PREHARVEST FORECASTING OF MUSTARD YIELD ON THE BASIS OF WEATHER VARIABLES IN BANASKANTHA DISTRICT OF GUJARAT

S.R. DAKA¹*, G.K. CHAUDHARY², P.B. MARVIYA^3
1Department of Agricultural Statistics, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar,385506, India
²Department of Agricultural Statistics, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar,385506, India
³Department of Agricultural Statistics, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar,385506, India
* Corresponding Author : prashantmalaviya3@gmail.com

Received : 02-04-2019     Accepted : 27-04-2019     Published : 30-04-2019
Volume : 11     Issue : 8       Pages : 8325 - 8328
Int J Agr Sci 11.8 (2019):8325-8328

Keywords : Forecast, Time trend, Weather variables, Mustard yield, Step-wise regression
Academic Editor : Dr N Umashankar Kumar
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are thankful to Department of Agricultural Statistics, Sardarkrushinagar Dantiwada Agricultural University, Sardarkrushinagar, 385506, India
Author Contribution : All authors equally contributed

To suggest most suitable pre-harvest forecasting model on mustard crop for Banaskantha district of Gujarat state past 32 years (1982-83 to 2013-14) weather data (weekly average of maximum and minimum temperature, morning and evening relative humidity, bright sunshine hours/day and rainfall (mm) from 42nd to 3rd meteorological standard weeks (MSW) were collected from the Agro-Meteorological Observatory, Agronomy Instruction Farm, S. D. Agricultural University, Sardarkrushinagar. The time trend was also included as independent variable. The data on average mustard yield (dependent variable) of Banaskantha district were obtained from the Directorate of Agriculture, Gujarat state, Gandhinagar. The step-wise regression procedure was employed by using 28 years data. The prediction equations and forecast of subsequent years were obtained separately for 26 to 28 years data set. The positive and significant effect of rainfall was observed. Effect of time trend was not observed suggested that technological advancement was not found significant with the mustard yield. The correlation coefficient as weight approach was found superior compared to other approaches. This approach provided suitable pre-harvest forecasting model predicting yield 4 weeks before actual harvest and explained more than 51 % variation in mustard yield.

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