[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2167":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":33,"doi":34,"paper":35,"created_at":60},2167,"Crop Simulation Model: A Digital Tool for Sustainable Water Management in Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95667","Efficient management of agricultural water resources is increasingly important under climate variability and freshwater scarcity. Accurate estimation of crop water requirements (CWR) supports crop productivity, irrigation scheduling and water-use efficiency. Conventional approaches, including crop-coefficient, Penman–Monteith and soil-water-balance methods, remain widely used but can require extensive field observations and may have limited capacity to represent dynamic soil–crop–atmosphere interactions. Crop simulation models provide a process-based digital framework that integrates weather, soil, crop and management information to simulate crop growth, evapotranspiration, yield and water productivity under different environmental and management conditions. This review synthesises the roles of widely used models, including DSSAT, APSIM, AquaCrop, CROPWAT and CropSyst, in estimating crop water requirements and supporting irrigation management. Applications across maize, wheat, rice, cotton and other crops show that these models can evaluate irrigation schedules, deficit-irrigation strategies, water-saving practices and potential responses to future climate conditions. Model performance, however, depends on appropriate input data, local calibration and validation. The review also identifies opportunities to strengthen crop simulation through integration with hydrological information, remote sensing, artificial intelligence and modules for biotic stresses. Overall, crop simulation models provide useful decision-support tools for evaluating water-management options and improving the evidence base for sustainable agricultural water-resource planning.","在气候变率和淡水稀缺背景下，农业水资源的高效管理日益重要。准确估算作物需水量（CWR）有助于支撑作物生产力、灌溉调度和水分利用效率。传统方法，包括作物系数法、Penman–Monteith法和土壤水量平衡法，仍被广泛使用，但可能需要大量田间观测，且在表征动态的土壤–作物–大气相互作用方面能力有限。作物模拟模型提供了一种基于过程的数字化框架，可整合天气、土壤、作物和管理信息，以模拟不同环境和管理条件下的作物生长、蒸散发、产量和水分生产力。本文综述了广泛使用的模型，包括DSSAT、APSIM、AquaCrop、CROPWAT和CropSyst，在估算作物需水量和支持灌溉管理中的作用。在玉米、小麦、水稻、棉花及其他作物上的应用表明，这些模型能够评估灌溉制度、亏缺灌溉策略、节水措施以及对未来气候条件的潜在响应。然而，模型性能取决于适当的输入数据、本地校准和验证。本综述还指出了通过与水文信息、遥感、人工智能及生物胁迫模块集成来加强作物模拟的机遇。总体而言，作物模拟模型为评估水资源管理方案和改善可持续农业水资源规划的证据基础提供了有用的决策支持工具。",null,"International Journal of Environment and Climate Change","2026-09-10T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,8,1,"系统综述DSSAT、APSIM、AquaCrop等作物模拟模型在作物需水估算与灌溉决策中的应用，并提出与遥感、AI融合方向，对农业水资源数字化管理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","农业人工智能","农业水资源","遥感","灌溉管理","作物模拟模型",0,"10.9734\u002Fijecc\u002F2026\u002Fv16i95667",{"doi":34,"openalex_id":36,"authors":37,"venue":10,"cited_by_count":33,"oa_url":6,"card":52,"direction":58,"ingested_from":59},"W7212194041",[38,40,42,44,46,48,50],{"name":39,"orcid":9},"P. S. Manju",{"name":41,"orcid":9},"N. Manikandan",{"name":43,"orcid":9},"A.P. Ramaraj",{"name":45,"orcid":9},"V. S. Jinsy",{"name":47,"orcid":9},"K. V. Sumesh",{"name":49,"orcid":9},"V. Dhanalakshmi",{"name":51,"orcid":9},"N. Gopika",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"综述作物模拟模型在估算作物需水与灌溉管理中的应用及改进方向。","综述DSSAT、APSIM、AquaCrop、CROPWAT、CropSyst等","模型可评估灌溉策略与节水实践，但精度依赖本地校准与输入数据。","农业人工智能与决策模型","可探索作物模型与遥感、AI及水文信息耦合，提升需水估算与灌溉决策精度。","农业遥感与作物表型","openalex","2026-09-11T23:30:30.176174Z"]