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

Daniela De Benedetto, Emanuele Barca, Mirko Castellini, Stefano Popolizio, Giovanni Lacolla and Anna Maria Stellacci
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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

Land, 2022, vol. 11, issue 3, 1-18

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.

Keywords: SOC spatial distribution; regression kriging (RK); multivariate adaptive regression splines (MARS); secondary variables; electromagnetic induction technique (EMI); ground-penetrating radar (GPR) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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