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Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates

Author

Listed:
  • Daniela De Benedetto

    (Council for Agricultural Research and Economics-Agriculture and Environment Research Center (CREA-AA), 70126 Bari, Italy)

  • Emanuele Barca

    (Water Research Institute (IRSA)—National Research Council (CNR), 70185 Bari, Italy)

  • Mirko Castellini

    (Council for Agricultural Research and Economics-Agriculture and Environment Research Center (CREA-AA), 70126 Bari, Italy)

  • Stefano Popolizio

    (Department of Soil, Plant and Food Sciences, University of Bari “A. Moro”, 70126 Bari, Italy)

  • Giovanni Lacolla

    (Department of Agricultural and Environmental Science, University of Bari “A. Moro”, 70126 Bari, Italy)

  • Anna Maria Stellacci

    (Department of Soil, Plant and Food Sciences, University of Bari “A. Moro”, 70126 Bari, Italy)

Abstract

Knowledge of the spatial distribution of soil organic carbon (SOC) is of crucial importance for improving crop productivity and assessing the effect of agronomic management strategies on crop response and soil quality. Incorporating secondary variables correlated to SOC allows using information often available at finer spatial resolution, such as proximal and remote sensing data, and improving prediction accuracy. In this study, two nonstationary interpolation methods were used to predict SOC, namely, regression kriging (RK) and multivariate adaptive regression splines (MARS), using as secondary variables electromagnetic induction (EMI) and ground-penetrating radar (GPR) data. Two GPR covariates, representing two soil layers at different depths, and X geographical coordinates were selected by both methods with similar variable importance. Unlike the linear model of RK, the MARS model also selected one EMI covariate. This result can be attributed to the intrinsic capability of MARS to intercept the interactions among variables and highlight nonlinear features underlying the data. The results indicated a larger contribution of GPR than of EMI data due to the different resolution of EMI from that of GPR. Thus, MARS coupled with geophysical data is recommended for prediction of SOC, pointing out the need to improve soil management to guarantee agricultural land sustainability.

Suggested Citation

  • Daniela De Benedetto & Emanuele Barca & Mirko Castellini & Stefano Popolizio & Giovanni Lacolla & Anna Maria Stellacci, 2022. "Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates," Land, MDPI, vol. 11(3), pages 1-18, March.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:3:p:381-:d:764233
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    References listed on IDEAS

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    3. Rita Leogrande & Carolina Vitti & Mirko Castellini & Marcello Mastrangelo & Francisco Pedrero & Gaetano Alessandro Vivaldi & Anna Maria Stellacci, 2021. "Comparison of Two Methods for Total Inorganic Carbon Estimation in Three Soil Types in Mediterranean Area," Land, MDPI, vol. 10(4), pages 1-11, April.
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    Cited by:

    1. Long Li & Yongjie Yue & Fucang Qin & Xiaoyu Dong & Cheng Sun & Yanqi Liu & Peng Zhang, 2022. "Multi-Scale Characterization of Spatial Variability of Soil Organic Carbon in a Semiarid Zone in Northern China," Sustainability, MDPI, vol. 14(15), pages 1-14, July.

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