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

摘要/Abstract

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、人工智能的集成技术体系，构建适用于不同时空尺度、高效、高精度的产量混合预报模型，通过开展针对不同区域和不同作物的适用性分析，进一步提高农业气象精细化、准确化和全面化的服务能力。

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

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

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.

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.

图/表 5

表1

基于关键气象因子的作物气象产量预报研究"

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]

表1

表2

表3

表4

基于历史丰歉气象因子指数的作物气象产量预报研究"

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]

表4

表5

多种统计学方法集成的气象产量预报研究"

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]

表5

参考文献 153

[1]	Zhao Y X, Xiao D P, Bai H Z, et al. The prediction of wheat yield in the North China plain by coupling crop model with machine learning algorithms[J]. Agriculture, 2023, 13: 99-118.
[2]	Li L C, Wang B, Feng P Y, et al. Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China[J]. Agricultural and Forest Meteorology, 2021, 308- 309(4): 108558-108569.
[3]	Fu Z P, Jiang J, Gao Y, et al. Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle[J]. Remote Sensing, 2020, 12(3): 508-526.
[4]	Leng G Y. Maize yield loss risk under droughts in observations and crop models in the United States[J]. Environmental Research Letters, 2021, 16: 024016-024027.
[5]	李朋磊, 张骁, 王文辉, 等. 基于高光谱和激光雷达遥感的水稻产量监测研究[J]. 中国农业科学, 2021, 54(14): 2965-2976.
	[Li Penglei, Zhang Xiao, Wang Wenhui, et al. Assessment of terrestrial laser scanning and hyperspectral remote sensing for the estimation of rice grain yield[J]. Scientia Agricultura Sinica, 2021, 54(14): 2965-2976.] doi: 10.3864/j.issn.0578-1752.2021.14.004
[6]	Chapagain R, Remenyi T A, Harris R M B, et al. Decomposing crop model uncertainty: A systematic review[J]. Field Crops Research, 2022, 279: 108448-108459.
[7]	Cao J, Zhang Z, Tao F L, et al. Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches[J]. Agricultural and Forest Meteorology, 2021, 297: 108275-108289.
[8]	Paudel D, Boogaard H, de Wit A, et al. Machine learning for large-scale crop yield forecasting[J]. Agricultural Systems, 2021, 187: 103016-103028.
[9]	高雁鹏, 陈文俊. 1984—2020年辽宁省极端气温时空变化及粮食产量响应研究[J]. 地理科学, 2021, 41(11): 2052-2062. doi: 10.13249/j.cnki.sgs.2021.11.018
	[Gao Yanpeng, Chen Wenjun. Spatial and temporal variation of extreme temperature and grain yield response in Liaoning Province from 1984 to 2020[J]. Scientia Geographica Sinica, 2021, 41(11): 2052-2062.] doi: 10.13249/j.cnki.sgs.2021.11.018
[10]	贾建英, 刘一锋, 彭妮, 等. 基于积分回归法甘肃省冬小麦产量动态预报[J]. 气象与环境学报, 2016, 32(2): 100-105.
	[Jia Jianying, Liu Yifeng, Peng Ni, et al. Dynamic forecast of winter wheat yield based on an integral regression method in Gansu Province[J]. Journal of Meteorology and Environment, 2016, 32(2): 100-105.]
[11]	姜骁, 许静, 潘丽娟, 等. 花生产量相关性状与气象因子多环境相关性分析[J]. 作物学报, 2023, 49(11): 3110-3121. doi: 10.3724/SP.J.1006.2023.24218
	[Jiang Xiao, Xu Jing, Pan Lijuan, et al. Peanut yield-related traits and meteorological factors correlation analysis in multiple environments[J]. Acta Agronomica Sinica, 2023, 49(11): 3110-3121.] doi: 10.3724/SP.J.1006.2023.24218
[12]	Kakati N, Deka R L, Das P, et al. Forecasting yield of rapeseed and mustard using multiple linear regression and ANN techniques in the Brahmaputra valley of Assam, North East India[J]. Theoretical and Applied Climatology, 2022, 150: 1201-1215.
[13]	Bouras E, Jarlan L, Er-Raki S, et al. Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in Morocco[J]. Remote Sensing, 2021, 13: 3101-3122.
[14]	刘维, 宋迎波. 基于不同空间尺度的作物产量集成预报——以江苏一季稻为例[J]. 气象科学, 2021, 41(6): 828-834.
	[Liu Wei, Song Yingbo. Comparative analysis of different regional scales integration yield prediction: A case study of single rice in Jiangsu[J]. Journal of the Meteorological Sciences, 2021, 41(6): 828-834.]
[15]	Clark R, Dahlhaus P, Robinson N, et al. Matching the model to the available data to predict wheat, barley, or canola yield: A review of recently published models and data[J]. Agricultural Systems, 2023, 211: 103749.
[16]	Huang S, Lv L, Zhu J, et al. Extending growing period is limited to offsetting negative effects of climate changes on maize yield in the North China Plain[J]. Field Crops Research, 2018, 215: 66-73.
[17]	Idrissi A, Nadem S, Boudhar A, et al. Review of wheat yield estimating methods in Morocco[J]. African Journal on Land Policy and Geospatial Sciences, 2022, 5(4): 2657-2664.
[18]	Rezaei E E, Webber H, Asseng S, et al. Climate change impacts on crop yields[J]. Nature Reviews Earth & Environment, 2023, 4: 831-846.
[19]	IPCC. Climate Change 2021:The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[R]. Cambridge: Cambridge University Press, 2021.
