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 C-13 isotope composition of SOC (delta(CSOC)-C-13)-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 delta(CSOC)-C-13 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 delta(CSOC)-C-13 (- 27.0 +/- 0.44 parts per thousand) 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 delta(CSOC)-C-13. 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.