International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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Original Research Articles                      Volume : 8, Issue:5, May, 2019

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : /
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(5): 910-926

Development of Prediction Equations for Tomato Leaf Curl Virus on Tomato at Different Dates of Planting using Logistic and Gompertz Model
Madhumita Maity, Poly Saha* and Partha Sarathi Nath
Department of Plant Pathology, Bidhan Chandra Krishi Viswavdyalaya, Nadia, West Bengal, India
*Corresponding author

Tomato leaf curl virus (ToLCV) has become a major threat of Tomato (Solanum lycopersicum) production in the world including tropical and subtropical tomato growing regions due to its the endemic presence. The aim of this study is to develop a forewarning strategy for the farmers. The components included in the experiment were, a susceptible tomato variety “Patharkuchi” planted at 15 days interval starting from16th August to 29th December during both the experimental year 2012-13 and 2013-14 under field condition. Different dates of planting also allowed the plants to interact with the different weather factors prevailed through out the growing period. Here, six independent weather variables like maximum and minimum temperature and their differences, maximum and minimum relative humidity and rainfall were considered and natural epiphytotic conditions were permitted. Disease severity was measured and expressed as AUDPC. Prediction equations were developed for each treatment separately through step down multiple regression analysis which showed that different meteorological factors having different influence on disease severity and these were explained after logistic and gompertz transformation of the realized observed value of the disease severity (expressed as AUDPC). Logitic and gompertz are the two transformation models through which the disease progress curve move over time and its comparative expression are also presented graphically in this study. Different dates of planting showed differences in disease severity. Lowest disease severity was found when tomato was planted in (D1=16th August) (AUDPC=94.08) and 97.01) and maximum disease severity was noticed (D4=30th September) (AUDPC=101.91 and 102.66) in the two respective years. Results disclosed that two models tested were not equally fit for predicting disease progress curve in every treatments, though both the models can be used to express disease progression but for linearization of AUDPC following the two models (logit and gompit) showed that logit fit better than gompit for the prediction of tomato leaf curl virus and this was confirmed by the low standard error estimate (MSE) of logit in most of the treatments. The co-efficient of determination value (R2) showed that variation in disease severity can be explained up to88.5% (maximum) in logistic as well as 98.7% (maximum) in Gompertz with combined effect of the weather variables included in the present study. The result also suggested with delay in planting time the disease severity (AUDPC) increases. Minimum disease severity (AUDPC) observed between planting time 16th August to 31st August. So, in West Bengal condition planting of tomato between these periods may be recommended with an expectation of minimum disease severity (AUDPC).

Keywords: Area under disease progress curve (AUDPC), Dates of planting (DOP), Logistic and gompertz model, Prediction equation, Tomato leaf curl virus (ToLCV) and weather parameters

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How to cite this article:

Madhumita Maity, PolySaha and Partha Sarathi Nath. 2019. Development of Prediction Equations for Tomato Leaf Curl Virus on Tomato at Different Dates of Planting using Logistic and Gompertz Model.Int.J.Curr.Microbiol.App.Sci. 8(5): 910-926. doi:
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.