[20]	Heino M, Kinnunen P, Anderson W, et al. Increased probability of hot and dry weather extremes during the growing season threatens global crop yields[J]. Scientific Reports, 2023, 13: 3583-3595. doi: 10.1038/s41598-023-29378-2 pmid: 36869041
[21]	Farooq M, Wahid A, Kobayashi N, et al. Plant drought stress: Effects, mechanisms and management[J]. Agronomy for Sustainable Development, 2009, 29: 185-212.
[22]	Liu E K, Mei X R, Yan C R, et al. Efects of water stress on photosynthetic characteristics, dry matter translocation and WUE in two winter wheat genotypes[J]. Agricultural Water Management, 2016, 167: 75-85.
[23]	褚鹏飞, 于振文, 王东, 等. 小麦灌水时期与灌水量对花后果聚糖积累与转运及水分利用效率的影响[J]. 应用生态学报, 2009, 20(11): 2691-2698.
	[Chu Pengfei, Yu Zhenwen, Wang Dong, et al. Effects of irrigation stage and amount on winter wheat fructan accumulation and translocation after anthesis and water use efficiency[J]. Chinese Journal of Applied Ecology, 2009, 20(11): 2691-2698.] pmid: 20136002
[24]	Pan J, Sharif R, Xu X, et al. Mechanisms of waterlogging tolerance in plants: Research progress and prospects[J]. Frontiers in Plant Science, 2020, 11: 627331-627346.
[25]	Mohammadi S, Rydgren K, Bakkestuen V, et al. Impacts of recent climate change on crop yield can depend on local conditions in climatically diverse regions of Norway[J]. Scientific Reports, 2023, 13: 3633-3644. doi: 10.1038/s41598-023-30813-7 pmid: 36869138
[26]	Fatima Z, Ahmed M, Hussain M, et al. The fingerprints of climate warming on cereal crops phenology and adaptation options[J]. Scientific Reports, 2020, 10: 18013-18034. doi: 10.1038/s41598-020-74740-3 pmid: 33093541
[27]	Eyshi Rezaei E, Webber H, Gaiser T, et al. Heat stress in cereals: Mechanisms and modelling[J]. European Journal of Agronomy, 2015, 64: 98-113.
[28]	马晓玲, 周焱博, 袁杰, 等. 阳泉市玉米生育期气象要素变化特征及相关性分析[J]. 中国农学通报, 2024, 40(7): 118-122. doi: 10.11924/j.issn.1000-6850.casb2023-0281
	[Ma Xiaoling, Zhou Yanbo, Yuan Jie, et al. Variation characteristics and correlation analysis of meteorological elements during maize growth period in Yangquan City[J]. Chinese Agricultural Science Bulletin, 2024, 40(7): 118-122.] doi: 10.11924/j.issn.1000-6850.casb2023-0281
[29]	娄伟平, 张寒, 孙永飞, 等. 光温条件对浙中晚稻抽穗期和结实率的影响[J]. 中国农业气象, 2006, 27(1): 49-52.
	[Lou Weiping, Zhang Han, Sun Yongfei, et al. Effects of sunlight and temperature conditions on heading period and seed setting rate of late rice[J]. Chinese Journal of Agrometeorology, 2006, 27(1): 49-52.]
[30]	Zandalinas S I, Mittler R. Plant responses to multifactorial stress combination[J]. New Phytologist, 2022, 234: 1161-1167.
[31]	Siebert S, Webber H, Rezaei E E. Weather impacts on crop yields-searching for simple answers to a complex problem[J]. Environmental Research Letters, 2017, 12: 081001-081005.
[32]	李小芳, 赵鹏, 张向荣, 等. 生育期气候因子对陕西安康烟区烤烟产量、质量的影响[J]. 西北农林科技大学学报, 2015, 43(9): 97-102.
	[Li Xiaofang, Zhao Peng, Zhang Xiangrong, et al. Influence of climate factors in growth period on yield and quality of flue-cured tobacco in Ankang, Shaanxi[J]. Journal of Northwest A & F University, 2015, 43(9): 97- 102.]
[33]	何娜, 范雨娴, 袁小康, 等. 基于关键气象因子的湘西烤烟产量预报模型构建[J]. 中国农学通报 2023, 39(24): 96-102. doi: 10.11924/j.issn.1000-6850.casb2022-0794
	[He Na, Fan Yuxian, Yuan Xiaokang, et al. Construction of flue-cured tobacco yield forecast model in Xiangxi autonomous prefecture based on key meteorological factors[J]. Chinese Agricultural Science Bulletin, 2023, 39(24): 96-102.] doi: 10.11924/j.issn.1000-6850.casb2022-0794
[34]	Tan B T, Fam P S, Firdaus R R, et al. Impact of climate change on rice yield in Malaysia: A panel data analysis[J]. Agriculture, 2021, 11(6): 569-586.
[35]	Wang D, Heckathorn S A, Barua D, et al. Effects of elevated CO₂ on the tolerance of photosynthesis to acute heat stress in C3, C4, and CAM species[J]. American Journal of Botany, 2008, 95: 165-176. doi: 10.3732/ajb.95.2.165 pmid: 21632342
[36]	Manderscheid R, Erbs M, Weigel H J. Interactive effects of free-air CO₂ enrichment and drought stress on maize growth[J]. European Journal of Agronomy, 2014, 52: 11-21.
[37]	Xiong W, Reynolds M P, Crossa J, et al. Increased ranking change in wheat breeding under climate change[J]. Nature Plants, 2021, 7: 1207-1212. doi: 10.1038/s41477-021-00988-w pmid: 34462575
[38]	Lobell D B, Schlenker W, Costa-Roberts J. Climate trends and global crop production since 1980[J]. Science, 2011, 333: 616-620. doi: 10.1126/science.1204531 pmid: 21551030
[39]	Abbas S, Kousar S, Khan M S. The role of climate change in food security; empirical evidence over Punjab regions, Pakistan[J]. Environmental Science and Pollution Research, 2022, 29(35): 53718-53736.
[40]	Ozdemir D. The impact of climate change on agricultural productivity in Asian countries: A heterogeneous panel data approach[J]. Environmental Science and Pollution Research, 2022, 29(6): 8205-8217.
[41]	Huang N, Song Y, Wang J, et al. Climatic threshold of crop production and climate change adaptation: A case of winter wheat production in China[J]. Frontiers in Ecology and Evolution, 2022, 10: 1019436-1019450.
