设施黄瓜白粉病流行动态与预测模型

doi:10.3969/j.issn.1004-1524.2022.01.13

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (1): 104-111.DOI: 10.3969/j.issn.1004-1524.2022.01.13

设施黄瓜白粉病流行动态与预测模型

吴燕君¹(), 洪文英¹, 章忠梅², 吴耀¹, 缪强²

1.杭州市农业技术推广中心,浙江杭州 310016
2.富阳区农业技术推广中心,浙江杭州 311400

收稿日期:2020-09-30 出版日期:2022-01-25 发布日期:2022-02-05
作者简介:吴燕君(1984—),女,浙江嘉兴人,农艺师,主要从事植保和农产品质量安全工作。E-mail: yanjun2284333@hz.cn
基金资助:
杭州市科技计划(绿色农产品生产质量安全控制技术的研究及应用)

Epidemic dynamic and prediction model of cucumber powdery mildew under protected cultivation

WU Yanjun¹(), HONG Wenying¹, ZHANG Zhongmei², WU Yao¹, MIAO Qiang²

1. Hangzhou Centre for Agricultural Technology Extension, Hangzhou 310016, China
2. Fuyang Centre for Agricultural Technology Extension, Hangzhou 311400, China

Received:2020-09-30 Online:2022-01-25 Published:2022-02-05

摘要/Abstract

摘要：

通过持续监测2007—2019年设施黄瓜白粉病的田间流行动态,研究其田间流行规律并建立预测模型。结果表明,不同年份设施黄瓜白粉病盛发期时间总体较一致,春黄瓜、秋黄瓜发病最高峰分别为6月中旬、10月上旬;年度间的峰期持续时间、发生量差异较大,春黄瓜以2019年高峰期发病最重,秋黄瓜以2009年发病最重,2013年春黄瓜和秋黄瓜上均发病重但峰期持续时间短;该病的流行与气候条件密切相关,干湿交替频繁、冷暖起伏大的环境条件下发病重。在此基础上,根据该病害的田间系统调查资料及每年3—10月的气象因素,采用逐步回归分析法,建立了春黄瓜、秋黄瓜高峰期发生量预测模型。经拟合率检验,各模型均达75分以上的准确度,说明拟合结果与实测值相符,可用于瓜类白粉病的流行预测。

关键词: 设施栽培, 黄瓜白粉病, 流行动态, 发生量, 预测模型

Abstract:

To determine the occurrence rule and prediction model, the field dynamic of cucumber powdery mildew under protected cultivation in Hangzhou during the years from 2007 to 2019 was investigated. The results showed that the peak period of the disease occurrence was very similar among different years. The powdery mildew severity in spring cucumber reached the highest peak in the middle ten days of June, while the occurrence of the disease would reach the peak in early October for autumn cucumber. There were significant differences in the peak duration and quantity of cucumber powdery mildew among years. The disease of spring and autumn cucumber led to peak quantities in 2019 and 2009, respectively. It was an unusual year in 2013 that the disease was found to be serious both in spring and autumn but short in the peak duration. In addition, it was found that the field dynamic of cucumber powdery mildew was closely related to the climate condition. For example, the disease would break out if the air temperature or environmental humidity exchanged frequently. Based on the monitoring data including the disease index of powdery mildew and climate factors from March to October every year, the prediction mathematical models of occurrence quantity for the disease in spring and autumn cucumber were established using the method of stepwise regression. Testing of the prediction accuracy of these models showed that the predicted values were consistent with the measured values and the scores representing prediction accuracy were over 75. Thus, these models could accurately predict the degree of occurrence for cucurbit powdery mildew.

Key words: protected cultivation, cucumber powdery mildew, epidemic dynamic, occurrence amount, prediction model

中图分类号:

S436.421

吴燕君, 洪文英, 章忠梅, 吴耀, 缪强. 设施黄瓜白粉病流行动态与预测模型[J]. 浙江农业学报, 2022, 34(1): 104-111.

