Somaiye Moghimi; Yahya Parvizi; Mohammad Hossein Mahdian; Mohammad Hassan Masihabadi
Abstract
Soil organic carbon is one of the most important soil characteristics, and any changes in its content and composition, affects soil physical, chemical, and biological characteristics. Enhancing soil organic carbon improves soil structure, increases water and nutrients in soils, reduces soil erosion and ...
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Soil organic carbon is one of the most important soil characteristics, and any changes in its content and composition, affects soil physical, chemical, and biological characteristics. Enhancing soil organic carbon improves soil structure, increases water and nutrients in soils, reduces soil erosion and degradation and thus greater productivity of plants and water quality are expected in watersheds and ultimately soil and ecosystem reclamation happens. Climatic, topographic and managerial factors affect soil organic carbon content. In local scale, climatic factors have not high efficiency on soil organic carbon and topographic factors play more important role compared to climate on soil organic carbon variability. The objective of this study was to predict and evaluate the effects of topographic factors such as elevation, slope percent, aspect, hill shade, and curvature on the soil organic carbon content of a rangeland in Mereg watershed, Kermanshah, Iran. Stepwise Multi Linear Regression (MLR) and Artificial Neural Network (ANN) were employed to develop models to predict soil organic carbon. AMulti-Layer Perceptrons (MLP) ANN withback-propagationerror algorithm was applied to this research.Theresult showed that themulti linear regression and ANN models explained53and 77percent of the total variability of soil organiccarbon, respectively. The calculated RMSE and MBE were 0.40 and 0 for the MLR and 0.16 and 0.003 for MLP models. Results indicated that designated ANN model with 5-9-1 arrange was more feasible than multi linear regression for predicting soil organic carbon. Elevation with 0.79, hill shade with 0.64 and slope percent with 0.24, were identified as the important factors that explained the variability of soil organic carbon.
Reza Sokouti Oskooei; Mohammad Hossein Mahdian; Shahla Mahmoodi; Mohammad Hasan Masihabadi
Volume 2, Issue 3 , October 2010, , Pages 161-169
Abstract
Planning and suitable management is necessary for optimal use of soil and for this; spatial variability of soil characteristics is important which may be edcarried out through geostatistical methods of parametric and non-parametric predictors such as TPSS, WMA, Kriging and Co-kriging. This research work ...
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Planning and suitable management is necessary for optimal use of soil and for this; spatial variability of soil characteristics is important which may be edcarried out through geostatistical methods of parametric and non-parametric predictors such as TPSS, WMA, Kriging and Co-kriging. This research work was done in Southern part of Uromieh plain with 36690 ha surface area in order to study the spatial variability of soil lime, sand and saturation moisture percentage. Distance between soil profiles ranged 1300 to 4700 meters. For estimation and prediction of them in non-sampled points, the Kriging, Co- kriging and Weighted Moving Average were used in Geographic Information System environment. For selecting suitable interpolation method, Cross validation and MAE and MBE parameters were used. Selected method was also used for estimating and mapping of the selected soil characteristics. The Sturges rule was used for defining map classification. Results showed that the Kriging method has the highest accuracy with correlation coefficient of 0.83 and error of 3.98 percent for prediction of soil characteristics in non-sampled points.