[42]	房世波. 分离趋势产量和气候产量的方法探讨[J]. 自然灾害学报, 2011, 20(6): 13-18.
	[Fang Shibo. Exploration of method for discrimination between trend crop yield and climatic fluctuant yield[J]. Journal of Natural Disasters, 2011, 20(6): 13-18.]
[43]	Piekutowska M, Niedbała G, Piskier T, et al. The application of multiple linear regression and artificial neural network models for yield prediction of very early potato cultivars before harvest[J]. Agronomy, 2021, 11(5): 885-902.
[44]	胡园春, 安广池, 杨宁, 等. 主要气象因子与冬小麦产量的灰色关联度分析[J]. 农学学报, 2020, 10(2): 92-95. doi: 10.11923/j.issn.2095-4050.cjas20190700115
	[Hu Yuanchun, An Guangchi, Yang Ning, et al. Main meteorological factors and winter wheat yield: Grey correlation degree analysis[J]. Journal of Agriculture, 2020, 10(2): 92-95.] doi: 10.11923/j.issn.2095-4050.cjas20190700115
[45]	王占林, 张海春. 基于多元回归的高寒地区油菜产量预测模型[J]. 中国农学通报, 2019, 35(14): 32-35. doi: 10.11924/j.issn.1000-6850.casb18010155
	[Wang Zhanlin, Zhang Haichun. Prediction model of rapeseed yield in Alpine region: Based on multiple regression[J]. Chinese Agricultural Science Bulletin, 2019, 35(14): 32-35.] doi: 10.11924/j.issn.1000-6850.casb18010155
[46]	徐芳, 黄帆. 基于SPSS的梧州早稻产量预测模型构建[J]. 气象研究与应用, 2016, 37(3): 98-101.
	[Xu Fang, Huang Fan. Building of early season rice yield prediction model of Wuzhou by SPSS[J]. Journal of Meteorological Research and Application, 2016, 37(3): 98-101.]
[47]	Rashid M, Bari B S, Yusup Y, et al. A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction[J]. IEEE Access, 2021, 9: 63406-63439
[48]	彭晓丹, 欧善国. 广东增城荔枝产量预报方法研究[J]. 农业工程, 2021, 11(1): 119-122.
	[Peng Xiaodan, Ou Shanguo. Yield predication method of Litchi in Zengcheng of Guangdong Province[J]. Agricultural Engineering, 2021, 11(1): 119-122.]
[49]	黄珍珠, 李寅, 陈慧华, 等. 基于气象关键因子的广东省橡胶产量预报[J]. 热带农业科学, 2018, 38(2): 107-112.
	[Huang Zhenzhu, Li Yin, Chen Huihua, et al. Forecast of rubber production based on meteorological key factors in Guangdong Province[J]. Chinese Journal of Tropical Agriculture, 2018, 38(2): 107-112.]
[50]	刘春涛, 慕臣英, 李德萍, 等. 青岛市崂山地区樱桃产量预报方法研究[J]. 气象与环境学报, 2017, 33(5): 108-112.
	[Liu Chuntao, Mu Chenying, Li Deping, et al. Study on forecast method of cherry yield in Laoshan district of Qingdao[J]. Journal of Meteorology and Environment, 2017, 33(5): 108-112.]
[51]	李琳琳, 王婷, 李雨鸿, 等. 基于关键气象因子的辽宁省水稻产量动态预报[J]. 大麦与谷类科学, 2017, 34(4): 50-54.
	[Li Linlin, Wang Ting, Li Yuhong, et al. The Dynamic prediction of single-season rice yield based on key meteorological factors in Liaoning Province[J]. Barley and Cereal Sciences, 2017, 34(4): 50-54.]
[52]	杨宁, 孔令刚, 甄铁军, 等. 夏玉米产量与主要气象因子灰色关联度分析[J]. 农学学报, 2020, 10(11): 37-42. doi: 10.11923/j.issn.2095-4050.cjas20191000250
	[Yang Ning, Kong Linggang, Zhen Tiejun, et al. Grey correlation analysis of summer corn yield and main meteorological factors[J]. Journal of Agriculture, 2020, 10(11): 37-42.] doi: 10.11923/j.issn.2095-4050.cjas20191000250
[53]	杨小兵, 杨峻, 杨晨, 等. 安徽省花生产量与气象因素的关联度分析及预测模型研究[J]. 中国农学通报, 2020, 36(34): 100-103. doi: 10.11924/j.issn.1000-6850.casb20191100823
	[Yang Xiaobing, Yang Jun, Yang Chen, et al. Peanut yield in Anhui: correlation with meteorological factors and forecast model[J]. Chinese Agricultural Science Bulletin, 2020, 36(34): 100-103.] doi: 10.11924/j.issn.1000-6850.casb20191100823
[54]	薛思嘉, 魏瑞江, 王朋朋, 等. 基于关键气象因子的河北省马铃薯产量预报[J]. 干旱气象, 2021, 39(1): 138-143.
	[Xu Sijia, Wei Ruijiang, Wang Pengpeng, et al. Potato yield forecast in Hebei Province based on meteorological key factors[J]. Journal of Arid Meteorology, 2021, 39(1): 138-143.]
[55]	雷玉红, 李春晖, 妥淑贞, 等. 基于关键气象因子的柴达木枸杞产量预报模型研究[J]. 中国农学通报, 2023, 39(23): 55-61. doi: 10.11924/j.issn.1000-6850.casb2022-1043
	[Lei Yuhong, Li Chunhui, Tuo Shuzhen, et al. Study on the yield forecast model of Qaidam Lycium barbarum based on key meteorological factors[J]. Chinese Agricultural Science Bulletin, 2023, 39(23): 55-61.]
[56]	孙贵拓, 杨若翰, 杨柯, 等. 基于气候适宜度的水稻发育期预报模型[J]. 安徽农业科学, 2019, 47(16): 231-234.
	[Sun Guituo, Yang Ruohan, Yang Ke, et al. Prediction model of rice developmental phase based on climatic suitability[J]. Journal of Anhui Agricultural Science, 2019, 47(16): 231-234.]
[57]	Wang P J, Wu D R, Yang J Y, et al. Summer maize growth under different precipitation years in the Huang-HuaiHai Plain of China[J]. Agricultural and Forest Meteorology, 2020, 285: 107927-107938.
[58]	Li J D, Lei H M. Impacts of climate change on winter wheat and summer maize dual-cropping system in the North China Plain[J]. Environmental Research Communications, 2022, 4(7): 075014.
[59]	李阳, 刘静, 马力文, 等. 宁夏中南部山区马铃薯气候适宜度时空变化特征[J]. 干旱气象, 2020, 38(6): 1001-1008.
	[Li Yang, Liu Jing, Ma Liwen, et al. Temporal-spatial characteristics of climate suitability of potato in mountainous area of central and southern Ningxia[J]. Journal of Arid Meteorology, 2020, 38(6): 1001-1008.]
