Use of crop condition based dummy regressor and weather input for parameter estimation of mustard yield forecast models
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Abstract
Parameter estimation in statistical modelling plays a crucial role in the real world phenomena. Several alternative analyses may be required for the purpose. In this paper, standard linear regression and multivariate statistical analyses were carried out to achieve the district-level rapeseed-mustard yield estimation in Haryana State (India). The study revealed that the zonal weather models incorporating crop condition term as dummy regressor(s) had the desired predictive accuracy. The model based mustard yield(s) indicated good agreement with State Department of Agriculture (DOA) yield estimates by showing 5-10 percent deviations in most of the mustard growing districts however for two-three districts, it gave 12-13 percent deviations possibly due to the smaller set of data available for those districts. The crop yield estimates on the basis of developed models may be obtained 4-5 weeks in advance of the harvest time.
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Clustering, Dummy variables, Linear time trend, Multiple linear regression, Principal component scores
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