Spatiotemporal variations of cropland phosphorus runoff loss in China

Abstract

Quantitative assessment of cropland phosphorus (P) loss via surface runoff is essential for developing effective pollution mitigation strategies. In this study, we compiled 812 datasets from 114 peer-reviewed papers for cropland P loss across China. We then developed machine learning (ML) approaches to estimate temporal and spatial variations in P runoff loss across China from 1990 to 2020. Four prevalent ML models were considered, namely, multiple linear regression (MLR), random forest (RF), classification and regression trees (CART), and boosted regression trees (BRT). Among these four models, RF exhibited the highest predictive accuracy for both uplands (calibration: R² = 0.86, n = 293; validation: R² = 0.61, n = 96) and paddy fields (calibration: R² = 0.88, n = 137; validation: R² = 0.60, n = 44). According to RF, China’s croplands are estimated to have lost an average of 148 ± 27 Gg P yr⁻¹ from 1990 to 2020, with uplands and paddy fields contributing 114 ± 26 Gg P yr⁻¹ and 34 ± 4 Gg P yr⁻¹, respectively. There was a significant increase in upland TP runoff loss over the study period (p < 0.001), whereas paddy field TP loss remained relatively constant. Regions in southern, eastern, and southwestern China, notably in Hainan, Guangxi, and Fujian provinces, were identified as hotspots of cropland TP runoff loss. Improved cropland management scenarios were predicted to reduce TP runoff loss by 1.4–11.8 %, with the best results obtained by minimizing runoff depth. To effectively mitigate TP runoff loss, an integrated management approach involving water, soil, and fertilizer is recommended. This study enhances quantitative understanding of cropland TP runoff loss in China, providing crucial insights for efficient cropland P management, which is key to managing nonpoint source pollution on a national level.