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volume 49 issue 3 (june 2015) : 367-372,   Doi: 10.5958/0976-0555.2015.00143.0

Forecasting eggs production in India

D
D.J. Chaudhari*
A
A.S. Tingre
1PGAV NAIP Project, Department of Agricultural Economics and Statistics, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola- 444 104 India.
Cite article:- Chaudhari* D.J., Tingre A.S. (2025). Forecasting eggs production in India . Indian Journal of Animal Research. 49(3): 367-372. doi: 10.5958/0976-0555.2015.00143.0.

ABSTRACT

With changing eating habits and rise in prices of pulses there is an increase in demand for protein rich food like poultry products. Considering the pace of population growth and surge in demand for the poultry products like eggs, the country needs to multiply its output. Therefore it is necessary to know the extent of eggs production in future with available resources. The present study aimed to forecast the eggs production in India by using the eggs production data for the period from 1979-80 to 2010-11. To forecast the eggs production ARIMA models were used. To test the reliability of model R2, Mean Absolute Percentage Error (MAPE), and Bayesian Information Criterion (BIC) were used. ARIMA (0,1,0) was the best fitted model. Based on model results the estimated eggs production in India would increase from 64749.84 million during 2011-12 to 75104.87 million during 2017-18.

REFERENCES

  1. Box, G. E. P. and Jenkins, J. M. (1970) Time Series Analysis -Forecasting and Control. Holden- Day Inc., San Francisco, CA.
  2. Brockwell, P.J. and Richard, A. (2002) Introduction to Time Series and Forecasting, 2nd Edn., Springer, New York.
  3. Farhan Ahmed, Hassnain Shah, Irum Raza and Abdul Saboor (2011) Forecasting milk production in Pakistan. Pakistan J. Agric. Res. 24: 82-85.
  4. Jai Shankar, T. and Prabhakaran, R. (2012) Forecasting milk production in Tamilnadu. International Multidisciplinary Research Journal, 2(1) : 10-15.
  5. Padhan, P.C., (2012) Application of ARIMA model for forecasting agricultural productivity in India. J. Agric. Soc. Sci., 8: 50–56.
  6. Satya Pal, Ramasubramanian V. and Mehta, S.C.(2006) Statistical Models for Forecasting Milk Production in India J. Indian Soc. Agri. Statist.60: 175-236.
  7. Slutzky, E. (1973) The summation of random causes as the source of cyclic processes. Econometrica 5:105-146.
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    volume 49 issue 3 (june 2015) : 367-372,   Doi: 10.5958/0976-0555.2015.00143.0

    Forecasting eggs production in India

    D
    D.J. Chaudhari*
    A
    A.S. Tingre
    1PGAV NAIP Project, Department of Agricultural Economics and Statistics, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola- 444 104 India.
    Cite article:- Chaudhari* D.J., Tingre A.S. (2025). Forecasting eggs production in India . Indian Journal of Animal Research. 49(3): 367-372. doi: 10.5958/0976-0555.2015.00143.0.

    ABSTRACT

    With changing eating habits and rise in prices of pulses there is an increase in demand for protein rich food like poultry products. Considering the pace of population growth and surge in demand for the poultry products like eggs, the country needs to multiply its output. Therefore it is necessary to know the extent of eggs production in future with available resources. The present study aimed to forecast the eggs production in India by using the eggs production data for the period from 1979-80 to 2010-11. To forecast the eggs production ARIMA models were used. To test the reliability of model R2, Mean Absolute Percentage Error (MAPE), and Bayesian Information Criterion (BIC) were used. ARIMA (0,1,0) was the best fitted model. Based on model results the estimated eggs production in India would increase from 64749.84 million during 2011-12 to 75104.87 million during 2017-18.

    REFERENCES

    1. Box, G. E. P. and Jenkins, J. M. (1970) Time Series Analysis -Forecasting and Control. Holden- Day Inc., San Francisco, CA.
    2. Brockwell, P.J. and Richard, A. (2002) Introduction to Time Series and Forecasting, 2nd Edn., Springer, New York.
    3. Farhan Ahmed, Hassnain Shah, Irum Raza and Abdul Saboor (2011) Forecasting milk production in Pakistan. Pakistan J. Agric. Res. 24: 82-85.
    4. Jai Shankar, T. and Prabhakaran, R. (2012) Forecasting milk production in Tamilnadu. International Multidisciplinary Research Journal, 2(1) : 10-15.
    5. Padhan, P.C., (2012) Application of ARIMA model for forecasting agricultural productivity in India. J. Agric. Soc. Sci., 8: 50–56.
    6. Satya Pal, Ramasubramanian V. and Mehta, S.C.(2006) Statistical Models for Forecasting Milk Production in India J. Indian Soc. Agri. Statist.60: 175-236.
    7. Slutzky, E. (1973) The summation of random causes as the source of cyclic processes. Econometrica 5:105-146.
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