JWSS - Journal of Water and Soil Science

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Qualitative land suitability evaluation is based on the physical parameters affecting the yield agricultural crops. The socio-economic factors are not considered in such evaluations. This study was carried out to assess the qualitative land suitability for irrigated wheat, barley, maize and rice in northern Baraan. Five soil series and 25 different phases were first delineated. Land utilization types commonly practiced in the region with intermediate input levels were considered. Physical requirements of the crops were determined and ranked using the available information. FAO framework and the proposed method of Sys were used for the evaluation. Qualitative evaluation was carried out according to both simple limitation and parametric methods by comparing land and climatic characteristics with crop requirements.

 It was shown that in addition to climatic factors, soil salinity, drainage and some soil physical properties such as texture, gravel and lime are the most limiting factors. Land physical suitability for rice production was very low in most land units. The results from physical evaluation by parametric method were very close to those from the simple limitation approach.

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In quantitative land suitability evaluation, economic aspects land evaluation such as impact of environmental physical factors on crop production and the amount of yield per surface unit are considered. The purpose of this research was to study quantitative land suitability of northern Baraan area located in eastern Isfahan. The study was a continuation of the previously accomplished qualitative land suitability evaluation of the area. The economic studies include economic data collection, matching inputs and outputs and gross margins analysis. Radiation-thermal production potential of the crops was calculated based on plant physiology and temperature (FAO model), which was 10.45, 10.11, 13.64 and 11.93 ton/ha for wheat, barley, maize and rice, respectively. Radiation-thermal production potential, observed and marginal yields and the results of the qualitative land evaluation were used to perform quantitative land evalution and to determine the corresponding suitability classes. The predicted yield in different land units varies between 1.64 and 9.17 ton/ha for wheat, 1.81 and 9 ton/ha for barley, 2.06 and 9.42 ton/ha for maize and 2.35 and 7.14 ton/ha for rice. Presence of significant statistical correlation between the observed and the predicted yield values reveals the validity of the evaluation methods used. The results from quantitative land evaluation show that most of the land units are slightly to moderately suitable for wheat, barley, maize and rice, while a few are not. The best land utilization type in each land unit can be selected through a combined consideration of quantitative land evaluation results, benefitability of each land unit and impact of the land use on the soil. Rice cultivation is not recommended in the study area, because of its adverse effect on soil physical properties and soil drainage.

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Paleosols include soils formed under climatic condition different from the present. Although such soils are widespread in central Iran region, adequate investigations of them are yet to be carried out. Micromorphology is one of the most important tools in plaeoclimatological studies. This investigation was carried out to study microscopic features of two paleosols from Isfahan province to reconstruct the paleoclimatic condition during the Quaternary. The results of this study indicate that strong clay coatings are presented in Sepahanshahr paleosol, indicating moisture regime in the past. This paleosol is polygenetic due to calcite and gypsum accumulation during drier periods compared to clay illuviation condition. Micromorphological features in Segzi paleosol indicate that this area has experienced a swampy environment during the younger Dryas. The overall results from this study indicate that climatic oscillation evidences during Quaternary have been preserved in paleosols from Isfahan region.

