The intensive development of the Yellow River Delta has caused huge transportation of non-point pollutants into the Bohai Sea through source river estuaries and thus poses a considerable threat to eco-environmental security in the region. Long-term irrigation in the Yellow River basin, with occasional heavy rainfall and the related effects of ensuring hydrological processes and human activities in terms of nitrate N transport via surface water systems, is unclear. Using stable isotope (δ²H-H₂O and δ¹⁸O-H₂O, δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻) and auxiliary geographic data, the ISO source model was run to quantitatively analyze the supply relationship of river systems and the rapid change in the spatial pattern of nitrate N due to heavy rainfall in the estuarine delta. This analysis made clear the dominant contribution of agricultural activities and urbanization to NO₃⁻-N emission, on which basis refined management measures were proposed to deal with NO₃⁻ in surface water from the “source-process”. The results of the study show that: (1) The relationship of surface water replenishment in the Yellow River Delta was affected not only by rainfall, irrigation, and other water conservancy measures but also the proportion of water from Yellow River flow declined from the source to estuary; (2) To a certain extent, rainfall diluted the concentration of nitrate N in the river and increased instantaneous flux of nitrate N into the sea, where nitrate N flux continuously increased from upstream to downstream; (3) The rapid deposition of nitrate in the estuary delta was driven by heavy rainfall and human activities such as excessive use of nitrogen fertilizers, rapid urbanization, and livestock waste discharge, and; (4) Scientific measures were needed to realize the interactive effect of the output of non-point source pollutants and the carrying and absorption capacity of coastal fragile ecosystems of the exogenous inputs.

Freshwater resources are becoming increasingly scarce in coastal areas, limiting crop productivity in coastal farmlands. Although the characteristic of crop water use is an important factor for water conservation in coastal farmlands, it has not been studied extensively. This study aimed to depict the water use process of soil–plant systems under saline stress in coastal ecosystems and optimize water management. An intensive observation experiment was performed within China’s Yellow River Delta to identify the water use processes and crop coefficients (K_C) and also quantify the impacts of salt stress on crop water use. The results show that shallow groundwater did not contribute to soil water in the whole rotation; K_C values for wheat–maize, wheat–sorghum, and wheat–soybean rotation systems were 45.0, 58.4, and 57% less, respectively, than the FAO values. The water use efficiency of the maize (8.70) and sorghum (9.00) in coastal farmlands was higher than that of the soybean (4.37). By identifying the critical periods of water and salt stress, this paper provides suggestions for water-saving and salinity control in coastal farmlands. Our findings can inform the sustainable development of coastal farmlands and provide new insights to cope with aspects of the global food crisis.

Optimising nitrogen management has the potential to enhance crop productivity while mitigating greenhouse gas emissions. Nevertheless, it has low adoption rates, due to the complex interactions of crop types, environments (climate and soil) and management combinations, posing significant challenges to advancing climate-smart agriculture. In this study, a hybrid modelling approach was developed to target a minimum of 90% of the potential yield, while simultaneously increasing nitrogen use efficiency and optimising N inputs, reducing net GHG emissions and GHG intensity. A 30-year field trial was conducted on a wheat-maize rotation system in the North China Plain. The observations (annual yields, SOC and N2O emissions) were then used to validate the process-based DNDC model, and the NSGA-Ⅲ machine learning algorithm was applied for multi-objective optimisation. This hybrid modelling approach simulated and optimised three levels of nitrogen management under future climate scenarios (level 1: fertilizer rates; level 2: fertiliser rates, timing, frequency, and crop schedules; level 3: level 2 plus irrigation and residue retention). From 1990 to 2100, the optimised practice combinations were identified: delaying and reducing basal fertilization (+5 d, -52.8 kg N ha-1) while advancing top-dressing in wheat (-5 d) and both events in maize (-9 d, -3 d); postponing wheat sowing (+5 d) and advancing maize sowing (-9 d); aligning irrigation event with fertilization, and adding one irrigation event during the maize bell stage; and lowering residue retention (-0.2). Integrating additional practices with fertiliser rates (levels 2 and 3) proves effective in meeting these climate-smart objectives. Under SSP245 and SSP585, the optimal level 3 practices, compared to maintaining current practices unchanged (conventional practices), increase annual crop yields by 5.6% and 1.7%, respectively, while concurrently reducing net GHG emissions by 9.4% and 8.4%, respectively. Optimal level 3 practices, in comparison to level 2, increased yields by only 0.7%, but significantly reduced net GHG emissions by 8.7%. Furthermore, the implementation of optimal level 3 practices, compared to conventional practices, led to a reduction in N inputs, irrigation water use and residue inputs by 17.2%, 6.7% and 20.0%, respectively. The findings of this study demonstrate that the optimal practices continually adapted in order to respond to the changing climate conditions. It is imperative for decision-makers to consider the trade-off between achieving greater GHG reductions and the potentially higher implementation costs associated with adjusting practices, given the minimal yield differences but significant GHG emission disparities across levels.