Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus

Fuat Kaya, Ali Keshavarzi, Rosa Francaviglia, Gordana Kaplan, Levent Başayiğit and Mert Dedeoğlu
Additional contact information
Fuat Kaya: Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Isparta University of Applied Sciences, Isparta 32260, Türkiye
Ali Keshavarzi: Laboratory of Remote Sensing and GIS, Department of Soil Science, University of Tehran, P.O. Box 4111, Karaj 31587-77871, Iran
Rosa Francaviglia: Research Centre for Agriculture and Environment, Council for Agricultural Research and Economics, 00184 Rome, Italy
Gordana Kaplan: Institute of Earth and Space Sciences, Eskisehir Technical University, Eskisehir 26555, Türkiye
Levent Başayiğit: Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Isparta University of Applied Sciences, Isparta 32260, Türkiye
Mert Dedeoğlu: Department of Soil Science and Plant Nutrition, Agriculture Faculty, Selçuk University, Konya 42130, Türkiye

Agriculture, 2022, vol. 12, issue 7, 1-27

Abstract: Predicting soil chemical properties such as soil organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC and Ava-P is influenced by both natural and anthropogenic factors. This study aimed at (1) predicting SOC and Ava-P in a piedmont plain of Northeast Iran using the Random Forests (RF) and Cubist mathematical models and hybrid models (Regression Kriging), (2) comparing the models’ results, and (3) identifying the key variables that influence the spatial dynamics of soil SOC and Ava-P under different agricultural practices. The machine learning models were trained with 201 composite surface soil samples and 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) and key soil features (S) according to the SCORPAN digital soil mapping framework, which can predictively represent soil formation factors spatially. Clay, one of the most critical soil properties with a well-known relationship to SOC, was the most important predictor of SOC, followed by open-access multispectral satellite images-based vegetation and soil indices. Ava-P had a similar set of effective variables. Hybrid approaches did not improve model accuracy significantly, but they did reduce map uncertainty. In the validation set, Ava-P was calculated using the RF algorithm with a normalized root mean square (NRMSE) of 96.8, while SOC was calculated using the Cubist algorithm with an NRMSE of 94.2. These values did not change when using the hybrid technique for Ava-P; however, they changed just by 1% for SOC. The management of SOC content and the supply of Ava-P in agricultural activities can be guided by SOC and Ava-P digital distribution maps. Produced digital maps in which the soil scientist plays an active role can be used to identify areas where concentrations are high and need to be protected, where uncertainty is high and sampling is required for further monitoring.

Keywords: digital soil mapping; landsat 8 OLI; sentinel 2A MSI; soil organic carbon; phosphorus; environmental covariates; machine learning; hybrid techniques; land use; arid regions (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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