References of "McParland, S"
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See detailCan chamber and SF6 CH4 measurements be combined in a model to predict CH4 from milk MIR spectra?
Vanlierde, Amélie ULiege; Vanrobays, Marie-Laure ULiege; Dehareng, F. et al

in Book of Abstracts of the 66th Annual Meeting of the European Federation of Animal Science (2015, September 02)

Methane (CH4) naturally produced by dairy cows during ruminal fermentation is an important greenhouse gas. An equation based on 446 reference data has been developed to predict easily individual CH4 ... [more ▼]

Methane (CH4) naturally produced by dairy cows during ruminal fermentation is an important greenhouse gas. An equation based on 446 reference data has been developed to predict easily individual CH4 emissions from milk mid-infrared (MIR) spectra. This equation was based on CH4 data measured exclusively with the SF6 technique on 146 distinct Holstein, Jersey and Holstein×Jersey cows. As breeds, managements, diets, etc. are different from one geographical area to another, representative reference data have to be included in the calibration set before applying this equation in a location. However, the local CH4 data needed are likely to be collected with different techniques (chambers, GreenFeed, etc.) depending on the research team and its equipment. A first study has therefore been conducted (1) to test the performance of the actual equation on data obtained in open-circuit chambers and (2) to analyse the impact of the inclusion of these data in the calibration set. A total of 60 chamber measurements of CH4 and milk MIR spectra were obtained from 30 lactating Brown-Swiss cows. The correlation between actually measured and predicted CH4 (C1) was 0.48. This result is in the range of expectations given the R2c of the equation (0.75), the correlation known between SF6 and chamber methods (~0.80), and the breed and diet differing between calibration sets. The correlation was about 0.70 after the inclusion of the chamber data (and so the inherent variability) in the calibration set (C2). As chambers are known as the gold standard method, the C1 observed confirms the relevance of using milk MIR technique. Moreover, C2 is very encouraging regarding the possibility to include data coming from chambers into the existing CH4 equation. [less ▲]

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See detailImprovement of a method to predict individual enteric methane emission of cows from milk MIR spectra
Vanlierde, Amélie ULiege; Dehareng, F.; Froidmont, E. et al

in Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science (2013, August 27)

Besides being a greenhouse gas, enteric methane (CH produced by ruminants during rumination is also associated with the loss of 6 to 12% of gross energy intake. Mitigation of those emissions could be ... [more ▼]

Besides being a greenhouse gas, enteric methane (CH produced by ruminants during rumination is also associated with the loss of 6 to 12% of gross energy intake. Mitigation of those emissions could be based on combined actions on diet, herd management and animal genetics. In order to investigate easily the relationship between these parameters and the CH4 emissions on a large scale, an equation to predict individual enteric CH4 emissions from the whole individual milk mid-infrared (MIR) spectra was developed. To build this equation a total of 452 CHA reference were obtained using the sF6 method. on Jersey, Holstein and Holstein-Jersey crossbred cows. In parallel a 40 ml sample of individual milk was collected at each milking (morning and evening) and was analyzed using MIR spectrometry Then, these spectra were averaged proportionally function of the milk production to have one spectrum for one CH4 ment. Data were collected on 146 different cows (63, 36, 18, 29 a in parity one to fourt, respectively) receiving different diets. The calibration model was developed using Foss wINISI 4 software on spectral data after applying the first derivative and using pLs regression. The CH4 emission prediction (g showed a calibration coefficient of determination (R2c) of 0.76, a cross-validation coefficient of determination (R2cv) and the standard error of calibration was of 62 g/day. Results are very promising and showed the possibility to predict the eructed CH4 from the milk spectra. relationship between measurements and predictions is linear and thereby allowing the distinction between low and high emitting cows. [less ▲]

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See detailPrediction of the individual enteric methane emission of dairy cows from milk mid-infrared spectra
Vanlierde, Amélie ULiege; Dehareng, F.; Froidmont, E. et al

in Advances in Animal Biosciences (2013, June)

The livestock sector is considered the largest producer of methane (CH4) from anthropogenic sources, world wide contributing 37% of emissions (FAO, 2006). An important step to study and develop mitigation ... [more ▼]

The livestock sector is considered the largest producer of methane (CH4) from anthropogenic sources, world wide contributing 37% of emissions (FAO, 2006). An important step to study and develop mitigation methods for livestock emissions is to be able to measure them on a large scale. However, it is difficult to obtain a large number of individual CH4 measurements with the currently available techniques (chambers or SF6). The aim of this study was to develop a high throughput tool for determination of CH4 emissions from dairy cows. Anaerobic fermentation of food in the reticulorumen is the basis of enteric CH4 production. End-products of that enteric fermentation can be found in the milk (e.g., volatile fatty acids). Therefore individual enteric CH4 emissions could be quantified from whole milk mid-infrared (MIR) spectra which reflect milk composition and can be obtained at low cost (e.g., national milk recording). Prediction equations of individual CH4 emissions (determined using the SF6 method) from milk MIR spectra have been established (Dehareng et al., 2012; Soyeurt et al., 2013). The results presented here are the improvement of this methodology by using a multiple breed and country approach. [less ▲]

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See detailAvancées dans le développement d’une équation permettant de prédire les émissions de méthane des vaches laitières grâce aux spectres moyens infrarouges du lait Progress in the development of an equation for predicting methane emission from dairy cows using milk mid-infrared spectra
Vanlierde, Amélie ULiege; DEHARENG, F.; FROIDMONT, E. et al

in Rencontres autour des Recherches sur les Ruminants (2013), 20

Le secteur de l'élevage contribue à 37% des émissions de méthane (CH4) d’origine anthropique dans le monde (Steinfeld et al., 2006). Afin de pouvoir étudier ces émissions et ainsi développer des méthodes ... [more ▼]

Le secteur de l'élevage contribue à 37% des émissions de méthane (CH4) d’origine anthropique dans le monde (Steinfeld et al., 2006). Afin de pouvoir étudier ces émissions et ainsi développer des méthodes permettant de les réduire il est nécessaire de pouvoir les mesurer à grande échelle. Dans cette optique, des équations permettant de prédire les émissions individuelles de CH4 directement à partir du spectre laitier mesuré en moyen infrarouge (MIR) ont été établies (Dehareng et al., 2012; Soyeurt et al., 2013). Les avancées de ces équations présentent désormais une approche internationale et multi-race. [less ▲]

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

in Advances in Animal Biosciences (2013), 4(2),

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See detailGenetic parameters for methane emissions predicted from milk mid-infrared spectra in dairy cows
Kandel, Purna Bhadra ULiege; Vanrobays, Marie-Laure ULiege; Vanlierde, Amélie ULiege 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 ULiege 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 ULiege 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 ULiege; Berry, D.; Gengler, Nicolas ULiege 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 ULiege; Vanlierde, Amélie ULiege; 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 ULiege; Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege et al

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

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