From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach
Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotop...
| Main Authors: | , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Springer
2024
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| Online Access: | https://hdl.handle.net/10568/152095 |
| _version_ | 1855517027543810048 |
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| author | Carbajal, M. Ramírez, D. Turin, C. Schaeffer, S.M. Konkel, J. Ninanya, J. Rinza, J. De Mendiburu, F. Zorogastua, P. Villaorduña, L. Quiroz, R. |
| author_browse | Carbajal, M. De Mendiburu, F. Konkel, J. Ninanya, J. Quiroz, R. Ramírez, D. Rinza, J. Schaeffer, S.M. Turin, C. Villaorduña, L. Zorogastua, P. |
| author_facet | Carbajal, M. Ramírez, D. Turin, C. Schaeffer, S.M. Konkel, J. Ninanya, J. Rinza, J. De Mendiburu, F. Zorogastua, P. Villaorduña, L. Quiroz, R. |
| author_sort | Carbajal, M. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (δ13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (“bofedales”) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variables using remote sensing data, land-use and land-cover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and δ13CSOC prediction, whereas ANN was the best to model RSOC. “Bofedales” showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted δ13CSOC (− 27.0 ± 0.44 ‰) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of “bofedales” as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and δ13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands. |
| format | Journal Article |
| id | CGSpace152095 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1520952025-12-08T09:54:28Z From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach Carbajal, M. Ramírez, D. Turin, C. Schaeffer, S.M. Konkel, J. Ninanya, J. Rinza, J. De Mendiburu, F. Zorogastua, P. Villaorduña, L. Quiroz, R. crops neural networks grasslands soil organic carbon machine learning Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (δ13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (“bofedales”) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variables using remote sensing data, land-use and land-cover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and δ13CSOC prediction, whereas ANN was the best to model RSOC. “Bofedales” showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted δ13CSOC (− 27.0 ± 0.44 ‰) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of “bofedales” as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and δ13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands. 2024-11 2024-09-10T20:53:32Z 2024-09-10T20:53:32Z Journal Article https://hdl.handle.net/10568/152095 en Open Access Springer Carbajal, M.; Ramírez, D.A.; Turin, C.; Schaeffer, S.M.; Konkel, J.; Ninanya, J.; Rinza, J.; De Mendiburu, F.; Zorogastua, P.; Villaorduña, L.; Quiroz, R. 2024. From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach. Ecosystems. ISSN 1435-0629. https://doi.org/10.1007/s10021-024-00928-7 |
| spellingShingle | crops neural networks grasslands soil organic carbon machine learning Carbajal, M. Ramírez, D. Turin, C. Schaeffer, S.M. Konkel, J. Ninanya, J. Rinza, J. De Mendiburu, F. Zorogastua, P. Villaorduña, L. Quiroz, R. From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach |
| title | From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach |
| title_full | From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach |
| title_fullStr | From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach |
| title_full_unstemmed | From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach |
| title_short | From Rangelands to Cropland, Land-Use Change and its impact on soil organic carbon variables in a Peruvian Andean Highlands: A Machine Learning Modeling approach |
| title_sort | from rangelands to cropland land use change and its impact on soil organic carbon variables in a peruvian andean highlands a machine learning modeling approach |
| topic | crops neural networks grasslands soil organic carbon machine learning |
| url | https://hdl.handle.net/10568/152095 |
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