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Spatial Prediction and Digital Mapping of Soil Texture Classes in a Floodplain Using Multinomial Logistic Regression

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Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation (INFUS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 308))

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Abstract

The spatial distribution of physical soil properties is an important requirement in practice as basic input data. Most effective of these properties is soil texture that governs water holding capacity, nutrient availability, and root development. Detailed information on soil texture variability in lateral dimension is crucial for proper crop and land management and environmental studies. Soil texture classes are determined in the soil survey. It may be consist of two or more texture classes for each polygon according to soil mapping units. There is a spatial discrepancy due to variability in soil texture within the mapping polygon. Digital soil mapping (DSM) offers major innovations in removing some of the inconsistencies in traditional soil mapping. DSM methodology can integrate the various raster-based spatial environmental data that field-based soil morphology, soil analyses, and effects of soil formation factors. In this study, the potential of environmental variables generated from digital data to predict soil texture classes were investigated. Curvature parameters indicating the shape of the slope were determined as the most important predictive variables in a flood plain. Overall accuracy was calculated as 63.9% and 47.60% for the training set and the test set, respectively. Digital soil map can be used effectively by farmers in the management of crops in this plain.

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Acknowledgement

This study was supported by Scientific Research Fund of Isparta University of Applied Sciences. Project number: 2019-YL1–0044.

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Authors and Affiliations

  1. Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Isparta University of Applied Sciences, 32260, Isparta, Çünür, Turkey

    Fuat Kaya & Levent Başayiğit

Authors
  1. Fuat Kaya
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  2. Levent Başayiğit
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Corresponding author

Correspondence to Fuat Kaya .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  3. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  4. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Basar Oztaysi

  5. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

  6. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

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Kaya, F., Başayiğit, L. (2022). Spatial Prediction and Digital Mapping of Soil Texture Classes in a Floodplain Using Multinomial Logistic Regression. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A.C., Sari, I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-030-85577-2_55

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