tractor


Mapping the Risk of Nitrate Concentration in Groundwater Resources of Shahrekord Plain (Iran) Using Parametric

Geostatistics

J. Mohammadi Soil Sci. Dept., Agric. College, Shahrekord Univ., Shahrekord, Iran.

Abstract The sustainable use of water resources involves managing both the quantity and quality of these resources. In Iran, groundwater is an important source of water supply for drinking, agricultural irrigation, and industrial activities. In order to evaluate the nitrate concentration, as a main chemical index of water pollution, 234 water samples were collected from wells located in Shahrekord plain in 2001. The average concentration of nitrate is 8.2 mg L-1 with standard deviation of 3.3 and minimum 0.1 to maximum 34.7 mg L-1. The results indicate that at 95% of the sampled wells, water contained more than 2 mg L-1 NO3

-, 86% of samples have values of more than 5 mg L-1 NO3

-, and 24% of observations show nitrate contents more than 10 mg L-1.Results obtained from geostatistical analysis indicate that from entire sampled area, 1250 km2,2% of all estimated locations (25 km2) have nitrate concentration less than 5 mg L-1, 38% of all estimated location illustrate nitrate content of 5-8 mg L-1, 55% of the whole estimated sites (686 km2) located in class 8-10 mg L-1, and finally, 5% (63 km2) of all estimated nitrate concentrations exceed the value of 10 mg L-1. The risk maps of nitrate pollution were prepared using threshold values of 5 and 10 mg L-1. The results illustrate that there is a probability of 0.8 for about 78% of the whole area (980 km2) that estimated wells contain more than 5 mg L-1 NO3

-.About 3% of the all estimated sites (37 km2) show a probability of more than 0.5 that the nitrate concentration exceeds the threshold value of 10 mg L-1, which is close to maximum admissible concentration of NO3

- legislated by European Communities. The results indicate the increasing trend in groundwater pollution in the study area. Furthermore, providing a measure of uncertainty associated with the estimated value by geostatistics, make kriging an attractive method to model and map the risk of environmental variables.

Keywords: Disjunctive Kriging, Nitrate Pollution, Risk Mapping, Variogram, Water Quality.

Introduction The sustainable use of water resources involves managing both the quantity and quality of the resources. The area of central Zagros, located almost in central of Iran, is a fast growing part of the country where groundwater is the main source of water supply for drinking, irrigation and other domestic uses. Consequently, it is important to protect the quality of this vital source. Unfortunately, the quality of the groundwater resources in central Zagros received less attention comparing to quantity of them. Furthermore, there is no water quality-monitoring program in the area providing information to answer questions relating to the management groundwater resources and its own catchments.One of the common properties of environmental data is their spatial dependence; that is, observation close to each other are more similar than observations separated by a larger distance. Therefore, when a spatial estimation is performed, this spatial dependence should be taken into account. In many environmental applications not only the estimations themselves do matter, but also the uncertainties associated with the estimated values. A conventional statement such as “the


nitrate concentration in groundwater which is 10 mgL-1” is likely become less acceptable that “the concentration of nitrate has a probability of 85% of being greater than 10 mgL-1”. This will result to a series of risk maps and the user can then choose which probability of exceedance is acceptable and so which risk he or she is willing to take. The principle attraction of kriging algorithm is that it allows water quality parameters to be estimated without bias and with minimum and known variance by taking into account the spatial dependence. Furthermore, nonlinear type of kriging, called disjunctive kriging, provides both estimates of the property and of the conditional probabilities. Such estimated conditional probabilities can be used as an input to the decision-making process about water management to provide a quantitative means for determining whether management actions are necessary (Webster and Oliver, 2001). The aim of this paper is to map the probability that the some water quality parameters exceed certain accepted tolerable thresholds. By this mean, one can show where people should attempt to counteract the water quality problem, where they should be on their guard to prevent its increase and where, for the present at least, they need not worry.

MATERIALS AND METHODS

The Study Area, Sample Collection and Analysis The watershed of Shahrekord plain, covering about 1250 km2, located in northeast of Chaharmahal va Backtiari province almost southeast of Iran. It is a part of central Zagros area, one of the main origins of Karoon River (Fig. 1). The mean annual precipitation in the region is 320 mm. The mean annual temperature is 24.5º C.Water samples were collected from 234 actively pumped wells used mainly for drinking, domestic and agricultural purposes. Water samples were collected in polyethylene bottles and were taken to the laboratory and analysis was carried out immediately. An ion analyzer model JENWAY determined NO3

-.

Theory of Disjunctive Kriging

Most applications of kriging to environmental data used ordinary kriging (Webster and Oliver, 2001). Procedure of ordinary kriging (OK) is carried out in two steps including spatial structure modeling of the regionalized variable (i.e., nitrate concentration in groundwater) through the variogram calculation and estimation of the values at unsampled locations using the selected model of spatial structure. The value of unobserved point at location x is estimated by a linear combination of the values of n neighboring points:

( ) ( )∑=

=n

iiiOK xZaxZ

1

* [1]

Where ai is the weight of the ith neighboring value and Z(xi) is measured value.In disjunctive kriging (DK) the weights are replaced by the functions fi, to give:

( ) ( )∑=

=n

iiiDK xZfxZ

1

* [2]

These functions are found to minimize the estimation variance. To obtain Gaussian DK estimator, the original data must be transformed into a new variable, Y(x), with a standard


normal distribution where pairs of sample values are bivariate normal (Yates et al., 1986a; Yates et al., 1986b). This can be achieved by a linear combination of Hermite polynomials:

( ) ( ){ }ii xYxZ Φ= [3] where Y(xi) is the transform of Z(xi) and Φ is linear combination of Hermite polynomials such that:

( ){ } ( )[ ]∑∞

=

=Φ0k

kki xYHCxY [4]

where the values for Y(x) are obtained by taking the inverse, ( ) ( )[ ]xZxY 1−Φ= , and Hk[Y(x)] is a Hermite polynomials of order k, and Ck are Herimitian coefficients which can be evaluated by Hermite integration. The DK estimator is then a linear combination of the estimates of the Hermite polynomials of the transformed sample values:

( ) ( )[ ]∑=

=k

k

kkDK xYHCxZ0

** [5]

and

( )[ ] ( )[ ]∑=

=n

iikikk xYHbxYH

1

* [6]

where the series in Equation 5 has been truncated to k terms and bik are disjunctive kriging weights. The DK method utilizes the autocorrelation function in determining the weighting coefficients for a series of Hermite polynomials, Equation 6:

( ) ( )21 σ

γρ hh −= [7]

where ρ(h) is the autocorrelation function, γ(h) is the variogram function, and σ2 is the variance. One of the interesting features of DK is that an estimate of the conditional probability that the value at an estimation site is greater than an arbitrary threshold values, Zc, can be calculated. The conditional probability is obtained by defining an indicator variable that is equal to unity if Z*(x) Zc and zero otherwise. This conditional probability is a useful means for determining the risk of different water quality parameters. Many case studies using disjunctive kriging in environmental management have been reported. Yates et al. (1986a, 1986b) set it in the context of soil water, and Yates and Yates (1988) used DK technique to estimate soil pollution by sewage. Wood et al. (1990) described an application of disjunctive kriging to estimate the salinity of soil. Von steiger et al. (1996) estimated the concentrations of heavy metals in polluted soil by disjunctive kriging.

RESULTS AND DISCUSSION

Comparison With the Recommended StandardsSummary statistics of nitrate concentration is given in Table 1. In view of the USEPA (1999) and WHO (1993) standards, which suggested the limit of 45 and 50 mg L-1, respectively for NO3

-, as the highest tolerable NO3

- content in drinking water, all of the water samples fall within these limits.


However, concerns regarding the environmental impact of elevated nutrient concentrations in water resources are much more reflected in recent legislation enacted in many parts of the world. For nitrates both guide levels (G) and maximum admissible concentrations (MAC) in all water intended for human consumption is given as 5.35 and 11.3 mg L-1, respectively (CEC, 1975; 1980; 1991a; 1991b).). Setting the maximum contaminated level, defined as the highest level of a specific compound that allowed in treated drinking water, of 10 mg L-1 is based on epidemiological studies that showed this value to be the limit above which methemoglobinemia could occur. At this level, there appears to be little margin of safety for some infants (Handbook of public water systems, 2001; Cohn et al., 1999). Accordingly, about 86% and 25% of water samples have values greater than 5 and 10 mg L-1 NO3

-, respectively.

Mapping the Risk of Nitrate Contents In disjunctive kriging the data are normalized using Hermite polynomials. We did this and then computed the variogram on the transformed values (Fig. 2). The calculated variogram is clearly bounded and the best-fitted model was spherical. The parameters of the model are given on the graph. Combining this model and the transformed data one can estimate the nitrate concentration in groundwater. Figure 3 shows the estimated map of nitrate concentration. It is clear that the highest NO3

- values are located almost in the central parts of the plain. That is the urban areas where a large population is accommodated. We suggest that most part of nitrate concentration in groundwater is originated from municipal origins. The kriged map indicated that more than 98% of the interpolated sites, covering 1250 km2,shows nitrate concentration of more than 2.0 mg L-1. Furthermore, about 38% of the estimated sites, 476 km2 shows a class of nitrate concentration 5-8 mg L-1. About 5% of the estimated area, 63 km2 shows nitrate concentration of more than 10 mgL-1. In these areas one could expect growing risk of nitrate pollution. To estimate the conditional probability that the true values exceed nitrate concentration thresholds, we used values of 5.0 and 10.0 mg L-1NO3

-, as critical values (i.e., G and MAC values) for nitrate concentrations. The resulting probability maps are shown in Figure 4. These maps illustrate the same general patterns of nitrate concentration in groundwater as those obtained for nitrate estimates. Considering the threshold value of 5 mg L-1, about 78% of the study area shows a probability of more than 0.8 that the nitrate concentration exceeds this threshold value. If we decrease the acceptable probability level from 0.8 to 0.5, then for 97% of the area is estimated to be more than 5 mg L-1 in groundwater resources. For the threshold value of 10 mg L-1, the result indicates that there is a chance of 0.5 for about 95% of the whole area the groundwater resources contain nitrate of less than 10 mg L-1.Assuming that a probability level of 0.8 and more is acceptable, then all estimated sites are contained nitrate less than 10 mg L-1.Although the results may direct us to this conclusion that there is no high risk for nitrate pollution in the area for this moment, but considering the geographical occurrences of points with a risky nitrate consideration, it is important to keep monitoring action on assessing the quality of groundwater in the region (Burden et al., 2002).


REFERENCES Burden, F.R., McKelvin, I., Forstner, U., Guenther, A. (2002) Environmental monitoring handbook. McGraw-Hill Handbooks. Cohn, P.D., Cox, M., Berger, P.S. (1999) Health and aesthetic aspects of water quality. Water quality and treatments. American Water Works Association, 5th edition, McGraw-Hill, NY. 63 p. Council of the European Communities. (1975) Directive concerning the quality required of the surface waters intended for the abstraction of drinking water in member states. 75/440/EEC, OJ No. L194/26, June 16. Council of the European Communities. (1980) Directive relating to the quality of water intended for human consumption. 80/778/EEC, OJ No. L229, July 21. Council of the European Communities. (1991a) Directive concerning urban wastewater treatment. 91/271/EEC, OJ No. L135, May 21. Council of the European Communities. (1991b) Directive concerning the protection of water against pollution caused by nitrates from agricultural sources. 91/676/EEC, OJ No. L375, December 12. Handbook of public water systems. (2001) 2nd edition. HDR Engineering, Inc. Omaha, NE. John Wiley & Sons. NY. 1136 p. USEPA (U.S. Environmental Protection Agency). (1999) Drinking water regulations and health advisories. 822-b-96-002. Washington D.C. Von Steiger, B., Webster, R., Schulin, R., Lehmann, R. (1996) Mapping heavy metals in polluted soil by disjunctive kriging. Environmental Pollution 94: 205-215. Webster, R., Oliver, M.A. (2001) Geostatistics for Environmental Scientists, John Wiley & Sons, LTD. WHO (World Health Organization). (1993) Guidelines for drinking-water quality. Volume 1, Recommendations, 2nd edition, Geneva. Wood, G., Oliver, M.A., Webster, R. (1990) Estimating soil salinity by disjunctive kriging. SoilUse and Management 6: 97-104. Yates, S.R., Yates, M.V. (1988) Disjunctive kriging as an approach to management decision making. Soil Science Society of America Journal 52:1554-1558. Yates, S.R., Warrick, A.W., Myers, D.E. (1986a) Disjunctive kriging. I. Overview of estimation and conditional probability. Water Resources Research 22: 615-621. Yates, S.R., Warrick, A.W., Myers, D.E. 91986b) Disjunctive kriging. II. Examples. Water Resources Research 22: 623-630.

Table 1. Summary statistics of nitrate concentration (n = 234).

Statistics (mgL-1)Mean 8.2 Range 0.1 – 34.7

Standard deviation 3.3


Tehran

Caspian Sea

Persian Gulf

Chaharmahal Bakhtiari

Isfahan

Brujen

Farsan Shahrekord

Kharadji

Farokhshahr

Tomanak

Taghanak

Ben

Figure 1. Schematic map of Iran and location of the study area with sampling points.

0 2000 4000 6000 8000 0

0.2

0.4

0.6

0.8

1

Distance (m)

(|h|) γγγγ

62

734

1144 1492 1784 2082 2128 1959 2202

Figure 2. Variogram of nitrate concentration after normalization by Hermite transformation with the model fitted. The curve is a spherical model with a nugget of 0.219, a sill of 0.756, and a range of

3926 m.


Figure 3. Kriged map of nitrate concentration (mgL-1).

(a) (b)

Figure 4. Maps of the conditional probability that (a) NO3 ≥≥≥≥ 5 mgL-1, (b) NO3 ≥≥≥≥ 10 mgL-1.


Discriminating Factors Affecting the Spatial Variability of Selected Soil Quality Attributes in Different Ecosystems of

Central Zagros, Iran

J. Mohammadi1 and H. Khademi2

1- Department of Soil Science, College of Agriculture, Shahrekord University, Shahrekord, Iran. 2- Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran

Abstract In order to achieve a sustainable management of land resources and improve land quality, quantitative assessment of effective factors and soil quality indicators are necessary. The aim of this study was to evaluate spatial variability of selected soil quality attributes in central Zagros affected by such factors as region, land use and management practices. Twelve sites were selected in three provinces including Chaharmahal va Backtiari (Sabz Ku, Broujen), Isfahan (Semyrum), and Kohkeloye va Boyerahmad (Yasoj). Different management practices were considered such as: protected pasture, intensive grazing, controlled grazing, dryland farming, irrigated wheat cultivation, legume-farming practice, protected forest, and degraded forest. Systematic sampling with taking 50 samples of surface soil in each site was carried out. The results of univariate and multivariate analysis revealed that all factors significantly influenced the spatial variability of selected soil quality attributes namely phosphatase activity, microbial respiration, soil organic matter, and total nitrogen. The results obtained from discriminant analysis indicated that all selected soil quality parameters could significantly be used as soil quality indicators in order to recognize and discriminate sustainable agricultural and forestry ecosystems and/or optimal management practices. It is suggested that in order to properly assess the quality of range land ecosystems, besides soil chemical and biological indicators, soil physical properties should be considered as an important criteria of soil quality. Furthermore, quantification of soil quality assessment needs not only spatial analysis of soil quality indicators but also their temporal variability.

Keywords: Phosphatase activity, Microbial respiration, Soil organic matter, Total nitrogen, Geostatistics, Variogram, Kriging

Introduction Increasing human populations, decreasing natural resources, drastic changes in land use, and environmental degradation at an alarming rate in the most part of central Zagros area of Iran is among the most pressing concerns. The quality of many soils in the Zagros area has declined significantly since grasslands and forests were converted to arable agriculture and cultivation was initiated. Deforestration effects on soil physical and chemical properties in some parts of central Zagros was studied by Hajabbasi et al. (1997). They found that forests clear-cutting and converting to farmlands resulted in a lower soil quality. Decreasing soil organic matter and aggregate size, increasing soil bulk density and plasticity index were reported to be the consequence of land use changes. Beside physical and chemical soil properties as indicators of soil quality, soil biological attributes can provide key indicators for soil quality evaluation. Microbial and enzyme activity may hold potential as sensitive indicators for soil quality assessment (Dick, 1997). For any soil biological attributes to be used as an effective indicator of soil quality, the spatial variability of such attributes and associated factors controlling their variability should be evaluated. An understanding of the spatial patterns of variability and the influence of spatially variable soil on the concerned variables may help elucidate the factors driving spatial variability, allowing for proper sampling and their assessment.


However, there is little information about the spatial variability of soil biological properties at regional scale regarding the impact of parent material, soil type, land use and different management systems. There is considerable evidence, that these factors affect soil quality (Brussaard, 1994). The aim of the study is to evaluate the spatial variability of the soil quality status by means of biological parameters at a regional scale in order to get basic information for further defining soil quality standards.

Materials and Methods

Study Area and Sampling The study area was the Zagros area covered almost a large central part of Iran (Fig. 1). For the regional scale investigation 12 sites were selected in three province namely Isfahan (Semyrum area), Chaharmahal va Bakhtiari (Sabz Ku and Broujen areas), and Kohkeloye va Boyerahmad (Yasoj area). According to different land use and management systems, each site was sampled on a regular grid with 50 samples. In total 600 samples of 0-15 cm soil surface were collected. In the whole study area we investigated 12 sites with different land use management including protected ungrazed area (UG), intensively grazed area (IG), contrloed-grazed area (CG), degraded dryland area (DD), cultivated land under irrigated wheat (IW), cultivated land under alfalfa (AF), protected forest (PF), and degraded forest (DF).The area of Broujen has a temperate and humid climate with mean annual temperature of 11.5 °C. The mean annual rainfall is 280 mm. Soils of the study area have been developed on alluvial plains with an overall slope of 1-3%. Sabz ku is a montainous area with mean annual temperature of 6.8 °C and the mean annual rainfall is 800 mm. Soils of the Semyrum area have been developed on alluvial plains with an overall slope of 1-2%. The mean annual temperature is 9.0 °C and the mean annual rainfall is 350 mm. The elevation of the sites located in Yasoj area was about 2500 m with the same slope, aspect and parent materials to ensure that the soils have been formed under the same forming factors. The area mean annual temperature is 14.9 ºC, and its mean annual precipitation is about 500 mm. The study area was originally covered with oak (Quercus persica), Amygdalus scoparia, Amygdalus erioclada, Lonicera arborescence, Pistacia motica, Crataegus spp., and Popolus nigra. The main herbal coverage consists of Tymus kotchianus, andgrasses. Fifty soil samples were collected from 0-15 cm depth in each site. The phosphatase assay of Tabatabai (1986) was used to evaluate the potential of phosphatase activity. Moreover, microbial respiration (Anderson, 1982), soil organic matter content (Nelson and Summers, 1982) and total nitrogen (Bremner and Mulvany, 1982) were measured as selected biological soil quality attributes.

Statistical Analysis To reveal the main structures of the variation pattern of the whole data from the regional scale investigation discriminant analysis was performed using SPSS 11.0 software package. A multivariate discriminant analysis is useful to describe and interprete significant effects from a former ANOVA analysis since it assesses predictor variables which best explain a group separation. A detailed introduction into the theory of discriminant analysis was given by Klecka (1980). To detect the significance of the main effects area (3 provinces and 12 sites), land use (agriculture, range, and forest) and management practices (excluded range, degraded range, grazing range under controling, dryland farming, irrigated wheat, legum


cultivation, protected forest, degraded forest) on the four measured biological indicators of soil quality, F statistics were applied.

Results and Discussion The results obtained from a multivariate analysis of variance are given in Table 1. They showed that all investigated soil quality indicators were significantly related to all factors including area, land use, management practices and sites. It implies that all investigated soil properties are significantly related to the spatial soil heterogeneity of the study area (factor area, in table 1), management practices and interaction of environmental conditions with land use and management. Almost all factors were significant at p<0.001 for all soil properties. However, the factor land use was only significant at p<0.05 for the microbial respiration. The results of mean comparisons of different soil properties under different groups of factors were also given in Table 1. The mean values for phosphatase activity were higher in protected forest (Yasoj area) than other land use. The same rsults were obtained for microbial respiration, organic matter and total nitrogen. Furthermore, deforestation resulted in lower mean values for all soil quality parameters. The same trend was observed for different range management. Among different agricultural management practices, alfalfa cultivation showed much higher mean values for all soil properties than other management. Considering different range management, protection of the pasture from heavy grazing practice resulted in improving of soil quality performances. In order to determine the capability of discriminant analysis in separating different groups based on different factors, a series of biplots were created (Figure 2). The results indicated that the first two discriminant functions could describe about 98% variability of soil quality indicators. The overall accuracy of discriminanting four areas (Sabz Ku, Broujen, Semyrum, Yasoj) was 79 percent. The least discrimination accuracy was found for Yasoj area with about 27% overlapping with Semyrum area. Furthermore, there is almost a large overlapping between Broujen and Semyrum areas. Hence, the whole study area could be divided into three distinct sub-areas. It means that both areas of Broujen and Semyrum represent almost similar environmental characteristics. It might be due to similarity in their physiogrphy, topography, soil formation processes. Considering land use factor, overall accuracy of discriminanting three land use classes (range, agriculture, forest) was 67 percent. Among them, only forest land use was discriminated well. It seems that grouping different activities under general heading of agriculture and range land use might mask the inherent differences of these land uses. The managemet practices were not well discriminated. The overall accuracy was about 40%. However the first discriminant function could discriminate legum management practice from dryland farming. On the other hand the second discriminant function seperated well the protection strategy of forest from non-limited utilization of forest resources. The discrimination analysis based on factor sites illustrated that soil properties like organic matter, total nitrogen and phosphatase activity contributed highly to separation of different studied sites.

ConclusionsThe discriminant analysis was used to evaluate spatial variability of selected soil quality attributes in central Zagros affected by factors such as area, land use, management practices, and sampling sites. The results indicated that the spatial pattern of the examined biological soil quality parameters in the large-scale area of central Zagros was significantly related to soil properties like phosphatase activity, microbial respiration, soil orgainc matter and total nitrogen content.


Furthermore, the results obtained from discriminant analysis indicated that all selected soil quality parameters could significantly be used as soil quality indicators in order to recognize and discriminate sustainable agricultural and forestry ecosystems and/or optimal management practices. It is suggested that in order to properly assess the quality of range land ecosystems, besides soil chemical and biological indicators, soil physical properties should be considered as an important criteria of soil quality. Furthermore, quantification of soil quality assessment needs not only spatial analysis of soil quality indicators but also their temporal variability.

References Anderson, J.P.E (1982) Soil respiration. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of soil analysis, Part 2, Soil Science Society of America, Madison, Wisconsin. pp. 831-872.Bremner, J.M., Mulvany, C.S (1982) Nitrogen, total. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of soil analysis, Part 2, Soil Science Society of America, Madison, Wisconsin. pp. 595-624. Brussaard L (1994) Interrelationships between biological activities, soil properties and soil management. In Greenland D. J. and Szabolcs I., Soil resilience and sustainable land use. CAB international, Wallingford, UK. Pp: 309-329. Dick, R.P (1994) Soil enzyme activities as indicators of soil quality. In: Doran, J.W., Coleman, D.C., Bezdicek, D.F., and Stewart, B.A. (Editors), Defining soil quality for a sustainable environment. Pp. 107-124. Soil Sci. Soc. Am. Special Publication, No. 35, Madison, USA. Hajabbasi, M.A., Jalalian, A., Karimzadeh, H.R (1997) Deforestation effects on soil physical and chemical properties, Lordegan, Iran. Plant and Soil 190: 301-308. Klecka, W.R (1980) Discriminant analysis. Sage Publications, 70 p. Nelson, D.W., Sommers, L.E (1982) Total carbon, organic carbon and organic matter. In: Page, A.L., Miller, R.H., and Keeney, D.R. (Eds.), Methods of soil analysis, Part 2, Soil Science Society of America, Madison, Wisconsin. pp. 539-580. Tabatabai, M.A (1986) Soil enzymes. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of soil analysis, Part 2, Soil Science Society of America, Madison, Wisconsin. pp. 903-943.


Table 1. Multivariate analysis of variance to show the significance of the main effects area, land use, management, and sites on the biological soil quality parameters (p<0.001 ***, p<0.05 **). Data are means of investigated parameters. Different letters indicate significant differences between mean values (Dunkan test; p<0.05).

Factors df Phosphatase activity µmol p-NP/g.h

Microbial respiration mgCO2/gr.day

Organic matter (%)

Total nitrogen (%)

Area1- Broujen 2- Semyrum 3- Sabz Ku 4- Yasoj

3 (F=132.5)***1.489a1.237b3.999c3.120d

(F=154.5)***0.156a0.062b0.037c0.135d

(F=291.3)***0.823a0.871a2.880b3.061b

(F=197.6)***0.048a0.049a0.129b0.102c

Land use 1- range 2- agriculture 3- forest

2 (F=23.8)*** 1.997a1.733a3.117b

(F=2.97)* 0.153a0.173b0.135a

(F=120.4)***1.390a0.919b3.061c

(F=35.3)*** 0.069a0.055b0.102c

Management 1- UG 2- IG 3- CG 4- DD 5- IW 6- AF 7- PF 8- DF

7 (F=32.4)*** 2.521a1.659b1.392b0.538c1.365b3.258d3.919e2.314a

(F=14.3)*** 0.171a0.165a0.063b0.091c0.174a0.257d0.164a0.105c

(F=64.4)*** 1.578a1.386a0.819b0.575c0.842b1.345a4.125d2.011e

(F=28.9)*** 0.077a0.069a0.044b0.035c0.051d0.079a0.135e0.070a

Site 1- UG (Broujen) 2- IG (Broujen) 3- DD (Broujen) 4- IW (Broujen) 5- AF (Broujen) 6- UG (Semyrum) 7- CG (Semyrum) 8- IG (Semyrum) 9- UG (Sabz Ku) 10- IG (Sabz Ku) 11- PF (Yasoj) 12- DF (Yasoj)

11 (F=82.6)*** 1.605a0.678b0.538b1.365a3.258d1.570a1.390a0.712b4.350f3.630d3.920de2.314c

(F=62.2)*** 0.125bc0.129c0.091ab0.174bc0.256d0.065c0.063bc0.056bc0.327g0.308g0.164h0.105e

(F=142.5)***0.829bc0.504a0.575ab0.842bc1.345d0.937c0.819bc0.854bc2.970g2.790g4.125h2.010e

(F=95.4)*** 0.039a0.036a0.035a0.051bc0.078d0.058c0.044ab0.045ab 0.133e0.125e0.135e0.069d


Tehran

Caspian Sea

Persian Gulf

Zagros Mountains

Isfahan

Shahrekord

Yasoj

Figure 1. Map of Iran with geographical distribution of Zagros Mountains and capitals of three provinces (Isfahan as capital of Isfahan, Shahrekord capital of Chaharmahal va Bakhtiari, and Yasoj capital of Kohkeloye va Boyerahmad).

(a) (b)

(c) (d)

Figure 2. Biplots of the discriminant models for different factors: (a) area, (b) land use, (c) management, and (d) sampling sites. Numbers (refer to table 1 for explanation) represent values for the first two functions at the gravity points of different levels of different factors.


The Effect of Soil and Water Salinity on The Growth and Yield of Different Wheat Genotypes

Ahmad Reza Mohammadzadeh1 , Hamid Siadat2

1- Khorasan Agricultural and Natural Resources Center, Department of Soil and Water Research, Phone : 0511-3400301 – 4. P.O.Box:91735-488, Mashhad – Iran. Email:Ahmadreza _ Mohammadzadeh @yahoo.com. 2-Soil and Water Research Institute. Phone : 0211-8011067, 8021069. P.O.Box:14155-6185, Tehran – Iran. Email: Swri @iranswri.com.

Abstract Salinity is a major environmental stress that drastically affects plant growth and productivity. To achieve optimal crop production in saline regions, the most appropriate, logical choice is growing salt-tolerant varieties best suited for these regions. A 2 – yr field study was conducted at two loacations on saline soils of Nishabour to investigate the effect of salinity on growth and yield of 22 wheat (Triticum aestivum.L) genotypes. Two salinity levels were imposed on two silt loam soils by irrigating with saline waters. Electerical conductivities of applied waters were ≈ 4 and 6-8 dsm-1. The experimental design was a randomized complete block with three replications. The studied genotypes were Rowshan, Siossons, Falat, 4213, Ghods, Agosta/Sefid, 4211*, Bezostaya, 4211, Alvand, Gaspard, Cross Shahi, Gascogne, Marvedasht, Mahdavi, M.V.17, Hirmand, Alamout, 4209, Azar2, Cross Arvand and Chamran. Yield and yield componenets were measured at grain physiological maturity. The results showed that grain yield, biological yield, number of seeds per spike and kernel size (weight) were Significantly decreased as a result of increasing salinity. Rowshan had the highest grain and biological yield. Harvest index of most genotypes were decreased by increasing salinity. Rowshan and Mahdavi had the highest kernel size(weight). Ingeneral grain protein contents were increased with increasing salinity. Alamout, Bezostaya and Rowshan had the highest percentage of grain proteins. Na+ and Cl- contents in the leaf and stem tissues were increased with increasing salinity in all genotypes. Rowshan had the lowest concenteration of Na+ in its leaf. Gaspard had the lowest Na+/K+ ratio in its seed. According to results, It seems that Rowshan, Siossns and 4213 genotypes were the most tolerant and Cross Arvand , 4209 and Alamout were sensitive genotypes, respectively.

Key words : Salinity stress ,Wheat genotypes , yield

IntroductionSalinity is one of the major constrains which limits crop production in numerous part of the world especially in arid and semiarid regions. Salinity effects on plants are complex. The general effects of salinity are the results of both osmotic and ionic stresses (Greenway and Munns, 1980). The initial and primary effect of salinity, especially at moderate salinity concenterations, is due to its osmotic effects (Munns and Termaat, 1986, Jacoby, 1994). At the whole plant level, ion concenteration in plant tissues increase as a result of salinity. Ion toxicity or nutrition deficiency will be caused by the overdominance of a specific ion (Bernstein etal, 1974). Development of salt tolerant crop cultivars would complement salt management programs to help maximum yields in these years. Wheat (Triticum aestivum L.) continues to be a predominant crop in the agriculture of the Iran. Wheat is grown in many regions under slightly to moderately saline conditions. Wheat is considered to be relatively salt tolerant species (Maas and Hoffman, 1977). The responses of wheat cultivars to salt–stress have been reported by several workers (Francois et al, 1986; Maftoun and Sepaskhah, 1989; Pessarakli et al, 1991; Kafi and Stewart, 1998). However little information is available on relative salt tolerance and salinity criteria of wheat cultivars used in the present study. Information on the salt tolerance of wheat cultivars is


needed as an aid in selecting cultivars to fit into the overall land use plan with minimum management problems and to provide bases for breeding programs. Therefore the growth responses of several wheat cultivars under two salinity levels of irrigation water were investigated.

Materials and Methods Field experiments were carried out during two growing seasons (1999-2000, 2000-2001) at the Neishabour flood plain locacated in northwest of Khorasan Province . The soil type at two selected sites (Feizabad and Daghestani) was silt loam (Coarse loamy over sandy, mixed, thermic Xeric Torriorthents, and fine–loamy, mixed, thermic Xerofluentic Haplocambids respectively). The electeric conductivities of applied waters on Feizabad and Daghestani sites were 4 and 6-8 dSm-1 respectively. Soil samples were collected from experimental sites each fall prior to treatment applications. Samples to a depth of 0-30 and 30-60 cm were taken at two sites. Routine soil test procedures (Ehyaei, 1997) were used to determine EC, pH, T.N.V. Organic matter content, available P and K. Results are summarized in table 1. A randomized complete block experimental design with three replications was used. The studied genotypes were Rowshan, Siossons, Falat, 4213, Ghods, Agosta/Sefied, 4211*, Bezostaya, 4211, Alvand, Gaspard, Cross Shahi, Gascogne, Marvedasht, Mahdavi, M.V.17, Hirmand, Alamout, 4209, Azar 2 , Cross Arvand and Chamran. The crops were grown in 2.0 by 1.0 – m plots. There was 1m of border between plots and 4m borders between replicates. Recommended rates of N, P and/or K were applied where soil tests showed a need. The sources of N, P and K were urea, triple superphosphate and potassium suphate, respectively. Fall–applied urea, P and/or K fertilizer were applied preplant and incorporated. Urea topdressing was broadcast at shooting and heading stages.Wheat cultivars were hand planted in 2-m rows at a rate of 250kgha- in early November in both years. The cultural practices used at planting were identical for both experiments. Each plot condtained 6 rows of wheat. The rows were planted 0.15m apart. Weeds were controlled by hand. Irrigation was provided to all plot through a basin irrigation method. During both growing seasons, all plots were irrigated approximately every 10 days. Both irrigation waters were analyzed for several times. To determine grain yield, straw yield and thousand grain weight of each cultivar, four inner rows of each plot by 1m were harvested by hand above the soil surface at physiological maturity. The flag leaves of each cultivar were sampled after spike emergence. The leaves were washed, dried at 70oC and finely ground in a blender. Representative samples were dry–shed with calcium oxide. Chloride contents were determined on water extracts of the leaf material by titration by 0.05N silver nitrate(Chapman and Pratt, 1961; Waling et al, 1989). Na and Cl were determined by flame photometer. Grain samples were ground and analyzed for total N(Kjeldahl). Crude protein was derived by multiplying N concentration by 5.70 (Parvaneh, 1992).Data collected were subjected to the analysis of variance using SAS soft ware (version 6.12) The means were compared and grouped by Duncan,s multiple range tests (Ott,1993). The 5% level of significance was used in all comparisons.


