Author(s): Monika Devi, Joginder and Dalip Kumar Bishnoi

Abstract: This study delves into the intricate relationship between climatic variables and sugarcane productivity in India, offering valuable insights into the factors affecting crop yield. This paper's major goal is to make estimates of how climatic factors affect sugarcane productivity. Pre- harvest models; i.e., Principal component analysis, discriminant function analysis and Post-harvest models; i.e., ARIMA and ARIMAX models are all used to examine the consistency of empirical results. The data set includes data spanning 40 years, from 1980 to 2019. All of these models have productivity of the sugarcane in Yamuna Nagar district as a dependent variable. Accuracy results revealed that univariate models have lesser accuracy as compared to the models with weather parameters. Discriminant function analysis has the higher level of accuracy in sugarcane yield forecasting and found best among all tried models. Also, selected model was found significant along-with individual scores. In discriminant function analysis 20th fortnight (16th Oct-31th Oct) is the best time for forecasting the sugarcane yield. Hence, use of weather parameters was found contributing positively towards the yield forecasting of sugarcane crop.

Pages: 118-123 | Views: 80 | Downloads: 12

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