Commenced in January 2007
Paper Count: 30850
Sensitivity Analysis for Determining Priority of Factors Controlling SOC Content in Semiarid Condition of West of Iran
Abstract:Soil organic carbon (SOC) plays a key role in soil fertility, hydrology, contaminants control and acts as a sink or source of terrestrial carbon content that can affect the concentration of atmospheric CO2. SOC supports the sustainability and quality of ecosystems, especially in semi-arid region. This study was conducted to determine relative importance of 13 different exploratory climatic, soil and geometric factors on the SOC contents in one of the semiarid watershed zones in Iran. Two methods canonical discriminate analysis (CDA) and feed-forward back propagation neural networks were used to predict SOC. Stepwise regression and sensitivity analysis were performed to identify relative importance of exploratory variables. Results from sensitivity analysis showed that 7-2-1 neural networks and 5 inputs in CDA models output have highest predictive ability that explains %70 and %65 of SOC variability. Since neural network models outperformed CDA model, it should be preferred for estimating SOC.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056791Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1142
 M. Amini, K.C.Abbaspour, H.vKhademi, N.vFathianpour, M. Afyuni,, R. Schulin, Neural network models to predict cation exchange capacity in arid regions of Iran. Europ. J. of Soil Sci. vol. 56, pp. 551-559, 2005.
 J.A. Anderson.. Introduction to neural networks. Prentice-Hall of India, New Delhi. 2001
 APERI, Mahidasht-Sanjabi plain study :phase 1: climate study. TAM consulting engineers, Ministry of Agriculture, Iran. ) vol. 2, 2004.
 T.W. Beers, Dress, P.E., Wensel, Aspect transformation in site productivity research. J. of Forertry, vol. 64, pp. 691-692. 1966.
 C.E.P. Cerri, M. Easter, K. Paustian, K. Killian, K. Coleman, M. Bernoux, P. Falloon, D.S. Powlson, N.H. Batjes, E. Milne, C.C. Cerri, Predicted soil organic carbon stocks and changes in the Brazilian Amazon between 2000 and 2030. Agric., Ecosys. and Environ. Vol. 122, pp. 58-72, 2007.
 IPCC, Land-use, land-use change, and forestry. In: Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J., Dokken, D.J. (Eds.), A Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. 2000.
 M. Kim, T. Kim, A neural classifier with fraud density map for effective credit card fraud detection. IDEAL, pp. 378-383. 2002.
 Lal, R.,. Soil carbon dynamics in cropland and rangeland. Environ. Pollut. Vol. 116, pp. 353-362, 2002.
 E.R. Levine, D.S. Kimes, Predicting soil carbon in Mollisols using neural networks. pp. 473-484. In R. Lal et al. (ed.) Soil process and the carbon cycle, CRC Press, Boca Raton, FL. 1997.
 E.R. Levine, D.S. Kimes, V.G. Sigilitto, Modeling soil structure using artificial neural network. Ecol. Modell. Vol. 92, pp. 101-108, 1996.
 D. Liu, Z. Wang, B. Zhang, K. Song, X. Li, J. Li, F. Li, H. Duan, Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China. Agric., Ecosys. and Environ. Vol. 113, pp. 73-81, 2006.
 A.B. McBratney, B. Minasny, S.R. Cattle, R.W. Vervoot, From pedotransfer function to soil inference system. Geoderma vol. 109, pp. 41-73, 2002.
 J. McCullagh, A modular neural network architecture for rainfall estimation, Artificial Intelligence and Applications. Innsbruck, Austria, pp. 767-772, 2005.
 K. McVay, C. Rice, Soil organic carbon and the global carbon cycle. Technical Report MF-2548, Kansas State University, Kansas. 2002.
 B. Minasny, A.B. McBratney, The neuron-m method for fitting neuralnetwork parametric pedotransfer functions. Soil Sci. Soc. Am. J. vol. 66, pp. 352-361, 2002.
 A. Nemes, M.G. Shaap, J.H. Wo, M. Sten, Functional evaluation of pedotransfer functions derived from different scales of data collection. Soil Sci. Soc. Am. J. vol. 67, pp. 1093-1193, 2003.
 M. Omid, A. Baharlooei, H. Ahmadi, Modeling Drying Kinetics of Pistachio Nuts with Multilayer Feed-Forward Neural Network. Drying Tech. vol. 27, no.10, pp. 1-9, 2009.
 S.J. Park, P.L.G. Vlek, Environmental correlation of three dimensional soil spatial variability: A comparison of three adaptive. Geoderma , vol. 109, pp. 117-140, 2002.
 F. Sarmadian, R. Taghizadeh Mehrjardi, A. Akbarzadeh, Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan province, north of Iran. Austral. J. of Basic and Applied Sci. vol. 3, no. 1, 323-329, 2009.
 S. Somaratne, G. Seneviratne, U. Coomaraswamy, Prediction of Soil Organic Carbon across Different Land-use Patterns:A Neural Network Approach. Soil Sci. Soc. Am. J. vol. 69, pp. 1580-1589, 2005.
 G.P. Sparling, D. Wheeler, E.T. Wesely, L.A. Schipper, What is soil organic matter worth?. J. Environ. Qual. Vol. 35, pp. 548-557, 2006.
 M.J. Spencer, T. Whitfort, J. McCullagh, Dynamic ensemble approach for estimating organic carbon using computational intelligence. Proceedings of the 2nd IASTED international conference on Advances in computer science and technology. Puerto Vallarta, Mexico, 2006.
 Z. Tan, R. Lal, Carbon Sequestration Potential Estimates with Changes in Land Use and Tillage Practice in Ohio, USA. Agric., Ecosys. and Environ., vol. 126, pp. 113-121, 2005.
 Z. Tan, R. Lal, N. Smeck, F. Calhoun, Relationships between surface soil organic carbon pool and site variables, Geoderma, vol. 121, pp. 187- 195, 2004.
 P.J. Werbos, Backpropagation, basic and new developments. pp. 134- 139. In Arbib, M.A., (ed.) The handbook of brain behavior and neural networks. The MIT Press, Cambridge, MA. 1995.
 B. Wolf, G.H. Snyder, Sustainable Soils: the place of organic matter in sustaining soil and their productivity. Food Products Press. New York, 2003.
 G. Zhang, Neural Networks in Business Forecasting, IRM Press, Hershey, PA., 2004.