The growth of Indian economy mainly depends on agriculture sector as it accounts 18 percent of national GDP. Agriculture sector was one of the main area to impact by climate change. Pre-harvest forecast based on weather parameters plays very important role in developing countries. Rice is the most significant principal food in India which play fundamental role in day-to-day requisite of diet. In the current study statistical crop modeling was engaged to provide forecast in advance. In this paper discriminant function analysis and logistic regression techniques were used for estimating average rice yield for Valsad district in south Gujarat. The weather indices were developed for the years from 1990 to 2012 and utilized for model construction. The cross validation of the developed forecast model were confirmed using data of the years 2013 to 2016. The study discovered that high value of Adj. R2 was obtained in the model and which indicated that it was appropriate forecast model than other models. Based on the outcomes in Valsad district, Logistic regression analysis is found better as compared to Discriminant function for pre harvest forecasting of rice crop yield. | Comparison of pre-harvest forecast models of Kharif rice using weather parameters in Valsad district of Gujarat state
