This research investigates the influence of water–nitrogen coupling on soil water content, nitrogen dynamics, and root distribution in farmland, along with the interactions among soil water, nitrogen transport, root distribution, and crop yield. A field experiment was conducted under moderate drought stress (50–60% of field capacity) and three nitrogen application rates (100, 200, and 300 kg·ha⁻¹, split-applied at 50% during sowing and 50% at the jointing stage, labeled as N₁, N₂, and N₃) at the two critical growth stages (jointing stage P₁ and tasseling-silking stage P₂) of maize (Denghai 605). The results demonstrated that maize root morphological parameters exhibited the trend N₂ > N₁ > N₃ under different nitrogen treatments. Compared to N₂, low nitrogen (N₁) decreased root morphological parameters by 35.01–49.60% on average, whereas high nitrogen (N₃) led to a reduction of 49.93–61.37%. The N₂ treatment consistently maintained greater water uptake, with the highest yield of 13,336 kg·ha⁻¹ observed under the CKN₂ treatment, representing increases of 16.1% and 9.2% compared to the P₁N₂ and P₂N₂ treatments, respectively. Drought stress at the jointing stage (P₁) inhibited root development more severely than at the tasseling-silking stage (P₂), demonstrating a bidirectional adaptation strategy characterized by deeper vertical penetration under water stress and increased horizontal expansion under nitrogen imbalance. Correlation analysis revealed a positive correlation between soil nutrient content and maize yield indicators. At the same time, root characteristic values were significantly negatively correlated with yield (p < 0.05). Appropriate water–nitrogen management effectively stimulated root growth, mitigated nitrogen leaching risks, and improved yield. These findings offer a theoretical foundation for optimizing water and nitrogen management in maize production within the Yellow River Basin.

This study presents a comprehensive bibliometric analysis of the DSSAT crop modeling field from 1990 to 2024, identifying its evolutionary trajectory and emerging frontiers. A comprehensive bibliometric analysis and network visualization were conducted using VOSviewer, CiteSpace, and Bibliometrix. Analyzing 6165 Scopus-indexed publications, we found the research focus has shifted from foundational yield simulation and calibration toward addressing complex climate-water-food challenges. Three distinct developmental phases were identified: an initial establishment phase, a methodological refinement phase, and a current technology integration phase dominated by machine learning and remote sensing applications. The results reveal that machine learning, model-data fusion, and sustainability assessment represent the most active research frontiers. This analysis provides a systematic map of the field’s intellectual structure and offers evidence-based predictions for its future development, highlighting the transition of DSSAT from a specialized crop model to an interdisciplinary decision-support platform for sustainable agriculture.

Summer maize (Zea mays L.) production on the North China Plain is highly dependent on variable seasonal rainfall, which increases the likelihood that inappropriate water and nitrogen allocation will cause yield fluctuations and ecological and environmental risks. Previous studies have mainly relied on single-site field comparisons or basic statistical evaluation methods, limiting the understanding of the dynamic response mechanisms of drought stress coupled with nitrogen application during the jointing and grain-filling stages. Based on field experiments conducted in 2024–2025, the DSSAT model was used to simulate aboveground dry matter accumulation (CWAM), grain yield, leaf area index (LAI), dry matter evapotranspiration productivity (DMPEM), and dry matter nitrogen productivity (DPNAM) of summer maize under different water–nitrogen treatments at different growth stages. Then, historical meteorological data for Henan Province from 2003 to 2023 were imported. The years were classified into three hydrological year types: wet years, normal years, and dry years. Subsequently, Principal Component Analysis (PCA), the TOPSIS method, and the Rank-Sum Ratio (RSR) method were employed to construct a multidimensional evaluation system for assessing water and nitrogen management strategies under different hydrological year types. The results showed that the nitrogen application rate had a significant regulatory effect on yield, DPNAM, and DMPEM. All three initially increased and then decreased as the nitrogen application rate rose, with the optimal performance observed under the normal nitrogen (N2) treatment. Under drought conditions during the same growth stage, the increase in the maximum yield under the N2 treatment was approximately 8.1% and 50% higher than that under the high-nitrogen (N1) and low-nitrogen (N3) treatments, respectively. Compared with drought during the grain-filling stage, drought during the jointing stage had a smaller negative effect on CWAM and LAI. A comprehensive evaluation with long-term meteorological data reflects that drought during the jointing stage combined with normal nitrogen (Q2) is the optimal water–nitrogen management strategy for wet years (with an RSR value of 0.994). The treatments of drought during the jointing stage combined with high nitrogen (Q1) and drought during the grain-filling stage combined with normal nitrogen (H2) reveal greater adaptability and favorable universality across different hydrological year types. The model’s reliability under various water–nitrogen coupling conditions was validated by integrating field experiments, DSSAT model simulations, and a multidimensional evaluation system. This study lays a scientific theoretical foundation for achieving high and stable yields in summer maize under different water–nitrogen coupling conditions and across various hydrological year scenarios.