[60]	金志凤, 叶建刚, 杨再强, 等. 浙江省茶叶生长的气候适宜性[J]. 应用生态学报, 2014, 25(4): 967-973.
	[Jin Zhifeng, Ye Jiangang, Yang Zaiqiang, et al. Climate suitability for tea growing in Zhejiang Province[J]. Chinese Journal of Applied Ecology, 2014, 25(4): 967-973.] pmid: 25011287
[61]	马树庆. 气候变化对东北粮食产量的模拟研究[J]. 资源科学, 1994, 16(1): 34-40.
	[Ma Shuqing. Simulation study of climate change on grain production in Northeast China[J]. Resources Science, 1994, 16(1): 34-40.]
[62]	王连喜, 顾嘉熠, 李琪, 等. 江苏省冬小麦适宜度时空变化研究[J]. 生态环境学报, 2016, 25(1): 67-75. doi: 10.16258/j.cnki.1674-5906.2016.01.010
	[Wang Lianxi, Gu Jiayi, Li Qi, et al. Study on the temporal and spatial variation of winter wheat suitability in Jiangsu Province[J]. Ecology and Environmental Sciences, 2016, 25(1): 67-75.]
[63]	张建涛, 李国强, 陈丹丹, 等. 两种冬小麦气候适宜度评价模型的比较[J]. 作物杂志, 2016, 32(2): 159-164.
	[Zhang Jiantao, Li Guoqiang, Chen Dandan, et al. Comparison of using DSSAT and fuzzy mathematics for climatic suitability model of winter wheat[J]. Crops, 2016, 32(2): 159-164.]
[64]	张佩, 田娜, 赵会颖, 等. 江苏省冬小麦气候适宜度动态模型建立及应用[J]. 气象科学, 2015, 35(4): 468-473.
	[Zhang Pei, Tian Na, Zhao Huiying, et al. Establishment and preliminary evaluation of dynamic model of climatic suitability on winter wheat in Jiangsu Province[J]. Journal of the Meteorological Sciences, 2015, 35(4): 468-473.]
[65]	黄淑娥, 田俊, 吴慧峻. 江西省双季水稻生长季气候适宜度评价分析[J]. 中国农业气象, 2012, 33(4): 527-533.
	[Huang Shu’e, Tian Jun, Wu Huijun. Evaluation on climatic suitability during growth season of double rice in Jiangxi Province[J]. Chinese Journal of Agrometeorology, 2012, 33(4): 527-533.]
[66]	郭志鹄, 孙佳, 黄翔, 等. 昌江芒果花期气候适宜度变化特征分析[J]. 中国农学通报, 2019, 35(31): 95-100. doi: 10.11924/j.issn.1000-6850.casb18060125
	[Guo Zhihu, Sun Jia, Huang Xiang, et al. Variation characteristics of climate suitability of mango flowering period in Changjiang[J]. Chinese Agricultural Science Bulletin, 2019, 35(31): 95-100.] doi: 10.11924/j.issn.1000-6850.casb18060125
[67]	张艳红, 吕厚荃, 钱永兰. 1987—2012年黄淮海地区冬小麦生育期气候适宜指数时空分布特征[J]. 中国农学通报, 2014, 30(36): 48-54. doi: 10.11924/j.issn.1000-6850.2014-1717
	[Zhang Yanhong, Lv Houquan, Qian Yonglan. Spatial-temporal distributions of climatic suitability index of winter wheat over the Huanghuaihai region of China in 1987-2012[J]. Chinese Agricultural Science Bulletin, 2014, 30(36): 48-54.] doi: 10.11924/j.issn.1000-6850.2014-1717
[68]	姚树然, 王鑫, 李二杰. 河北省棉花气候适宜度及其时空变化趋势分析[J]. 干旱地区农业研究, 2009, 27(5): 24-29.
	[Yao Shuran, Wang Xin, Li Erjie. Climatic suitability of cotton and its spatial and temporal trend in Hebei Province[J]. Agricultural Research in the Arid Areas, 2009, 27(5): 24-29.]
[69]	张建军, 陈晓艺, 马晓群. 安徽油菜气候适宜度评价指标的建立与应用[J]. 中国农学通报, 2012, 28(13): 155-158.
	[Zhang Jianjun, Chen Xiaoyi, Ma Xiaoqun. Establishment and application of climatic suitability indicator of rape in Anhui Province[J]. Chinese Agricultural Science Bulletin, 2012, 28(13): 155-158.] doi: 10.11924/j.issn.1000-6850.2012-0225
[70]	刘新, 赵艳丽, 刘林春, 等. 内蒙古玉米气候适宜度及其变化特征[J]. 干旱气象, 2018, 36(6): 1020-1026.
	[Liu Xin, Zhao Yanli, Liu Linchun, et al. Climate suitability of maize and its changes in Inner Mongolia[J]. Journal of Arid Meteorology, 2018, 36(6): 1020- 1026.]
[71]	武晋雯, 孙龙彧, 纪瑞鹏, 等. 辽宁水稻气候适宜度日尺度评价研究[J]. 资源科学, 2017, 39(8): 1605-1613. doi: 10.18402/resci.2017.08.16
	[Wu Jinwen, Sun Longyu, Ji Ruipeng, et al. Intraday evaluation modeling of climatic suitability for rice in Liaoning Province[J]. Resources Science, 2017, 39(8): 1605-1613.] doi: 10.18402/resci.2017.08.16
[72]	蒲金涌, 姚小英, 姚茹莘. 近40年甘肃河东地区夏秋作物气候适宜性变化[J]. 干旱地区农业研究, 2011, 29(5): 253-258.
	[Pu Jinyong, Yao Xiaoying, Yao Ruxin. Variations of summer and autumn grain crops’ climatic suitability in the areas east of Yellow River in Gansu in recent 40 years[J]. Agricultural Research in the Arid Areas, 2011, 29(5): 253-258.]
[73]	姚小英, 蒲金涌, 姚茹莘, 等. 气候暖干化背景下甘肃旱作区玉米气候适宜性变化[J]. 地理学报, 2011, 66(1): 59-67.
	[Yao Xiaoying, Pu Jinyong, Yao Rushen, et al. Variation of climate suitability of maize in arid area in Gansu under the condition of climate dry-warming[J]. Acta Geographica Sinica, 2011, 66(1): 59-67.] doi: 10.11821/xb201101006
[74]	刘晓英, 周鹏, 闫利霞, 等. 廊坊地区夏玉米气候适宜度评价分析[J]. 中国农学通报, 2016, 32(6): 151-159. doi: 10.11924/j.issn.1000-6850.casb15080018
	[Liu Xiaoying, Zhou Peng, Yan Lixia, et al. Analysis of climate suitability for summer maize in Langfang region[J]. Chinese Agricultural Science Bulletin, 2016, 32(6): 151-159.] doi: 10.11924/j.issn.1000-6850.casb15080018
[75]	景毅刚, 高茂盛, 范建忠, 等. 陕西关中冬小麦气候适宜度分析[J]. 西北农业学报, 2013, 22(8): 27-32.
	[Jing Yigang, Gao Maosheng, Fan Jianzong, et al. Climate suitability analysis of winter wheat in Guanzhong area of Shaanxi[J]. Acta Agriculturae Boreali-Occidentalis Sinica, 2013, 22(8): 27-32.]
[76]	何永坤, 张建平. 渝东地区烤烟气候适宜度及其变化特征研究[J]. 西南大学学报(自然科学版), 2014, 36(9): 140-146.
	[He Yongkun, Zhang Jianping. Climatic suitability of flue-cured tobacco and its changes in eastern Chongqing[J]. Journal of Southwest University, 2014, 36(9): 140-146.]
