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Advances of Modern Agrometeorological Service and Technology in China
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摘要: 农业气象业务技术是开展农业气象服务的基础和前提，因此，农业气象业务技术的研发一直是国家级农业气象业务服务的核心工作。近年来，国家级农业气象业务技术已逐步迈向精细化、定量化，涵盖了农业气象监测评价、作物产量预报、农业气象灾害监测评估与影响预报、农用天气预报、农林病虫害发生发展气象等级预报等诸多领域。随着农业气象业务技术的发展，支撑农业气象服务的客观产品更加丰富和多样化，既有站点产品，又有格点产品，涵盖了日、周、月、季、年等不同时间尺度。以农业气象指标、数理统计模型、作物生长模拟、卫星遥感、地理信息系统、大数据等技术为核心的国家级农业气象业务平台（CAgMSS）已成为全国农业气象业务系统的重要品牌。面向现代农业发展对气象服务日益增长的需求，精细化、精准化的农业气象灾害监测与风险评估技术、作物长势评估与产量预报综合集成技术、农业应对气候变化技术以及农业气象大数据挖掘与人工智能技术将是未来国家级农业气象业务技术发展的重点。Abstract: Agrometeorological operational technology is the foundation and premise of modern agrometeorological service in China. The development of agrometeorological science and technology is always the core for national agrometeorological service. In recent years, national agrometeorological service grows quickly with high quality, and now it covers various fields such as agrometeorological monitoring and assessment, crop yield forecast, agrometeorological disasters monitoring and prediction, meteorological forecasting of disease and pests, and weather forecast for agricultural activities. With the development of agrometeorological technology, more and more numerical products are proposed to support agrometeorological services, e.g., daily, weekly, monthly and yearly products at both stations and grid points. Advances of agrometeorological service and technology are illustrated here, involving fields of agrometeorological monitoring and assessment, crop yield forecast, agrometeorological disasters monitoring and prediction, meteorological forecasting of disease and pests, weather forecast for agricultural activities and agrometeorological service system. Agrometeorological monitoring and assessment includes timely automatic monitoring of soil humidity, assessment of climate suitability based on temperature, precipitation, radiation and integrated functions, quantified assessment of crop growing situation based on three ways (i.e., field observation, remote sensing (RS) monitoring and crop model simulation). Several grades are defined to classify the level of crop growing. Crop yield forecast is mainly upon mathematical statistics methods based on relations between crop yield and key affecting factors, crop model simulation based on growing mechanism process for crops, and RS estimation based on relationships between yield and vegetation index. For some inevitable limitations in each method, multiple methods are integrated to forecast crop yield and the accuracy is generally above 99% during 2008-2017. Agrometeorological disasters monitoring, and prediction are always implemented in some ways, including classification based on single and multiple agrometeorological index, statistical analysis based on field survey, RS monitoring and crop model simulation. Risk analysis is the prerequisite for disasters assessment, involving hazard, vulnerability, sensibility and prevention. Meteorological forecasting of disease and pests currently refers to the linking models of meteorological factors and the occurrence of disease and pests. Key technologies for agricultural activities related weather forecast are the selection of index for key time farming, e.g., planting, flowering and harvest. China Agrometeorological Service System (CAgMSS), in which agrometeorological index, mathematical statistical models, crop growth simulation, RS, GIS, agrometeorological big data and other technologies integrated, is the highlight of national agrometeorological service and extended to provincial agrometeorological institutions. With an increasing demand of modern agricultural development, the meticulous and accurate agricultural meteorological disaster monitoring with risk assessment technology, the integrated technology of crop growth assessment and yield forecast, agricultural climate change impact, big data mining and artificial intelligence technology will become the focus of agrometeorological service in the coming decade.
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图 1 2018年8月10日20 cm土壤湿度(a)和土壤墒情对比(b)
Fig. 1 Relative humidity(a) and comparative humidity(b) of soil at depth of 20 cm on 10 Aug 2018
图 2 全国冬小麦适宜度逐旬动态变化
Fig. 2 Dynamic variation of climate suitability at every ten days for winter wheat
图 5 2008—2017年粮食单产预报值(a)和预报准确率(b)
Fig. 5 Yield prediction of food crops(a) and their precision rate(b) during 2008-2017
图 6 农业干旱综合监测(a)和遥感干旱指数(b)
Fig. 6 Comprehensive monitoring of agricultural drought(a) and remote sensing monitoring index of agricultural drought(b)
图 8 春玉米(a)和一季稻(b)春耕春播气象适宜度
Fig. 8 Climate suitability of planting for spring maize(a) and single-season rice(b)
表 1 作物长势遥感监测指标
Table 1 Crop growth condition classification using vegetation index
长势分级 差值植被指数 植被状态指数 偏差 [-100，-8) [-100，55) 略偏差 [-8，-4) [55，65) 持平 [-4，4) [65，75) 略偏好 [4，8) [75，85) 偏好 [8，100) [85，100) 
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表 2 基于生物量的作物长势分类指标
Table 2 Crop growth condition classification using biomass simulated from crop model
评价结果 地上干物质总重/% 穗重/% 叶重/% 偏好 [20，100) [10，100) [10，100) 略偏好 [10，20) [5，10) [5，10) 持平 [-10，10) [-5，5) [-5，5) 略偏差 [-20，-10) [-10，-5) [-10，-5) 偏差 [-100，-20) [-100，-10) [-100，-10)
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表 3 小麦赤霉病发生发展气象分级标准
Table 3 Criteria of epidemic level for wheat scab
发生发展气象等级 气象条件描述 流行程度 判别指标(达标日数) 1 适宜 大流行 不低于10 d 2 基本适宜 中等 [5 d，10 d) 3 不适宜 轻发生、局部轻发生 [0，5 d)
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表 4 内蒙古地区草原蝗虫发生发展气象分级标准
Table 4 Criteria of development level for grasshopper in Inner Mongolia
发生发展气象条件 虫害气象适宜度指数(Z) 等级 适宜 Z≥12 1 较适宜 8≤Z＜12 2 不适宜 0≤Z＜8 3
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