设为首页
加入收藏
Advances of Modern Agrometeorological Service and Technology in China
-
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.
-
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) 
下载: 导出CSV
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)
下载: 导出CSV
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)
下载: 导出CSV
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
下载: 导出CSV
-
[1] 王建林.现代化农业气象业务.北京:气象出版社, 2010. [2] 毛留喜, 吕厚荃.国家级农业气象业务技术综述.气象, 2010, 36(7):75-80. http://d.old.wanfangdata.com.cn/Periodical/qx201007013 [3] 王良宇, 何延波.自动土壤水分观测数据异常值阈值研究.气象, 2015, 41(8):1017-1022. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qx201508011 [4] 陈金华, 杨再强, 杨太明, 等.安徽省土壤水分监测预测系统.应用气象学报, 2011, 22(2):249-256. doi: 10.3969/j.issn.1001-7313.2011.02.014 [5] 谭方颖, 王建林, 程路.东北地区单季稻温度适宜性及其变化特征.生态学杂志, 2017, 36(3):719-724. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=stxzz201703020 [6] 侯英雨, 王良宇, 毛留喜, 等.基于气候适宜度的东北地区春玉米发育期模拟模型.生态学杂志, 2012, 31(9):2431-2436. http://d.old.wanfangdata.com.cn/Periodical/stxzz201209039 [7] 张建军, 马晓群, 许莹.安徽省一季稻生长气候适宜性评价指标的建立与试用.气象, 2013, 39(1):88-93. http://d.old.wanfangdata.com.cn/Periodical/qx201301011 [8] 罗怀良, 闫宁.区域种植业气候适宜度及其对种植活动的响应——以四川省盐亭县为例.生态学报, 2016, 36(24):7981-7991. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=stxb201624009 [9] 侯英雨, 张艳红, 王良宇, 等.东北地区春玉米气候适宜度模型.应用生态学报, 2013, 24(11):3207-3212. http://d.old.wanfangdata.com.cn/Periodical/yystxb201311025 [10] 钱永兰, 侯英雨, 延昊, 等.基于遥感的国外作物长势监测与产量趋势估计.农业工程学报, 2012, 28(13):166-171. http://d.old.wanfangdata.com.cn/Periodical/nygcxb201213027 [11] 马玉平, 王石立, 王馥棠.作物模拟模型在农业气象业务应用中的研究初探.应用气象学报, 2005, 16(3):293-303. doi: 10.3969/j.issn.1001-7313.2005.03.003 [12] 黄健熙, 贾世灵, 马鸿元, 等.基于WOFOST模型的中国主产区冬小麦生长过程动态模拟.农业工程学报, 2017, 33(10):222-228. doi: 10.11975/j.issn.1002-6819.2017.10.029 [13] 侯英雨.作物模型业务应用技术指南.北京:国家气象中心, 2016. [14] 王春乙, 张雪芬, 孙忠富, 等.进入21世纪的中国农业气象研究.气象学报, 2007, 67(5):815-824. doi: 10.3321/j.issn:0577-6619.2007.05.017 [15] 宋迎波, 王建林, 杨霏云, 等.粮食安全气象服务.北京:气象出版社, 2006. [16] 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.应用气象学报, 2015, 26(1):103-111. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20150111&flag=1 [17] 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.应用气象学报, 2009, 20(5):622-626. doi: 10.3969/j.issn.1001-7313.2009.05.014 [18] 任玉玉, 千怀遂.河南省棉花气候适宜度变化趋势分析.应用气象学报, 2006, 17(1):87-93. doi: 10.3969/j.issn.1001-7313.2006.01.012 [19] 李树岩, 刘伟昌.基于气象关键因子的河南省夏玉米产量预报研究.干旱地区农业研究, 2014, 32(5):223-227. http://d.old.wanfangdata.com.cn/Periodical/ghdqnyyj201405038 [20] 唐余学, 罗孳孳, 范莉, 等.