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Influence of weather factors, soil temperature and moisture on collar rot disease of betel vine (Piper betle L.) under coastal saline zone of West Bengal

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A study was conducted to find out the influence of weather factors, soil temperature and soil moisture on the incidence of Sclerotium rolfsii Sacc. induced collar rot disease in betelvine (Piper betle L.), during 2016 to 2018. Fourteen soil and weather factors, taken from the agrometeorological observatory located at instructional farm of Ramkrishna Ashram Krishi Vigyan Kendra, Nimpith and recorded from a nearby betelvine boroj, were subjected to multiple regression, binary logistic regression and canonical discriminant analysis to develop a suitable disease forewarning model. The binary logistic model, Y(0/1) = 5.899 + 0.865 (Tmax) – 0.569 (SM) + 0.097 (BRHmin) was able to predict the disease risk with 78 per cent accuracy and correctly classified 94 per cent of cases during model validation in 2018. The weekly averages of maximum temperature (Tmax), soil moisture (SM) and minimum relative humidity inside the boroj (BRHmin) were found to be the most significant predictors of disease incidence, in this model. The soil moisture at 69 - 72 per cent of field capacity, minimum temperature of 25 - 27oC, maximum temperature of 33 - 36oC, average soil temperature of 28 - 30oC, minimum relative humidity of 60 - 72 per cent inside the boroj and maximum relative humidity of 83 - 89 per cent inside the boroj were found to be highly congenial for collar rot disease incidence in betelvine under coastal saline zone of West Bengal.
Title: Influence of weather factors, soil temperature and moisture on collar rot disease of betel vine (Piper betle L.) under coastal saline zone of West Bengal
Description:
A study was conducted to find out the influence of weather factors, soil temperature and soil moisture on the incidence of Sclerotium rolfsii Sacc.
induced collar rot disease in betelvine (Piper betle L.
), during 2016 to 2018.
Fourteen soil and weather factors, taken from the agrometeorological observatory located at instructional farm of Ramkrishna Ashram Krishi Vigyan Kendra, Nimpith and recorded from a nearby betelvine boroj, were subjected to multiple regression, binary logistic regression and canonical discriminant analysis to develop a suitable disease forewarning model.
The binary logistic model, Y(0/1) = 5.
899 + 0.
865 (Tmax) – 0.
569 (SM) + 0.
097 (BRHmin) was able to predict the disease risk with 78 per cent accuracy and correctly classified 94 per cent of cases during model validation in 2018.
The weekly averages of maximum temperature (Tmax), soil moisture (SM) and minimum relative humidity inside the boroj (BRHmin) were found to be the most significant predictors of disease incidence, in this model.
The soil moisture at 69 - 72 per cent of field capacity, minimum temperature of 25 - 27oC, maximum temperature of 33 - 36oC, average soil temperature of 28 - 30oC, minimum relative humidity of 60 - 72 per cent inside the boroj and maximum relative humidity of 83 - 89 per cent inside the boroj were found to be highly congenial for collar rot disease incidence in betelvine under coastal saline zone of West Bengal.

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