[77]	段海来, 千怀遂, 李明霞, 等. 中国亚热带地区柑桔的气候适宜性[J]. 应用生态学报, 2010, 21(8): 1915-1925.
	[Duan Hailai, Qian Huaisui, Li Mingxia, et al. Climatic suitability of citrus in subtropical China[J]. Chinese Journal of Applied Ecology, 2010, 21(8): 1915-1925.] pmid: 21043095
[78]	魏瑞江, 宋迎波, 王鑫. 基于气候适宜度的玉米产量动态预报方法[J]. 应用气象学报, 2009, 20(5): 622-627.
	[Wei Ruijiang, Song Yingbo, Wang Xin. Method for dynamic forecast of corn yield based on climatic suitability[J]. Journal of Applied Meteorological Science, 2009, 20(5): 622-627.]
[79]	刘伟昌, 陈怀亮, 余卫东, 等. 基于气候适宜度指数的冬小麦动态产量预报技术研究[J]. 气象与环境科学, 2008, 31(2): 21-24.
	[Liu Weichang, Chen Huailiang, Yu Weidong, et al. Dynamic output forecast research for winter wheat based on climatic suitability index[J]. Meteorological and Environmental Sciences, 2008, 31(2): 21-24.]
[80]	赖纯佳, 千怀遂, 段海来, 等. 淮河流域双季稻气候适宜度及其变化趋势[J]. 生态学杂志, 2009, 28(11): 2339-2346.
	[Lai Chunjia, Qian Huaisui, Duan Hailai, et al. Climate suitability and its change trend of double-cropping rice in Huaihe River Basin[J]. Chinese Journal of Ecology, 2009, 28(11): 2339-2346.]
[81]	蔡福, 张淑杰, 纪瑞鹏, 等. 近30年辽宁玉米水分适宜度时空演变特征及农业干旱评估[J]. 应用生态学报, 2015, 26(1): 233-240.
	[Cai Fu, Zhang Shujie, Ji Ruipeng, et al. Spatiotemporal dynamics of maize water suitability and assessment of agricultural drought in Liaoning Province, China from 1981 to 2010[J]. Chinese Journal of Applied Ecology, 2015, 26(1): 233-240.] pmid: 25985675
[82]	易雪, 王建林, 宋迎波, 等. 早稻产量动态集成预报方法研究[J]. 中国水稻科学, 2011, 25(3): 307-313. doi: 10.3969/j.issn.1001-7216.2011.03.012
	[Yi Xue, Wang Jianlin, Song Yingbo, et al. Study on dynamic integrated prediction of early rice yield[J]. Chinese Rice Science, 2011, 25(3): 307-313.]
[83]	王贺然, 李晶, 张慧, 等. 基于气候适宜度的辽宁省春玉米产量动态预报研究[J]. 安徽农业科学, 2018, 46(23): 121-125.
	[Wang Heran, Li Jing, Zhang Hui, et al. Study on dynamic prediction of spring maize yield based on climate suitability in Liaoning Province[J]. Journal of Anhui Agricultural Science, 2018, 46(23): 121-125.]
[84]	柳芳, 薛庆禹, 黎贞发. 天津棉花气候适宜度变化特征及其产量动态预报[J]. 中国农业气象, 2014, 35(1): 48-54.
	[Liu Fang, Xue Qingyu, Li Zhenfa. Climatic suitability variation and yield dynamic prediction model of cotton in Tianjin[J]. Chinese Journal of Agrometeorology, 2014, 35(1): 48-54.]
[85]	易灵伟, 杨爱萍, 刘文英, 等. 湖北中稻气候适宜度指标构建及其对产量影响的定量评估与应用[J]. 中国农学通报, 2015, 31(23): 109-115. doi: 10.11924/j.issn.1000-6850.casb15020036
	[Yi Lingwei, Yang Aiping, Liu Wenying, et al. Index construction of climatic suitability on middle-season rice in Hubei Province and quantitative evaluation and application of its effect on yield[J]. Chinese Agricultural Science Bulletin, 2015, 31(23): 109-115.] doi: 10.11924/j.issn.1000-6850.casb15020036
[86]	李树岩, 余卫东. 基于气候适宜度的河南省夏玉米产量预报研究[J]. 河南农业大学学报, 2015, 49(1): 27-34.
	[Li Shuyan, Yu Weidong. Research of summer maize yield forecasting based on climate suitability in Henan[J]. Journal of Henan Agricultural University, 2015, 49(1): 27-34.]
[87]	邱美娟, 宋迎波, 王建林, 等. 耦合土壤墒情的气候适宜度指数在山东省冬小麦产量动态预报中的应用[J]. 中国农业气象, 2015, 36(2): 187-194.
	[Qiu Meijuan, Song Yingbo, Wang Jianlin, et al. Application of climate suitability index coupling soil moisture in dynamic yield prediction of winter wheat in Shandong Province[J]. Chinese Journal of Agrometeorology, 2015, 36(2): 187-194.]
[88]	魏瑞江, 王鑫, 康西言. 国内气候适宜度模型中水分适宜度模型的改进与应用[J]. 地球科学进展, 2022, 37(5): 496-504. doi: 10.11867/j.issn.1001-8166.2022.018
	[Wei Ruijiang, Wang Xin, Kang Xiyan. Improvement and application of a water suitability model in a domestic climate suitability model[J]. Advances in Earth Science, 2022, 37(5): 496-504.] doi: 10.11867/j.issn.1001-8166.2022.018
[89]	刘维, 李祎君, 吕厚荃. 早稻抽穗开花至成熟期气候适宜度对气候变暖与提前移栽的响应[J]. 中国农业科学, 2018, 51(1): 49-59. doi: 10.3864/j.issn.0578-1752.2018.01.005
	[Liu Wei, Li Yijun, Lv Houquan. Responses of heading to flowering to maturity of early rice to climate change and different transplant periods[J]. Scientia Agricultura Sinica, 2018, 51(1): 49-59.] doi: 10.3864/j.issn.0578-1752.2018.01.005
[90]	帅艳民, 武梦瑾, 吴昊, 等. 东北春玉米全生育期气候适宜度评价[J]. 干旱地区农业研究, 2022, 40(3): 238-247.
	[Shuai Yanmin, Wu Mengjin, Wu Hao, et al. Evaluation of climate suitability of spring maize during the whole growth period in Northeast China[J]. Agricultural Research in the Arid Areas, 2022, 40(3): 238-247.]
[91]	张迎杰, 王海梅, 李鑫杨. 气候变化背景下内蒙古翁牛特旗玉米气候适宜度变化[J]. 农学学报, 2023, 13(2): 83-88. doi: 10.11923/j.issn.2095-4050.cjas2021-0190
	[Zhang Yingjie, Wang Haimei, Li Xinyang. Climate suitability variation of maize under climate change in Wengniute of Inner Mongolia[J]. Journal of Agriculture, 2023, 13(2): 83-88.] doi: 10.11923/j.issn.2095-4050.cjas2021-0190
[92]	赵秀兰, 徐玲玲, 张艳红, 等. 未来黄淮海地区夏玉米光温水资源适宜度及灾害风险演变特征[J]. 海洋气象学报, 2023, 43(3): 88-103.
	[Zhao Xiulan, Xu Lingling, Zhang Yanhong, et al. Evolution characteristics of future light temperature and water suitability and disaster risk for summer maize in Huang-Huai-Hai region[J]. Journal of Marine Meteorology, 2023, 43(3): 88-103.]
