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Vol. 11, Issue 6 (2022)

Multiple regression analysis for prediction of anthracnose disease in mango (Mangifera indica L.)

Author(s):
Mamta, KP Singh and Sharad Pandey
Abstract:
Mango (Mangifera indica L.) is one of the highly demanded fruit in India. However, the crop is vulnerable to numerous diseases at all stages of its development. Among these diseases, Anthracnose disease caused by Colletotrichum gloeosporioides is one of the most serious and widespread disease. The purpose of this study was to carry out multiple regression analysis for prediction of anthracnose disease of mango. The experiment was conducted on 15 years old plants of twenty cultivars of mango namely Pantsinduri, Dashehari, Amarpalli, Neelum, Hathijhul, Rasgulla, Redtotapari, Langra, Nashpati, Ramkela, Gaurjeet, Golajafrani, Gulabkhas, Gorakhpurlangra, Kalahafus, Karela, Tamancha, Barahmasi, Husnara and Chausa in 2013 and 2014 at Horticulture Research Station (H.R.C.) of G. B. Pant University of Agriculture and Technology, Pantnagar, Dist. Udham Singh Nagar, Uttarakhand. Prevailing weather variables such as temperature, relative humidity and rainfall were obtained corresponding to the mango seasons for both years (2013 and 2014) from agrometeorological section of GBPUAT, Pantnagar. These data were also utilized for working out disease weather correlations. Significant correlation coefficient was used to work out multiple regressions for prediction of anthracnose in mango. The coefficient of multiple determinations (R2) value of twenty cultivars showed that variation of disease incidence in the development of disease was maximum (94%) in Nashpati cultivar and minimum (84%) in Pantsinduri. Observing the meterological data, it was found the relative humidity and rainfall was more in 2014 (96%, 119.8 mm respectively) as compared to 2013 (97%, 105.8 mm respectively) during early stage of disease development. The minimum temperature was less in 2014 in comparison to 2013 (5.9 0C, 6.2 0C respectively) meanwhile, maximum temperature was recorded high in 2014 as compared to 2013(41.0 0C, 39.0 0C respectively). It was revealed further that free moisture/relative humidity and temperature were also very high which, contributed to the initial stage of disease development.
Pages: 1374-1377  |  401 Views  210 Downloads


The Pharma Innovation Journal
How to cite this article:
Mamta, KP Singh, Sharad Pandey. Multiple regression analysis for prediction of anthracnose disease in mango (Mangifera indica L.). Pharma Innovation 2022;11(6):1374-1377.

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