Advances and Applications in Statistics
Volume 39, Issue 1, Pages 25 - 35
(March 2014)
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IMPACT OF CLIMATIC VARIABLES ON SUGARCANE YIELD PREDICTION IN HARYANA (INDIA)
U. Verma, H. P. Piepho, M. Goyal, A. Goyal and P. Verma
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Abstract: An attempt has been made to assess the impact of weather variables on sugarcane yield prediction in Haryana. The time-series data on crop yield and weather variables from 1979-80 to 2007-08 have been used to develop the zonal weather models by following multiple linear regression and principal component analyses. A comparative evaluation of the predictive performances of the fitted models showed that the multiple linear regression models incorporating weather variables as predictors had the higher predictive accuracy. The percent deviations from observed yields and RMSEs at zonal level indicate the preference of using developed zonal models for district level crop yield estimation quite well in advance of the crop harvest. |
Keywords and phrases: multiple linear regression, eigenvector, principal component score, percent deviation, root mean square error, DOA yield. |
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