Yu (2026): Machine learning-based assessment of soil organic carbon dynamics in soybean–wheat rotations in eastern China
Zhi Yu, IN: Nature Scientific Reports, https://doi.org/10.1038/s41598-026-38105-6
Soil organic carbon (SOC) is a critical component of agroecosystems, influencing carbon cycling, soil fertility, and structure, thereby affecting crop productivity. This study evaluated the spatial distribution and dynamics of SOC stocks in eastern China under soybean–wheat rotations using advanced machine learning models. Data were collected from Anhui, Hebei, Henan, Jiangsu, Shandong, Tianjin, and Beijing, measuring SOC at two soil depths (0–15 cm and 15–30 cm) before sowing and after harvest during 2022–2024. Among the models tested, Random Forest (RF) provided the most accurate SOC predictions, particularly in the 0–15 cm layer (R² = 0.89, RMSE = 0.95, PRD = 3.41).