Lv Mingzi, Tian Delong, Wang Guoshuai, Fan Ting, Li Weiping, Hou Chenli, Zhou Jie, Miao Xianyang
Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of WaterResources and Hydropower Research, Beijing, 100038, China.
School of Energy and Environment, Inner Mongolia University of Science and Technology, Baotou, 014000, Inner Mongolia, China.
Sci Rep. 2025 Apr 9;15(1):12108. doi: 10.1038/s41598-025-92058-w.
Forage crop production globally faces challenges of low water and nitrogen use efficiency alongside increased environmental pressures, particularly in arid and semi-arid regions. The Hetao Irrigation District, a representative area of such regions, is characterized by limited rainfall, high evaporation, insufficient water, and severe soil salinization, which significantly hinder agricultural development. The findings from this study not only address local challenges but also provide insights applicable to other regions facing similar climatic and environmental constraints. This study aimed to optimize water and nitrogen management strategies for alfalfa production in the Hetao Irrigation District by calibrating and validating the DSSAT-FORAGES-Alfalfa model using field experimental data collected during 2022-2023. The calibration involved adjusting key parameters to minimize discrepancies between simulated and observed values, and validation was performed using an independent dataset, evaluated based on metrics such as MAE, RMSE, and R. The novelty of this research lies in its comprehensive calibration and validation process, which provides a robust framework for simulating alfalfa growth under diverse management scenarios. Various management scenarios were simulated, including six nitrogen application rates (0, 50, 100, 150, 200, and 250 kg ha) and seven irrigation levels (45, 55, 65, 75, 85, 95, and 105 mm), to comprehensively evaluate their impacts on alfalfa yield and quality. These scenarios were designed to cover a wide range of practices, from deficit to excessive irrigation and fertilization, providing a robust assessment of optimal management strategies. Results indicated high predictive accuracy, with all performance metrics (e.g., MAE, RMSE, and R) demonstrating the model's reliability in simulating alfalfa growth under varying management conditions. Specifically, the normalized RMSE was below 10%, and the coefficient of determination (R) exceeded 0.9, confirming the model's robustness. Optimal management involving 260-340 mm of irrigation and 100-150 kg ha nitrogen application over the full growth cycle not only maximizes water use efficiency, but also achieves over 9% water saving and more than 25% reduction in nitrogen fertilizer application, compared to traditional local practices. These findings demonstrate the potential of optimized management strategies to significantly reduce ecological pressures while maintaining high crop productivity.
全球饲料作物生产面临着水分和氮素利用效率低下的挑战，同时环境压力不断增加，特别是在干旱和半干旱地区。河套灌区作为这类地区的典型代表，降雨稀少、蒸发量大、水资源匮乏且土壤盐渍化严重，这些因素显著阻碍了农业发展。本研究的结果不仅解决了当地面临的挑战，还为其他面临类似气候和环境限制的地区提供了参考。本研究旨在通过利用2022 - 2023年收集的田间试验数据对DSSAT - FORAGES - 苜蓿模型进行校准和验证，从而优化河套灌区苜蓿生产的水氮管理策略。校准过程包括调整关键参数，以尽量减少模拟值与观测值之间的差异，验证则使用独立数据集，并基于平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R）等指标进行评估。本研究的新颖之处在于其全面的校准和验证过程，为模拟不同管理情景下苜蓿的生长提供了一个可靠的框架。模拟了各种管理情景，包括六种施氮量（0、50、100、150、200和250千克/公顷）和七种灌溉水平（45、55、65、75、85、95和105毫米），以全面评估它们对苜蓿产量和品质的影响。这些情景旨在涵盖从亏缺灌溉到过量灌溉和施肥的广泛实践，从而对最佳管理策略进行可靠评估。结果表明该模型具有较高的预测准确性，所有性能指标（如MAE、RMSE和R）都证明了该模型在模拟不同管理条件下苜蓿生长方面的可靠性。具体而言，归一化RMSE低于10%，决定系数（R）超过0.9，证实了该模型的稳健性。与当地传统做法相比，在整个生长周期内进行260 - 340毫米的灌溉和100 - 150千克/公顷的施氮量的优化管理，不仅能最大限度地提高水分利用效率，还能节水超过9%，减少氮肥施用量超过25%。这些发现表明，优化管理策略在保持高作物生产力的同时，具有显著降低生态压力的潜力。
