Integrated Technology of Yield Dynamic Prediction of Winter Wheat in Shandong Province

Qiu Meijuan; Song Yingbo; Wang Jianlin; Wu Dingrong; Liu Ling; Liu Jiandong

doi:10.11898/1001-7313.20160207

Vol.27, NO.2, 2016

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Article Navigation > Journal of Applied Meteorological Science > 2016 > 27(2): 191-200

Qiu Meijuan, Song Yingbo, Wang Jianlin, et al. Integrated technology of yield dynamic prediction of winter wheat in Shandong Province. J Appl Meteor Sci, 2016, 27(2): 191-200. DOI: 10.11898/1001-7313.20160207.

Citation:

Qiu Meijuan, Song Yingbo, Wang Jianlin, et al. Integrated technology of yield dynamic prediction of winter wheat in Shandong Province. J Appl Meteor Sci, 2016, 27(2): 191-200. DOI: 10.11898/1001-7313.20160207.

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Integrated Technology of Yield Dynamic Prediction of Winter Wheat in Shandong Province

DOI: 10.11898/1001-7313.20160207

1.
Jilin Institute of Meteorological Science, Changchun 130061
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
3.
National Meteorological Center, Beijing 100081

Received Date: 2015-06-24
Rev Recd Date: 2015-11-17
Publish Date: 2016-03-31

Abstract

Abstract

Using winter wheat yield and growth data of 17 prefecture-level city, daily meteorological data from 1980 to 2011, and daily 20 cm depth soil moisture data of 14 representative meteorological stations from 1992 to 2011, methods for dynamic prediction of winter wheat yield are established in 4 regions of Shandong Province, considering historical meteorological influence index for bumper or poor harvest of crop yield, key meteorological factors influence index, the climatic suitability influence index and the WOFOST crop growth model, respectively. A newly developed statistical method, cluster analysis of statistical test (CAST), which divides planting areas of winter wheat in Shandong Province into four regions. These four methods are used to predict yield of winter wheat in regions of Shandong Province from 2004-2011. An integrated prediction method is established in which the weight coefficients of each method is determined based on the prediction accuracy, and the prediction method with accuracy lower than 90.0% in each period is removed.The comparison result shows the prediction accuracy in each region and period of four single yield prediction method is very unstable and has a large fluctuation range. Forecast results of the historical meteorological influence index for bumper or poor harvest of crop yield are relatively good in region of C1 and C3. The accuracy of key meteorological factor influence index in region C1 and C2 is relatively consistent, while not quite stable in region C3. The prediction accuracy of the climatic suitability influence index generally is more than 80%. And the prediction accuracy of WOFOST in four regions all reaches 90.0%, except for certain instability and fluctuation. Through integrating these methods, the accuracy in each region and each period is significantly improved, which is generally above 95.0%, and the prediction result is stable. Therefore, the integrated prediction method could overcome shortcomings of the single forecast method, and it is more suitable for application.
- yield historical bumper or poor harvest;
- key meteorological factors;
- climatic suitable index;
- integrated prediction method;
- WOFOST

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References(34)

Figures and Tables

Fig. 1 Regionalization of winter wheat in Shandong Province

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Fig. 2 Forecasting result of method based on meteorological influence index of yield historical harvest conditions

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Fig. 3 Forecasting result of method based on influence index of key meteorological factors

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Fig. 4 Forecasting result of method based on climatic suitable index

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Fig. 5 Forecasting result of WOFOST crop model

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Fig. 6 Forecasting result of method based on integrated forecasting technology

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Table 1 Correlation coefficient of meteorological yield and climatic suitable index in different developmental stages of winter wheat

区域	播种至1月下旬	播种至2月下旬	播种至3月下旬	播种至4月下旬	播种至5月下旬
C1	0.713	0.704	0.704	0.721	0.714
C2	0.703	0.747	0.776	0.797	0.770
C3	0.716	0.736	0.737	0.765	0.781
C4	0.566	0.586	0.743	0.766	0.797

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Table 2 The weight coefficient of four methods at different forecasting time

