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The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Author

Listed:
  • Katarzyna Pentoś

    (Institute of Agricultural Engineering, Wrocław University of Environmental and Life Sciences, 37b Chełmońskiego Street, 51-630 Wrocław, Poland)

  • Krzysztof Pieczarka

    (Institute of Agricultural Engineering, Wrocław University of Environmental and Life Sciences, 37b Chełmońskiego Street, 51-630 Wrocław, Poland)

  • Kamil Serwata

    (Agri Solutions Sp. z o.o., Ligota Wielka 34, 56-400 Oleśnica, Poland)

Abstract

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

Suggested Citation

  • Katarzyna Pentoś & Krzysztof Pieczarka & Kamil Serwata, 2021. "The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil," Agriculture, MDPI, vol. 11(2), pages 1-11, February.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:2:p:114-:d:491037
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    References listed on IDEAS

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    1. Katarzyna Pentoś & Krzysztof Pieczarka & Krzysztof Lejman, 2020. "Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions," Complexity, Hindawi, vol. 2020, pages 1-11, January.
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    Cited by:

    1. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.

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