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应用生态学报 ›› 2025, Vol. 36 ›› Issue (5): 1579-1589.doi: 10.13287/j.1001-9332.202505.012

• 综合评论 • 上一篇    下一篇

作物生长模型发展及应用中的问题探讨

郭建平*   

  1. 中国气象科学研究院, 北京 100081
  • 收稿日期:2024-10-28 修回日期:2025-03-16 出版日期:2025-05-18 发布日期:2025-11-18
  • 通讯作者: *E-mail: gjp@cma.gov.cn
  • 作者简介:郭建平, 男, 1963年生, 博士, 研究员。主要从事农业气象灾害监测预警、农业气候资源利用技术研究。E-mail: gjp@cma.gov.cn
  • 基金资助:
    十四五国家重点研发计划项目(2022YFD2001003)

Discussion on problems in the development and application of crop growth model

GUO Jianping*   

  1. Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • Received:2024-10-28 Revised:2025-03-16 Online:2025-05-18 Published:2025-11-18

摘要: 智慧农业是未来农业发展的方向,作物生长模型作为作物生产精确管理与智能决策的数字化工具,是智慧农业的核心技术之一,被称为智慧农业大脑。本文首先介绍了近几十年来作物生长模型的发展历程,包括萌芽阶段、起步阶段、快速研发阶段、深度发展阶段、完善和应用阶段,重点介绍了在世界上被广泛应用的几个典型模型(Wageningen系列模型、DSSAT模型、APSIM模型、STICS模型等)的特色和局限性。当前的作物生长模型在应用中仍存在许多不足,主要表现为: 作物生长模型的泛化能力弱、迁移性差,限制了模型在区域上的应用能力;作物生长发育对环境要素响应的机理过程认识不足,定量表达模型还需不断完善;极端气候事件、病虫害等不利因素影响的定量描述欠缺,影响模型的模拟精度;模型的复杂性和应用的便利性存在矛盾,难以平衡;作物生长模型的应用与当前新技术的结合不够;作物生长模型中遗传参数的可解释性不足影响模型预测能力等。

关键词: 作物生长模型, 模型发展, 模型应用, 问题探讨

Abstract: Smart agriculture is an important direction for agricultural development. As a digital tool for accurate management and intelligent decision-making of crop production, crop growth model is one of the core technologies of smart agriculture, which is called smart agricultural brain. Here, I introduced the development history of crop growth models in recent decades, which included germination stage, initial stage, rapid research and development stage, deep development stage, improvement and application stage. The characteristics and limitations of several typical models (Wageningen series models, DSSAT model, APSIM model, STICS model, etc.) widely used in the world were emphatically introduced. There are many shortcomings in the application of current crop growth models, mainly manifested in the weak generalization ability and poor migration ability of crop growth models, which limited the regional application ability of the models. The mechanism of response of crop growth and development to environmental factors was not well understood. Meanwhile, the quantitative expression model needed to be improved. The lack of quantitative description of adverse effects such as extreme weather events, pests and diseases affected the simulation accuracy of the model. It was difficult to balance the complexity of the model and the convenience of application. The application of crop growth models was not sufficiently integrated with current new technologies. The insufficient interpretability of genetic parameters in crop growth models impacted the prediction ability of the models.

Key words: crop growth model, model development, model application, problem and discussion