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Overgrazing is the most important agent which causes accelerated soil erosion and land degradation in arid and semi-arid zones of Iran. Appropriate planning and land use in these areas based on land suitability evaluation provide a suitable base for conserving the land and controling desertification. Land evaluation identifies possible alternatives in land use which will more effectively meet national or local needs and assists in assessing the consequences of these alternatives. Extensive grazing refers to the land utilization type in which animals feed in natural pastures. This study was performed to evaluate physical potential of the given watershed for grazing by sheep and goats, and assess the limiting factors for the land utilization type in Mehr watershed, Sabzevar, Khorasan province. Land qualities which were evaluated include accessibility to animals, soil erodibility, moisture availability, rooting conditions, salinity and alkalinity, and drinking water availability for animals. Above mentioned land qualities were assessed by appropriate land characteristics. The requirement of grazing land utilization type was defined in terms of rated land characteristics. Matching of requirements of LUT with the land qualities of each pixel of DEM (prepared in 200×200m by GIS software) resulted in a rating for every land characteristics. Some characteristics such as slope, aspect, and distance to drinking water for animals were calculated directly by GIS. Land index for every pixel was calculated by square root method. Finally, qualitative and physical land suitability classes were determined based on land indices and classified to polygons which would be suitable in grazing management. The results were interpreted under two different scenarios. In the first scenario, drinking water for animals was supplied by permanent sources and in the second one, the supplying of water was developed to temporary rivers besides the permanent sources. With the analysis of spatial modeling it was possible to assess the land suitability with higher accuracy. Overall results showed that the given area was not highly suitable for grazing at all. The most limiting factors included moisture availability for plant growth, slope, rock fragment and outcrops and distance to drinking water. Also during the late winter, spring and early summer, when the seasonal rivers were supplying the drinking water, the limitation of given area was decreased.

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In Iran, the development of cultivated areas becomes gradually impossible due to ever-increasing population growth and urban area development. Therefore, it is very important to use the existing cultivated lands more efficiently. Land suitability evaluation makes the sustainable use of the lands feasible. The objective of this study was qualitative, quantitative and economic assessment of land suitability in Talandahst area for rainfed wheat, barley and chickpea. Talandasht plain with a surface area of 4500 ha is located southwest of Kermanshah city. The climate is semi-arid with cold winter and moderate summer. The successive stages of this research included soil survey in the field, soil analysis, qualitative and quantitative and economic evaluations of land suitability. In qualitative evaluation of land, climatic, topographic and soil suitability classes were determined according to the degree of the matching. Limitation and parametric methods were used in qualitative evaluation. Quantitative and economic evaluations made based on the observed yield and gross benefit, respectively. Based on qualitative evaluation, the studied area is marginally suitable for rainfed farming of wheat, barley and chickpea. This is due to water deficiency occurring during some stages of the growing cycle. The solution for this problem is supplementary irrigation. In addition to climate limitation, there are also topographic and soil restriction for the growth of the studied crops. On the basis of observed yield, the land units are moderately to highly suitable for rainfed wheat and barley production. Among the three named crops, the most and least profitable ones are chickpea and barley, respectively, and wheat ranks between them.

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Evaluating agricultural land management practices requires a thorough knowledge of soil spatial variability and understanding their relationships. This study was conducted at a traditionally operated wheat field in Sorkhankalateh district, located about 25 km northeast of Gorgan, in Golestan province, Iran. Soil samples of the 0-30 cm depth were collected right after planting at the end of autumn 2004 , 100 × 180m plot in a nested grid pattern (n=101). A 1 m² plot of wheat was harvested at each of 101 sites previously sampled at the end of spring. Statistical results showed that frequency distribution of all data was normal. The highest and lowest CV was related to grain yield (20.40%) and pH (0.59%) respectively. Variogram analysis showed that all parameters had spatial structure and the range values showed considerable variability among the measured parameters. The ranges of spatial dependence showed a variation from 23.99m for total N up to 93.92m for K. Among the parameters, total N and ESP had stronger spatial dependence while P had the lower spatial dependence. Interpolated maps of Kriging demonstrated that crop and soil properties did not have a random pattern but had a spatial distribution. The spatial distribution of total N was similar to organic matter and also there was similarity between spatial distribution of harvest index and available P. The results demonstrated that, the spatial distribution and spatial dependence level of soil properties can be different even within similarly managed farms. Variography and Kriging can be useful tools for designing soil sampling strategies, characterizing management zones and variable application rates of inputs in the precision agriculture.

