Forewarning of stripe rust (Puccinia striiformis) of wheat in Jammu plains

被引：2

作者：

Khushboo S.S. ^{[1
]}

Gupta V. ^{[1
]}

Pandit D. ^{[1
,2
]}

机构：

[1] Division of Plant Pathology, Sher-e-Kashmir University of Agricultural Science and Technology of Jammu, Jammu and Kashmir, Jammu

[2] Shoolini University of Biotechnology and Management Sciences, Himachal Pradesh, Solan

来源：

Indian Phytopathology | 2023年 / 76卷 / 3期

关键词：

Forewarning; Meteorological parameters; Regression model; Stripe rust; Validation; Wheat;

D O I：

10.1007/s42360-023-00639-4

中图分类号：

学科分类号：

摘要：

Relationship of meteorological parameters with the onset and progress of stripe rust of wheat was investigated to develop forewarning models by the time series and multiple linear regression. Autoregressive Integrated Moving Average ARIMA (2,1,1)(1,1,1)7 with minimum temperature (°C) and rainfall (mm) with lag 1, adjusted best having maximum accuracy of 96.00 per cent in predicting stripe rust of wheat for short-term period. Severity of stripe rust had highly significant positive correlation (0.89 and 0.91; 0.91 and 0.75) with the maximum and minimum temperatures, whereas, morning relative humidity had significantly negative correlation (− 0.84 and − 0.80) in 2005–17 and 2017–2019, respectively. Rainfall had non-significant correction with the disease during 2005–17 and 2017–19, respectively. Model viz., Y = − 502.1392 + 0.6373X1 + 8.5741X2 + 3.0402X3 + 1.4227X4 + 0.5764X5 and Y = 322.5683 + 9.4103X1− 4.1446X2− 2.5589X3− 0.7089 + 0.2609X5 were developed by multiple regression for 2005–17 and 2017–2019, and were highly significant in the prediction of stripe rust of wheat. Both the models exhibited that 91 and 89 per cent variation in the disease severity was influenced by the maximum and minimum temperatures, maximum and minimum relative humidity and rainfall. © 2023, Indian Phytopathological Society.

引用

页码：767 / 776

页数：9

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