A Daily Meteorological Impact Index of Maize Yield Based on Weather Elements

Liu Wei; Song Yingbo

doi:10.11898/1001-7313.20220310

Vol.33, NO.3, 2022

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Liu Wei, Song Yingbo. A daily meteorological impact index of maize yield based on weather elements. J Appl Meteor Sci, 2022, 33(3): 364-374. DOI: 10.11898/1001-7313.20220310.

Citation:

Liu Wei, Song Yingbo. A daily meteorological impact index of maize yield based on weather elements. J Appl Meteor Sci, 2022, 33(3): 364-374. DOI: 10.11898/1001-7313.20220310.

Liu Wei, Song Yingbo. A daily meteorological impact index of maize yield based on weather elements. J Appl Meteor Sci, 2022, 33(3): 364-374. DOI: 10.11898/1001-7313.20220310.

Citation:

Liu Wei, Song Yingbo. A daily meteorological impact index of maize yield based on weather elements. J Appl Meteor Sci, 2022, 33(3): 364-374. DOI: 10.11898/1001-7313.20220310.

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A Daily Meteorological Impact Index of Maize Yield Based on Weather Elements

DOI: 10.11898/1001-7313.20220310

Liu Wei,
Song Yingbo^,

National Meteorological Center, Beijing 100081

Received Date: 2022-02-22
Rev Recd Date: 2022-04-02
Publish Date: 2022-05-31

Abstract

Abstract

Ten-day and monthly climate suitability has been widely used in agrometeorological research and operation, but it will underestimate the impact of short-term meteorological disasters on crops. In order to dynamically reflect the effect of meteorological conditions on crop yields, the daily precipitation suitability is optimized by calculating a weighed 10-day mean of daily precipitation including previous 9 days. Daily climate suitability is constructed consideringthe suitability of temperature, sunshine and precipitation. The correlation coefficient and Euclidean distance of daily climate suitability before the forecast period is used to identify three similar years and the comprehensive similar year. The whole growth climate suitability sequence of crop is established based on daily climate suitability before the forecast period and daily climate suitability in the similar years after the forecast period. And the climate suitability index is integrated from daily climate suitability sequence. The yield forecast model is established by using crop meteorological yields and the daily climate suitability. Daily crop meteorological yield impact index is designed to indicate the effect of meteorological conditions on crop yields. A daily yield forecast model is constructed to analyze the accuracy of daily yield forecast in the main maize-producing provinces in Northeast China and to indicate the accuracy of the meteorological impact index on crop yield. The results show that the use of comprehensive similar years can improve the accuracy of forecasts. The interannual fluctuation of daily forecast accuracy in Heilongjiang smaller than that in the other three provinces. The forecast accuracy is the lowest in Liaoning. Under the comprehensive similar years in monthly time scale, the advancement of the maize fertility process and the access of real-time meteorological data will improve the accuracy of monthly average forecasts. The accuracy on 31 August is generally higher that on 31 July. The daily forecast can provide a reference for yield forecast. The daily scale forecast in Liaoning varies greatly.Daily forecast yield and the announced yield are gradually approaching with the advancement of the maize fertility process and the accuracy of daily forecast yield is also improved. The impact index based on daily meteorological data can quantitatively assess the effect of meteorological conditions on crop yields at different time scales. To a certain extent, the daily meteorological impact index on crop yield can improve the quantitative evaluation level of agrometeorology operation.
- daily weather elements;
- similar year;
- daily meteorological impact index on crop yield;
- maize in Northeast China

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References(27)

Figures and Tables

Fig. 1 Weight coefficients of precipitation in previous 10 days

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Fig. 2 Comparison on the accuracy of yield in Northeast China from 2011 to 2020

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Fig. 3 Yield in Northeast China from 2011 to 2020

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Fig. 4 Comparison on the accuracy of yield in different month under the comprehensive similar year in Northeast China from 2011 to 2020

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Fig. 5 Daily forecast yield, announced yield and forecast accuracy of Liaoning in 2014(a) and 2018(b)

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Fig. 6 The daily crop meteorological yield impact index(a) and same period index(b)of Liaoning in 2014 and 2018

