[1]武强,韩旭,唐余学,等.2种基于历史丰歉气象影响指数的茎瘤芥产量动态预报方法比较[J].江苏农业学报,2021,(06):1443-1450.[doi:doi:10.3969/j.issn.1000-4440.2021.05.011]
　WU Qiang,HAN Xu,TANG Yu-xue,et al.Comparison of two methods for yield prediction of Brassica juncea var. tumida Tsen & Lee based on meteorological influence index of historical yield[J].,2021,(06):1443-1450.[doi:doi:10.3969/j.issn.1000-4440.2021.05.011]
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2种基于历史丰歉气象影响指数的茎瘤芥产量动态预报方法比较()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2021年06期
页码:
1443-1450
栏目:
耕作栽培·资源环境
出版日期:
2021-12-30

文章信息/Info

Title:
Comparison of two methods for yield prediction of Brassica juncea var. tumida Tsen & Lee based on meteorological influence index of historical yield
作者:
武强1; 2; 3; 韩旭3; 唐余学1; 2; 3; 徐倩倩4; 阳园燕1; 2; 3
（1.重庆市气象科学研究所，重庆401147；2.重庆市农业气象与卫星遥感工程技术研究中心，重庆401147；3.重庆市江津现代农业气象试验站,重庆402260；4.合肥市气象局，安徽合肥230041）
Author(s):
WU Qiang1; 2; 3; HAN Xu3; TANG Yu-xue1; 2; 3; XU Qian-qian4; YANG Yuan-yan1; 2; 3
（1.Chongqing Institute of Meteorological Sciences, Chongqing 401147, China;2.Chongqing Engineering Research Center of Agrometeorology and Satellite Remote Sensing, Chongqing 401147, China;3.Jiangjin Modern Agrometeorology Experimental Station, Chongqing 402260, China;4.Hefei Meteorological Bureau, Hefei 230041, China）
关键词:
产量预报; 大概率法; 加权平均分析法; 气象条件相似年; 茎瘤芥
Keywords:
yield prediction; large probability method; weighted average method; similarity year of meteorological conditions; Brassica juncea var. tumida Tsen &;  Lee
分类号:
S165+.27
DOI:
doi:10.3969/j.issn.1000-4440.2021.05.011
文献标志码:
A
摘要:
以涪陵茎瘤芥为例，基于历史丰歉气象影响指数，综合诊断筛选气象要素相似年型，采用大概率法与加权平均分析法建立茎瘤芥产量预报模型，并验证不同方法的茎瘤芥产量动态预报准确性。结果表明：同一年份不同起报时间的气象条件相似年型差异主要发生在热量条件的相似年，其次是光照条件相似年，而水分条件相似年在同一年份不同起报时间无变化。但同一起报时间不同年份的气象条件相似年，无明显的重复性，即年际间的气象条件具有差异性，能够综合反映气象条件对茎瘤芥产量丰歉的决定性。比较大概率法与加权平均分析法的茎瘤芥产量预报结果，在产量丰歉趋势预报方面，大概率法在较早的起报时间易出现较大的偏差，但是随着起报时间推后，产量丰歉趋势趋于正确；加权平均分析法在大多数年份的产量丰歉趋势预报准确性较高，但是个别年份会出现不同起报时间的持续性预报偏差。在单产预报准确率方面，加权平均分析法预报结果明显优于大概率法，但2种方法均在1月1日起报时间表现出最高的预报准确率，大概率法1月1日起报的单产预报准确率平均值为89.5%，加权平均分析法1月1日为起报时间单产预报准确率平均值为98.9%。在进入2月份也即茎瘤芥瘤茎膨大后期，预报准确率降低。
Abstract:
Based on the meteorological influence index of historical yield, the similarity year of meteorological conditions was selected by integrated diagnosis. The large probability method and weighted average method were used to establish the yield prediction model of Brassica juncea var. tumida Tsen & Lee, and the accuracy of different methods was verified. The results showed that the difference of similarity year of meteorological conditions with different starting times in the same year mainly occurred in the heat condition. Secondly, the similarity year of light conditions was different in some years, while the water conditions had no change in different starting times. However, there was no obvious repeatability in the similarity years of meteorological conditions with the same starting time, that was, the meteorological conditions were different among years, which could comprehensively reflect the decisive effect of meteorological conditions on the yield of Brassica juncea var. tumidaTsen & Lee. Comparing with the results of the large probability method and the weighted average method, the large probability method was prone to large deviation in the early starting time, the yield trend tended to be correct with the delay of reporting time. The accuracy of the weighted average method was high, but the persistent forecasting deviation with different starting time could occur in some years. In terms of the prediction accuracy of unit yield, the prediction result of weighted average method was obviously better than that of large probability method, both methods showed the highest forecast accuracy on the starting time of January 1, the highest forecast accuracy of large probability method and weighted average method were 89.5% and 98.9%. In February, the late period of stem enlargement of Brassica juncea var. tumida Tsen & Lee, the accuracy of prediction decreased.