[93]	金林雪, 于水燕, 宋海清, 等. 内蒙古马铃薯气候适宜度时空演变及产量动态预报研究[J]. 江西农业学报, 2024, 36(9): 66-73.
	[Jin Linxue, Yu Shuiyan, Song Haiqing, et al. Study on spatiotemporal evolution of climate suitability and dynamic prediction of potato yield in Inner Mongolia[J]. Acta Agriculturae Jiangxi, 2024, 36(9): 66-73.]
[94]	杨月婷, 李凯伟, 张继权, 等. 气候变暖背景下中国北方地区谷子生长季气候适宜性分析[J]. 中国农业气象, 2022, 43(3): 215-228.
	[Yang Yueting, Li Kaiwei, Zhang Jiquan, et al. Climatic suitability analysis of millet growing season in Northern China region in the context of climate warming[J]. Chinese Journal of Agrometeorology, 2022, 43(3): 215-228.]
[95]	梁颖, 马树庆, 张明. 基于模糊隶属函数的吉林市烤烟种植气候适宜性区划[J]. 气象与环境学报, 2022, 38(1): 100-105.
	[Liang Yin, Ma Shuqing, Zhang Ming. Climatic suitability regionalization of flue-cured tobacco cultivation in Jilin City based on fuzzy membership function[J]. Journal of Meteorology and Environment, 2022, 38(1): 100-105.]
[96]	曾晓珊, 张波, 孙思思, 等. 气候变化背景下贵州烤烟生长季气候适宜度评价分析[J]. 中国烟草科学, 2023, 44(4): 9-16.
	[Zeng Xiaoshan, Zhang Bo, Sun Sisi, et al. Climatic suitability analysis of flue-cured tobacco growing season in Guizhou in the context of climate change[J]. Chinese Tobacco Science, 2023, 44(4): 9-16.]
[97]	何亮, 毛留喜. 气候变化背景下东北大豆种植区气候适宜性变化[J]. 中国生态农业学报, 2023, 31(5): 690-698.
	[He Liang, Mao Liuxi. Change of soybean climatic suitability in Northeast China under climate change[J]. Chinese Journal of Eco-Agriculture, 2023, 31(5): 690-698.]
[98]	刘聪, 李凯伟, 张继权, 等. 基于气候适宜度的南方柑橘种植精细化气候区划[J]. 应用气象学报, 2021, 32(4): 421-431.
	[Liu Cong, Li Kaiwei, Zhang Jiquan, et al. Refined climatic zoning for citrus cultivation in Southern China based on climate suitability[J]. Journal of Applied Meteorological Science, 2021, 32(4): 421-431.]
[99]	刘武, 莫家尧, 李政, 等. 广西柑橘气候适宜度模型[J]. 中国农学通报, 2021, 37(25): 109-114. doi: 10.11924/j.issn.1000-6850.casb2020-0634
	[Liu Wu, Mo Jiayao, Li Zheng, et al. Climate suitability model of citrus in Guangxi[J]. Chinese Agricultural Science Bulletin, 2021, 37(25): 109-114.] doi: 10.11924/j.issn.1000-6850.casb2020-0634
[100]	朱婷艳, 武英娇, 李文琛, 等. 宁夏番茄气候适宜度变化特征分析[J]. 农学学报, 2022, 12(4): 67-74. doi: 10.11923/j.issn.2095-4050.cjas20200300083
	[Zhu Tingyan, Wu Yingjiao, Li Wenchen, et al. Climate suitability of tomato in Ningxia: Variation characteristics[J]. Journal of Agriculture, 2022, 12(4): 67-74.] doi: 10.11923/j.issn.2095-4050.cjas20200300083
[101]	Qu G, Shuai Y M, Shao C Y, et al. County scale corn yield estimation based on multi-source data in Liaoning Province[J]. Agronomy, 2023, 13: 1428-1449.
[102]	陈雪, 高梦竹, 赵晶, 等. 高寒地区大豆产量动态预报研究[J]. 农学学报, 2023, 13(6): 91-96. doi: 10.11923/j.issn.2095-4050.cjas2022-0065
	[Chen Xue, Gao Mengzhu, Zhao Jing, et al. Study on dynamic forecast of soybean yield in Alpine region[J]. Journal of Agriculture, 2023, 13(6): 91-96.] doi: 10.11923/j.issn.2095-4050.cjas2022-0065
[103]	徐敏, 徐经纬, 高苹, 等. 不同统计模型在冬小麦产量预报中的预报能力评估——以江苏麦区为例[J]. 中国生态农业学报, 2020, 28(3): 438-447.
	[Xu Min, Xu Jingwei, Gao Ping, et al. Evaluation of winter wheat yield prediction ability of different statistical models: A case study of Jiangsu wheat-growing region[J]. Chinese Journal of Eco-Agriculture, 2020, 28(3): 438-447.]
[104]	邱美娟, 刘布春, 刘园, 等. 两种不同产量历史丰歉气象影响指数确定方法在农业气象产量预报中的对比研究[J]. 气象与环境科学, 2019, 42(1): 41-46.
	[Qiu Meijuan, Liu Buchun, Liu Yuan, et al. Comparative study of two different methods for determining meteorological impact index of historical yield in agrometeorological yield prediction[J]. Meteorological and Environmental Sciences, 2019, 42(1): 41- 46.]
[105]	薛思嘉, 魏瑞江, 王朋朋, 等. 基于产量历史丰歉气象影响指数的河北省马铃薯产量预报[J]. 沙漠与绿洲气象, 2021, 15(3): 137-143.
	[Xue Sijia, Wei Ruijiang, Wang Pengpeng, et al. A dynamic prediction method for potato yield based on influence index for bumper or poor harvest from historical yield in Hebei Province[J]. Desert and Oasis Meteorology, 2021, 15(3): 137-143.]
[106]	帅细强, 樊清华, 谢佰承. 基于历史丰歉气象影响指数的湖南油菜产量动态预报[J]. 湖南农业科学, 2021(6): 82-85.
	[Shuai Xiqiang, Fan Qinghua, Xie Baicheng. Dynamic forecast of rape yield in Hunan Province based on historical weather impact index[J]. Hunan Agricultural Sciences, 2021(6): 82-85.]
[107]	郑昌玲, 杨霏云, 王建林, 等. 早稻产量动态预报模型[J]. 中国农业气象, 2007, 28(4): 412-416.
	[Zheng Changling, Yang Feiyun, Wang Jianlin, et al. Study on dynamic prediction model of early rice yield per unit[J]. Chinese Journal of Agrometeorology, 2007, 28(4): 412-416.]
[108]	郑昌玲, 王建林, 宋迎波, 等. 大豆产量动态预报模型研究[J]. 大豆科学, 2008, 27(6): 943-948.
	[Zheng Changling, Wang Jianlin, Song Yingbo, et al. Dynamic prediction model of soybean yield per unit[J]. Soybean Science, 2008, 27(6): 943-948.]
[109]	王贺然, 张慧, 王莹, 等. 基于两种方法建立辽宁大豆产量丰歉预报模型对比[J]. 中国农业气象, 2018, 39(11): 725-738. doi: 10.3969/j.issn.1000-6362.2018.11.004
