Prediction of mungbean yellow mosaic virus disease using multiple regression models

Abstract

Background: The main cause for low mungbean [Vigna radiata (L.) R. Wilczek] productivity is mungbean yellow mosaic virus (MYMV). Generally, the management of MYMV relies upon frequent insecticide sprays to control its vector (whitefly) and genetic resistance. However, disease forecast models can help to economize the pesticide sprays. Hence current study was designed to identifying environmental factor that promote disease development and developing a disease prediction model. Methods: One hundred and twenty-seven mungbean accessions were planted for two years (2012 and 2013) and infection was dependent on natural inoculum. Weekly and daily data on disease incidence and environmental variables were collected and analyzed using correlation and stepwise regression analysis. Results: Wind velocity and high temperature had a negative relation with disease occurrence during both years, whereas low temperature, rainfall, and relative humidity, had positive relationship based on linear regression. The environmental conditions responsible for the highest disease incidence were, maximum temperature (32–34 °C), relative humidity (72–75 %), minimum temperature (27–29 °C), rainfall (1.8–2.1 mm) and wind velocity (3–4.5 km/hr) during both growing seasons Overall, five environmental variable multiple regression model encompassing relative humidity, wind speed, rainfall, suboptimum temperature, and optimum temperatures accommodate the data rightly explaining 83 % variation in disease outgrowth. Conclusion: The observed MYMV disease occurrence values for most of the mungbean genotypes, and those predicted by the model were very close. This multi-environmental variable model can be utilized to provide early warning forecasts for the management of MYMV in Pakistan.

Keywords

• Disease forecasting
• Legumes
• Prediction
• Cost-effective