Forecasting Livestock and Poultry Production in India
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Abstract
The challenges faced by India in achieving the food and nutritional security to the fast growing population need a concerted approach for growth of livestock sector. Various forecasting models like combinations of ARIMA models and Exponential Smoothing models like Simple, Holt, Brown and Damped trend were used to identify the growth patterns and to predict the future trends in livestock and poultry production in India. Time series data on milk production from 1950-51 to 2017-18 were used to forecast the milk production in India up to 2050-51. Brown Exponential Smoothing was the best fit model for forecasting of milk production in India. The forecast obtained showed that milk production would increase to 207.57, 311.86, 416.16 and 520.45 MT respectively during 2020-21, 2030-31, 2040-41 and 2050-51 from 121.80 MT during the year 2010-11. ARIMA (0,1,1) was the best fit model for forecasting meat and wool production, the forecasted values of meat production showed that meat production would increase from 1.08 MT in 2000-01 to 5.18, 7.63, 10.07 and 12.52 MT, respectively during 2020-21, 2030-31, 2040-41 and 2050-51. Wool production would increase appreciably over years with a production of 46.26, 52.06, 58.59 and 65.17 million kg, respectively during the years 2020-21, 2030-31, 2040-41 and 2050-51 from 43.00 million kg in 2010-11. The Brown Exponential Smoothing model was the best fit model for forecasting egg production in India. The forecasted values obtained from the best fitted Brown ES was 103341.99, 144374.02, 185406.04 and 222334.86 million numbers during the years 2020-21, 2030-31, 2040-41 and 2050-51, respectively.
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References
Box, G.E.P., Jenkins, G.M and Reinsel, G.C. (2007). Time — series Analysis: Forecasting and control. (3). Pearson education, India.
Brown, R.G. (1963). Smoothing,
Forecasting and Prediction of Discrete Time Series. Englewood Cliffs NJ: Prentice-Hall.
Chaudhari, D.J and Tingre, A.S. (2015). Forecasting eggs production in India. Indian Journal of Animal Research, 49(3):367-372.
Deshmukh, S.S and Paramasivam, R. (2016). Forecasting of milk production in India with ARIMA and VAR time series models. Asian Journal of Dairy and Food Research, 35(1): 17-22.
Gardner, E.S and McKenzie, E. (1985). Forecasting trends in time series. Management Science, 31(10):1237¬1246.
Hanke, J. E and Wichern, D. W. (2008). Business Forecasting. 8th Ed. Pearson Education International; Harlow, Essex.
Hossain, J and Hassan, F. (2013). Forecasting of milk, meat and egg production in Bangladesh. Research Journal of Animal, Veterinary and Fishery Sciences, 1(9):7-13.
Jaisankar, T and Prabakaran, R. (2012). Forecasting milk production in Tamil Nadu. International journal of multidisciplinary research and development, 2(1):10-15.
Paul, R. K., Alam W and Paul, A. K. (2014). Prospects of livestock and dairy production in India under time series framework. Indian Journal of Animal Science, 84 (4):462-466.
Sharpe, R., Vaux, R.D and Velleman, P.F. (2010). Business Statistics, 2nd Edition, Addison Vesley - Pearson Education; Boston.
Talwar, A and Goyal, C.K. (2019). A comparative study of various exponential smoothing models for forecasting coriander price in Indian commodity market. International bulletin of management and economics, 10:143-155.
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