干旱区研究2025，Vol.42Issue(4)：730-753,24.DOI:10.13866/j.azr.2025.04.14
中国区域作物气象产量统计预报研究进展
Advances in statistical prediction of crop meteorological yields in China
摘要
Abstract
Accurate crop yield prediction is crucial for governments to understand production levels,plan agricul-tural activities,and ensure national food security.Meteorological factors critically influence crop yields,and yield prediction methods and technology systems based on these factors serve as important references.Meteorological yield prediction predominantly employs statistical methods because of their simplicity,ease of implementation,and high accuracy,making them the most widely used techniques in China.This study reviews the application of the most commonly used statistical methods in meteorological yield prediction in China—including the key mete-orological factor,climate suitability,and historical meteorological impact index methods.Through extensive data collection and investigation,a detailed overview is provided regarding the crop types and regions where each sta-tistical method has been applied,the quantities and time scales of selected meteorological factors,various calcula-tion approaches for meteorological indicators,and the modeling techniques adopted.The paper elaborates on the effectiveness of each statistical method across different regions and crops,evaluates the performance of integrat-ed statistical models,and compares the forecast accuracy of different approaches.In doing so,several issues in the statistical prediction of meteorological yields are identified.For example,the key meteorological factor meth-od offers advantages such as easy model parameter acquisition and operational applicability;however,it primari-ly considers the effects of light,temperature,and water,potentially overlooking other meteorological factors and disasters.The climate suitability method comprehensively accounts for the light,temperature,and water resourc-es required for crop growth but mainly focuses on average states with lower temporal resolution,making it diffi-cult to capture the impact of short-term disastrous weather.The historical meteorological impact index method ob-jectively and quantitatively predicts the influence of meteorological conditions on crop yields;however,it is chal-lenging to identify truly similar years.These issues contribute to unstable forecast results.To overcome these limi-tations,future efforts can focus on integrating data from multiple sources(such as satellite remote sensing,wire-less sensor networks,Internet of Things,etc.),introducing advanced data analysis technologies and new statistical methods(such as machine learning and deep learning algorithms),and combining these with crop growth models to establish an integrated technology system based on agriculture,meteorology,remote sensing,and artificial in-telligence.This will facilitate the development of mixed forecasting models suitable for various spatiotemporal scales,which are efficient and highly accurate.By conducting applicability analyses for different regions and crops,the precision,accuracy,and comprehensiveness of agricultural meteorological services will be enhanced.关键词
统计预报/作物/气象产量/遥感/人工智能算法/中国区域Key words
statistical prediction/crop/meteorological yield/remote sensing/artificial intelligence algorithms/China引用本文复制引用
方锋,王静,贾建英,王兴,黄鹏程,殷菲,林婧婧..中国区域作物气象产量统计预报研究进展[J].干旱区研究,2025,42(4):730-753,24.基金项目
甘肃省科技重大专项项目(25ZDFA011) （25ZDFA011）
中国气象局人才计划(QXLJRC2024-05-0026(2)) （QXLJRC2024-05-0026(2）
中国气象局西部人才(QXYXRC2022-02-0125(5)) （QXYXRC2022-02-0125(5）
中央引导地方科技发展资金项目(25ZYJA035) （25ZYJA035）
甘肃省陇原青年英才(GSLQ-QX202201) （GSLQ-QX202201）
甘肃省科技计划项目(24JRRA1181) （24JRRA1181）
甘肃省气象局重点项目(Zd2023-01,Zd2023-04) （Zd2023-01,Zd2023-04）
甘肃省气象人才专项(2425rczx-D-JCRC-02) （2425rczx-D-JCRC-02）
