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See detailGenetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows
Kandel, Purna Bhadra ULg; Vanrobays, Marie-Laure ULg; Vanlierde, Amélie ULg et al

in Journal of Dairy Science (2013), 95(E-1), 388

Genetic selection of low methane (CH4) emitting animals is additive and permanent but the difficulties associated with individual CH4 measurement result in a paucity of records required to estimate ... [more ▼]

Genetic selection of low methane (CH4) emitting animals is additive and permanent but the difficulties associated with individual CH4 measurement result in a paucity of records required to estimate genetic variability of CH4 traits. Recently, it was shown that direct quantification of CH4 emissions by mid-infrared spectroscopy (MIR) from milk. The CH4 prediction equation was developed using 452 SF6 CH4 measurements with associated milk spectra and the calibration equation was developed using PLS regression. The obtained SD of predicted CH4 was 126.39 g/day with standard error of cross validation 68.68 g/day and a cross-validation coefficient of determination equal to 70%. The equation was applied on a total of 338,917 spectra obtained from milk samples collected between January 2007 and August 2012 during the Walloon milk recording for first parity Holstein cows. The prediction of MIR CH4 was 547 ± 111 g/d and MIR CH4 g/kg of fat and protein corrected milk (FPCM) was 23.66 ± 8.21.Multi-trait random regression test-day models were used to estimate the genetic variability of MIR predicted CH4 and milk production traits. The heritability, phenotypic and genetic correlations between MIR predicted CH4 traits and milk traits are presented in Table 1. Estimated heritability for CH4 g/day and CH4 g/kg of FPCM were lower than common production traits but would still be useful in breeding programs. While selection for cows emitting lower amounts of MIR predicted CH4 (g/d) would have little effect on milk production traits, selection on MIR predicted CH4 (g/kg of FPCM) would decrease FPCM, fat and protein yields. These genetic parameters of CH4 indicator traits might be entry point for selection that accounts mitigation of CH4 from dairy farming. Table 1. Heritability (diagonal), phenotypic (below the diagonal) and genetic (above the diagonal) correlations between MIR predicted CH4 and production traits Traits MIR CH4 (g/d) MIR CH4 ((g/kg of FPCM) FPCM Fat yield Protein yield MIR CH4 (g/d) 0.11 0.42 0.03 0.19 0.04 MIR CH4 (g/kg of FPCM)0.59 0.18 -0.83 -0.72 -0.77 FPCM -0.02 -0.65 0.20 0.95 0.91 Fat yield 0.01 -0.58 0.76 0.22 0.70 Protein yield -0.01 -0.61 0.78 0.69 0.20 [less ▲]

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See detailPhenotyping of robustness and milk quality
Berry, D.P.; McParland, S.; Bastin, Catherine ULg et al

in Advances in Animal Biosciences (2013), 4(3), 600-605

A phenotype describes the outcome of the interacting development between the genotype of an individual and its specific environment throughout life. Animal breeding currently exploits large data sets of ... [more ▼]

A phenotype describes the outcome of the interacting development between the genotype of an individual and its specific environment throughout life. Animal breeding currently exploits large data sets of phenotypic and pedigree information to estimate the genetic merit of animals. Here we describe rapid, low-cost phenomic tools for dairy cattle. We give particular emphasis to infrared spectroscopy of milk because the necessary spectral data are already routinely available on milk samples from individual cows and herds, and therefore the operational cost of implementing such a phenotyping strategy is minimal. The accuracy of predicting milk quality traits from mid-infrared spectroscopy (MIR) analysis of milk, although dependent on the trait under investigation, is particularly promising for differentiating between good and poor-quality dairy products. Many fatty acid concentrations in milk, and in particular saturated fatty acid content, can be very accurately predicted from milk MIR. These results have been confirmed in many international populations. Albeit from only two studied populations investigated in the RobustMilk project, milk MIR analysis also appears to be a reasonable predictor of cow energy balance, a measure of animal robustness; high accuracy of prediction was not expected as the gold standard method of measuring energy balance in those populations was likely to contain error. Because phenotypes predicted from milk MIR are available routinely from milk testing, longitudinal data analyses could be useful to identify animals of superior genetic merit for milk quality and robustness, as well as for monitoring changes in milk quality and robustness because of management, while simultaneously accounting for the genetic merit of the animals. These sources of information can be very valuable input parameters in decision-support tools for both milk producers and processors. [less ▲]

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See detailImplementation in breeding programmes
Coffey, M.P.; McParland, S.; Bastin, Catherine ULg et al

in Advances in Animal Biosciences (2013), 4(3), 626-630

Genetic improvement is easy when selecting for one heritable and well-recorded trait at a time. Many industrialised national dairy herds have overall breeding indices that incorporate a range of traits ... [more ▼]

Genetic improvement is easy when selecting for one heritable and well-recorded trait at a time. Many industrialised national dairy herds have overall breeding indices that incorporate a range of traits balanced by their known or estimated economic value. Future breeding goals will contain more non-production traits and, in the context of this paper, traits associated with human health and cow robustness. The definition of Robustness and the traits used to predict it are currently fluid; however, the use of mid-infrared reflectance spectroscopic analysis of milk will help to create new phenotypes on a large scale that can be used to improve the human health characteristics of milk and the robustness of cows producing it. This paper describes the state-of-the-art in breeding strategies that include animal robustness (mainly energy status) and milk quality (as described by milk fatty acid profile), with particular emphasis on the research results generated by the FP7-funded RobustMilk project [less ▲]

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See detailGenetics of body energy status of Holstein cows predicted by mid-infrared spectrometry
Bastin, Catherine ULg; Berry, D.; Gengler, Nicolas ULg et al

in Journal of Dairy Science (2013), 96(E-Suppl. 1),

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See detailRelationship between milk composition estimated from mid infrared and methane emissions in dairy cows
Kandel, Purna Bhadra ULg; Vanlierde, Amélie ULg; Dehareng, F et al

Scientific conference (2012, December 03)

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See detailGenome-wide association study for milk fatty acid composition using cow versus bull data
Bastin, Catherine ULg; Gengler, Nicolas ULg; Soyeurt, Hélène ULg et al

in Book of Astracts of the 63rd Annual Meeting of the European Federation of Animal Science (2012)

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