Abstract
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.
Highlights
Betelvine (Piper betle L.) is a dioecious, perennial creeper, grown in the tropical humid climate of South East Asia
Attempts were made to understand the possibility of using weather factors, soil moisture and soil temperature with durations that allowed prediction of Sclerotium rolfsii induced collar rot incidence in betelvine before it occurred, using linear multiple regression analysis, logistic regression analysis and canonical discriminant analysis
Soil moisture (p
Summary
Betelvine (Piper betle L.) is a dioecious, perennial creeper, grown in the tropical humid climate of South East Asia. It is very difficult to manage and eradicate the collar rot disease, caused by the soil borne pathogen Sclerotium rolfsii Sacc, which is reported to cause 17-100% crop loss in West Bengal (Dasgupta et al, 2000; Garain et al, 2020). Though S. rolfsii is a soil borne fungi, infecting the collar region and roots of various crops, the environmental factors like temperature, relative. Forecasting models that predict the likelihood of collar rot outbreak may provide important information for betelvine growers to execute a timely disease management plan. Attempts were made to understand the possibility of using weather factors, soil moisture and soil temperature with durations that allowed prediction of Sclerotium rolfsii induced collar rot incidence in betelvine before it occurred, using linear multiple regression analysis, logistic regression analysis and canonical discriminant analysis
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