中国区域作物气象产量统计预报研究进展

doi:10.13866/j.azr.2025.04.14

Abstract

Abstract:

Accurate crop yield prediction is crucial for governments to understand production levels, plan agricultural 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 meteorological 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 statistical method has been applied, the quantities and time scales of selected meteorological factors, various calculation 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 integrated 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 method offers advantages such as easy model parameter acquisition and operational applicability; however, it primarily 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 resources required for crop growth but mainly focuses on average states with lower temporal resolution, making it difficult to capture the impact of short-term disastrous weather. The historical meteorological impact index method objectively and quantitatively predicts the influence of meteorological conditions on crop yields; however, it is challenging to identify truly similar years. These issues contribute to unstable forecast results. To overcome these limitations, future efforts can focus on integrating data from multiple sources (such as satellite remote sensing, wireless 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 intelligence. 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.

Key words: statistical prediction, crop, meteorological yield, remote sensing, artificial intelligence algorithms, China

FANG Feng, WANG Jing, JIA Jianying, WANG Xing, HUANG Pengcheng, YIN Fei, LIN Jingjing. Advances in statistical prediction of crop meteorological yields in China[J].Arid Zone Research, 2025, 42(4): 730-753.

Figures/Tables 5

Tab. 1

Research in crop meteorological yield prediction based on key meteorological factor"

作物	地点	气象要素				产量预报				文献来源
作物	地点	指标	因子数	时间范围	时间尺度	预报因子	时间范围	建模方法	关键气象因子数量	文献来源
小麦	山东（日照、枣庄）、河南（郑州地区）	降水量、平均气温、最高和最低气温、日照时数、地温、蒸发、相对湿度、风速、冬前积温、负积温、需冷量、≥0 ℃积温、≥10 ℃积温、≥20 ℃积温、太阳辐射	6~150	1970—2019年	日、旬、月、年、各生育期和全生育期	气象产量、产量、千粒重	全生育期，前期、抽穗-成熟期	灰色关联、相关分析、多元回归、逐步回归	2~7	[2,44]
水稻	辽宁、湘北、黑龙江、广西（梧州）、四川（南充）、浙江（金华）	降水量、平均气温、最高和最低气温、日照时数、相对湿度、平均风速、最大风速、平均相对湿度、最小相对湿度、土壤湿度	6~135	1980—2022年	候、旬、各生育期和全生育期	产量、气象产量、相对气象产量	全生育期	相关分析、灰色关联、多元线性回归、逐步回归、主成分分析	3~11	[2,46,51]
玉米	山东（枣庄）	降水量、平均气温、最高和最低气温、日照时数、夜间降水量、土壤湿度	15~41	1990—2018年	旬、月、全生育期	气象产量、产量	前期、全生育期	相关分析、灰色关联、逐步回归	11~14	[2,52]
马铃薯	河北（21个站）	降水量、平均气温、日照时数、水汽压、相对湿度	13~360	1949—2018年	旬、月	气象产量	全生育期	相关分析、逐步回归、多元回归	5~11	[54]
花生	全国（10个主产省）、安徽（16个市）	降水量、平均气温、日照时数	12~8	1980—2017年	日、旬、月	产量、气象产量	全生育期	正交变换、积分回归、灰色关联、相关分析、逐步回归	5~10	[53]
油菜	青海（贵南）	降水量、平均气温、日照时数、无霜期、相对湿度、平均风速、≥0 ℃积温	24	1991—2015年	月、全生育期	产量	全生育期	相关分析、逐步回归	9	[45]
橡胶	广东	降水量、平均气温、最高和最低气温、日照时数、风速、相对湿度、降雨日数、水汽压	>100	1992—2014年	旬、月、全生育期	气象产量	全年、割胶期	多元线性回归、相关分析、逐步回归	5~6	[49]
荔枝	广东（增城）	降水量、平均气温、最高气温、降水日数、日照时数、相对湿度	216	1999—2018年	旬	气象产量	上年8月—当年7月	相关分析、逐步回归	11	[48]
樱桃	山东（青岛）	降水量、平均气温、最高和最低气温、日照时数	>40	2000—2016年	旬	产量、气象产量	生殖生长期	相关分析、逐步回归	4	[50]
枸杞	青海（柴达木）	平均气温、最低气温、气温日较差、≥5 ℃积温、平均日照时数及降水量	42	1991—2020年	日、各生育期	气象产量	全生育期	相关分析、多元线性回归	10	[55]
烤烟	湖南（湘西）	平均气温、最高和最低气温、降水量、日照时数、相对湿度、≥10 ℃有效积温、≥12 ℃有效积温、≥13 ℃有效积温、降水日数、20~26 ℃日数、干旱日数、高温日数	184	2019—2021年	日、各生育期	产量	全生育期	相关分析、逐步回归	12	[33]

Tab. 1

Tab. 2

Tab.3

Tab. 4

Research in crop meteorological yield prediction based on historical meteorological factor index"

