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The objectives of this study were to analyze the genetic properties of three measures of lactation persistency in Polish Holstein-Friesian cows, and possibly to choose one measure which could be used for estimation of breeding value for lactation persistency in the Polish dairy cattle population.Data included 117,327 first three lactations of 110,141 cows calved in 1995-2009. The lactation curie model of Ali and Schaeffer was fitted to test-day milk yields. The first definition of persistency (P2:1)was milk yield in the second 100 days in milk (DIM) divided by yield in the first 100 DIM. The second definition (P3:1) was milk yield in the third 100 DIM divided by yield in the first 100 DIM, and the third definition (Pd) was milk yield at 280 DIM divided by milk yield at 60 DIM. The multipletrait REML method was applied for (co)variance component estimation. Heritabilities for three measures of persistency were very low, and ranged from 0.01 to 0.08. Genetic correlations were highest between P3:1 and Pd (0.96-0.99), and lowest between P2:1 and Pd (0.66-0.81),in the first three lactations. The correlations between 305-d milk yield and P3:1 or Pd in each of the first three lactations, and P2:1 in the second lactation, were negative and moderate. The phenotypic correlations between 305-d milk yield and persistency measures were low in the first three lactations.The phenotypic correlation between milk yield and Pd in each lactation was almost the same (0.14-0.15); the correlation between milk yield and P3:1 (0.11-0.17) or P2:1 (0.08-0.13) showed little variation in the first three lactations. All three compared measures of persistency were low-heritable and practically uncorrelated with total milk yield of 305-d lactations, so any of them could be used in the breeding program. However, the Pd measure could be recommended for use in practice because it is easy to calculate and interpret.
The objective of this study was to estimate genetic correlations of lactose percentage and urea concentration in milk with conformation traits related to udder and legs of Polish Holstein-Friesian cows. Data consisted of 5,813 test-day records and type scores of 791 primiparous cows. The analysis involved two descriptive traits (udder, feet and legs, scored from 50 to 100) and 11 linearly scored traits (describing udder: fore udder height, rear udder height, central ligament, udder depth, udder width, fore teat placement, teat length, rear teat placement; describing legs: rear legs - side view, foot angle, rear legs - rear view; on a scale of 1 to 9). Genetic correlations were calculated based on (co)variances estimated using the Bayesian method via Gibbs sampling and the multitrait animal model. Genetic correlations between lactose content and conformation traits ranged from -0.18 to 0.23, while those between milk urea concentration and conformation traits ranged between -0.02 and 0.43, respectively. Absolute values of average genetic correlations with daily lactose percentage exceeded 0.15 only for udder (descriptive trait) and several linearly scored traits, i.e. central ligament, udder depth, rear teat placement, and rear legs - rear view. Milk urea content was weakly or moderately genetically correlated with six type traits: udder, and five linearly scored traits: fore udder height, central ligament, udder width, teat length, and rear legs - side view. Absolute values of genetic correlations between these traits exceeded 0.15. Our results showed that type traits connected with udder were more highly genetically correlated with both lactose and milk urea contents than type traits describing legs. It meant that an increase in both lactose percentage and urea concentration in milk might be expected as an indirect response to selection for better udder, whereas selection for improvement of legs would not affect lactose percentage and milk urea content.
Celem pracy było porównanie kilku metod szacowania dobowej wydajności mleka na podstawie jednego udoju: porannego lub wieczornego, krów ocenianych metodą AT4. Materiał stanowiły dane z robotów udojowych udostępnione przez Polską Federację Hodowców Bydła i Producentów Mleka (PFHBiPM). Wybrano 43 309 udojów wykonanych między 5. a 305. dniem doju dla krów z jednym lub dwoma udojami w ciągu doby. Odstęp między danym dojem a poprzednim podzielono na klasy (MIC), a laktacje podzielono na 10 miesięcznych faz. Wydajności dobowe szacowano na podstawie równań regresji liniowej. Porównano 3 równania: w obrębie laktacji pierwszych i pozostałych (wariant 1, modele 1-3) lub łącznie dla wszystkich laktacji (wariant 2, modele 4-6). Współczynniki dla modelu 1. wyznaczono w obrębie pory doju, klasy MIC i fazy laktacji. Współczynniki modelu 2. obliczono w obrębie pory doju, a odstęp między danym dojem i poprzednim uwzględniono jako dodatkową współzmienną. Współczynniki dla modelu 3. wyznaczono w obrębie pory doju, klasy MIC, a fazę laktacji zastąpiono regresją na dzień doju. Rekomendowany do wdrożenia jest model 1., który stanowi prosty model regresji, łatwy do implementacji w praktyce. Wyznaczenie współczynników w obrębie laktacji podzielonych na pierwsze i pozostałe pozwoli na dokładniejsze szacowanie wydajności dobowej.
