作物生长模型在农作物生产上的应用创新

Application Innovation of Crop Growth Models in Crop Production

摘要: 随着全球人口增长与气候变化加剧，农作物生产面临产量–品质协同提升与资源环境约束的双重挑战。作物生长模型作为融合农学、数学与信息科学的交叉工具，为解决上述挑战提供了科学路径。本文系统构建“环境驱动–生理响应–决策支持”的三层模型应用框架，结合江苏南京溧水某草莓基地“物联网 + 模型”精准灌溉实践、南京某都市型浆果园区模型应用案例等，揭示模型在产量预测、资源优化、品质调控中的核心机制。研究发现，模型驱动的精准管理可显著提升农业生产的经济与生态效益。针对当前数据获取成本高、模型本地化难等问题，提出多源数据融合、智能算法优化等对策，为作物生长模型在智慧农业中的深度应用提供理论支撑与实践参考。研究表明，作物生长模型正从单一田块模拟迈向区域尺度决策，成为农业数字化转型的核心使能技术。

Abstract: With the global population growth and intensifying climate change, crop production faces dual challenges: the synergistic improvement of yield and quality, and constraints from resource environments. As an interdisciplinary tool integrating agronomy, mathematics, and information science, crop growth models provide a scientific pathway to address these challenges. This paper systematically constructs a three-layer model application framework of “Environmental Driving-Physiological Response-Decision Support”. By combining practical cases such as the “Internet of Things (IoT) + Model” precision irrigation practice in a strawberry base in Lishui, Nanjing, Jiangsu, and the model application in an urban berry park in Nanjing, it reveals the core mechanisms of models in yield prediction, resource optimization, and quality regulation. The study finds that model-driven precision management can significantly enhance the economic and ecological benefits of agricultural production. Aiming at current issues such as high data acquisition costs and difficulties in model localization, strategies such as multisource data fusion and intelligent algorithm optimization are proposed, providing theoretical support and practical references for the in-depth application of crop growth models in smart agriculture. The research indicates that crop growth models are evolving from single-field simulation to regional-scale decision-making, becoming a core enabling technology for agricultural digital transformation.