Comparative Study on Main Crop Yield Separation Methods

Li Xinyi; Zhang Yi; Zhao Yanxia; Du Zixuan; Yang Shenbin

doi:10.11898/1001-7313.20200107

Vol.31, NO.1, 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2020 > 31(1): 74-82

Li Xinyi, Zhang Yi, Zhao Yanxia, et al. Comparative study on main crop yield separation methods. J Appl Meteor Sci, 2020, 31(1): 74-82. DOI: 10.11898/1001-7313.20200107.

Citation:

Li Xinyi, Zhang Yi, Zhao Yanxia, et al. Comparative study on main crop yield separation methods. J Appl Meteor Sci, 2020, 31(1): 74-82. DOI: 10.11898/1001-7313.20200107.

Li Xinyi, Zhang Yi, Zhao Yanxia, et al. Comparative study on main crop yield separation methods. J Appl Meteor Sci, 2020, 31(1): 74-82. DOI: 10.11898/1001-7313.20200107.

Citation:

Li Xinyi, Zhang Yi, Zhao Yanxia, et al. Comparative study on main crop yield separation methods. J Appl Meteor Sci, 2020, 31(1): 74-82. DOI: 10.11898/1001-7313.20200107.

PDF( 1056 KB)

Comparative Study on Main Crop Yield Separation Methods

DOI: 10.11898/1001-7313.20200107

1.
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044
2.
Chinese Academy of Meteorological Sciences, Beijing 100081
3.
Henan Institute of Meteorological Sciences, Zhengzhou 450003

Received Date: 2019-10-08
Rev Recd Date: 2019-11-25
Publish Date: 2020-01-31

Abstract

Abstract

Crop yield separation is one of the important steps in analyzing the impact of meteorological factors on yield. Statistical rice yield data for 1985-2018 from 24 counties in Jiangsu are used to analyze the rationality of different separation methods. Six separation methods are 3-year moving mean, 5-year moving mean, five-point quadratic smoothing, quadratic exponential smoothing, HP filter and year-to-year increment. Consistencies and differences are analyzed from aspects of trend yield and meteorological yield. In order to select better methods that could accurately capture the yield variation caused by meteorological factors, the meteorological yield based on different methods are compared with the typical annual increase and decrease of rice yield records. Finally, as mentioned above, the selected methods are calibrated by the rationality of the relationship between meteorological factors and yield. Results show that the trend yield curves fitted by different methods are in line with the process of social technology development. Compared with the average trend yield, almost all the consistency correlation coefficients are greater than 0.5. It suggests that different methods do not differ much in trend fitting. Characteristics of meteorological yield separated by 3-year moving mean, 5-year moving mean, five-point quadratic smoothing and quadratic exponential smoothing in each county are simultaneously increasing or decreasing. And their standard deviation values are significantly smaller than HP filter method and year-to-year increment method. The result suggests that the rationality of separating the meteorological yields by 3-year moving mean, 5-year moving mean, five-point quadratic smoothing, and quadratic exponential smoothing is higher than the other two methods. Five-point quadratic smoothing method and 3-year moving mean method can capture almost 100% of typical annual meteorological yield changes in the whole research area. Further verification results show that the positive and negative effects of meteorological factors captured by 3-year moving mean and five-point quadratic smoothing method are more consistent with the response to meteorological factors. Overall, separation methods of five-point quadratic smoothing method and 3-year moving mean method are more suitable for this research area and match well with meteorological factors.
- climate change;
- rice;
- trend yield;
- meteorological yield;
- separation methods

FullText(HTML)

References(35)

Figures and Tables

Fig. 1 The research region and distribution of meteorological stations

DownLoad: Full-Size Img PowerPoint

Fig. 2 Rice yield trends fitted by 5 methods from 1987 to 2016

DownLoad: Full-Size Img PowerPoint

Fig. 3 Regional mean value and standard deviation series of relative meteorological yield seperated by 6 methods

DownLoad: Full-Size Img PowerPoint

Table 1 Records of typical years

典型年型	年份	水稻生育期内对产量影明显的气象条件
减产年	1997	3月下旬—6月中旬江淮长期干旱少雨, 水稻栽秧用水严重短缺
	1999	水稻分蘖期、抽穗扬花期与灌浆盛期先后出现寡照阴雨天气
	2003	6月21日—7月12日连续多次大范围暴雨到大暴雨过程, 田块被淹
	2007	6月19日—7月24日江淮地区降水特多, 夏秋两季发生梅雨涝灾及台风
丰产年	2002	8月6日—18日出现罕见的凉爽连续阴雨天气, 对水稻形成大穗有利