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Table 1 Average accuracy of 10 years under the different similar yearsin Northeast China from 2011 to 2020 (unit:%)

年型	辽宁	内蒙古东部	黑龙江	吉林
第1相似年	90.3	93.9	91.8	91.9
第2相似年	90.9	93.7	91.4	91.7
第3相似年	90.3	93.6	92.4	91.8
综合相似年	91.2	94.1	93.4	92.4

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Table 2 Monthly average forecast accuracy in Northeast China from 2011 to 2020 (unit:%)

月份	辽宁	内蒙古东部	黑龙江	吉林
5	89.9	94.1	91.6	92.2
6	89.9	94.4	92.5	92.5
7	89.7	94.4	93.5	92.0
8	92.5	94.1	95.1	92.5
9	93.8	93.5	94.4	92.6

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Table 3 Forecast accuracy in Northeast China on 31 Jul and 31 Aug from 2011 to 2020 (unit:%)

年份	辽宁		内蒙古东部		黑龙江		吉林
年份	07-31	08-31	07-31	08-31	07-31	08-31	07-31	08-31
2011	94.2	94.5	83.3^*	89.3^*	95.1	98.4	95.9	95.2
2012	95.9	98.7	91.7	94.2	95.0	98.9	92.0	91.1
2013	87.7^*	89.9^*	99.1	96.7	94.8	90.3	94.5	93.2
2014	73.8^*	83.2^*	86.3^*	86.3^*	91.3	93.3	91.9	98.3
2015	96.2	96.2	93.3	89.8^*	89.0^*	87.8^*	93.6	95.5
2016	91.2	93.9	95.9	96.7	92.8	97.5	93.4	90.9
2017	91.2	92.4	99.6	96.7	92.2	93.7	90.5	96.1
2018	99.5	99.7	99.9	99.2	97.6	98.4	80.7^*	85.7^*
2019	84.8^*	90.6	98.5	95.5	94.1	94.2	92.9	89.8^*
2020	93.4	95.0	97.6	95.8	100.0	90.6	93.6	90.0
注：*表示逐日预报准确率低于90%。

