IDEAS home Printed from https://ideas.repec.org/a/caa/jnlswr/v13y2018i1id139-2016-swr.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://swr.agriculturejournals.cz/doi/10.17221/139/2016-SWR.html
    Download Restriction: free of charge
    File URL: http://swr.agriculturejournals.cz/doi/10.17221/139/2016-SWR.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/139/2016-SWR?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Manuel Chaves-Maza & Eugenio M. Fedriani Martel, 2020. "Entrepreneurship support ways after the COVID-19 crisis," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 662-681, December.
    2. Mingoti, Sueli A. & Lima, Joab O., 2006. "Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1742-1759, November.
    3. Andreas Wunsch & Tanja Liesch & Stefan Broda, 2022. "Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 39-54, January.
    4. repec:onb:oenbwp:y:2005:i:9:b:1 is not listed on IDEAS
    5. Guobin Fu & Stephanie R. Clark & Dennis Gonzalez & Rodrigo Rojas & Sreekanth Janardhanan, 2023. "Spatial and Temporal Patterns of Groundwater Levels: A Case Study of Alluvial Aquifers in the Murray–Darling Basin, Australia," Sustainability, MDPI, vol. 15(23), pages 1-18, November.
    6. Pérez-Campuzano, Darío & Rubio Andrada, Luis & Morcillo Ortega, Patricio & López-Lázaro, Antonio, 2022. "Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance," Journal of Air Transport Management, Elsevier, vol. 101(C).
    7. Paradi, Joseph C. & Zhu, Haiyan & Edelstein, Barak, 2012. "Identifying managerial groups in a large Canadian bank branch network with a DEA approach," European Journal of Operational Research, Elsevier, vol. 219(1), pages 178-187.
    8. Lozano, S. & Guerrero, F. & Onieva, L. & Larraneta, J., 1998. "Kohonen maps for solving a class of location-allocation problems," European Journal of Operational Research, Elsevier, vol. 108(1), pages 106-117, July.
    9. Onsel Sahin, Sule & Ulengin, Fusun & Ulengin, Burc, 2004. "Using neural networks and cognitive mapping in scenario analysis: The case of Turkey's inflation dynamics," European Journal of Operational Research, Elsevier, vol. 158(1), pages 124-145, October.
    10. Antonio Russo & Ian Smith, 2012. "Attractive regions: for whom? And how does that matter?," ERSA conference papers ersa12p362, European Regional Science Association.
    11. Pankaj Kumar Medhi & Sandeep Mondal, 2016. "A neural feature extraction model for classification of firms and prediction of outsourcing success: advantage of using relational sources of information for new suppliers," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6071-6081, October.
    12. Ramin Baghai‐Wadji & Rami El‐Berry & Stefan Klocker & Markus Schwaiger, 2006. "Changing investment styles: style creep and style gaming in the hedge fund industry," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(4), pages 157-177, October.
    13. Ozer, Muammer, 2005. "Fuzzy c-means clustering and Internet portals: A case study," European Journal of Operational Research, Elsevier, vol. 164(3), pages 696-714, August.
    14. Ulengin, Fusun & Ulengin, Burc & Onsel, Sule, 2002. "A power-based measurement approach to specify macroeconomic competitiveness of countries," Socio-Economic Planning Sciences, Elsevier, vol. 36(3), pages 203-226, September.
    15. Marcus Deetz, 2019. "K-Means Clustering of Self-Organizing Maps: An Empirical Study on the Information Content of Self-Classification of Hedge Fund Managers," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 5(3), pages 43-57, March.
    16. Z Hua & S Li & Z Tao, 2006. "A rule-based risk decision-making approach and its application in China's customs inspection decision," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(11), pages 1313-1322, November.
    17. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    18. Eun Sun Kim & Yunjeong Choi & Jeongeun Byun, 2019. "Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    19. Curry, B. & Morgan, P. H., 2004. "Evaluating Kohonen's learning rule: An approach through genetic algorithms," European Journal of Operational Research, Elsevier, vol. 154(1), pages 191-205, April.
    20. Atefeh Abdolhay & Bahram Saghafian & Mohd Soom & Abdul Ghazali, 2012. "Identification of homogenous regions in Gorganrood basin (Iran) for the purpose of regionalization," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 61(3), pages 1427-1442, April.
    21. Machado de CAMPOS, Silvia Regina & Henriques, Roberto & Yanaze, Mitsuru Higuchi, 2019. "Knowledge discovery through higher education census data," Technological Forecasting and Social Change, Elsevier, vol. 149(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlswr:v:13:y:2018:i:1:id:139-2016-swr. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.