Digital Mapping of Soil Properties Using Ensemble Machine Learning Approaches in an Agricultural Lowland Area of Lombardy, Italy

Odunayo David Adeniyi, Alexander Brenning, Alice Bernini, Stefano Brenna and Michael Maerker ()
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Odunayo David Adeniyi: Department of Earth and Environmental Sciences, University of Pavia, 27100 Pavia, Italy
Alexander Brenning: Department of Geography, Friedrich Schiller University Jena, 07743 Jena, Germany
Alice Bernini: Department of Earth and Environmental Sciences, University of Pavia, 27100 Pavia, Italy
Stefano Brenna: ERSAF, Regione Lombardia Milan, 20124 Milano, Italy
Michael Maerker: Department of Earth and Environmental Sciences, University of Pavia, 27100 Pavia, Italy

Land, 2023, vol. 12, issue 2, 1-17

Abstract: Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear and nonlinear ML models in predicting and mapping soil properties in an agricultural lowland landscape of Lombardy region, Italy. We further evaluated the ability of an ensemble learning model, based on a stacking approach, to predict the spatial variation of soil properties, such as sand, silt, and clay contents, soil organic carbon content, pH, and topsoil depth. Therefore, we combined the predictions of the base learners (ML models) with two meta-learners. Prediction accuracies were assessed using a nested cross-validation procedure. Nonetheless, the nonlinear single models generally performed well, with RF having the best results; the stacking models did not outperform all the individual base learners. The most important topographic predictors of the soil properties were vertical distance to channel network and channel network base level. The results yield valuable information for sustainable land use in an area with a particular soil water cycle, as well as for future climate and socioeconomic changes influencing water content, soil pollution dynamics, and food security.

Keywords: digital soil mapping; ensemble machine learning; stacking model; terrain attributes; Lombardy lowland (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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