Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood's model

  • Grzesiak, Wilhelm (Department of Ruminants Science, West Pomeranian University of Technology) ;
  • Zaborski, Daniel (Department of Ruminants Science, West Pomeranian University of Technology) ;
  • Szatkowska, Iwona (Department of Ruminants Science, West Pomeranian University of Technology) ;
  • Krolaczyk, Katarzyna (Department of Animal Anatomy and Zoology, West Pomeranian University of Technology)
  • Received : 2019.12.07
  • Accepted : 2020.03.16
  • Published : 2021.04.01
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Abstract

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

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References

  1. Lefebvre D, Marchand D, Leonard M, Thibault C, Block E, Cannon T. Gestion de la performance du troupeau laitier: des outils à exploiter. In: Proceedings of the Symposium Sur Les Bovins Laitiers. Cahier de Conference. Montreal, Canada: Conseil des Productions Animales du Quebec Inc.; 1995. p. 13-56.
  2. Hortet P, Seegers H. Loss in milk yield and related composition changes resulting from clinical mastitis in dairy cows. Prev Vet Med 1998;37:1-20. https://doi.org/10.1016/S0167-5877(98)00104-4
  3. Beever DE. Opportunities to improve the performance and profitability of dairy farms through better nutrition. Knowledge agriculture. In: Perspectives towards a new model of milk production. Carlow, Irland: R. Keenan & Co.; 2004. pp. 6-8.
  4. Fathi Nasri MH, France J, Odongo NE, Lopez S, Bannink A, Kebreab E. Modelling the lactation curve of dairy cows using the differentials growth functions. J Agric Sci 2008;146:633-41. http://dx.doi.org/10.1017/S0021859608008101
  5. Olori VE, Brotherstone S, Hill WG, McGuirk BJ. Fit of standard models of the lactation curve to weekly records of milk production of cows in a single herd. Livest Prod Sci 1999;58:55-63. https://doi.org/10.1016/S0301-6226(98)00194-8
  6. Silvestre AM, Martins AM, Santos VA, Ginja MM, Colaco JA. Lactation curves for milk, fat and protein in dairy cows: a full approach. Livest Sci 2009;122:308-13. https://doi.org/10.1016/j.livsci.2008.09.017
  7. Jeretina J, Babnik D, Skorjanc D. Modeling lactation curve standards for test-day milk yield in Holstein, Brown Swiss and Simmental cows. J Anim Plant Sci 2013;23:754-62.
  8. Box GEP, Jenkins GM. Time series analysis. Warsaw, Poland: PWN; 1983.
  9. Pal S, Ramasubramanian V, Mehta SC. Statistical models for forecasting milk production in India. J Indian Soc Agric Stat 2007;61:80-3.
  10. Paul RK, Alam W, Paul AK. Prospects of livestock and dairy production in India under time series framework. Indian J Anim Sci 2014;84:130-4.
  11. Macciotta NPP, Cappio-Borlino A, Pulina G. Time series autoregressive integrated moving average modeling of test-day milk yields of dairy ewes. J Dairy Sci 2000;83:1094-103. https://doi.org/10.3168/jds.S0022-0302(00)74974-5
  12. Osman MM, EL-Bayomi KHM, Abd El-Aziz AA, Moawed SHAM. Prediction of weekly and lactation yields of milk using time series models. Suez Canal Vet Med J 2008;13:483-96.
  13. Fernandez C, Gomez J, Sanchez-Seiquer P, et al. Prediction of weekly goat milk yield using autoregressive models. S Afr J Anim Sci 2004;34(Suppl 1):169-72.
  14. Grzesiak W, Blaszczyk P, Lacroix R. Methods of predicting milk yield in dairy cows-predictive capabilities of Wood's lactation curve and artificial neural networks (ANNs). Comput Electron Agric 2006;54:69-83. https://doi.org/10.1016/j.compag.2006.08.004
  15. Murphy MD, O'Mahony MJ, Shalloo L, French P, Upton J. Comparison of modelling techniques for milk-production forecasting. J Dairy Sci 2014;97:3352-63. https://doi.org/10.3168/jds.2013-7451
  16. Zhang F, Murphy MD. Comparative efficiency of lactation curve models using irish experimental dairy farms data. In: Proceedings of the 2016 ASABE Annual International Meeting. MI, USA: American Society of Agricultural and Biological Engineers; 2016. pp. 1. https://doi.org/10.13031/aim.20162455147
  17. Gantner V, Jovanovac S, Raguz N, Klopcic M, Solic D. Prediction of lactation milk yield using various milk recording methods. Biotechnol Anim Husb 2008;24:9-18.
  18. Salamonczyk E, Gulinski P. The course of milk production, lactation length and milk yield of primiparous depending on the age at first calving. Sci Ann Pol Soc Anim Prod 2010;6:155-63.
  19. Wang Y, Wang J, Zhao G, Dong Y. Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of China. Energy Policy 2012;48:284-94. https://doi.org/10.1016/j.enpol.2012.05.026
  20. Pankratz A. Forecasting with univariate box-Jenkins models. New York, USA: John Wiley & Sons, Inc.; 1983.
  21. Box GEP, Jenkins GM, Reinsel GC. Time series analysis: forecasting and control. 3rd ed. Englewood Cliffs, NJ, USA: Prentice Hall; 1994.
  22. Ljung GM, Box GEP. On a measure of lack of fit in time series models. Biometrika 1978;65:297-303. https://doi.org/10.1093/biomet/65.2.297
  23. Melard G. Algorithm AS 197: a fast algorithm for the exact likelihood of autoregressive-moving average models. J R Stat Soc Ser C Appl Stat 1984;33:104-14. https://www.jstor.org/stable/2347672
  24. McLeod AI, Sales PRH. Algorithm AS 191: an algorithm for approximate likelihood calculation of ARMA and seasonal ARMA models. J R Stat Soc Ser C Appl Stat 1983;32:211-23. https://doi.org/10.2307/2347301
  25. Hurvich CM, Tsai CL. Regression and time series model selection in small samples. Biometrika 1989;76:297-307. https://doi.org/10.1093/biomet/76.2.297
  26. Xia J-H, Kumta AS. Feedforward neural network trained by BFGS algorithm for modeling plasma etching of silicon carbide. IEEE Trans Plasma Sci 2010;38:142-8. https://doi.org/10.1109/TPS.2009.2037151
  27. Mokhtari A, Ribeiro A. RES: regularized stochastic BFGS algorithm. IEEE Trans Signal Process 2014;62:6089-104. https://doi.org/10.1109/TSP.2014.2357775
  28. Wood PDP. Algebraic model of the lactation curve in cattle. Nature 1967;216:164-5. https://doi.org/10.1038/216164a0
  29. Cieslak M. Economic forecasting. Methods and applications. Warsaw, Poland: PWN; 1997.
  30. Wade KM, Lacroix R. The role of artificial neural networks in animal breeding. In: Smith C, editor. Proceedings of the 5th World Congress on Genetics Applied to Livestock Production; 1994 Aug 7-12: Guelph, Canada. pp. 31-4.
  31. Zhang F, Murphy MD, Shalloo L, Ruelle E, Upton J. An automatic model configuration and optimization system for milk production forecasting. Comput Electron Agric 2016;128:100-11. https://doi.org/10.1016/j.compag.2016.08.016
  32. Abudu S, King JP, Sheng Z. Comparison of the performance of statistical models in forecasting monthly total dissolved solids in the Rio Grande. JAWRA J Am Water Resour Assoc 2012;48:10-23. https://doi.org/10.1111/j.1752-1688.2011.00587.x