Asian-Australasian Journal of Animal Sciences

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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The effect of lactation number, stage, length, and milking frequency on milk yield in Korean Holstein dairy cows using automatic milking system

  • Vijayakumar, Mayakrishnan (Dairy Science Division, National Institute of Animal Science, Rural Development Administration) ;
  • Park, Ji Hoo (Dairy Science Division, National Institute of Animal Science, Rural Development Administration) ;
  • Ki, Kwang Seok (Dairy Science Division, National Institute of Animal Science, Rural Development Administration) ;
  • Lim, Dong Hyun (Dairy Science Division, National Institute of Animal Science, Rural Development Administration) ;
  • Kim, Sang Bum (Dairy Science Division, National Institute of Animal Science, Rural Development Administration) ;
  • Park, Seong Min (Dairy Science Division, National Institute of Animal Science, Rural Development Administration) ;
  • Jeong, Ha Yeon (Dairy Science Division, National Institute of Animal Science, Rural Development Administration) ;
  • Park, Beom Young (Dairy Science Division, National Institute of Animal Science, Rural Development Administration) ;
  • Kim, Tae Il (Dairy Science Division, National Institute of Animal Science, Rural Development Administration)
  • Received : 2016.11.18
  • Accepted : 2017.03.19
  • Published : 2017.08.01
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Abstract

Objective: The aim of the current study was to describe the relationship between milk yield and lactation number, stage, length and milking frequency in Korean Holstein dairy cows using an automatic milking system (AMS). Methods: The original data set consisted of observations from April to October 2016 of 780 Holstein cows, with a total of 10,751 milkings. Each time a cow was milked by an AMS during the 24 h, the AMS management system recorded identification numbers of the AMS unit, the cow being milking, date and time of the milking, and milk yield (kg) as measured by the milk meters installed on each AMS unit, date and time of the lactation, lactation stage, milking frequency (NoM). Lactation stage is defined as the number of days milking per cows per lactation. Milk yield was calculated per udder quarter in the AMS and was added to 1 record per cow and trait for each milking. Milking frequency was measured the number of milkings per cow per 24 hour. Results: From the study results, a significant relationship was found between the milk yield and lactation number (p<0.001), with the maximum milk yield occurring in the third lactation cows. We recorded the highest milk yield, in a greater lactation length period of early stage (55 to 90 days) at a $4{\times}$ milking frequency/d, and the lowest milk yield was observed in the later stage (>201 days) of cows. Also, milking frequency had a significant influence on milk yield (p<0.001) in Korean Holstein cows using AMS. Conclusion: Detailed knowledge of these factors such as lactation number, stage, length, and milking frequency associated with increasing milk yield using AMS will help guide future recommendations to producers for maximizing milk yield in Korean Dairy industries.

