Comparison of the fit of automatic milking system and test-day records with the use of lactation curves

  • Sitkowska, B. (Department of Biotechnology and Animal Genetics, Faculty of Animal Breeding and Biology, UTP University of Science and Technology) ;
  • Kolenda, M. (Department of Biotechnology and Animal Genetics, Faculty of Animal Breeding and Biology, UTP University of Science and Technology) ;
  • Piwczynski, D. (Department of Biotechnology and Animal Genetics, Faculty of Animal Breeding and Biology, UTP University of Science and Technology)
  • Received : 2019.03.07
  • Accepted : 2019.06.07
  • Published : 2020.03.01


Objective: The aim of the paper was to compare the fit of data derived from daily automatic milking systems (AMS) and monthly test-day records with the use of lactation curves; data was analysed separately for primiparas and multiparas. Methods: The study was carried out on three Polish Holstein-Friesians (PHF) dairy herds. The farms were equipped with an automatic milking system which provided information on milking performance throughout lactation. Once a month cows were also subjected to test-day milkings (method A4). Most studies described in the literature are based on test-day data; therefore, we aimed to compare models based on both test-day and AMS data to determine which mathematical model (Wood or Wilmink) would be the better fit. Results: Results show that lactation curves constructed from data derived from the AMS were better adjusted to the actual milk yield (MY) data regardless of the lactation number and model. Also, we found that the Wilmink model may be a better fit for modelling the lactation curve of PHF cows milked by an AMS as it had the lowest values of Akaike information criterion, Bayesian information criterion, mean square error, the highest coefficient of determination values, and was more accurate in estimating MY than the Wood model. Although both models underestimated peak MY, mean, and total MY, the Wilmink model was closer to the real values. Conclusion: Models of lactation curves may have an economic impact and may be helpful in terms of herd management and decision-making as they assist in forecasting MY at any moment of lactation. Also, data obtained from modelling can help with monitoring milk performance of each cow, diet planning, as well as monitoring the health of the cow.


