Özet:

The aim was of this study was to discover the adaptation indicators of different breeds first-calving cows to voluntary (robotic) milking system during the first month of lactation. The research was carried out in a robotic farm on German Holstein, French Holstein breeds and Brown Swiss breed of cows. During the adaptation period, the German Holsteins were differed from the rench Holsteins and Brown Swiss breed by milk yield, multiplicity of milking, and the amount of consumed concentrated feed. On the 30th day (end of adaptation period), the German Holstein breed dominated over the French Holstein breed and Brown Swiss having average daily milk yield by 0.73 and 4.12 kg, milking times by 0.26 and 0.34, the amount of consumed concentrated feed by 0.32 and 0.61 kg higher. In addition, the German Holstein breed during the adaptation period was distinguished by higher multiplicity of passages through the selection gate and the number of visits to feed stations. Important indicators of adaptation to free keeping and voluntary milking are the number of cases of forced milking. On the 5th day of lactation, some 69.4–86.2% of cows of all the groups did not enter the milking robot. This indicator has been steadily declining every five days. On the 30th day of lactation the cases of operator-forced milking were 24.3–35.9% and they were the lowest in cows of the German Holstein breed, indicating the best adaptive qualities of this cattle. The German Holstein breed had more lower (by 0.30 and 0.26 mS/cm) electrical milk conductivity on the 30th day compared with French and Brown Swiss breeds. In addition, the number of somatic cells in its milk was lower by 19.4 and 17.1 thousand cells per cm3 compared with French Holstein and Brown Swiss breed. Therefore, we suggested that the German Holstein breed has more higher stress tolerance, and hence a shorter adaptation time to the keeping. Key words: Robotic milking; Cow breeds; Adaptation; Productivity; Milking multiplicity; Milk electrical conductivity References Bach, A. & Cabrera, V. (2017). Robotic milking: Feeding strategies and economic returns. Journal of Dairy Science, 100, (9), 7720–7728. doi: 10.3168/jds.2016-11694 Bomko, V., Kropyvka, Yu., Bomko, L., Chernyuk, S., Kropyvka, S., Gutyj, B. (2018). Effect of mixed ligand complexes of Zinc, Manganese, and Cobalt on the Manganese balance in high-yielding cows during first 100-days lactation. Ukrainian Journal of Ecology, 8(1), 420–425. doi: 10.15421/2018_230 Bonnefont,C., Toufeer, M., Caubet, C., Foulon, E., Tasca, C., Aurel, M.R., Bergonier, D., Boullier, S., Robert-Granié, C., Foucras, G., & Rupp, R. (2011). Transcriptomic analysis of milk somatic cells in mastitis resistant and susceptible sheep upon challenge with Staphylococcus epidermidis and Staphylococcus aureus. BMC Genomics 2011, 12:208. doi: 10.1186/1471-2164-12-208 Borshch, A.A., Borshch, A.V., Lutsenko, M.M., Merzlov, S.V., Kosior, L.T., Lastovska, I.A., & Pirova, L.V. (2018). Amino acid and mineral composition of milk from local Ukrainian cows and their crossbreedings with Brown Swiss and Montbeliarde breeds. Journal of the Indonesian Tropical Animal Agriculture, 43 (3), 238–246. doi: 10.14710/jitaa.43.3.238-246 Borshch, O.O., Borshch, O.V., Donchenko, T.A., Kosior, L.T., & Pirova, L.V. (2017) Influence of low temperatures on behavior, productivity and bioenergy parameters of dairy cows kept in cubicle stalls and deep litter system. Ukrainian Journal of Ecology, 7(3), 73–77. doi: 10.15421/2017_51 Córdova, H.A., Alessio, D.R., Cardozo, L.L., & Neto, A.T. (2018). Impact of the factors of animal production and welfare on robotic milking frequency. Pesq. agropec. bras., Brasília, 53 (2),238-246. doi: 10.1590/s0100-204x2018000200013 Drach, U., Halachmi, I., Pnini, T., Izhaki, I., & Degani, A. (2017). Automatic herding reduces labour and increases milking frequency in robotic milking. Biosystems Engineering, 155, 134–141. doi: 10.1016/j.biosystemseng.2016.12.010 Faly, L. I., Kolombar, T. M., Prokopenko, E. V., Pakhomov, O. Y., & Brygadyrenko, V. V. (2017). Structure of litter macrofauna communities in poplar plantations in an urban ecosystem in Ukraine. Biosystems Diversity, 25(1), 29–38. doi: 10.15421/011705 Feng, S., Salter, A.M., Parr, T., & Garnsworthy, P.C. (2007). Extraction and Quantitative Analysis of Stearoyl-Coenzyme A desaturase mRNA from Dairy Cow Milk Somatic Cells. Journal of Dairy Science, 90(9), 4128–4136. doi: 