| Title | Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates |
| Abstract | 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. |
| Source | Land (Basel) |
| Keywords | SOC spatial distribution; regression kriging (RK); multivariate adaptive regression splines (MARS); secondary variables; electromagnetic induction technique (EMI); ground-penetrating radar (GPR) |
| Journal | Land (Basel) |
| Editor | Molecular Diversity Preservation International, Basel, |
| Year | 2022 |
| Type | Articolo in rivista |
| Authors | Daniela De Benedetto 1 , Emanuele Barca 2,* , Mirko Castellini 1 , Stefano Popolizio 3 , Giovanni Lacolla 4 and Anna Maria Stellacci 3 |
| Text | 474946 2022 SOC spatial distribution; regression kriging RK ; multivariate adaptive regression splines MARS ; secondary variables; electromagnetic induction technique EMI ; ground penetrating radar GPR Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates Daniela De Benedetto 1 , Emanuele Barca 2, , Mirko Castellini 1 , Stefano Popolizio 3 , Giovanni Lacolla 4 and Anna Maria Stellacci 3 1 Council for Agricultural Research and Economics Agriculture and Environment Research Center CREA AA , 70126 Bari, Italy; daniela.debenedetto@crea.gov.it D.D.B. ; mirko.castellini@crea.gov.it M.C. 2 Water Research Institute IRSA National Research Council CNR , 70185 Bari, Italy 3 Department of Soil, Plant and Food Sciences, University of Bari A. Moro , 70126 Bari, Italy; stefano.popolizio@uniba.it S.P. ; annamaria.stellacci@uniba.it A.M.S. 4 Department of Agricultural and Environmental Science, 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. Published version Prediction of Soil Organic Carbon at Field Scale by Regression Kriging and Multivariate Adaptive Regression Splines Using Geophysical Covariates file pdf land 11 00381 v2 1 .pdf Articolo in rivista Molecular Diversity Preservation International 2073 445X Land Basel Land Basel emanuele.barca BARCA EMANUELE |