参考文献/References:

[1]刘佩英. 中国芥菜[M].北京:中国农业出版社,1996:77-120.
[2]罗梦森,景元书,熊世为. 基于遗传优化BP神经网络的水稻气象产量预报模型[J].气象科学,2012,32(6):665-670.
[3]郭亚菲，樊超，闫洪涛. 基于主成分分析和粒子群优化神经网络的粮食产量预测[J]. 江苏农业科学，2019，47(19)：241-245.
[4]唐俊，赵成萍，周新志，等. 基于EVI-RBF的玉米长势监测及产量预测[J].江苏农业学报，2020,36(3):577-583.
[5]李环，孙素芬，罗长寿. 基于NARX神经网络的粮食产量预测模型[J]. 江苏农业科学，2020，48(22):228-232.
[6]刘树泽,张宏铭,蓝鸿第. 作物产量预报方法[M].北京:气象出版社,1987:11-35.
[7]王书裕. 作物产量的预报方法[J].气象学报,1984,42(3):349-355.
[8]李明星,刘建栋,王馥棠,等. 分布式水文模型在陕西省冬小麦产量模拟中的应用[J].水土保持通报,2008(5):148-154.
[9]熊伟. CERES-Wheat模型在我国小麦区的应用效果及误差来源[J].应用气象学报,2009,20(1):88-94.
[10]BOOGAARD H, WOLF J, SUPIT I, et al. A regional implementation of WOFOST for calculating yield gaps of autumn-sown wheat across the European Union[J]. Field Crops Research, 2013, 143:130-142.
[11]MA G, HUANG J, WU W, et al. Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield[J]. Mathematical & Computer Modelling, 2013, 58(3/4):634-643.
[12]王利民,姚保民,刘佳,等. 基于SWAP模型同化遥感数据的黑龙江南部春玉米产量监测[J].农业工程学报,2019,35(22):285-295.
[13]李军玲,郭其乐,彭记永. 基于MODIS数据的河南省冬小麦产量遥感估算模型[J].生态环境学报,2012,21(10):1665-1669.
[14]邓坤枚,孙九林,陈鹏飞,等. 利用国产环境减灾卫星遥感信息估测春小麦产量——以内蒙古陈巴尔虎旗地区为例[J].自然资源学报,2011,26(11):1942-1952.
[15]单璐璐,谭丽静,白华,等. 低温阴雨对丹东地区粮食作物产量的影响[J].气象科技,2017,45(4):745-749.
[16]肖卉,沈瑱,刘杰,等. 连云港水稻产量的气象影响因子分析[J].气象科学,2011(S1):81-86.
[17]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.
[18]朱海霞,李东明,王铭,等. 基于积分回归法黑龙江省作物产量动态预报研究[J].气象与环境学报,2018,34(3):86-92.
[19]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[J].气象,2016,42(7):885-891.
[20]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.
[21]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.
[22]武文辉,吴战平,袁淑杰,等. 贵州夏旱对水稻、玉米产量影响评估方法研究[J].气象科学,2008(2):232-236.
[23]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[J].中国农业气象, 2010, 31(3): 427-430.
[24]宋迎波,王建林,杨霏云,等. 粮食安全气象服务[M].北京:气象出版社,2006:182-196.
[25]邱美娟,刘布春,刘园,等. 春玉米产量动态预报技术的改进方法探索[J].气象与环境科学,2020,43(1):1-8.
[26]陈上,窦子荷,蒋腾聪,等. 基于聚类法筛选历史相似气象数据的玉米产量DSSAT-CERES-Maize预测[J].农业工程学报,2017,33(19):147-155.

相似文献/References:

[1]武强,唐余学,闫梦玲,等.2种茎瘤芥产量丰歉动态预报方法的对比[J].江苏农业学报,2022,38(02):486.[doi:doi:10.3969/j.issn.1000-4440.2022.02.024]
　WU Qiang,TANG Yu-xue,YAN Meng-ling,et al.Accuracy comparison of two methods for dynamic yield prediction of Brassica juncea var. tumida[J].,2022,38(06):486.[doi:doi:10.3969/j.issn.1000-4440.2022.02.024]

备注/Memo

备注/Memo:
收稿日期：2021-03-03基金项目：国家自然科学基金面上项目（42175193）；重庆市技术创新与应用发展专项项目（cstc2020jscx-msxmX0111）；中国气象局创新发展专项项目(CXFZ2021J068、CXFZ2021J073)；重庆市气象部门智慧气象技术创新团队项目（ZHCXTD-202016）；重庆市气象部门业务技术攻关项目（YWJSGG-201905）；重庆市气象局科技计划项目（QNJJ-201703）作者简介：武强（1989-），男，山西河曲人，硕士，工程师，主要从事农田小气候与气象仪器研究。(E-mail)theodorus@yeah.net通讯作者：唐余学， (E-mail)cqtangyx@foxmail.com
更新日期/Last Update: 2022-01-07