Non-point source pollution from excessive use of fertilizers in agriculture is a major cause of the eutrophication problem in China. Understanding farmers’ decision-making concerning fertilization and identifying the influencing factors in this process are key to tackling overfertilization and related pollution issues. This paper reports a study on modelling decisions about fertilizer use based on data collected from 200 farmer households in the Three Gorges Reservoir area of China, using a well-fitted artificial neural network (ANN) with incorporated variance-based sensitivity analysis. The rate of fertilizer use estimated from the model is in good agreement with observed data. The model is further validated and tested by comparing the simulated and observed values. Results show that the model is able to identify the influencing factors and their interactions causing the variation in fertilizer use and to help pinpoint the underlying reasons. It is found that the farmers’ fertilization behavior is greatly affected by the area of cultivated land, followed by the interaction among farmers’ education level, annual income, and awareness of the importance of environmental protection. Future land consolidation is one of several ways to achieve more sustainable fertilization strategies.

Evaluating changes in streamflow, sediment, and nutrient fluxes, as well as quantifying their influencing factors, is crucial for regional water resource protection. While the relationships between major influencing factors and these indicators have been widely studied, the quantitative contributions of the separate and interactive effects of these influencing factors have not been fully explored. This study quantitatively evaluated the changing characteristics of streamflow, sediment discharge, total nitrogen (TN) and total phosphorus (TP), as well as the separate and interactive effects of various major influencing factors such as-rainfall, temperature, evapotranspiration (ET), revegetation, dam construction, and land use change-by applying the GeoDetector method to account for their spatial heterogeneity and contributions. Our findings reveal that the influence of these factors has gradually intensified over time, with dam construction and land use change emerging as the most significant contributors to changes in sediment discharge and TN, respectively. Notably, the interactive effects between dam capacity and vegetation cover on streamflow and sediment discharge was twice as strong as their separate impacts, highlighting the effectiveness of integrating dam construction with reforestation to control erosion and sediment transport. Similarly, the interaction of dam capacity and land use change had a 1.5 times greater impact on TN and TP than their separate effects, indicating that reducing fertilizer application at the source and in the meantime implementing direct interception measures are more effective ways to control water pollution. These findings provide a solid foundation for policymakers to develop integrated water management strategies targeting multiple factors simultaneously, that address both water quantity and quality concerns in the Yangtze River Basin and similar regions.

Soil erosion represents a primary threat to soil systems with adverse implications for ecosystem services, crop production, potable water, and carbon storage. While numerous studies have quantified the spatial distribution of Above-Ground Biomass (AGB), soil erosion, and Soil Organic Carbon (SOC) in the Yangtze River Basin (YRB) in China, limited attention has been given to assessing the contributions of different land use types and especially crop types to AGB, soil erosion, and SOC. In most studies, cropland is taken as a land use class, while detailed crop types and rotation patterns, and their effect on soil erosion and SOC, vary significantly. In this study, we used the Metronamica model to generate a detailed crop rotation and distribution map across the YRB and subsequently employed the PESERA model to simulate the spatial distribution of AGB, soil erosion, and SOC on a monthly basis. PESERA model simulations indicate an average soil erosion rate across the entire YRB of 7.7 t/ha/yr, with erosion hotspots concentrated in the Sichuan Basin and the central-southern regions. The southwestern region and western Sichuan show elevated levels of AGB and SOC, while the eastern plains display lower levels. Erosion rates are lowest in areas designated as artificial land, pasture and grassland, whereas cropland and fruit trees experience the highest erosion rates. In terms of crop types, the highest erosion rates and lowest AGB are observed in fallow and potato cultivation, while the lowest erosion rates and highest AGB are found in rice-wheat rotation fields. To the best of our knowledge, this is the first study including detailed crop types and patterns into account while evaluating their effect on relatively large scale (i.e. YRB). These findings can help to develop sustainable soil management and (cropping) conservation strategies.