ArticleScientific reports2026
Machine learning-based assessment of soil organic carbon dynamics in soybean-wheat rotations in eastern China.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 (R
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