Results and DiscussionThe comparison of means for grain yield, straw yield and thousand grain weight on Feizabad and Daghestani are presented in table2. The results showed that grain yield, biological yield thousand grain weight were significanly decreased as a result of increasing salinity. Rowshan cultivar had the highest grain, biological and straw yield. Harvest index of most genotypes were decreased by increasing salinity. Rowshan and Mahdavi cultivars had the highest thousand grain weight. In general, nitrogen content of most genotypes increased with increasing level of salinity (table 3). An increase in nitrogen content of grain with increasing level of salinity of growth medium or irrigation water also recorded Bable (1981) and Chhipa and Lai(1992). Na+ and Cl- contents in the leaf tissues were increased with increasing salinity in all genotypes. Moreover, the Cl- concenteration in wheat leaves was considerably higher than the Na+ concenteration. This is in agreement with the findings of Gates et al (1970) who observed that although both Cl and Na ions readily enter the plant root, most Na ions tended to be retained in the roots, whereas a greater proportion of Cl ions was translocated to the leaves. The data in table4 indicates that changes in plant Cl- and Na+ concenteration were primarily due to soil salinity and to a lesser extent were related to wheat cultivars. Rowshan cultivar had the lowest concenteration of Na+ in its leaves. The variation observed among the genotypes tested here supports the idea that wide variation exist among wheat genotypes for character of salt tolerance. It seems that Rowshan, Sissons and 4213 genotypes were the most tolerant and Cross Arvand, 4209 and Alamout were sensitive genotypes respectively.

References 1- Bable A(1981) Effect of application of saline water at different stages of Barley (BL.2).M.Sc.(Ag). Thesis, University of Udaipur(Raj). 2-Bernstein L , Francois LE , Clark RA (1974) Interactive effects of salinity and fertility on yields of grain and vegetables. Agron. J. 66:412-421. 3- Chapman HD, Pratt PF (1961)Methods of analysis for soils, plants and waters. Univ of California, Div of Agric Sci. 4- Chhipa BR, Lai P(1992) Effect of soil salinity on the pattern of nutrient uptake by susceptible and tolerant varieties of wheat. Agrochimica. Vol 36, N. 5-Ehyaei M (1997). Methods of chemical analysis of soils. Soil and Water Res Institute. 6-Greenway H , Munns R (1980) Mechanisms of salt tolerance in non – halophytes, Annu. Rev. Plant Physiol. 31 : 149-190. 7-Jacoby B (1994) Mechanisms involved in salt tolerance by plants. P. 97-123. In Pessaraki M(ed) Hand book of plant and crop stress. Marcel Dekker, New York. 8-Kafi M , Stewart WS (1998) The effects of salinity on growth and yield of nine wheat cultivars. J. Agri Sci and Tech. Vol 12. No 1. 77-93. 9- Maas EV , Hoffman GJ (1977) “Crop salt tolerance – current assessment”. J. Irrig and Drainage Div., ASCE 103 (IR2) : 115-134. 10-Munns R , Termmat A (1986) Whole – Plant responses to salinity. Aust. J. Plant Physiol. 13 : 143 – 160. 11- Ott R (1993) An introduction to statistical methods and data analysis, 4 th ed., Duxbury Press, Belmont, CA. 12-Parvaneh V (1992) Qualitative control and chemical analysises of Food Thehran Univ Press. P.18. 13- SAS Institute(1994) SAS/STAT users guide. Version 6. 4th ed. SAS INST., Cary, NC. 14- Waling IW, Van Vark W, Houba VJG, Vander Lee JJ(1989). Soil and Plant Analysis, a series of syllabi. Part 7, Plant Analysis Procedures. Wageningen Agric. Univ.


Table1. Selected physical and chemical properties of soils at two sites Site Year Depth EC PH T.N.V Organic C P(ava) K(ava) Texture

cm dSm-1 ____ % ____ ___ mgkg- ___

0-30 18.7 7.7 11.5 0.68 10.2 207 Silt loam 1999 30-60 15.4 7.7 13 0.49 4.8 203 Silt loam

0-30 9.4 7.9 14 0.37 9.3 186 Silt loam Feizabad 2000 30-60 11 7.8 13.5 0.27 4.5 183 Silt loam

0-30 12.7 7.8 12.3 1.12 15 263 Silt loam 1999 30-60 11.6 7.8 13.3 0.7 7 276 Silt loam

0-30 12.9 7.9 13.1 0.82 12.6 463 Silt loam Daghestani 2000 30-60 13.9 7.9 12.8 0.55 9.8 485 Silt loam

Table2. Comparison of means for grain yield, straw yield and thousand grain weight of wheat genotypes.*

Feizabad Daghestani Genotype Grain Straw Thousand

grain weight Grain Straw Thousand

grain weight

_____ Kgha- _____ gr _____ Kgha- _____ gr

Rowshan 3950ab** 7833a 34.5a 3867a 7983a 26.5ab

Siossons 3500abcd 6234abcd 24.0gh 3133ab 4850bcdef 22.5abc

Falat 3434abcd 5584bcd 29.0bcde 2300bcd 4333def 24.0abc

4213 3750abc 7683ab 30.0bcd 2967abc 6184abcd 22.5abc

Ghods 3100bcd 6116abcd 25.0fgh 2817abc 6116abcd 21.5abc

4211* 4167a 7167abc 27.0cdefgh 2850abc 6033abcd 20.5abc

Bezostaya 3100bcd 6397abcd 28.5cdef 2833abc 5900abcde 26.0ab

4211 3967ab 5433cd 29.0bcde 2817abc 5267bcde 23.5abc

Alvand 3500abcd 7133abc 30.0bc 2700abcd 6633abc 23.0abc

Gaspard 3266abcd 6017abcd 25.0fgh 2683bcd 6267abcd 21.0abc

Cross Shahi 3300abcd 7100abcd 27.0cdefgh 2667bcd 6917ab 22.0abc

Gascogne 3317abcd 5217cd 28.0cdef 2633bcd 5233bcde 24.0abc

Marvedasht 3800ab 6700abcd 24.5fgh 2500bcd 5450bcde 20.5bc

Mahdavi 3484abcd 6567abcd 30.3bc 2183bcd 5717bcde 21.0abc

Chamran 3167bcd 4967d 28.0cdefg 2133bcd 3833ef 26.5a

M.V.17 3317bcd 6033abcd 23.0h 2000bcd 5183bcde 19.0c

Hirmand 2833cd 5633bcd 31.0abc 2000bcd 4617cdef 24.0abc

Alamout 2833cd 6167abcd 25.5efgh 1917cd 5083bcde 21.0abc

4209 2150d 5800abcd 25.5efgh 1883cd 4917bcdef 19.0c

Cross Arvand 2716d 5667bcd 32.5ab 1567d 2983f 22.0abc

*Means of two years **Mean Separation with columns by Duncan,s multiple range testes, 5% level


Table3. Comparison of means for grain nitrogen percent of wheat genotypes at Feizabad and Dageatani sites.*

Grain N % Genotype Feizabad Daghestani 1999-2000 2000-2001 Mean 1999-2000 2000-2001 Mean Rowshan 3.07ab 2.70abc 2.79abc 3.25abc 2.58bc 2.93a

Siossons 3.12ab 2.23defg 2.90ab 3.03abcde 2.35cd 2.92a

Falat 2.96abcd 2.86a 2.88ab 3.19abcd 2.34cd 2.91a

4213 2.86ab 2.18efg 2.67bcd 2.85bcde 2.45bcd 2.87a

Ghods 2.72b 2.38bcdefg 2.69bc 3.01abcd 2.51bcd 2.84ab

4211* 2.83ab 2.23defg 2.60bcd 2.72cde 2.51bcd 2.82ab

Bezostaya 3.06ab 2.74ab 2.78abc 3.35ab 2.57bcd 2.81ab

4211 2.69b 2.54abcdef 2.55dc 2.69de 2.43bcd 2.80ab

Alvand 2.96ab 2.46abcdef 2.75abc 3.05abcde 2.31d 2.80ab

Gaspard 3.23a 2.80abcdef 2.36d 3.07abcde 2.49bcd 2.79ab

Cross Shahi 2.93ab 2.63abcd 3.01a 3.08abcde 2.54bcd 2.78ab

Gascogne 2.89ab 2.49abcdef 2.79abc 2.95abcde 2.40bcd 2.76ab

Marvedasht 3.22a 2.13fg 2.72abc 2.95abcde 2.80a 2.76ab

Mahdavi 2.79ab 2.41bcdef 2.67bcd 3.00abcde 2.64ab 2.69abc

Chamran 3.05ab 2.39bcdefg 2.73abc 3.12abcd 2.40bcd 2.68abc

M.V.17 2.75ab 1.96g 2.69bc 3.09abcd 2.50bcd 2.61abc

Hirmand 3.08ab 2.19defg 2.52cd 3.22abcd 2.38cd 2.65abc

Alamout 3.05ab 2.57abcdef 2.53cd 3.43a 2.44bcd 2.64abc

4209 2.87ab 2.50abcdef 2.61bcd 3.15abcd 2.54bcd 2.55bc

Cross Arvand 3.01ab 2.29cdefg 2.65bcd 2.55e 2.38cd 2.46c

Agosta/Sefid øø 2.75ab ___ øø 2.50bcdøø

Azar2 ___ 2.61abcde ___ øø 2.52bcdøø

*Mean Separation with columns by Duncan,s multiple range tests, 5% level


Table4. Na and Cl percent in leaves of wheat genotypes at feizabad and Daghestani sites. Feizabad Daghestani

1999-2000 2000-2001 1999-2000 2000-2001 Genotype Na Cl Na Cl Na Cl Na Cl

________________________________ % _________________________ Rowshan 0.15 1.02 0.12 0.85 0.23 0.94 0.28 0.98 Siossons 0.33 1.07 0.18 0.85 0.49 1.11 0.28 0.68 Falat 0.28 0.89 0.15 1.06 0.35 0.96 0.4 0.83 4213 0.32 1.16 0.14 1.17 0.37 1.24 0.63 0.87 Ghods 0.32 0.85 0.20 1.00 0.42 1.15 0.26 0.59 4211* 0.36 1.00 0.19 1.13 0.33 1.11 0.42 1.13 Bezostaya 0.29 1.24 0.14 0.94 0.35 0.94 0.94 0.70 4211 0.42 1.29 0.18 0.85 0.43 1.31 0.4 0.76 Alvand 0.28 0.92 0.11 0.94 0.45 1.11 0.33 0.93 Gaspard 0.30 1.11 0.14 0.83 0.32 1.20 0.30 0.64 Cross Shahi 0.40 0.83 0.15 1.2 0.49 1.53 0.66 1.06 Gascogne 0.33 1.00 0.13 0.75 0.41 1.37 0.31 0.64 Marvedasht 0.41 0.87 0.10 0.83 0.49 1.15 0.17 0.62 Mahdavi 0.23 0.89 0.15 0.93 0.25 1.16 0.64 1.15 Chamran 0.27 1.20 0.13 0.62 0.44 1.77 0.30 0.74 M.V.17 0.23 0.98 0.40 1.02 0.36 0.44 0.32 1.80 Hirmand 0.15 0.81 0.12 0.75 0.37 0.98 0.39 0.62 Alamout 0.32 1.15 0.19 0.87 0.24 0.94 0.59 0.98 4209 0.36 1.22 0.15 0.81 0.69 1.53 0.47 0.89 CrossArvand

0.40 1.29 0.18 0.98 0.74 1.48 0.69 0.98

Agosta/Sefid ___ ___ 0.10 0.53 ___ ___ 0.43 0.77 Azar2 ___ ___ 0.18 0.87 ___ ___ 0.25 0.55


Relationship between some nutrient uptake and early falling of seeds in ash (Fraxinus excelsior) M 20030

Moraghebi F . 1,2, Khanjanishirazi B.2, Teimouri M.2 and Korori S.A. A2

Shahr-e- Rey Unit. Islamic Azad University. Tehran-Iran. Tel: + 0098 - 0261-6619517 , E-mail: [email protected] , Ecophysiology and biotechnology. Forest Division. Research Institute of Forests and Rangelands.. Tehran-Iran. Tel: + 0098 - 0261-6619517. E-mail: [email protected] and [email protected]

AbstractAsh (Fraxinus excelsior) has a distribution from Astara in Gilan to Gildaghi in Golestan province in North of Iran. This species is used widely in reforestation programs because of its suitable growth, production and resistance against cold and drought. But investigation on metabolic evaluation of seeds has shown that most of them were hollow and early falling. In this investigation, the effect of plant nutrition was studied during 2 years in Gisum region in Gilan province. The amount of potassium, calcium, natrium, magnesium and phosphorus was measured by atomic absorption and spectrophotometer in leaves. Samplings were done in four months (June, July, August and September). Sampling from soil was done and the chemical and physical properties were determined. The amount of elements showed that the amount of Mg was optimum but phosphorus was more and calcium was much more than required. In spite of optimum amount of potassium in soil, measurement of K in leaves showed a severely deficient. Results indicated that pH of soil has changed about 1- 2 unit from neutral to acidic (5-5.2) reaction during past 30 years. In acidic soils, the absorption of K by roots is limited but the absorption of Ca is increased. This caused disorder in Ca/K ratio. This situation along with climatical changes caused reduction in production and remaining of seeds in ash.

Introduction Forest is a complex system according to ecological definition and there is balance between different parameter (quantitatively and qualitatively). By exact understanding of these parameters, determining the damaging factors and then preventing of its damage is possible. Fraxinus excelsior is one of the valuable and industrial species that grow and regenerate rapidly in suitable condition such as ecological and elite maternal stands and produce seedlings with good quality. Sabeti studied the distribution of Fraxinus excelsior in North of Iran ( Sabeti ,1985 ). The best sites of Fraxinus excelsior are in north of Iran (Tabari, 2002). This species has been used widely in afforestation and reforestation plans because of its resistance against frost and drought. The production of seeds has decreased because of forest damage. Most of the seed are empty or early falling (Korori et al, 2002). This situation will cause extinction of ash.The nutritional demands of ash are high and establish only in fertile soils. The occurrence deficit of nutrients in ash is a signal that indicates other species will suffer from deficit of nutrients easily. The amount of elements in soil and optimum amount of them in leaves was studied in some of forest trees including ash (Zarrin kafsh, 2001). The nutritional disorders was investigated in ash and other species and determined the optimum amount for suitable growth and regeneration (Bageri, 2001). The deficiency of phosphorous in leaves was related to deficiency of nitrogen and phosphorous in soil of habitat (Zarrinkafsh, 1982). Investigation soil fertility in Khirood Kenar forest showed deficiency of all nutrient except for iron due to the composition of soil bed (Mohammadi, 1991). The low growth of Juniperus, Pectacia and Amigdalus was related to deficiency of macro and microelements in soil of Malek region in Kerman province (Ordokhani, 1998). The nutritional quality was lower in hazel- nut natural


forest in compare to artificial habitats because of nutrient deficiency in soil of natural habitats (Moraghebi, 2001). The optimum, deficiency and excess level of different elements was reported in some forest trees (Bonneaue, 1995). The direct relationship was seen between deficiency of elements in soil and Juniperus excelsa (Esphandiaripour, 1998). The effect of soil properties in absorbance of Calcium by plants and antagonistic effect on Fe and Mg caused early falling of seeds (Genni, 1998). Aims of this study were investigation of nutritional situation in Fraxinus excelsior following dramatic changes in Gisoum region due to wood and pulp factory establishment, making road and spreading of tourism in this region and show their effect on seed production in this species.

Material and Methods - Study area Selected trees (Figure 1) locate in Gismo region in Gilan province between 49o, 5 Eastern longitude and 37o, 35 Northern latitude 10 m from sea level. Its annual precipitation is about 1958 mm. The minimum and maximum temperatures are 11 and 38/5 o C, respectively. Most of the soil bed is composed of clay, volcanic stones and rarely sandy rocks. The plant cover is 20% and the light percent 60% (according to plants crown). Sampling Sampling from leaves was done in June, July, August and September in two successive years (2002-2003). Samples were transferred to laboratory at 4 C. The percent of dry weight and organic and inorganic matter were determined. Leaves were dried in 70 C for 48 hours and extracted by dry ashing and by use of HCl (2M) in 80 C for measuring elements. The amount of calcium, magnesium, natriun, potassium was measured by atomic absorption spectrophotometer Phoenix 896 in extractions and by use of related standard solution and calculated as g/100g. The amount of phosphorous was measured by molibdate- vanadate method and calculated as g/100g (Emami, 1996). Soil samples were taken from 0-20 cm depth and analyzed according to standard methods.

Results and Dscussion Ten Fraxinus stands were chosen in Gisum region that their height are presented in Table 1. The percent of dry weight is presented in Table 2. Results indicated decreasing amount of water from the beginning up to end of season and increasing of inorganic component from June to August but with a little decreasing in September indicating efflux of elements from leaves (Ebrahimzadeh, 1994; Zarinkafsh, ,2001). The amount of element in 2002 and 2003 are presented in Table 3 and 4. During two years, the amount of calcium was more adequcy. The amount of Mg and Na was optimum (Zarinkafsh, 2001; Bageri , 2001; Bonneaue,1995). Although the amount of K changed during different months but comparison of its amount with standards showed a sever deficiency in leaves (Bonneaue, 1995; Zarinkafsh, 2001). The role of K in plant physiology is very important and its deficiency makes plants sensitive to weeds and disease (Hagparast, 1991; Salari, 1989). Also in some plants causes seeds early falling during drought stress. (Moraghebi, 2001; Ebrahimzadeh, 1994; Hagparast, 1991). The role of calcium is very important in plant physiology so that its uptake increases during stress (Ebrahimzadeh, 1994; Bakrdjeva, 1996; Maghouli, 1996; Pvan, 1993). The K/Ca ratio is very important in flower and yield (Ebrahimzadeh, 1994; Hagparast, 1991). The antagonistic relationship has been reported among Ca, K and Mg uptake especially Ca effects on K uptake (Ebrahimzadeh, 1994; Hagparast, 1991; Salari, 1989; Genni, 1998). The ratio of K/Ca is 1-3 in suitable condition. The precipitation rate was 2mm and 16 mm in July 2002 and 2003, respectively.


Then the increase in Ca uptake is predictable because its uptake increases during drought stress. Results showed this ratio was 0.36 and the ratio of K/Ca+Mg was 2.5-5 times less than normal ratio. These condition cauased the decreasing of yield especially in stress. Demancy for K increases during the stress. The flower has been hurt as a result of late cold in Gilan during 3 last years and the rate of fecundated flowers has decreased drastically. Sensitivity of trees against late cold can be explained by two mechanisms: 1- The late cold occurred after appearing of flowers in comparison to last years with 15-20 day delay. 2- The rate of buds decreased because of tree sensitivity against late cold following K deficiency. Abnormality in K/Ca and K/Ca+Mg caused decreasing of yield and losing of more seed during growth season. This phenomenon intensified by occurring of drought in June and July months.The soil analysis did not show K deficiency (Table 5). The reaction of soil was acidic (pH 5-5.2). The acidic reaction limits the uptake of K and then its deficiency in trees in spite of proper amount in soil (Tavallaei, 2001) The soil acidity changed from neutral 6/9 in 1975 to acidic 5 in 2002 because of different factories activity (for example by wood factories). Trees were cut for wood factory demand and then this region was planted with nonendemic species that caused drastic changes in plant cover and ecosystem. Improving tourism, increasing the population of region are other possible reasons for ecosystem alteration. Local road has changed to highway with jam traffic. Making road in South and West directions of this region interrupted in natural drainage function. The level of underground water will increase as a sequence of low drainage, producing acidic humus. In acidic soil, interruption in tree physiology and nutrient uptake occur that finally cause reduction in seed production.

Reverences 1- Bakrdjeva NT, Christora NV, Cristov K. (1996) Reaction of peroxidase from different plant species to incavsed temperatures and the effect of calcium and zinc ions , Plant peroxidase IV international symposium. Geneva 2 Bageri A (2001) Nutritional disorders in ornamental plants. Parks organization publication. 3- Birang, N (1990) Plant cover. University Publication Center. 4- Bonneaue M (1995) Fertilisation des forests dona les payes temperes. Engerf 5- Ebrahimzadeh, H (1994) Plant physiology. Tehran University Publication 6- Emami A (1996) Methods for plant analysis. Soil and water research Institute publication. 7- Espahbodi, K., SH, Mohammadnejadkiasari. H, Barimaniuvarandi. H. Ghobadian (2003) The best density and mixed plantation of Acer and Fraxinus. Forest and Populous research. 11: 19-34 8- Esphanidarpour, P (1998) Investigation relationship between physico- chemical properties of soil with plant covers in Juniperus habitats in Malek region in Kerman province. MSc thesis. Soil Science. Azad University 9- Gahraman A. (1994) Iranian chromophytes. Tehran University Publications. 10- Gennei E , F Bussotti, L Galeotti ( 1998) The declune of Pinus nigra . Reforestation stands on limetone substructure. Ann. Sci. For . Elsevier /Iran. 11 - Hagparast M (1991) Plant physiology. Gillian University Publication 12- Korori, S.A.A, M khoshnevis, F Maghuli, M Jebelli, B Khanjanishirazi (2002) Metabolic evaluation of fraxinus seeds by use of enzymes and cations alterations. Journal of Forest and Populous research. 9: 83-149 13- Mohammadi F (1991) Investigation of trees nutrition and fertility of different ecosystems in Khiroodkenar forest. MSc thesis. Tehran University. 14- Moraghebi F (2001) Ecological studies and environmental adoption and plant sociology in hazel- nut sites in north of Iran. Plant science. Ph.D. thesis. Azad University


15- Maghouli F (1996) physiological reasons of yellowing in natural and artificial habitats of Haloxylon sp. Plant science. MSc thesis. Azad University 16- Mosaddegh A (1996) Silviculture. Tehran University Publications 17- Ordoukhani K (1998) Ecophyiological studies of Juniperus, Pictacia and Amygdalus forest in Malek region in Kerman province. MSc thesis. Azad University 18- Pvan X ( 1993). Association of calcium and calmudoline to peroxidase secretion and activation . J plant Physiology . 141: 141-146 19-Pourasghari, MA ( 1996) Iranian nurseries in north. Forestation and parks office. Publication of Forests and Rangelands Organization 20-Sabeti H (1985) Iranian trees and shrubs. Iranian botanical garden publication. 21-Salari AA (1989) Plant nutrition. University Publication Center 22- Tabari M (2002) Forest population and environmental requirement of Fraxnius excelsiorin north of Iran. Pajuhesh va Sazandegi . 55: 94-103 23-Tavallaei, M (2001) Hydroponics cultures in commercial scale. Publication of agricultural. 24- Zarrinkafsh. M (1982) Physico-chemical properties of soil in Lajim (Zarab). Agriculture faculty. Tehran university 25-Zarrinkafsh. M (2001) Forest soils. Research Institute of Forests and Rangelands Publication.

Figure 1- The Fraxinus excelsior site in Gisume region in Gillan province.


Table1- Morphological parameter in selected trees Tree number Height (m) Diameter (cm)

1 17 30 2 20 45 3 18 40 4 12 25 5 15 35 6 15 45 7 13 55 8 25 50 9 23 55 10 27 60

Table 2- Dry weight and inorganic matter percentage in leaves in different months. June July August September

%Dry weight (2002) - 35.2 44 46.1 %Dry weight (2002) 31.2 32.7 35.9 41.6

%Inorganic matter (2001) - 10 14.5 12.5 %Inorganic matter (2002) 8.2 8.26 10.74 10.08

Table 3- The average concentration of element in leaves of ash in 2002 (dry weight%) P Ca K

July 0.354 1.09 .946 August 0.253 2.29 1.169

September 0.049 2.35 1.174

Table 4- The average concentration of element in leaves of ash in 2003 (dry weight%) P Ca K Mg Na

June 2.65 1.489 0.725 0.235 0.06 July 0.724 1.63 0.601 0.227 0.065

August 0.684 2.09 0.72 0.226 0.071 September 0.506 1.65 0.71 0.222 0.072

Table5- Some chemical properties of soil K Na Mg Lime% Ca Cl %OC %N %Chalk P

373.52 83.02 9.12 0.78 46.4 0.66 4.12 0.346 * 32.92

K, Mg, Ca, P as mg/ kg soil Cl as mg/g soil

* trace


Cd and Mn uptake and bioaccumulation in Trifolium alexandrinumL.: interaction with mycorrhizal colonization

Habiballah Nadian Crop Production Department, Ramin Agricultural Research and Educational School, Shahid Chamran University, Ahvaz, IRAN, Phone: +611- 4431630 E-mail: [email protected]

Abstract A randomized complete block greenhouse pot experiment was carried out to study the effect of mycorrhizal colonization on the concentration of Cd and Mn in shoot and root of T. alexandrinm.In this study Cd at rates of 0, 1.5, 3.0, 10 and Mn at rates of 0 and 100 mg kg-1 soil were added to a sterilized soil. Five two-day old seedlings were transplanted into each pot (containing 2700 g air dry soil) and inoculated by Glomus intraradices (in mycorrhizal treatments). The plants were grown in a glasshouse and harvested 7 weeks after transplanting. The results of this study indicated that shoot and root dry weights of mycorrhizal clover plants were significantly greater than those of non-mycorrhizal clover plants at all levels of Cd and Mn concentrations. The observed increase in mycorrhizal growth response was due to the colonization of clover root and thus improved phosphorus nutrition of the host plant. The concentration of Cd in both mycorrhizal and non-mycorrhizal clover plants (shoot and root) significantly increased as Cd applied to soil was increased. However, the concentration of Cd in both root and shoot of mycorrhizal plants significantly was lower than that in non-mycorrhizal plants. A similar trend to that of Cd was observed for Mn. The observed decrease in the accumulation of Cd and Mn per unit weight of mycorrhizal plants might be due to sequestration of these elements in external hyphae of the fungus, for example, via a chemical reaction between these elements and fungal polyphosphate granules. The lower concentration of Mn in mycorrhizal plants was also attributed to a significant decline in the proportion of Mn-reducers of the total microbial population in the rhizosphere of mycorrhizal plants. It has also been shown that the number of Mn reducers in the rhizosphere of non- mycorrhizal plants was 20-30 times that in the rhizosphere of mycorrhizal plants, and this led to a higher Mn concentration in the non-mycorrhizal plants. The results of this study clearly showed that the concentration of heavy metals like Cd and Mn in mycorrhizal T. alexandrinum was lower than those in non-mycorrhizal T. alexandrinum. This was mainly attributed to the sequestration of the heavy metals by fungal structure and might be regarded as a mechanism facilitating exclusion of metals from the shoot, and thus avoiding metal toxicity.

Keywords: Mycorrhizal colonization, Cd, Mn, mycorrhizal growth response, clover

Introduction Accumulation of heavy metals in crop plants is of great concern due to the potential for food chain contamination through the soil-root interface. The potential for contamination of the food chain with heavy metals through their bioaccumalation in plants has recently received increased attention. Of these metals, Cd and to some extent Mn, are of particular concern. Although Cd is not essential for plant growth, it is easily taken up and accumulated by plants in appreciable quantities.


Arbuscular mycorrhizal (AM) fungi establish their intimate association with more than 80% of plant species (Smith and Gianinazzi-Pearson, 1988). The greatest beneficial effect of mycorrhizal symbiosis has been related to improved P nutrition of the host plant. Root colonization with AM fungi can enhance the uptake of P by plant roots by providing a larger absorbing surface for uptake of P and by overcoming problems relating to development of depletion zone, via translocation in external hyphae to the host plant root (Cooper and Tinker, 1978; Sanders and Tinker, 1973). In addition, there is some evidence which indicates that mycorrhizal colonization helps plants to thrive in arid conditions (Nelson and Safir, 1982), deters root pathogens (Gianinazzi-Pearson and Gianinazzi, 1983), increases soil aggregation in eroded soil (Koske et al., 1975; Tisdall, 1991) and decreases the negative effect of soil compaction on growth and nutrient uptake by plant (Nadian et al., 1998). It has also been shown that the concentrations of heavy metals such as Cd, Ba and Mn in non-mycorrhizal plants (e.g., maize, sorghum, millet, durum wheat) are higher than those in mycorrhizal plants (Kothari et al., 1991; Turnau et al., 1993). In fact, heavy metals can be sequestrated in external hyphae of AM fungi and thus avoiding heavy metal toxicity for the host plant. Recently, the latter beneficial effect of mycorrhizal colonization has received special attention in sustainable agriculture, particularly in the food chain. Heavy metals uptake varies considerably with plant species, particularly in the presence of AM fungi. There have been no published reports of how mycorrhizal colonization may affect the concentration of Cd and Mn in Trifolium alexandrinum. Thus, this work was amid to study of the uptake of Cd and Mn by mycorrhizal and non-mycorrhizal Trifolium alexandrinum plants.

Materials and Methods For the purpose of the experiments a soil with low P content was collected from the 0-20 cm layer at Ahvaz in Western South of Iran. Some properties of the soil are given in Table 1. The soil was passed through a 2 mm mesh sieve and autoclaved at 121 °C for 1 hour on two consecutive days. Cd and Mn at different amounts (0, 1,5, 3.0, 10 and 0, 100 mg kg-1 soil, respectively) and sufficient distilled water to bring water content of the soil to 0.2 kg kg-1 soil were mixed throughout the soil.

Plant material and growth conditions Seeds of clover plant (Trifolium alexandrinum) were sterilized with NaOCl solution (5 g dm-3)and germinated on moist filter paper at 23°C. Five 2-day old seedlings were transplanted into each pot containing 2700 g air-dry soil. AM inoculum was obtained from pot cultures of clover (T. alexandrinum) grown in a soil:sand mixture (1:9) containing 10% of a dry inoculum from pot cultures of Glomus intraradices. Roots from the pot culture were harvested three months after transplanting and washed with water. The roots were chopped into segments about 1 cm long, thoroughly mixed and used as fresh inoculum. For the mycorrhizal treatments each seedling was inoculated by placing 0.2 g fresh inoculum in each planting hole. Seedlings, for the non-mycorrhizal treatments, received 0.2 g non-colonized clover roots. In all treatments, each seedling received a 0.5 cm-3 dense suspension of Rhizobium leguminosarum biovar trifolii. The plants were grown in a glasshouse for 8 weeks. The water content of the soil was brought to field capacity by adding distilled water.

Measurements


In all treatments, the tops of the clover plants were harvested after 8 weeks and dried at 70°Cfor 48 hours and the roots were separated from the soil by washing with water. The roots were cut into segments about 1 cm long and thoroughly mixed. Sub-samples of roots were taken for determination of dry weight and root length, and for the assessment of percentage of root length colonized by G. intraradices. The percentage of root length colonized was measured, using a grid intersect method, under a dissecting microscope (Giovannetti and Mosse, 1980) after staining with trypan blue. Dry ground shoots and roots were digested in a mixture of nitric and perchloric acids and P concentrations of the plant materials were determined colorimetrically using the phosphovanado-molybdate method. Cd, Mn, Ni, Cu and Zn were determined using an atomic absorption spectrophotometer. The percentage of mycorrhizal growth response was calculated from the following equation:

Shoot dry wt. of mycorrhizal plants − shoot dry wt. of non-mycorrhizal plants _______________________________________________________________________________________________ ×

Shoot dry wt. of non-mycorrhizal plants The experiment had a randomized block design with 4×Cd levels, 2×Mn levels, 2×mycorrhiza arranged in factorial combination with 3 replications, totally 48 pots. Data were analyzed with MSTATC software and multiple linear comparisons between means were made with Duncan’s Multiple Range Test.

