2023, Vol. 8, Special Issue 6

Agriculture is one of the most important sector of an Indian Economy. Sugarcane is one of the most important commercial crops grown in India. Pre-harvest forecasts have significant value in agricultural planning and play important role in doubling the farmers’ income. Therefore proper forecast of such important commercial crop is necessary for future planning, policy making and sustainable production. In the present investigation, relationship between yearly cane yield and weekly weather parameters were studied by using Karl-Pearson’s correlation coefficient approach. The study revealed that all the weather variables were significantly correlated with cane yield in different weeks of cropping season. The sugarcane yield forecasting models were developed using 26 years of cane yield and corresponding weather data (1991-92 to 2016-17). The statistical tools of multiple linear regression (MLR) and discriminant function analysis were used for model development. The study showed that Model-1h, developed through MLR technique have high R2 value (92.6%) and low value of RMSE (6.42) as compared to remaining models. Therefore, study concluded that multiple linear regression (MLR) was more reliable as compared to discriminant function analysis approach and which provide yield forecast well in advance of actual harvesting of the crop.