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  Soil erosion is one of the most threatening issues for crop production and environmental qualities, especially for soil and water resources. Appropriate knowledge about total soil loss and runoff is valuable in order to perform soil and water conservation practices in watersheds. EUROSEM, "a single event, dynamic and distributed model," was developed to simulate soil loss, sediment transportation and deposition by rill and interrill processes. This study was conducted to evaluate EUROSEM model in order to simulate soil loss and runoff in Sulijan sub-basin, which covered 20 ha, from Charmah-Bakhtari province. The sub-basin was divided in to 19 homogeneous elements using topographic, land use, plant cover, slope and channel properties throughout it. Soil, plant cover, land surface and climate characteristics were measured and evaluated by field observations and laboratory measurements. Actual soil loss and runoff for studied events were determined by direct measurement in the field. After sensitivity analysis, calibration and validation steps were carried out to simulate runoff and soil loss. The results of sensitivity analysis showed that the EUROSEM model for predicting runoff was more sensitive to hydraulic conductivity, capillary drive and initial soil moisture. On the other hand the model for predicting soil loss was more sensitive to Manning's coefficient and soil cohesion. The results showed that the EUROSEM model was able to simulate well the total runoff, peak of runoff discharge, total soil loss and time for the peak of soil loss discharge. But that could not simulate well the peak of soil loss discharge and time for the peak of runoff. Although it seems that EUROSEM is able to predict soil loss and runoff partially well in individual events, it is necessary to evaluate the efficiency of the models for different basins with varieties of soil, plant cover and climatic properties.

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  Extension of cultivation areas becomes gradually impossible due to ever-increasing population growth and urban area development in Iran. Therefore, it is very important to use the existing cultivated lands more efficiently. Land suitability evaluation makes the sustainable use of the lands feasible. The objective of this study was qualitative, quantitative and economic suitability evaluation of irrigated croplands for wheat, maize and sesame in Mehran plain, Ilam Province. Soil survey in the field, laboratory analysis of the soil samples, qualitative, quantitative and economic evaluation were different successive stages of this research. In qualitative evaluation, climatic, topographic and soil suitability classes were determined according to the degree of the matching with plant requirements, by parametric (square root) method. Quantitative and economic evaluations were done based on observed, potential and marginal yield analysis. Results of the qualitative land evaluation showed that most of the land units were classified moderately suitable for given crops because of soil limitations. Qualitatively, most of the land units were classified in the same classes as, or in lower classes than quantitative suitability classes for wheat and maize production, due to high management level at the farms. Whereas quantitative classes of sesame were determined lower than qualitative classes induced by low management level for this crop. Economic land suitability classification showed that the wheat production was the most economic land utilization type. Results of the economic assessment suggested that the cultivation of wheat in rotation with sesame would produce the most income for different units and could be increased in future using improvement in management level in the study area for sesame cultivation.

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Understanding distribution of soil properties at the field scale is important for improving agricultural management practices and for assessing the effects of agriculture on environmental quality. Spatial variability within soil occurs naturally due to pedogenic factors as well as land use and management strategies. The variability of soil properties within fields is often described by classical statistical and geostatistical methods. This research was conducted to study what factors control the spatial variability of soil nutrients using an integration of principal component analysis and geostatistics in Appaipally Village, Andra Pradesh, India. 110 soil samples were randomly collected from 0-30 cm and prepared for laboratory analyses. Total N, available P, Ca, K, Na, Mg, S, B, Mn, Fe, Zn were measured using standard methods. Statistical and geostatistical analysis were then performed on raw data. The results of PCA analysis showed that 4 PC's had Eigen-value of more than 1 and explained 71.64 % of total variance. The results of geostatistical analysis revealed that three PC's had isotropic distribution based on surface variogram. Spherical model was fitted to all PC's. Ranges of model were 288 and 393 m for PC1 and PC3 respectively. On the other hand the range for PC2 was significantly different (877m). The most important elements in PC2 such as Fe, Mn, and Zn probably had similar range of effectiveness (700-900m). The comparison of PC's distributions indicated that PC1 and PC3 including total N, available Mg, K, Cu, Ca and P, were in accordance with farming plots dimensions and management practices. Therefore, it is necessary to improve the appropriate fertilizers used by farmers. The pattern of PC2 distribution was not consistent with farmer's plots, but had the best concordance with soil acidity. Therefore, the most correlated elements with this PC including Fe, Mn, and Zn are mainly controlled by soil acidity and not affected by management practices. However, spatial variability of these elements in areas lower than critical values should be considered for site-specific management.

