Discriminant Analysis and Prediction of Rice Blast Epidemic Based on Virulence of Magnaporthe oryzae and Meteorological Factors

doi:10.11924/j.issn.1000-6850.casb2022-0769

Abstract

Abstract:

Rice blast is a destructive disease of rice. Forecasting the epidemics of rice blast by using scientific model function is an effective way to prevent and control rice blast. Using the rainfall days, rainfall amount, temperature, pathogenicity of Magnaporthe oryzae, and avirulence gene as variables and the occurrence index of rice blast disease as the dependent variable, a model function for discriminating the epidemic of rice blast disease in Jiangxi Province was established. The results showed that the occurrence index of rice blast was positively correlated with the rainfall days, rainfall amount and the pathogenicity of M. oryzae, while the avirulence gene frequency and temperature were negatively correlated. The prediction function model had a high correct discrimination rate (above 80%) for the occurrence grade of rice blast, and the discrimination result did not show the exponential sliding. Therefore, this model function could be used as a method for the prediction of rice blast disease to guide the safe production of rice.

Key words: rice blast, discriminant analysis, prediction model, meteorological factor, virulence of Magnaporthe oryzae

LAN Bo, YIN Changfa, CHEN Jian, SHI Weitao, YANG Aiping, ZHANG Xiaoyang, KUANG Hongmin, XIAO Hui, LI Xiangmin, YANG Yingqing. Discriminant Analysis and Prediction of Rice Blast Epidemic Based on Virulence of Magnaporthe oryzae and Meteorological Factors[J]. Chinese Agricultural Science Bulletin, 2023, 39(25): 116-121.

Figures/Tables 6

References 18

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年份	区组	降雨量/mm	降雨天数/d	温度/℃	上年菌群致病力/%	上年菌群无毒基因频率/%	发生指数
2007	A	1295.2	136.2	19.0	69.73	26.01	3
2008	A	1539.0	138.3	18.3	67.45	27.32	2
2009	A	1382.4	137.0	18.7	66.37	28.15	2
2010	A	2117.3	161.0	18.3	67.92	26.95	3
2011	A	1272.1	136.7	18.1	81.63	22.75	3
2012	A	2214.8	178.7	17.9	83.35	23.25	3
2013	A	1454.5	130.0	18.9	78.01	22.95	3
2014	A	1689.7	150.2	18.7	54.7	35.75	3
2015	A	2296.2	174.8	18.6	78.49	32.35	4
2016	A	2294.6	165.7	18.9	86.92	33.15	4
2017	A	1720.2	151.1	18.9	57.96	32.85	3
2018	A	1547.7	157.6	18.8	65.38	28.38	2
2019	A	1722.1	157.5	18.9	61.57	39.65	2
2020	A	1870.9	157.8	19.0	62.05	35.65	2
2007	B	1264.3	145.0	19.1	51.62	34.22	1
2008	B	1521.5	141.0	18.5	68.92	28.65	3
2009	B	1527.0	148.0	18.7	63.75	33.15	2
2010	B	2315.2	163.0	18.3	81.35	21.56	4
2011	B	1074.5	147.0	18.2	64.82	30.75	2
2012	B	2334.7	186.0	17.9	83.15	20.16	4
2013	B	1328.7	136.0	19.1	62.01	31.38	2
2014	B	1908.7	159.0	18.7	69.1	24.65	3
2015	B	1919.3	177.0	18.8	67.55	22.42	3
2016	B	1689.1	167.0	18.8	61.36	28.83	2
2017	B	1638.5	154.0	19.1	53.75	36.96	1
2018	B	1545.2	155.0	18.5	55.86	38.79	1
2019	B	1749.1	158.0	18.6	64.21	31.65	2
2020	B	1957.1	171.0	18.7	65.83	32.47	2
2007	C	954.0	130.0	18.9	58.63	36.85	2
2008	C	1317.6	134.0	18.1	61.25	37.03	2
2009	C	1320.4	123.0	18.2	59.75	35.28	2
2010	C	1743.4	140.0	17.9	68.25	25.65	3
2011	C	1109.3	115.0	17.6	62.35	36.85	2
2012	C	1593.8	161.0	17.5	70.19	24.39	3
2013	C	1078.4	118.0	18.8	60.33	37.62	2
2014	C	2404.5	149.0	18.1	79.69	22.61	4
2015	C	1681.6	161.0	17.0	62.87	35.85	2
2016	C	1968.1	164.0	17.3	73.96	24.13	3
2017	C	1782.9	145.0	17.2	56.88	36.42	2
2018	C	1530.4	170.0	17.4	59.52	35.96	2
2019	C	1198.3	146.0	17.4	60.03	36.35	2
2020	C	2048.5	170.0	17.5	70.85	23.95	3

