Vol. 7, Issue 6 (2019)
Pre- harvest forecast model using linear regression model based on weather indices
Author(s): Sarvesh Kumar, VN Rai, Mo Azfar, Annu and Ravi Prakash Gupta
Abstract: In the present paper, an application of regression analysis of weather variables (minimum & maximum temperature, relative humidity 7 hr & 14 hr, sun shine hour and wind velocity) for developing suitable statistical models to forecast Rapeseed and Mustard yield in Sultanpur district of Eastern Uttar Pradesh has been demonstrated. Time series data on Rapeseed and Mustard yield for 25 years (1990-91 to 2014-15) have been used in the regression mode. The forecast yield of Rapeseed and Mustard have been obtained from this model for the year 2012-13, 2013-14 and 2014-15, which were not included in the development of the model. This model has been found to be most appropriate on the basis of Adj R2, percent deviation of forecast, percent root mean square error (%RMSE) and percent standard error (PSE) for the reliable forecast of Rapeseed and Mustard yield about one and half months before the crop harvest.
Pages: 2960-2962 | 206 Views 47 Downloads
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How to cite this article:
Sarvesh Kumar, VN Rai, Mo Azfar, Annu, Ravi Prakash Gupta. Pre- harvest forecast model using linear regression model based on weather indices. Int J Chem Stud 2019;7(6):2960-2962.