Results and Discussion The result of this study indicated that mycorrhizal colonization had a significant effect (p < 0.01) on shoot dry weight (table 2). Shoot dry weight of mycorrhizal plants was greater than that of non-mycorrhizal plants at all treatments used in this experiment (Fig 1). Cd applied to the soil had no significant effect on shoot dry weight of clover plants in both mycorrhizal and non-mycorrhizal plants. A similar trend to that of Cd was observed for Mn (Fig. 1). Mycorrhizal growth response was not affected by application of Cd and Mn to the soil (results not shown). There was a significant difference between total root length of mycorrhizal and non-mycorrhizal plants in treatments with Cd and Mn applied (Fig. 2). In both mycorrhizal and non-mycorrhizal clover plants, total root length was decreased as the concentration of Cd in the soil was increased (Fig. 2). This decrease was more pronounced for non-mycorrhizal plants than for mycorrhizal plants. A similar trend was observed for Mn application for control plant, but not for mycorrhizal plant (Fig. 2). A similar trend to that of total root length was observed for root dry weight. The percentage of root length of T. alexandrinum colonized by Glomus intraradices increased as concentration of Cd was increased (Fig. 3). A similar increase in the percentage of root length colonized was observed when the concentration of Mn application to the soil was increased (result not shown).Shoot P concentration of mycorrhizal plants was significantly greater than that of non-mycorrhizal plants in treatment with Cd and Mn (resuls not shown). The decreased shoot P concentration of non-mycorrhizal plant due to the increased Cd concentration was more pronounced than that in shoot P concentration of mycorrhizal plant. This indicated that mycorrhizal colonization improved P nutrition even in the presence of high Cd concentration. In both mycorrhizal and non-mycorrhizal plants, shoot P concentration was not affect by application


of Mn. At similar trend to that of shoot P concentration was observed for root P concentration when the soil was supplied with Cd and Mn (results not shown). Shoot Cd concentration was increased as Cd application to the soil was increased (Fig. 4). The increase shoot Cd concentration was greater for non-mycorrhizal plant than for mycorrhizal plant. Turnau et al. (1993) used electron energy loss spectroscopy to investigate the possibility that heavy metals are sequestrated in the hyphae and not transferred to the host plant. They found a greater accumulation of Cd, Ba and Ti in the fungal structures than in the root cells themselves. Similarly, Bradley et al. (1981) found that the concentration of Cu in roots was higher than in shoots of plants ericoid mycorrhizas. They suggested that internal proliferation of the hyphal complexes that occurs within cortical cells of the host roots increases the area of wall material available for complexion by several orders of magnitude. However, further work is needed to confirm these suggestions. A similar increase to that of shoot Cd concentration was also observed for root Cd concentration for both mycorrhizal and non-mycorrjizal plants. Shoot Mn concentration of mycorrhizal plants was significantly lower than that in non-mycorrhizal plants in all treatments with Cd and Mn applications (Fig. 4). The observed decline in the concentrations of Mn mycorrhizal shoots might be due to tissue dilution of Mn in larger plants or to a significant decline in the proportion of Mn-reducers of the total microbial population in the rhizosphere of mycorrhizal plants. The second suggestion is supported by the results of Kothari et al. (1991), who found that the number of Mn-reducers in the rhizosphere of non-mycorrhizal plants was 20-30 times that in the rhizosphere of mycorrhizal plants. The result of this study also indicated that the concentration of Ni in mycorrhizal plants was significantly lower than that in non-mycorrhizal plants. This might also due to sequestration of Ni in the fungus, for example, via a chemical reaction between Ni and fungal polyphosphate granules, as has been shown for Ca and Fe by Nadian (1998). In conclusion, the results of this study clearly showed that the concentration of heavy metals like Cd and Mn in mycorrhizal T. alexandrinum was lower than those in non-mycorrhizal T.alexandrinum. This was mainly attributed to the sequestration of the heavy metals by fungal structure and might be regarded as a mechanism facilitating exclusion of metals from the shoot, and thus avoiding metal toxicity.

References Bradley R., Burt AJ, Read DJ (1981) Mycorrhizal infection and resistance to heavy metal toxicity

in Calluna vulgaris. Nature 292: 335-337Cooper KM, Tinker PB (1978) Translocation and transfer of nutrients in vesicular-arbuscular

mycorrhiza. II. Uptake and translocation of phosphorus, zinc and sulfur. New Phytologist 81: 43-52

Giovannetti M, Mosse B (1980) An evaluation of techniques for measuring vesicular-arbuscular mycorrhizal infection in roots. New Phytologist 84: 489-500

Gianinazzi-Pearson V, Gianinazzi S (1983) The physiology of vesicular-arbuscular mycorrhizal roots. Plant and soil 71: 197-209

Koske RE, Sutton JC, Sheppard BR (1975) Ecology of Endogone in Lake Huron sand and dunes. Canadian Journal of Botany 53: 87-93

Kothari SK Marschner H, Romheld V (1991) Contribution of the VA mycorrhizal hyphae in acquisition of phosphorus and zinc by maize grown in a calcareous soil. Plant and Soil 131: 177-185


Nadian H, Smith SE, Alston AM, Murray RS, Siebert BD (1998) Effects of soil compaction on P uptake and growth of Trifolium subterraneum L. colonized by four species of vesicular-arbuscular mycorrhizal fungi. New Phytologist 135: 303-311

Nelson CE, Safir GR (1982) Increased drought tolerance of mycorrhizal onion plants caused by improved phosphorus nutrition. Planta 154: 407-413

Smith SE, Gianinazzi-Pearson V (1988) Physiological interaction between symbionts in vesicular-arbuscular mycorrhizal plants. Annual Review of Plant Physiology and Molecular Biology 39: 221-244

Sanders FE, Tinker PB (1973) Phosphate flow into mycorrhizal roots. Pesticide Science 4: 385-395

Tennant D (1975) A test of a modified line intersect method of estimating root length. Journal of Ecology 63: 995-1001

Tisdall JM (1991) Fungal hyphae and structural stability of soil. Australian Journal of Soil Research 29: 729-743

Turnau K, Kottle I, Oberwinkler F (1993) Element localization in mycorrhizal roots of pterdium aquilinum (L.) Kuhn collected from experimental plots treated withcadmium dust. New Phytologist 123: 313-323

Table 1: Some chemical and physical characteristics of the soil used in this experiment

pH EC dSm-1

Organic C g kg-1

Pmg kg-1

Cdmg kg-1

Mnmg kg-1

Nimg kg-1

Soil texture

7.6 2.6 8.0 3.6 0.6 1.4 0.8 Loam

Table 2: Results of ANOVA on shoot dry wt., root length, root length colonized,

concentration of P, Cd, Mn and Ni in shoot of clover plant

Source of variance

df Shoot d. wt.

Rootlength

Root length colonized (%)

Shoot P concentation

Cd Mn Ni

Replication 2 ns ns ns ns ns ns ns Mycorrhiza (M) 1 ** ** ** ** ** ** ** Mn 1 ns ** ** ** ** - ** Mn × M 1 ns ** ** ** ** - ** Cd 3 ns ** ** ** - ** ** Cd × M 3 ns ** ** ** - ** ** Mn × Cd 3 ns ** ** ** - - ** M × Cd × Mn 3 ns * ** ** - - ** Error 30

ns, not significant * and **, significant at p = 0.05 and 0.01 respectively


Shoo

t dry

wei

ght (

g po

t-1)

0.0

0.4

0.8

1.2

0 1.5 3 10

MNM

Cd applied (mg kg-1 soil)

0.0

0.4

0.8

1.2

0 100

NMM

Mn applied (mg kg-1 soil)

Fig. 1: Shoot dry weights of mycorrhizal (M) and non-mycorrhizal (NM) clover plants as affected by Cd (left) and Mn (right) applied to the soil

Tota

l roo

t len

gth

(m p

ot-1

)

0

4

8

12

16

20

0 1.5 3 10

MNM


0

4

8

12

16

20

0 100

MNM


Fig. 2: Total root length of mycorrhizal (M) and non-mycorrhizal (NM) clover plants as affected by Cd (left) and Mn (right) applied to the soil


Rro

ot le

ngth

colo

nize

d (%

)

M

0

10

20

30

40

0 1.5 3 10


Fig. 3: Percentage of root length colonized as affected by Cd applied

Shoo

t Cd

conc

entra

tion

(µ g

-1 d

. wt.)

0

10

20

30

40

50

0 1.5 3 10

MNM


Shoo

t Mn

conc

entra

tion

(µ g

-1 d

. wt.)

0

200

400

600

800

0 100

MNM


Fig. 4: Shoot Cd concentration (left) and Shoot Mn concentration (right) of mycorrhizal (M) and non-mycorrhizal (NM) clover plants


Effect of level and time of nitrogen fertilizer application and cutting height on yield and yield component of rice

ratooning

Mortaza Nassiri1, Hemmatollah Pirdashti 2and Taghi. Naij Nejad1

1-Rice Research Institute of Iran, Deputy of Mazandaran, Amol , P.O.Box: 145, Tel and Fax: 0121-3253137 ; Email: [email protected]; 2- Agronomy and Plant Breeding Department, Agriculture Faculty, Mazandaran University, Sari, Iran. Email: [email protected]

Abstract In order to study the effect of level and time of nitrogen fertilizer application and cutting height on yield and yield component of rice ratooning (Tarom genotype, a traditional cultivar in Mazandaran Province, Iran) an experiment was conducted at Rice Research Institute of Iran, Deputy of Mazandaran, Amol. The experiment was set-up in factorial design based on randomized completely block design with 3 replications. The level of nitrogen fertilizer in four levels (0, 11.5, 23 and 34.5 kg N ha-1), time of nitrogen application in two levels (immediately and one month after main crop harvest) and cutting height in three levels (0, 20 and 40 cm from above ground) were the treatments. The results showed that different levels of N fertilizer did not significantly affect ratoon yield, harvest index, panicle number per meter squared, grain number per panicle, filled grain number and 1000-grain weight but N applied immediately after main crop harvest significantly affected ratoon yield and grain number per panicle. Cutting height had a significant effect on ratoon plant height, grain number per panicle and filled grain number. Ratoon yield, grain number per panicle and filled grain number was significantly higher when the main crop was cut at 40 cm above ground.

Key Words: Rice, ratoon, yield, nitrogen and cutting height

Introduction Rice (Oryza sativa L.) is the important primary cereal crop in the world. It is the staple food for more than two-third of the world’s population (Singh, 1993). The world population by the year 2050 has been projected to be approximately 11 billion people, of which 90% will reside in the developing countries of the South (Krattiger, 1996). Ratoon cropping of rice is the practice of obtaining a second crop from the stubble of a previously harvested (main) crop (Jones, 1993; Coale and Jones, 1994). Two rice crops per year are possible in tropical climates but the relatively short rice-growing season in the Iran prevents the production of two rice crops per year with currently acceptable rice cultivars. Rice ratooning has several stated advantages: low production costs, high water use efficiency, and reduced growth duration (Jones, 1993). In an effort to better understand the factors influencing rice ratoon crop growth, the International Rice Research Institute (IRRI) published a comprehensive report (Chauhan et al., 1988) identifying key factors influencing a rice cultivar’s ratoon potential: plant maturity at main crop harvest, main crop harvest height, main crop cultural practices, temperature, sunlight, leaf senescence, and carbohydrate and N content of main crop stubble. Level and time of nitrogen application and the height of harvest of the main crop (cutting height) are critical management factors in ratoon cropping. Cutting or stubble height determines the number of buds available for re-growth (Chauhan et al., 1988; Vergara et al., 1988). Ratoon characters most affected


by cutting height are grain yield, tillering, and growth duration (De Datta and Bernasor, 1982). Main crop cutting heights ranging from 0 to 0.5 m from the soil surface have been used for ratoon crop production. The effect of cutting height on ratoon grain yield performance varies. Generally, ratoon yield increased with increased cutting height (Vergara et al., 1988). On the other hands, Reddy et al. (Reddy et al., 1979) and Balasubramanian et al. (Balasubramanian et al., 1970) found no differences in ratoon yields with different cutting height. In other studies, several reports claim that higher cutting heights decreased ratoon yields (Parago, 1963, prashar, 1970). Nitrogen fertilizer is another important factor that greatly influences growth and yield of ratoons. Nitrogen has been observed to improve tillering and increase grain yield of the ratoon crop (Vergara et al., 1988; De Datta and Bernasor, 1988; Bahar and De Datta, 1977). Nitrogen application immediately after harvest of the main crop is recommended (Mengel and Wilson, 1981). The objective of the present study was to determine the effect of time and level of N fertilizer and cutting height on the ratoon crop yield of Tarom cultivar.

Materials and Methods This study was conducted at the Rice Research Institute of Iran- Deputy of Mazandaran (Amol) located in north of Iran (52°22 E 36°28 N). The experiment was conducted in a factorial arranged in a completely randomized block design with three replications. The first factor (cutting height) had three levels (0, 0.2, and 0.4 m above ground level), the second one (fertilizer level) had four levels (0, 25, 50, and 75 kg urea ha-1 or 0,11.5,23, and 34.5 kg N ha-1) and the third one (time of N application) had two levels (immediately after main crop harvesting and 30 days after main crop harvesting). The seeds of Tarom cultivar (a traditional variety that generally uses for ratoon cropping in Mazandaran province) were sawn in seedbed and transplanted 35 d after sowing in lowland field under continuos condition. At maturity plots were drained approximately 1 wk before harvest. Entire plots were then cut, using a cutting guide at heights of 0, 0.2, and 0.4 m above the ground level. Three rates of N including 0, 25, 50 and 75 kg.ha-1 were applied as urea. All N was applied immediately after main crop harvesting or 30 d after main crop harvesting. At maturity, plots were drained and mature plant heights were recorded. Grain yield was determined from a harvest area of 6 m2 (96 hills). All plants from harvest area were dried at 70°C for total dry matter determination, and harvest index was calculated as yield/total dry matter. Panicle number per m2 was determined at dough stage from five randomly sampled hills per plot. Before harvesting for determination of total dry matter and fertility of panicles, five randomly sampled panicles per plot were counted for filled and unfilled grains to determine percentage of filled grains. All grains were dried in the hot air oven at 70°C for 5 days and 1000 grain weight was calculated from the number and seed weight of filled grains. Data were statistically evaluated by analysis of variance using the SAS data processing package (SAS Institute, 1996).

Results and Discussion Main crop harvest cutting height (CH) had a significant (P <0.01) effect on total grain number, filled grains number, and ratoon plant height (Table 1). However, main crop cutting height had not a significant effect on ratoon grain yield as well as one of the three yield components (Table 1). These findings are in agreement with some researchers (Reddy et al., 1979; Balasubramanian et al., 1970), yet conflict with others (Parago, 1963; Prashar, 1970). Ratoon tiller origin varies between cultivars, with some cultivars initiating the majority of ratoons at the either basal, near basal, or


axillary nodes (Jones, 1993). Therefore, cutting heights can have variable effects on ratoon performance, depending on the characteristic of ratoon initiation of the cultivar studied. Such variability in ratooning habit may explain some of the disagreement in the literatures regarding the effect of CH on ratoon yield. Filled grain number was significantly (p <0.01) lower at the 0.0 m CH than at the 0.2 and 0.4 m CH. Plant height also was significantly (p <0.01) higher at the 0.4 m CH than at the 0.0 m CH (Table 2). Ratoon crop grain yield, yield components, and agronomic traits was not affected significantly (p > 0.05) by level of N application (Table 1). Although, Bahar (1976) has reported that N level did not affect tiller and panicle number, 100-grain weight of ratoon crop (Bahar, 1976) but some studies showed that tiller number (Balasubramanian et al., 1970; Sun et al., 1988) and grain yield (Prashar, 1970; Bahar and De Datta, 1977) increased with increasing N level. Significant differences (p < 0.01) were observed between the two time of N application for the ratoon grain yield and grain number per panicle (Table 1). Nitrogen application immediately after main crop harvest had significantly (p < 0.05) higher grain yields than N application 30 day after main crop harvest (Table 3). This yields advantage was due to more grain number per panicle in the N application immediately after main crop harvest (Table 4). Among yield components, panicle per meter squared and grain number per panicle has been reported to account for most of variations in ratoon grain yield (Jones and Snyder, 1987). Cutting height (CH) × level of N application interactions had a significantly (p<0.01) effect on ratoon grain yield and plant height (Table 1). Cutting height at 0.4 m above ground with 0, and 23 kg N per hectare had significantly (p< 0.05) higher grain yields than other conditions (Table 5). Cutting height and time of nitrogen application interaction, however, had not a significantly effect (p<0.01) on ratoon grain yield and agronomic traits (data not shown). Meanwhile, level of N fertilizer × time of N application interaction had a significantly (p<0.01) effect on ratoon grain yield and plant height. The level of 23 kg N per hectare that immediately after main crop harvest were applied had significantly higher grain yield and plant height than other treatments (data not shown). Results of this study indicated that ratoon (Tarom cultivar) can be optimized by leaving the main crop stubble at a height of 0.2 to 0.4 m and application of N fertilizer immediately after main crop harvest.

Acknowledgements Financial support by the Rice Research Institute of Iran- Deputy of Mazandaran (Amol) was greatly appreciated.

References Bahar, FA (1976) Prospects of Raising Productivity of Rice by Ratooning. MS thesis,

University of the Philippines at Los Banos, Laguna, Philippines. Bahar, FA and De Datta, SK (1977) Prospects of increasing tropical rice production

through ratooning. Agronomy. Journal 69: 536-540. Balasubramanian, B, Modrachan, YB and Kaliappa, R (1970) Studies on ratooning in

rice: I. growth attributes and yield. Madras Agricultural Journal 57: 565-570. Chauhan, JS, Singh, BN, Chauhan, VS and Sahu, SP (1988). Screening of

photoinsensitive summer rice (Oryza sativa L.) genotypes for ratoon cropping. Journal of Agronomy and Crop Science 160: 113-115.

Coale, FJ and Jones, DB (1994). Reflood timing for ratoon rice grown on Everglades histosols. Agronomy Journal 86: 478-482.


De datta SK and Bernasor, PC (1988) Agronomic principles and practices of rice ratooning. In: Rice Ratooning. International Rice Research Institute. Los Banos, Philippines., 163-176.

Jones, DB (1993) Rice ratoon response to main crop harvest cutting height. AgronomyJournal 85: 1139-1142.

Jones DB and Synder, GH (1987). Seeding rate and row spacing effects on yield and yield components of ratoon rice. Agronomy Journal 79: 627-629

Krattiger, AF (1996) The role of the private sector in biotechnology transfer to developing countries. International Conference on Agricultural Biotechnology, Saskatoon, Canada, 143-144.

Mengel, DB and Wilson, FE (1981) Water management and nitrogen fertilization of ratoon crop rice. Agronomy Journal 1008-1010.

Parago JF (1963) Rice ratoon culture. Indian Agricultural Research 25: 15, 45, 47. Prashar CRK (1970) Paddy ratoons. World Crops 22: 145-147. Reddy TG, Mahadevappa M and Kulkarni KR (1979) Rice ratoon crop management

in hilly region of Karnataka, India. International Rice Research Newsletter 4: 22-23

SAS Institute, 1996. SAS/STAT User’s Guid, Version 6.12 SAS Institute, Cary, NC, USA. Singh, RB (1993) Research and development strategies for intensification of rice

production in the Asia-Pacific region. In: Muralidharan KE and A. Siddiq, (Eds), New Frontiers in Rice Research. Hyderabad, India: Directorate of Rice Research., 25-44.

Sun X., Zhang, J and Liang, Y (1988) Ratooning with rice hybrids. In: Rice Ratooning. International Rice Rrsearch Institute, Manila, Philippines, 155-161.

Vergara BS., Lopez, FSS and Chauhan, JS (1988) Morphology and physiology of ratoon rice. In: Rice Ratooning. International Rice Research Institute. Los Banos, Philippines, 31-40.

Table 1: Grain yield and other plant characters of Tarom ratoon as affected by cutting height of main crop CH (m)

GrainYield

(kg/ha)

HarvestIndex (%)

GrainNumber

perPanicle

Filled Grainper

Panicle

Unfilled Grainper

Panicle

1000-Grain

Weight (gr)

Panicle Numberper m2

Plant Height (cm)

0 873.0 b 27.8 a 39.2 b 31.8 b 7.3 b 22.3 a 156.8 a 84.8 b 0.2 899.7 ab 24.9 b 42.7 a 34.1 ab 8.6 a 22.3 a 149.6 a 84.8 b 0.4 973.8 a 25.3 ab 44.6 a 36.4 a 8.2 ab 22.3 a 154.2 a 95.9 a

Table 2: Grain yield and other plant characters of Tarom ratoon as affected by N level N level (kg/ha)

GrainYield

(kg.ha-1)

HarvestIndex (%)

GrainNumber

perPanicle

Filled Grainper

Panicle

Unfilled Grainper

Panicle

1000-Grain

Weight (gr)


Plant Height (cm)

0 908.1 ab 24.85 b 40.36 a 32.47 a 7.89 ab 22.37 a 152.3 a 89.9 a 25 872.6 b 24.82 b 42.96 a 35.23 a 7.73 b 22.19 a 155.1 a 85.5 b 50 986.4 a 25.69 ab 42.18 a 34.71 a 7.42 b 22.71 a 154.7 a 89.5 ab 75 893.5 ab 28.7 a 43.16 a 34.12 a 9.01 a 21.95 a 146.9 a 89.0 ab


Table 3: Grain yield and other plant characters of Tarom ratoon as affected by time of nitrogen application

Time of N application

GrainYield

(kg.ha-1)

HarvestIndex (%)

GrainNumber

perPanicle

Filled Grainper

Panicle

Unfilled Grainper

Panicle

1000-Grain

Weight (gr)


Plant Height (cm)

IAMH 978.8 a 26.6 a 44.1 a 35.8 a 8.5 a 22.1 a 156.8 a 92.2 a AMH 851.5 b 25.5 a 40.3 a 32.8 a 7.6 b 22.9 a 148.8 a 84.8 b

Table 4: Grain yield and other plant characters of Tarom ratoon as affected by cutting height (CH) × N levels

CH(m)

N level (kg.ha-1)

Grain Yield

(kg.ha-1)

Harvest Index (%)

Grain Number

perPanicle

Filled Grain per Panicle

Unfilled Grain

perPanicle

1000-Grain

Weight (gr)

Panicle Number per m2

Plant Height (cm)

0 0 905.2 bc 28.1 abc 36.4 c 29.6 c 6.7 b 22.6 ab 8.7 ab 82.1 de 0 25 843.8 bc 25.9 abc 38.2 bc 31.4 bc 6.8 b 21.9 ab 10.7 ab 76.8 e 0 50 888.6 bc 27.5 abc 40.4 abc 33.1 abc 7.2 b 22.6 ab 10.3 ab 88.1 bcd 0 75 850.6 bc 29.98 a 41.6 abc 33.2 abc 8.4 ab 22.1 ab 9.4 ab 92.2 bc 0.2 0 777.7 c 23.8 abc 40.8 abc 31.9 bc 8.9 ab 21.1 b 8.5 b 81.7 de 0.2 25 859.4 bc 23.1 a 43.8 ab 35.6 abc 8.1 ab 22.5 ab 8.9 ab 84.1 de 0.2 50 995 b 24.8 abc 43.3 ab 35.8 abc 7.5 ab 23.6 a 10.7 ab 87.1 cd 0.2 75 966.5 bc 28.7 ab 43.1 ab 33.3 abc 9.7 a 21.9 ab 9.3 ab 86.2 cd 0.4 0 1276.2 a 22.6 bc 43.8 ab 35.9 ab 8.1 ab 23.3 a 11.3 a 106.1 a 0.4 25 914.6 bc 26.5 abc 46.9 ab 38.6 a 8.3 ab 22.2 ab 9.4 a 95.5 b 0.4 50 1276.2 a 24.7 abc 42.8 ab 35.2 abc 7.6 ab 21.8 ab 8.9 ab 93.5 bc 0.4 75 863.6 bc 27.4 abc 44.8 ab 35.8 ab 8.9 ab 21.8 ab 8.8 ab 88.6 bcd

Table 5: Grain yield and other plant characters of Tarom ratoon as affected by cutting height (CH) ×Time of N application

CH(m)

TNA Grain Yield

(kg.ha-1)

Harvest Index (%)

Grain Number

perPanicle

Filled Grain

perPanicle

Unfilled Grain

perPanicle

1000-Grain

Weight (gr)

Panicle Number per m2

Plant Height (cm)

0 IMAH 931.9 ab 29.2 a 40.9 bc 32.9 bc 8.1 ab 22.1 a 10.3 a 86.9 bc 0 AMH 812.2 b 26.5 a 37.3 c 30.8 c 6.7 b 22.5 a 9.3 a 82.6 c 0.2 IMAH 979.1 a 24.9 a 43.8 ab 35.2 ab 8.6 a 22.0 a 9.4 a 87.0 bc 0.2 AMH 820.3 b 24.8 ab 41.7 b 33.1 bc 8.5 a 22.6 a 9.3 a 82.5 c 0.4 IMAH 1025.5 a 25.5 ab 47.3 a 38.6 a 8.7 a 22.0 a 9.9 a 102.6 a 0.4 AMH 922.1 ab 25.1 ab 41.8 b 34.2 bc 7.7 ab 22.6 a 9.3 a 89.2 b

IAMH= immediately after main crop harvesting, and AMH= 30 d after main crop harvesting In a column, means followed by a common letter are not significantly different at the 5% level


Soil organic matter and nutrients under different traditional cropping systems in an intensively managed agroecosystem

Fayez Raiesi and Shiva Asadian Soil Science Dept., Faculty of Agriculture, ShahreKord University, P.O.Box 115, Shahre Kord, Iran. Phone: + 98 – 381-4424428 ; Email: [email protected] ([email protected])

Abstract Diverse farming systems may influence soil attributes that are of great importance for agricultural production in managed ecosystems. However, very little is known about the influence of cropping systems on soil organic matter and the amount of plant-available nutrients in intensively cropped soils. The aim of the present study was to determine effects of diverse cropping systems on soil nutrients, C content, bulk density and pH in arable lands of Saman area, Iran. The traditional cropping systems consisted of (1) continuous rice (RI); (2) continuous almond (AL); (3) continuous grape (GR); (4) almond-grape (AG) inter-cropping (5) walnut-grape inter-cropping (WG) and (6) wheat-alfalfa (WA) rotation. Composite soil samples from each of nine replicates of the six cropping systems were sampled to a depth of 30 cm in 2002, and analyzed for soil bulk density, pH, EC, nutrients, organic carbon and wet aggregate stability. Our results showed that soil pH did not vary among various cropping systems. However, significant differences (p<0.05) in soil bulk density, total soil organic matter, plant-available P and K contents were observed among soils of six cropping systems. The soil under walnut-grape inter-cropping (WG) and continuous almond (AL) had the greatest soil organic matter contents. The total N contents were the greatest in the soil under wheat-alfalfa rotation (WA) and continuous rice (RI) systems, but the difference was not significant among the cropping systems. The soil C/N ratios for the cropping systems were statistically different, and it was greater in walnut-grape inter-cropping (WG) system. The plant-available P content followed the trend RI = AG > GR = AL = WA = WG. The available K contents in AG, AL and GR systems were the highest, followed by WA, WA and RI systems. The aggregate stability in the wheat-alfalfa rotation was significantly greater than that in other cropping systems. It seems that the walnut-grape cropping system comparatively favors soil conditions required for carbon sequestration in the soil, while other cropping systems induce carbon loss from the soil surface. It is also suggested that including alfalfa in the crop sequence can be used to enhance soil N storage and therefore sustain N availability for crop productivity in this region. In conclusion, concentrations of plant-available P and K in the six cropping systems are greater than the amount required for maximum yield production, and consequently, further application of P and K fertilizers might lead to nutrient storage in the top soil.

Keywords: Cropping systems; C sequestration; Nutrients content; Calcareous soils; Soil attributes

Introduction Soil, as a renewable resource, is a key element for agriculture and productivity; thus, maintenance of high soil quality is one of the main goals of sustainable agriculture (Herrick, 2000). Soil organic matter (SOM) is an important component of the soil–plant system. The importance of soil organic matter is due to its impact on physical, chemical and biological indicators of soil quality. Reduction of soil organic matter causes a loss in water holding capacity, poor aggregation, acceleration in soil erosion, poor retention of applied nutrients,


reduced soil biological and enzymatic activities. A combination of these factors causes loss in crop productivity. Loss in SOM may also result in poor ground and surface water quality. Therefore, maintenance and improvement of SOM in agricultural soils is crucial to land sustainability (Doran and Parkins, 1994; Gregorich et al., 1994; Campbell et al., 1999). Land use changes or agricultural management practices lead to changes in SOM content. However, these changes often occur gradually and therefore against the larger background, small changes are difficult to detect in the short or medium-term (Bolinder et al., 1999).

The maintenance of soil organic matter and nutrients is affected by the overall crop management systems. The quantity of soil organic matter in soil is considered to be a function of the net input of plant residues by cropping systems and factors affecting carbon turnover (Gregorich et al., 1996). Therefore, soil and crop management practices such as cultivation, crop rotation, residue management, fertilization, irrigation, and herbicide exert a substantial influence on the quantity of organic matter retained over time. Previous studies have indicated that cropping systems changed soil moisture content, bulk density, porosity, nutrient distribution and structure stability (Zhang et al., 1988; Unger, 1991; Islam and Weil, 2000; Mikhailova et al., 2000; Kanchikerimath and Singh, 2001; Lobe et al., 2001). These changes may decrease or increase the content and dynamics of C and N, and may, therefore, have an influence on nutrient uptake by plants (Broersma et al., 1996). Application of chemical fertilizers, especially N, for improving nutrient availability may also have an effect on the C and N contents of soil (Mahmood et al., 1997; Liang et al., 1998; Raiesi, 2004). Sprague and Triplett (1986) reported that different cropping systems affected the C and N status of soil in a short time period. In general, systems returning small amounts of residue resulted in larger losses of C and N than systems returning large amounts of residue. Crop rotations that include legumes have long been known to increase soil aggregation and maintain organic matter and nitrogen contents at higher levels than do continuous row crops (Willson et al., 2001).

Many arid and semi-arid soils are poor in soil organic matter (SOM) and rely on high inputs of external chemical fertilizers to maintain soil quality and subsequent fertility for crop production. On the other hand, the success and sustainability of agricultural production systems in the arid and semi-arid regions depends on the production and conservation of SOM. In additions, these soils are undergoing an intensive cropping and agricultural activity. The effects of these cropping systems on SOM quantity are not well understood. Since SOM is an important factor influencing soil fertility, this lack of understanding may affect the proper management of soils in these regions for sustainable production. This paper deals with the evaluation of the influence of diverse farming systems on some physical and chemical properties of soil, particularly on SOM.