作物	地点	气象要素			对比指标	气象因子变化量	单产变化量	历史相似年		预报年丰歉气象影响指数		文献来源
作物	地点	指标	时间范围	时间尺度	对比指标	气象因子变化量	单产变化量	判断方法	数量	计算方法	时间尺度	文献来源
水稻	黑龙江（30个站）、全国（95 个站）	最高和最低气温、降水量、日照时数	1960—2008年	日、候	平均温度、有效温度、积温、有效积温；累积降水量、分段累积降水量、标准化降水量、分段标准化降水量、有效降水量、分段有效降水量；累积日照时数、分段累积日照时数、标准化日照时数、分段标准化日照时数、有效日照、分段有效日照时数	相邻两年气象因子的差值	单产丰歉值	综合聚类指标（相关系数/欧氏距离）	3、6、9	算术平均法、加权平均法、大概率法、符号一致平均法、综合影响指数	月	[107]
玉米	吉林（50个站）	最高和最低气温、日照时数、降水量	1980—2016年	日	平均气温、平均日照时数、平均降水量、累积降水量、平均积温、标准化降水量、累积日照时数	相邻两年气象因子的差值	单产丰歉值	综合聚类指标（相关系数/欧氏距离）；欧氏距离（气象要素种类标准化后）	9	加权平均法、大概率法	旬、月	[104]
油菜	四川（13个站）、湖南（4个站）、全国（71个站）	平均气温、最高和最低气温、日照时数、降水量	1961—2019年	日	平均温度、有效温度、积温、有效温度累积，累积降水量、分段累积降水量、标准化降水量、分段标准化降水量，累积日照时数、分段累积日照时数、标准化日照时数、分段标准化日照时数	相邻两年气象因子的差值	单产丰歉值	综合聚类指标（相关系数/欧氏距离）	9	加权平均法、大概率法、符号一致平均法、综合诊断指标法	候、月	[106]
马铃薯	河北（24个站）	最高和最低气温、日照时数、降水量	1982—2018年	日	平均气温、降水量、日照时数、平均积温、标准化降水量、累积日照时数	相邻两年气象因子的差值	单产丰歉值	综合聚类指标（相关系数/欧氏距离）	9	加权平均法、大概率法	月	[105]
大豆	全国（61个站）	最高和最低气温、降水量、日照时数	1960—2004年	日、候	平均温度、日有效温度、积温、分段累积降水量、分段累积日照时数、标准化降水量、分段标准化降水量、标准化日照时数和分段标准化日照时数	相邻两年气象因子的差值	单产丰歉值	综合聚类指标（相关系数/欧氏距离）	3、6	算术平均法、符号一致平均法、综合影响指数	月	[108]

Tab. 4

Tab. 5

Research in crop meteorological yield prediction by integrating multiple statistical methods"

作物	地点	气象数据			关键气象因子		气候适宜度		历史丰歉气象影响指数			集成方法	预报效果	文献来源
作物	地点	指标	时间范围	时间尺度	气象因子	关键气象因子	方法	建模因子	气象因子类型	相似年数	预报方法	集成方法	预报效果	文献来源
小麦	山东（17个站）	最高和最低气温、降水量、日照时数、风速、水汽压、土壤水分	1980—2011年	日、旬	平均气温、降水量、日照时数（旬）（72个因子）	8~12	温度、降水、土壤墒情、水分、日照、气候适宜度	气候适宜度	积温、标准化降水量、累积日照时数	9	大概率法	准确率加权方法	集成预报准确率和稳定性更高	[110]
	湖北（荆州）	平均气温、日照时数、降水量	1970—2016年	日、候			温度、降水、日照、气候适宜度	气候适宜度	积温、累积日照、累积降水量	9	加权平均分析法、大概率法		丰歉指数方法的预报准确率更高	[115]
	江苏（69个站）	平均气温、最高和最低气温、降水量、日照时数、高温和低温日数、降雨和大雨日数、土壤相对湿度	1993—2018年	日	平均气温、≥0 ℃的积温、≥30 ℃和≤0 ℃的日数、降水量、降雨和大雨日数、可照时数（生育期）（72个因子）	无	温度、降水、日照、气候适宜度	气候适宜度	积温、标准化降水量、累积日照时数	9	加权平均分析法、大概率法	准确率加权方法	气候适宜度方法和集成预报准确率更高	[103]
水稻	湖南（15个站）	最高和最低气温、降水量、日照时数	1961—2008年	日			温度、降水、日照、气候适宜度	气候适宜度	降水量、日照、温度	9	算术平均法、符号一致平均法	预报误差加权方法	集成预报准确率更高	[82]
	湖南（15个站）	平均气温、最高和最低气温、降水量、日照时数、风速、水汽压	1962—2012年	日	平均温度、降水量、日照时数（旬）（36个因子）	8	温度、降水、日照、气候适宜度	气候适宜度					气候适宜度方法趋势预报准确性最高	[113]
	云南（125个站）	平均气温、降水量、日照时数	2000—2018年	日	平均气温（旬）、降水量（月）（20个因子）	2			累积日照时数、平均温度、≥15 ℃和≥18 ℃有效积温、累积降水	15	大概率法		2种模型预报准确率均较高	[112]
大豆	辽宁（56个站）	最高和最低气温、降水量、日照时数	1981—2016年	日	平均气温、降水量、日照时数（候）（30个因子）	7	温度、降水、日照、气候适宜度	气候适宜度					气候适宜度预报准确率和稳定性更高	[109]
油菜	江西（87个站）	平均气温、降水量、日照时数	1990—2015年	日	平均气温、累积降水量、累积日照时数（生育期）（15个因子）	6	温度、降水、日照、气候适宜度	气候适宜度				准确率加权方法	辐热积模型拟合效果最佳	[111]

Tab. 5

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Advances in statistical prediction of crop meteorological yields in China

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