National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
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.
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