A Comparative Study on Dynamic Forecasting of Early Rice Yield by Using Different Methods in Hunan Province

Shuai Xiqiang; Lu Kuidong; Huang Wanhua

doi:10.11898/1001-7313.20150111

Vol.26, NO.1, 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 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 Appl Meteor Sci, 2015, 26(1): 103-111. DOI: 10.11898/1001-7313.20150111.

Citation:

Shuai Xiqiang, Lu Kuidong, Huang Wanhua. A comparative study on dynamic forecasting of early rice yield by using different methods in Hunan Province. J Appl Meteor Sci, 2015, 26(1): 103-111. DOI: 10.11898/1001-7313.20150111.

Citation:

PDF( 453 KB)

A Comparative Study on Dynamic Forecasting of Early Rice Yield by Using Different Methods in Hunan Province

DOI: 10.11898/1001-7313.20150111

Shuai Xiqiang^{1,2
,
,},
Lu Kuidong^1,2,
Huang Wanhua^1,2

1.
Meteorological Science Institute of Hunan Province, Changsha 410118
2.
Key Lab of Hunan Province for Meteorological Disaster Prevention and Mitigation, Changsha 410118

Received Date: 2014-05-13
Rev Recd Date: 2014-09-22
Publish Date: 2015-01-31

Abstract

Abstract

The crop yield forecasting is one of the most important aspects of meteorological services for agricultural production. In order to improve the prediction accuracy, different forecasting methods are compared, and dynamic forecasting models of early rice yield are established based on climatic suitability, key meteorological factors and crop growth simulation model. Daily mean, maximum and minimum temperatures, precipitation, sunshine duration, wind velocity and vapor pressure data of 15 representative meteorological stations are used, as well as the early rice growth and yield data of 12 representative agricultural meteorological stations in Hunan Province from 1962 to 2002. Fitting test is performed by constraining the margin of error less than 5%. Extrapolation test is performed using data from 2003 to 2012, showing the accuracy of three methods are similar, all higher than 93.8%, and the dynamic forecasting models practically pass the test of 0.02 level, except for failing the test of 0.10 level on 30 April. Forecasting models from rifeness tiller to elongating stage pass the test of 0.01 level, and forecasting models at reproductive stage pass the test of 0.001 level too. The method based on climatic suitability improves the accuracy by 4%-6% comparing to that based on key meteorological factors and is 8%-10% more accurate than that based on crop growth simulation model. In quantitative forecast, the method based on crop growth simulation model is optimum, leading to obviously more samples whose margin of error is less than 5%. According to the analysis, the better method of early rice yield forecasting is screened out for Hunan Province. The method based on climatic suitability is chosen to carry out trend prediction of early rice yield, and the method based on crop growth simulation model is used to make quantitative forecast. It also provides reference for dynamic forecasting method research of early rice yield in other areas of China.
- climatic suitability;
- key meteorological factor;
- crop growth simulation model;
- yield forecasting method

FullText(HTML)

References(38)

Figures and Tables

Table 1 The minimum, maximum and optimum temperatures at different growth stages of early rice in Hunan Province (unit:℃)

生育期	最低温度	最高温度	适宜温度
播种期	10	40	18
出苗期	14	40	20
移栽、返青期	15	35	28
分蘖期	17	33	25
孕穗期	17	40	25
抽穗期	18	35	25
乳熟、成熟期	13	35	23

DownLoad: Download CSV

Table 2 Dynamic forecasting models for bumper or poor harvest of early rice yield based on climatic suitability in Hunan Province

预报时间	预报模型	显著性水平
04-30	ΔY=(34.2f-0.60)×100%	未达到0.10显著性水平
05-10	ΔY=(34.4f-10.70)×100%	0.02
05-20	ΔY=(38.3f-18.68)×100%	0.01
05-31	ΔY=(38.2f-16.99)×100%	0.01
06-10	ΔY=(37.0f-7.64)×100%	0.01
06-20	ΔY=(37.8f-17.60)×100%	0.01
06-30	ΔY=(37.9f-17.07)×100%	0.001
07-10	ΔY=(36.7f-26.39)×100%	0.001
07-20	ΔY=(35.9f-18.98)×100%	0.001
注：ΔY表示湖南省早稻产量丰歉值预报，f表示从播种到预报时间止的早稻气候适宜指数。

