Abstract
In this paper Principal Components (PC) and Multiple Linear Regression (MLR) Technique were used for development of pre-harvest model for rice yield in the Navsari district of south Gujarat. The weather indices were developed and utilized for development of pre-harvest forecast models. The data of rice yield and weather parameters from 1990 to 2012 were utilized. The cross validation of the developed forecast model were confirmed using data of the years 2013 to 2016. It was observed that value of Adj. R2 varied from 89 to 96. The appropriate forecast model was selected based on high value of Adj. R2. Based on the outcomes in Navsari district, MLR techniques found to be better than PCA for pre harvest forecasting of rice crop yield. The Model-2 found competent to forecast rice yield in Navsari district before eight weeks of actual harvest of crop (37th SMW) i.e during reproductive stage of the crop growth period.
Highlights
MATERIALS AND METHODSConsidering the specific objectives of the study, kharif rice yield data were collected from the Directorate of Economics and Statistics, Government of Gujarat, Gandhinagar, Gujarat from 1990 to 2016
Indian rural economy mainly depends on Agriculture
An effort is made in the present paper to develop statistical models for pre-harvest forecast of the rice yield based on Principal component analysis (PCA) and Multiple Linear Regression (MLR)
Summary
Considering the specific objectives of the study, kharif rice yield data were collected from the Directorate of Economics and Statistics, Government of Gujarat, Gandhinagar, Gujarat from 1990 to 2016. The study utilized weekly weather data which were collected from the Department of Agrometeorology, Navsari Agricultural University, Navsari. The maximum temperature (X1), minimum temperature (X2), Morning relative humidity (X3), Evening relative humidity (X4), and total rain fall (X5) considered for studying the effect on kharif rice grain yield. The weekly weather data related to kharif rice crop season starting from a first fortnight before sowing to last of. Vol 21, No 3 reproductive stage were utilized for the development of statistical models. For the each year weather data, from May-June (23rd standard meteorological week, SMW) to October (40th SMW) were utilized for kharif rice crop
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