Forecasting Mustard Yield Using Weather Parameters : An Application of the ARIMAX Model
Keywords:
Time Series, ARIMAX, RMSE, MAE, PRDAbstract
Accurate forecasting of crop yields is essential for effective agricultural planning, policymaking, and ensuring food security. This study focuses on forecasting mustard yield in the Bhiwani district of Haryana using the ARIMAX model, a time-series approach that incorporates weather parameters as exogenous variables. The analysis leverages historical data on mustard yield from 1980 to 2023 and weather parameters including minimum temperature, maximum temperature, and rainfall during the crop-growing season.
References
Ahmer, S. A., Singh, P. K., Ruliana, R., Pandey, A. K., & Gupta, S. (2023). Comparison of ARIMA, Sutte ARIMA, Holt-Winters and NNAR models to predict food grain in India. Forecasting 5(1), 138152.
Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21, 243-47.
Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and Control. Holden Day, San Francisco. Dharmaraja, S., Jain, V., Anjoy, P., & Chandra, H. (2020). Empirical analysis for crop yield forecasting in India, Agricultural Research, 9(1), 132-138.