DownLoad: Download CSV

Table 3 Correlation coefficients between ten-day climate factors and bumper or poor harvest of early rice yield in Hunan Province

时间	相关系数
时间	平均温度	降水量	日照时数
3月下旬	0.1034	-0.0557	0.1356
4月上旬	-0.1861	0.0020	-0.2122
4月中旬	0.0694	0.0172	0.0150
4月下旬	-0.1519	-0.1392	0.0606
5月上旬	0.2481	-0.1633	0.3845
5月中旬	0.1655	-0.1668	0.1368
5月下旬	0.0326	0.1756	-0.0387
6月上旬	-0.2882	0.0052	-0.1673
6月中旬	-0.1820	-0.3055	0.1947
6月下旬	-0.1389	-0.0346	-0.0284
7月上旬	0.1567	-0.2169	0.1942
7月中旬	-0.0906	0.0058	-0.0592

DownLoad: Download CSV

Table 4 Dynamic forecasting models for bumper or poor harvest of early rice yield based on key meteorological factors in Hunan Province

预报时间	预报模型	显著性水平
04-30	ΔY=(0.075x_M-0.043x_A+2.91)×100%	未达到0.10显著性水平
05-10	ΔY=(0.171x₁-4.58)×100%	0.02
05-20	ΔY=(0.163x₁+0.46x₂-14.56)×100%	0.01
05-31	ΔY=(0.15x₁+0.55x₂+0.031x₃-18.11)×100%	0.01
06-10	ΔY=(0.12x₁+0.60x₂+0.037x₃-1.40x₄+16.92)×100%	0.01
06-20	ΔY=(0.11x₁+0.45x₂+0.034x₃-1.425x₄-0.049x₅+25.09)×100%	0.001
06-30	ΔY=(0.12x₁+0.64x₂+0.041x₃-1.38x₄-0.057x₅-1.6x₆+62.01)×100%	0.001
07-10	ΔY=(0.11 x₁+0.83x₂+0.029x₃-1.38x₄-0.063x₅-1.76x₆-0.054x₇+66.79)×100%	0.001
07-20	ΔY=(0.11x₁+0.79x₂+0.028x₃-1.36x₄-0.060x₅-1.71x₆-0.057x₇-0.39x₈+76.70)×100%	0.001
注：ΔY表示湖南省早稻产量丰歉值预报，x_M表示3月下旬日照时数，x_A表示4月下旬降水量，x₁表示5月上旬日照时数，x₂表示5月中旬平均气温，x₃表示5月下旬降水量，x₄表示6月上旬平均气温，x₅表示6月中旬降水量，x₆表示6月下旬平均气温，x₇表示7月上旬降水量，x₈表示7月中旬平均气温。

DownLoad: Download CSV

Table 5 Fitting test for dynamic forecasting method of early rice yield based on climatic suitability from 1962 to 2002

预报时间	趋势预报准确性/%	预报准确率/%	误差5%以内样本百分率/%	误差7%以内样本百分率/%
05-10	66	94.5	54	68
05-20	71	94.6	56	73
05-31	68	94.6	59	68
06-10	66	94.3	51	66
06-20	73	94.5	51	66
06-30	73	94.5	54	66
07-10	71	94.7	61	68
07-20	71	94.7	56	68

DownLoad: Download CSV

Table 6 Fitting test for dynamic forecasting method of early rice yield based on key meteorological factors from 1962 to 2002

预报时间	趋势预报准确性/%	预报准确率/%	误差5%以内样本百分率/%	误差7%以内样本百分率/%
05-10	54	94.3	54	66
05-20	63	94.4	54	69
05-31	59	94.4	51	66
06-10	61	94.4	54	66
06-20	68	94.5	49	63
06-30	71	95.2	56	63
07-10	71	95.2	59	73
07-20	68	95.2	61	73

DownLoad: Download CSV

Table 7 Fitting test for dynamic forecasting method of early rice yield based on crop growth simulation model from 1962 to 2002

预报时间	趋势预报准确性/%	预报准确率/%	误差5%以内样本百分率/%	误差7%以内样本百分率/%
04-30	66	93.6	59	63
05-10	54	93.6	51	63
05-20	59	94.1	56	63
05-31	54	93.9	59	76
06-10	54	93.6	46	59
06-20	59	93.8	56	63
06-30	59	94.0	59	61
07-10	66	93.8	66	73
07-20	66	93.9	59	71