Materials and Methods The study area is located in the croplands of the Saman area (northwest of Shahre Kord), characterized by very intensive agricultural systems, in central Iran. The mean annual rainfall is 280 mm and mean annual temperature is 7 oC. The landscape is dominated by agricultural land use, and agriculture using various traditional farming systems is the main activity. Almond (Prunus dulcis Mill. ), grape (Vitis vinifera L.), black walnut (Juglans nigra L.), wheat (Triticum aestivum L.) and alfalfa (Medicago sativa L.) are the principle crops of the region and are grown as either inter-cropping or mono-cropping system. Black walnut, almond and grape are well-


adapted crops which are widely grown in the region. Cultivation of almond, walnut, grape, wheat and alfalfa, either mixed or inter-cropped, represent a large part of the study area. The land use units have a field size of about 0.5-2 ha within a short distance of 50 m. Soils of the area are calcareous with more than 30 % calcium carbonate in the top layer, and have developed in limestone. The soil of the study area, which has been conventionally cultivated, is a clay loam Luvic Calcisol. The traditional cropping systems consisted of (1) continuous rice (RI); (2) continuous almond (AL); (3) continuous grape (GR); (4) almond-grape (AG) inter-cropping (5) black walnut-grape inter-cropping (WG) and (6) wheat-alfalfa (WA) rotation. The inter-cropping systems are the most common agricultural practices in the area, and the WA rotation is generally alternated every three years. Composite soil samples from each of nine replicates of the six cropping systems were sampled to a depth of 30 cm in 2002, and analyzed for major chemical and physical analyses. Soil samples were mixed thoroughly to obtain a composite and representative sample for each replicate. Samples were air-dried, and sieved through a 2-mm mesh before laboratory analysis. Wet oxidation (potassium-dichromate- sulfuric acid) and back titration with iron ammonium sulfate, according to the Walkley & Black method determined total organic carbon. Soil pH (pHH2O in 1:2.5 ratio) was measured using a glass electrode. Total nitrogen (the Kejldahl method), extractable P (the Olsen's method) and exchangeable K were determined following procedures described in Page et al. (1991). Total porosity was calculated assuming a particle density of 2.65 g cm-3. Soil aggregate stability was determined by the mean weight diameter (MWD) and the geometric mean diameter (GMD) using the wet sieving method described in Kemper (1965). The data are expressed as the means, and the significant differences in soil data among the six cropping systems were determined by one-way analysis of variance (ANOVA) procedure. Differences were considered significant only when p values were lower than 0.05, unless mentioned otherwise. All statistical calculations were carried out using the SAS software.

Results and Discussion Selected physical and chemical characteristics of soil samples taken from different cropping systems are shown in Table 1. All soils were basic and there was no significant effect of cropping system on soil pH. Similarly, Broersma et al. (1996) reported no significant effect of cropping systems on soil pH. The amount of CaCO3 contents in the 0-30 cm depth was maximum in the wheat-alfalfa rotation system and minimum in the continuous almond system. The EC values for the cropping systems ranged from 0.29 in the continuous almond (AL) to 0.55 dS m-1 in the walnut-grape inter-cropping (WG) systems, and were statistically different. Soil bulk density varied among different cropping systems, and was greater in the system with continuous rice. Soil total porosity was significantly greater in the almond-grape inter-cropping (AG) system, and showed the following trend: AG = AL=GR=WA>RI>WG.

Soil OM and nutrient contents in different cropping systems are presented in Table 2. Significant differences (p<0.01) in the total soil OM contents were observed among soils of six cropping systems. The soil under walnut-grape inter-cropping (WG) and continuous almond (AL) had the greatest OM contents. So, cropping system that included walnut and grape crops had higher organic matter than the other systems, perhaps because the slow decomposition rate of plant materials incorporated into the soil surface. The total N contents were the greatest in the soils under wheat-alfalfa rotation (WA) and continuous rice (RI) systems, but the difference was not significant among the cropping systems. In one study by Broersma et al. (1996), different cropping


systems had no influence on total nitrogen in the surface layer. Willson et al. ( 2001) indicated that crop rotations might also have some effects on N content, although increased N availability may account for only a small portion of the increased production commonly associated with rotations that include legumes.

The plant-available P content ranged from 25.8 to 62.3 mg kg-1 soil and followed the trend RI = AG > GR = AL = WA = WG. The available K contents in AG, AL and GR systems were the highest, followed by WA, WA and RI systems. Results showed significant difference (p<0.01) in the plant-available P and K contents among soils of six cropping systems (Table 2). The continuous rice and almond-grape inter-cropping soils had the highest contents of plant available P, while K concentration was the greatest in AG, AL and GR systems. The C/N ratios for the cropping systems ranged from 3.40 to 16.7 and were statistically different. However, Broersma et al. (1996) reported opposite results. They observed no significant change in the C/N ratios for the cropping systems.

The MWD and GMD values showed rather similar trends in all cropping systems (Fig. 1). The aggregate stability in the wheat-alfalfa rotation was significantly greater than that in other cropping systems (Fig. 1). Furthermore, the aggregate stability did not correlate well with the soil organic matter, suggesting that it can not be estimated from organic matter content. Among the six cropping systems, the accumulation of organic matter was in the order WG>AL=RI> WA=GR>AG, but the aggregate stability did not show any consistent pattern with organic matter content in these systems. The results indicated that despite the periodic addition of organic matter from almond, grape and walnut to the soil, their mixed cropping systems did not improve the aggregate stability as much as the wheat-alfalfa rotation system.

ConclusionsAfter several years of traditional farming system, differences in total organic C were observed among soils of six cropping systems. The soil under continuous walnut-grape intercropping and continuous almond and rice had the greatest C contents. However, the total N contents were not different among the six cropping systems. It seems that the walnut-grape cropping system comparatively favors soil conditions required for carbon sequestration in the soil, while other cropping systems induce carbon loss from the soil surface. It is also suggested that including alfalfa in the crop sequence can be used to enhance soil N storage and therefore sustain N availability for crop productivity. Finally, concentrations of plant-available P and K in the six cropping systems are greater than the amount required for maximum yield production, and consequently, further application of P and K fertilizers might lead to nutrient storage in the topsoil.

Acknowledgements We acknowledge the funding of this work provided by The Research Department of Shahre Kord University.

References Bolinder MA, Angers DA, Gregorich EG, Carter MR (1999) The response of soil quality

indicators to conservation management. Canadian Journal of Soil Science 79:37–45


Broersma K, Juma NG, Robertson JA (1996) Net nitrogen mineralization from a Gray Luvisol under diverse cropping systems in the Peace River region of Alberta. Canadian Journal of Soil Science 76: 117–123

Campbell CA, Biederbeck VO, McConkey BG, Curtin D, Zentner RP (1999) Soil quality- Effect of tillage and fallow frequency. Soil Biology and Biochemistry 31:1–7

Doran JW, Parkins TB (1994) Defining and assessing soil quality. In: Doran JW, Coleman DC, Bezdicek DF, Stewart BA (Eds.), Defining Soil Quality for a Sustainable Environment, Soil Science Society of America, Madison, pp. 3–21

Gregorich EG, Carter MR, Angers DA, Monreal CM, Ellert BH (1994) Towards a minimum data set to assess soil organic matter quality in agricultural soils. Canadian Journal of Soil Science74:367–385

Gregorich EG, Ellert BH, Drury CF, Liang BC (1996) Fertilization effects on soil organic matter turnover and corn residue storage. Soil Science Society of American Journal 60: 472–476.

Herrick JE (2000) Soil quality: an indicator of sustainable land management? Applied Soil Ecology 15: 75–83

Islam KR, Weil RR (2000) Land use effects on soil quality in a tropical forest ecosystems of Bangladesh. Agriculture, Ecosystem and Environment 79:9-16

Kanchikerimath M, Singh D (2001) Soil organic matter and biological properties after 26 years of maize-wheat-cowpea cropping as affected by manure and fertilization in a Cambisol in semiarid region of India. Agriculture, Ecosystem and Environment 86:155-162

Kemper WD (1965) Aggregate stability. In: Black CA (Ed.), Methods of Soil Analysis. Am. Soc. Agron., Madison, Wisconsin, USA, pp. 511-519

Liang BC, MacKenzie AF, Schnitzer M, Monreal CM, Voroney PR, Beyaert R.P (1998) Management-induced change in labile soil organic matter under continuous corn in eastern Canadian soils. Biology and Fertility of Soils 26:88-94

Lobe I, Amelung W, Du Preez CC (2001) Losses of carbon and nitrogen with prolonged arable cropping from sandy soils of the South African Highveld. European Journal of Soil Science52:93-101

Mahmood T, Azam F, Hussain F, Malik KA (1997) Carbon availability and microbial biomass in soil under an irrigated wheat-maize cropping systems receiving different fertilizer treatments. Biology and Fertility of Soils 25:63-68

Page AL (1991) Methods of Soil Analysis Part 2: Chemical and microbiological properties (2nd). Am. Soc. Agron., Madison, Wisconsin, USA

Raiesi F (2004) Soil properties and N application effects on microbial activities in two winter wheat cropping systems. Biology and Fertility of Soils 40: 88–92

Sprague A, Tnplett CV (1986) No-Tillage and Surface Tillage Agriculture. Wiley-lnterscience Publications. New York. NY 465 p

Unger PW (1991) Organic matter, nutrient, and pH distribution in no and conventional-tillage semiarid soils. Agronomy Journal 83:186-189

Willson TC, Paul EA, Harwood RR (2001) Biologically active soil organic matter fractions in sustainable cropping systems. Applied Soil Ecology 16:63-76

Zhang H, Thompson ML, Sandor JA (1988) Compositional differences in organic matter among cultivated and uncultivated Argiudolls and Hapludalfs derived from loess. Soil Science Society of American Journal 52:216-222


Table 1 Some physical and chemical characteristics of the topsoil layer (0-30 cm) under different traditional cropping systems. Each value represents mean (n=9).

Cropping system pH (-)

CaCO3

(%)EC(dS m-1)

BD (g cm-3)

Porosity (%)

Continuous almond (AL) 8.07 A 8.40 C 0.293 C 1.44 BC 39.2 ABC

Continuous grape (GR) 7.95 A 16.2 AB 0.323 BC 1.51 B 42.0 AB

Continuous rice (RI) 7.96 A 16.4 AB 0.431 AB 1.69 A 34.3 BC

Walnut-grape inter-cropping (WG) 8.09 A 19.2 AB 0.556 A 1.40 C 33.9 C

Almond-grape inter-cropping (AG) 7.87 A 14.1 B 0.373 BC 1.43 BC 45.0 A

Wheat-alfalfa rotation (WA) 8.06 A 21.1 A 0.373 BC 1.51 B 38.8 ABC

Mean 7.99 15.89 0.392 1.50 38.9

P 0.06 0.0004 0.004 <0.0001 0.04

LSD0.05 0.15 5.34 0.134 0.095 7.78 BD; bulk density Similar letters indicate no significant difference among the cropping systems at p<0.05.

Table 2 Soil C and macronutrient contents of 0-30 cm soils as affected by different traditional cropping systems. Each value represents mean (n=9).

Cropping system OM (%)

TN(%)

Pa(ppm)

K(ppm)

C/N(-)

Continuous almond (AL) 3.49 AB 0.37 A 29.9 B 1111 A 10.9 AB

Continuous grape (GR) 2.13 DE 0.21 A 30.7 B 1073 A 10.2 ABC

Continuous rice (RI) 2.86 BC 0.52 A 62.3 A 357 C 4.70 BC

Almond-grape inter-cropping (AG) 1.62 E 0.23 A 48.7 A 1259 A 6.39 BC

Walnut-grape inter-cropping (WG) 4.07 A 0.34 A 26.1 B 850 AB 16.7 A

Wheat-alfalfa rotation (WA) 2.30 CD 0.62 A 25.8 B 490 BC 3.40 C

Mean 2.74 0.38 37.3 857 8.72

P <0.0001 0.08 <0.0001 <0.0001 0.04

LSD0.05 0.672 0.32 15.3 0.095 7.78 OM; Organic matter, TN; Total Nitrogen, Pa; Available P Similar letters indicate no significant difference among the cropping systems at p<0.05.


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

AL GR RI AG WG WA

MW

D (

mm

)

BCC BCC

A

BC

LSD0.05=0.0705p<0.001

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

AL GR RI AG WG WA

Cropping System

GM

D (

mm

)

CB

BCD

AB

LSD0.05=0.0285p<0.001

Figure 1 The impact of different traditional cropping systems on soil wet aggregate stability determined as MWD (top) and GMD (bottom).


Land use Effect on the occurrence and distribution of Azotobacter in Hamadan soils, Iran

A.A. Safari Sinegani1 and Z. Sharifi2

1- Soil Science Department, Faculty of Agriculture, Bu-Ali Sina University, Hamadan, Iran, Phone: +98- 811- 4223367, Email: [email protected]. 2- Soil Science Department, Faculty of Agriculture, Bu-Ali Sina University, Hamadan, Iran.

Abstract Azotobacter is a nitrogen-fixing bacterium, found in soils world-wide, with many features relevant to energy consumption and carbon sequestration. For assessment the effects of agricultural practices and land use on the occurrence and distribution of Azotobacter, the present investigation was made in the pastures, deciduous and coniferous woodlands, and dry and irrigated (with sewage and river waters) farmlands of Hamadan in northwestern of Iran. Sampling was carried out at depth of 0-30 cm with maximum of plant cover diversity in may, 2003. According to heterogeneity of lands, sampling plan was completely randomized with unequal numbers of repetitions. Some soil physical, chemical and biological properties were investigated. Data statistically analyzed for standard deviation (s), and F-test to assess the land use effect on each soil property. Means were calculated and Duncan’s new multiple range test was made to assess the soil management systems. The highest population of Azotobacter was found in soil sampled from sewage water irrigated farmland. Among soils, dry farmlands and deciduous woodland soils had the lowest fertility. The lowest population of Azotobacter was found in soil sampled from deciduous woodland. The occurrence of Azotobacter correlated positively with soil organic matter (SOM), electrical conductivity, total nitrogen, available phosphorous, available potassium, C/N ratio, and substrate-induced respiration (SIR) and negatively with soil carbonates.

Key Words: Azotobacter, management practices, soil properties.

Introduction Soil microorganisms promote plant growth in many ways. Azotobacter can affect plant growth directly, either by the nitrogen it fixes (Zapater et al. 1982 and Zaid, 1992), or through growth promoting substances, indol-3-acetic acid, gibberellins and cytokinins (Barea and Brown, 1974; Pareek et al. 1996 and Zahir et al. 1997), or indirectly by change in the microflora of the rhizosphere (Barea and Brown, 1974). Preliminary study of occurrence of Azotobacter chroococcum in soils have shown that its population is lower than 105 in 1 g of dry soil (Subba Rao, 1993). Occurrence of Azotobacter is strongly depended on soil properties and agricultural practices. Azotobacter was abundant in soils near neutrality, and decreased in accordance with increasing acidity, being absent at pH values of 5.80 and below (Harris, 1973). Soil organic matter is an important factor affecting soil biological activities. The stimulating influences of humic and fulvic acid on the growth and efficiency of nitrogen fixation of Azotobacter have been reported by Gaur and Mathur, (1966) and Bhardwaj and Gaur, (1970). Mishustin and Shilnikova, (1971) reported that application of nitrogen and phosphorus fertilizers have significant negative and positive effects on Azotobacter population respectively. Hartley and Schlesinger (2002) used the acetylene reduction assay to analyze soil nitrogenase activity at the Jornada Long-Term Ecological Research site (northern Chihuahuan Desert, New Mexico, U.S.A.). Their findings indicated that labile carbon and


inorganic N may exert a stronger control on nitrogenase activity than phosphorus or micronutrient levels.The objectives of this study were (i) to determine the effects of management practices on the occurrence and distribution of Azotobacter in soil, and (ii) to investigate the relationships of Azotobacter populations with some biological and physico-chemical properties of soil in different uses in Hamadan, northwestern of Iran.

Materials and Methods The present investigation was made in dry and irrigated farmlands, pastures, deciduous and coniferous forests of Hamadan, in northwestern of Iran. In may, 2003, sampling was carried out at depth of 0-30 cm from root zone of plants with maximum diversity. According to heterogeneity of the experimental sites, sampling plan was completely randomized with unequal numbers of repetitions (>3). Soil samples were analyzed for clay, silt, sand, equivalent CaCO3 (CCE), pH, electrical conductivity (EC), Cation-exchange capacity (CEC), organic carbon (OC), total nitrogen (TN), Olsen available phosphorus, available K, basal respiration, and substrate induced respiration (SIR) according to methods of soil analysis parts 1 and 2: published by SSSA (Klute, 1986; Page, et al, 1982), and applied methods in soil biology and biochemistry (Kassem and Nannipieri, 1995). Azotobacter populations were estimated by plate count method. Soil suspension and dilutions were prepared. Two media were used for study of Azotobacter population in soil samples. The first one was Ashby’s mannitol agar. For inhibition of the growth of gram positive bacteria and actinomycetes it was modified by addition of 1 ml crystal violet solution (0.5 % in ethanol). The second media for Azotobacter enumeration was LG medium. Colony forming unites on the solid media were numbered after a week of incubation at 27 oC (Kassem and Nannipieri, 1995; Subba Rao, 2001).Data statistically analyzed for standard deviation (s), and F-test to assess the land use effect on Azotobacter populations. Means were calculated and Duncan’s new multiple range test was made to assess the soil management systems. Pearson linear correlations were performed to ascertain whether the Azotobacter populations were correlated with soil physical and chemical properties. So, the relationship between Azotobacter populations and the other soil properties were analyzed by correlation analysis.

ResultsAzotobacter populations in soils sampled from farmlands irrigated with wastewater and river water numbered on Ashby’s medium were 10.06∈106 and 6.13∈106 g-1 soil respectively. They are significantly higher than that numbered in the other soils (FIG.1). Likewise, Azotobacter population in soil sampled from farmlands irrigated with wastewater numbered on LG medium was 30.65∈106 g-1 soil, having a significant difference with the other soils. Azotobacter population in soils of the coniferous forest was the lowest one. It was also considerably low in soils sampled from the ranges and dry farmlands. Estimate for Azotobacter population on LG medium was 11.66∈106 g-1 in soils sampled from farmlands irrigated with river water. It was 10.65∈106 g-1 in soils from deciduous forest. These estimates are significantly different from the population estimated for the coniferous forest (3.75∈106 g-1 soil).Estimates for Azotobacter population by LG medium with compare to Ashby’s medium were higher. It is related to the differences between their constituents. Carbon and energy sources used


in the both media are different, and crystal violet used in modified Ashby’s medium may be more toxic than bromothymol blue used in LG medium.Land use and management practices can change soil properties, controlling soil microbial population and activities. Table 1 shows the relationship between the Azotobacter population and some of the other soil properties. In these calcareous soils the correlation between Azotobacter population and soil carbonates was negative and significant. The correlation between Azotobacter population and soil salinity was positive in these non-saline soil. The correlation coefficients between Azotobacter population and soil organic carbon, total nitrogen, available P and K were positive and strong. Strikingly basal respiration exhibited a significant negative correlation with Azotobacter numbered on LG medium. Soil basal respiration highly depends upon both the soil organic matter and microbial populations. The investigated soil had low organic matter so organic carbon limitation on basal respiration is more than limitation of microbial populations. There were significant positive correlation coefficients between substrate induced respiration and soil Azotobacter populations numbered on Ashby’s and LG medium. The associations and positive correlation coefficients between SOM, microbial biomass, and fine particles are well documented in vegetated soils (Gupta and Germida, 1988; Schnitzer and Kodama, 1992; Hassink et al., 1993; Kiem and Kandeler, 1997; Ley, et al., 2001). Maximum respiration response upon addition of substrate (SIR) is proportional to the size of the living microbial biomass (Anderson and Domsch, 1978). Azotobacter populations numbered on Ashby’s and LG medium showed strong positive correlations with soil C/N ratio.

DiscussionThe results indicated that there are significant differences between the biological parameters of soils, used and managed differently. Coniferous forest with low level of fertility showed a lower biological activity namely Azotobacter population. Allelopathic compounds such as tannins and aromatics may be toxic for bacteria and especially Azotobacter species (Inderjit and Weston, 2001). Irrigated farmlands especially those irrigated with raw municipal wastewater compared to coniferous forest had the highest microbiological activities. This study supports earlier findings that organic fertilization rapidly benefits soil microbial biomass and activity, but provide few indications that the irrigation with wastewater affect soil microbial biomass, community structure, or activity. Although it is beyond the scope of this study to address possible effects of use of the wastewater in irrigation, it appears that there are strong and positive relationships between soil fertility and some microbiological indices.

Acknowledgments This study was supported by funds allocated by the Vice-President for Research of Bu-Ali Sina University.

References Anderson JPE, Domsch KH (1978) A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10:214–221. Barea JM, Brown ME (1974) Effect on plant growth produced by Azotobacter paspali related to synthesis of plant growth regulating substances. J. Appl. Bact. 37: 583-593. Bhardwaj KKR, Gaur AC (1970) The effects of humic and fulvic acid on the growth and efficiency of nitrogen fixation of Azotobacter chroococcum. Folia Microbiol. 15:364-367.


Gaur AC, Mathur RS (1966) Stimulating influences of humic substances on nitrogen fixation by Azotobacter, Sci. Cult., 32:319-32. Gupta VVSR, Germida JJ (1988) Distribution of microbial biomass and its activity in different soil aggregate size classes as affected by cultivation. Soil Biol. Biochem. 20:777–786. Harris JO (1973) Azotobacter of the Konza Prairie. In L.C. Hulbert (ed): Third Midwest Prairie Conference Proceedings. Manhattan, KS: Division of Biology, Kansas State University, 53-54. Hartley AE, Schlesinger WH (2002) Potential environmental controls on nitrogenase activity in biological crusts of the northern Chihuahuan Desert. J. Arid Environ. 52:293-304. Hassink J, Bouwman LA, Zwart KB, Bloem J, Brussard L (1993) Relationships between soil texture, physical protection of organic-matter, soil biota, and C-mineralization in grassland soils. Geoderma, 57:105–128. Inderjit, Weston LA (2001) Root interactions in higher plants: allelopathy and competition (in press). In C.W.P.M. Blom and E.J.W. Visser (ed.) Root ecology. Springer-Verlag, Heidelberg. Kassem A, Nannipieri P (1995) Methods in applied soil microbiology and biochemistry, Academic Press, Harcourt Brace & Company, Publishers, London. Kiem R, Kandeler E (1997) Stabilization of aggregates by the microbial biomass as affected by soil texture and type. Appl. Soil Ecol. 5:221–230. Klute A (1986) Method of soil analysis, part 1: Physical and mineralogical methods, Soil Science Society of America, Madison, Wisconsin USA. Ley RE, Lipson DA, Schmidt SK (2001) Microbial biomass levels in barren and vegetated high altitude talus soils. Soil Sci. Soc. Am. J. 65:111–117. Mishustin EN, Shilnikova VK (1971) Biological fixation of atmospheric nitrogen, MacMillan, London. Page AL, Miller RH, Keeney DR (1982) Methods of Soil Analysis. Part 1. Chemical and microbiological properties. ASA. SSSA. Madison, WI. USA. Pareek SK, Srivastava VK, Maheshwari ML, Gupta R (1996) Effect of Azotobacter cultures in relation to nitrogen application on growth, yield and alkaloidal composition of opium poppy (Papa versomniferum). Indian Journal of Agronomy, 41: 321-328. Schnitzer M, Kodama H (1992) Interactions between organic and inorganic components in particle-size fractions separated from four soils. Soil Sci. Soc. Am. J. 56:1099–1105. Subba Rao NS (2001) Soil microbiology (Forth edition of soil microorganisms and plant growth). SciencePublishers, Inc. Enfield (NH). USA. Subba Rao NS (1993) Biofertilizers in agriculture and forestry. 3rd Edition, Oxford and IBH Publishing Co., New Delhi. Zahir AZ, Arshad M, Azam M, Hussein A (1997) Effect of an auxin precursor tryptophan and Azotobacter inoculation on yield and chemical composition of potato under fertilized conditions. J. of Plant Nutrition, 20: 745-0752. Zaid HA (1992) Effect of Rhizobacteria and nitrogen fertilization on the yield of barley. J. Agric. Sci. Mansoura Uni., 17: 3981-3986. Zapater RM, Graham PH, Harris SC (1982) Effect of Azotobacter inoculation and nitrogen fertilization on the yield of seed potatoes in the coastal area of Peru. Biological Nitrogen Fixation Technology for Tropical Agriculture, 533-535.


1 2 3 4 5 6

Ashby mediumLG medium

c

bbc

bcb

a

c c c cb

a

0

5

10

15

20

25

30

35

Azo

tob

acte

r C

FU

's

(*1E

6/g

so

il o

f ro

ot

zon

e)

Land use

Figure 1. Effects of management practices on the Azotobacter population in different land. 1) Coniferous forest, 2) Deciduous forest, 3) Ranges, 4) Dry farmlands, 5) Farmlands irrigated with river water and 6) Farmlands irrigated with untreated municipal wastewater. (Values for each medium followed by different letters are significantly different at the 0.05 probability level).

Table 1. Pearson correlation coefficients of the soil Azotobacter populations and some soil physico-chemical properties. Azotobacter in Ashby’s medium Azotobacter in LG medium Sand 0.170248 0.141409 Silt -0.2475 -0.31134 *

Clay -0.00683 0.118442 CEC 0.02781 -0.07043 CCE -0.23201 * -0.27356 **

EC 0.40437 ** 0.35574 **

.pH -0.07953 -0.12382 OC 0.43995 ** 0.46560 **

Total N 0.45153 ** 0.49216 **

Available P 0.59433 ** 0.35217 **

Available K 0.52382 ** 0.31009 **

C/N ratio 0.233015 * 0.211862 *

Basal respiration -0.01963 -0.20405 *

SIR 0.49180 ** 0.4142 **

Azotobacter in Ashby’s m. 1 0.47497 **

** Correlation is significant at the 0.01 level. * Correlation is significant at the 0.05 level.


Fractal Analysis of Temporal Yield Variation of Five Main Crops in Iran

Mohammad Hassan Salehi1, Mohammad Rafieiolhossaini2 and Jahangard Mohammadi3

1-Assistant Prof., Soil Science Department, College of Agriculture, Shahrekord University, Shahrekord, Iran, E-mail: [email protected], 2- Lecturer, Department of Agronomy, College of Agriculture, Shahrekord University, Shahrekord, Iran, E-mail: [email protected], Telefax: +98-381-4424428; 3- Associate Prof., Soil Science Department, College of Agriculture, Shahrekord University, Shahrekord, Iran, E-mail: [email protected], Telefax: +98-381-4424428

Abstract Temporal variability of five main crops over 38 years was studied using fractal theory. To do this, first, variograms of different variables consisting yield of wheat, barely, paddy, sugar beat and cotton were calculated. It is a mathematical function that describes temporal pattern of yield variability. Then, fractal dimension (D-value) was estimated by fitting a linear model to log-transformed variograms. Fractal dimension (D-value) was used as indicator of long-term or short-term variation. The long-term variation will be pronounced if fractal dimension is close to unit. Increasing the D-value indicate the importance of short-range variability in crop yield. The results illustrated that among different crops, cotton showed a higher D-value followed by sugar beat. On the other hand, wheat, barely and paddy showed lower D-values. Such a high D-value could be related to the economical and environmental conditions imposed on cotton and sugar beet production.

Key Words: Temporal variability, Fractal dimension, Wheat, Barley, Paddy, Sugar beat, Cotton.

Introduction Temporal and spatial variability of environmental factors such as soil and plant properties is described by specific statistical analyses. Variograms are important tools to determine the structure of this variability for environmental data. Semivariance is a mathematical function that describes temporal and/or spatial pattern of observations and can be used for determining patterns in data (Clark, 1979 and Burrough, 1983). Fractal analysis, which is based on statistical self-similarity, means the variability pattern at one scale is repeated at other scales (Mandelbort, 1982 and Burrough, 1983). By decreasing the scale number in different stages, more details with high resolution are recognized (Fig. 1). Several researchers used fractal analysis in order to characterize plant and soil variability (Burrough, 1981; Palmer, 1988; Perfect and Kay, 1991; Eghball et al., 1993; Eghball and Power, 1995). One of the main parameter in fractal description is fractal dimension which is scale independent (Mendelbort, 1982). This factor for each variable can be estimated by semivariance function and used as an indicator of temporal variation of crop yield (Burrough, 1983; Eghball and Power,1995). There has been an increasing need in sustainable crop production in recent years. To attain this purpose, determining the yield variability over the time for different crops is necessary. Such studies are also extremely useful to develop models that predict the response of crops to different environmental conditions. The objective of this study is to apply the fractal theory for evaluating the temporal variability of five strategic crops over 38 years in Iran.

Materials and Methods Average yield of five main crops consists of wheat, barely, paddy, cotton and sugar-beat from 1961 to 1999 were obtained (Iran Agriculture Ministry). Then, semivariance function was calculated for each crop for different year interval (h), following equation:


[ ]2

))(()(2

1)(

)(

1∑

=

+−=hN

i

ii hxZxZhN

hγ) (1)

where γ) (h) is the estimate of γ(h) or calculated semivariance, Z(xi) and Z (xi+h) are the observed values of Z (yield) at xi and xi+h respectively, and N(h) is the number of paired comparisons at that lag (h). By changing h, an ordered set of values is obtained and this is the sample or experimental variogram. Then, the slope of regression line of log semivariance vs. log h for each crop was used to calculate fractal dimension. Fractal dimension (D-value), which is an indication of the pattern of yield variability, was estimated for each crop according to below formula (Burrough, 1981):

SlopeD2

12 −= (2)

Different stages for calculation of fractal dimension can be seen in Fig. 2. A small D-value (near 1) indicates the dominance of long-term variation, whereas a large D-value (near 2) reflects the dominance of short-term variation and non-dominance or lack of long-term variation (Burrough, 1983).

Results and Discussion Yield variations of five strategic crops during 1961 to 1999 are shown in figure 3. Yield production of wheat has increased about threefold over this period. There are about 120, 250, 590, and 680 percent for cotton, barely, paddy and sugar beet, respectively. Improving the yield production might be affected by many factors like increasing in cultivation area, using more fertilizers and pesticides, new varieties and also the promotion of agricultural knowledge of farmers in the country. The least increased yield observed for cotton might be due to prevalence of pests such as bollworm and lack of appropriate financial supports of farmers for this crop. On the other hand, the most increase belongs to sugar beet that can be attributed to utilizing of new germplasms and sufficient investment from the government. Estimated fractal dimensions for five crops are given in Table 1. Fractal dimensions ranged from 1.29 to 1.70. The results illustrated that among different crops, cotton has the highest D-value followed by sugar beet. Such a high D-value could be related to the economical and environmental conditions imposed on cotton and sugar beet production. A higher production risk for cotton and sugar beet and competition with other sugar crops like sugar cane in the country may be affected on importance of short-range variability in these crops. Eghball and Power (1995) obtained a lower value (D=1.42) for cotton in United States, while the estimated D-values for barely and wheat are almost similar. On the other hand, barely showed the lowest D-values that indicate the importance of long-term variability (less short-range variation) for this crop. Factors such as social and economical conditions and especially inherent resistance of barely in respect to environmental variations can explain such temporal variability.

ConclusionFactors such as plant breeding, using more fertilizers and pesticides and improved management practices has increased yield production of all crops over 38 years. In spite of this increase, fractal dimension suggesting long-term variation as well as short-term variation in yield of the crops studied. Although the average yield levels varied widely for different crops, it is possible to compare temporal yield variation with these techniques. This


information can also help the researchers who want to know more about the reasons of temporal yield variability in the region.

ReferencesIran Agriculture Ministry (2000) Agricultural statistics Book. Ministry of Agriculture,

Planning and Budget Affairs, Main Office of Statistic and Information, Bulletin No. 79/17, 4th edition, 234p.

Burrough PA (1981) Fractal dimension of landscapes and other environmental data. Nature(London) 294: 240-242.

Clark I (1979) Practical geostatistics. Appl. Sci. Publ., London. Burrough PA (1983) Multiscale sources of spatial variation in soil: 1. The application of

fractal concepts to nested levels of soil variation. J. Soil Sci. 34:577-597. Eghball B, Settimi JA, Maranville JW, Parkhurst AN (1993) Fractal analysis for

morphological description of corn roots under nitrogen stress. Agron. J. 85:287-289. Eghball B, Power JF (1995) Fractal description of temporal yield variability of 10

crops in the United States. Agron. J. 87 (2): 152-156. Mandelbrot BB (1982) The fractal geometry of nature. W.H. Freeman, San Francisco. Perfect E, Kay BD (1991) Fractal theory applied to soil aggregation. Soil Sci. Soc. Am.

J. 55:1552-1558. Frey KJ (1984) Future crop technology. pp. 310-338. In: B.C. English et al. (ed.),

Future agricultural technology and resource conservation. Iowa State Univ. Press, Ames.

Palmer MW (1988) Fractal geometry: A tool for describing spatial pattens of plant communities. Vegetatio. 75:91-102.

Table 1. Fractal dimension (D-value) and slope of fitted line of log semivariance vs. log year interval for five crops.

Fractal Dimension (D)

SlopeCrop

1.321.36Wheat1.291.43Barely1.391.23Paddy1.630.77Sugar beet1.700.69Cotton

Fig. 1. Schematical presentation of fractal characteristics.

