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Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

Author

Listed:
  • Marzieh MOKARRAM

    (Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran)

  • Mahdi NAJAFI-GHIRI

    (Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran)

  • Abdol Rassoul ZAREI

Abstract

Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

Suggested Citation

  • Marzieh MOKARRAM & Mahdi NAJAFI-GHIRI & Abdol Rassoul ZAREI, 2018. "Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 13(1), pages 11-17.
    • Handle: RePEc:caa:jnlswr:v:13:y:2018:i:1:id:139-2016-swr
    DOI: 10.17221/139/2016-SWR
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    References listed on IDEAS

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    1. Mangiameli, Paul & Chen, Shaw K. & West, David, 1996. "A comparison of SOM neural network and hierarchical clustering methods," European Journal of Operational Research, Elsevier, vol. 93(2), pages 402-417, September.
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