Chinese Journal of Agrometeorology ›› 2019, Vol. 40 ›› Issue (07): 444-459.doi: 10.3969/j.issn.1000-6362.2019.07.004

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Research Progress in Application of Crop Growth Models

SUN Yang-yue, SHEN Shuang-he   

  1. 1. College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2. Collaboration Innovation Center on Forecast and Evaluation of Meteorological Disaster of Nanjing University of Information Science & Technology, Nanjing 210044
  • Online:2019-07-20 Published:2019-07-08

Abstract: 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.

Key words: Crop growth model, Crop model, Climate change, Remote sensing, Research progress