“…First, considering the underlying nonlinearities and interactions among the rural livelihood, land use decisions, and family traits (Kuang et al, 2019), we used a non‐parametric boosted regression tree to analyze the influences on the agricultural income of rural households arising from a series of external factors, with special attention to land management intensity. The use of boosted regression trees has grown in popularity recently as it is viewed as a powerful multivariate analysis tool in the land system science domain due to its capability to reveal complex nonlinearities and handle large datasets that may have outliers (Ma et al, 2020; Prishchepov et al, 2021). Aside from the metrics of agricultural input, a set of survey variables that characterize the household, namely, farm size, education, and health, were selected as the predictors in the model.…”