Stage 1 Stage 2 Stage 3Stage 0


Fig. 2. Different stages for calculating the fractal dimension; a) Variability of a environmantal variable as a function of temporal position of observations along a transect which is indicated in gray scale, b) Variogram of the variable and sill value which showes that semivariance is closed to the observations’ variance, c) The log semivariance vs. log distance as well as fitted line for calculation of fractal dimension.

Fig.3. Average yield of five main crops in Iran from 1961 to 1999.

Wheat Barely Paddy

Sugar beet Cotton


Sources and Processes of Salt Accumulation in Segzi Valley of Isfahan, IRAN

MOHAMMAD HASSAN SALEHI1 and MOHAMMAD RAFIEIOLHOSSAINI2

1-Assistant Prof., Soil Sci. Dept., Shahrekord University, Shahrekord, IRAN, E-mail: [email protected], Telefax: +98-381-4424428; 2-Instructor, Dept. of Agronomy, Shahrekord University, Shahrekord, IRAN, E-mail: [email protected], Telefax: +98-381-4424428

Abstract Salinity and alkalinity are the major problems in soils of arid regions. Wind erosion also creates land degradation in drylands through loss of crops, pollution and jeopardised sustainability. Concentrations of soluble salts, through their high osmotic pressures, affect plant growth by restricting the uptake of water by the roots. In this study, factors influencing salt accumulation in Segzi valley of Isfahan were investigated. Because of the vicinity of the study area to irrigated farms and the city of Isfahan, wind erosion is also one of the most important agriculturally related problems in the area. Thirteen soil mapping units, which were different in their surface characteristics and salt accumulation, separated in an area of 43000 ha. In these soil- mapping units, 25 pedons were described in the field and 16 pedons selected for laboratory analyses. Physical and chemical soil properties including texture, percentage of stable aggregates in wet sieving, EC, SAR, soluble cations and anions, pH, percentage of calcium carbonate, gypsum and organic matter were determined by routine procedures. Factors such as moist subsoil, increase of EC and SAR toward the soil surface, redoximorphic features and salt efflorescence indicates that high water table is the major source of salt accumulation in these soils. Because of excessive pumping, water table has dropped lower in recent years. Wind also plays an important role in distribution of salts in the area as indicated by aeolian deposits, ripple marks and nebkas. Textural discontinuity in soils and human activity in the area also influence salt distribution but factors such as topography, soil parent material and surface runoff are less important in the area. Halite, gypsum and mirabilite were major evaporites identified in saturated extracts by XRD. Crusted soils had stable aggregates during wet sieving as compared to non-crusted soils. Because of high salinity and susceptibility of soils to wind erosion, open mining for gypsum, sand and clay is not recommended and special care must be taken to preserve surface crust in the area. Further investigation should be done to find tolerant plants to wind erosion and salty-gypsiferous soils with impermeable subsurface horizons.

Key words: Isfahan; Salt accumulation, Wind erosion,

Introduction Saline and sodic soils occupy about 15 percent of Iran. Although they are extensive, little research has been done about factors influencing salt accumulation in these soils. Salinization is the process by which water-soluble salts accumulate in the soil. Since few plants grow well on saline soils; therefore, salinization often restricts options for cropping in a given land area. About 10 million ha of irrigated lands have to be taken out of production because of salinity problems (Rhoades and Loveday, 1990). The released salts are transported away from their source of origin through surface or groundwater streams. Saline soils also facilitate desertification processes due to their unfavorable properties. Abu-Sharar et al. (1987) have mentioned in saline and sodic soils, reducing of hydraulic conductivity is corrolated with aggregate dispersion.


Wind erosion can be a major threat to soil productivity if care is not taken to protect the soil surface with an appropriate soil cover. World Bank (1984) mentioned that the dominant man induced causes of land degradation in the drylands are salinity and alkalinity problems, poor farming practices, population pressure, overgrazing, soil erosion, deforestation, and the use of livestock manure and crop residue for fuel as energy resource of the rural households. Therefore, evaluation of salinity hazards can help to prevent irrevocable damage of soil quality. The objective of this study is to determine sources and processes of salt accumulation in Segzi valley of Isfahan, Iran.

Materials and Methods The area under investigation is about 43000 ha. It is located between 51° 56' 29'' and 52° 7' 30'' E and 32° 52' 18'' and 32° 23' 50'' N in eastern part of Isfahan, Iran. Because of the vicinity of the study area to irrigated farms, airport and the city of Isfahan and also main road of Isfahan toYazd, salinity and wind erosion are two major important problems in the region. This area is characterized by a dry climate with high temperature. The mean annual rainfall and air temperature is about 100mm and 14.7ºC, respectively with an altitude of 1500m above the sea level. The soil moisture and temperature regimes in the area are aridic and thermic respectively. The dominant wind direction is from the east to the west in this region. Total of 25 pedons were excavated in 13 selected mapping units, which were different in topsoil properties and salt accumulation. After describing the pedons in the field, 16 pedons representing soils and including secondary carbonates, gypsum and salts were selected for laboratory analyses. Physical and chemical soil properties including texture, percentage of stable aggregates (MWD) in wet sieving, ECe, soluble cations and anions, pH, percentage of calcium carbonate, gypsum and organic matter were determined by routine procedures. Sodium adsorption ratio (SAR) of saturated extracts was calculated by following equation:

[ ] [ ] 2/122 )2//( +++ += MgCaNaSAR

Results and Discussion Results show that all soils in the region are considered as saline-sodic soils based on their pH, SAR and ECe values and classified as Gypsic Aquisalids, Gypsic Haplosalids and Typic Haplosalids subgroups according to soil taxonomy (Soil Survey Staff, 1999). In all pedons studied, ECe and SAR increase toward the soil surface (Fig.1). This increase indicate that high water table was the major source for salt accumulation in the soils. Soil morphology is also an indicator of seasonal changes of the shallow water table. In most subsurface horizons, redoximorphic features present that water table has been near the soil surface but because of excessive pumping and artificial drainage, water table has dropped lower in recent years. In spite of artificial drainage, water table approaches to the surface by decreasing the slope in the area. Therefore, the overall salt accumulation increases toward the center of the playa. A dark layer has been observed in some subsurface horizons. This layer consists of about threefold organic matter in comparison with surface horizons. This can suggest that high water table and moist subsoil have prevented the decomposition of soil organic matter. Khademi (1997) believed that this study area was a part of the post-Tethyan sea environment that covered by shallow lagoons in the late Tertiary. The Gavkhouni swamp located 50 Km east of the area studied is considered to be the remnant of such lagoons. Palynological investigations by Ayoubi (2002) on Segzi paleosols also showed


different aquatic species and a lot of macrofossil shells of Gastropods. All of these documents confirm the existence of a swampy environment in the past conditions of the area. Evaporation of water raised by capillary movement could leave considerable amounts of salts on the soil surface. From the top to the bottom of soils, the order of salt follows soluble salts, gypsum and calcite. This succession shows that because of evaporation of water table, more soluble salts accumulate near the soil surface whereas less soluble salts gather in subsoil horizons. Salt efflorescence and puffy grounds in the region presents the growth of salt crystals under crust in the soil surface.In a few soils studied, a cemented horizon containing high soluble salts has been distinguished. Since this horizon slakes in water and also is a root-limiting layer, petrosalic (zm as suffix symbol) is suggested as a new horizon in Soil Taxonomy. Chemical analyses show high gypsum and salinity contents in wind deposited materials. Also, halite, gypsum and mirabilite were major evaporites identified in soils and wind deposits by XRD (Fig. 2). Therefore, wind plays an important role in distribution of salts in the area as also indicated by ripple marks and nebkas. Textural discontinuity in soils is another indicator for wind activity, which is an important factor in formation of salic and gypsic horizons. This property reduces the rate of water movement and causes salt accumulation between horizon boundary. Calculated mean weight diameter shows that crusted soils have stable aggregates during wet sieving as compared to non-crusted soils. This can suggest that special care must be taken to preserve crust, desert pavement and also poor native vegetation in the area. To protect the soil surface, open mining and over-exploitation of gypsum, sand and clay is not recommended. These activities in addition to soil and climate conditions have created a favorable environment for wind erosion. With respect to results obtained in different positions of the area, other factors such as topography, soil parent material and surface runoff are less important in salt accumulation. Further investigation should be done to find tolerant plants to wind erosion as well as salty-gypsiferous soils with impermeable subsurface horizons because planting of Halloxilon and Atriplex species has not been successful in last decades probably because of low porosity and heavy texture in the soils.

ConclusionHigh water table and wind activity is two major factors in salt accumulation and distribution in Segzi valley. Textural discontinuity in soils and human activity in the area also influence salt distribution but factors such as topography, soil parent material and surface runoff are less important in the area. Further investigations should be done to find plants adaptable with soil and climate conditions in the area. Better management of land and vegetation cover in the region can reduce wind erosion, thus preventing salt accumulation in soils further a way from Segzi. Because of high salinity and susceptibility of soils to wind erosion, open mining for gypsum, sand and clay is not recommended and special care must be taken to preserve surface crust in the region.

References Abu-Sharar TM, Bingham FT, Rhodes JO (1987) Reducing in hydraulic conductivity

in relation to clay dispersion and disaggregation. Soil Sci. Soc. Am. J. 51:342-346.


Ayoubi S (2002) Pedogenic evidence of Quaternary climatic change recorded in paleosols from Isfahan and Emam-Gheis (Chaharmahal Bakhtiari province), Ph D thesis, Isfahan University of Technology, Isfahan, Iran.

Khademi H (1997) Stable isotope geochemistry, mineralogy, and microscopy of gypsiferous soils from central Iran Ph D thesis, Univ. Saskatchewan, Saskatoon, Canada.

Rhoades JD, Loveday J (1990) Salinity in irrigated agriculture. pp. 1089-1142. In: B. A. Srewart and D. R. Nielsen (ed.), Irrigation of agricultural crops. Agron. Monogr. 30. ASA, CSSA, and SSSA, Madison, WI.

Soil Survey Staff (1999) Soil Taxonomy: a basic system of soil classification for making and interpreting soil surveys. USDA, NRCS, US. Govt. Print. Office, Washington, D.C.

World Bank (1984) Ethiopia issues and options in the energy sector. Report No. 4741-ET of the Joint UNDP/WB Energy Sector Assessment Programme.

Fig.1. Increase of ECe and SAR toward the soil surface in pedons studied.

ECe (dS/m)

SAR


Fig.2. Evaporite minerals identified by XRD in saturated extracts of different depth of pedons (H: Halite, M: mirabilite, G: Gypsum).


Soil Characteristics Changes in a Flood Spreading System

Amir SarreshtehdariSoil Conservation and Watershed Management Research Institute, P.O.Box 13445-1136, Tehran, I.R. Iran. Phone: +98-21-4901240~47 Email: [email protected], [email protected],

Abstract Water shortage is a critical problem in arid and semi-arid areas. At this time, this problem is threatening the areas in different cases. There are several methods for solving this problem with different management ways. One of the methods which is implementing now in Iran, is Flood Spreading Project (FSP). This project is operating and actually, that is a research case. The FSP monitoring step has been started and it is continuing. The flood spreading objectives are water harvesting, soil conservation (water and wind) and vegetation cover improvement in the desert area with a multiple purpose point of view. Since the implementation of the FSP, there has not been any systematic evaluation to review the project. Therefore, this research tried that operates an impact assessment of the project based on soil productivity factors evaluation. There was assessed the changes before and after the implementation of the FSP. This project has been implemented in the province of Kerman in Abbarik site. This area has been located in margin of the Lout desert in alluvial fans of Jebal-e-Barez Mountain. There were carried out soil sampling, from the FSP and control area which measured factors were, nitrogen, phosphorous, potassium, organic carbon, pH, electrical conductivity (EC), transferred sediment depth and infiltration rate. The results showed that sediment depth, infiltration rate, phosphorous and organic carbon increased significantly after the FSP. There were also significantly changes of some soil properties between the dikes. The results of this research indicates that there has been a generally improvement in soil condition. It illustrates that The FSP could be effective on soil productivity as a positive operation.

Key Words: Soil Characteristics, Flood spreading, Abkhandari, Aquifer management

Introduction The world population is increasing fast and this population needs more food and water. The world’s resources are limited and that includes arable lands, forest, rangelands and water resources. Accelerated soil erosion, deforestation, desertification, pollution and other environmental problems are related to over exploitation of these resources. Iran is located in the arid and semi arid zones of West Asia. More than half of its area is desert or salt desert with the main problems being water shortage, inadequate plant cover and vegetation, accelerated soil erosion (windy and water) and violent floods from upside catchments toward flood plains (these flood flows are caused by irregular rain with high intensity). Approximately one-half of Iran's water supplies come from surface water, with most of the remainder coming from ground water aquifers, which are significantly overdrawn. Drought is an ever-present phenomenon in Iran and in the area (Kowsar, 1992). “Flood spreading in aquifer” is one of the more effective watershed management methods. It results in acquiring and saving water flow floods. These results might be a solution the water shortage problem in arid and semi arid zones and also it can change some of soil properties on the system as a good condition. If we are able to regenerate soil, vegetation and water resources to improve the condition in the rural area, then it might be an appropriate method for arid and


semi-arid areas based on development management in rural areas. In this article, the author has presented some research results about impact assessment of the system based on soil characteristics changes. Floodwater spreading is an easy method of harnessing sediment, which is usually wasted. Nutrient-rich waters have a number of important uses: to satisfy the water requirements of annual and perennial crops, range land plants, shrubs and trees, either immediately or over time by using surface reservoirs and aquifers; to recharge aquifers to prevent the intrusion of salt water into water-bearing strata; to stabilize drifting sands through precipitation of the suspended load; to grade land on sloping and eroded surfaces; to reduce gully erosion and control downstream flooding; and to leach saline soils (Kowsar, 1992). In Iran, the mean annual water harvested from aquifers and ground water is 53.5 billions cubic meter. Recently, over-exploitation of ground water resources has resulted in lowering the ground water table. Continuation of this problem creates drying up of wells problem. In addition to this, people left the area and the area gradually become a desert. One of the most important conditions in setting up flood spreading area is slope (should be between 1 to 5 percent) and also a flood-spread site must be in high infiltration soil situation. Some studies show that rainwater infiltration in sandy soils is influenced by rainfall characteristics. At low initial soil water content, rainwater infiltration is controlled by rainfall depth while at high preliminary soil water content the raindrop impact forming the surface crusts, is the deciding factor also rainwater infiltration significantly decreases with increasing basin slope and reducing the slope length (Sharma et al., 1983). This indicates that texture of the flood plain is very important for infiltration of water. “Experimental results indicated that sand ditches increased both the percentage of rainfall stored in the soil matrix and the infiltration depth of water during the two winter seasons from 1996 to 1998. (Abu Zreig et al., 2000). In Australia water harvesting is widely practiced. There are several techniques for implementation based on farming and watershed management policy. Also soil conservation service of New South Wales has implemented some water spreading techniques for soil conservation goals (Reij et al., 1988). Sedimentation after flood spreading is a normally condition. This is very important in where erosion is particularly severed, rates may reach 10000 ton/km2/year. Studies have shown that the domains of this range could be much more (100-20000) (Meijerink, 1995). In high erosion watershed floodplain is threaten by largely sedimentation that comes from top of the catchments. As a result there is no more information about infiltration basins considering based on economically feasible, Hence there must be focused on this condition as an important criterion in impact assessment because in addition in creating a problem for the floodplain, it can be generate a degradation condition of lands. In one new research on the first flood-spreading site1 where a researcher used remote sensing (RS) tools, the results show that irrigated farms increased from 184 ha to 2832 ha in after project implementation. Reduction of wind erosion and, poor rangeland from 7500 ha to 2352 ha was as well. These results indicate, “System has directly or indirectly affected on the environment of more than 10000 ha of lands (Nejabat, 1999). The other research in this background and in the same area has been emphasized “Optimal use of rainfall through floodwater systems not only reduces the negative effects of the flood in low lands but also reduces the water shortage during

1 This site is located in Gareh-Baygan through the Fars Province. Actually results of this site are background for Flood spreading program in wide situation.


dry seasons. It needs a simple technology and little investment and promises an environmental development.” (Nejabat, 2000)

Materials and Methods Narmashir located in Bam County falls within Kerman province. Narmashir general location is a part of Lout desert. This area is limited from South and South-West to Jiroft catchment, from east to Zahedan County, from North to Lout desert and from West to Dehbakry county. This desert is the hottest desert in Iran. This system in Abbarik works very simply. Some dikes have been made on iso-altitude lines. There are some overflows for leading extra floodwater to the others dike. The distance of overflow spacing is related to design method conditions. Immediately after each dike, also is made a settlement basin for water relaxation and giving time for more penetration again. After inflowing of water to this basin, water will reach to a particular level (0.2 m) for spreading inside the FSP region. One sketch map which explain it very good schematically. (Figure 1) The following materials were used in this study: a) Remote sensed images / Aerial photograph,b) Topographic, land use and geology maps, c), d) ILWIS, Minitab and Statistica software. As mentioned to article objective, the method was based on selecting appropriate ways for reaching objective. The main target of research is comparison between before and after the FSP implementation in some of soil characteristics. The activities were performed and sequences followed in the study are divided into three main phases: A) pre-fieldwork, B) fieldwork (Ground observation), C) after fieldwork (Data analysis).Pre-fieldwork step were included: literature review, collection of the available data, design sampling method based on grid system (that was based on planning map at 1:2000 scales), aerial photograph interpretation. Fieldwork step were included: soil sampling from 0-30 cm depth (based on grid system), sending soil samples to laboratory for measuring Nitrogen by Kjeldahl method, Phosphorous by Olsen’s method, Potassium by Flame photometer, pH by pH meter, EC by EC meter and Organic Carbon by Walkly Black method (Klute, 1986). There was also identification of soil texture by Bouyoucos Hydrometer (USDA triangle). Finally recognition between transferred sediment and base topsoil in area was capability of auger applying in the soil. Control area does not have transferred sediment (except in waterways) and this matter showed that might be use from this point for sediment and original soil. For exact comparison between before and after the FSP implementation, sampling operation was done in control land around the project area that it is exactly similar to primary situation of areas. Numbers of samples in inside of project were 30 and for control area also were 30. Due to kind of soil classification that it is Typic Torriorthent (based on American classification) majority of soil profile is sand, gravel and stone and in deeper situation even boulders. The activities after fieldwork were done as follows: Data and statistical analysis by two sample t-tests (with using of 30 samples within the FSP area and 30 samples in control area for each factor separately.), Kruskal-Wallis test and Mann-Whitney test.A two-sample t-test analysis was used to compare the soil properties of the FSP and the control areas. There have been defined follow hypothesis for each factor: H0: µcontrol = µFSP for all of factors, H1: µcontrol < µFSP (Organic Carbon, Nitrogen, Phosphorous, Potassium), H1: µcontrol > µFSP

(Infiltration rate).

Results


The results show that the amount of nitrogen increased a little after the flood spreading implementation in comparison to the previous condition of area. The mean difference between the FSP area and control is not significant at the level of 5 %. Simultaneously the results show that the amount of phosphorous, Potassium and organic carbon have increased significantly at the level of 5 % after the flood-spreading (Table 1). Finally the results show that the amount of infiltration rate has decreased very much after the flood-spreading implementation by comparison with the previous condition of area. The mean difference between the FSP area and control is very highly significant at the level of 1 % (Table 1). There was done Kruskal-Wallis test, which is a Non-parametric test. This test has been carried out for reaching to this result whether there are any changes between dikes in the flood-spreading project. On the other hand, how do the soil properties change within the flood spreading dikes? P-value in Kruskal-Wallis test for nitrogen amount versus dikes in the FSP area is 0.554. This test shows that amount of nitrogen has not changed significantly. P-value in Kruskal-Wallis test for phosphorous, potassium and organic carbon amounts versus dikes in the FSP area are respectively: 0.026, 0.178 and 0.197. This tests show that the amount of phosphorous has increased in some dikes and that is from top to down respectively. Phosphorous has increased in the FSP area but was decreased from top to bottom (Table 2). On the other hand, the result shows that amount of organic carbon has not increased from top to down in the FSP area. P-value in Kruskal-Wallis test for Infiltration rate and sediment depth versus dikes in the FSP area are 0.008 and 0.012 respectively. The result shows that Infiltration rate has changed (increased) significantly from top to down in the FSP area and vise versa for sediment depth. (Tables 3). The result shows that there is only one strong relation between dikes for nitrogen amount, which is between dike 5 and 10 at 5% level and also the result shows that there are moderate differences between dikes 1, 2, 3, 4 and 5 medians with dike 10 for phosphorous amount. About comparison of organic carbon percentage between each dike the result shows that there are moderate differences between medians of line 4, 5 and 7 with line 10 at level 5%. The result about comparison of infiltration rate between each dike shows that the most variation is between line 1 with the others lines. Variation of Infiltration rate is clear between dikes in line 1 with lines 3, 4, 7, 8, 9 and 10 at 6% level. In this time also there are medians differences between some the others lines to each other at level 10% and higher. This case is confirmed in comparison to transferred sediment depth results which are in table 4.

DiscussionAs mentioned above nitrogen has not increased significantly within the area after the FSP implementation. This is because nitrogen is a mobile element in soil and plant. Based on nitrogen amount in sample point distribution of nitrogen is more or less uniform except around the west of area that it is related to sedimentation. Research in the USA that compares between drip and flood irrigation in sugarcane farming showed soil nitrate in all of drip treatment has been higher than that with flood irrigation treatment (Sharmasarkar et al., 2001). Di et al., (1998) have reported that the lower nitrate concentrations in leaches process flood irrigation and they attribute the greater loss of nitrogen by de-nitrification and the greater dilution of soil solution nitrate by the larger volume of irrigation water applied. In the other study on water quality and effects on drain outflow controlled drainage had a significant hydrological and environmental effect during the 2 years of study. A comparison between conventional subsurface drainage (CD) and control drainage (CTW), show that the total drain outflow from CWT was 79% less in Year


1 and 94% in Year 2. The total reduction in nitrate losses with CWT corresponded to the reduced outflow rates. Compared with CD, the total amounts of nitrate in drain outflow were 78% less in Year 1 and 94% in Year 2. The highest concentrations of nitrate were measured at the time of the largest outflow rates. These results confirmed the amount of mobile nitrogen in water seepage and overflow (Wesstrom et al., 2001).Statistical analysis show that phosphorous increased significantly and it regularly decreased from the top to the bottom of the FSP (in water flow direction), which is related to sedimentation. There was significant increase in the first three dikes. This may be due to floodwater being brought by sediment from upper catchment, and it may have caused an increase of phosphorous. Phosphorus does not behave in the some way as nitrogen and it settles on clay and silt. When sediment is deposited behind the dikes, the amount of phosphorous in comparison with the amount before the FSP was implemented increased. Phosphorous could supersede on silt and clay. Some phosphorous would be dissolved in water and then enter within the FSP area. Based on the FSP targets, organic carbon (OC) is important because it supports vegetation growth. OC is related to organic matter and it is correlated to the amount of vegetation cover in the area. Organic matter is one of the major sources of soil fertility particularly for nitrogen and phosphorous (Tesfai, 2001) In this research results show that OC has changed significantly and increasing of sediment, vegetation establishment and soil erosion control with the FSP implementation was able to increase OC simultaneously. Consequently vegetation growth also has increased the OC. As mentioned above, this result proves that the FSP can increase OC through sediment and vegetation growing. Lack of homogenous sedimentation, non-homogenous vegetation growth, shortage of vegetation in the previous soil in the area and consumption of OM by new plantations, caused that an irregular distribution of OC. At the same time the OC distribution is a function of sedimentation pattern but it is also high in the west part of the area because there are good conditions for vegetation cover growth. Sediment is always accompanied by floodwater. The amount of sediment is related to parent material and topsoil condition in upper catchment. Based on geological studies, Abbarik catchment does not have much sediment. The effect of sedimentation on soil depth, water preservation capacity, infiltration rate etc. becomes positive except for soil horizon development (Tesfai, 2001). After performance of the FSP, transferred sediment has increased significantly. Majority of this sediment is silt particle. The FSP was able to increase sediment but it is not homogenous. Statistical test shows that the amount of sediment is changing in relation to dikes. This case is normally because the first dikes can entrap most sediment and therefore bottom dikes receive less sediment. Of course there is a highly sedimentation in the middle of the FSP area in dike 5, that this case implies probably to some problems in dikes design or sometimes to high flood value with high sediment. It is not clear what the problem is exactly? But it could be for this reason that the dike has not been designed very well or distribution of water has not been done as well. In any case, sedimentation has occurred and this is unstable. When the system works very well that water spreading and sediment would be homogenous and constant. In a study in Abbarik basin, results show that transferred sediment in the FSP area has effect on soil moisture significantly and this is increased with increasing sediment depth and soil depth (Heidari Mourchehkhorti, 2000). In the other research, it has been reported that due to sedimentation after several flood events, soil properties of area are leaded toward a good soil genesis class. On the other hand there is a problem of sediment fillingness behind the dikes and also rising of surface level at the back of them. In a research on spate irrigation it has been reported that there is a large amount of sediment deposited on the canal beds, which block the


passage of water, and decrease the life span of the irrigation structures. The sedimentation inside the spate irrigated fields raise the level of soil surface every year (Tesfai, 2001). Infiltration rate has changed very much without doubt. Entering sediment from upper catchment into the dikes reduced infiltration rate. The results of one research about Band-sars (one flood harvesting system) show that infiltration rate has been reduced 5.3 times in Band-sars treatments as compared to the control area (Arabkhedri et al., 1997). One research, which has been done in Abbarik site, shows that infiltration rate in the control area around the site with sandy texture are very high. This amount of infiltration rate is less in bed of waterway, which has sediment after each flood naturally. At the same time it is high in the others area except waterways (Heidari Mourchehkhorti, 2000). That study has also illustrated that sedimentation in this location after the FSP implementation was reduced infiltration rate very much. This reduction has two aspects in here. As mentioned above, this site has been a desert with a non-suitable soil profile from infiltration point of view. This condition is not good for planting new species because water penetrates into the profile quickly and it comes out from plant root at the same time this soil profile is not good plant establishment. This research illustrated that the FSP has been able to increase the sediment. Simultaneously this can create problems in the long run. If maintenance of the system would not be done well, it could be very dangerous after 10 years because soil will not have a good infiltration and after that vegetation will be destroyed. Results and land cover map from satellite images shows that infiltration rate does not have uniform distribution, as well as sedimentation depth but it is vice versa (Sarreshtehdari, 2002). The main aim for this research is assess the changes in soil properties before and after FSP. The results show that sediment depth, infiltration rate, phosphorous and organic matter changed significantly improved after the FSP. There were variations of some of these soil properties between the dikes. Nitrogen and organic carbon indicated a strong linear relation while between phosphorous and organic carbon showed a moderate linear correlation. As pointed out in the text, the soil has been changed significantly for most of factors. These alterations that include improvement in texture, increasing in some nutrients in soil profile and improvement of soil properties generally are the most important soil properties. References Abu Zreig M, Attom M, Hamasha N (2000) Rainfall Harvesting Using Sand Ditches in Jordan.

Agricultural Water Management 46:183-192.Arabkhedri M, Sarreshtehdari A, Kamali K (1997) The Long Time Effect of Flood Harvesting

on Infiltration Rate. Proceeding of the 8th International Conference on Rainwater Catchment System, Vol. 2. p. 1157-1158.

Heidari Mourchehkhorti F, (2000) Impact Assessment of Different Sediement Depth on Soil Moisture: A Case Study in Flood Spreading Project in Bam (Persian). MSc, Gorgan University.

Kowsar A, (1992) Desertification control floodwater spreading in Iran. Unasylva 43:27-30.Meijerink AMJ, (1995) Erosion and Sediment Yield in Catchment. ITC publication. Nejabat M, (1999) Improving Environmental Characteristics in a Wide Area Around a Flood

Water Spreading System, "A case study" 9th International congress on rainwater cachment system.Brazil, Brazil. URL: www.cpatsa.embrapa.br/doc/ technology/4_20_Masoud_Nejabat.doc

Nejabat M, (2000) An Indirect Estimation of Water Balance for Groundwater in Artificial Recharge Project 10th world water congress. Melborn. Australia., Australia. URL: www.icms.com.au/worldwater/session/393.html.


Sarreshtehdari A, (2002) The impact of a flood spreading project on soil properties : a case study in Iran, Kerman Province, Bam Abbarik, MSc thesis, ITC, Enschede, Netherlands

Sharma KD, Singh HP, Pareek OP, (1983) Rainwater infiltration into a bare loamy sand. Hydrological Sciences Journal 28:417-424.

Sharmasarkar FC, Sharmasarkar S, Miller SD, Vance GF, Zhang R, (2001) Assessment of drip and flood irrigation on water and fertilizer use efficiencies for sugar beets. Agricultural Water Management 46:241-251.

Tesfai M, Stroosnijder L, (2001) The Eritrean spate irrigation system. Agricultural Water Management 48:51-60.

Wang B, Wang B, Zhang Fu E, Wang BR, Wang BT, Zhang F, (1996). Runoff forestry experiment on the Loess Plateau. Journal of Beijing Forestry University, English edition 5:36-44.

Klute A, Dirksen C, (1986) Methods of Soil Analysis Methods of Soil Analysis: Part I, SSSA publisher.

Tables Legends Table 1 – Summary of two sample t-test results for significant factors variables

N Mean StDev 99% CI SE Mean P-value DF

P Control 30 1.593 0.364 0.066

P FSP 30 2.21 1.46 (-1.368,0.140) 0.27 0.033 29

K Control 30 99.35 9 1.6

K FSP 30 128 31.9 (-45.16, -12.04) 5.8 0.000 29

OC Control 30 0.104 0.154 0.028

OC FSP 30 0.201 0.185 (-0.2141, 0.0204) 0.034 0.032 29

INF Control 30 389.6 36.3 6.6

INF FSP 30 9.7 22.2 (359.06, 400.78) 4.1 0.000 29

Table 2 - Summary of Kruskal-Wallis test for Phosphorous FP N Median Ave Rank Z 1 4 2.00 18.8 0.79 2 3 4.10 25.5 2.07 3 3 3.40 22.2 1.38 4 3 2.00 18.2 0.55 5 3 1.80 18.8 0.69 7 3 2.20 16.8 0.28 8 3 2.00 14.0 -0.31 9 3 1.00 5.8 -2.00

10 5 1.00 5.2 -2.87 Overall ----- ----- 15.5 -----

H = 17.46 DF = 8 P = 0.026H = 17.78 DF = 8 P = 0.023 (adjusted for ties)


Table 3 - Summary of Kruskal-Wallis test results for infiltration rate FINF N Median Ave Rank Z

1 4 0.035 4.3 -2.75

2 3 1.000 11.2 -0.90

3 3 7.000 23.2 1.59

4 3 4.000 18.3 0.59

5 3 0.040 5.0 -2.18

7 3 3.200 12.5 -0.62

8 3 3.600 17.3 0.38

9 3 8.700 23.2 1.59

10 5 8.800 23.2 2.14

Overall ----- ----- 15.5 -----

H = 20.69 DF = 8 P = 0.008 DF = 8 P = 0.008 (adjusted for ties)

Table 4 - The result of p-value in Mann – Whitney test for transferred sediment depth

SED1 SED2 SED3 SED4 SED5 SED7 SED8 SED9 SED10

SED1 **** 0.2159 0.0518 0.0518 1.0000 0.0518 0.0518 0.0518 0.0200

SED2 0.2159 **** 0.3827 0.6625 0.2752 1.0000 0.5127 0.2752 0.2330

SED3 0.0518 0.3827 **** 0.2752 0.0809 0.0809 0.3827 1.0000 1.0000

SED4 0.0518 0.6625 0.2752 **** 0.0809 0.1904 1.0000 0.5127 0.1797

SED5 1.0000 0.2752 0.0809 0.0809 **** 0.0809 0.0809 0.0809 0.0369

SED7 0.0518 1.0000 0.0809 0.1904 0.0809 **** 0.3827 0.1904 0.0736

SED8 0.0518 0.5127 0.3827 1.0000 0.0809 0.3827 **** 0.5127 0.3711

SED9 0.0518 0.2752 1.0000 0.5127 0.0809 0.1904 0.5127 **** 0.8815

SED10 0.0200 0.2330 1.0000 0.1797 0.0369 0.0736 0.3711 0.8815 ****

Figure Legends Figure 1 – Schematic longitudinal section of dikes (sizes are changeable)

Figure 2- Sketch map for showing of sample (FSP) and control (C) points. Dikes have been shown by blue line along the altitude iso-line. (Base topographic map had scale 1:50000)

Spreading area Next

Water

Sandy dike


Overflow

Water direction

Top of the area


Effect of crop rotation on mobility of heavy metals in undisturbed soil columns

Gholamabbas Sayyad1, Majid Afyuni1, Seyyed Farhad Musavi2, Rainer Schulin3, Karim Abbaspour4

1 Department of soil science, College of Agriculture, Isfahan University of Technology, 84154, Isfahan, Iran. From left to right respectively: Phone: +98-311-3912693 Email: [email protected]; Phone: +98-311-3912868: Email: [email protected] ,2Department of irrigation, College of Agriculture, Isfahan University of Technology, 84154, Isfahan, Iran: Phone: +98-311-3913083 Email: [email protected], 3Institute of Terrestrial Ecology, ETH Zürich, Grabenstrasse 3/11a, CH-8952 Schlieren, Switzerland: Phone: +411-6336071 Email: [email protected],4Swiss Federal Institute for Environmental Science and Technology, EAWAG,Ueberlandstr 133 P.O. Box 611, 8600 Dübendorf, Switzerland: Phone: +411-8235359 Email: [email protected]

Abstract Heavy metal contamination of groundwater has received much attention in recent years. In this study we assessed the mobility of Cd, Cu, Pb, and Zn under two different rooting systems (fibrous root and tap root), and two different rotations (crop-fallow and crop-crop). The study was conducted on undisturbed soil columns. In each column, the top 10 cm of the soil was removed, contaminated with Cd, Cu, Pb, and Zn at 15.2, 585.9, 117.2 and 1093.8 mg kg-1,respectively and then replaced. Half of the columns were respectively planted with wheat and safflower based on their previous history. Leachate was collected in continuous intervals and analyzed for heavy metals during the experiment. After harvesting, soil samples were collected at 10 cm intervals and analyzed for total and DTPA-extractable heavy metal concentrations. Results showed that rotation had a significant effect on heavy metal transport as in the crop-crop rotation heavy metal concentrations of the subsoil were more than the crop-fallow rotation. The influence of rooting systems on the HM movement although observed but was not significant. The DTPA extractable levels of metals were higher in the subsoil depths of polluted soils, indicating that the metals had been redistributed from the surface layer in more soluble forms into the deeper zones in the profile.

