农业机械学报2024，Vol.55Issue(2)：1-15,27,16.DOI:10.6041/j.issn.1000-1298.2024.02.001
作物生长模型研究现状与展望
Progress and Perspective of Crop Growth Models
摘要
Abstract
Crop growth models have evolved from initial crop development models to agricultural decision support models,playing an increasingly important role in scientific research,agricultural management,and policy-making.In the paper the development process of crop growth models was firstly reviewed.Based on the main driving factors,the models were categorized into three types:soil factors,photosynthetic factors,and human factors,and comprehensive introductions to each category were provided.Then a comparative analysis of typical models was presented from ten aspects,including model modules,spatiotemporal scales,and range of crop types that can be simulated.Furthermore,the applications of crop growth models in climate change assessment,production management decision support,and resource management optimization were discussed.The challenges faced by these models were also highlighted,such as extreme conditions,complex agricultural landscapes,and model complexity.Based on the comprehensive discussions,two promising directions for the future development of crop growth models were identified:remote sensing data assimilation and twin farming.Remote sensing data assimilation techniques have the potential to significantly enhance the spatial range and accuracy of the simulations,providing more precise information for agriculture.Twin farming,on the other hand,offers virtual replicas of actual farming systems,enabling comprehensive analysis and optimization of crop growth.These research findings provide valuable insights for selecting and improving crop growth models,driving advancements in this field.关键词
作物生长模型/长势监测/作物估产/驱动因子/遥感/孪生农场Key words
crop growth models/growth monitoring/yield forecasting/driving factor/remote sensing/twin farm分类
农业科技引用本文复制引用
蒙继华,王亚楠,林圳鑫,方慧婷..作物生长模型研究现状与展望[J].农业机械学报,2024,55(2):1-15,27,16.基金项目
国家重点研发计划项目(2022YFD2001102)、广西科技重大专项(2022AA01030)、国家自然科学基金项目(41871261)和雪川农业科研专项(E1H2053802) （2022YFD2001102）
