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

• Autores: 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
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:null:20.500.12955/2576
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2576; https://doi.org/10.1007/s10021-024-00928-7
• Nivel de acceso: acceso abierto
• Materia: 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; 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.