Different approaches on pre harvest forecasting of wheat yield

Abstract

Agriculture is backbone of Indian economy, contributing about 40 per cent towards the Gross National Product and provide livelihood to about 70 per cent of the population. According to the national income published in Economic survey 2014-15, by the CSO, the share of agriculture in total GDP is 18 percent in 2013-14. The Rabi crops data released by the Directorate of Economics and Statistics recently indicates that the total area coverage has declined; area under wheat has gone down by 2.9 per cent. Therefore needs to be do research to study weather
situation and effect on crop production. Pre harvest forecasting is true essence, is a branch of anticipatory sciences used for identifying and foretelling alternative feasible future. Crop yield forecast provided useful information to farmers, marketers, government agencies and other agencies. In this paper Multiple Linear Regression (MLR) Technique and discriminant function analysis were derived for estimating wheat productivity for the district of Varanasi in eastern Uttar Pradesh. The value of Adj. R² varied from 0.63 to 0.94 in different models. It is observed that high value of Adj. R² in the Model-2 which indicated that it is appropriate forecast model than other models, also the value of RMSE varied from minimum 1.17 to maximum 2.47. The study revealed that MLR techniques with incorporating technical and statistical indicators (Model 2) was found to be better for forecasting of wheat crop yield on the basis of both Adjusted R² and RMSE values.