Alain Gillon

Modeling milk urea of Walloon dairy cows in management perspectives.

The aim of this study was to develop an adapted random regression test-day model for milk urea (MU) and to study the possibility of using predictions and solutions given by the model for management purposes. Data included 607,416 MU test-day records of first-lactation cows from 632 dairy herds in the Walloon Region of Belgium. Several advanced features were used. First, to detect the herd influence, the classical herd x test-day effect was split into 3 new effects: a fixed herd x year effect, a fixed herd x month-period effect, and a random herd test-day effect. A fixed time period regression was added in the model to take into account the yearly oscillations of MU on a population scale. Moreover, first autoregressive processes were introduced and allowed us to consider the link between successive test-day records. The variance component estimation indicated that large variance was associated with the random herd x test-day effect (48% of the total variance), suggesting the strong influence of herd management on the MU level. The heritability estimate was 0.13. By comparing observed and predicted MU levels at both the individual and herd levels, target ranges for MU concentrations were defined to take into account features of each cow and each herd. At the cow level, an MU record was considered as deviant if it was <200 or >400 mg/L (target range used in the field) and if the prediction error was >50 mg/L (indicating a significant deviation from the expected level). Approximately 7.5% of the MU records collected between June 2007 and May 2008 were beyond these thresholds. This combination allowed for the detection of potentially suspicious cows. At the herd level, the expected MU level was considered as the sum of the solutions for specific herd effects. A herd was considered as deviant from its target range when the prediction error was greater than the standard deviation of MU averaged by herd test day. Results showed that 6.7% of the herd test-day MU levels between June 2007 and May 2008 were considered deviant. These deviations seemed to occur more often during the grazing period. Although theoretical considerations developed in this study should be validated in the field, this research showed the potential use of a test-day model for analyzing functional traits to advise dairy farmers.

Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice.

Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra

Changes in milk production traits (i.e., milk yield, fat, and protein contents) with the pregnancy stage are well documented. To our knowledge, the effect of pregnancy on the detailed milk composition has not been studied so far. The mid-infrared (MIR) spectrum reflects the detailed composition of a milk sample and is obtained by a nonexhaustive and widely used method for milk analysis. Therefore, this study aimed to investigate the effect of pregnancy on milk MIR spectrum in addition to milk production traits (milk yield, fat, and protein contents). A model including regression on the number of days pregnant was applied on milk production traits (milk yield, fat, and protein contents) and on 212 spectral points from the MIR spectra of 9,757 primiparous Holstein cows from Walloon herds. Effects of pregnancy stage were expressed on a relative scale (effect divided by the squared root of the phenotypic variance); this allowed comparisons between effects on milk traits and on 212 spectral points. Effect of pregnancy stage on production traits were in line with previous studies indicating that the model accounted well for the pregnancy effect. Trends of the relative effect of the pregnancy stage on the 212 spectral points were consistent with known and observed effect on milk traits. The highest effect of the pregnancy was observed in the MIR spectral region from 968 to 1,577 cm-1. For some specific wavenumbers, the effect was higher than for fat and protein contents in the beginning of the pregnancy (from 30 to 90 or 120 d pregnant). In conclusion, the effect of early pregnancy can be observed in the detailed milk composition through the analysis of the MIR spectrum of bovine milk. Further analyses are warranted to explore deeply the use of MIR spectra of bovine milk for breeding and management of dairy cow pregnancy.

Bayesian single-step genomic evaluations combining local and foreign information in Walloon Holsteins

Most dairy cattle populations found in different countries around the world are small to medium sized and use many artificial insemination bulls imported from different foreign countries. The Walloon population in the southern part of Belgium is a good example for such a small-scale population. Wallonia has also a very active community of Holstein breeders requesting high level genetic evaluation services. Single-step Genomic BLUP (ssGBLUP) methods allow the simultaneous use of genomic, pedigree and phenotypic information and could reduce potential biases in the estimation of genomically enhanced breeding values (GEBV). Therefore, in the context of implementing a Walloon genomic evaluation system for Holsteins, it was considered as the best option. However, in contrast to multi-step genomic predictions, natively ssGBLUP will only use local phenotypic information and is unable to use directly important other sources of information coming from abroad, for example Multiple Across Country Evaluation (MACE) results as provided by the Interbull Center (Uppsala, Sweden). Therefore, we developed and implemented single-step Genomic Bayesian Prediction (ssGBayes), as an alternative method for the Walloon genomic evaluations. The ssGBayes method approximated the correct system of equations directly using estimated breeding values (EBV) and associated reliabilities (REL) without any explicit deregression step. In the Walloon genomic evaluation, local information refers to Walloon EBV and REL and foreign information refers to MACE EBV and associated REL. Combining simultaneously all available genotypes, pedigree, local and foreign information in an evaluation can be achieved but adding contributions to left-hand and right-hand sides subtracting double-counted contributions. Correct propagation of external information avoiding double counting of contributions due to relationships and due to records can be achieved. This ssGBayes method computed more accurate predictions for all types of animals. For example, for genotyped animals with low Walloon REL (<0.25) without MACE results but sired by genotyped bulls with MACE results, the average increase of REL for the studied traits was 0.38 points of which 0.08 points could be traced to the inclusion of MACE information. For other categories of genotyped animals, the contribution by MACE information was also high. The Walloon genomic evaluation system passed for the first time the Interbull GEBV tests for several traits in July 2013. Recent experiences reported here refer to its use in April 2016 for the routine genomic evaluations of milk production, udder health and type traits. Results showed that the proposed methodology should also be of interest for other, similar, populations.