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Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques

Author

Listed:
  • Mohamed A. E. AbdelRahman

    (National Authority for Remote Sensing and Space Sciences, Cairo 11769, Egypt)

  • Yasser M. Zakarya

    (Faculty of Agriculture, Ain-Shams University, Shubra Gardens 11241, Egypt)

  • Mohamed M. Metwaly

    (National Authority for Remote Sensing and Space Sciences, Cairo 11769, Egypt)

  • Georgios Koubouris

    (Institute for Olive Tree, Subtropical Crops and Viticulture, ELGO DEMETER, Agrokipio, 73100 Chania, Greece)

Abstract

Detailed knowledge of soil properties is fundamentally important for optimizing agriculture practices and management. Meanwhile, the spatial distribution of soil physicochemical properties is considered a fundamental input of any sustainable agricultural planning. In the present study, ordinary kriging, regression kriging and IDW were chosen for deciphering soil spatial variability and mapping soil properties in a reclaimed area of the Behera Governorate of Egypt where soil arose from two different types, one sandstone and the other limestone. Geostatistics were used to show the interrelationships and conditions of soil properties (available phosphorus, potassium and nitrogen, EC, pH, Sp, ESP, CEC, OC, SAR, and CaCO 3 ). The results of mapping spatial soil variability by Geostatistics could be used for precision agriculture applications. Based on the soil test results, nutrient management recommendations should be applied regarding variable rates of fertilizers. The performance of the maps was evaluated using Mean square error (MSE). Inverse distance weight (IDW) showed higher efficiency than Kriging as a prediction method for mapping the studied soil properties in the study area. The results of the present study suggest that the application of the selected fit model worldwide in any relevant study of soil properties of different geological sources is feasible.

Suggested Citation

  • Mohamed A. E. AbdelRahman & Yasser M. Zakarya & Mohamed M. Metwaly & Georgios Koubouris, 2020. "Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques," Sustainability, MDPI, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:194-:d:469256
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    References listed on IDEAS

    as
    1. Xueling Yao & Bojie Fu & Yihe Lü & Feixiang Sun & Shuai Wang & Min Liu, 2013. "Comparison of Four Spatial Interpolation Methods for Estimating Soil Moisture in a Complex Terrain Catchment," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-13, January.
    2. Runhong Liu & Yuanfang Pan & Han Bao & Shichu Liang & Yong Jiang & Hongrun Tu & Juanli Nong & Wanqing Huang, 2020. "Variations in Soil Physico-Chemical Properties along Slope Position Gradient in Secondary Vegetation of the Hilly Region, Guilin, Southwest China," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
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    Cited by:

    1. Jesús Barrena-González & Joaquín Francisco Lavado Contador & Manuel Pulido Fernández, 2022. "Mapping Soil Properties at a Regional Scale: Assessing Deterministic vs. Geostatistical Interpolation Methods at Different Soil Depths," Sustainability, MDPI, vol. 14(16), pages 1-20, August.

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