Effect of sire, Dam, parity and year of calving on parameters and characteristics of lactation curve of Friesian-Holstein cows
DOI:
https://doi.org/10.65405/sfftf568Keywords:
Active learning, Basic education, Curricula, School administration.Abstract
This study aimed to model the lactation curves of Friesian-Holstein cows to estimate key curve characteristics and to quantify the effects of sire, dam, parity, and year of calving on these parameters.A total of 290 lactation records from 85 cows (progeny of 34 sires) were collected from the Alexandria University herd between 2003 and 2013. The incomplete Gamma function (Wood's model) was fitted to individual lactation data to derive parameters: initial yield (a), ascending rate to peak (b), and post-peak decline rate (c). These were used to calculate total milk yield (TMY), peak yield (PMY), time to peak (PW), lactation length (LL), and persistency measures (LnS, PER). Data were analyzed using an ANOVA model that included sire and dam (within sire) as random effects, and parity and year of calving as fixed effects.The results revealed substantial variation in all studied traits. The mean TMY was 5820.7 kg, with a high coefficient of variation (40.97%). The average PMY was 231.6 kg/week, and cows reached their peak yield around the 5th week. The analysis of variance indicated that the year of calving had a highly significant (P < 0.0001) influence on TMY and LL. The maternal effect (Dam within Sire) was significant for the post-peak decline rate (c) (P < 0.0001) and for TMY (P = 0.0018). In contrast, the sire effect and parity did not significantly affect most of the curve parameters or production traits. It concluded that the study concludes that annual environmental and management factors (year of calving) are the primary drivers of total milk production and lactation length in this herd. Maternal effects are crucial for lactation persistency. The high variability in key traits presents an opportunity for genetic improvement; however, enhancing overall herd management remains the most critical strategy for boosting productivity, as most traits were predominantly influenced by environmental conditions.
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References
1) Dekkers, J. C. M., Ten Hag, J. H., and Weersink, A. (1998). Economic aspects of persistency of lactation in dairy cattle. Livestock Production Science, 53(3), 237–252. https://doi.org/10.1016/S0301-6226(97)00124-3
2) Jakobsen J.H., Madsen P., Jensen J., Pedersen J., Christensen L.G., Sorensen D.A. Genetic parameters for milk production and persistency for Danish Holsteins estimated in random regression models using REML. Journal of Dairy Science, 2002, 85(6): 1607-1616 (doi: 10.3168/jds.S0022-0302(02)74231-8).
3) Koeck, A., Jamrozik, J., Schenkel, F. S., Moore, R. K., Lefebvre, D. M., Kelton, D. F., and Miglior, F. (2014). Genetic analysis of milk β-hydroxybutyrate and its association with fat-to-protein ratio, body condition score, clinical ketosis, and displaced abomasum in early first lactation of Canadian Holsteins. Journal of Dairy Science, 97(11), 7286–7292. https://doi.org/10.3168/jds.2014-8405
4) Macciotta, N. P. P., Dimauro, C., Rassu, S. P. G., Steri, R., and Pulina, G. (2011). The mathematical description of lactation curves in dairy cattle. Italian Journal of Animal Science, 10(4), e51. https://doi.org/10.4081/ijas.2011.e51
5) Nørgaard, J. V., Sørensen, M. T., Theil, P. K., Sehested, J., and Sejrsen, K. (2008). Effect of pregnancy and feeding level on cell turnover and expression of related genes in the mammary tissue of lactating dairy cows. Animal, 2(4), 588–594. https://doi.org/10.1017/S1751731108001626
6) Rekik, B., Ben Gara, A., Ben Hamouda, M., and Hammami, H. (2003). Fitting lactation curves of dairy cattle in different types of herds in Tunisia. Livestock Production Science, 83(2-3), 309-315.
7) Seangjun, A., Koonawootrittriron, S., and Elzo, M. A. (2009). Characterization of Lactation Patterns and Milk Yield in a Multibreed Dairy Cattle Population in the Central Thailand. Asian-Australasian Journal of Animal Sciences, 22(5), 644-653.
8) Solodneva, E. V., Smolnikov, R. V., Bazhenov, S. A., Vorobyeva, D. A., and Stolpovsky, Y. A. (2022). Lactation curves as a tool for monitoring the health and performance of dairy cows — A mini-review. Veterinary World, 15(3), 765-771.
9) Togashi, K., and Lin, C. Y. (2003). Modifying the lactation curve to improve lactation milk and persistency. Journal of Dairy Science, 86(4), 1487–1493. https://doi.org/10.3168/jds.S0022-0302(03)73734-5
10) Weller J.I., Ezra E., Leitner G. Genetic analysis of persistency in the Israeli Holstein popula-tion by the multitrait animal model. Journal of Dairy Science, 2006, 89(7): 2738-2746 (doi: 10.3168/jds.S0022-0302(06)72350-5).
11) Wood, P. D. P. (1967). Algebraic model of the lactation curve in cattle. Nature, 216(5111), 164–165. https://doi.org/10.1038/216164a0
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