DownLoad: Download CSV

Table 8 Extrapolation test for dynamic forecasting method of early rice yield based on climatic suitability from 2003 to 2012

预报时间	趋势预报准确性/%	预报准确率/%	误差5%以内样本百分率/%	误差7%以内样本百分率/%
05-10	50	96.5	60	100
05-20	60	97.0	70	90
05-31	60	96.0	60	80
06-10	50	95.5	30	70
06-20	60	96.5	50	80
06-30	60	96.6	50	80
07-10	60	96.2	40	80
07-20	60	96.1	50	80

DownLoad: Download CSV

Table 9 Extrapolation test for dynamic forecasting method of early rice yield based on key meteorological factors from 2003 to 2012

预报时间	趋势预报准确性/%	预报准确率/%	误差5%以内样本百分率/%	误差7%以内样本百分率/%
05-10	60	95.9	50	90
05-20	50	95.8	50	90
05-31	50	95.6	60	90
06-10	40	95.5	60	90
06-20	50	95.2	50	60
06-30	50	96.0	60	90
07-10	70	95.9	70	80
07-20	60	96.2	70	80

DownLoad: Download CSV

Table 10 Extrapolation test for dynamic forecasting method of early rice yield based on crop growth simulation model from 2003 to 2012

预报时间	趋势预报准确性/%	预报准确率/%	误差5%以内样本百分率/%	误差7%以内样本百分率/%
04-30	60	97.0	80	90
05-10	50	96.8	90	90
05-20	50	96.7	90	90
05-31	50	96.7	80	90
06-10	50	96.4	70	90
06-20	40	94.7	50	70
06-30	50	94.1	60	70
07-10	50	94.7	60	60
07-20	50	95.3	60	60

