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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 54 issue 7 (july 2020) : 912-917,   Doi: 10.18805/ijar.B-3918

Modelling and Forecasting of Milk Production in Chhattisgarh and India

P
P. Mishra
C
Chellai Fatih
H
H.K. Niranjan
S
Shiwani Tiwari
M
Monika Devi
A
Anurag Dubey
1College of Agriculture, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Powarkheda-461 001, Madhya Pradesh, India. 
Cite article:- Mishra P., Fatih Chellai, Niranjan H.K., Tiwari Shiwani, Devi Monika, Dubey Anurag (2020). Modelling and Forecasting of Milk Production in Chhattisgarh and India. Indian Journal of Animal Research. 54(7): 912-917. doi: 10.18805/ijar.B-3918.

ABSTRACT

India is accounting for almost 20 percent of total milk production in the world and 70 percent of this share is coming from small, marginal farmers and landless people of the country residing in rural areas and this shows that dairy industry has an important role in social and economic development in India. Dairy is growing with a positive rate as per capita availability has reached to 375 (gms/day) in 2017-18 from 178 (gms/day) in 1990-91. In this study, time series data (2001-02 to 2015-16) on milk production and different milching species population of Chhattisgarh have been used to find out the suitable forecasting models for milk production and population of these mulching animals of Chhattisgarh. To meet the objective of study different Autoregressive Integrated Moving Average (ARIMA) models have been tried and among all ARIMA (0,2,0) model has been found more suitable for production of milk in India and Chhattisgarh both. Availability of milk is forecasted suitably by ARIMA (0,2,1) and ARIMA(0,1,1) for India and Chhattisgarh respectively. Similarly different ARIMA models have been fitted for population of different species animals. By this study milk production is expected to reach 219.73 MMT and 1.599 MMT by 2022-23 in India and Chhattisgarh respectively.

REFERENCES

  1. Akaike, H. (1979). A Bayesian extension of the minimum AIC procedure of autoregressive model fitting. Biometrika. 66: 237–242.
  2. Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control, Holden-Day, San Fransisco.
  3. Breusch, T. (1978). Testing for Autocorrelation in Dynamic Linear Models. Australian Economic Papers. 17: 334-355. doi: 10.1111/j.1467-8454.1978.tb00635.x
  4. Breusch, T.S. and A.R. Pagan, (1979). A simple test for hetero scedasticity and random coefficient variation, Econometrica. 47: 1287- 1294.
  5. Chaudhari, D.J. and Tingre, A.S. (2015). Forecasting eggs pro-    -duction in India. Indian Journal of Animal Research. 49 (3): 367-372.
  6. 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.
  7. Durbin, J. and Watson, G.S. (1950). Testing for serial correlation in least squares regression: I. Biometrika. 37 (3/4): 409-428.
  8. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica. 50: 987–1008.
  9. GoI (2018). MSME - Development Institute, Raipur (Ministry of MSME, Govt. of India,) http://www.msmediraipur.gov.in/
  10. Holt, C. C. (1957). Forecasting seasonal and trends by exponentially weighted moving averages. Office of Naval Research, Research Memorandum, No. 52.
  11. Hyndman, R.J. and Athanasopoulos, G. (2018). “Forecasting: principles and practice”, 2nd ed., O Texts, Melbourne, Australia. Section 3.4 “Evaluating forecast accuracy”. https://otexts. org/fpp2/accuracy.html
  12. Landes, M., Cessna, J., Kubarka, L. and Jones, K. (2017). India’s Dairy Sector: Structure, Performance and Prospects, Economic Research Service/USDA. LDPM-272-01
  13. Ministry of Agriculture and Farmers Welfare. (2018). National Action plan of Diary development, Ministry of Agriculture and Farmers Welfare.
  14. Pablo M. Pincheira and Carlos A. Medel. (2016). Forecasting with a Random Walk. Finance a úvìr-Czech Journal of Economics and Finance. 66(6):18-23.
  15. Schwarz, G. (1978). Estimating the dimension of a model. Ann Stat. 6(2): 461–466.
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Indian Journal of Animal Research
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    Research Article

    volume 54 issue 7 (july 2020) : 912-917,   Doi: 10.18805/ijar.B-3918

    Modelling and Forecasting of Milk Production in Chhattisgarh and India

    P
    P. Mishra
    C
    Chellai Fatih
    H
    H.K. Niranjan
    S
    Shiwani Tiwari
    M
    Monika Devi
    A
    Anurag Dubey
    1College of Agriculture, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Powarkheda-461 001, Madhya Pradesh, India. 
    Cite article:- Mishra P., Fatih Chellai, Niranjan H.K., Tiwari Shiwani, Devi Monika, Dubey Anurag (2020). Modelling and Forecasting of Milk Production in Chhattisgarh and India. Indian Journal of Animal Research. 54(7): 912-917. doi: 10.18805/ijar.B-3918.

    ABSTRACT

    India is accounting for almost 20 percent of total milk production in the world and 70 percent of this share is coming from small, marginal farmers and landless people of the country residing in rural areas and this shows that dairy industry has an important role in social and economic development in India. Dairy is growing with a positive rate as per capita availability has reached to 375 (gms/day) in 2017-18 from 178 (gms/day) in 1990-91. In this study, time series data (2001-02 to 2015-16) on milk production and different milching species population of Chhattisgarh have been used to find out the suitable forecasting models for milk production and population of these mulching animals of Chhattisgarh. To meet the objective of study different Autoregressive Integrated Moving Average (ARIMA) models have been tried and among all ARIMA (0,2,0) model has been found more suitable for production of milk in India and Chhattisgarh both. Availability of milk is forecasted suitably by ARIMA (0,2,1) and ARIMA(0,1,1) for India and Chhattisgarh respectively. Similarly different ARIMA models have been fitted for population of different species animals. By this study milk production is expected to reach 219.73 MMT and 1.599 MMT by 2022-23 in India and Chhattisgarh respectively.

    REFERENCES

    1. Akaike, H. (1979). A Bayesian extension of the minimum AIC procedure of autoregressive model fitting. Biometrika. 66: 237–242.
    2. Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control, Holden-Day, San Fransisco.
    3. Breusch, T. (1978). Testing for Autocorrelation in Dynamic Linear Models. Australian Economic Papers. 17: 334-355. doi: 10.1111/j.1467-8454.1978.tb00635.x
    4. Breusch, T.S. and A.R. Pagan, (1979). A simple test for hetero scedasticity and random coefficient variation, Econometrica. 47: 1287- 1294.
    5. Chaudhari, D.J. and Tingre, A.S. (2015). Forecasting eggs pro-    -duction in India. Indian Journal of Animal Research. 49 (3): 367-372.
    6. 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.
    7. Durbin, J. and Watson, G.S. (1950). Testing for serial correlation in least squares regression: I. Biometrika. 37 (3/4): 409-428.
    8. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica. 50: 987–1008.
    9. GoI (2018). MSME - Development Institute, Raipur (Ministry of MSME, Govt. of India,) http://www.msmediraipur.gov.in/
    10. Holt, C. C. (1957). Forecasting seasonal and trends by exponentially weighted moving averages. Office of Naval Research, Research Memorandum, No. 52.
    11. Hyndman, R.J. and Athanasopoulos, G. (2018). “Forecasting: principles and practice”, 2nd ed., O Texts, Melbourne, Australia. Section 3.4 “Evaluating forecast accuracy”. https://otexts. org/fpp2/accuracy.html
    12. Landes, M., Cessna, J., Kubarka, L. and Jones, K. (2017). India’s Dairy Sector: Structure, Performance and Prospects, Economic Research Service/USDA. LDPM-272-01
    13. Ministry of Agriculture and Farmers Welfare. (2018). National Action plan of Diary development, Ministry of Agriculture and Farmers Welfare.
    14. Pablo M. Pincheira and Carlos A. Medel. (2016). Forecasting with a Random Walk. Finance a úvìr-Czech Journal of Economics and Finance. 66(6):18-23.
    15. Schwarz, G. (1978). Estimating the dimension of a model. Ann Stat. 6(2): 461–466.
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