WU Yanjun, HONG Wenying, ZHANG Zhongmei, WU Yao, MIAO Qiang. Epidemic dynamic and prediction model of cucumber powdery mildew under protected cultivation[J]. Acta Agriculturae Zhejiangensis, 2022, 34(1): 104-111.

图/表 6

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年份Year	Y₁	X₂	X₅	X₆	X₇	X₈	X₁₀	X₁₆	X₂₅	X₃₀	X₃₂
2007	26.2	12.74	11	105.4	13.45	5.45	16.50	3.43	11.62	61.9	3.12
2008	54.8	12.75	8	154.9	2.96	2.61	17.10	2.65	15.34	75.6	2.12
2009	36.8	11.12	15	109.6	10.58	4.53	17.55	3.49	14.69	168.4	2.29
2010	46.2	10.28	15	118.5	29.22	4.60	14.02	3.45	16.33	109.6	2.60
2011	29.2	9.97	9	148.8	4.39	2.81	17.29	4.20	13.93	77.9	2.65
2012	16.6	10.15	19	103.7	22.05	4.13	19.00	2.64	15.07	64.3	2.44
2013	51.93	12.26	13	130.1	8.89	4.13	16.91	3.94	13.19	79.2	3.15
2014	45.45	12.73	12	127.2	6.20	4.87	16.99	1.96	14.97	90.0	1.85
2015	53.19	11.68	16	102.2	13.50	4.31	17.33	4.88	13.13	72.9	2.58
2016	38.77	12.30	10	128.1	5.08	3.80	17.63	1.93	15.83	105.9	2.81
2017	42.86	11.33	14	121.3	16.68	2.06	18.71	3.53	12.83	101.1	1.85
2018	32.49	13.41	16	144.1	4.87	4.46	18.95	4.03	15.24	177.9	3.27
2019	69.82	12.41	18	127.1	9.12	3.12	18.13	4.12	17.38	118.5	2.52

年份Year	Y₁	X₂	X₅	X₆	X₇	X₈	X₁₀	X₁₆	X₂₅	X₃₀	X₃₂
2007	26.2	12.74	11	105.4	13.45	5.45	16.50	3.43	11.62	61.9	3.12
2008	54.8	12.75	8	154.9	2.96	2.61	17.10	2.65	15.34	75.6	2.12
2009	36.8	11.12	15	109.6	10.58	4.53	17.55	3.49	14.69	168.4	2.29
2010	46.2	10.28	15	118.5	29.22	4.60	14.02	3.45	16.33	109.6	2.60
2011	29.2	9.97	9	148.8	4.39	2.81	17.29	4.20	13.93	77.9	2.65
2012	16.6	10.15	19	103.7	22.05	4.13	19.00	2.64	15.07	64.3	2.44
2013	51.93	12.26	13	130.1	8.89	4.13	16.91	3.94	13.19	79.2	3.15
2014	45.45	12.73	12	127.2	6.20	4.87	16.99	1.96	14.97	90.0	1.85
2015	53.19	11.68	16	102.2	13.50	4.31	17.33	4.88	13.13	72.9	2.58
2016	38.77	12.30	10	128.1	5.08	3.80	17.63	1.93	15.83	105.9	2.81
2017	42.86	11.33	14	121.3	16.68	2.06	18.71	3.53	12.83	101.1	1.85
2018	32.49	13.41	16	144.1	4.87	4.46	18.95	4.03	15.24	177.9	3.27
2019	69.82	12.41	18	127.1	9.12	3.12	18.13	4.12	17.38	118.5	2.52

