Weather based models in predicting powdery mildew of mango for Malihabad mango belt Shukla P.K.*, Adak Tarun, Gundappa, Misra A.K. Central Institute for Subtropical Horticulture, Rehmankhera, Lucknow-226 101, Uttar Pradesh, India *E-mail: pksmush@gmail.com
Online published on 30 November, 2019. Abstract Weather based models may be used to predict the disease incidence and its development over a period of time as a function of soil-plants-atmospheric systems and thereby screening various mitigation options to combat impinging climate change. Although much attention has been paid in recent years to find the applicability of various crop growth simulation models at regional/country levels, the agroclimatic based regression models may be paid due attention to quantify the real time crop responses using daily weather information in different agro-ecological zones. Keeping this in view, powdery mildew disease of mango has been predicted using weather based regression models following field experimentations carried out in two consecutive flowering seasons of 2013 and 2014 in Dashehari mango belt of Malihabad and Kakori of Lucknow district. Peak occurrence of powdery mildew was recorded during last week of March i.e. 13th standard meteorological week of 2013 and 2014. Highest incidence and severity of powdery mildew was recorded 32.0 and 41.12 per cent during 2013 at Rehmankhera and 18.0 and 43.66 per cent during 2014 at Meethenagar. Minimum incidence and severity of the disease was observed 2.0 and 13.0 per cent at Kakori during 2013 and 4.0 and 22.5 per cent at Ulrapur during 2014. However, average incidence and severity of the disease were recorded 12.2 and 15.9 per cent during 2013 and 13.5 and 34.01 per cent during 2014. Linear regression models were developed in which Growing Degree Days (GDD), Heliothermal Unit (HTU) and Photothermal Unit (PTU) have been used as independent variables. These units were cumulated up to maximum disease incidence and severity. Models developed from pooled data showed significant and positive correlations existed between weather variables with disease. Around 77 per cent variations in progressive changes in powdery mildew disease incidence and severity could be predicted by GDD (Disease incidence = 0.059 × GDD -40.54; R2 = 0.77** and Disease severity = 0.1282 × GDD -89.76; R2 = 0.76**). Regression models generated may be used for predicting disease incidence at farmer's field and forewarning system may be adopted to put in use for agro advisory services to the farmers, so that time bound control measures may be adopted. Top Keywords Powdery mildew, mango, regression models, heliothermal unit, photothermal unit, GDD. Top |