Keywords

References

  1. De Koning CJAM. Automatic milking-A common practice on dairy farms. In: Proceedings of the First North American Conference on Precision Dairy Management; Mar 2-Mar 6; Toronto, Canada: Ontario Agriculture; 2010. p. 52-67.
  2. Jacobs JA, Siegford JM. Invited review: The impact of automatic milking systems on dairy cow management, behavior, health, and welfare. J Dairy Sci2012;95:2227-47. https://doi.org/10.3168/jds.2011-4943
  3. Svennersten-Sjaunja KM, Pettersson G. Pros and cons of automatic milking in Europe. J Anim Sci 2008;86:37-46. https://doi.org/10.2527/jas.2007-0527
  4. Speroni M, Pirlo G, Lolli S. Effect of automatic milking systems on milk yield in a hot environment. J Dairy Sci 2006;89:4687-93. https://doi.org/10.3168/jds.S0022-0302(06)72519-X
  5. Smith JW, Ely LO, Graves WM, Gilson WD. Effect of milking frequency on DHI performance measures. J Dairy Sci 2002;85:3526-33. https://doi.org/10.3168/jds.S0022-0302(02)74442-1
  6. McNamara S, Murphy JJ, O'Mara FP, Rath M, Mee JF. Effect of milking frequency in early lactation on energy metabolism, milk production and reproductive performance of dairy cows. Livest Sci 2008;117:70-8. https://doi.org/10.1016/j.livsci.2007.11.013
  7. Erdman RA, Varner M. Fixed yield responses to increased milking frequency. J Dairy Sci 1995;78:1199-203. https://doi.org/10.3168/jds.S0022-0302(95)76738-8
  8. Remond R, Pomies D, Julien C, Guinard-Flament J. Performance of dairy cows milked twice daily at contrasting intervals. Animal 2009;3: 1463-71. https://doi.org/10.1017/S1751731109990371
  9. Amani Z, Gader A, Khair M, Lutfi A, Peters K. Milk yield and reproductive performance of Friesian cows under Sudan tropical conditions. Arch Tierz 2007;50:155-64.
  10. Ray DE, Halbach TJ, Armstrong DV. Season and lactation number effects on milk production and reproduction of dairy cattle in Arizona. J Dairy Sci 1992;75:2976-83. https://doi.org/10.3168/jds.S0022-0302(92)78061-8
  11. Arbel R, Bigun Y, Ezra E, Sturman H, Hojman D. The effect of extended calving intervals in high lactating cows on milk production and profitability. J Dairy Sci 2001;84:600-8. https://doi.org/10.3168/jds.S0022-0302(01)74513-4
  12. Cilek S. Milk yield traits of Holstein cows raised at Polatli state farm in Turkey. J Anim Vet Adv 2009;8:6-10.
  13. Sorensen MT, Norgaard JV, Theil PK, Vestergaard M, Sejrsen K. Cell turnover and activity in mammary tissue during lactation and the dry period in dairy cows. J Dairy Sci 2006;89:4632-9. https://doi.org/10.3168/jds.S0022-0302(06)72513-9
  14. Wathes DC, Cheng Z, Bourne N, et al. Differences between primiparous and multiparous dairy cows in the inter-relationships between metabolic traits, milk yield and body condition score in the periparturient period. Domest Anim Endocrinol 2007;3:203-25.
  15. Hossain AM. A study of average percentage of fat, total solids, solidsnot-fat content of local cow's milk and the variation of milk fat percentage with the stage of lactation [Master's thesis]. Mymensingh, East Pakistan: Agricultural University; 1968.
  16. Sekerden O. Relationships between lactation stage with milk yield and constituents, and heritabilities of milk constituents. Hay Uret J Anim Prod 2002;43:61-7.
  17. Alshaikh MA, Salah MS. Effect of milking interval on secretion rate and composition of camel milk in late lactation. J Dairy Res 1994;61: 451-6. https://doi.org/10.1017/S0022029900028375
  18. Pearson RE, Fulton LA, Thompson PD, Smith JW. Three times a day milking during the first half of lactation. J Dairy Sci 1979;62:1941-50. https://doi.org/10.3168/jds.S0022-0302(79)83526-2
  19. Poole DA. The effects of milking cows three times daily. Anim Prod 1982;34:197-201. https://doi.org/10.1017/S0003356100000672
  20. Archer P 1983. Milking three times a day. Milk Marketing Board Report, 34. Farm Management Service (FMI). Great Britain. 33.
  21. Archer P 1983. Milking three times a day: Farm management service (FMI). Great Britain: Milk Marketing Board; 1983.
  22. Stelwagen K. Effect of milking frequency on mammary functioning and shape of the lactation curve. J Dairy Sci 2001;84:E204-E11. https://doi.org/10.3168/jds.S0022-0302(01)70219-6
  23. Salama AAK, Such X, Caja G, et al. Effects of once versus 2x daily milking throughout lactation on milk yield and milk composition in dairy goats. J Dairy Sci 2003;86:1673-80. https://doi.org/10.3168/jds.S0022-0302(03)73753-9
  24. VanBaale MJ, Ledwith DR, Thomson JM, et al. Effect of increased milking frequency in early lactation with or without recombinant bovine somatotropin. J Dairy Sci 2005;88:3905-12. https://doi.org/10.3168/jds.S0022-0302(05)73076-9
  25. Wilde CJ, Addey CVP, Boddy LM, Peaker M. Autocrine regulation of milk secretion by protein in milk. Biochem J 1995;305:51-8. https://doi.org/10.1042/bj3050051
  26. Peaker M. The effect of raised intramammary pressure on mammary function in the goat in relation to the cessation of lactation. J Physiol 1990;301:415-28.
  27. Stelwagen K, Davis SR, Farr VC, Prosser CG, Sherlock RA. Mammary epithelial cell tight junction integrity and mammary blood flow during an extended milking interval in goats. J Dairy Sci 1994;77:426-32. https://doi.org/10.3168/jds.S0022-0302(94)76969-1
  28. Wilde CJ, Henderson AJ, Knight CH, et al. Effects of long-term 3x daily milking on mammary enzyme activity, cell population and milk yield in the goat. J AnimSci 1987;64:533-9.
  29. Jurjanz S, Laurent F, Graupner M. Effect of increased milk frequency on milk composition. Mh Vet Med 1993;48:631-4.
  30. Van der Iest R, Hilerton JE. Short-term effects of frequent milking of dairy cows. J Dairy Res 1989;56:587-92. https://doi.org/10.1017/S0022029900029101

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