Development of crop yield forecast models under FASAL- a case study of kharif rice in West Bengal

Abstract

Crop yield forecasts are prepared at District, State and National level under the ongoing project “Forecasting Agricultural output using Space, Agrometeorology and Land based observations (FASAL)”, operational at Ministry of Agriculture, Govt. of India in collaboration with Space Application Centre (SAC), Institute of Economic Growth (IEG) and India Meteorological Department (IMD). As per the mandate of the project, crop yield forecasts are being generated for 13 major crops. Different models are used by these organizations for generation of crop yield forecasts. Under FASAL project, IMD in collaboration with 46 Agromet Field units (AMFU) located at different State Agricultural Universities (SAUs), ICAR institutes, IITs, develops intra-seasonal operational yield forecast for the major crops during kharif and rabi seasons using statistical model. Within IMD, in addition to the Agricultural Meteorology Division, all the Regional Meteorological Centres (RMCs) and Meteorological Centres (MCs), located in different states are also working in this project. Long period crop yield data as well as weekly weather data as per meteorological standard week have been used for development of the district level yield forecast models. For developing the yield forecast, models using composite weather variables have been studied. Simple and weighted weather indices have been prepared for individual weather variables as well as for interaction of two at a time considering throughout the crop growing season. Minimum data set required to develop the statistical model have also been mentioned in this paper. In order to demonstrate the data requirement, methodology for crop yield forecast through regression technique along with the interpretations, the relevant data of West Bengal have been taken up. National level crop yield forecast is prepared by the Mahalanabis National Crop Forecasting Centre (MNCFC) based on the state level forecast generated in IMD.