DownLoad: Download CSV

Table 2 Consistency statistics of trend yield series fitted by 5 methods at 24 counties in Jiangsu and average trend yield series of research areas

相关系数	样本量
相关系数	3年滑动平均	5年滑动平均	五点二次平滑	二次指数平滑	HP滤波
0≤ρ_c < 0.5	1	1	1	3	2
0.5≤ρ_c < 0.85	12	11	16	15	10
0.85≤ρ_c < 1	11	12	7	6	12

DownLoad: Download CSV

Table 3 Comparison between the meterorological yield seperated by different methods and records of 5 typical years at 24 counties in Jiangsu

吻合比例/%	样本量
吻合比例/%	3年滑动平均	5年滑动平均	五点二次平滑	二次指数平滑	HP滤波	年际增量
100	21	12	23	5	5	13
80	3	10	1	9	14	8
60	0	2	0	10	5	3

DownLoad: Download CSV

Table 4 Parameters of meteorological yield models for Zhenjiang and Jinhu

地区	分离方法	正相关		负相关
地区	分离方法	气象因子	回归系数	气象因子	回归系数
镇江	3年滑动平均	10月上旬日照时数	0.555	6月上旬最高气温 5月下旬最低气温 9月降水	-0.661 -0.382 -0.269
镇江	五点二次平滑	10月上旬日照时数	0.637	6月上旬最高气温 5月下旬最低气温 9月降水	-0.626 -0.301 -0.267
金湖	3年滑动平均	10月上旬日照时数	0.435	7月上旬降水	-0.456
金湖	五点二次平滑	10月上旬日照时数	0.491	7月上旬降水	-0.395

