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

作　　者：方锋 王静[2] 贾建英 王兴 黄鹏程 殷菲 林婧婧 FANG Feng;WANG Jing;JIA Jianying;WANG Xing;HUANG Pengcheng;YIN Fei;LIN Jingjing(Lanzhou Regional Climate Center,Lanzhou 730020,Gansu,China;Lanzhou Institute of Arid Meteorology,China Meteorological Administration,Lanzhou 730020,Gansu,China)

机构地区：[1]兰州区域气候中心,甘肃兰州730020 [2]中国气象局兰州干旱气象研究所,甘肃兰州730020

出　　处：《干旱区研究》2025年第4期730-753,共24页Arid Zone Research

基　　金：甘肃省科技重大专项项目(25ZDFA011);中国气象局人才计划(QXLJRC2024-05-0026(2));中国气象局西部人才(QXYXRC2022-02-0125(5));中央引导地方科技发展资金项目(25ZYJA035);甘肃省陇原青年英才(GSLQ-QX202201);甘肃省科技计划项目(24JRRA1181);甘肃省气象局重点项目(Zd2023-01,Zd2023-04);甘肃省气象人才专项(2425rczx-D-JCRC-02)。

摘　　要：准确的作物产量预报对于政府提前了解作物产量信息、合理规划农业生产以及保障国家粮食安全至关重要。气象因子是影响作物产量的重要因素,基于气象因子建立的气象产量预报方法和技术体系为作物产量预报提供了重要参考。气象产量预报主要采用统计学方法实现,该方法简单易行、准确率高,是目前中国区域应用最广泛的气象产量预报技术。本文综述了气象产量预报中常用的统计学方法(关键气象因子、气候适宜度和历史丰歉气象影响指数)在中国区域的应用现状。通过广泛地搜集和调查,详尽地给出了各统计学方法所应用的作物品种和研究区域,选取的气象因子类型、数量和时间尺度,气象指标的多种计算方法,以及采用的建模技术等应用现状;阐述了各统计学方法在不同区域、不同作物中的应用效果;评述了统计学方法的集成模型效果,比较了各统计学方法的预报准确率。通过这些深入调查,明确了作物气象产量统计预报中存在的问题。其中,关键气象因子方法虽然易于业务化且模型参数获取方便,但由于主要考虑光照、温度和水分的影响,可能会忽略其他气象因子及气象灾害的作用;气候适宜度方法能够充分考虑到作物生长所需的光温水资源,但该方法主要关注气象要素的平均态,且时间分辨率较低,难以反映短时灾害性天气对作物产量的影响;历史丰歉气象影响指数方法可以客观地预报气象条件对作物产量丰歉趋势的影响,但在确定真正的相似年方面存在挑战。这些问题导致了预报结果的不稳定性。为了克服这些局限性,未来的研究可通过融合更多来源的数据(如卫星遥感、无线传感器网络、物联网等),引入先进的数据分析技术和新的统计方法(如机器学习和深度学习算法),结合作物生长机理模型,建立基于农业、气象、遥感、人工智能的集成技�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 t

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 

分 类 号：S162[农业科学—农业气象学]