基于关键气象因子的中稻单产动态预报, 中国农业气象, 2011, 32(1):140-143. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QK201103810292 [21] 易灵伟, 杨爱萍, 余焰文, 等.基于气候适宜指数的江西晚稻产量动态预报模型构建及应用.气象, 2016, 42(7):885-891. http://d.old.wanfangdata.com.cn/Periodical/qx201607012 [22] 杜春英, 李帅, 王晾晾, 等.基于历史产量丰歉影响指数的黑龙江省水稻产量动态预报.中国农业气象, 2010, 31(3):427-430. doi: 10.3969/j.issn.1000-6362.2010.03.019 [23] 宋迎波, 王建林, 陈晖, 等.中国油菜产量动态预报方法研究.气象, 2008, 34(3):93-99. doi: 10.3969/j.issn.1673-8411.2008.03.032 [24] 易雪, 王建林, 宋迎波, 等.早稻产量动态集成预报方法研究.中国水稻科学, 2011, 25(3):307-313. doi: 10.3969/j.issn.1001-7216.2011.03.012 [25] 王桂芝, 胡慧, 陈纪波, 等.基于BP滤波的Fourier模型在粮食产量预测中的应用.中国农业气象, 2015, 36(4):472-478. doi: 10.3969/j.issn.1000-6362.2015.04.011 [26] 王海军, 柳敏燕, 高娟.利用遗传算法和支持向量机测算农用地理论单产和可实现单产.农业工程学报, 2013, 29(19):244-252. doi: 10.3969/j.issn.1002-6819.2013.19.030 [27] 向昌盛, 周子英, 武丽娜.粮食产量预测的支持向量机模型研究.湖南农业大学学报(社会科学版), 2010, 11(1):6-10. doi: 10.3969/j.issn.1009-2013.2010.01.003 [28] 李哲, 张军涛.人工神经网络与遗传算法相结合在作物估产中的应用.生态学报, 2001, 21(5):716-720. doi: 10.3321/j.issn:1000-0933.2001.05.005 [29] Li H, Jiang Z W, Chen Z X, et al.Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation.Journal of Integrative Agriculture, 2016, 15(10):60345-60347. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgnykx-e201710019 [30] 秦鹏程, 刘敏, 万素琴.不完整气象资料下基于作物模型的产量预报方法.应用气象学报, 2016, 27(4):407-416. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160403&flag=1 [31] 王雪姣, 潘学标, 王森, 等.基于COSIM模型的新疆棉花产量动态预报方法.农业工程学报, 2017, 33(8):160-165. http://d.old.wanfangdata.com.cn/Periodical/nygcxb201708022 [32] 陈鹏飞, 杨飞, 杜佳.基于环境减灾卫星时序归一化植被指数的冬小麦产量估测.农业工程学报, 2013, 29(11):124-131. http://d.old.wanfangdata.com.cn/Periodical/nygcxb201311018 [33] Huang J, Wang X, Li X, et al.Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.Plos One, 2013, 8(8):e70816. doi: 10.1371/journal.pone.0070816 [34] 张东霞, 张继贤, 常帆, 等.遥感技术在主要粮食作物估产中的应用.测绘学报, 2014, 39(11):95-98;103. http://d.old.wanfangdata.com.cn/Periodical/chkx201411021 [35] 谭昌伟, 罗明, 杨昕, 等.运用PLS算法由HJ-1A/1B遥感影像估测区域小麦实际单产.农业工程学报, 2015, 31(15):161-166. doi: 10.11975/j.issn.1002-6819.2015.15.022 [36] 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.应用气象学报, 2016, 27(2):191-200. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160207&flag=1 [37] 陈昌为, 朱秀芳, 蔡毅, 等.一种基于趋势单产和遥感修正模型的混合估产模型.中国农业科学, 2017, 50(10):1792-1801. doi: 10.3864/j.issn.0578-1752.2017.10.005 [38] Li Z H, Jin X L, Zhao C J, et al.Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing.European Journal of Agronomy, 2015, 71:53-62. doi: 10.1016/j.eja.2015.08.006 [39] Huang J X, Tian L Y, Ma H Y, et al.Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model.Agricultural and Forest Meteorology, 2015, 204:106-121. doi: 10.1016/j.agrformet.2015.02.001 [40] 黄健熙, 马鸿元, 田丽燕, 等.基于时间序列LAI和ET同化的冬小麦遥感估产方法比较.农业工程学报, 2015, 31(4):197-203. doi: 10.3969/j.issn.1002-6819.2015.04.028 [41] Yao F M, Tang Y J, Wang P J, et al.Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain.Physics and Chemistry of the Earth, 2015, 87:142-152. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4f1b889430cda01c0003836055ddd092 [42] 郭建平.农业气象灾害监测预测技术研究进展.应用气象学报, 2016, 27(5):620-630. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160510&flag=1 [43] 王春乙, 张继权, 霍治国, 等.农业气象灾害风险评估研究进展与展望.气象学报, 2015, 68(1):1-19. doi: 10.3969/j.issn.1005-0582.2015.01.001 [44] 霍治国, 王石立.农业和生物气象灾害.北京:气象出版社, 2009. [45] 王春乙, 娄秀荣, 王建林.中国农业气象灾害对作物产量的影响.自然灾害学报, 2007, 16(5):37-43. doi: 10.3969/j.issn.1004-4574.2007.05.006 [46] 郭建平.农作物低温冷害监测预测理论与实践.北京:气象出版社, 2009. [47] GB/T 21985-2008.主要农作物高温危害温度指标.北京:中国标准出版社, 2008. [48] QX/T 82-2007.小麦干热风灾害等级.北京:气象出版社, 2007. [49] QX/T 88-2008.作物霜冻害等级.北京:气象出版社, 2008. [50] QX/T 167-2012.北方春玉米冷害评估技术规范.北京:气象出版社, 2012. [51] QX/T 107-2009.冬小麦、油菜渍涝等级.北京:气象出版社, 2009. [52] 谢五三, 王胜, 唐为安, 等.干旱指数在淮河流域的适用性对比.应用气象学报, 2014, 25(2):176-184. doi: 10.3969/j.issn.1001-7313.2014.02.007 [53] Liu X F, Zhu X F, Pan Y Z, et al.Agricultural drought monitoring:Progress, challenges, and prospects.Journal of Geographical Sciences, 2016, 26(6):750-767. doi: 10.1007/s11442-016-1297-9 [54] Hao Z.Drought characterization from a multivariate perspective:A review.J Hydrol, 2015, 527:668-678. doi: 10.1016/j.jhydrol.2015.05.031 [55] BG/T 32136-2015.农业干旱等级.北京:中国标准出版社, 2015. [56] Ma Y P, Wang S L, Li W J.Monitoring and predicting of maize chilling damage based on crop growth model in Northeast China.Acta Agronomica Sinica, 2011, 37(10):1868-1878. doi: 10.3724/SP.J.1006.2011.01868 [57] 张建平, 赵艳霞, 王春乙, 等.基于WOFOST作物生长模型的冬小麦干旱影响评估技术.生态学报, 2013, 33(6):1762-1769. http://d.old.wanfangdata.com.cn/Periodical/stxb201306009 [58] 张建平, 王靖, 何永坤, 等.基于WOFOST作物模型的玉米区域干旱影响评估技术.中国生态农业学报, 2017, 25(3):451-459. http://d.old.wanfangdata.com.cn/Periodical/stnyyj201703016 [59] 张建平, 王春乙, 赵艳霞, 等.基于作物模型的低温冷害对我国东北三省玉米产量影响评估.生态学报, 2012, 32(13):4132-4138. http://d.old.wanfangdata.com.cn/Periodical/stxb201213018 [60] 张丽文, 王秀珍, 姜丽霞, 等.用MODIS热量指数动态监测东北地区水稻延迟型冷害.遥感学报, 2015, 19(4):690-701. http://d.old.wanfangdata.com.cn/Periodical/ygxb201504015 [61] 侯英雨, 柳钦火, 延昊, 等.我国陆地植被净初级生产力变化规律及其对气候的响应.