Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: the case of the Tuojiang River Basin

Cropland soil organic carbon (SOC) is key to maintain soil fertility for plant growth and mitigating climate change by storing considerable amount of organic carbon. Accurate mapping of cropland SOC is essential for improving soil management in agriculture and assessing the potential of different strategies aiming at enhancing regional carbon sequestration. Digital Soil Mapping represents an intermediate approach between labor-intensive soil measurement survey and uncertain SOC modelling. However, most of the widely-used environmental predictors employed in current cropland SOC mapping describe the natural conditions. Indeed, anthropogenic activities, particularly agricultural management practices have profound impacts on agricultural soils, but have rarely been considered in previous research on SOC digital mapping.
Here, we filled that gap by incorporating within the Extreme Gradient Boosting (XGBoost) model several key cropland management practices including carbon input, length of cultivation, and irrigation as management covariates, together with natural variables in order to predict the spatial distribution of cropland SOC in a traditional agricultural area in the Tuojiang River Basin, China. This approach revealed the dominant role of carbon input in explaining SOC variation in this intensively cultivated areas, followed by elevation and soil pH. Adding cropland management practices to natural variables improved prediction accuracy, with the coefficient of determination (R²), the root mean squared error (RMSE) and Lin’s Concordance Correlation Coefficient (LCCC) improving by 16.67%, 17.75% and 5.62%, respectively. Our research highlights the necessity of considering cropland management practices alongside environmental predictors in order to provide more reliable prediction of cropland SOC. We conclude that the construction of spatio-temporal database of agricultural management practices is a research priority as it has a very strong potential, not only to provide accurate digital SOC maps when incorporated within XGBoost model, but also to better initialize the SOC stocks in process-oriented model, such as Dynamic Vegetation Models and Earth System Models.