Methodology and Application of Weather Level Forecasting of Rice Sheath Blight

doi:10.11924/j.issn.1000-6850.casb2022-1008

Abstract

Abstract:

To establish a prediction model for rice sheath blight based on weather forecasting products, so that we can judge the degree of the influence of weather trends on disease occurrence and provide early warning of it, and improve control efficiency. By means of comprehensive analysis of rice sheath blight field observation data from 1987 to 2019, we screened out disease monitoring periods and disease promoting meteorological indicators. Next, we proposed a daily disease promoting index calculation method based on the disease occurrence mechanism. Meanwhile, we constructed a disease plant rate prediction model and grading scheme on account of daily disease promoting index, combined with weather forecasting products for operational application in 2020. The results showed that there was a highly significant linear correlation between the daily disease promotion index and daily disease plant rate which came to 0.797 (P<0.001), Y=-0.032+0.147X (R²=0.634). In addition, the accuracy of model back verification was 83.3%; the average forecast accuracy of the applied model from 2018 to 2020 was 82.4%. In consequence, the meteorological forecast levels are in good agreement with the actual disease in the field and can be used in daily weather level forecasting operations for rice sheath blight.

Key words: rice sheath blight, daily disease promotion index, daily disease plant rate, prediction, application

CHU Daiwei, ZHANG Zhouna, WANG Qiongjie, HONG Ran, XUE Wenjing. Methodology and Application of Weather Level Forecasting of Rice Sheath Blight[J]. Chinese Agricultural Science Bulletin, 2023, 39(29): 74-78.

Figures/Tables 5

References 26

[1]	洪剑鸣, 童贤明. 中国水稻病害及其防治[M]. 上海: 上海科学技术出版社, 2006:62-68.
[2]	周而勋, 曹菊香, 杨媚, 等. 我国南方六省(区)水稻纹枯病菌遗传多样性的研究[J]. 南京农业大学学报, 2002, 25(3):36-40.
[3]	周而勋, 杨媚, 陈友林, 等. 土壤环境因素对水稻纹枯病菌腐生定殖能力的影响[J]. 植物病理学报, 2002, 32(3):214-218.
[4]	黄世文, 王玲, 陈惠哲, 等. 氮肥施用量和施用方法对超级杂交稻纹枯病发生的影响[J]. 植物病理学报, 2009, 39(1):104-109.
[5]	张静文, 张竞成, 张雪雪, 等. 耦合气象影响因素和Logistic方程的水稻纹枯病发病等级动态预测模型研究[J]. 植物保护学, 2022, 48(3):172-180.
[6]	王艳青. 近年来中国水稻病虫害发生及趋势分析[J]. 中国农学通报, 2006, 22(2):343-347.
[7]	桑海旭, 王井士, 刘郁, 等. 水稻纹枯病对水稻产量及米质的影响[J]. 北方水稻, 2013(1):10-13.
[8]	金书秦, 邢晓旭. 农业面源污染的趋势研判、政策评述和对策建议[J]. 中国农业科学, 2018, 51(3):593-600. doi: 10.3864/j.issn.0578-1752.2018.03.016
[9]	马畅, 刘新刚, 吴小虎, 等. 农田土壤中的农药残留对农产品安全的影响研究进展[J]. 植物保护, 2020, 46(2):6-11.
[10]	肖志强, 李宗明, 樊明, 等. 陇南山区小麦条锈病流行程度预测模型[J]. 中国农业气象, 2007(3):350-353.
[11]	于彩霞, 霍治国, 黄大鹏, 等. 基于大尺度因子的小麦白粉病长期预测模型[J]. 生态学杂志, 2015, 34(3):703-711.
[12]	张蕾, 霍治国, 姜燕, 等. 典型区域小麦白粉病发生气象等级动态预警模型[J]. 中国农业气象, 2015, 36(5):631-639.
[13]	巩文峰, 帅玉婷, 马占鸿. 林芝地区小麦条锈菌与气象因子的关系及流行动态模型的构建[J]. 植物保护学报, 2018, 45(1):167-172.
[14]	HUGHES G, BURNETT F J. Evaluation of probabilistic disease forecasts[J]. Phytopathology, 2017, 107(10):1136-1143. doi: 10.1094/PHYTO-01-17-0023-FI pmid: 28471321
[15]	SHAH D A, D E WOLF E D, PAUL P A, et al. Predicting Fusarium head blight epidemics with boosted regression trees[J]. Phytopathology, 2014, 104(7):702-714. doi: 10.1094/PHYTO-10-13-0273-R pmid: 24450462
[16]	SUN S S, BAO Y X, LU M H, et al. A comarison of models for the short-term prediction of rice stripe virus disease and its association with biological and meteorological factors[J]. Acta ecologica sinica, 2016, 36(3):166-171. doi: 10.1016/j.chnaes.2016.04.002 URL
[17]	ZHANG J C, PU R L, YUAN L, et al. Tntegrating remotely sensed and meteorological observations to forecast wheat powdery mildew at a regional scale[J]. IEEE journal of selected topics in applied earth observations & remotes sensing, 2017, 7(11):4328-4339.
[18]	姚渝丽, 杨信东, 郭明智, 等. 利用天气促病指数表模型预测稻叶瘟发病趋势[J]. 气象, 2003, 29(7):52-55.
[19]	李颖丽, 刘二明, 任佐华, 等. 水稻纹枯病病情指数预测模型研究[J]. 中国植保导刊, 2006, 36(1):47-53.
[20]	黄珍珠, 杨永生, 陈慧华, 等. 广东省水稻纹枯病发生的气象等级监测和预报方法[J]. 广东气象, 2009, 31(4):28-30.
[21]	万素琴, 徐荣钦, 刘志雄, 等. 用通径分析方法建立水稻纹枯病气象等级模型[J]. 湖北农业科学, 2012, 51(2):286-294.
[22]	周伟, 李涵茂. 早稻纹枯病气象等级预测研究[J]. 中国农学通报, 2014, 30(22):246-250.
[23]	刘庭洋, 李烨, 蒲仕磊, 等. 基于BP神经网络的稻瘟病预测预报研究[J]. 西南农业学报, 2017, 30(7):1546-1553.
[24]	张涛宏. 水稻纹枯病流行原因及综合防控对策探讨[J]. 农村经济与科技, 2019, 30(20):19-20.
[25]	许昌燊, 朱寿燕, 张文明, 等. 农业气象指标大全[M]. 北京: 气象出版社, 2004:159-160.
[26]	郭瑞鸽, 杜筱玲, 刘文英, 等. 江西早稻穗瘟发生的气象条件监测预警方法[J]. 气象与减灾研究, 2008, 31(3):48-51.