DownLoad: Download CSV

References

[1]	Rogelj J, Elzen M D, Höhne N, et al.Paris Agreement climate proposals need a boost to keep warming well below 2.0℃.Nature, 2016, 534(7609):631-639. doi: 10.1038/nature18307
[2]	IPCC.Special Report on Global Warming of 1.5℃.New York:Cambridge University Press, 2018.
[3]	周广胜, 何奇瑾, 汲玉河.适应气候变化的国际行动和农业措施研究进展.应用气象学报, 2016, 27(5):527-533. doi: 10.11898/1001-7313.20160502
[4]	郭建平.气候变化对中国农业生产的影响研究进展.应用气象学报, 2015, 26(1):1-11. doi: 10.11898/1001-7313.20150101
[5]	孙茹, 韩雪, 潘婕, 等.全球1.5℃和2.0℃升温对中国小麦产量的影响研究.气候变化研究进展, 2018, 14(6):573-582. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qhbhyjjz201806004
[6]	李茂松, 李章成, 王道龙, 等.50年来我国自然灾害变化对粮食产量的影响.自然灾害学报, 2005, 14(2):55-60. doi: 10.3969/j.issn.1004-4574.2005.02.010
[7]	吕晓敏, 周广胜.双季稻主要气象灾害研究进展.应用气象学报, 2018, 29(4):385-395. doi: 10.11898/1001-7313.20180401
[8]	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. doi: 10.11898/1001-7313.20160510
[9]	张倩, 赵艳霞, 王春乙.长江中下游地区高温热害对水稻的影响.灾害学, 2011, 26(4):57-62. doi: 10.3969/j.issn.1000-811X.2011.04.011
[10]	钱永兰, 毛留喜, 周广胜.全球主要粮食作物产量变化及其气象灾害风险评估.农业工程学报, 2016, 32(1):226-235. http://d.old.wanfangdata.com.cn/Periodical/nygcxb201601032
[11]	霍治国, 尚莹, 邬定荣, 等.中国小麦干热风灾害研究进展.应用气象学报, 2019, 30(2):129-141. doi: 10.11898/1001-7313.20190201
[12]	胡春丽, 李荣平, 王婷, 等.辽宁水稻年景预报模型研究.干旱气象, 2018, 36(3):501-506. http://d.old.wanfangdata.com.cn/Periodical/ghqx201803019
[13]	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.气象科学, 2012, 32(6):665-670. doi: 10.3969/2012jms.0103
[14]	Iizumi T, Luo J J, Challinor A J, et al.Impacts of El Nio Southern Oscillation on the global yields of major crops.Nat Commun, 2014, 5(1):1-7.
[15]	Nguyen-Huy T, Deo R C, Mushtaq S, et al.Modeling the joint influence of multiple synoptic-scale, climate mode indices on Australian wheat yield using a vine copula-based approach.Eur J Agron, 2018, 98:65-81. doi: 10.1016/j.eja.2018.05.006
[16]	王培娟, 张佳华, 谢东辉, 等.A2和B2情景下冀鲁豫冬小麦气象产量估算.应用气象学报, 2011, 22(5):549-557. doi: 10.3969/j.issn.1001-7313.2011.05.004
[17]	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. doi: 10.11898/1001-7313.20160207
[18]	王桂芝, 陆金帅, 陈克垚, 等.基于HP滤波的气候产量分离方法探讨.中国农业气象, 2014, 35(2):195-199. doi: 10.3969/j.issn.1000-6362.2014.02.012
[19]	房世波.分离趋势产量和气候产量的方法探讨.自然灾害学报, 2011, 20(6):13-18. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zrzhxb201106004
[20]	牛浩, 陈盛伟.山东省玉米气象产量分离方法的多重比较分析.山东农业科学, 2015, 47(8):95-99. http://d.old.wanfangdata.com.cn/Periodical/shandnykx201508026
[21]	赵东妮, 王艳华, 任传友, 等.3种水稻趋势产量拟合方法的比较分析.中国生态农业学报, 2017, 25(3):345-355. http://d.old.wanfangdata.com.cn/Periodical/stnyyj201703005
[22]	温克刚, 卞光辉.中国气象灾害大典(江苏卷).北京:气象出版社, 2008.
[23]	王馥棠.气候变化与我国的粮食生产.中国农村经济, 1996(11):19-23. http://d.old.wanfangdata.com.cn/Periodical/jrhb-zxk201409035
[24]	谢云.中国粮食生产对气候资源波动响应的敏感性分析.资源科学, 1999, 21(6):13-17. doi: 10.3321/j.issn:1007-7588.1999.06.004
[25]	史印山, 王玉珍, 池俊成, 等.河北平原气候变化对冬小麦产量的影响.中国生态农业学报, 2008, 16(6):1444-1447. http://d.old.wanfangdata.com.cn/Periodical/stnyyj200806020
[26]	魏凤英.现代气候统计诊断与预测技术.北京:气象出版社, 2007.
[27]	莫旭昱, 张勇, 秦雨, 等.南北盘江流域降水的时空变化分析.云南地理环境研究, 2012, 24(1):7-11. doi: 10.3969/j.issn.1001-7852.2012.01.002
[28]	王长江.指数平滑法中平滑系数的选择研究.中北大学学报(自然科学版), 2006, 27(6):558-561. doi: 10.3969/j.issn.1673-3193.2006.06.022
[29]	Hodrick R J, Prescott E C.Postwar U.S.Business cycles:An empirical investigation.J Money, Credit Bank, 1997, 29(1):1-16. doi: 10.2307/2953682
[30]	郑昌玲, 王建林, 宋迎波, 等.大豆产量动态预报模型研究.大豆科学, 2008, 27(6):943-948. http://d.old.wanfangdata.com.cn/Periodical/ddkx200806009
[31]	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. doi: 10.11898/1001-7313.20150111
[32]	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.中国农业气象, 2018, 39(11):725-738. doi: 10.3969/j.issn.1000-6362.2018.11.004
[33]	田俊, 霍治国.江西省早稻雨洗花灾害指标构建与灾损评估.应用气象学报, 2018, 29(6):657-666. doi: 10.11898/1001-7313.20180602
[34]	房茜, 吴文祥, 周扬.气候变化对农作物产量影响的研究方法综述.江苏农业科学, 2012, 40(4):12-16. doi: 10.3969/j.issn.1002-1302.2012.04.005
[35]	李永华, 高阳华, 张建平, 等.气候波动对重庆水稻产量的影响及对策.中国农业气象, 2008, 29(1):75-78. doi: 10.3969/j.issn.1000-6362.2008.01.018