DownLoad: Download CSV

References

[1]	莫喆, 刘中秋, 吴永常.国内外农业生产监测及产量预报系统的现状与分析.农业信息科学, 2008, 24(5):434-437. http://www.cnki.com.cn/Article/CJFDTOTAL-ZNTB200805092.htm
[2]	Douglas K B, Mark A F.Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics.Agricultural and Forest Meteorology, 2013, 173:74-84. doi: 10.1016/j.agrformet.2013.01.007
[3]	Juraj B, van der Velde M, Erwin S, et al.Pan-European crop modelling with EPIC:Implementation, up-scaling and regional crop yield validation.Agricultural Systems, 2013, 120:61-75. doi: 10.1016/j.agsy.2013.05.008
[4]	Yannick C, de Wit A, Gregory D, et al.Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment.Agricultural and Forest Meteorology, 2011, 151:1843-1855. doi: 10.1016/j.agrformet.2011.08.002
[5]	兰洪第, 段运怀, 章庆辰, 等.东北地区粮豆产量预报.科学通报, 1982, 27(6):383. http://www.cnki.com.cn/Article/CJFDTOTAL-KXTB198206021.htm
[6]	赵四强.应用海温预报粮食产量的初步探讨.科学通报, 1982, 27(20):126-127. http://www.cnki.com.cn/Article/CJFDTOTAL-SEAC198304004.htm
[7]	钱拴, 王建林.农业气象作物产量预报的特点与思考.气象科技, 2003, 31(5):33-38. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200305000.htm
[8]	王石立, 马玉平, 刘文泉, 等.面向Internet的农业气象产量动态预报.气象, 2004, 30(4):42-46. doi: 10.7519/j.issn.1000-0526.2004.04.011
[9]	王建林, 宋迎波.棉花产量动态预测方法研究.中国棉花, 2002, 29(9):5-7. http://www.cnki.com.cn/Article/CJFDTOTAL-ZMZZ200209002.htm
[10]	王建林, 杨霏云, 宋迎波.西北地区玉米产量动态业务预报方法探讨.应用气象学报, 2004, 15(1):51-57. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040107&flag=1
[11]	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: 10.7519/j.issn.1000-0526.2008.03.014
[12]	宋迎波, 王建林, 杨霏云.粮食安全气象服务.北京:气象出版社, 2006:188-195.
[13]	杨霏云, 王建林.晚稻单产动态预测方法研究.气象科技, 2005, 33(5):433-436. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200505010.htm
[14]	郑昌玲, 杨霏云, 王建林, 等.早稻产量动态预报模型.中国农业气象, 2007, 28(4):412-416. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200704017.htm
[15]	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://www.cnki.com.cn/Article/CJFDTOTAL-DDKX200806012.htm
[16]	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. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY201003020.htm
[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: 10.11898/1001-7313.20090514
[18]	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.气象与环境科学, 2008, 31(2):21-24. http://www.cnki.com.cn/Article/CJFDTOTAL-HNQX200802005.htm
[19]	李曼华, 薛晓萍, 李鸿怡.基于气候适宜度指数的山东省冬小麦产量动态预报.中国农学通报, 2012, 28(12):291-295. doi: 10.11924/j.issn.1000-6850.2012-0337
[20]	游超, 蔡元刚, 张玉芳.基于气象适宜指数的四川盆地水稻气象产量动态预报技术研究.高原山地气象研究, 2011, 31(1):51-55. http://www.cnki.com.cn/Article/CJFDTOTAL-SCCX201101009.htm
[21]	钱锦霞, 郭建平.郑州地区冬小麦产量构成要素的回归模型.应用气象学报, 2012, 23(4):500-504. doi: 10.11898/1001-7313.20120414
[22]	刘布春, 王石立, 马玉平.国外作物模型区域应用研究进展.气象科技, 2002, 30(4):194-203. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ200204000.htm
[23]	高永刚, 王育光, 殷世平, 等.世界粮食研究模型在黑龙江省作物产量预报中的应用.中国农业气象, 2006, 27(1):27-30. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200601008.htm
[24]	高永刚, 顾红, 姬菊枝, 等.近43年来黑龙江气候变化对农作物产量影响的模拟研究.应用气象学报, 2007, 18(4):532-538. doi: 10.11898/1001-7313.20070414
[25]	袁东敏, 尹志聪, 郭建平.SRES B2气候情景下东北玉米产量变化数值模拟.应用气象学报, 2014, 25(3):284-292. doi: 10.11898/1001-7313.20140304
[26]	帅细强, 王石立, 马玉平.基于水稻生长模型的气象影响评价和产量动态预测.应用气象学报, 2008, 19(1):71-81. doi: 10.11898/1001-7313.20080112
[27]	高亮之.农业模型学基础.上海:天马图书有限公司, 2004:186-206.
[28]	潘学标.作物模型原理.北京:气象出版社, 2003:273-303.
[29]	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: 10.11898/1001-7313.20050303
[30]	刘春, 张春辉, 郭萨萨.基于能量模型的水稻生长模型.应用气象学报, 2013, 24(2):240-247. doi: 10.11898/1001-7313.20130212
[31]	薛昌颖, 杨晓光, Bam B, 等.ORYZA2000模型模拟北京地区旱稻的适应性初探.作物学报, 2005, 31(12):1567-1571. doi: 10.3321/j.issn:0496-3490.2005.12.007
[32]	韩家炜, 堪博.数据挖掘:概念与技术.北京:机械工业出版社, 2007:8-21.
[33]	王海峰, 张健, 黄晓亚.数据挖掘技术及其在渔情预报中的应用.计算机时代, 2007(11):52-53. doi: 10.3969/j.issn.1006-8228.2007.11.020
[34]	石扬, 张燕平, 赵姝, 等.基于商空间的气象时间序列数据挖掘研究.计算机工程与应用, 2007, 43(1):201-203. http://www.cnki.com.cn/Article/CJFDTOTAL-JSGG200701059.htm
[35]	Kropff M J, van Laar H H, Ten B H F M.ORYZA1:A Basic Model for Irrigated Lowland Rice Production.Wageningen:Centre for Agrobiological Research, 1993:76-83.
[36]	Kropff M J, van Laar H H, Matthews R.ORAZA1, An Eco-physiological Model for Irrigation Rice Production.SARP Research Proceedings, 1994:110.
[37]	Bouman B A M, van Keulen, van Laar H H, et al.The school of de Wit crop growth simulation models:A pedigree and historical overview.Agricultural Systems, 1996, 52:171-198. doi: 10.1016/0308-521X(96)00011-X
[38]	Matthews R B, Hunt L A.A model describing the growth of cassava.Field Crops Res, 1994, 36:69-84. doi: 10.1016/0378-4290(94)90054-X