In the future an approach incorporating cows’ measured phenotypes and marker genotypes of cows and bulls within a single model can be applied. The most important advantage of such a model is the simultaneous use of pedigree and marker-based genomic relationship data. Such a solution allows the use of both genotyped and non-genotyped animals in the prediction procedure. This pilot study is aimed towards implementation of a one-step approach in a random regression test day model context for the Polish Holstein Friesian population, considering various ways of adjusting the relationship matrix. Data consisted of 890 animals (10 genotyped bulls, 100 cows with phenotypic data and 780 ancestors without genotypes or phenotypes). Random regression test day models with a polygenic effect on milk yield modeled by second order Legendre polynomials for the estimation of variance-covariance parameters and were used for prediction of genomically enhanced breeding values (GEBV). In this model, a matrix combining pedigree and marker-based information was used instead of a traditional numerator relationship matrix. In this matrix the proportions of information coming from pedigree and markers were defined by weighting parameters w and 1-w for pedigree and marker-based information matrices, respectively. Various weights of the two sources of information were considered. The accuracy of GEBV both for genotyped bulls and for cows with phenotypes was highest for weighting parameter w=0 and lowest for w=l. Incorporating genomic information into a conventional genetic evaluation improves reliabilities of breeding value prediction, however, pedigree information is important to maintain the stability of evaluation for non-genotyped animals. Implementation of the single-step approach in a random regression test day model framework is very attractive for genomic prediction in dairy cattle, since it allows to incorporate genomic information directly into a conventional genetic evaluation. However, for accurate predictions it is essential to achieve the right balance between the numerator relationship and markers-based relationship information.
The objective of the study was to compare five lactation curve models, Ali and Schaeffer, Guo, Wilmink, and third- and fourth-order normalized Legendre polynomials, for their ability to reliably predict 305-d lactation yields from test-day records. The analysis covered 27 589 422 test-day yields from 3 350 638 first three lactations of 1 621 796 Polish Holstein-Friesian cows. All of the functions were fitted to the test-day yields of milk, fat and protein using a multiple-trait prediction (MTP) method. Based on the fitted curves, 305-d yields were calculated and the results were compared with the official lactation yields of cows assumed as true yields. Lactation curve models differed significantly (P < 0.01) when 305-d yields of milk traits were estimated and compared. Legendre polynomials showed the smallest bias and the highest accuracy among five compared models for all milk traits, whereas the Guo model for milk and the Wilmink model for fat and protein had the highest bias and the lowest accuracy. All of the functions produced similar estimates of milk, fat and protein yields at peak day; the estimated day of peak depended on the model.
The objective of this study was to compare five mathematical functions used for modeling lactation curves and to choose the most suitable one for Polish Holstein-Friesian cows. The data used were 1,944,818; 1,548,700; and 1,081,107 test-day milk yields from 220,487 first, 181,165 second, and 128,774 third lactations. Five models were fitted to the test-day data: the Ali and Schaeffer (ALI), Guo (GUO), and Wilmink (WIL) functions, and thirdorder (LEG3), and fourth-order (LEG4) Legendre polynomials. The milk lactation curves were fitted using a multiple-trait prediction method. Several criteria based on the analysis of residuals were used to compare the models. Of the five models, five-parameter functions (ALI and LEG4) gave the best goodness of fit for standard lactations (305-d), whereas three-parameter functions (GUO and WIL) were the worst. For extended lactations (400-d), the ALI function ensured the most correct prediction.
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