Abstract
The study, conducted in 2020–2021, aimed to provide a better understanding of the effect of an automated milking system (AMS) on lactation curves by comparing them to these of the conventional milking system (CMS), from mainly pasture-based farms in South-East Australia. Ten farms in Tasmania, Western Victoria and New South Wales were enrolled (five of each AMS and CMS). The objectives of this study were to create lactation curves for (1) pasture-based AMS and CMS in South-East Australia; (2) 3 age categories: primiparae (parity 1); mid-age category (2nd, 3rd and 4th parity) and old age category (>4th parity). At overall, this study demonstrated that lactation curves did vary with the type of milking system. The variability of cow average yield was observed among age categories and farms. In the AMS, the descending stage of the lactation curve was slightly more persistent from approximately 150 days-in-milk (DIM) onwards. Variability in average cow milk yield observed between age categories was higher between mid-age and old-age cows in AMS. Herd structure, when grouped by age category, consisted of more old-age cows on AMS (25.6%) compared to CMS (16.3%). Feeding system had a great impact on the milk yield in South-East Australia, being 21.1 L and 38.9 L for pasture-based and total mixed ration (TMR)-based AMS.
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References
Jago, J., Burke, J.: An evaluation of two pastoral dairy production systems using automatic milking technology. In: Proceedings of the New Zealand Grassland Association, pp. 109–116 (2010)
Cole, J.B., Null, D.J., De Vries, A.: Short communication: best prediction of 305-day lactation yields with regional and seasonal effects. J. Dairy Sci. 94(3), 1601–1604 (2011)
Kamphuis, C., Burke, J.K., Taukiri, S., Petch, S.F., Turner, S.A.: Shorter sampling periods and accurate estimates of milk volume and components are possible for pasture based dairy herds milked with automated milking systems. J. Dairy Res. 83(3), 326–333 (2016)
Ebrahimi, M., Mohammadi-Dehcheshmeh, M., Ebrahimie, E., Petrovski, K.R.: Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: deep learning and gradient-boosted trees outperform other models. Comput. Biol. Med. 114, 103456 (2019)
Petrovski, K.R., Trajcev, M., Buneski, G.: A review of the factors affecting the costs of bovine mastitis. J. S. Afr. Vet. Assoc. 77(2), 52–60 (2006)
Rutten, C.J., Velthuis, A.G.J., Steeneveld, W., Hogeveen, H.: Invited review: Sensors to support health management on dairy farms. J. Dairy Sci. 96(4), 1928–1952 (2013)
Schwanke, A.J., Dancy, K.M., Didry, T., Penner, G.B., DeVries, T.J.: Effects of concentrate location on the behavior and production of dairy cows milked in a free-traffic automated milking system. J. Dairy Sci. 102(11), 9827–9841 (2019)
Jacobs, J.A., Siegford, J.M.: Invited review: the impact of automatic milking systems on dairy cow management, behavior, health, and welfare. J. Dairy Sci. 95(5), 2227–2247 (2012)
Lyons, N.A., Kerrisk, K.L., Garcia, S.C.: Milking frequency management in pasture-based automatic milking systems: a review. Livest. Sci. 159, 102–116 (2014)
Siewert, J.M., Salfer, J.A., Endres, M.I.: Factors associated with productivity on automatic milking system dairy farms in the upper midwest United States. J. Dairy Sci. 101(9), 8327–8334 (2018)
Schaeffer, L.R., Minder, C.E., McMillan, I., Burnside, E.B.: Nonlinear techniques for predicting 305-day lactation production of Holsteins and Jerseys. J. Dairy Sci. 60(10), 1636–1644 (1977)
Keeper, D.M., Kerrisk, K.L., House, J.K., Garcia, S.C., Thomson, P.: Demographics, farm and reproductive management strategies used in Australian automatic milking systems compared with regionally proximal conventional milking systems. Aust. Vet. J. 95(9), 325–332 (2017)
Siewert, J.M., Salfer, J.A., Endres, M.I.: Milk yield and milking station visits of primiparous versus multiparous cows on automatic milking system farms, upper midwest United States. J. Dairy Sci. 102(4), 3523–3530 (2019)
Adediran, S.A., Nish, P., Donaghy, D.J., Ratkowsky, D.A., Malau-Aduli, A.E.O.: Genetic and environmental factors influencing milk, protein and fat yields of pasture-based dairy cows in Tasmania. Anim. Prod. Sci. 50(4), 265–275 (2010)
Bach, A., Cabrera, V.: Robotic milking: Feeding strategies and economic returns. J. Dairy Sci. 100(9), 7720–7728 (2017)
Endres, M.I., Salfer, J.A.: Feeding cows in a robotic milking system. In: 26th Tri-State Dairy Nutrition Conference, Fort Wayne, Indiana, USA, pp. 61–68. Ohio State University (2017)
John, A.J., Freeman, M.J., Kerrisk, K.F., Garcia, S.C., Clark, C.E.F.: Robot utilisation of pasture-based dairy cows with varying levels of milking frequency. Animal 13(7), 1529–1535 (2019)
Scott, V.E., Thomson, P.C., Kerrisk, K.L., Garcia, S.C.: Influence of provision of concentrate at milking on voluntary cow traffic in a pasture-based automatic milking system. J. Dairy Sci. 97(3), 1481–1490 (2014)
Schuster, J.C., Barkema, H.W., De Vries, A., Kelton, D.F., Orsel, K.: Invited review: academic and applied approach to evaluating longevity in dairy cows. J. Dairy Sci. 103(12), 11008–11024 (2020)
Vredenberg, I., Han, R., Mourits, M., Hogeveen, H.: Steeneveld, W: An empirical analysis on the longevity of dairy cows in relation to economic herd performance. Front. Vet. Sci. 8, 646672 (2021)
Nikulina, D.Y., Surovtsev, V.: Economic efficiency factors of automatic milking system in Russia: a case study. In: Agriculture Digitalization and Organic Production: Proceedings of the Second International Conference, ADOP 2022, St. Petersburg, Russia, pp. 245–257. Springer Nature Singapore, Singapore (2022)
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Perov, I., Petrovski, K., Ebrahimie, E. (2023). Differences in Milk Production Curves on Ten Dairy Farms with Automated and Conventional Milking System in South-East Australia. In: Ronzhin, A., Kostyaev, A. (eds) Agriculture Digitalization and Organic Production. ADOP 2023. Smart Innovation, Systems and Technologies, vol 362. Springer, Singapore. https://doi.org/10.1007/978-981-99-4165-0_20
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