This research utilized various statistical tools to analyze and predict the area and production of pomegranate crops in Himachal Pradesh, India. The secondary data on the area and production of pomegranate in Himachal Pradesh were collected from Directorate of Horticulture, Shimla, for the period 2001-2023. To analyze trends, various regression models including linear, non-linear, and time-series models were employed. The statistically most suited regression models were selected based on adjusted R 2 , RMSE, significant regression co-efficient, and Theil’s inequality. The annual growth rate were was analysed analyzed using the linear and compound models, which indicated an increasing growth rate in both area and production. Appropriate time-series models were fitted after judging the data for stationarity. The statistically appropriate model was selected on the basis ofbased on various goodness of fit criteria viz. AIC, BIC, RMSE, MAPE, and MAE. The cubic model was found to be the best fit for predicting both the area ( 2 = 0.99) and production ( 2 = 0.91) of pomegranate. In exponential smoothing the Holt’s linear trend models is the best fit for both area (AIC = 274.42) and production (AIC = 348.41) of pomegranate. The ARIMA models were also applied to forecast pomegranate area and production. ARIMA (0,2,0) and ARIMA (0,1,1) models were obtained as ideal models for forecasting area (AIC = 236.51) and production (AIC = 344.63), respectively.
Verma et al. (Sun,) studied this question.