  1. Ferreira AGT, Henrique DS, Vieira RAM, Maeda EM, Valotto AA. Fitting mathematical models to lactation curves from holstein cows in the southwestern region of the state of Parana, Brazil. An Acad Bras Cienc 2015;87:503-17.
  2. Ptak E, Barc A, Jagusiak W. Development of methods for assessing the breeding value of animals on the example of dairy cattle in a retrospective approach. Przeglad Hod 2015;2:1-3.
  3. Karangelil M, Abas Z, Koutroumanidis T, Malesios C, Giannakopoulos C. Comparison of models for describing the lactation curves of Chios sheep using daily records obtained from an automatic milking system. In: M. Salampasis, A. Matopoulos, editors. Proceedings of the International Conference on Communication Technologies for Sustainable Agri-production and Environment. 8-11 Sept 2011; Skiathos, Greece. p. 571-89.
  4. Elahi Torshizi M, Aslamenejad AA, Nassiri MR, Farhangfar H. Comparison and evaluation of mathematical lactation curve functions of Iranian primiparous Holsteins. S Afr J Anim Sci 2011;41:104-15.
  5. Nasri MHF, France J, Odongo NE, Lopez S, Bannink A, Kebreab E. Modelling the lactation curve of dairy cows using the differentials of growth functions. J Agric Sci 2008;146:633-41.
  6. Rowlands GJ, Lucey S, Russell AM. A comparison of different models of the lactation curve in dairy cattle. Anim Prod 1982;35:135-44.
  7. Bahashwan S. Lactation curve modeling for dhofari cows breed. Asian J Anim Vet Adv 2018;13:226-31.
  8. Otwinowska-Mindur A, Ptak E. Factors affecting the shape of lactation curves in Polish Holstein-Friesian cows. Anim Sci Pap Rep 2016;34:373-86.
  9. Macciotta NPP, Dimauro C, Rassu SPG, Steri R, Pulina G. The mathematical description of lactation curves in dairy cattle. Ital J Anim Sci 2011;10:e51.
  10. Jingar S, Mehla RK, Singh M, Roy AK. Lactation curve pattern and prediction of milk production performance in crossbred cows. J Vet Med 2014;2014:Article ID 814768.
  11. Pytlewski J, Antkowiak I, Skrzypek R, Kesy K. The effect of dry period length on milk performance traits of Black-and-white Polish Holstein-friesian and Jersey cows. Ann Anim Sci 2009;9:341-53.
  12. Soleimani A, Moussavi AH, Mesgaran MD, Golian A. Effects of dry period length on, milk production and composition, blood metabolites and complete blood count in subsequent lactation of Holstein dairy cows. World Acad Sci Eng Technol 2010;44:1072-7.
  13. Knight CH. Extended lactation: turning theory into reality. Adv Dairy Technol 2005;17:113-23.
  14. Gulinski P. Domestic cattle. Breeding and use. 1st ed. Warszawa, Poland: Wydawnictwo Naukowe PWN SA; 2017.
  15. Patton J, Kenny DA, McNamara S, et al. Relationships among milk production, energy balance, plasma analytes, and reproduction in Holstein-Friesian cows. J Dairy Sci 2007;90:649-58.
  16. Wood PDP. Algebraic model of the lactation curve in cattle. Nature 1967;216:164-5.
  17. Macciotta NPP, Vicario D, Cappio-Borlino A. Detection of different shapes of lactation curve for milk yield in dairy cattle by empirical mathematical models. J Dairy Sci 2005;88:1178-91.
  18. Khalifa M, Hamrouni A, Djemali M. The estimation of lactation curve parameters according to season of calving in Holstein cows under North Africa environmental conditions: the case of Tunisia. J New Sci Agric Biotechnol 2018;50:3048-53.
  19. Sahin A, Ulutas Z, Yildirim A, Yuksel A, Serdar G. Lactation curve and persistency of anatolian buffaloes. Ital J Anim Sci 2015;14:3679.
  20. Wilmink JBM. Adjustment of test-day milk, fat and protein yield for age, season and stage of lactation. Livest Prod Sci 1987;16:335-48.
  21. Melzer N, Tri$\ss$l S, Nurnberg G. Short communication: Estimating lactation curves for highly inhomogeneous milk yield data of an F2 population (Charolais ${\times}$ German Holstein). J Dairy Sci 2017;100:9136-42.
  22. Silvestre AM, Petim-Batista F, Colaco J. The accuracy of seven mathematical functions in modeling dairy cattle lactation curves based on test-day records from varying sample schemes. J Dairy Sci 2006;89:1813-21.
  23. SAS Institute Inc. SAS/STAT 9.4 User's guide. Cary, NC, USA: SAS Institute Inc.; 2014.
  24. Kocaman I, Kurc HC. A Research on the determination of lactation length and milk yield of anatolian water buffaloes under different environmental conditions. J Sci Eng Res 2018; 5:39-44.
  25. Elahi Torshizi ME, Aslamenejad AA, Nassiri MR, Farhangfar H. Comparison and evaluation of mathematical lactation curve functions of Iranian primiparous Holsteins. S Afr J Anim Sci 2011;41:104-15.
  26. Jankovic M, Leko A, Suvak N. Application of lactation models on dairy cow farms. Croat Oper Res Rev 2016;7:217-27.
  27. De Marchi M, Penasa M, Cassandro M. Comparison between automatic and conventional milking systems for milk coagulation properties and fatty acid composition in commercial dairy herds. Ital J Anim Sci 2017;16:363-70.
  28. Lovendahl P, Chagunda MGG. Covariance among milking frequency, milk yield, and milk composition from automatically milked cows. J Dairy Sci 2011;94:5381-92.
  29. Ettema JF, Santos JEP. Impact of age at calving on lactation, reproduction, health, and income in first-parity Holsteins on commercial farms. J Dairy Sci 2004;87:2730-42.