应用生态学报, 2007, 18(7):1546-1553. doi: 10.3321/j.issn:1001-9332.2007.07.023 [62] Zhang L, Huo Z G, Zhang L Z, et al.Integrated risk assessment of major meteorological disasters in paprika pepper in Huanan Province.Journal of Tropical Meteorology, 2017, 23(3):334-344. http://www.cqvip.com/QK/85390X/201703/673385616.html [63] 张蕾, 霍治国, 黄大鹏, 等.10-11月海南省瓜菜苗期湿涝风险评估与区划.应用气象学报, 2015, 26(4):432-441. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20150405&flag=1 [64] 薛昌颖, 张弘, 刘荣花.黄淮海地区夏玉米生长季的干旱风险.应用生态学报, 2016, 27(5):1521-1529. http://d.old.wanfangdata.com.cn/Periodical/yystxb201605022 [65] Zhang D, Wang G L, Zhou H C.Assessment on agricultural drought risk based on Variable Fuzzy Sets Model.Chinese Geographical Science, 2011, 21(2):167-175. doi: 10.1007/s11769-011-0456-2 [66] 高晓容, 王春乙, 张继权, 等.东北地区玉米主要气象灾害风险评价模型研究.中国农业科学, 2014, 47(21):4257-4268. doi: 10.3864/j.issn.0578-1752.2014.21.011 [67] Sun Z, Zhang J Q, Yan D H, et al.The impact of irrigation water supply rate on agricultural drought disaster risk:A case about maize based on EPIC in Baicheng City, China.Natural Hazards, 2015, 78(1):23-40. doi: 10.1007/s11069-015-1695-9 [68] 王春乙, 张玉静, 张继权.华北地区冬小麦主要气象灾害风险评价.农业工程学报, 2016, 32(增刊Ⅰ):203-213. http://d.old.wanfangdata.com.cn/Periodical/nygcxb2016z1029 [69] Zhang L, Yang B Y, Li S, et al.Disease-weather relationships for wheat powdery mildew under climate change in China.Journal of Agricultural Science, 2017, 155:1239-1252. doi: 10.1017/S0021859617000442 [70] 于彩霞, 霍治国, 张蕾, 等.中国稻飞虱发生的大气环流指示指标.生态学杂志, 2014, 33(4):1053-1060. http://d.old.wanfangdata.com.cn/Periodical/stxzz201404030 [71] 卢小凤, 霍治国.桂林地区稻飞虱发生等级气象预报模型.生态学杂志, 2013, 32(9):2444-2450. http://d.old.wanfangdata.com.cn/Periodical/stxzz201309030 [72] 张蕾, 霍治国, 王丽, 等.河北省小麦白粉病发生气象等级动态预警.生态学杂志, 2015, 34(11):3139-3145. http://d.old.wanfangdata.com.cn/Periodical/stxzz201509014 [73] 武荣盛, 陈素华.内蒙古地区玉米螟发生的气象条件适宜度预报.中国农业气象, 2011, 32(3):471-474. doi: 10.3969/j.issn.1000-6362.2011.03.025 [74] 郭安红, 王建林, 王纯枝, 等.内蒙古草原蝗虫发生发展气象适宜度指数构建方法.气象科技, 2009, 37(1):42-47. doi: 10.3969/j.issn.1671-6345.2009.01.008 [75] 李轩, 郭安红, 庄立伟.基于GIS的主要农作物病虫害气象等级预报系统研究.国土资源遥感, 2012, 24(1):104-109. http://d.old.wanfangdata.com.cn/Periodical/gtzyyg201201019 [76] 闵庆文.农用天气预报.北京:气象出版社, 1988. [77] 刘锦銮, 何键, 陈新光.广东省农用天气预报技术研究.气象, 2006, 32(2):116-120. doi: 10.3969/j.issn.1000-0526.2006.02.021 [78] 马树庆, 陈剑, 王琪, 等.东北地区玉米整地、播种和收获气象适宜度评价模型.气象, 2013, 39(6):782-788. http://d.old.wanfangdata.com.cn/Periodical/qx201306015 [79] 庄立伟, 卫建国, 毛留喜.软件设计模式在农业气象系统开发中的应用.应用气象学报, 2011, 22(5):631-640. doi: 10.3969/j.issn.1001-7313.2011.05.014 -
图(8) / 表(4)
计量
- 摘要浏览量: 6633
- HTML全文浏览量: 1506
- PDF下载量: 293
- 被引次数: 0