Keywords: Environmental pollution, Heavy metals, Rooting systems, Preferential flow,

Introduction Environmental problems associated with heavy metal (HM) pollution have increased during the last decade (Hinz and Selim, 1994; Richards et al., 1998; Schwab et al. 2001). Industrial and agricultural activities have introduced large amounts of HMs into the surrounding atmosphere, waterways and soils. Several factors have led to soil contamination in Isfahan, central Iran. For example, the Isfahan steel factory and other factories introduce their wastewater into the lowland areas where groundwater level is high (< 2m) (Shirani, 1996). Also, large-scale use of sewage sludge in agricultural lands can introduce large amounts of HMs into the soils. The potential mobility of HMs in soils has been investigated for several decades (Giordano and Mortvedt, 1976; Richards et al., 1998). Many researchers concluded that there is little potential for heavy metal mobility via water percolating through the soil profile resulting in contamination of groundwater (Chang et al., 1984; Williams et al., 1987). However, an examination of recent and past works suggests that the case of potential metal mobility is not closed. Because many laboratory scale studies that have reported metal immobility use homogenized soil columns (Giordano and Mortvedt, 1976) there is little chance HM movement through preferential pathways. Also, in most laboratory and field studies, the lack


of noticeable increase in HM concentrations below the incorporated topsoil is mentioned as the evidence for immobility of HM (Chang et al., 1984). Interestingly, many of these studies had mass balance errors in the order of 30-50% missing HM mass. The reasons for this error are given as tillage dispersion (Williams et al., 1987), incomplete analytical recovery and changes in bulk density (Chang et al., 1984). Another reason often cited is the possibility that metals are transported to lower depths in the soil through preferential paths (Camobreco et al., 1996; Schwab et al., 2001). The objectives of this study were to assess the mobility of Cd, Cu, Pb and Zn in agricultural soils as influenced by two different rooting systems, wheat (fibrous root) and safflower (tap root), and two different rotations, rotation 1 (crop-fallow) and rotation 2 (crop-crop). These factors create different preferential pathways in the soil profile.

Materials and Methods The study was conducted on undisturbed soil columns (22.5 cm in diameter and 50 cm in depth) in a greenhouse. The treatments consisted of the following factors: uncontaminated top soil, contaminated top soil with Zn, Cu, Cd, Pb, fibrous root (wheat), tap root (safflower), crop-follow rotation, and crop-crop rotation. The experiment used Randomized Complete Block design with three replications. Soil columns from wheat and safflower farms comprised 3 taxonomic layers (Typic Haplocalcids) and had continuous canals with a 0.5-3 cm thickness which started from second horizon and stretches to C horizon. Table 1 summarizes some chemical and physical properties of the soils. Soil moisture characteristic curves (SMCC) determined using pressure plate at 0.1, 0.3, 0.5, 1, 3, 5, 10 and 15 atmosphere. SMCC was used for calculating available water storage, which was used to calculate the required irrigation To contaminate the top soil the upper 10 cm of soil was carefully removed and for the half of the columns, metal solutions (CdCl2, CuSO4, Pb (NO3)2, and ZnCl2) were sprayed and incorporated completely. The rate of application was as follows: Cd: 19.5 (kg ha-1), Cu: 750 (kg ha-1), Pb: 150 (kg ha-1), Zn: 1400 (kg ha-1)After adding the metals and replacing the topsoil, the plants were sown in half of the columns according to the previous use of the soil and crops, respectively. Plants were seeded manually on 30 March 2003, at a density of 200 (seeds m-2 for wheat) and 20 (seeds m-2 for safflower). Water content in columns was measured using horizontal TDR probes placed at 15, 30 and 45 cm depths. Irrigation was based on 60% depletion of available water and was done using a scaled cylinder. Leachate was collected after applying irrigation water and was kept in refrigerator until analyzed for heavy metals. Evaporation, maximum and minimum temperatures ware measured in the greenhouse using a class A evaporation pan and a maximum-minimum thermometer, respectively. Actual evaporation (Eact) and potential evapotranspiration (Etp) were calculated using Jackson (Jackson et. al., 1977) and Thornthwaite (Abbaspour, 1991) methods, respectively. Plants were harvested on 9 June 2003. Subsamples of 0.2 g were digested with a mixture of 6 ml 65% HNO3 and 2 ml 10% H2O2 and 2 ml distilled water , heated for 25 minutes in 100 °C , filtrated and analyzed using a Graphite Furnace (Varian Spectra 300-400). After plant harvesting, soil samples were collected from each column at 10-cm increments. The Soil samples were extracted for total (Sposito et. al. 1989) and DTPA-extractable metal concentrations (Soltanpour et al., 1991). The metal concentrations in the extracts were analyzed using atomic absorption Spectroscopy or ICP (Inductively Coupled Plasma). The comparison between treatments was made by ANOVA (p=0.05) using SPSS software (Version 9.0).

Results and discussion


Metals in soil profile Figure 1 gives the DTPA-extractable concentrations of heavy metals in soil profile. In crop-fallow rotation (rotation 1) and crop-crop rotation (rotation 2) although the Cu concentration increased through the profiles but it was not significant except for the top 10 cm of the soil for crop-fallow rotation and 20 cm for crop-crop rotation. The Zn and Pb concentration increased significantly in crop-crop rotation up to 30 and 40 cm depth for wheat and safflower columns, respectively. In deeper depths the concentration of Zn in polluted columns was more than the unpolluted columns. The Cd concentration for crop-crop rotation for wheat column increased significantly up to 40 cm depth and for safflower it increased significantly up to 50 depth. Metals in Plant Polluting the columns increased the heavy metal uptake in the plants. The average metal uptakes by both plants in unpolluted columns were 0.04, 1.66, 0.07 and 9.48 mg for Cd, Cu, Pb and Zn, respectively. The values for the polluted columns were, respectively, 0.66, 2.43, 0.19 and 26.36 mg. Metals in discharge

Polluting the columns increased metal leaching for all the elements for both rotations (Table 2). The absence of plants in columns with crop-fallow rotation resulted in increased discharge. Mass balance Mass balance calculations showed that the recovered metals were more than 75% for most treatments. The main reasons for the lack of heavy metal recovery could be the efficiency of extraction method and also errors in bulk density measurement. ConclusionThis study showed that even in calcareous soils, plants could enhance metal mobility through the soil profile. As Table 2 shows, the metal concentrations in leachate increased in polluted columns, despite the lack of significant increases in the metal concentrations in the subsoils. Although safflower produces deeper roots than wheat and has finer roots at the deeper soil layers, the difference between the two plants was not significant for metal mobility.

References: Abbaspour KC (1991) A comparison of different methods of estimating energy-limited

evpotranspiration in the Peace River region of British Columbia. Atmosphere-Ocean 29:686-698.

Camobreco VJ, Richards BK, Steenhuis TS, Peverly JH, and McBride MB (1996) Movement of heavy metals through undisturbed and homogenized soil columns. Soil Sci. 161: 740-750.

Chang AC, Warneke JE, and Page AL, Lund LJ (1984) Accumulation of heavy metals in sewage sludge treated soils. J. Environ. Qual. 13: 87-91.

Giordano PM, Mortvedt JJ (1975) Nitrogen effects on mobility and plant uptake of sewage sludge applied to soil columns. J. Environ. Qual. 5:165-168.

Hinz C, Selim H M (1994) Transport of Zinc and Cadmium in soils: Experimental evidence and modeling approaches. Soil Sci. Soc. Am. J. 58:1316-1327.

Jackson RD, Idso SB, and Reginato RJ (1976) Calculating of evaporation rates during the transition from energy-limiting to soil-limiting phases using albedo data. Water Resour. Res. 12:23-26.

Richards BK, Steenhuis TS, Peverly JH, and McBride MB (1998) Metal mobility at an old, heavily loaded sludge application site. Environ. Pollu. 99: 365-377.


Richards BK, Steenhuis TS, Peverly JH, and McBride MB (2000) Effect of sludge-processing mode, soil texture and soil pH on metal mobility in undisturbed soil columns under accelerated loading. Environ. Pollu. 109: 327-346.

Schwab AP, Banks MK, and Erickson LE (2002) Fate and transport of heavy metals and radionuclides in soil: The impact of vegetation. Kansas State University. http//www.engg.ksu.edu/HSRC/fate.html/.

Shabanpour M, Musavi SF, Afyuni M (2000) Bromide transports under field conditions. Iranian J. of Soil and Water Sci. 4: 92-98.

Shirani Bidabadi H (1996) Investigation of morphological and chemical variability of the Zarinshahr soils and evaluation of their properties for sewage water applications. Master of Science thesis, College of Agriculture, Isfahan University of Technology, Isfahan, Iran.

Soltanpour PN (1991) Determination of nutrient availability and element toxicity by AB-DTPA soil test and ICPS. Adv. Soil Sci. 16:165-190.

Sposito G, Lund L, and Chang A (1981) Trace metal chemistry in arid-zone field soils amended with sewage sludge: I. Fractionation of Ni, Cu, Zn, Cd and Pb in solid phases. Soil Sci. Soc. Am. J. 46: 260-264.

Williams DE, Vlamis J, Pukite AH, Corey JE (1987) Metal movement in sludge amended soils: a nine year study. Soil Sci. 143: 124-131.

Table 1- Some chemical and physical soil properties wheat farm safflower farm

Horizon Ap Bw Bk Ap Bw Bk Depth m 0-0.25 0.25-0.45 0.45-.60 0-0.25 0.25-0.45 0.45-0.60

pH Sat. paste 7.7 7.9 7.9 7.6 7.7 7.7 ECe dS m-1 10.5 6.6 6.5 9.8 7.8 5.9

CaCO3 % 38.2 35.6 37.6 37.2 35.9 36.2 Cd mgkg-1 1.6 1.7 1.7 1.6 1.7 1.6 Cu mgkg-1 20.3 24.5 19.3 19.3 19.1 14.1

Pb mgkg-1 42.0 42.0 48.0 45.0 42.0 45.0 Zn mgkg-1 33.0 24.0 27.0 30.0 30.0 39.0 Cl % 2.4 1.6 1.6 2.1 1.6 1.1

CEC1 Cmol kg-1 14.8 13.7 14.6 13.8 14.6 14.3 O.M.2 % 1.0 0.7 0.7 0.5 0.3 0.4 Sand % 17.7 17.2 17.8 17.9 17.1 18.2 Silt % 43.4 42.5 43.1 43.2 42.4 43.3

Clay % 38.9 40.3 39.2 39.0 40.5 38.4 Bulk density Mg m-3 1.3 1.4 1.4 1.3 1.4 1.4 1. Cation exchange capacity 2. Organic matter

Table 2. Total mass of heavy metals transported by discharge (mg) plant treatment Cd Cu Pb Zn

mg Rot2-Npol 0.0 4.9 0.7 14.2 Rot1-Npol 0.0 6.9 1.3 12.7

Wheat Rot2-pol 0.2 8.1 2.5 43.7 Rot1-pol 0.3 9.0 5.6 33.8 Rot2-Npol 0.0 1.1 0.2 8.9


Safflower Rot1-Npol 0.0 2.6 1.1 21.8 Rot2-pol 0.2 3.7 1.5 31.5 Rot1-pol 0.2 5.2 1.5 41.3

Cd

-50

-40

-30

-20

-10

0

0.0 0.5 1.0 1.5 2.0 2.5

concentration(mg/ kg)

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Figure 1. DTPA-extractable metal concentrations in different soil depths In the legend: w stands for Wheat, s for safflower, cc for crop-crop rotation, and cf for crop-fallow

rotation


Modelling of land production potential for irrigated cotton in Qom masileh, Qom province

Seyed Alireza Seyed Jalali1

1. Scientific member of Soil and Water Research Instotute, Kargar Shomali Avenue, Jalal Ale-Ahmad Road, Tehran, 14155/6185, IRAN. Tel: 0098-21- 8021089 Fax: 0098-21-634006 Email: [email protected]

Abstract The study area is about 75000 hectares and is located in Qom-Masileh area, Qom Province of Iran. It is located between 34° 30′ and 34° 52′ north latitude and 50° 47′ and 51° 19′ east longitude. Based on nearest synoptic station to the study area, Qom station, the climatic type of the area is desertic. The maximum daily air temperature is 34.5 °C in June and the minimum daily air temperature is 3.6 °C in December. The annual rainfall is about 145 mm. The aim of this research was to elaborate an approach for the prediction of land production potential for irrigated cotton taking into account the environmental condition in the study area. The methodology considers different hierarchically ordered production situations. In the first hierarchically production situation, the radiation thermal production potential (RPP) or the irrigated yield for cotton has been calculated. In the second hierarchical production situation, land production potential (LPP) for phases of soil families has been calculated considering soil indices. The result of the first hierarchically production situation showed that irrigated potential yield based on FAO crop growth model for cotton (lint+seed) is 6460 kg/ha and cotton (lint) is 2308 kg/ha in the study area. The result of the second hierarchically production potential situation showed land production potential for irrigated cotton (lint+seed) 65 to 5623 and cotton (lint) 23 to 1968 kg/ha due to lime, gypsum, gravel, soil depth, salinity and alkalinity limitations.

Key words : Cotton , land suitability, modelling of land production potential, , Qom soils

IntroductionCotton, Gossypium spp., is an important crop that is grown in warmer climates throughout the world. It is grown primarily for lint fibers, which are used in the textile industry. Oil, meal, seed hulls, and linters are also important cotton products. Research efforts are essential for cotton to remain aviable competitive renewable agricultural resource (Peace Corps ICE, 1985). In year 2000, the total area under cotton cultivation in Iran was 186640 ha which from this amount Qom Province had area 6427 ha with average yield for irrigated cotton 2888 kg/ha (Fig. 1). The aim of this research was to elaborate an approach for the prediction of the land production potential for irrigated cotton taking into account the environmental condition in the study area.

Materials and methods


The study area is about 75000 ha. It lies between 34º 30 ´ and 34º 52 north latitude and 50º 47 ´ and 51º 19 east longitude in Qom Provnice of Iran (fig 2).The elevation from sea level is 928 m.The climatic type of area is desertic. The annual precipitation is about 145 mm. The maximum daily air temperature is about 41°C in June and the minimum daily air temperature is 3°C in January. The soils were classified based on key of soil taxonomy (1998), 9 subgroup were determined for these subgroups, 19 families and 32 phases of families were recognised (Delavar and Seyed Jalali, 2004). Soil Subgroups in the study area are Fluventic Haplocambids, Sodic Haplocambids,TypicTorriorthents,Typic Haplocambids, Typic Haplocalcids, Typic Calcigypsids, Typic Haplogypsids, Sodic Haplogypsids and Gypsic Haplosalids. The methodology considers different hierarchically ordered production situations. In the first hierarchically production situation, the radiation thermal potential (RPP) or the irrigated yield for cotton has been calculated. This model permits to estimate, from radiation and temperature data, the net biomass production and yield of a high-yielding crop variety, cultivar or clone that is optimally supplied with water and nutrients grown in the absence of pests and diseases. Calculation of total net biomass production (Bn) is based on FAO (1979) which is as following:

( )Bnbgm K LAI

L ct= × ×

+ ×0 36

1 0 25.

.

Where Bn = total net biomass production (kg CH2O ha-1 ) Bgm = maximum gross biomass production (kg CH2O ha-1 hr -1 ) KLAI = correction factor for LAI < m2/m2. L = number of days between sowing and maturation of crop Ct = respiration coefficient (ct= 0.028*(0.044+0.0029+0.001*t2) with t being the mean daily temperature

The yield of a crop can finally be written as:

RPP Bn Hi= ×

Where RPP = Radiation-thermal production potential or potential irrigated cotton yield, Hi = harvest index, fraction of the total net biomass that is economically useful (FAO, 1979)

In the second hierarchical production situation, land production potential (LPP) for phases of soil families has been calculated considering soil indices.The land production potential has been calculated using an equation in which the effects of climate (radiation, temperature) and soil characteristics on crop yield have been combined. Since water requirements is provided by irrigation then RPP=CPP and finally LPP can be calculated by following formula:

100

SI*CPP=LPP


Where LPP = land production potential CPP = Climatic production potential

SI = soil index (calculation based on parametric approach with the Square root method).

ResultsThe result of the first hierarchically production situation showed that irrigated potential yield based on FAO crop growth model for cotton (lint+seed) is 6460 kg/ha and cotton (lint) is 2308 kg/ha in the study area. The result of the second hierarchically production potential situation showed land production potential for irrigated cotton (lint+seed) 65 to 5623 and cotton (lint) 23 to 1968 kg/ha due to lime, gypsum, gravel, soil depth salinity and alkalinity limitations. Table 1 shows land production potential of cotton for different phases of soil families and their distribution in the area was shown in figure 3.

Discussion Climate, landscape and soil characteristics data were used by the model applied in this study to assess the potential production of different soil family phases for irrigated cotton cultivation. Based on the results obtained in this study the following discussion can be made: The climatic conditions in the study area are moderately suitable for irrigated cotton due to low temperature during late season which can reduce the yield somewhat. The evaluation of landscape and soil characteristics revealed limitations of salinity and alkalinity, calcium carbonate, gravel, soil depth for most studied maping units. Problems with salinity and alkalinity can be improved by management practices such as drainage and leaching if sufficient water can be supplied, wwhereas physical soil conditions such as calcium carbonate, soil depth, gravel content and low temperature during late season can not be improved. Assuming that no soil improvement works is needed. Irrigated production potential for irrigated cotton (lint+seed) is 6460 kg/ha and cotton (lint) is 2308 kg/ha in the study area. The result of the second hierarchically production potential situation showed land production potential for irrigated cotton (lint+seed) is 65 to 5623 and cotton (lint) 23 to 1968 kg/ha due to lime, gypsum, gravel, soil depth salinity and alkalinity limitations. Taking into account management practices to improve the soil conditions, the future land production potential after land improvement such as drainage, irrigation and leaching can be increased to considerable amount for about 50 percent of the area with potential irrigated cotton (lint+seed) of 6460 kg/ha and cotton (lint) of 2308 kg/ha in the study area Accurate and reliable estimates of crop yield are fundamental to all procedures of agricultural planning and models such as the one applied in this study can be very useful tools for predicting the production potential. However, any model needs to be validated by confronting the results with detailed and relevant field observations. Therefore it is absolutely necessary to have information on actual yields corresponding to land for which soil data is available. Furthermore, the model could be greatly improved if management characteristics would be considered.


References1. Delavar MA, and Seyed Jalali SA (2004). Land suitability evaluation for winter wheat, barly and cotton in Qom-Masileh, Qom Province. Soil and Water Research Institute. Tehran. Iran. 2. Doorenbos J and Kassam AH (1979). Yield response to water, Irrigation and Drainage paper 33. FAO, Rome. 3. Food and Agricultural Organization (1979). Report on Agro-Ecological Zones project. Vol. 1: Methodology and result for Africa. World soil resources report No. 48, FAO, Rome. 4. Khiddir SM (1986). A statistical approach in the use of parametric systems applied to the FAO framework for land evaluation. Ph. D. Thesis, State university of Ghent, Belgium. 5. Peace Corps ICE (1985). New Crop Production Handbook. 390 pp. 6. Seyed Jalali SA (2004). Determination of Land production potential for winter wheat, barly and cotton in Qom-Masileh, Qom. Province. Soil and Water Research Institute. Tehran. Iran. 7. Soil survey staff (1998). Keys to soil taxonomy. Six edition, Soil Conservation Service. United States Department of Agriculture. 8. Sys C; Van Ranst E & Debaveye J (1991). Land evaluation part I, II & III (1993). General Administration for Development Cooperation, Brussels.

Figure Legends

Area and yield of cotton in Iran ( 2002)

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Figure 1: Area and yield of cotton in 2002


Tables Legends

Table 1: Results of Soil index, potential irrigated yield and land production potential for cotton Phases of soilfamilis codes SI RPP(kg/ha)

LPP seed+lint (kg/ha)

LPP lint (kg/ha)

Area(ha)

Area(%)

1.1 37.3 6463 2411 844 508 0.68 2.1 9.7 6463 627 219 86 0.11 3.1 1.8 6463 116 41 1228 1.64 4.1 30.1 6463 1945 681 764 1.02 5.1 15.4 6463 995 348 327 0.44 6.2 36.1 6463 2333 817 1939 2.59 8.1 44.3 6463 2863 1002 1576 2.11 8.2 23.1 6463 1493 523 971 1.30 9.1 48.4 6463 3128 1095 514 0.69 9.2 36.1 6463 2333 817 1100 1.47 11.1 44.9 6463 2902 1016 2587 3.46 12.1 50.3 6463 3251 1138 2578 3.45 14.1 38.3 6463 2475 866 2823 3.77 14.2 50.8 6463 3283 1149 332 0.44 16.1 46.7 6463 3018 1056 496 0.66 17.1 45.7 6463 2954 1034 1495 2.00 10.1-10.2 47.7 6463 3083 1079 12003 16.05 13.1-13.2 46.6 6463 3012 1054 1794 2.40 15.1-15.2 43.3 6463 2798 979 3087 4.13 17.2-17.3 20.5 6463 1325 464 5095 6.81 18.1-18.2 2.6 6463 168 59 7623 10.19 19.1-19.2 2.5 6463 162 57 7284 9.74 5.2-5-3 1.6 6463 103 36 1733 2.32 7.1-7.2-7.3 31.9 6463 2062 722 10276 13.74 Hills 431 0.58 MicellaneousArea

6950.93

River Wash 1675 2.24 Ueban Areas 3778 5.05 Sum 74798 100.00 SI: Soil Index RPP: Radiation-thermal production potential or potential irrigated cotton LPP: Land production potential


Figure 2 : Location of study area (A)

(B)Figure 3: Land production potential for cotton (lint) (A); and cotton (seed and lint) (B) in the study area.


Root morphology, yield and nitrogen uptake in two potato clonal selections of Russet Norkotah at different soil nitrogen levels

Mehdi Sharifi1, Bernie. J. Zebarth2, Mohammad A. Hajabbasi1 and Mahmoud Kalbasi1

1 Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan, Iran, Phone: 0311-3913471 Email: [email protected]; [email protected]; [email protected]; 2- Potato Research Centre, Agriculture and Agri-Food Canada, PO Box 20280, Fredericton, New Brunswick, Canada E3B 4Z. Phone: +1-506-4524828 Email: [email protected]

Abstract The low vine vigor and high N requirement of Russet Norkotah may lead to higher risk of nitrate leaching compared to other cultivars. Recent clonal selections from Texas have produced strains that have larger and stronger vines, which may alter N requirements. The importance of clonal variation in N requirements and root morphological properties is not known. A field experiment was conducted during 2002 to evaluate yield, N uptake and root morphological characteristics of two Russet Norkotah clonal selections at different soil N levels. The standard clone (SRC) and Texas selection 112 (TX112) of Russet Norkotah were used. Whole plants were excavated and partitioned to different components. Root length (RL), root length density (RLD), root average diameter (RAD) and root dry weight (RDW) were measured. Tuber yield, dry weight and N concentration of different component and plant N content were determined. Soil inorganic N was measured at planting and at harvest. The two clones of Russet Norkotah had quite different partitioning of dry matter and N but there was little difference in their tuber yield, total dry weight and plant N content. Vines dry weight, RDW, RL and RLD was higher in TX112 than SRC, whereas tuber nitrogen concentration was higher in SRC than TX112 clone. The nitrogen fertilization increased tuber yield, total dry weight, vine dry weight and tuber and vine N concentrations but significantly decreased the RL and RLD. The TX112 vine growth was more responsible to N application than SRC whereas the SRC root growth was more sensitive to N application than the TX112 clone. Although, Root length and RLD were significantly higher for TX112 clone compared to SRC but cultivar Russet Norkotah has a more limited root system compare to other potato cultivars. Soil nitrate concentration was not affected by N fertilization and clone after harvest. Under the conditions of this study, use of new clonal selections of cultivar Russet Norkotah did not have any yield or nitrate leaching advantages over the standard cultivar. Nitrogen fertilization increased mostly vegetative growth and risk of nitrate leaching to groundwater especially in early season.

Key Words: dry matter production, nitrogen accumulation, root characteristics, Solanum tuberosum L., tuber yield

Introduction Potato (Solanum tuberosum L.) is the fourth most important world food crop, after rice, wheat, and maize which require high inputs of N and water for optimum production. Combination of superficial rooting system, high nitrogen fertilizer application rates and heavy rain or irrigation greatly increases the potential for nitrate leaching which consequently is more costly and may pose environmental pollution. While numerous studies have explored improved N management practices as a strategy for minimizing N loss, there is potential for exploiting the genetic variability among cultivars and mutant strains of asexually propagated crop species such as potato for improved N uptake. Significant variations in N use efficiency characteristics have been identified among potato cultivars, clonal selections, and ascensions of wild potato species (Errebhi et al., 1999; Zebarth et al., 2004; Zvomuya et al., 2002). This


suggests that there may be the potential to reduce the risk of nitrate leaching through selection of more efficient potato cultivars or clonal selections of cultivars. Russet Norkotah was released as an early fresh market russet in 1987 (Johansen et al., 1988). It is popular because of its relatively high yield, high percentage of U.S. No. 1 tubers and excellent tuber type. This cultivar has low vine vigor, is susceptible to early die-down, and requires higher fertilizer N inputs than many other cultivars (Zvomuya et al., 2002). Residual soil nitrate measured in commercial potato fields was almost twice as high for Russet Norkotah compared to Russet Burbank and Shepody, suggesting an increased risk of nitrate leaching with Russet Norkotah production (Zebarth et al., 2003). This was attributed to higher N fertilization rates, (Zebarth et al., 2003) and lower N uptake efficiency (Zebarth et al., 2004) for Russet Norkotah compared to Russet Burbank and Shepody. Recent clonal selections of Russet Norkotah have identified strains with higher yield potential and larger and stronger vines that may require lower fertilizer N inputs (Miller et al., 1999). Miller et al. (1995) demonstrated that clonal selection could result in the improvement of existing cultivars with modification of traits such as stronger vines, which can improve productivity under stressful conditions. New clonal selections of Russet Norkotah from Texas produced greater biomass than the standard clone of Russet Norkotah per kg of N applied when N rates were low and per kg of fertilizer N absorbed by the plant (Zvomuya et al., 2002). Their superior yield has been attributed to increased vine vigor and resistance to verticillium wilt (Miller et al., 1999). Although, the physiological basis of genotypic variation in nitrogen use efficiency of potato is poorly understood but the difference in root morphological characteristics have been considered as one of the possible mechanism. Sattelmacher et al. (1989) attribute differing nitrogen uptake efficiency of two commercial potato cultivars to differences in root morphology.The objective of this study was to determine the effect of N fertilization on plant dry matter, N accumulation and root morphological parameters of two clonal selections of Russet Norkotah.

Materials and Methods The study site was at Potato Research Centre, Agriculture and Agri-Food Canada, Fredericton, New Brunswick, Canada (45° 55' N; 66° 37' W). Soil was a coarse-textured developed on till deposits, and classified as Haplorthods with unfertilized barley (Hordeumvulgare L.) as preceding crop. Soil (0-15 cm) had 505 g kg-1 sand , 346 g kg-1 silt, and 149 g kg -1 clay (hydrometer method); pH of 6.2 (1:1 water), and organic carbon content of 20.7 g kg -1 (combustion method). Soil inorganic N was measured at planting and at harvest as a possible indicator of N uptake efficiency. Soil samples (0-30 cm) were taken and frozen for subsequent analysis. Soils were passed through a 4.75-mm sieve to remove coarse mineral fragments, extracted with 1.7 M KCl, and the extract analyzed colorimetrically for NO3

- and NH4

+ concentrations using a Technicon TRAACS 800 auto-analyzer (Zebarth and Milburn, 2003).A factorial arrangement of treatments in a randomized complete block design with four replications was used with two fertilizer N rates (0 and 150 kg N ha-1) and two clonal selections of potato cultivar Russet Norkotah. The two clonal selections were the original Russet Norkotah (Johansen et al., 1988) and Texas 112 (Miller et al., 1999). Each plot contained six rows 10 m in length, with two outer rows acting as guards. Hand-cut 57 g (± 7 g) seed was hand-planted on May 24 (2002) at 0.30 m within-row spacing in rows 0.91 m apart. Nitrogen fertilizer was applied as ammonium nitrate (NH4NO3), banded at planting approximately 7.5 cm to each side, and 5 cm below the seed pieces. All plots received 150 kg


P2O5 and K2O ha-1 banded similar to N at planting time. Standard commercial practices were used for tillage and, weed, insect, and disease control. One representative plant was taken from each plot on August 26, 2002 (96 DAP). Whole plants were excavated and partitioned into roots, stolons, tubers, vines and fruit. The plant components were dried at 55 oC and weighed. Nitrogen concentration in each component was determined by combustion using a Leco CNS-1000. Tuber yield, tuber dry weight, vine dry weight, total dry weight (TDW) and total plant N content (PN) were calculated as described by Zebarth and Milburn (2003).Before drying, roots were washed using a hydropneumatic elutriation root washer (sieve size = 0.47 mm) (Smucker et al., 1982). Roots photos were taken using an Epson PhotoPC 750z digital camera and were analysis by WinRHIZO Version 2002C PRO software (Arsenault et al., 1995). Root length and average diameter were calculated. Data were subjected to analysis of variance (ANOVA) using the General Linear Model of SAS (SAS Institute Inc., Cary, NC, Version 8). Treatments interaction means were compared using Fisher’s Protected Least Significant Difference (LSD) test. A logarithmic transformation of soil nitrate concentration was performed prior to statistical analyses.