年份Year	Y₂	X₃₄	X₃₆	X₃₈	X₃₉	X₄₀	X₄₁	X₄₆	X₄₉	X₆₂	X₆₄
2007	43.2	10.80	67.39	9	189.9	3.97	1.90	11	1.43	3	10.45
2008	32.6	8.20	69.42	12	225.4	3.80	1.62	11	2.40	3	0.59
2009	54.4	8.44	68.74	9	204.7	7.68	3.33	18	2.21	1	0.29
2010	32.8	9.20	76.87	16	156.9	8.91	1.81	9	2.39	1	0.24
2011	27.6	6.70	69.55	12	185.2	4.30	1.82	20	2.26	3	0.37
2012	44.1	14.99	64.16	9	230.5	1.71	2.05	16	2.24	1	0
2013	49.1	6.06	50.81	4	289.5	0.27	1.56	10	2.78	4	15.04
2014	26.4	7.10	77.77	15	155.8	6.92	2.55	19	2.73	0	0
2015	48.6	24.59	80.68	18	118.6	13.14	3.86	14	2.89	7	1.48
2016	20.8	5.92	73.65	14	204.1	2.61	2.60	8	2.04	5	1.69
2017	41.5	20.39	66.19	10	272.9	2.45	2.61	16	2.00	3	0.83
2018	14.3	9.70	72.94	15	240.8	5.13	1.70	13	1.53	2	0.23
2019	40.1	13.73	78.32	14	152.5	7.37	3.68	12	2.48	3	0.88

年份Year	Y₂	X₃₄	X₃₆	X₃₈	X₃₉	X₄₀	X₄₁	X₄₆	X₄₉	X₆₂	X₆₄
2007	43.2	10.80	67.39	9	189.9	3.97	1.90	11	1.43	3	10.45
2008	32.6	8.20	69.42	12	225.4	3.80	1.62	11	2.40	3	0.59
2009	54.4	8.44	68.74	9	204.7	7.68	3.33	18	2.21	1	0.29
2010	32.8	9.20	76.87	16	156.9	8.91	1.81	9	2.39	1	0.24
2011	27.6	6.70	69.55	12	185.2	4.30	1.82	20	2.26	3	0.37
2012	44.1	14.99	64.16	9	230.5	1.71	2.05	16	2.24	1	0
2013	49.1	6.06	50.81	4	289.5	0.27	1.56	10	2.78	4	15.04
2014	26.4	7.10	77.77	15	155.8	6.92	2.55	19	2.73	0	0
2015	48.6	24.59	80.68	18	118.6	13.14	3.86	14	2.89	7	1.48
2016	20.8	5.92	73.65	14	204.1	2.61	2.60	8	2.04	5	1.69
2017	41.5	20.39	66.19	10	272.9	2.45	2.61	16	2.00	3	0.83
2018	14.3	9.70	72.94	15	240.8	5.13	1.70	13	1.53	2	0.23
2019	40.1	13.73	78.32	14	152.5	7.37	3.68	12	2.48	3	0.88

模型编号 Model No.	预测模型 Prediction model	R²
1	Y₁=392.889+7.056X₂+6.724X₅-0.403X₆-2.593X₇-19.339X₈-19.034X₁₀-3.779X₁₆- 1.391X₂₅-0.036X₃₀-0.678X₃₂	0.998 6^**
2	Y₂=69.839+0.911X₃₄+0.405X₃₆-5.617X₃₈-0.078X₃₉+2.194X₄₀+0.979X₄₁-0.860X₄₆+ 5.054X₄₉-0.033X₆₂-0.623X₆₄	0.997 4^**

设施黄瓜白粉病流行动态与预测模型

Epidemic dynamic and prediction model of cucumber powdery mildew under protected cultivation

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参考文献 18

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预测对象 Forecasting object	a	a₁	a₂	t/d	δ	S_D	结论 Conclusion
春黄瓜病指高峰值	41.87	69.82	75.55	7	14.08	81.85	准确Accurate
The maximum value of disease index in spring cucumber
秋黄瓜病指高峰值	36.58	40.14	44.88	7	11.96	78.53	准确Accurate
The maximum value of disease index in autumn cucumber

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