M. Hashemi; Ahmad Gholamalizadeh Ahangar; Abolfazl Bameri; F. Sarani; A. Hejazizadeh
Abstract
Introduction: In order to provide a database, it is essential having access to accurate information on soil spatial variation for soil sustainable management such as proper application of fertilizers. Spatial variations in soil properties are common but it is important for understanding these changes, ...
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Introduction: In order to provide a database, it is essential having access to accurate information on soil spatial variation for soil sustainable management such as proper application of fertilizers. Spatial variations in soil properties are common but it is important for understanding these changes, particularly in agricultural lands for careful planning and land management.
Materials and Methods: To this end, in winter 1391, 189 undisturbed soil samples (0-30 cm depth) in a regular lattice with a spacing of 500 m were gathered from the surface of Miankangi land, Sistan plain, and their physical and chemical properties were studied. The land area of the region is about 4,500 hectares; the average elevation of studied area is 489.2 meters above sea level with different land uses. Soil texture was measured by the hydrometer methods (11), Also EC and pH (39), calcium carbonate equivalent (37) and the saturation percentage of soils were determined. Kriging, Co-Kriging, Inverse Distance Weighting and Local Polynomial Interpolation techniques were evaluated to produce a soil characteristics map of the study area zoning and to select the best geostatistical methods. Cross-validation techniques and Root Mean Square Error (RMSE) were used.
Results and Discussion: Normalized test results showed that all of the soil properties except calcium carbonate and soil clay content had normal distribution. In addition, the results of correlation test showed that the soil saturation percentage was positively correlated with silt content (r=0.43 and p
Ahmad Gholamalizadeh Ahangar; F. Sarani; M. Hashemi; A. Shabani
Abstract
Knowledge of organic carbon spatial variations in different land uses will help to interpret and simulate the behavior of terrestrial ecosystems facing environmental and climate changes. The purpose of this study is comparing regression, geostatistics and artificial neural network (ANN) methods for predicting ...
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Knowledge of organic carbon spatial variations in different land uses will help to interpret and simulate the behavior of terrestrial ecosystems facing environmental and climate changes. The purpose of this study is comparing regression, geostatistics and artificial neural network (ANN) methods for predicting organic carbon content in 192 samples of surface soil (0 to 30 cm) of Sistan plain (Miankangi region). In this study, Only 25% of organic carbon variations were explained by variables used in linear regression model in the study area (R2= 0.25). Moreover, simple co-kriging (with clay as co-variable) which was the best geostatistical method in the current study, predicted organic carbon content weakly (R2= 0.23 and RMSE= 0.127). However, using latitude and longitude parameters, ANN performed much better than linear regression and geostatistical methods for predicting organic carbon content (R2= 0.79 and RMSE= 0.044).
