Need for Documentation of Traditional Knowledge (TK) of Rainfall Forecasting in Murshidabad District of West Bengal: A Survey in the Light of the Attitudes of the Young Generation towards TK and the Need for Documentation of TK
Abstract
This study documents the traditional knowledge of rainfall forecasting in Murshidabad district of West Bengal. It explores the attitudes of the farmers, especially young generation of farmers equipped with modern and formal education, towards the Traditional Knowledge. Altogether, sixteen 16 different TKs relating to rainfall prediction have been found. The farmers in the study area predict rainfall through their observation, experiences and understanding of nature. Traditionally they use indicators like plant phenology, animal behaviour, direction and intensity of wind and position of moon in the sky etc. to predict seasonal rainfall. It has been found that 44.32% people interviewed rely on their traditional beliefs and knowledge for rainfall prediction. Education of the respondents has great impact on the attitudes towards traditional knowledge. Illiterate people largely think that TK works. Respondents attaining primary to secondary education are also not sure of TK’s scientific base. 76% of the respondents completing Higher Secondary education are in the opinion that TK is unscientific. But among the graduates and masters, 46%and 100% respectively think TKs to be reliable and scientific. This clearly indicates the importance of TK and the need of the participation of local people to link TK system of weather forecast to modern system of weather forecasting for more accurate prediction.
Keywords: IMD, traditional knowledge, weather forecasting, IPCC, mKisan
Cite this Article
Ali Ahammad Taher Hasan, Sabuj Kumar Chaudhuri. Need for Documentation of Traditional Knowledge (TK) of Rainfall Forecasting in Murshidabad District of West Bengal: A Survey in the Light of the Attitudes of the Young Generation towards TK and the Need for Documentation of TK. Journal of Advancements in Library Sciences. 2019; 6(2): 88–95p.
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