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Forecasting of Area, Production, and Productivity of Food Grains in India : Application of ARIMA Model


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1 Dean, School of Business Studies, Professor and Head, Department of Economic Studies and Planning, Central University of Karnataka, Kadaganchi, Aland Road, Gulbarga - 585 367, Karnataka, India

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Food grains occupy a dominant place in Indian agriculture. The demand for food grains is continuously increasing due to steady increase in the population. Food grains are an important source of energy and protein to majority of the Indians, who are vegetarians. Apart from this, the Government of India enacted the National Food Security Act (NFSA) which came into force with effect from July 5, 2013. This further put pressure on the demand for food grains in the country. Realizing the importance of food grains, the Government of India initiated various measures to boost the production and productivity of food grains since independence. As a result of this, the production of food grains has increased since 1950s, but still there is a gap between demand for and supply of food grains in the country which needs to be addressed urgently. In view of this, the projections for the area, production, and productivity of food grains for 5 years starting from 2016-17 onwards, based on the univariate time series analysis known as ARIMA analysis, was conducted in this paper. ARIMA (2,1,2), ARIMA (4,1,0), and ARIMA (3,1,3) models were fitted to the data on area, production, and productivity of food grains, respectively and these models were found to be adequate. The forecast values indicated that production and productivity will increase during the forecast period but that of area exhibited near stagnancy, calling for timely measures to enhance the supply of food grains to meet the increasing demand in the years to come.

Keywords

Forecasts, Autocorrelation, Partial Autocorrelation, Residuals

C22, C32, C53

Paper Submission Date : November 18, 2017 ; Paper sent back for Revision : December 6, 2017 ; Paper Acceptance Date : December 13 , 2017.

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  • Forecasting of Area, Production, and Productivity of Food Grains in India : Application of ARIMA Model

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Authors

Pushpa Savadatti
Dean, School of Business Studies, Professor and Head, Department of Economic Studies and Planning, Central University of Karnataka, Kadaganchi, Aland Road, Gulbarga - 585 367, Karnataka, India

Abstract


Food grains occupy a dominant place in Indian agriculture. The demand for food grains is continuously increasing due to steady increase in the population. Food grains are an important source of energy and protein to majority of the Indians, who are vegetarians. Apart from this, the Government of India enacted the National Food Security Act (NFSA) which came into force with effect from July 5, 2013. This further put pressure on the demand for food grains in the country. Realizing the importance of food grains, the Government of India initiated various measures to boost the production and productivity of food grains since independence. As a result of this, the production of food grains has increased since 1950s, but still there is a gap between demand for and supply of food grains in the country which needs to be addressed urgently. In view of this, the projections for the area, production, and productivity of food grains for 5 years starting from 2016-17 onwards, based on the univariate time series analysis known as ARIMA analysis, was conducted in this paper. ARIMA (2,1,2), ARIMA (4,1,0), and ARIMA (3,1,3) models were fitted to the data on area, production, and productivity of food grains, respectively and these models were found to be adequate. The forecast values indicated that production and productivity will increase during the forecast period but that of area exhibited near stagnancy, calling for timely measures to enhance the supply of food grains to meet the increasing demand in the years to come.

Keywords


Forecasts, Autocorrelation, Partial Autocorrelation, Residuals

C22, C32, C53

Paper Submission Date : November 18, 2017 ; Paper sent back for Revision : December 6, 2017 ; Paper Acceptance Date : December 13 , 2017.




DOI: https://doi.org/10.17010/aijer%2F2017%2Fv6i6%2F120114