年份	区组	降雨量/mm	降雨天数/d	温度/℃	上年菌群致病力/%	上年菌群无毒基因频率/%	发生指数
2007	A	1295.2	136.2	19.0	69.73	26.01	3
2008	A	1539.0	138.3	18.3	67.45	27.32	2
2009	A	1382.4	137.0	18.7	66.37	28.15	2
2010	A	2117.3	161.0	18.3	67.92	26.95	3
2011	A	1272.1	136.7	18.1	81.63	22.75	3
2012	A	2214.8	178.7	17.9	83.35	23.25	3
2013	A	1454.5	130.0	18.9	78.01	22.95	3
2014	A	1689.7	150.2	18.7	54.7	35.75	3
2015	A	2296.2	174.8	18.6	78.49	32.35	4
2016	A	2294.6	165.7	18.9	86.92	33.15	4
2017	A	1720.2	151.1	18.9	57.96	32.85	3
2018	A	1547.7	157.6	18.8	65.38	28.38	2
2019	A	1722.1	157.5	18.9	61.57	39.65	2
2020	A	1870.9	157.8	19.0	62.05	35.65	2
2007	B	1264.3	145.0	19.1	51.62	34.22	1
2008	B	1521.5	141.0	18.5	68.92	28.65	3
2009	B	1527.0	148.0	18.7	63.75	33.15	2
2010	B	2315.2	163.0	18.3	81.35	21.56	4
2011	B	1074.5	147.0	18.2	64.82	30.75	2
2012	B	2334.7	186.0	17.9	83.15	20.16	4
2013	B	1328.7	136.0	19.1	62.01	31.38	2
2014	B	1908.7	159.0	18.7	69.1	24.65	3
2015	B	1919.3	177.0	18.8	67.55	22.42	3
2016	B	1689.1	167.0	18.8	61.36	28.83	2
2017	B	1638.5	154.0	19.1	53.75	36.96	1
2018	B	1545.2	155.0	18.5	55.86	38.79	1
2019	B	1749.1	158.0	18.6	64.21	31.65	2
2020	B	1957.1	171.0	18.7	65.83	32.47	2
2007	C	954.0	130.0	18.9	58.63	36.85	2
2008	C	1317.6	134.0	18.1	61.25	37.03	2
2009	C	1320.4	123.0	18.2	59.75	35.28	2
2010	C	1743.4	140.0	17.9	68.25	25.65	3
2011	C	1109.3	115.0	17.6	62.35	36.85	2
2012	C	1593.8	161.0	17.5	70.19	24.39	3
2013	C	1078.4	118.0	18.8	60.33	37.62	2
2014	C	2404.5	149.0	18.1	79.69	22.61	4
2015	C	1681.6	161.0	17.0	62.87	35.85	2
2016	C	1968.1	164.0	17.3	73.96	24.13	3
2017	C	1782.9	145.0	17.2	56.88	36.42	2
2018	C	1530.4	170.0	17.4	59.52	35.96	2
2019	C	1198.3	146.0	17.4	60.03	36.35	2
2020	C	2048.5	170.0	17.5	70.85	23.95	3

序号	判别函数	方差解释率/%
1	LD₁=-0.003237974Rainfall+0.031923512Raining_days-0.094898323Temperature-0.152661428Pathogenicity+0.006104285Gene_frequency	85.22
2	LD₂=-0.0009443542Rainfall-0.0014218725Raining_days-0.2628766829Temperature-0.1060013217Pathogenicity-0.2920317222 Gene_frequency	11.13
3	LD₃=0.001853144Rainfall+0.012100144Raining_days+1.127973157Temperature-0.105364742Pathogenicity-0.070895932Gene_frequency	3.65

序号	判别函数	方差解释率/%
1	LD₁=-0.003237974Rainfall+0.031923512Raining_days-0.094898323Temperature-0.152661428Pathogenicity+0.006104285Gene_frequency	85.22
2	LD₂=-0.0009443542Rainfall-0.0014218725Raining_days-0.2628766829Temperature-0.1060013217Pathogenicity-0.2920317222 Gene_frequency	11.13
3	LD₃=0.001853144Rainfall+0.012100144Raining_days+1.127973157Temperature-0.105364742Pathogenicity-0.070895932Gene_frequency	3.65

实际指数	预测发病指数
实际指数	1	2	3	4
1	2	1	0	0
2	0	19	1	0
3	0	2	11	1
4	0	0	0	5
判别准确率/%	88.09