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. Machine learning (ML) and data-driven approaches are increasingly used inmany research areas. Extreme gradient boosting (XGBoost) is a tree boosting method that has evolved intoa state-of-the-art approach for many ML challenges. However, it has rarelybeen used in simulations of land use change so far. Xilingol, a typicalregion for research on serious grassland degradation and its drivers, wasselected as a case study to test whether XGBoost can provide alternativeinsights that conventional land-use models are unable to generate. A set of20 drivers was analysed using XGBoost, involving four alternativesampling strategies, and SHAP (Shapley additive explanations) to interpretthe results of the purely data-driven approach. The results indicated that,with three of the sampling strategies (over-balanced, balanced, andimbalanced), XGBoost achieved similar and robust simulation results. SHAPvalues were useful for analysing the complex relationship between thedifferent drivers of grassland degradation. Four drivers accounted for99 % of the grassland degradation dynamics in Xilingol. These four driverswere spatially allocated, and a risk map of further degradation wasproduced. The limitations of using XGBoost to predict future land-use changeare discussed.

Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China