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Crop yield, soil properties and erosion are strongly affected by terrain parameters. Therefore, knowledge about the effects of terrain parameters on strategic crops such as wheat production will help us with sustainable management of landscape. This study was conducted in 900ha, of Ardal district, Charmahal and Bakhtiari Province to develop regression models on wheat yield components vs. terrain parameters. Wheat yield and its components were measured in 100 points. Points were distributed randomly in stratified geomorphic surfaces. Yield components were measured by harvesting of 1 m2 plots. Terrain parameters were calculated by a 3×3 m spacing from digital elevation model. The result of descriptive statistics showed that all variables followed a normal distribution. The highest and lowest coefficient of variance (CV) was related to grain yield (0.36) and thousand seeds weight (0.13), respectively. Multiple regression models were established between yield components and terrain parameters attributes. The predictive models were validated using validation data set (20% of all data). The regression analysis revealed that wetness index and curvature were the most important attributes which explained about 45-78% of total yield components variability within the study area. The overall results indicated that topographic attributes may control a significant variability of rain-fed wheat yield. The result of validation analysis confirmed the above-stated conclusion with low RMSE and ME measures.

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Gypsum is the most common sulfate mineral in soils of arid and semi-arid regions. Two hundred million hectares of the earth’s surface and about 28 million hectares of Iranian arid soils are covered by gypsiferous soils. The studied soils consist of alluvial plane and eolian plains in northern Iran. Soils were classified as Haplocambids, Aquisalids and Endoaquepts. In this study, genesis, formation and development of gypsum in soils were investigated. Study area includes different geomorphic surfaces from the upper watershed to alluvial plains. Gypsum crystals without considering their size, shape, arrangement are determined as pedogenic. In the weakly-developed horizons, in aridic moisture regimes, formation of euhedral pedogenic gypsum crystals and incomplete infilling of voids was dominant. In the well-developed soils, in xeric moisture regime, gypsum crystals are oriented and the perpendicular in arrangement of crystals along pores is dominants. Gypsum crystals occur in different shapes such as lenticular, tabular, and rosette-like and fan arrangement. Origin of gypsum in weakly-developed soils was weathered gypsiferous marls in upper Atrak basin. The source of gypsum in the soils with shallow ground water is high content of sulfate dissolved in soil solutions. In horizons affected by fluctuation of ground water contaning sulphur, euhedral tabular shape is dominant. In well-developed soils, based on Snowball morphology, gypsum is classified as stage II nodule. Results showed that there is no relationship between formation of the snowball morphology, b-fabric and kind of diagnostic horizons (A, B and C).

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Digital soil mapping includes soils, spatial prediction and their properties based on the relationship with covariates. This study was designed for digital soil mapping using binary logistic regression and boosted regression tree in Zarand region of Kerman. A stratified sampling scheme was adopted for the 90,000 ha area based on which, 123 soil profiles were described. In both approaches, the occurrence of relevant diagnostic horizons was first mapped, and subsequently, various maps were combined for a pixel-wise classification by combining the presence or absence of diagnostic horizons. Covariates included a geomorphology map, terrain attributes and remote sensing indices. Among the predictors, geomorphology map was identified as an important tool for digital soil mapping approaches as it helped increase the prediction accuracy. After geomorphic surfaces, the terrain attributes were identified as the most effective auxiliary parameters in predicting the diagnostic horizons. The methods predicted high probability of salic horizon in playa landform, gypsic horizon in gypsiferous hills and calcic horizon in alluvial fans. Both models predicted Calcigypsids with very low reliability and accuracy, while prediction of Haplosalids and Haplogypsids was carried out with high accuracy.