Results and Discussion Yield and Dry Matter Production

Although, vine and root dry weight were significantly higher for the TX112 clone compare to SRC but clones did not significantly differ in tuber yield and TDW (Table 1 and 2). The SRC partitioned 12% more dry matter to tubers than did TX112 clone, whereas TX112 clone partitioned 11% more dry matter to vines than did SRC. Zvomuya et al. (2002) reported that the genotype main effect was not significant for any of the yield parameters but harvest index (HI) was 7% greater for SRC than for TX selections, reflecting the larger vine growth that characterizes the selections. Nitrogen fertilization increased tuber yield, vine dry weight and total dry weight by 42, 139 and 42%, respectively, but did not affect other parameters (Table 1). This suggests that N application resulted in an increase in the proportion of TDW that was partitioned to the vines. Millard et al. (1989) reported similar result for N fertilization effect on TDW and tuber dry weight in Maris Piper potato. The difference in effect of N fertilization on tuber dry weight and tuber yield confirms reducing of tuber specific gravity with N application. There was a significant clone by N rate interaction on vine dry weight which N fertilization resulted in higher increase of vine dry weight for the TX112 clone than SRC (Fig. 1). The vine dry weight, therefore, was significantly higher for TX112 clone than for SRC only in fertilized treatments. Results from this study contradict earlier reports of consistently higher tuber yields with Texas Norkotah strains compared to standard Russet Norkotah (Miller et al., 1999). This discrepancy may be due to different environmental conditions of the previous study sites compared to the present study. The Texas strains were developed for improved performance under the stressful conditions of Texas (Zvomuya et al., 2002). The lack of a significant yield response in this study may reflect the lack of significant disease pressure or moisture stress, as well as the generally shorter growing season in Atlantic Canada compared to other growing regions. It may also relate to later maturity of Texas selections (Zvomuya et al., 2002). The difference in vine dry weight between the clones corresponds to superior vine growth associated with Texas selection compared with standard cultivars.

Nitrogen Concentration of Vines, Roots and Tubers at Harvest


Genotype main effects were significant for tubers N concentration but roots and vines N concentrations were not affected by clone (Table 2). N fertilization significantly affected vine and tuber N concentration. There were significant clone x N rate interaction for tubers N concentration. Nitrogen fertilization resulted in higher increase of tuber N concentration for SRC than TX112 clone at harvest (Fig. 2). The tubers N concentration, therefore, was significantly higher for SRC than for TX112 clone only in fertilized treatments. This may be related to translocation of N from the canopy to tubers occurred earlier in SRC than TX112 clone due to its earlier maturity. It is consistent with lower value (but not significant) of vine N concentration in TX112 clone than SRC. Zvomuya et al. (2002) reported that vine, tuber and total N uptake increased linearly as N rate increased from 28 to 336 kg ha-1.

Nitrogen Uptake

Nitrogen uptake like dry matter production was not affected by genotype but N fertilization increased N uptake by 160%. Zebarth et al. (2004) showed that N accumulation increased linearly with increasing crop N supply.

Root Morphology

Root length and RLD were significantly higher for TX112 clone compared to SRC; however, RAD was not affected by clone. Nitrogen fertilization significantly decreased RL and RLD but did not affect RAD. There was a significant clone by N rate interactions on RLD (Table 2). Rate of decrease in RLD with N fertilization was higher for the SRC compared with the TX112 clone. The SRC had significantly lower RLD in fertilized compared to non-fertilized treatments (Fig. 3). Mean values of RL and RLD were about 0.60 km plant-1 (2.2 km m-2) and 0.60 cm cm-3

respectively (Table 1). These values are small in comparison to previously published values for potato (Vos and Groenwold, 1986; Lesckynski and Tanner, 1976; Stalham and Allen, 2001). RL and RLD differences among various experiments could be attributed in part to genotypic differences and response of these genotypes to various treatments. Russet Norkotah cultivar is an early maturing, determinate variety, and thus has a relatively limited root system compared with other potato cultivars. Root average diameter was about 0.34 mm, which is greater than those previously reported for other potato cultivars. Lesczynski and Tanner (1976) stated that the major portion of Russet Burbank potato roots had diameters less than 0.2 mm. Vos and Groenwold (1986) found that 91% of root diameters were smaller than 0.44 mm.

Soil Mineral Nitrogen Concentrations

Soil inorganic N content (0-30 cm) at planting was 7 kg NH4-N ha-1 and 14 kg NO3-N ha-1.Soil nitrate and ammonium concentrations for 0-30 cm depth were not affected by clone or N fertilization at harvest (Table 2). The highest risk of nitrate leaching, therefore, is early in the growing season prior to rapid N uptake by crop, and outside of the growing season when soil residual nitrate and mineralized nitrogen may leach.

ConclusionsResults indicate that there are significant genotypic differences in root morphology among clonal selections of Russet Nokotah. The standard Russet Norkotah clone has a more limited root system in comparison to TX112 clone. Significance of the size of rooting system for yield or nitrogen uptake is much greater when experimental condition such as soil type, soil


humidity and soil fertility become growth limiting factors (El Bassam, 1981). Soil fertility and nitrogen mineralization in our experiment was perhaps still high, even for the non-fertilized treatments. In more adverse conditions (low nitrogen supply and moisture stress), TX112 clone with stronger root system may show some advantages over SRC. By comparison of root parameter values obtained in this study with published results, we may conclude that cultivar Russet Norkotah has a more limited root system which may pose higher risk of nitrate leaching as compared with other potato cultivars (Zebarth et al., 2003). Two clones of Russet Norkotah had quite different partitioning of dry matter and nitrogen but there was little difference in their total dry weight and total plant N content. Under the condition of this study, use of new clonal selections of cultivar Russet Norkotah did not have any yield or nitrate leaching advantages over standard cultivars. Nitrogen fertilization increased mostly vegetative growth, total plant N content and risk of nitrate leaching to groundwater especially in early season. Nitrogen fertilization also increased tuber yield in standard Russet Norkotah although its tuber gravity was decreased.

References Arsenault J L, Pouleur S, Messier C, Guary R (1995) WinRHIZOTM, a root-measuring system with a unique overlap correction method. HortSci. 30: 906. El Bassam N (1981) Genetical variation in efficiency of plant root system. pp 295-299. In Brouwer, R., Ed. Structure and Function of Plant Roots, Martinus Nijhoff/Dr. Junk Publishers.Errebhi M, Rosen C J, Lauer F I, Martin M W, Bamberg J B (1999) Evaluation of tuber-bearing Solanum species for nitrogen use efficiency and biomass partitioning. Am. J. Pot. Res. 76: 143-151. Johansen R H, Farnsworth B, Nelson D C, Secor G A, Gudmestad N, Orr P H (1988) Russet Norkotah: A new russet-skinned potato cultivar with wide adaptation. Am. Pot. J. 65: 597-604.Lesczynski D B, Tanner C B (1976) Seasonal variation in root distribution of irrigated field-grown Russet Burbank potatoes. Am. Pot. J. 53: 69-78. Millard P, Robinson D, Mackie-Dawson L A (1989) Nitrogen partitioning within the potato (Solanum tuberosum L.) plant in relation to nitrogen supply. Annals of Botany 63: 289-296.Miller J C Jr, Smallwood D G, Miller J P, Fernandez G C J (1995) Norgold Russet and Norgold Russet M: Aditional evidence for genetic dissimilarity. Am. Potato J. 72: 273-286. Miller J C Jr, Scheuring D C, Miller J P, Fernandez G C J (1999) Selection, evaluation, and identification of improved Russet Norkotah strains. Am. J. Pot. Res. 76: 161-167. Sattelmacher B, Klotz F, Marschner H (1989) Influence of the nitrogen level on root growth and morphology of two potato varieties differing in nitrogen acquisition. Plant Soil 123: 131-137.Smucker A J M, MacBurney S L, Srivastava A K (1982) Quantitative separation of root from compacted soil profile by hydropneumatic elutriation system. Agron. J. 74: 500-503. Stalham M A, Allen E J (2001) Effect of variety, irrigation regime and planting date on depth, rate, duration and density of root growth in the potato (Solanum tuberosum) crop. J.Agric. Sci. Camb. 137: 251-270. Vos J, Groenwold J (1986) Root growth of potato crops on a marine-clay soil. Plant Soil 94: 17-33.Zebarth B J, Milburn P H (2003) Spatial and temporal distribution of soil inorganic nitrogen concentration in potato hills. Can. J. Soil Sci. 83: 183-195Zebarth B J, Leclerc Y, Moreau G, Gareau R, Milburn P H (2003) Soil inorganic nitrogen content in commercial potato fields in New Brunswick. Can. J. Soil Sci. 83: 425-429.


Zebarth B J, Tai G, Tarn R, de Jong H, Milburn P H (2004) Nitrogen use efficiency characteristics of commercial potato cultivars. Can. J. Plant Sci. 84: 589-598. Zvomuya F, Rosen C J, Miller J C Jr (2002) Response of Russet Norkotah clonal selection to nitrogen fertilization. Am. J. Pot. Res. 79: 231-239.

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Figure 1. The effect of N fertilization on vine dry weight of standard Russet Norkotah clone (SRC) and Texas112 clone of Russet Norkotah (TX112). Same lower case letters indicate non-significant difference at the 5% level of probability.

Figure 2. The effect of N fertilization on tuber N concentrations of standard Russet Norkotah clone (SRC) and Texas112 clone of Russet Norkotah (TX112). Same lower case letters indicate non-significant difference at the 5% level of probability.

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Figure 3. The effect of N fertilization on root length density (RLD) of standard Russet Norkotah clone (SRC) and Texas112 clone of Russet Norkotah (TX112). Same lower case letters indicate non-significant difference at the 5% level of probability.

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0.8

1

1.2

1.4

SRC TX112

Clone

Tub

er N

con

cent

rati

on (

%)


Pro

ceed

ings

of T

he F

ourt

h In

tern

atio

nal I

ran

& R

ussi

a C

onfe

renc

e 65

4

Tab

le 1

. R

oot

leng

th (

RL

), r

oot

leng

th d

ensi

ty (

RL

D),

roo

t av

erag

e di

amet

er (

RA

D),

pla

nt p

artit

ions

and

tot

al d

ry w

eigh

t, pl

ant

part

ition

s N

co

ncen

trat

ion,

tube

r yi

eld,

pla

nt N

con

tent

(PN

), s

oil n

itrat

e co

ncen

trat

ion

at h

arve

st (

NO

3- ) an

d so

il am

mon

ium

con

cent

ratio

n at

har

vest

(N

H4+

)fo

r st

anda

rd (

SRC

) an

d T

exas

112

(T

X11

2) c

lone

s of

cul

tivar

Rus

set N

orko

tah

at tw

o ni

trog

en le

vel (

0 an

d 15

0 kg

N h

a-1).

Val

ues

are

mea

ns o

f 4

repl

icat

ions

.

Para

met

ers

Dry

wei

ght (

g pl

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gen

conc

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n (%

) T

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eld

PN

NO

3- N

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Sour

ce o

f v

aria

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T

reat

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RL

(k

m)

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(cm

cm

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RA

D

(mm

)R

oots

T

uber

s V

ines

T

otal

R

oots

T

uber

s V

ines

(g

pla

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)(µ

g g-1

)

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0.

60

0.60

0.

34

7.

40

272.

15

54.6

7 33

4.72

2.17

0.

91

1.98

1110

.8

3.85

5.95

2.

22

Clo

ne

TX

112

0.65

0.

65

0.34

12.0

2 22

2.78

87

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323.

72

2.

22

0.81

2.

02

87

1.2

4.05

6.32

2.

66

0 (k

g N

ha-1

) 0.

68

0.66

0.

34

9.

14

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37

42.1

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2.19

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59

1.66

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4 2.

20

6.

25

2.37

N

rat

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) 0.

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0.

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56

100.

49

386.

24

2.

31

1.13

2.

34

11

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5.

72

6.

00

2.52

Tab

le 2

.Sta

tistic

al a

naly

ses

for

root

len

gth

(RL

), r

oot

leng

th d

ensi

ty (

RL

D),

roo

t av

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D),

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nt p

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ions

and

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l dry

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plan

t par

titio

ns N

con

cent

ratio

n, tu

ber

yiel

d, p

lant

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nt (

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l nitr

ate

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n at

har

vest

(N

O3- )

and

soil

amm

oniu

m c

once

ntra

tion

at h

arve

st (

NH

4+)

resu

lts p

rese

nted

in T

able

1.

Para

met

ers

Dry

wei

ght

Nit

roge

n co

ncen

trat

ion

Sour

ce o

f v

aria

tion

R

L

RL

D

RA

D

R

oots

T

uber

s V

ines

T

otal

R

oots

T

uber

s V

ines

T

uber

yi

eld

PN

N

O3-

NH

4+

Blo

ck

**

**

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

Clo

ne (

C)

* *

ns

**

ns

**

* ns

ns

* ns

ns

ns

ns

ns

N

rat

e (N

) **

* **

ns

ns

ns

***

**

ns

**

* **

*

* **

*

ns

ns

C x

Nns

*

ns

ns

ns

*

ns

ns

*

ns

ns

ns

ns

ns

CV

(%

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19

20

10

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24

27

*, *

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Not

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nifi

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at t

he 0

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leve

l of

prob

abili

ty


Soil diversity relevant to landscape evolutionary processes in Zayandeh rud valley

Norair Toomanian 1, Ahmad Jalalian 2, Hossein Khademi 3

1- Soil Science Department, College of Agriculture, Isfahan University of Technology, Phone: 0311 3912255, member of scientific board, Iranian Soil and Water Research Institute, E-mail: [email protected]. 2- Soil Science Department, College of Agriculture, Isfahan University of Technology, Phone: 0311 3912255 E-mail: [email protected]. 3- Soil Science Department, College of Agriculture, Isfahan University of Technology, Phone: 0311 3912255 E-mail: [email protected].

AbstractSoil spatial distributions are responses to underlying fundamental controls. Hydrologic and geomorphic processes are the basic factors affecting the soil continuous and discontinuous variability in arid landscapes. In arid region of Isfahan, these two processes along with the soil forming processes are responsible for all spatial consequences. There is no information about the composition, origin and the evolutionary processes of surficial sediments in Zayandeh-rud valley. The homogeneity and or heterogeneity of land resources in this area have not been determined yet. The soil resources of Zayandeh-rud valley (all piedmonts excluded) from Habib-abad in northwest to Varzane in east normally are assumed to be River alluviums that had been laid in different Quaternary time sections. Highlighting the heterogeneity of land resources in Zayandeh rud valley and the role of land resources and geomorphic landforms in diversity and behavior of studied soils are the main objectives of this study. To cover all factors affecting soil diversity in such environment, we have undertaken the survey of 0.3 million hectares in Zayandeh-rud plain. This area (51º 30' 00"- 52º 15' 00" E, and 32º 30' 00" – 32º 52' 30" N) contains a complete set of arid geomorphic units (landscapes, landforms and geomorphic surfaces). Air photo interpretation recognized about 45 different geomorphic surfaces in this area. Hundred and ninety profile descriptions and 750-sample analysis proved that the soil diversity is partly concordant with geomorphic units, also when we take the apparent land resources into account in process of geomorphic surfaces stratification. The genetic differences, color, texture, structure and vertical sequence of different soil horizons and layers showed that there are some other soil resources, which affect on diversity and behavior of soils in studied area. Supervised classification of remotely sensed landsat images highlighted some differences. Air photo interpretation of studied area supported with field witnesses showed that the pathway of Zayandeh Rud River had shifted twice in past Quaternary period. Tectonic activities, playa formation and lagoonization of Segzi area, and subsequent climatic changes via different geomorphic and hydrologic processes had changed the land resources distribution in Zayandeh-rud valley. Subsequent wind erosion had covered the surface of previous sediments in some places and also different sediments had been overlaid on each other and made some complex land resources. In our studied area, we could differentiate six different sedimentation processes that had created some compound land resources. Piedmont and River (along the existing and previous pathways) alluviations, Playa, Lagoonal and Wind sedimentations, and also temporal stream alluviations were the main creative processes in our study area. Overlaying these stratified land resources on API interpretation map and using a complete set of environmental knowledge about the land resources evolutionary processes, we could define and stratify the landscape more precisely. This could enable us to relate the soil diversity to geomorphic land units more properly. Using the genetic characteristics of soils and their 3D contiguity we could describe the evolutionary history of Zayandeh-rud valley during Quaternary period.


Key Words: Geomorphic map units, Land resources, Soil diversity

Introduction Recently, natural Resources management (NRM) is becoming a specific agenda on scientific and applied protocols. It is important to recognize the accurate diversity and behavior of all sections of ecosystem to predict their responses against management practices. Precision management demands higher detailed information with high certainty (Hengl, 2000b). Effective soil management requires well understanding of soil distribution patterns within the studied landscape. Such knowledge permits us to use the landscape within its constraints, and enables to make wise decisions (McBratney et al. 2000). As the spatial dependent models and environmental management systems improved, demand for more accurate, dens and multi-functional information increased. Thus, an accurate quantitative soil survey should be improved and establish to offer such amount of information via a cost-effective methods (Hengl, 2003). To keep up with this ever-growing phenomenon in each scale of management, try should be kept to improve some knowledge-based and quantitative soil survey method. Landform analysis, as a basic technique of differentiating the soil diversity resources, commonly is used in recent hydrologic, geomorphologic and pedologic surveying techniques (Park et al., 2001). Many scientists, when trying to determine soil diversity of their target area, have used the landform parameterization methods (Moore et al. 1993). Standard method of delineating the land resources in complete spectrum of diversity is drawing their distribution on aerial photos by means of stereoscopic analysis (Hengl T., and D. G. Rossiter, 2003). To delineate such stratifications in a quantitative manner statistical correlation (Irvin et al., 1997) and supervised classification of images established on environmental and field measured terrain attributes are greatly used (Hengl T., and D. G. Rossiter, 2003). In geomorphological and hydrological studies, various quantitative methods have been developed to characterize the landscape morphology and land units (Pennock, 2003; Dikau, 1989). But results of these quantitative methods were more accurate and reliable in high relief landscapes. In flat or low relief regions, where DEM and its measured attributes are not able to differentiate the landforms as accurate as in undulating landscapes, it is advised to use the traditional API landform stratifying method (Hengl T., and D. G. Rossiter, 2003). DEM’s accuracy depends more on vertical resolution of data set extracted from topo maps, aerial photos and satellite images. Because of some difficulties (expense, software and hardware) the vertical resolution of extracted DEMs are not satisfying in flat areas. So, using the measured attributes from the extracted DEMs in landscape analysis are seemingly not reliable. Landscapes and /or soilscapes are a mixture of patches that vary in structure and phylogenic processes. These features are under continuous influence of environmental events. The necessity to understand the importance of landscape dynamics, heterogeneity and recognition of the diversity of soils are completely acknowledged (Saldana and Ibanez, 2004?). Measurements of diversity were introduced to pedology few years ago. Several approaches to pedodiversity analysis have been proposed: Taxonomic pedodiversity (the diversity of soil classes); Functional pedodiversity (soil behavior under different uses) and diversity of soil properties. The aim of this article is to show the heterogeneity of land resources resulted from landscape evolutionary processes in Zayandeh rud valley and the soil diversity evolved with different landforms.

Materials and Methods Study area


Study area is located in western part of central basin of Iranian plateau. Precisely, the area is bounded by 51 30 00- 52 15 00, 32 30 00 - 32 52 30 (547000- 617000 E, 35696000- 3638000 N) Eastern longitude and Northern latitude lines having Isfahan city in center (Fig. 1). This area includes 4.5 topographic map of 1/50000 or 18 of 1/25000. Geologic infrastructure of the area is mainly made of Cretaceous limestone overlaying Mesozoic shale and sandstone. As it is obvious from the watershed of Zayandeh-rud River that surrounds the study area (Fig. 12), the mountains bound the north, west and south part of the study area, and landscape faces to east. The erosional and depositional processes during the geologic time, especially in late Tertiary and early Quaternary, had been the main processes of geo-formation in the area. Considering the sedimentological proves and paleoclimatological witnesses, it is clear that the Gavkhooni marsh had a quite bigger extent during late Tertiary period and its sediments are vast spread in area. After uplifting of Zagros Mountains (Alpian Orogeny), the Zayandeh-rud cuts down its underlain parent materials and washes the debris to east, forming the terraces along its path in eastern part of the watershed toward the Gavkhooni marsh. Due to low infiltration and extreme aridity (high evapotranspiration), the inverse movement of water gathers the soluble ions and minerals in surface of the soil. Salinity of soils increases eastwardly with increasing the aridity and by decreasing the altitude. Other pathway of material movement was the Khoshkeh rud pathway. This seasonal fluviation inters to study area at northwest corner and obliquely passes the area from southeastern corner. This pathway coming from Mourcheh khort, Shahin-Shahr, transfers some great amount of gypsiferous and soluble minerals from northern mountains (Toomanian et al. 1999). Alluvial fans and bajadas, extending from mountain front in the north to Borkhar and Segzi playas reaching to central river alluvial sediments, construct the geomorphic configuration of the most of inward sloping surfaces. The soils are characterized by calcic (increasing westward) and gypsic (mainly in fan and bajadas) horizons. Saline horizons are the simultaneous alternative for each of mentioned soils, when the move is to the east. The aridity and the temperature of soils increase toward the southeastern direction. The region has economically, industrially and, agriculturally extreme importance for the province and country, therefore the fundamental studies are considered as basic steps toward sustainable development.

Figure 1- the study area in central Iran.


Methods Soils at each landscape position are formed and developed under direct influence of soil forming factors and their complex interactions along with Hydrological and Geomorphological processes. Therefore, the diversity of the stratification of land resources with different historical phylogeny would determine the soil variability. For landscape stratification or ground cover clustering, supervised image classification and air photo interpretation of landforms are the most used techniques. A set of Landsat 7 ETM+ (2001) images was imported to ILWIS software and geo-referenced using reference points. Air photo interpretation was based on manual hierarchically geomorphologic landform classification. During stereoscopic delineation of landform unites, pedologic knowledge of surveyor from soil-landscape relations together with factors of geology, topography, geomorphology, is considered. Stereoscopically air photo interpreted map of study area were imported to GIS software and geo-referenced using benchmark points for each photo. The Natural False Color Composite (using 742 bands) of landsat images as a base and the photo-interpreted boundaries displayed on top enables us to select the training samples. These sample sites are typical representatives of landform classes when observed stereoscopically. Two methods for creating the training samples were used. First, to create the training samples, the entire area of some different API interpreted units, were selected. Second, samples were created by manual on-screen selection of about 100 pixels within each photo-interpreted unit. The training samples were then used as input to maximum likelihood classifiers (Lillesand and kiefer, 2000) with no distance thresholds, so that all pixels were classified. Automatic (classification) and manual (photo) API final maps were compared visually (Rossiter, 2001, home page). Because, these two methods judge only by considering the surficial morph genetic characteristics and or reflex of tin surficial layer for stratifying the area, thus none of the geomorphologic and hydrologic historical events that had deeply affected the landscape evolution, are considered in detection of land resources. To check the ability of these two methods in highlighting the accurate diversity of soils, we plan to check it with direct sampling of the whole soilscape. It is thought that, sampling design should comprise a triple section to cover all variations existed in real world and depicted in prepared land unit maps. I) Taking soil samples in a grid of 7 x 7 km distances in uniform landforms of whole study area. II) Stratified random sampling within landforms having little surface from lattice intervals. III) Short transects in locations with violent variation due to slops (from mountains to central plains).

ResultsIt was assumed that all the flat and fine alluviated parts of Zayandeh Rud valley are River alluviums. Now we can trustfully reject it. Natural false color composite of Landsat images of the studied area (Fig. 1) shows the global distribution of land resources. In this on-screen interpretation method, the analyst judges on geomorphic differences by using the color contrast. As it is evident from the image, this method couldn’t differentiate the land resources accurately, but could separate the previous gross River alluvial plain to two different parts, which are the Ap (111, 121) and Pl (111, 112, 211, 311, 411) geomorphic units in API method. In photo-interpretation (API) method, a three-dimensional model is constructed in the analyst’s visual perception by comparing adjacent photos of stereo- pair. Through differentiating the homogene units on this virtual space analyst can stratify the landforms. The stratified land units resulted from photo-interpretation method are listed in table 1. This stratifying method more than previous method, could determine two shifting pathways of Zayandeh Rud River, River


Terraces, different Playas, and seasonal stream alluviums that were draining the whole playa surfaces. Table 2 shows that there is remaining some soil Taxonomic and Functional pedodiversity or heterogeneity in defined units, which have not been detected through this method. These diversities are due to existing of different river terraces in Ap 111 and Ap 121 units, are due to slope and the differences in stability of surfaces in Pi units, because of pathway shifting of Zayandeh Rud river during the Holocene period in Fp unit, due to salinization and gypsification processes intensity and dynamism of underground water in Pl units. Because, the landform evolution processes are more evident in air photos, therefore this method could differentiate the area better than previous one. Because, all of the soil variability is not considered in Taxonomic space, there are some geomorphic units with one taxonomic strata but different management problems. And also there are some land units with different soil strata (weakness of stratification due to scale of study). In the second case of interpretation, in some map units, the soil diversity was completely beyond the scope and definitions of only geomorphic map units. And there should be a different factor to define the spectrum of studied soil variability. Soil profile description in whole area of concern proved that the land resources are more complex than we had been determined via using two previous methods. Description of real soil parent material, soil horizon sequences, color of soil layers and layer discontinuities, proved that, we have six different sedimentation processes that had created some compound land resources. Piedmont and River (along the existing and previous pathways) alluviations, Playa, Lagoonal and Wind sedimentations, and also temporal stream alluviations were the main creative processes in our study area. Overlaying the map of these stratified land resources on API interpretation map and using a complete set of environmental knowledge about the land evolutionary processes, made us to stratify the geomorphic land units, which are completely concordant with Taxonomic and functional soil diversities. Table 3 shows the location of profiles and the land resources in which the soil have evoluted.

DiscussionDelineating the soil types and mapping of their distribution in any study area without considering the landscape evolutionary processes may result some delineation errors and impurities in map units. Determining landscape evolutionary processes and their resulted land resources in area of concern is a step for geomorphological landscape stratification, by which all differentiation factors are high lighted. Distinguishing the landscape constructing relatively chronological steps enables us to judge on soil evolution and relate the soil-forming processes to geomorphologic realities. With an excessively field work and synthesizing them with air photo interpretation and image processing we could come to this end that the Zayandeh Rud valley had evoluted by this chronological sequences:

1- In Mio-Pliocene times the sea regressed eastwards and intermountain lakes locally occupied the area. Some sandy limestone deposits were laid down unconformably on different older beds in the western part of the valley, and thick deposits of coarse conglomerates accumulated in the southwestern intermountain depressions. In the northeast some gypsiferous marls were deposited in a lagoonal environment, which may have represented the last remnants of the Miocene sea. Finally, the formation of recent faults, associated with dolomitization and mineralization, thrusting, reactivation of fold faults, magmatic activity, and appearance of hot springs are indications of later Alpine orogenic movements (Ministry of Industry and Mines, 1976).


2- In early Pleistocene times, the uplifting of Zagross chain, steepened the slope of Zayandeh Rud River basin, concordant with existing more humid situation, made it to cut its basin eastward and laid the debris around the pathway to a more bigger Gavkhooni marsh. The pathway was passing from south of Beheshti airport (Ghahjavarestan plain). Segzi playa was not this much isolated and was receiving water from the Zayandeh Rud River.

3- Much active Dastkan seasonal river was bringing the eroded material from northern part of Zayandeh Rud catchment and laying its coarse alluvium at most around the Shahin Shahr, and its fine alluviums passing from Borkhar area were receiving to local lagoonal eastern lakes.

4- Climate change toward a more arid condition in late Pleistocene, reduced hydrologic power of the river, resulted to a rejection of pathway. Through this rejection, the river turned its way to pass from Gavart, east of Isfahan.

5- Drying of local marshes in Segzi and isolation of Gavkhooni marsh in Far East of Isfahan was the result of later active Aridity climate. This process completed the formation of Borkhar and Segzi wet playas.

6- Sinking of eastern part of Valley (a kind of fault) caused a huge amount of erosion taking place around the Segzi in the north and Sin in the south of River. Relics of earlier piedmont course sediments are remained in form of uplifted terraces (Taljerd soil searies). Contemporaneous with this, the river made its last rejection to present pathway.

7- Deepened Aridity with draining the underground water by established draining systems and pulling up the remaining water by intensive agriculture ruined all green cover of the playa surfaces and abandoned the saline and alkaline soil to wind erosion.

References1- Dikau R., (1989) The application of a digital relief model to landform analysis in geomorphology. In: Raper ,J. (Ed.), Three Dimensional Applications in GIS. Taylor & Francis,London, pp. 151-175. 2- Hengl, T. (2000a) http://www.itc.nl/personal/hengl/topics.htm.3- Hengl, T., (2000b) Improving soil survey methodology using advanced mapping techniques and grid-based modeling. MSc. thesis, ITC, Enschede, 89 pp. 4- Hengl T., (2003) Pedometric mapping: briging the gaps between conventional and pedometric approaches. PhD. Thesis, Wageningen University and ITC. Enschede, Netherlands. 5- Hengl T., D. G. Rossiter, (2003) Supervised landform classification to enhance and replace Photo-interpretation in semi-detailed soil survey. Soil Sci. Soc. Am, J. 67: 1810-1822 6- Irvin, B.J., S.J. Ventura, and B.K. Slater, (1997) Fuzzy and isodata classification of landform elements from digital terrain data in Pleasant Valley, Wisconsin. Geoderma, 77: 137-154 7- Lillesand T. M. and Kiefer, R. W., 2000. Remote sensing and Image Interpretation, 4th edition, John Wiley and Sons, 715 pp. 8- McBratney, A.B.; I.O.A., Odeh; T.F.A., Bishop; M.S., Dunbar; T.M., Shatar, (2000) An overview of pedometric techniques for use in soil survey. Geoderma, 97: 293-327 pp. 9- McBratney. A.B., M.L. Mendoca Santos, B. Minasny, (2003) On digital Soil mapping. Geoderma, in press. 10- Ministry of Industry and Mines, (1976) Geologic survey of Iran, Geological Quadrangle No. F8


11- Moore, I.D., P.E. Gessler, G.A. Nielsen, and G.A. Peterson, (1993) Soil attribute prediction using terrain analysis. Soil Science Society of America Journal, 57(2): 443-452 12- Park, S.J., K. McSweeney, B. Lowery, (2001) Identification of the spatial distribution of soils using a process-based terrain characterization. Geoderma, 103: 249-272 13- Pennock D. G. (2003) Terrain attributes, landform segmentation, and soil redistribution. Soil& Tillage Research, 69: 15-26 14- Rossiter D. G., http://www.itc.nl/~rossiter/research/rsrch_ss_class.html 15- Saldana A., J. J. Ibanez, (2004?) Pedodiversity analysis at large scales: an example of three fluvial terrain of the Henares River (central Spain), Geoderma. In press. 16- Toomanian N., A. Jalalian, A. Zolanvar, (1999). Geologic Sources of Gypsum in Isfahan area. Journal of Agricultural Sciences & Natural Resources, Vol. 3, Isfahan University of Technology. (Persian).

Tables Legends Table 1- Part of table showing different soils in each landform Table 2- Part of profile locations with their original parent material which have evoluted from Table 3- Soil diversity depicted in each landform that was not able to differentiate in this scale of study

Figure Legends Figure 1: The study area in central Iran. Figure 2: Natural false color composite of Landsat images of the studied area

Fan

Plain

Segzi Playa

Mountain

Figure 2: Natural false color composite of Landsat images of the studied area

Borkhar Playa

Pro

ceed

ings

of T

he F

ourt

h In

tern

atio

nal I

ran

& R

ussi

a C

onfe

renc

e 66

2

Tab

le 1

- H

iera

rch

ical

lan

dfo

rm c

lass

ific

atio

n r

esu

lted

fro

m A

PI

No.

of

land

un

its

Land

scap

e La

ndfo

rm =

(re

lief -

mol

ding

) Li

thol

ogy

- fa

cies

G

eom

orph

ic s

urfa

ces

= (la

ndfo

rm)

1 M

ount

ain

Dis

ecte

d rid

ge

Dar

k gr

ey s

hale

with

lim

esto

ne (

naib

and

form

atio

n, T

n)

Str

uctu

red

surf

ace

2

Dis

ecte

d rid

ge

Mar

ly li

mes

tone

s (K

4, K

2)

Str

uctu

red

surf

ace

3

Roc

k pe

dim

ent

Err

oded

cal

care

ous

and

gyps

yfer

ous

rock

s S

carp

slo

pe

4Z

ayan

deh

Rud

Val

ley

Hill

land

, Dis

ecte

d rid

ge

Bas

al C

ongl

omer

ate

(OM

C)

C

liff+

Tal

us

5

Hill

land

, Err

oded

rid

ge

Dis

ecte

d da

rk g

rey

shal

e w

ith li

mes

tone

(T

n), G

ypsi

fero

us

Str

uctu

red

surf

ace

with

bra

ded

stre

am n

etw

ork

6

Hill

land

, Roc

ky h

igh

hill

Rem

nant

of s

hale

s (J

) S

lope

face

t com

plex

7

Pie

dmon

t, P

edim

ent

Rem

nant

of s

hale

s an

d di

ffere

nt li

mes

tone

s (J

, K2,

K4)

S

lope

face

t com

lex

with

out g

ypsu

m

8

Pie

dmon

t, P

edim

ent

Rem

nant

of s

hale

s (J

) S

lope

face

t com

plex

9

Pie

dmon

t, F

lush

floo

d fa

n de

lta

Qua

tern

ary

allu

vium

O

utw

ash

sedi

men

t (fin

er a

nd w

hite

) w

ith d

iver

gent

d

10

P

iedm

ont,

Flu

sh fl

ood

fan

delta

Q

uate

rnar

y al

luvi

um

Out

was

h se

dim

ent (

coar

ser)

11

P

iedm

ont,

Allu

vial

fan

Allu

vium

of f

oram

infe

ral l

imes

tone

and

bas

al c

ongl

omer

ate

(OM

, OM

C)

Api

cal p

art

12

P

iedm

ont,

Allu

vial

fan

Allu

vium

of d

ark

grey

sha

le w

ith li

mes

tone

(T

n, n

aiba

nd fo

rmat

ion)

A

pica

l par

t, sl

ope

face

t com

plex

13

P

iedm

ont,

Allu

vial

fan

Allu

vium

of m

arly

lim

esto

nes

(K2,

K4,

..)