10.3168/jds.2006-830 Green, M.J., Burton, P.R., Green, L.E., Schukken,Y.H., Bradley, A.J., Peeler, E.J., & Medley, G.F. (2004). The use of Markov chain Monte Carlo for analysis of correlated binary data: patterns of somatic cells in milk and the risk of clinical mastitis in dairy cows. Preventive Veterinary Medicine,64, (2–4), 157–174. Gutyj, B., Grymak, Y., Drach, M., Bilyk, O., Matsjuk, O., Magrelo, N., Zmiya, M., & Katsaraba, O. (2017). The impact of endogenous intoxication on biochemical indicators of blood of pregnant cows. Regulatory Mechanisms in Biosystems, 8(3), 438–443. doi: 10.15421/021768 Hansen, B.G. (2015). Robotic milking-farmer experiences and adoption rate in Jæren, Norway. Journal of Rural Studies, 41, 109–117. doi: 10.1016/j.jrurstud.2015.08.004 Holloway, L., & Bear, C. (2019). Beyond resistance: geographies of divergent more-that-human coduct in robotic milking. Geoforum, 104, 212–221. doi: 10/1016/j.geoforum.2019.04.030 Holloway, L., Wilkinson, K., & Bear, C. (2014). Robotic milking technologies and renegotiating situated ethical relationships on UK dairy farms. Agriculture and Human Values, 31(2), 185–199. doi: 10.1007/s10460-013-9473-3 Jacobs, J.A. & Siegford, J.M. (2012). Lactating dairy cows adapt quickly to being milked by an automatic milking system. Journal of Dairy Science, 95, (3), 1575–1584. doi: 10.3168/jds.2011-4710 Kozak, V. M., & Brygadyrenko, V. V. (2018). Impact of cadmium and lead on Megaphyllum kievense (Diplopoda, Julidae) in a laboratory experiment. Biosystems Diversity, 26(2), 128–131. doi: 10.15421/011820 Melin, M., Pettersson, G., Svennersten-Sjaunja, K., & Wiktorsson, H. (2007). The effects of restricted feed access and social rank on feeding behavior, ruminating and intake for cows managed in automated milking systems. Applied Animal Behaviour Science, 107(1), 13–21. doi: 10.1016/j.applanim.2006.09.026 Munksgaard, L., Rushen, J., de Passillé, A.M., & Krohn, C.C. (2011). Forced versus free traffic in an automated milking system. Livestock Science, 138, (1–3), 244–250. doi: 10.1016/j.livsci.2010.12.023 NRC. (2001). Nutrient Requirements of Dairy Cattle. 7th rev. ed. National. Academy Press, 2101 Constitution Avenue, N.W., Lockbox 285, Washington. 13–28. Pérez-Ramírez, E., Peyraud, J.L., & Delagarde, R. (2009). Restricting daily time at pasture at low and high pasture allowance: Effects on pasture intake and behavioral adaptation of lactating dairy cows. Journal of Dairy Science 92, (7), 3331–3340. doi: 10.3168/jds.2008-1951 Rousing, T., Badsberg, J. H., Klaas, I. C., Hindhede, J., & Sørensen, J. T. (2006). The association between fetching for milking and dairy cows' behaviour at milking, and avoidance of human approachdan on-farm study in herds with automatic milking systems. Livestock Science, 101(1), 219–227. doi: 10.1016/j.livprodsci.2005.11.013. Shoshani, E. & Chaffer, M. (2001). Robotic milking: a report of a field trial in israel Ezra Shoshani and Marcelo Chaffer. Proceedings of the Dutch-Israeli Seminar on Robotic Milking and Heat Stress, 41–50. Sitkowska, B., Piwczy??ski, D., Aerts, J., &Wa??kowicz, M. (2015). Changes in milking parameters with robotic milking. Archive Animal Breeding, 58, 137–143. doi: 10.5194/aab-58-137-2015 Slivinska, L., Fedorovych, V., Gutyj, B., Lychuk, M., Shcherbatyy, A., Gudyma, T., Chernushkin, B., Fedorovych, N. (2018). The occurrence of osteodystrophy in cows with chronic micronutrients deficiency. Ukrainian Journal of Ecology, 8(2), 24–32. doi: 10.15421/2018_305 Slivinska, L., Shcherbatyy, A., Gutyj, B., Lychuk, M., Fedorovych, V., Maksymovych, I., Rusyn, V., Chernushkin, B. (2018). Parameters of erythrocytopoiesis, acid resistance and population composition of erythrocytes of cows with chronic hematuria. Ukrainian Journal of Ecology, 8(1), 379–385. doi: 10.15421/2017_225 Slivinska, L.G., Shcherbatyy, A.R., Lukashchuk, B.O., Zinko, H.O., Gutyj, B.V., Lychuk, M.G., Chernushkin, B.O., Leno, M.I., Prystupa, O.I., Leskiv, K.Y., Slepokura, O.I., Sobolev, O.I., Shkromada, O.I., Kysterna, O.S., ??usiienko, O.V. (2019). Correction of indicators of erythrocytopoesis and microelement blood levels in cows under conditions of technogenic pollution. Ukrainian Journal of Ecology, 9(2), 127-135 Taylor, V.J., Beever, D.E., & Wathes, D.C. (2004). Physiological Adaptations to Milk Production that Affect the Fertility of High Yielding Dairy Cows. BSAP Occasional Publication, 29, 37–71. doi: 10.1017/S0263967X00040040

Anahtar Kelimeler: