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Winter wheat is the main grain crop in the Yellow River Basin, and optimizing water and nitrogen management can not only improve the yield, but also reduce water and fertilizer waste and environmental pollution. Using two years of winter wheat field trials, the Aquacrop model was calibrated and validated, and 36 different water and nitrogen application scenarios were simulated and analyzed. Among them, the field trials were divided into three N application levels, and three lower limits of water control. The results showed that: (1) the model has good applicability to simulate the growth process of winter wheat in the Yellow River Basin. (2) At a constant level of irrigation or nitrogen application, crop yield initially increased and subsequently decreased with the enhancement of either irrigation or nitrogen application. Finally, through response surface analysis of regression equations, the recommended irrigation volume was determined to be between 296 and 303 mm, and the fertilizer application rate was set between 229 and 261 kg/ha. At these levels, the PFP could achieve over 60% of its maximum. This research provides a theoretical basis for the water-saving, fertilizer-saving and high-yield water and fertilizer management system of winter wheat in the irrigated area of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Summer maize constitutes a major food crop in the Yellow River Basin. Optimizing nitrogen (N) application management for this crop not only elevates its yield but also reduces N leaching, thereby ensuring food security and lessening agricultural surface pollution. Utilizing two years of summer maize field experiments, the soil water heat carbon and N simulator (WHCNS) was calibrated and validated against empirical measurements. Subsequent analyses employed the calibrated WHCNS to analyze 56 different N management scenarios. These scenarios varied in terms of N application levels, basal N to topdress application ratios, and chase ratios. The entropy-weighted TOPSIS method was utilized for the optimization, considering agronomic, environmental, and economic aspects. The model's calibration accuracy was validated by root mean square errors, relative root mean square errors, and mean errors for soil volumetric water content and soil nitrate N content. The calibration results demonstrated that the new model was capable of simulating the soil hydraulic characteristics, N cycling, and the growth and development of summer maize during the reproductive phase in the Yellow River Basin. Scenario analyses revealed that increasing the N application initially elevated, then stabilized, summer maize yields, whereas the N agronomic efficiency first increased and then decreased. Moreover, reducing the basal N to topdress application ratios and increasing the chase ratios during the tasseling and flowering stages could minimize the nitrate N leaching and optimize both the yield and N fertilizer agronomic utilization. Specifically, the optimal N management for the current year involved applying 170 kg·ha−1 of N with a basal N to the topdress N application ratio of 1:5 and a chase ratio of 1:1 during the tasseling and flowering stages. This study lays the foundation for developing N fertilizer management strategies for summer maize cultivation in the Yellow River Basin. Furthermore, the methodology established here can be adapted for optimizing the management of diverse crops in different geographical regions. [ABSTRACT FROM AUTHOR]

This study aims to examine the impact of combined irrigation and fertilizer control on the summer maize yield, nitrogen use efficiency (NE), and nitrogen leaching (NL) in the Yellow River Basin. Based on the measured data from the field summer maize experiment in 2021 and 2022, a water-nitrogen movement model was constructed for 'Zhengdan 958' maize under two irrigation methods (wide furrow irrigation (G) and border irrigation (Q)), three fertilizer rates (120 kg/ha (N1), 220 kg/ha (N2), and 320 kg/ha (N3)), and three fertilizer frequencies (1 (T1), 2 (T2), and 3 (T3)), yielding 18 total treatments. Calculation of nitrogen leaching was based on water nitrogen transport modeling. The study then analyzed the factors and their combined effects. A multi-objective optimization genetic algorithm (NSGA-II) was established to evaluate maize yield, nitrogen use efficiency, and nitrogen leaching. The results indicate that the determination coefficients between simulated and measured water, nitrogen values exceeded 0.74. The rate optimized HYDRUS model effectively simulated the soil solute movement. The interaction of the irrigation method, fertilizer rate, and fertilizer application frequency did not significantly affect yield and nitrogen leaching, but did significantly impact nitrogen use efficiency (p < 0.05). Nitrogen leaching increased gradually as nitrogen application increased. The yield under wide furrow irrigation was 6.26% higher than that under border irrigation. The optimal coupling scheme of water and fertilizer was obtained using the genetic algorithm multi-objective optimization method, where the combined GN2T2 treatment was the optimal management model, the summer maize yield reached 14,077 kg/ha, nitrogen use efficiency and nitrogen leaching were reduced to 30.21 kg·kg−1 and 17.64 kg/ha, respectively. These findings can guide summer maize cultivation in the Yellow River Basin and assist in reducing nitrogen surface source pollution. [ABSTRACT FROM AUTHOR]

Strategic management of nitrogen fertilizers can not only mitigate agricultural nitrogen pollution but also significantly enhance crop yield and nitrogen use efficiency. This study was designed to determine the optimal nitrogen fertilizer management strategy for the Yellow River irrigation area. Leveraging two years of field data related to soil water nitrogen and summer maize growth indices, parameters for the Root Zone Water Quality Model 2 (RZWQM2) were calibrated and validated. Subsequently, various scenarios were generated to simulate the impacts of different nitrogen application rates and basal chasing ratios on summer maize yield, nitrogen agronomic efficiency, nitrogen physiological efficiency, and nitrogen apparent recovery rate. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method was employed for a comprehensive evaluation. RZWQM2 can effectively simulate the dynamic changes in soil moisture and nitrogen in the Yellow River irrigation area, and the results indicated that the mean relative error (MRE) between the simulated and observed values varied from 5.77% to 14.09%, and 4.36% to 33.01%, while the root mean square error (RMSE) ranged from 0.016 to 0.037 cm3/cm3, and 0.111 to 1.995 mg/kg. The normalized root mean square error (NRMSE) varied between 6.20% to 14.42% and 5.24% to 17.84%, respectively. The results validate the model's effectiveness in simulating summer maize yields and nitrogen metrics under varying nitrogen fertilizer management practices. A nitrogen application rate of 180–200 kg/hm2 (expressed in terms of pure nitrogen) in the Yellow River irrigation area could adequately meet the requirements for summer maize production. The recommended nitrogen fertilizer management strategy in the Yellow River irrigation area involves applying 200 kg/hm2 of nitrogen in a 1:2:1 ratio during the sowing, trumpeting, and anthesis stages. [ABSTRACT FROM AUTHOR]