作物生长模型的应用研究进展

作物生长模型不仅能够进行单点尺度上作物生长发育的动态模拟，而且能够从系统角度评价作物生长状态与环境要素的关系。本文通过梳理当前作物生长模型应用的诸多研究成果，剖析模型在气候变化对农业生产影响研究、作物生长模型区域应用中的关键问题，总结了当前以作物生长模型为核心的农业决策支持系统开发的研究情况，意在促进作物生长模型在生态、农业、区域气候资源和气候变化等研究中更广泛地应用。结果表明，作物生长模型在国内外的研究与应用广泛而深入，在气候变化背景下，应用作物生长模型进行历史时期气候条件和农业气象灾害对作物生产状况和产量的影响研究已相当广泛且相对成熟。利用全球气候模式（GCM）或区域气候模式（RCM）构建未来气候变化情景，再与作物生长模型耦合已发展成为评估未来气候变化对农业生产影响的重要手段。通过集成与整合多作物生长模型、多气候模式集合模拟、优化气候模拟数据订正方法可有效降低气候变化对农业生产影响评估的不确定性。遥感数据同化技术能够将站点模型运用到区域尺度上评价不同环境因子对农业生产的影响，拓宽了作物生长模型的应用尺度范围并有效提高作物产量估算的精度。以作物生长模型为核心的农业决策支持系统的研究与应用越来越多元化，是辅助农业生产管理和决策的重要工具。然而，由于作物生态系统的复杂性，作物生长模型模拟结果仍存在很大的不确定性，今后对作物生长机理及过程间耦合机制的探索还需加强，以便进一步完善和改进模型，促进作物生长模型更广泛地应用。

Research Progress in Application of Crop Growth Models

The crop growth model can not only simulate the dynamic growth of crops on a single point scale, but also evaluate the relationship between crop growth status and environmental factors from a systematic perspective. This paper reviews latest works related to crop growth model, with particular focuses on the research of climate change to agricultural production and application of crop growth model at regional scale. In addition, this paper summarizes the current research on the development of agricultural decision support systems(DSS) with crop growth models as the core. The research is intended to promote crop growth models to be more widely used in researches on ecology, agriculture, regional climate resources and climate change filed. Research results show that the crop growth model is widely and deeply used in China and abroad. Under the background of climate change, the application research of crop growth model to the impact of historical period climatic conditions and agrometeorological disasters on crop production status and yield has been extensive and relatively mature. Using global climate models (GCMs) or regional climate models (RCMs) to construct future climate change scenarios, coupled with crop growth models, has evolved into an important tool for assessing the impact of future climate change on agricultural production. By integrating and consolidating multi-crop growth model, multi-climate model ensemble simulation and optimizing climate simulation data correction methods, the uncertainty of climate change impact assessment on agricultural production can be effectively reduced. The remote sensing data assimilation technology can apply the site model to the regional scale to evaluate the impact of different meteorological factors on agricultural production, broaden the application scale range of the crop growth model and effectively improve the accuracy of crop yield estimation. The research and application of agricultural decision support system with crop growth model as the core is more and more diversified, and it is an important tool to assist agricultural production management and decision-making. However, due to the complexity of crop ecosystems, there are still great uncertainties in crop growth model simulation results. In the future, the exploration of crop growth and process coupling mechanism needs to be strengthened in order to improve the model and promote it more widely used.