1College of Agriculture, JNKVV, Powarkheda, Hoshangabad, Madhya Pradesh, India
2Department of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, NadiaWest Bengal, India
3Department of Mathematics & Statistics, CCSHAU, Hisar, Haryana, India
4Department of Based Education, University of Ferhat Abbas, Algeria
5BTC College of Agriculture and Research Station, IGKV, Bilaspur, Chhattisgarh, India
*Corresponding author: pradeepjnkvv@gmail.com (ORCID ID: 0000-0003-4430-886X)
Online Published on 31 August, 2022.
The world as well as in India, rice is playing a major role in food security. Production factors (like rainfall, minimum temperature, fertilizer consumption, an area under irrigation for a particular crop) are very crucial for crop productivity. Forecasting is always important for policy implication and planning purposes of the country. In the present investigation, the projection has been made using simple ARIMA and ARIMAX (with the inclusion of crop inputs in ARIMA models). In terms of less error in model and projection, wise ARIMAX model was found better compared to simple ARIMA. In this present study, forecasting has been attempted with the inclusion of meteorological factors using ARIMA modeling up to the year 2022. This study reveals the future trend of rice production as well as a factor affecting productivity. Among the major states, West Bengal would lead the state in India in rice production, with a productivity of 4758 kg/ha, while Punjab will be the leader in productivity in the year 2022. This prediction would be helpful for policy implication and food security of the country.
By and large, there has been an expansion in the area, production, and yield of rice in major growing states, including the whole of India. In the majority of the states, including the whole of India, the average rainfall has increased during the study period.
In rice area, production, and productivity data, none of the series is stationary; Ist differencing with original data makes all the series stationary.
Inclusions of different factors of productions in the best fitted time series model increase the accuracy and forecasting power compared to the simple ARIMA model.
Modelling, Forecasting, Rainfall, Temperature, Food security, ARIMAX