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...
| Autores principales: | , , , , , , , , , , |
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| Formato: | info:eu-repo/semantics/article |
| Lenguaje: | Inglés |
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Springer
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.12955/2576 https://doi.org/10.1007/s10021-024-00928-7 |
| _version_ | 1855028388960403456 |
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| author | Carbajal, Mariella Ramirez, David A. Turin Canchaya, Cecilia Claudia Schaeffer, Sean M. Konkel, Julie Ninanya, Johan Rinza, Javier De Mendiburu, Felipe Zorogastua, Percy Villaorduña, Liliana Quiroz, Roberto |
| author_browse | Carbajal, Mariella De Mendiburu, Felipe Konkel, Julie Ninanya, Johan Quiroz, Roberto Ramirez, David A. Rinza, Javier Schaeffer, Sean M. Turin Canchaya, Cecilia Claudia Villaorduña, Liliana Zorogastua, Percy |
| author_facet | Carbajal, Mariella Ramirez, David A. Turin Canchaya, Cecilia Claudia Schaeffer, Sean M. Konkel, Julie Ninanya, Johan Rinza, Javier De Mendiburu, Felipe Zorogastua, Percy Villaorduña, Liliana Quiroz, Roberto |
| author_sort | Carbajal, Mariella |
| collection | Repositorio INIA |
| 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 (d13CSOC)—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 variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and d13CSOC 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 d13CSOC (- 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 d13CSOC. 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 | info:eu-repo/semantics/article |
| id | INIA2576 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | INIA25762024-09-30T18:24:11Z 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, Mariella Ramirez, David A. Turin Canchaya, Cecilia Claudia Schaeffer, Sean M. Konkel, Julie Ninanya, Johan Rinza, Javier De Mendiburu, Felipe Zorogastua, Percy Villaorduña, Liliana Quiroz, Roberto Artificial neural networks Bofedales 13C isotope composition Extreme gradient boosting Grasslands Random forest Refractory C fraction Support vector machine https://purl.org/pe-repo/ocde/ford#4.01.04 Redes de neuronas Fishing nets Tierra húmeda Wetlands Isótopo Isotopes Gradiente de temperatura Temperature gradients Grasslands Pradera Machine learning Aprendizaje automático 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 (d13CSOC)—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 variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and d13CSOC 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 d13CSOC (- 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 d13CSOC. 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. 2024-09-30T18:24:09Z 2024-09-30T18:24:09Z 2024-09-09 info:eu-repo/semantics/article Carbajal, M.; Ramirez, D.A.; Turin-Canchaya, C.C.; Schaeffer, S.M.; Konkel, J.; Ninanya, J.; Rinza, J.; De Mendiburu, F.; Zorogastua, P.; Villaordun, 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 (2024). doi: 10.1007/s10021-024-00928-7 1435-0629 https://hdl.handle.net/20.500.12955/2576 https://doi.org/10.1007/s10021-024-00928-7 eng urn:issn:1435-0629 Ecosystems info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf application/pdf Springer US Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Artificial neural networks Bofedales 13C isotope composition Extreme gradient boosting Grasslands Random forest Refractory C fraction Support vector machine https://purl.org/pe-repo/ocde/ford#4.01.04 Redes de neuronas Fishing nets Tierra húmeda Wetlands Isótopo Isotopes Gradiente de temperatura Temperature gradients Grasslands Pradera Machine learning Aprendizaje automático Carbajal, Mariella Ramirez, David A. Turin Canchaya, Cecilia Claudia Schaeffer, Sean M. Konkel, Julie Ninanya, Johan Rinza, Javier De Mendiburu, Felipe Zorogastua, Percy Villaorduña, Liliana Quiroz, Roberto 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 | Artificial neural networks Bofedales 13C isotope composition Extreme gradient boosting Grasslands Random forest Refractory C fraction Support vector machine https://purl.org/pe-repo/ocde/ford#4.01.04 Redes de neuronas Fishing nets Tierra húmeda Wetlands Isótopo Isotopes Gradiente de temperatura Temperature gradients Grasslands Pradera Machine learning Aprendizaje automático |
| url | https://hdl.handle.net/20.500.12955/2576 https://doi.org/10.1007/s10021-024-00928-7 |
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