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.
Supplementary information: The online version contains supplementary material available at 10.1007/s10021-024-00928-7.
Keywords: 13C isotope composition; Artificial neural networks; Bofedales; Extreme gradient boosting; Grasslands; Random forest; Refractory C fraction; Support vector machine.
© The Author(s) 2024.