	[Wang Heran, Zhang Hui, Wang Ying, et al. A comparative study on forecast model for soybean yield by using different statistic methods in Liaoning Province[J]. Chinese Journal of Agrometeorology, 2018, 39(11): 725-738.] doi: 10.3969/j.issn.1000-6362.2018.11.004
[110]	邱美娟, 宋迎波, 王建林, 等. 山东省冬小麦产量动态集成预报方法[J]. 应用气象学报, 2016, 27(2): 191-200.
	[Qiu Meijuan, Song Yingbo, Wang Jianlin, et al. Integrated technology of yield dynamic prediction of winter wheat in Shandong Province[J]. Journal of Applied Meteorological Science, 2016, 27(2): 191-200.]
[111]	余焰文, 蔡哲, 姚俊萌, 等. 江西省油菜产量集成预测模型方法研究[J]. 气象与减灾研究, 2019, 42(3): 206-211.
	[Yu Yanwen, Cai Zhe, Yao Junmeng, et al. Study on integrated forecasting model of rape yield in Jiangxi Province[J]. Meteorology and Disaster Reduction Research, 2019, 42(3): 206-211.]
[112]	张加云, 陈瑶, 朱勇, 等. 基于相似气象年型和关键气象因子的云南一季稻动态产量预报[J]. 中国农学通报, 2020, 36(34): 96-99. doi: 10.11924/j.issn.1000-6850.casb20191100868
	[Zhang Jiayun, Chen Yao, Zhu Yong, et al. Dynamic prediction of single cropping rice yield based on similar meteorological year type and key meteorological factors in Yunnan[J]. Chinese Agricultural Science Bulletin, 2020, 36(34): 96-99.] doi: 10.11924/j.issn.1000-6850.casb20191100868
[113]	帅细强, 陆魁东, 黄晚华. 不同方法在湖南省早稻产量动态预报中的比较[J]. 应用气象学报, 2015, 26(1): 103-111.
	[Shuai Xiqiang, Lu Kuidong, Huang Wanhua. A comparative study on dynamic forecasting of early rice yield by using different methods in Hunan Province[J]. Journal of Applied Meteorological Science, 2015, 26(1): 103-111.]
[114]	侯英雨, 张蕾, 吴门新, 等. 国家级现代农业气象业务技术进展[J]. 应用气象学报, 2018, 29(6): 641-656.
	[Hou Yingyu, Zhang Lei, Wu Menxin, et al. Advances of modern agrometeorological service and technology in China[J]. Journal of Applied Meteorological Science, 2018, 29(6): 641-656.]
[115]	艾劲松, 孙雨轩, 刘凯文. 荆州市冬小麦产量动态预报方法对比研究[J]. 气象科技进展, 2018, 8(5): 36-39.
	[Ai Jinsong, Sun Yuxuan, Liu Kaiwen. Comparison of winter wheat output forecast methods in Jingzhou[J]. Advances in Meteorological Science and Technology, 2018, 8(5): 36-39.]
[116]	Ajith S, Debnath M K, Karthik R. Statistical and machine learning models for location-specifc crop yield prediction using weather indices[J]. International Journal of Biometeorology, 2024, 68: 2453-2475.
[117]	刘维, 宋迎波. 基于气象要素的逐日玉米产量气象影响指数[J]. 应用气象学报, 2022, 33(3): 364-374.
	[Liu Wei, Song Yingbo. A daily meteorological impact index of maize yield based on weather elements[J]. Journal of Applied Meteorological Science, 2022, 33(3): 364-374.]
[118]	Huang J, Islam A R M T, Zhang F, et al. Spatiotemporal analysis the precipitation extremes affecting rice yield in Jiangsu province, southeast China[J]. International Journal of Biometeorology, 2017, 61(6): 1-10.
[119]	Zhang Y, Wang Y S, Hao H, et al. Unmanned aerial vehicle (UAV) hyperspectral imagery mining to identify new spectral indices for predicting the field-scale yield of spring maize[J]. Sustainability, 2024, 16(24): 10916-10934.
[120]	孙法福, 赖宁, 耿庆龙, 等. 基于无人机高光谱影像的冬小麦叶片氮浓度遥感估测[J]. 干旱区研究, 2024, 41(6): 1069-1078. doi: 10.13866/j.azr.2024.06.15
	[Sun Fafu, Lai Ning, Geng Qinglong, et al. Estimation of nitrogen contentration in winter wheat leaves based on hyperspectral images of UAV[J]. Arid Zone Research, 2024, 41(6): 1069-1078.] doi: 10.13866/j.azr.2024.06.15
[121]	Yang W, Nigon T, Hao Z Y, et al. Estimation of corn yield based on hyperspectral imagery and convolutional neural network[J]. Computers and Electronics in Agriculture, 2021, 184(6): 106092-106102.
[122]	Zhu J, Yin Y M, Lu J S, et al. The relationship between wheat yield and sun-induced chlorophyll fluorescence from continuous measurements over the growing season[J]. Remote Sensing of Environment, 2023, 298(6): 113791.
[123]	Duveiller G, Filipponi F, Walther S, et al. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity[J]. Earth System Science Data, 2020, 12(2): 1101-1116. doi: 10.5194/essd-12-1101-2020
[124]	Duguma A L, Bai X. How the internet of things technology improves agricultural efficiency[J]. Artificial Intelligence Review, 2025, 58(63): 1-26.
[125]	Srivastava A K, Safaei N, Khaki S, et al. Winter wheat yield prediction using convolutional neural networks from environmental and phenological data[J]. Scientifc Reports, 2022, 12: 3215-3229.
[126]	Panigrahi B, Kathala K C, Sujatha M. A machine learning-based comparative approach to predict the crop yield using supervised learning with regression models[J]. Procedia Computer Science, 2023, 218: 2684-2693.
[127]	Jena P R, Majhi B, Kalli R, et al. Prediction of crop yield using climate variables in the south-western province of India: A functional artificial neural network modeling (FLANN) approach[J]. Environment, Development and Sustainability, 2023, 25(10): 11033-11056.
[128]	Ali I, Greifeneder F, Stamenkovic J, et al. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data[J]. Remote Sensing, 2015, 7(12): 16398-16421.
