Development of prediction equations for early blight leaf spot on tomato under different fungicides treatments
POLY SAHA and SRIKANTA DAS
Field experiment was conducted during 2008-09 and 2009-10 to study the influence of different weather factors on the development of early blight on tomato caused by Alternaria solani (Ell. and Mart.) under six different fungicidal treatments and also to compare the two transformation models viz. Logit and Gompertz through which the disease progress curve move over time. Here, different prediction equationswere developed for each fungicide treatment separately through step down multiple regression analysis which showed that different meteorological factors having different influence on disease severity and these were done after Logistic and Gompertz transformation of the realized observed value of the disease severity (expressed as AUDPC).Results revealed that among the six fungicides tested Mancozeb was superior in controlling the disease severity with the lowest (AUDPC: 97.34 and 95.74) for the two experimental years respectively. Linearization of area under disease progress curve (AUDPC) following the two models (Logit and Gompit) showed that Gompit fit better than Logit for the prediction of early blight disease severity and this was confirmed by the low standard error estimate value of Gompertz. The co-efficient of determination value (R2 ) showed thatvariation in disease severity can be explained up to 77 percent (with an exception 85percent in case of Mancozeb application in 2009-10) in Logistic as well as 88 percent in Gompertz with combined effect of the weather variables included in the present study. Among the seven meteorological factors considered only average temperature,RH and total rainfall were found to act positively and significantly,whereas, bright sunshine hours were found to have negative significant effect on early blight disease severity on tomato. These situations were observed in both the transformation models but vary with in the treatments and with in the years.
Area under disease progress curve, early blight, Logistic andGompertz model, prediction equation, weather parameters