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Heavy metals are known to have deleterious effects on human health. The main route of human exposure to heavy metals is the daily intake of food. This study was designed to investigate the heavy metal concentrations (Cu, Zn, Mn, Fe, Cr, Ni and Cd) in soil and major food crops (wheat, potato and corn) and estimate the health risks of metals to humans via soil and the crops consumed in Hamedan Province, using the total non-cancer hazard quotient. Daily metal intakes were estimated for three receptor groups and then compared with health guideline values. The non-cancer risk estimations showed that chromium, manganese, cadmium, zinc, Iron, Nickel and copper have oral Hazard Quotient values less than a value of one. The Hazard Index values were greater than 1 for all age groups, suggesting that adults and children in the study area may experience a potential non-cancer risk due to diet of heavy metal via wheat, corn and potato consumption and soil ingestion. Consumption of plant foods particularly wheat was found to be the major route of human exposure to heavy metal. The soil ingestion route is also important.

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Digital soil mapping techniques which incorporate the digital auxiliary environmental data to field observation data using software are more reliable and efficient compared to conventional surveys. Therefore, this study has been conducted to use k- Nearest Neighbors (k-NN) and artificial neural network (ANN) to predict spatial variability of soil salinity in Ardekan district in an area of 700 km², in Yazd province. In this study, 180 soil samples were collected in a grid sampling manner and then soil chemical and physical properties were measured in laboratory. Environmental auxiliary variables were included topographic attributes, remote sensing data (ETM+) and apparent electrical conductivity (ECa). The result of the study showed that the K-mean nearest neighborhood had higher accuracy than ANN models for predicting soil electrical conductivity (ECe). Overall, k-NN models could provide significant relationships between soil salinity data and environmental auxiliary variables. The k-NN model had the root mean square and coefficient of determination of 12.10 and 0.92, respectively, between predicted and observed ECe data. Also, apparent EC, and remotely sensed indices and wetness index were identified as the most important factors for predicating the soil salinity in the studied area.

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This study is aimed to investigate the spatial variation of soil macronutrients such as phosphorus, potassium and organic matter using different methods of Geostatistics and Geostatistical method combined with Fuzzy logic to estimate the values of this element to provide a spatial distribution map for the proper distribution of fertilizer in the plain of Uremia. Spatial variations in soil nutrients are natural but knowing these changes for careful planning and management particularly in the agricultural lands is simply inevitable. This information is necessary to increase the profitability and sustainable agricultural management. Therefore, to estimate the changes in the elements of places not sampled, the Kriging, Fuzzy Kriging, Cokriging and Inverse Distance Weighting  methods have been used in GS +. In this study, Matlab 9.1 software was used to fuzzification of the data and GIS was used for the final mapping. The parameters MAE, MBE and RMSE were used to compare these methods. The results showed that the combined method of Fuzzy Geostatistic with the mean absolute error values for the elements phosphorus, potassium and organic matter i.e. 0.17, 0.18 and 0.18, respectively, is recognized as the preferred method based on which zoning maps have been prepared for P, K and OC in GIS.

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Mapping the spatial distribution of soil taxonomic classes is important for useful and effective use of soil and management decisions. Digital soil mapping (DSM) may have advantages over conventional soil mapping approaches as it may better capture observed spatial variability and reduce the need to aggregate soil types. A key component of any DSM activity is the method used to define the relationship between soil observations and environmental covariates. This study aims to compare multiple logistic regression models and covariate sets for predicting soil taxonomic classes in Bam district, Kerman province. The environmental covariates derived from digital elevation models, Landsat imagery, geomorphology map and soil unit map that were divided into two different sets: (1) variables derived from digital elevation models, remote sensing and geomorphology map, (2) variables derived from digital elevation model, remote sensing, geomorphology map and the soil map. Stratified sampling schemes were defined in 100000 hectares, and 126 soil profiles were excavated and described. The results of accuracy model showed that data set 2 increased accuracy of model including overall accuracy, kappa index, user accuracy and reliability of the producer. The results showed that the multiple logistic regression model can promote traditional soil mapping and it can be used to large group of other scientific fields.
 

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