[129]	Prasad N, Patel N, Danodia A. Crop yield prediction in cotton for regional level using random forest approach[J]. Spatial Information Research, 2020, 29: 1-12.
[130]	Nti I K, Nyarko-Boateng O, Adekoya F A, et al. An empirical assessment of different kernel functions on the performance of support vector machines[J]. Bulletin of Electrical Engineering and Informatics, 2021, 10(6): 3403-3411.
[131]	Savas C, Dovis F. The impact of different kernel functions on the performance of scintillation detection based on support vector machines[J]. Sensors, 2019, 19: 5219-5235.
[132]	López Segura M V, Aguilar Lasserre A A, Fernández Lámbert G, et al. XGBoost sequential system for the prediction of Persian lemon crop yield[J]. Crop Science, 2023, 65: e21148-e21160.
[133]	Taye M M. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions[J]. Computers, 2023, 12(5): 91-117.
[134]	Zhou S, Xu L, Chen N. Rice yield prediction in Hubei Province based on deep learning and the effect of spatial heterogeneity[J]. Remote Sensing, 2023, 15(5): 1361-1378.
[135]	van Klompenburg T, Kassahun A, Catal C. Crop yield prediction using machine learning: A systematic literature review[J]. Computers and Electronics in Agriculture, 2020, 177: 105709-105727.
[136]	Mokhtar A, El-Ssawy W, He H, et al. Using machine learning models to predict hydroponically grown lettuce yield[J]. Frontiers in Plant Science, 2022, 13: 1-10
[137]	Hani N, Roy P, Isler V. A comparative study of fruit detection and counting methods for yield mapping in apple orchards[J]. Journal of Field Robotics, 2020, 37(2): 263-282.
[138]	Tian H, Wang P, Tansey K, et al. An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China[J]. Agricultural and Forest Meteorology, 2021, 310: 108629-108640.
[139]	Dang C, Liu Y, Yue H, et al. Autumn crop yield prediction using data-driven approaches: Support vector machines, random forest, and deep neural network methods[J]. Canadian Journal of Remote Sensing, 2021, 47: 162-181.
[140]	Oikonomidis A, Catal C, Kassahun A. Hybrid deep learning-based models for crop yield prediction[J]. Applied artificial intelligence, 2022, 36(1): e2031822-e2031841.
[141]	Sun J, Di L, Sun Z, et al. County-level soybean yield prediction using deep CNN-LSTM model[J]. Sensors, 2019, 19(20): 4363-4384.
[142]	Khaki S, Wang L, Archontoulis S V. A CNN-RNN framework for crop yield prediction[J]. Frontiers in Plant Science, 2020, 10: 1750-1764.
[143]	Yang F, Zhang D, Zhang Y, et al. Prediction of corn variety yield with attribute-missing data via graph neural network[J]. Computers and Electronics in Agriculture, 2023, 211: 108046.
[144]	Apolo-Apolo O E, Pérez-Ruiz M, Martínez-Guanter J, et al. A cloud-based environment for generating yield estimation maps from apple orchards using UAV imagery and a deep learning technique[J]. Frontiers in Plant Science, 2020, 11: 1086-1100. doi: 10.3389/fpls.2020.01086 pmid: 32765566
[145]	Kim N, Ha K J, Park N W, et al. A comparison between major artificial intelligence models for crop yield prediction: Case study of the midwestern United States, 2006-2015[J]. ISPRS International Journal of Geo-Information, 2019, 8: 240-263.
[146]	Wu S R, Yang P, Ren J Q, et al. Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm[J]. Remote Sensing of Environment, 2021, 255: 112276-112297.
[147]	冯克鹏, 许德浩, 庄淏然. WOFOST伴随率定三温模型的玉米农田遥感蒸散发估算方法[J]. 干旱区研究, 2025, 42(1): 166-178. doi: 10.13866/j.azr.2025.01.15
	[Feng Kepeng, Xu Dehao, Zhuang Haoran. An estimation method of remote sensing evapotranspiration in farmland based on the three-temperature model with adjoint calibrated of WOFOST[J]. Arid Zone Research, 2025, 42(1): 166-178.] doi: 10.13866/j.azr.2025.01.15
[148]	Junior F, dos Santos Vianna I M, Marin M, et al. Assimilating leaf area index data into a sugarcane process-based crop model for improving yield estimation[J]. European Journal of Agronomy, 2022, 136: 126501
[149]	Han D, Wang P, Ding Y, et al. Improving the simulation accuracy of summer maize growth and yield by pixel-based parameterization based on assimilating upscaled MODIS LAI[J]. Science of the Total Environment, 2024, 954: 176649.
[150]	Ziliani M G, Altaf M U, Aragon B, et al. Early season prediction of within-field crop yield variability by assimilating CubeSat data into a crop model[J]. Agricultural and Forest Meteorology, 2022, 313: 108736-108750.
[151]	Sarker I H. Deep Learning: A comprehensive overview on techniques, taxonomy, applications and research directions[J]. SN Computer Science, 2021, 2: 1-20.
[152]	Ren Y, Li Q, Du X, et al. Analysis of corn yield prediction potential at various growth phases using a process-based model and deep learning[J]. Plants, 2023, 12(3): 446-464.
[153]	Bai H Z, Xiao D P, Tang J Z, et al. Evaluation of wheat yield in North China Plain under extreme climate by coupling crop model with machine learning[J]. Computers and Electronics in Agriculture, 2024, 217: 108651.