等级	发病程度	气象适宜等级	日促病指数	日病株率/%
1	轻发生	较适宜	<3.0	<0.5
2	中等发生	适宜	[3.0，6.0]	[0.5，1.0]
3	大发生	很适宜	>6.0	>1.0

等级	发病程度	气象适宜等级	日促病指数	日病株率/%
1	轻发生	较适宜	<3.0	<0.5
2	中等发生	适宜	[3.0，6.0]	[0.5，1.0]
3	大发生	很适宜	>6.0	>1.0

等级	发生次数	预报准确次数	预报准确率/%
1	103	85	82.5
2	74	61	82.4
3	27	24	88.8

等级	发生次数	预报准确次数	预报准确率/%
1	103	85	82.5
2	74	61	82.4
3	27	24	88.8

地区	观测日期	日促病指数	气象预报等级	实际发生等级	预报检验
临安	2018-08-05	2.2	1	1	++
	2018-08-09	1.8	1	1	++
	2018-08-14	3.2	2	2	++
	2018-08-26	1.5	1	1	++
	2018-09-02	5.9	2	1	+
	2018-09-08	3.1	2	2	++
	2018-09-17	0.5	1	1	++
建德	2018-08-06	1.3	1	1	++
	2018-08-13	1.3	1	1	++
	2018-08-20	1.4	1	1	++
	2018-08-27	2.6	1	1	++
	2018-09-02	3.3	2	2	++
	2018-09-09	3.0	2	2	++
	2018-09-17	3.1	2	1	+
	2018-09-24	1.6	1	1	++
	2019-08-05	1.0	1	1	++
	2019-08-09	3.0	2	2	++
	2019-08-19	2.0	1	1	++
	2019-08-27	1.9	1	1	++
	2019-09-02	6.2	3	3	++
	2019-09-09	0.7	1	1	+
	2019-09-17	0.6	1	1	+
余杭	2020-08-05	1.8	1	1	++
	2020-08-10	1.6	1	1	++
	2020-08-15	0.8	1	1	++
	2020-08-20	0.2	1	2	+
	2020-08-25	1.0	1	2	+
	2020-08-31	3.3	2	2	++
	2020-09-05	1.0	1	1	++
	2020-09-10	0.4	1	1	++
	2020-09-15	5.6	2	1	+
	2020-09-20	7.4	3	3	++
	2020-09-25	3.4	2	1	+
	2020-09-30	0.4	1	1	++