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References

[1]	Wu M X, Zhuang L W, Hou Y Y, et al. The design and implementation of China Agricultural Meteorological Service System(CAgMSS). J Appl Meteor Sci, 2019, 30(5): 513-527. doi: 10.11898/1001-7313.20190501
[2]	Hou Y Y, Zhang L, Wu M X, et al. Advances of modern agrometeorological service and technology in China. J Appl Meteor Sci, 2018, 29(6): 641-656. doi: 10.11898/1001-7313.20180601
[3]	Zhang L, Hou Y Y, Zheng C L, et al. The construction and application of assessing index to crop growing condition. J Appl Meteor Sci, 2019, 30(5): 543-554. doi: 10.11898/1001-7313.20190503
[4]	Guo A H, Li S, He L, et al. Advance in operational technology of agrometeorological disaster forecasting and assessment in China in recent 10 years. Meteor Mon, 2021, 47(6): 693-702. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202106005.htm
[5]	Ma P L, Pu J Y, Zhao C Y, et al. Influence of light and temperature factors on biomass accumulation of winter wheat in field. Chinese J Appl Ecology, 2010, 21(5): 1270-1276. https://www.cnki.com.cn/Article/CJFDTOTAL-YYSB201005029.htm
[6]	Li K W, Zhang J Q, Wei S C, et al. Refined climatic zoning of spring soybean in Northeast China. J Appl Meteor Sci, 2021, 32(4): 408-420. doi: 10.11898/1001-7313.20210403
[7]	Liu C, Li K W, Zhang J Q, et al. Refined climatic zoning for citrus cultivation in southern China based on climate suitability. J Appl Meteor Sci, 2021, 32(4): 421-431. doi: 10.11898/1001-7313.20210404
[8]	Qiu M J, Liu B C, Liu Y, et al. Water requirement and precipitation suitability of apple planting in northern China. J Appl Meteor Sci, 2021, 32(2): 175-187. doi: 10.11898/1001-7313.20210204
[9]	Chu Z, Guo J P. Effects of climatic change on maize varieties distribution in the future of Northeast China. J Appl Meteor Sci, 2018, 29(2): 165-176. doi: 10.11898/1001-7313.20180204
[10]	Wei R J, Song Y B, Wang X. Method for dynamic forecast of corn yield based on climatic suitability. J Appl Meteor Sci, 2009, 20(5): 622-627. doi: 10.3969/j.issn.1001-7313.2009.05.014
[11]	Qiu M J, Song Y B, Wang J L, et al. Application of climate suitability index coupling moisture in dynamic yield prediction of winter wheat in Shandong Province. Chinese J Agrometeor, 2015, 36(2): 187-194. doi: 10.3969/j.issn.1000-6362.2015.02.009
[12]	Xu M, Xu J W, Gao P, et al. Evaluation of winter wheat yield prediction ability of different statistical models-A case study of Jiangsu wheat-growing region. Chinese J Eco-Agr, 2020, 28(3): 438-447. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTN202003014.htm
[13]	Liu W, Song Y B. Operational application of observed yield of winter wheat from agrometeorological stations in yield prediction. Meteor Mon, 2021, 47(12): 1546-1554. doi: 10.7519/j.issn.1000-0526.2021.12.010
[14]	Wei R J, Wang X. Progress and prospects of research on climatic suitability at home and abroad. Adv Earth Sci, 2019, 34(6): 584-595. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201906006.htm
[15]	Liu W, Li Y J, Lv H Q. Responses of heading to flowering to maturity of early rice to climate change and different transplant periods. Scientia Agricultura Sinica, 2018, 51(1): 49-59. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNYK201801005.htm
[16]	Hou Y Y, Zhang Y H, Wang L Y, et al. Climatic suitability model for spring maize in Northeast China. Chinese J Appl Ecology, 2013, 24(11): 3207-3212. https://www.cnki.com.cn/Article/CJFDTOTAL-YYSB201311026.htm
[17]	Liu X T. The Remediation of Degraded Landand Agricultural Development on Songnen Plain in China. Beijing: Science Press, 2001.
[18]	Ma S Q. Agroclimatic Research in Jilin Province. Beijing: China Meteorological Press, 1994.
[19]	Huang H. A study on the climatic ecology adaptability of the crop production in the red and yellow soils region of China. J Nat Resources, 1996, 11(4): 340-346. doi: 10.3321/j.issn:1000-3037.1996.04.007
[20]	Song Y B, Zheng C L, Tan F Y, et al. Meteorological Support Technology for Increasing Grain Production: Dynamic Forecasting Technology for Crop Yield. Beijing: China Meteorological Press, 2020.
[21]	Ma S Q. A simulating study on the influences of climate change on grain yield and the countermeasures in the Northeast China. Acta Meteor Sinica, 1996, 54(4): 484-492. doi: 10.3321/j.issn:0577-6619.1996.04.011
[22]	Li X Y, Zhang Y, Zhao Y X, et al. Comparative study on main crop yield separation methods. J Appl Meteor Sci, 2020, 31(1): 78-82. doi: 10.11898/1001-7313.20200107
[23]	Jin R H, Dai K, Zhao R X, et al. Progress and challenge of seamless fine gridded weather forecasting technology in China. Meteor Mon, 2019, 45(4): 445-457. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201904001.htm
[24]	Qin P C, Lin M, Wan S Q, et al. 2016. Methods for yield forecast based on crop model with incomplete weather observations. J Appl Meteor Sci, 2016, 27(4): 407-416. doi: 10.11898/1001-7313.20160403
[25]	Chen S, Dou Z H, Jiang T C, et al. Maize yield forecast with DSSAT-CERES-Maize model driven by historical meteorological data of analogue years by clustering algorithm. Transactions of the CSAE, 2017, 33(19): 147-155. doi: 10.11975/j.issn.1002-6819.2017.19.019
[26]	Liu W, Song Y B. Comparative analysis of different regional scales integration yield prediction-A case study of single rice in Jiangsu. J Meteor Sci, 2021, 41(6): 828-834. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKX202106012.htm
[27]	Hou Y Y, He L, Jin N, et al. Establishment and application of crop growth simulating and monitoring system in China. Transactions of the CSAE, 2018, 34(21): 165-175. doi: 10.11975/j.issn.1002-6819.2018.21.020