Parvizi, Y., Mahdian, M. (2014). Estimate Soil Organic Carbon Variability of Rainfed lands and Sensitivity Analysis of Predictor Variables. , 27(3), 31-38. doi: 10.22092/wmej.2014.106891
Yahya Parvizi; Mohammad Hossein Mahdian. "Estimate Soil Organic Carbon Variability of Rainfed lands and Sensitivity Analysis of Predictor Variables". , 27, 3, 2014, 31-38. doi: 10.22092/wmej.2014.106891
Parvizi, Y., Mahdian, M. (2014). 'Estimate Soil Organic Carbon Variability of Rainfed lands and Sensitivity Analysis of Predictor Variables', , 27(3), pp. 31-38. doi: 10.22092/wmej.2014.106891
Parvizi, Y., Mahdian, M. Estimate Soil Organic Carbon Variability of Rainfed lands and Sensitivity Analysis of Predictor Variables. , 2014; 27(3): 31-38. doi: 10.22092/wmej.2014.106891

Estimate Soil Organic Carbon Variability of Rainfed lands and Sensitivity Analysis of Predictor Variables

پژوهش های آبخیزداری
Article 4, Volume 27, Issue 3 - Serial Number 104, September 2014, Pages 31-38    PDF (635.07 K)
Document Type: Research
DOI: 10.22092/wmej.2014.106891
Authors
Yahya Parvizi* 1; Mohammad Hossein Mahdian2
1Assistant Professor, Soil and Watershed Protection Research Department, Agricultural and Natural Resources Research Center, Kermanshah
2Professor, Agricultural Research, Education and Extension Organization, Tehran
Abstract
Since Soil organic carbon (SOC) has a key role in production and soil productivity, it  has also important role in sustainable soil and crop specially in our country. Among soil particles, SOC variability in medium time scale is a management based practice. This research was conducted to investigate the effects of physical and management variables on SOC variations and to quantify the relative importance of these variables on SOC distribution in a rainfed watershed by use of artificial neural networks (ANNs) technique. Sampling was done based on randomized systematic method and simulation, analysis of physical and management factors impacts and sensitivity analysis of predictor variables were don using artificial neural networks (ANNs). Results indicated that among ANNs that applied to simulate SOC, those with 2 hidden layers and two nodes in each layer with than transfer function have high efficiency with MSE=0.04 – 0.07 in SOC prediction. ANN models with management exploratory variable were able to distinguish SOC contents more efficient. Best ANN model with all 31 exploratory variables can estimate about 78 percent of SOC variability. Sensitivity analysis showed that tillage, crop residue and rotation related prediction variables by controlling about 24, 24 and 18 of SOC variability, respectively,  have greatest effects.
Keywords
Soil organic carbon; Artificial Neural Networks; Rainfed; Sensitivity analysis
References
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