A stepwise multiple regression model to predict Fusarium wilt in lentil

Abstract The present study was designed to develop a disease predictive model based upon meteorological variables, that is, minimum and maximum temperatures, rainfall, and relative humidity to predict lentil wilt severity. Correlation and regression analyses were performed to determine the relationship of meteorological variables with disease severity. A significant correlation was found between all meteorological variables and lentil wilt severity. Maximum temperature showed negative correlation, while minimum temperature, rainfall, and relative humidity exhibited positive correlation with lentil wilt severity. Environmental variables and disease severity data of 2 years (2017–2018) were used to develop a disease predictive model using a stepwise multiple regression analysis. Maximum and minimum temperatures, rainfall, and relative humidity significantly contributed to disease development and explained 94.39% variability in disease severity. This model, based on 2 years data, was then validated with 5 years (2012–2016) meteorological variables and disease severity data set. Homogeneity of regression line in the multiple regression equations of 2 years (2017–2018) model and 5 years (2012–2016) model indicated that they validated each other. Lentil wilt severity was high at maximum (17–25°C) and minimum temperature (9.5–15.1°C), rainfall (4.5–6.5 mm), and relative humidity (55%–85%), respectively. The lentil wilt disease predictive model developed for four lentil varieties, namely M‐85, NL‐2, Mansehra‐89, and NARC‐08‐1, during the present investigation will be useful to predict disease severity before epidemic occurrence and the time of fungicide application. Hence, the model is helpful to reduce the use of fungicides, lessen environmental pollution, and help limit the cost of production of lentil growers.