Development of prediction model for early blight of tomato and its management by non-conventional chemicals

A field experiment was conducted to find out the effect of different weather factors on development of early blight in tomato (Solanum lycopersicum L.) caused by Alternaria solani (Ell. and Mart.) under six non-conventional chemical treatments during 2008-09 and 2009-10. Two transformation models, viz. logit and gompertz through which the disease progress curve moves over time were also compared. Different prediction equations were developed for each chemical treatment separately through step down multiple regression analysis. Different meteorological factors had different effect on disease severity. Among six non-conventional chemicals tested, Salicylic acid was superior in controlling the disease severity with AUDPC: 93.70 and 92.98 during both the years, respectively. Linearization of area under disease progress curve (AUDPC) following both models (logit and gompit) showed that gompit fit better than logit for prediction of early blight disease severity as confirmed by the low standard error estimate value of gompertz. The co-efficient of determination value (R2) showed that variation in disease severity can be explained up to 80% (maximum) in logistic as well as 92% (maximum) in Gompertz with combined effect of weather variables included in the present study. Among seven meteorological factors considered, only average temperature (Tmean), RHmean and total rainfall (Rt) were found to act positively and significantly, whereas bright sunshine hours (BSH) had negatively significant effect on increase the disease severity in tomato. These situations were observed in both the transformation models but varied within treatments and the year.