Prediction stock of soil organic carbon in Argentina

Authors: Olmedo, Guillermo F.; Angelini, Marcos E.; Schulz, Guillermo A.; Rodríguez, Darío M.; Taboada, Miguel A.; Pascale, Carla; Escobar, Dardo; +11 Authors

doi: 10.5281/zenodo.6323694 , 10.5281/zenodo.6323695

Prediction stock of soil organic carbon in Argentina

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Abstract

We standardized the Stocks soil organic carbon (SOC) at 0-30 cm depth for 5,073 soil samples. We spatially predicted SOC stock (kg/m2) using regression forest and associated prediction uncertainties using quantile regression forest at 1000 m resolution. Global accuracy based on cross-validation. We obtained a RMSE 2.624 and Rsquared 0.464.

Related Organizations

International Soil Reference and Information Centre
Netherlands
Wageningen University & Research
Netherlands
National Autonomous University of Mexico
Mexico
National Agricultural Technology Institute
Argentina

Keywords

digital soil mapping, soil organic carbon, quantile regression forest, data harmonization

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