Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru
Soil organic carbon stocks (SOCS) are critical components of the global carbon cycling and play a central role in climate change mitigation. However, their dynamics in high‐altitude Andean ecosystems remain poorly understood despite their importance for carbon sequestration. The significant spatial...
| Main Authors: | , , , , |
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| Format: | Preprint |
| Language: | Inglés |
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Elsevier B.V.
2025
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| Online Access: | http://hdl.handle.net/20.500.12955/2952 https://doi.org/10.1016/j.geodrs.2025.e01026 |
| _version_ | 1855490558650220544 |
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| author | Carbajal Llosa, Carlos Miguel Tumbalobos Dextre, Merely Condori Ataupillco, Levi Tatiana Cuellar Condori, Nestor Edwin Gavilan, Carla |
| author_browse | Carbajal Llosa, Carlos Miguel Condori Ataupillco, Levi Tatiana Cuellar Condori, Nestor Edwin Gavilan, Carla Tumbalobos Dextre, Merely |
| author_facet | Carbajal Llosa, Carlos Miguel Tumbalobos Dextre, Merely Condori Ataupillco, Levi Tatiana Cuellar Condori, Nestor Edwin Gavilan, Carla |
| author_sort | Carbajal Llosa, Carlos Miguel |
| collection | Repositorio INIA |
| description | Soil organic carbon stocks (SOCS) are critical components of the global carbon cycling and play a central role in climate change mitigation. However, their dynamics in high‐altitude Andean ecosystems remain poorly understood despite their importance for carbon sequestration. The significant spatial heterogeneity of SOCS in mountainous terrain makes accurate quantification and mapping challenging. This study evaluated the performance of geospatial regression and machine learning (ML) approaches for predicting SOCS in two Peruvian Andean basins: Torobamba and Coata. We compared Geographically Weighted Regression (GWR), GWR with collinearity analysis (GWRC), their kriging‐adjusted variants, and ML models (Random Forest, Gradient Boosting). Models were built using key SOCS covariates for each basin and validated through 5‐fold cross‐validation with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). In Torobamba, GWRC markedly improved performance, reducing the RMSE by 79–90% and achieving R² up to 0.99. In contrast, Coata, showed only modest improvements (RMSE reductions of 7.8–9.8%, R² = 0.30–0.39). ML models performed poorly (negative R²), likely due to feature selection, parameter tuning, or limited sample size. Overall, locally weighted regression approaches (GWRK/GWRCK) outperformed conventional ML methods for SOCS prediction in complex mountain environments, particularly with small to medium sample sizes. These results highlight the importance of accounting for spatial non‐stationarity in SOCS and provide methodological guidance for SOCS mapping in Andean ecosystems. |
| format | Preprint |
| id | INIA2952 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier B.V. |
| publisherStr | Elsevier B.V. |
| record_format | dspace |
| spelling | INIA29522025-12-03T16:57:54Z Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru Carbajal Llosa, Carlos Miguel Tumbalobos Dextre, Merely Condori Ataupillco, Levi Tatiana Cuellar Condori, Nestor Edwin Gavilan, Carla Digital soil mapping Soil organic carbon stock Geographically weighted regression Machine learning regression algorithms Andes Cartografía digital de suelos Reservas de carbono orgánico del suelo Regresión ponderada geográficamente Algoritmos de regresión de aprendizaje automático https://purl.org/pe-repo/ocde/ford#4.01.04 Regresión de paso cauteloso; stepwise regression; Cuenca hidrográfica; Watersheds; Perú; Peru Soil organic carbon stocks (SOCS) are critical components of the global carbon cycling and play a central role in climate change mitigation. However, their dynamics in high‐altitude Andean ecosystems remain poorly understood despite their importance for carbon sequestration. The significant spatial heterogeneity of SOCS in mountainous terrain makes accurate quantification and mapping challenging. This study evaluated the performance of geospatial regression and machine learning (ML) approaches for predicting SOCS in two Peruvian Andean basins: Torobamba and Coata. We compared Geographically Weighted Regression (GWR), GWR with collinearity analysis (GWRC), their kriging‐adjusted variants, and ML models (Random Forest, Gradient Boosting). Models were built using key SOCS covariates for each basin and validated through 5‐fold cross‐validation with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). In Torobamba, GWRC markedly improved performance, reducing the RMSE by 79–90% and achieving R² up to 0.99. In contrast, Coata, showed only modest improvements (RMSE reductions of 7.8–9.8%, R² = 0.30–0.39). ML models performed poorly (negative R²), likely due to feature selection, parameter tuning, or limited sample size. Overall, locally weighted regression approaches (GWRK/GWRCK) outperformed conventional ML methods for SOCS prediction in complex mountain environments, particularly with small to medium sample sizes. These results highlight the importance of accounting for spatial non‐stationarity in SOCS and provide methodological guidance for SOCS mapping in Andean ecosystems. To the Soil, Water, and Foliar Laboratory (LABSAF) network technicians, especially of La Molina, Canaan, ´ and Illpa Experimental Agrarian Stations headquarters. Special thanks go to Marilia Coila Mamani and Fredy Flores Galindo for their help collecting soil samples 2025-12-03T15:01:44Z 2025-12-03T15:01:44Z 2025-11-06 info:eu-repo/semantics/preprint Carbajal, C., Tumbalobos-Dextre, M., Condori-Ataupillco, T., Cuellar-Condori, N., & Gavilan, C. (2025). Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru. Geoderma Regional, e01026. https://doi.org/10.1016/j.geodrs.2025.e01026 2352-0094 http://hdl.handle.net/20.500.12955/2952 https://doi.org/10.1016/j.geodrs.2025.e01026 eng urn:issn:2352-0094 Geoderma Regional info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf Elsevier B.V. NL Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Digital soil mapping Soil organic carbon stock Geographically weighted regression Machine learning regression algorithms Andes Cartografía digital de suelos Reservas de carbono orgánico del suelo Regresión ponderada geográficamente Algoritmos de regresión de aprendizaje automático https://purl.org/pe-repo/ocde/ford#4.01.04 Regresión de paso cauteloso; stepwise regression; Cuenca hidrográfica; Watersheds; Perú; Peru Carbajal Llosa, Carlos Miguel Tumbalobos Dextre, Merely Condori Ataupillco, Levi Tatiana Cuellar Condori, Nestor Edwin Gavilan, Carla Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru |
| title | Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru |
| title_full | Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru |
| title_fullStr | Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru |
| title_full_unstemmed | Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru |
| title_short | Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru |
| title_sort | spatial prediction of soil organic carbon stocks across contrasting andean basins peru |
| topic | Digital soil mapping Soil organic carbon stock Geographically weighted regression Machine learning regression algorithms Andes Cartografía digital de suelos Reservas de carbono orgánico del suelo Regresión ponderada geográficamente Algoritmos de regresión de aprendizaje automático https://purl.org/pe-repo/ocde/ford#4.01.04 Regresión de paso cauteloso; stepwise regression; Cuenca hidrográfica; Watersheds; Perú; Peru |
| url | http://hdl.handle.net/20.500.12955/2952 https://doi.org/10.1016/j.geodrs.2025.e01026 |
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