Slo

pe fa

cet c

ompl

ex

14

P

iedm

ont,

Allu

vial

fan

Allu

vium

of m

arly

lim

esto

nes

(K2,

K4,

..)

Slo

pe fa

cet c

ompl

ex, c

ultiv

ated

15

P

iedm

ont,

Allu

vial

fan

Allu

vium

of m

arly

lim

esto

nes

(K2,

K4,

K7.

.)

Act

ive

fan,

Del

ta

16

P

iedm

ont,

Bah

ada

Allu

vium

of f

oram

infe

ral l

imes

tone

, bas

al c

ongl

omer

ate

and

volc

anic

s (O

M, O

MC

, Ev)

M

iddl

e pa

rt

17

P

iedm

ont,

Bah

ada

Allu

vium

of A

ndes

ite, g

rano

dior

ite a

nd fo

ram

infe

ral l

imes

tone

A

pial

par

t

18

P

iedm

ont,

Bah

ada

Allu

vium

of A

ndes

ite, g

rano

dior

ite a

nd fo

ram

infe

ral l

imes

tone

A

pial

par

t (ex

trea

mly

bra

ded

drai

nage

)

19

P

iedm

ont,

Bah

ada

Allu

vium

of

fora

min

fera

l lim

esto

ne (

OM

) D

ista

l par

t, w

ith d

ens

drai

age

netw

ork

20

P

iedm

ont,

Bah

ada

Allu

vium

of

fora

min

fera

l lim

esto

ne (

OM

) M

iddl

e pa

rt

21

P

iedm

ont,

Bah

ada

Allu

vium

of

fora

min

fera

l lim

esto

ne (

OM

) D

ista

l par

t, w

ith d

ens

drai

age

netw

ork

22

P

iedm

ont,

Bah

ada

Allu

vium

of

fora

min

fera

l lim

esto

ne (

OM

) D

ista

l par

t with

den

s dr

aina

ge n

etw

ork,

gyp

sife

rous

23

P

iedm

ont,

Bah

ada

Allu

vium

of

fora

min

fera

l lim

esto

ne (

OM

) D

ista

l par

t cal

care

ous

24

P

iedm

ont,

Bah

ada

Allu

vium

of

fora

min

fera

l lim

esto

ne (

OM

) D

ista

l par

t, sa

lt cr

uste

d, g

ypsi

fero

us

25

P

iedm

ont,

Bah

ada

Allu

vium

of m

arly

lim

esto

ns a

nd d

ark

grey

sha

le (

K4,

K2,

Tn)

M

iddl

e pa

rt w

ith p

aral

lel d

rain

age

patte

rn

26

P

iedm

ont,

Bah

ada

Allu

vium

of m

arly

lim

esto

ns a

nd d

ark

grey

sha

le (

K4,

K2,

Tn)

M

iddl

e pa

rt w

ith le

ss d

rain

age

27

P

iedm

ont,

Bah

ada

Allu

vium

of m

arly

lim

esto

ns a

nd d

ark

grey

sha

le (

K4,

K2,

Tn)

D

ista

l par

t with

par

alle

l net

wor

k

28

P

iedm

ont,

Dis

ecte

d ol

d ba

hada

A

lluvi

um o

f for

amin

fera

l lim

esto

ne (

OM

) P

aleo

terr

ace,

und

ulat

ing

plat

eau

29

P

iedm

ont,

Dis

ecte

d ol

d ba

hada

A

lluvi

um o

f for

amin

fera

l lim

esto

ne (

OM

) P

aleo

terr

ace,

with

bra

ded

inte

nse

netw

ork

30

P

iedm

ont,

Old

bah

ada

Mar

ly g

ypsi

fero

us a

lluvi

um

Pal

eote

rrac

e, fl

at, s

alty

31

P

iedm

ont,

Old

bah

ada

Qua

tern

ary

fine

allu

vium

P

aleo

terr

ace,

dis

tal p

art

32

P

iedm

ont,

Rol

ling

old

baha

da

Qua

tern

ary

coar

se,g

ypsi

fero

us a

lluvi

um

Pal

eote

rrac

e, g

ypsi

c pl

atea

u

Pro

ceed

ings

of T

he F

ourt

h In

tern

atio

nal I

ran

& R

ussi

a C

onfe

renc

e 66

3

33

R

iver

allu

vial

pla

in, A

lluvi

al fl

at

Fin

e riv

er a

lluvi

um

Upp

er te

rrac

e, fl

at, c

ultiv

ated

34

R

iver

allu

vial

pla

in, A

lluvi

al fl

at

Fin

e riv

er a

lluvi

um

Upp

er te

rrac

e, s

alty

35R

iver

allu

vial

pla

in, O

ld fl

ood

plai

n P

aleo

-riv

er a

lluvi

al te

rrac

e M

eand

erin

g co

mpl

ex fa

cet

36R

iver

allu

vial

pla

in, R

ecen

t flo

od

plai

n R

iver

allu

vium

C

hann

el m

argi

ne a

lluvi

um

37R

iver

allu

vial

pla

in, R

ecen

t flo

od

plai

n R

iver

allu

vium

C

hann

el s

edim

ents

38R

iver

allu

vial

pla

in, R

ecen

t flo

od

plai

n T

empo

raril

y st

reem

allu

vium

s C

hann

el m

argi

ne, f

ine

allu

vium

,sal

ty, o

ver

bank

flo w

39R

iver

allu

vial

pla

in, R

ecen

t flo

od

plai

n T

empo

raril

y st

reem

allu

vium

s C

hann

el s

edim

ent,

salty

40

P

laya

, Seg

zi b

asin

A

lluvi

o- la

guna

ry fi

ne s

edim

ents

, ext

ream

ly s

alty

and

gyp

sife

rous

W

et z

one,

flat

, sal

ty, c

ultiv

ated

41

P

laya

, Seg

zi b

asin

A

lluvi

o- la

guna

ry fi

ne s

edim

ents

, ext

ream

ly s

alty

and

gyp

sife

rous

W

et z

on, f

lat,

verr

y sa

lty

42

P

laya

, Seg

zi b

asin

A

lluvi

o- la

guna

ry fi

ne s

edim

ents

, ext

ream

ly s

alty

and

gyp

sife

rous

S

oft c

lay

flat,

very

alk

ali,

with

hig

h gr

ound

wat

er

43

P

laya

, Seg

zi b

asin

A

lluvi

o- la

guna

ry fi

ne s

edim

ents

, ext

ream

ly s

alty

and

gyp

sife

rous

S

oft c

lay

flat,

gyps

ifero

us (

win

d er

oded

), e

xtre

amly

s

44

P

laya

,Bor

khar

bas

in

Allu

vio-

lagu

nary

fine

sed

imen

ts, s

light

ly s

alty

hav

ing

caca

reuo

us d

ulls

S

oft c

lay

flat,

culti

vate

d

45

P

laya

, Mar

gh b

asin

A

lluvi

o- la

guna

ry fi

ne s

edim

ents

, gyp

sife

rous

P

uffy

gro

und,

lagu

nary

, gyp

sife

rous

1

Tab

le 2

- S

oil

div

ersi

ty d

epic

ted

in e

ach

lan

dfo

rm t

hat

was

un

able

to

dif

fere

nti

ate

in t

his

sca

le o

f st

ud

y

2

La

ndfo

rm

code

N

umbe

r of

pro

files

Stu

died

in th

is

land

form

F

requ

ency

C

lass

ifica

tion

1 M

o 32

1 N

o pr

ofile

0

2 M

o 33

1 N

o pr

ofile

0

3 P

i 421

18

7 -

1 18

7 T

ypic

Tor

riort

hent

s

4 H

i 111

1

-3 -

4 -

3

1- 3

Typ

ic T

orrio

rthe

nts

4 Li

ttic

Hap

logy

psid

s

5 H

i 411

38

-

1 38

Litt

ic T

orrio

rthe

nts

6 H

i 511

15

4 -

1 15

4 Li

ttic

Hap

logy

psid

s

7 P

i 111

10

4 -

105

- 10

6 -

107

- 10

8 -

109

- 13

13

0-10

5-10

6-10

7-10

8-10

9-12

2-18

9 T

ypic

Hap

loca

lcid

s

121

- 1

22 -

123

- 1

24 -

130

- 1

89 -

190

104

Typ

ic H

aplo

gyps

ids

121-

123

Typ

ic T

orrio

rthe

nts

190-

124

Typ

ic C

alci

gyps

ids

8 P

i 121

11

0 -

1 11

0 Li

ttic

Tor

riort

hent

s

9 P

i 211

46

- 5

0 -

2 46

-50

Typ

ic H

aplo

gyps

ids

10

Pi 2

12

45 -

1

45 T

ypic

Hap

logy

psid

s

11

Pi 8

21

7 -

1 7

Typ

ic T

orrio

rthe

nts

Pro

ceed

ings

of T

he F

ourt

h In

tern

atio

nal I

ran

& R

ussi

a C

onfe

renc

e 66

4

12

Pi 8

51

42 -

142

- 1

43 -

186

- 1

88

5 14

3-14

2-18

6 T

ypic

Hap

loca

lcid

s

42 T

ypic

Tor

riort

hent

s

188

Typ

ic H

aplo

gyps

ids

13

Pi 8

61

152

- 15

3 -

156

- 15

9 -

160

- 16

1 -

10

16

6-16

5-15

2-15

9-16

0 T

ypic

Hap

loca

lcid

s

165

- 16

6 -

168

- 16

9

153-

169

Typ

ic C

alci

gyps

ids

156-

161

Typ

ic T

orrio

rthe

nts

168

Typ

ic H

aplo

gyps

ids

14

Pi 8

62

170

-172

2

170-

172

- T

ypic

Tor

riort

hent

s

15

Pi 8

71

139

- 14

0 -

2 13

9 T

ypic

Cal

cigy

psid

s

140

Typ

ic H

aplo

calc

ids

16

Pi 9

11

6 -

1

6 T

ypic

Hap

logy

psid

s

17

Pi 9

21

8 -

14 -

2

8 T

ypic

Tor

riort

hent

s

14 T

ypic

Tor

riort

hent

s ov

er T

ypic

Hap

logy

psid

s

18

Pi 9

22

54 -

1

54 T

ypic

Tor

riort

hent

s

19

Pi 9

31

5 -

9 -

2 5

Typ

ic H

aplo

gyps

ids

9 T

ypic

Hap

loca

lcid

s

20

Pi 9

32

15 -

1

15 T

ypic

Hap

logy

psid

s

21

Pi 9

33

11 -

22

- 2

11-2

2 T

ypic

Hap

logy

psid

s

22

Pi 9

34

23 -

1

23 T

ypic

Hap

logy

psid

s

23

Pi 9

35

16 -

1

16 L

eptic

Hap

logy

psid

s

24

Pi 9

36

21 -

1

21 T

ypic

Hap

logy

psid

s

25

Pi 9

51

19 -

1

19 T

ypic

Hap

logy

psid

s

26

pi 9

52

20 -

157

- 1

58 -

162

- 1

63 -

164

- 1

67 -

17

38

20 L

eptic

Hap

logy

psid

s

157

Typ

ic H

aplo

gyps

ids

158-

163

Tip

ic C

alci

gyps

ids

162-

164

Typ

ic H

aplo

calc

ids

167-

173

Typ

ic T

orrio

rthe

nts

27

Pi 9

53

36 -

37

- 39

- 4

1 4

36-3

7-41

Typ

ic T

orrio

rthe

nts

39 T

ypic

Hap

logy

psid

s

28

Pi 1

011

10 -

53

- 55

3

10-5

5-53

Typ

ic H

aplo

gyps

ids

29

Pi 1

012

12 -

13

- 49

- 5

6 -

58 -

61

- 35

7

56-3

5-61

Typ

ic H

aplo

gyps

ids

12-1

3-58

Lep

tic H

aplo

gyps

ids

49 T

ypic

Tor

riort

hent

s

30

Pi 1

111

18 -

28

- 2

18 T

ypic

Hap

loca

mbi

ds

28 T

ypic

Cal

cigy

psid

s

Pro

ceed

ings

of T

he F

ourt

h In

tern

atio

nal I

ran

& R

ussi

a C

onfe

renc

e 66

5

31

Pi 1

121

33 -

1

33 T

ypic

Hap

loca

mbi

ds

32

Pi 1

211

67 -

74

- 76

3

76-7

4-67

Typ

ic H

aplo

gyps

ids

33

Ap

111

89-9

6-98

-100

-111

-113

- 28

17

5 T

ypic

Cal

ciar

gids

136-

137-

150-

171-

174-

191-

131-

132-

180-

181-

185-

129-

89-9

6-11

3 T

ypic

Hap

loca

mbi

ds

125-

127-

128-

129-

131-

132-

135-

191-

102

17

1 T

ypic

Hap

loca

lcid

s

175-

176-

178-

180-

181-

182-

183-

185

17

8-98

-99-

111-

174-

127-

102-

183-

135-

136-

137-

100

Typ

ic

Tor

riort

hent

s

125-

150-

176-

182

Typ

ic H

apla

rgid

s

34

Ap

121

72 -

79

- 82

- 8

4 -

88 -

87

- 77

- 1

33

8 87

-84-

72-8

2-13

3 T

ypic

Hap

loca

mbi

ds

79-7

7 T

ypic

Tor

riort

hent

s

88 T

ypic

Hap

larg

ids

35

Fp

311

91 -

97

- 99

- 1

12 -

114

- 1

20 -

101

- 1

03

8 99

-112

-114

-120

-101

-103

Typ

ic T

orrio

rthe

nts

97 T

ypic

Hap

loca

mbi

ds

91 G

ypsi

c H

aplo

salid

s

36

Fp

411

134

- 14

7 -

148

- 15

5 -

177

- 17

9 6

134

- 14

7 -

148

- 15

5 -

177

- 17

9 T

ypic

Tor

riort

hent

s

37

Fp

412

71 -

73

- 75

- 8

1

4 71

-73-

75 A

quic

Tor

riort

hent

s

81 T

ypic

Tor

riort

hent

s

38

Fp

421

No

prof

ile

R

iver

str

eem

39

Fp

422

85 -

1

85 T

ypic

Tor

ripss

amen

t

40

Pl 1

11

17 -

24

- 29

- 5

2 -

57 -

2

6 2-

29 T

ypic

Tor

riort

hent

s

17-5

2 T

ypic

Hap

loca

mbi

ds

24 T

ypic

Cal

ciar

gids

57 T

ypic

Hap

loca

lcid

s

41

Pl 1

12

25 -

27

- 48

- 5

1 -

60 -

64

- 66

- 6

9 -

14

92-4

8-43

-126

-66-

69-1

16 G

ypsi

c H

aplo

salid

s

92

- 93

- 9

5 -

116

- 12

6 -

43

51

-27-

60-9

3 T

ypic

Hap

losa

lids

25 T

ypic

Hap

logy

psid

s

64 C

alci

c H

aplo

salid

s

95 T

ypic

Tor

riort

hent

s

42

Pl 2

11

26 -

44

- 47

- 5

9 -

68 -

70

- 78

-

12

117-

26-4

4-59

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68-7

0-78

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Gyp

sic

Hap

losa

lids

80 -

86

- 11

7 -

118

- 83

80 T

ypic

Hap

losa

lids

86 T

ypic

Hap

logy

psid

s

47 T

ypic

Gyp

sarg

ids

43

Pl 3

11

62

- 63

- 9

0 -

94 -

115

- 1

19

6 63

-62-

94 L

eptic

Hap

logy

psid

s

119-

115-

90 G

ypsi

c H

aplo

salid

s

44

Pl 5

11

30-3

1-32

-34-

40-1

84-

65-1

44-1

45-1

46-

10

30-3

1-32

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65-T

ypic

Hap

loca

mbi

ds

40-1

84-1

45-1

46-T

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Tor

riort

hent

s

Pro

ceed

ings

of T

he F

ourt

h In

tern

atio

nal I

ran

& R

ussi

a C

onfe

renc

e 66

6

144-

Typ

ic H

apla

rgid

s

45

Pl 4

11

138

- 14

1 -

149

- 15

1 4

149-

138-

151

Typ

ic T

orrio

rthe

nts

141

Lept

ic H

aplo

gyps

ids

1

Tab

le 3

- P

art

of

pro

file

loca

tio

ns

wit

h t

hei

r o

rig

inal

par

ent

mat

eria

l wh

ich

is e

volu

ted

fro

m

No.

of

prof

ile

Geo

mor

phic

un

it O

rigin

al P

aren

t mat

er

Long

itude

La

titud

e G

enet

ic

horiz

on

dept

h

24P

l 111

P

laya

sed

imen

ts

51 5

8 23

.732

47

36.0

A

p -2

0

Btk

-6

0

C

-140

25P

l 112

P

laya

sed

imen

ts

51 5

6 46

.832

48

24.9

A

-2

0

By

-80

C

-1

50

26P

l 211

P

laya

sed

imen

ts

51 5

4 27

.132

48

58.4

A

z -1

4

By1

-3

5

By2

-8

0

By3

-1

50

27P

l 112

P

laya

sed

imen

ts

51 5

3 14

.332

50

53.5

Az

-18

B

w1

-47

B

w2

-85

B

k -1

40

28P

i 111

1 F

ine

pied

mon

t sed

imen

ts

51 5

3 42

.732

53

07.2

A

yz

-12

B

y1

-50

B

ky1

-83

B

ky2

-130

29P

l 111

P

laya

ove

r st

ream

sed

imen

ts

51 4

9 31

.632

50

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MORPHOLOGICAL AND PHYSICAL PROPERTIES OF SOME SOILS AT MONKIN, TARABA STATE, NIGERIA.

Morphological and physical properties of some soils at monkin, Taraba State, Nigeria

B. H. Usman1, I. E. Vahyala1 and B. B. Jakusko21 - Department of Soil Science, 2 – Department of Crop production and Horticulture, E-mail: [email protected], Tel: +79263045366, Federal University of Technology, P.M.B.2076 Yola, Adamawa State Nigeria.

Abstract. Irrigation practices have been used to increase food production in the world. In this study, a soil survey of Monkin, Zing L.G.A., Taraba state was conducted to ascertain its significance for irrigated agriculture. Six pedons were identified based on their morphological characteristics and were classified as Ustipsamments (USDA) or Haplic Leptosols (FAO), Typic Tropudul t(USDA)or Haplic Leptosols (FAO), Lithic Plinthustalf (USDA) or Lithic Lixisols (FAO), and a soil map was produced. Field determinations further suggests Typic Plinthustalf (USDA) or Plinthic Leptosols (FAO), Argillic Tropaqualf (USDA) or Haplic Luvisols (FAO). Their capability classification were 2e, 3e, class 4, class 6 and class 7 with irrigation suitability classification of class 2 irrigable, class 3 irrigable, class 4 restricted irrigable and class 6 non-irrigable. It is, therefore,recommended that the majority of land could be developed for irrigated agriculture as indicated by their potentials.

Key words: Nigeria, pedons, irrigable land, fadama, infiltration, soil classification, drainage

INRODUCTION:The development and expansion of agricultural lands for irrigation purposes requires adequate planning and projections. Some fundamental requirements for such land use planning are the knowledge of the morphological properties of the soils. This study was conducted at Monkin, Zing local government area of Taraba state, Nigeria, which lies between latitudes 8°40 , 9°60North and longitudes 10°20 , 11°57 East. Average annual rainfall of the area is 1520mm with a unimodal distribution and the peak discharge is usually in the months of July and August (TADP, 2002). The mean monthly temperature ranges between 20°C-25°C while the relative humidity is lowest (26%) in March and reaches 98% in August. The geology of the area could be described as a basement complex, having a well pronounced system of extrusions which form a part of the range of Adamawa Mountains (Adebayo and Tukur, 2000). Monkin, is well noted in Taraba state for agricultural produce such as yams, groundnuts, maize, cowpea and the river system allows for notable production of sugarcane. There exists’ a potential for irrigated agriculture through construction of a dam across river Monkin, hence the objective of this study is to ascertain the exact area which could be developed for irrigated agriculture and give appropriate management techniques for both soil and irrigation system so as to ensure land conservation and sustainable agricultural production.

MATERIALS AND METHOD: A grid survey system was employed as described by Anthony and David (1981). A baseline was chosen to coincide with the axis of the proposed dam across river Monkin, and perpendicular


transverses were drawn to cover the study area. Field work was carried out on a scale of 1:5,000 and auger holes were described at every 50m for soil thickness, texture, and moist soil colour according to Munsell colour chart notations (Munsell, 1975). Six profile pits were dug and described morphologically according to terms and codes of Soil Survey Staff (1975). Infiltration studies were conducted adjacent each of the profile pits using double ring infiltrometer method (ELE International, 1992). Soil samples were collected from identified genetic horizons for laboratory analysis. The abbreviations MK1-MK6 were used to describe the different soil series of Monkin,and a soil map was produced. The location of the study area (Monkin) is shown in Figures 1and 2.

Results and Discussions

Morphological characteristics:The soil depth in the study area ranges from a shallow soil less than 50cm having sandy to gravelly texture at the surface, with a dark yellowish brown colour (10YR 5/4) and a highly concretional subsurface having a poor structural development (MK1 & MK4), to deep soils, with depths greater than 120cm having a loamy textured surface horizon with a brown colour (10YR 4/6) and a clay or clay loam subsurface horizons having a dark brown colour (10YR 5/1) with a moderate structural development (MK2). The pedons identified as MK3 and MK5 have soil depths ranging between 50cm and 110cm. The soils have a loose structure less surface and subsurface horizons, with a sandy loam and sandy texture for the surface and subsurface horizons respectively. Horizon boundaries are generally clear or gradual and horizons were free from mottles except in a few. The changes in soil depths over the identified pedons follow the local relief, with shallow soils occurring on upper slopes and deep soil occurring down slope. The morphological properties of the soils of the study area are shown in Table 1.

Soil mapping and classification: The soil map of the survey area is shown in Figure 2.

Mapping unit MK1: This occurs on a level to gently undulating plains of river Monkin, some portions have upper and middle slopes with sand stone boulders. It consist of well drained to imperfectly drained soils with pockets of poorly drained areas and is characterized by scanty vegetation of shrubs and grasses. It is classified as Typic Plinthustalf (USDA) or Plinthic Leptosols (FAO). Land use comprises of arable agriculture with crops such as sorghum and millet. This mapping unit is found in south of Monkin town, occupying southern part of the river Monkin covering Danvo and Banta villages, with a total area of 564.25 hectares or 27.43% of the total area.

Mapping unit MK2: This occurs extensively to the south and north of river Ijadin, with gentle undulations, the soils are moderately deep, well drained to somewhat poorly drained. The unit is the major soil type covering an extensive portion of the total area surveyed. It is classified as Argillic Tropaqualf (USDA) or Haplic Luvisols, (FAO). It’s vegetation comprises trees, shrubs, tall and short grasses and is usually, cultivated with sorghum, maize, rice, yam, and cowpea. This unit covers a total of 977.25 hectares about 47.5% of the total area surveyed.

Mapping unit MK3: The soils of this unit have a loamy sandy surface texture and sandy textured subsurface horizons to a depth of 110cm with a remarkable infiltration capacity. It is


distributed on both sides of river Monkin and is classified as Typic Ustipsamment (USDA) or Haplic Leptosols (FAO). Its vegetations comprises of tall and short grasses with very few shrubs. It is cultivated with groundnuts, maize and sorghum, while sugar cane is cultivated along the river banks. It covers an area of 42.5hectares or 2.07% of the total area.

Mapping unit MK4: The soil in this unit occurs mostly on upper slopes having a shallow and poorly drained surface horizon, and a consolidated subsurface horizon. The texture is sandy to gravel and is characterized by scanty shrubs and short grasses. It is classified as Typic Plinthustalf (USDA) or Plinthic leptosols (FAO). It is usually not cultivated and has a total area of about 83.5hactares.

Mapping unit MK5: This unit occurs in northwestern part of the Monkin near Bodiga and Takuta villages. Pockets of this unit are fadama like in nature occurring near Busoboro village. It is classified as Typic Tropudult (USDA) or Haplic Leptosols (FAO). Extensive part of this unit occurs as a government forest reserve, and covers an area of 29.75hectares.

Mapping unit MK6: This unit consist of rocks outcrops (hills and inselbergs), although their fringes are cultivated. It’s classified as Lithic Plinthustalf (USDA) or Lithic Lixisols (FAO), with a total area of 359.76hectares.

Land capability classification and potential use: Various mapping units of the surveyed area can be grouped into the following capability classes (USDA): Class 2 - This capability class covers mapping unit MK1 and MK3, accounting for about 606.75 hectares. This land having minor physical limitations is suitable for a variety of crops and yields are expected to be high with optimum use of the right fertilizers .however, a sub-class 2e has been identified. Sub-class 2e - This is found within mapping unit MK1. The soils are sandy loam and located on a gently undulating plain where they are susceptible to erosion hazard. The erosion risk can be checked by proper land preparation practices. Class 3 - This class covers mapping unit MK2 with a total land of about 977.25 hectares. These soils are moderately good for cultivation. They are approaching marginality and therefore require maximum and rational application of fertilizers with more intensive and appropriate soil-crop management practices. A sub-class of 3e has been identified. Sub-class 3e - Erosion risks are high and obvious where the surfaces are sandy in nature. At some places where the subsoil’s are also sandy there is the tendency of gully erosion formation. The erosion hazard can be controlled by appropriate soil conservation measures. Class 4 - This capability class covers mapping unit MK4. The soils in this class should be put under cultivation with careful management. The soils have major limitations of fertility, depth, drainage, root zone and are prone to erosion hazard. This class occupies a total of 83.5 hectares of area surveyed. Class 7 - This capability class covers mapping unit MK5. This unit is under rough grazing and woodland. It is a forest reserve with an area of 29.75 hectares. Class 8 - This class covers mapping unit MK6. It occupies a total of 359.76 hectares of land. This class is unsuitable for any economic cropping activities and it is characterized by rock outcrops.


Basic infiltration rate. The results of infiltration studies are presented in Figure 1. The mean initial infiltration, final infiltration, and cumulative infiltrations are 93cm/hr,14.61cm/hr and 43.55cm respectively. The mean value of the steady state infiltration suggests a good intake rate (Folorunso, 1986). However, the infiltration capacity on MK5 (30cm/hr), is too high for conventional irrigation systems (ELE international, 1992). On Figure 3, MK1, MK4 and MK3 lies on several common points, and are therefore represented by their mean intake of 11cm/hr. Mk2, which has deep soils and an infiltration capacity of 4.7cm/hr, could be well utilized for both surface and sub surface irrigation systems. However, the shallow nature of the soils on Mk1 and Mk2 restricts its development to only surface or sprinkler irrigation system (Figure 4).

ConclusionsThe distribution of different soil classes in the area can be generally explained by the nature of the well pronounced system of extrusions resulting a variable of topography. Therefore, on upper slopes, shallow soils are commonly observed (Typic Plinthustalf-Mk1and Mk4), while very deep soils associated with colluvio-alluvial parent materials (Argillic Tropaqulf-Mk2 and Typic Ustipsamment-Mk3) occur in the depressions. The geology of the basement complex which is part of the range of Adamawa mountains and therefore has a range of fertility status, which provides opportunity for growing a wide range of crops, of which Monkin is well known. The infiltration capacity and drainage status of the soils provides ample opportunity for the development of an irrigation scheme, as shown by its suitability classification. However, proper management techniques must be employed on some of the soils due their extremely high infiltration capacity and structureless nature.

References ELE International (1992) Agronomics catalogue. Instrumentation for monitoring the agricultural

environment. The Artisan Press Limited, Leicester, U. K. FAO (1989) FAO/UNESCO. Soil Map of the World, Revised Legend. World Resources Report

60. Rome; FAO. Reprinted as Technical Paper 20, ISCRIC, Wageningen, 1989.138pp. Folorunso OA (1986) Distribution of field measured steady state infiltration rate for a Borno

State soil. Annals of Borno III 193-199. Munsell (1975) Munsell Soil Colour Charts. Baltimore. Tukur AL (2000) LANDFORMS In; Adamawa State in Maps. Edt. A.A.adebayo and

A.L.Tukur.Paraclete Publishers, Yola Nigeria. Soil Survey Staff (1975) Soil Taxonomy: a basic system of soil classification for making and

interpreting soil survey. U.S.D.A. Soil Conservation Service Agric Handbook. No. 436.


Table1. MORPHOLOGICAL CLASSIFICATION

Location.

Horizon

Depths Munsellcolor (moist)

Texture Structure Consistency

Boundary Remarks

AP 0-27 10YR5/2 Clay loam Sbk Sticky& Plastic

Boundary May fine andmediumroots

AB 27-38 10YR6/2 Clay loam Sbk Sticky& Plastic

Gradual& smooth

Many fine andmediumroots

Bt1 38-49 10YR5/2 Loamy sand

Loose Firm Gradual & smooth

-

Bt2 49-63 10YR4/6 Clay Massive Firm Gradual & smooth

-

Bt3 63-70 10YR4/2 Gravelly Massive Firm Gradual & smooth

-

Bt4 70-120 10YR5/1 Clay Massive Friable Gradual & smooth

-

Pedon1

Argillic Tropaqualf

Bt5 120-140 10YR5/4 Clay loam Sbk Friable Clear & smooth

-

AP 0-20 10YR5/4 Sandy loam

Sbk Friable - - Pedon2Typic Plinthustalf

AC 20-48 10YR4/6 Sandy loam

Sbk Friable Clear & smooth

Few tune roots

AP 0-26 10YR5/4 Sandy loam

Sbk Loose Clear & smooth

-

AC 26-50 10YR5/3 Gravelly Loose Firm - Few iron & Magnesium concretions

Pedon3Typic Tropudult

C 50-80 10YR4/6 Sandy Sbk Friable Gradual & way

Many gravels and few fine roots

AP 0-28 10YR5/4 Loamy sand

Loose Loose - Few distinct mottles

Pedon4Typic Plinthustalf AC 28-50 10YR4/6 Gravelly Sbk Friable Gradual

& smoothFew fine roots

Pedon5Typic

AP 0-22 10YR4/6 Loamy sand

Granular Loose - Gradual& smooth

Few fine roots


AC1 22-56 10YR4/6 Sandy Granular Loose Gradual & smooth

Few stones

AC2 56-84 10YR5/2 Sandy Loose Loose Clear & smooth

-

Ustipsamment

AC3 84-110 10YR4/6 Sandy Loose Loose - Common fine roots

AP1 0-19 10YR6/3 Loamy sand

- Loose Gradual & smooth

Few fine roots

AP2 19-23 10YR6/3 Gravelly Angular blocky

Firm Gradual & smooth

-

AC1 23-45 10YR6/3 Gravelly Angular blocky


-



Commonmottles

Pedon6Typic Plinthustalf


Massive Gradual & smooth

Commonmediumdistinct mottles

Basic infiltration rates

0

50

100

150

200

250

0 50 100 150 200

Time (minutes)

Inta

ke (c

m/h

r)

Site1,MK2

Site2,MK4

Site3,MK1

Site4,MK3

Site5,MK5

Site6,MK5

Figure 1. Basic Infiltration Rates.

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