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International Journal of Soil Science
  Year: 2012 | Volume: 7 | Issue: 1 | Page No.: 1-14
DOI: 10.3923/ijss.2012.1.14
 
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Determination the Factors Explaining Variability of Physical Soil Organic Carbon Fractions using Artificial Neural Network

Shamsollah Ayoubi and Parisa Mokhtari Karchegani

Abstract:
Limited information is available about the use of intelligent system such as Artificial Neural Networks (ANN) to determine the most affecting factors on variability of soil organic carbon fractions (SOC) in the landscape scale. Therefore, this study was conducted to estimate SOC fractions by topographic attributes, selected soil properties and Normalized Vegetation Index (NDVI) data using ANN models. A total of 108 samples from surface soils (0-10 cm depth) were collected and various physical soil organic fractions were determined. The developed ANN models could explain 78-91% of the total variability in SOC fractions in the site studied. Sensitivity analysis using ANN models developed showed that NDVI as indication of vegetation cover was the most important factor for explaining variability of SOC fractions at the site. Furthermore, soil properties such as clay, silt and calcium carbonate and some topographic attributes which indirectly affect the total SOC content, also significantly influence the variability of SOC fractions. In overall, the results showed that the ANN models provide reliable prediction of SOC fractions by considering the NDVI, soil properties and terrain attributes.
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How to cite this article:

Shamsollah Ayoubi and Parisa Mokhtari Karchegani, 2012. Determination the Factors Explaining Variability of Physical Soil Organic Carbon Fractions using Artificial Neural Network. International Journal of Soil Science, 7: 1-14.

DOI: 10.3923/ijss.2012.1.14

URL: https://scialert.net/abstract/?doi=ijss.2012.1.14

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