区域	预报时间	产量历史丰歉	关键气象因子	气候适宜度指数	WOFOST模型
C1	1月下旬	0.4827	0.0000	0.0000	0.5173
	2月下旬	0.4803	0.0000	0.0000	0.5197
	3月下旬	0.4833	0.0000	0.0000	0.5167
	4月下旬	0.4832	0.0000	0.0000	0.5168
	5月下旬	0.5047	0.0000	0.0000	0.4953
C2	1月下旬	0.2492	0.2422	0.2495	0.2591
	2月下旬	0.3301	0.3188	0.0000	0.3510
	3月下旬	0.3261	0.3279	0.0000	0.3460
	4月下旬	0.3312	0.3319	0.0000	0.3369
	5月下旬	0.3370	0.3338	0.0000	0.3292
C3	1月下旬	0.0000	0.3346	0.3329	0.3325
	2月下旬	0.4849	0.0000	0.0000	0.5151
	3月下旬	0.3353	0.0000	0.3181	0.3466
	4月下旬	0.3393	0.0000	0.3286	0.3321
	5月下旬	0.3351	0.0000	0.3277	0.3372
C4	1月下旬	0.3361	0.3307	0.0000	0.3332
	2月下旬	0.3275	0.3363	0.0000	0.3363
	3月下旬	0.0000	0.4977	0.0000	0.5023
	4月下旬	0.3218	0.3437	0.0000	0.3345
	5月下旬	0.3233	0.3392	0.0000	0.3375
注：以2009年为例。

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Table 3 Average accuracy of return test for the dynamic yield forecast model based on different years (unit:%)

预报方法	建模年份	C1区	C2区	C3区	C4区
关键气象因子	1980—2003	95.4	92.5	96.6	94.7
	1980—2004	95.2	92.5	96.6	94.1
	1980—2005	95.2	92.4	96.0	94.2
	1980—2006	95.7	93.3	95.7	94.5
	1980—2007	96.0	93.4	96.5	95.1
	1980—2008	95.6	93.0	95.5	95.0
	1980—2009	94.9	93.5	94.4	94.7
	1980—2010	94.9	93.6	94.8	94.4
气候适宜度指数	1992—2003	98.8	97.0	97.0	97.1
	1992—2004	98.4	97.0	96.6	96.9
	1992—2005	97.7	96.7	96.5	95.8
	1992—2006	97.3	96.4	96.1	95.5
	1992—2007	97.9	96.2	96.1	95.7
	1992—2008	97.1	95.8	95.6	95.8
	1992—2009	96.5	95.5	95.7	95.9
WOFOST模型	1980—2003	91.2	91.6	90.8	91.9
	1980—2004	91.4	92.0	92.3	90.7
	1980—2005	91.6	92.2	92.4	90.6
	1980—2006	91.9	92.2	91.6	92.9
	1980—2007	94.2	93.5	92.9	90.9
	1980—2008	92.0	92.8	92.6	91.3
	1980—2009	91.6	92.9	92.8	91.8
	1980—2010	93.7	93.7	92.2	91.9
注：预报为5月下旬。

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Table 4 Average accuracy of yield forecast of winter wheat in Shandong Province from 2004 to 2011(unit:%)

区域	预报时间	产量历史丰歉	关键气象因子	气候适宜度指数	WOFOST模型	集成预报方法
C1	1月下旬	93.7	92.0	90.6	94.7	96.9
	2月下旬	93.9	90.4	90.1	96.0	97.1
	3月下旬	93.3	89.9	90.0	95.4	97.6
	4月下旬	94.2	89.3	90.0	94.9	97.3
	5月下旬	94.5	89.1	90.0	93.0	98.5
C2	1月下旬	88.2	92.9	93.4	95.4	98.2
	2月下旬	86.8	93.4	92.1	96.6	97.7
	3月下旬	87.3	93.8	92.5	96.6	97.5
	4月下旬	86.7	93.2	92.7	95.0	96.5
	5月下旬	85.8	93.4	93.1	94.4	96.6
C3	1月下旬	90.2	93.6	94.4	93.4	96.5
	2月下旬	92.2	94.2	93.3	95.8	98.0
	3月下旬	93.5	93.2	92.4	92.8	98.1
	4月下旬	94.6	93.9	92.7	94.6	98.4
	5月下旬	94.6	94.1	93.0	92.9	97.9
C4	1月下旬	85.1	93.0	90.3	92.2	95.1
	2月下旬	86.4	91.3	90.3	93.6	96.2
	3月下旬	86.9	91.7	90.1	93.6	95.9
	4月下旬	87.0	92.1	90.7	93.3	96.3
	5月下旬	85.7	92.0	91.3	94.2	96.3

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