JWSS - Journal of Water and Soil Science

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A two-year study (1996-97) was conducted to verify tillage effects on several soil properties and corn yield. The soil (fine loamy, Thermic, typic Haplargids) was treated by conventional (CT) and no-till (NT) systems. Soil organic matter (OM), mean weight diameter (MWD), penetration resistance (Cl), bulk density (BD), total nitrogen (TN) and aggregate size distribution at depths of 0-20 and 20-40 cm were measured.

No-till system caused the OM to be twice as much as that in the conventional tillage system. Total nitrogen in the NT and at depths of 0-20 and 20-40 cm were higher by 30% and 20%, respectively. No differences obtained in bulk density and penetration resistance, but MWD in the NT was 20% and 10% higher than CT in the 0-20 and 20-40 cm depths, respectively. Mean weight diameter of the aggregates in the CT was smaller than that in NT. Aggregates of less than 0.25 mm at 0-20 cm depths were almost 25% higher in CT compared to NT system. The yield in the NT system was significantly lower than CT. Although reduced cultivation could bring a better soil physical condition, low initial organic matter, weak structure and heavy-textured soil produced unsuitable conditions for the crop roots and, consequently, resulted in low yield. Therefore, no-till system in this region would not be recommended.

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Remote sensing science and satellite data are widely used by researchers for agricultural studies. Vegetation spectral reflections recorded by satellite sensors have been used extensively for identifying plant types, plant cover, health community of plants and predicting yield. The TERRA satellite, with 5 sensors, provides an opportunity to observe land, atmosphere and ocean characteristics. The Moderate Resolution Imaging Spectroradiometer (MODIS) is on–board TERRA satellite. This sensor with 36 bands by 250m, 500m, and 1000m spatial resolution help us to study our environment. The MODIS vegetation indices are used to monitor photosynthetic activity radiation, change detection in plant communities, planted area estimation and plant health. A statistical analysis was done to analyze Near Infra Red (NIR) (841-876 nm) and Red (R) (620-670 nm) bands of MODIS images for a 16 day period. The images have been used for winter wheat in Mashhad (North East of IRAN) during agricultural season of 2004-05.Some image processing techniques were used to extract the related digital numbers (DN), showing the electromagnetic spectrum reflection for all of the pixels. The analysis shows a positive correlation between R and NIR spectrum (0.70 and 0.69) and decrease in NDVI (0.18 and 0.24) in the first and late wheat growth season. However, there is not such a good correlation in the middle of the season and NDVI increased very much. In spite of having wheat cover in the field, NIR reflection decreased very much in the late wheat growth season (0.5). Therefore the correlation relation between R and NIR band along with NDVI could be used effectively in precision agriculture management such as predicting of phonological stage, wheat yield estimation and wheat health condition.

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Industrial, agricultural and urban activities have contaminated soil by heavy metals that can also increase concentration of the metals in food chains. This study was carried out in Isfahan province where lots of such activities are in progress. The purpose of this study was to determine spatial variability of Arsenic )As) in Isfahan soils. In this research, the soil samples )0-20 cm) were collected in a stratified random sampling system at about 4 Km intervals in a study area of 6800 Km2. The positions of samples were recorded using a GPS. After laboratory preparation, soil samples were measured for total As. Spatial structures of total As were determined by directional variograms. Spherical model was the best model to describe spatial variability of As. Mean-square error )MSE) and correlation coefficient were used to validate variograms. Distribution map for Arsenic was prepared using the obtained information from element by point kriging method and by using Surfer software. Interpolation in blocks by dimensions of 1000×1000 m was made. The mine effective factors with high concentration of As are parent material, and direction of dominant wind has affected the spread of As in north-west of the study area.

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In this study, the influence of spatial heterogeneity of rainfall on flood hydrograph prediction in three mountainous catchments in south west of Iran was studied. Two interpolation techniques including Thiessen polygons method and Inverse Distance Weighting method were applied to compare the rainfall patterns of surrounding rain-gages in hydrograph simulation with rainfall patterns of nearest rain-gage from the catchment outlet. It was found that the best simulated hydrograph is obtained from rainfall pattern of the nearest rain gage. Moreover, the results did not show any relationship between spatial variation of rainfall and outlet hydrograph. Formation of different local rainfall patterns due to non-stationary rainfall field provoked by irregular topography and their effect on interpolation procedure caused important biases in interpolated rainfall hyetographs obtained by Thiessen and IDW methods. It seems that the observed biases in the response of the catchments are the result of inaccurate representation of spatially averaged rainfall rather than its spatial variability. Hence, in mountainous catchments with irregular topography, the lack of sufficient records caused by poor rain gage arrangement can be highlighted as the dominant source of uncertainty in modeling the spatial variations of rainfall.

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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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