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See detailConsequences of Selection for Environmental Impact Trait in Dairy Cows
Kandel, Purna Bhadra ULg; Vanderick, Sylvie ULg; Vanrobays, Marie-Laure ULg et al

Scientific conference (2014, February 07)

Environmental sustainability is gaining importance in dairy industry due to enteric methane (CH4) emission from dairy cows. We predicted CH4 indicator trait (CH4 intensity: CH4 g/kg of milk) from Mid ... [more ▼]

Environmental sustainability is gaining importance in dairy industry due to enteric methane (CH4) emission from dairy cows. We predicted CH4 indicator trait (CH4 intensity: CH4 g/kg of milk) from Mid-infrared spectra of milk samples and recorded milk yield. Genetic correlations between CH4 intensity and milk production traits were estimated on Holstein cows from correlations of estimated breeding values. Genetic correlations between CH4 intensity and milk yield (MY) was -0.67, fat yield (FY) -0.13, protein yield (PY) -0.46, somatic cell score (SCS) 0.02, longevity -0.07, fertility 0.31, body condition score (BCS) 0.27 and average of confirmation traits -0.23. Currently, there is no CH4 emission trait in genetic evaluation selection index. Putting an hypothetical 25% weight on CH4 intensity on current Walloon genetic evaluation selection index and proportional reduction on other selection traits, the response to selection will be reduction of CH4 emission intensity by 24%, increase in MY by 30%, FY by 17%, PY by 29%, SCS by -15%, longevity by 24%, fertility by -11%, BCS by -13% and conformation traits by 24%. In conclusion, introduction of environmental traits in current selection index will affect selection responses. As there is no economic value of these traits presently alternative methods like putting correlated traits with clear economic value (e.g. feed efficiency) in the selection objective could generate appropriate index weights. [less ▲]

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See detailGenetic effects of heat stress on milk yield and MIR predicted methane emissions of Holstein cows
Vanrobays, Marie-Laure ULg; Gengler, Nicolas ULg; Kandel, Purna Bhadra ULg et al

Conference (2013, August 28)

Dairy cows both contribute to and are affected by climate change. Breeding for heat tolerance and reduced methane (CH4) emissions is a key requirement to mitigate interactions between dairy cows and ... [more ▼]

Dairy cows both contribute to and are affected by climate change. Breeding for heat tolerance and reduced methane (CH4) emissions is a key requirement to mitigate interactions between dairy cows and climate change. This study was aimed to estimate genetic variation of milk yield and CH4 emissions over the whole trajectory of temperature humidity index (THI) using a reaction norm approach. A total of 257,635 milk test-day (TD) records and milk mid-infrared (MIR) spectra from 51,782 Holstein cows were used. Data were collected between January 2007 and December 2010 in 983 herds by the Walloon Breeding Association (Ciney, Belgium). The calibration equation developed by Vanlierde et al. (Abstract submitted to EAAP 2013; R² of cross-validation=0.70) was applied on the spectral data in order to predict CH4 emissions values (g CH4/d). These values were divided by fat and protein corrected milk yield (FPCM) defining a new CH4 trait (g CH4/kg of FPCM). Daily THI values were calculated using the mean of daily values of dry bulb temperature and relative humidity from meteorological data. Mean daily THI of the previous 3 days before each TD record was used as the THI of reference for that TD. Bivariate (milk yield and a CH4 trait) random regression TD mixed models with random linear regressions on THI values were used. Estimated average daily heritability for milk yield was 0.17 and decreased slightly at extreme THI values. However, heritabilities of MIR CH4 traits increased as THI values increase: from 0.10 (THI=28) to 0.14 (THI=75) for MIR CH4 (g/d) and from 0.14 (THI=28) to 0.21 (THI=75) for MIR CH4 (g/kg of FCPM). Genetic correlations between milk yield and MIR CH4 (g/d) ranged from -0.09 (THI=28) to -0.12 (THI=75) and those between milk yield and MIR CH4 (g/kg of FPCM) from -0.75 (THI=28) to -0.71 (THI=75). These results showed that milk production and CH4 emissions of dairy cows seemed to be influenced by THI. [less ▲]

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See detailHerd-test-day variability of methane emissions predicted from milk MIR spectra in Holstein cows
Vanrobays, Marie-Laure ULg; Kandel, Purna Bhadra ULg; Soyeurt, Hélène ULg et al

Poster (2013, August 26)

The aim of this study was to estimate the herd-test-day (HTD) effect on milk yield, fat and protein content, and methane (CH4) emissions of Walloon Holstein first-parity cows. A total of 412,520 test-day ... [more ▼]

The aim of this study was to estimate the herd-test-day (HTD) effect on milk yield, fat and protein content, and methane (CH4) emissions of Walloon Holstein first-parity cows. A total of 412,520 test-day records and milk mid-infrared (MIR) spectra of 69,223 cows in 1,104 herds were included in the data set. The prediction equation developed by Vanlierde et al. (Abstract submitted to EAAP 2013; R² of cross-validation=0.70) was applied on the recorded spectral data to predict CH4 emissions (g/d). Daily CH4 emissions expressed in g/kg of milk were computed by dividing CH4 emissions (g/d) by daily milk yield of cows. Several bivariate (a CH4 trait with a production trait) random regression test-day models including HTD and classes of days in milk and age at calving as fixed effects and permanent environment and genetic as random effects were used. HTD solutions of studied traits obtained from these models were studied and presented large deviations (CV=17.54%, 8.93%, 4.68%, 15.51%, and 23.18% for milk yield, fat and protein content, MIR CH4 (g/d), and MIR CH4 (g/kg of milk), respectively) indicating differences among herds, especially for milk yield and CH4 traits. HTD means per month of milk yield and fat and protein contents presented similar patterns within year. The maximum of monthly HTD means corresponded to the spring (pastern release) for milk yield and to the winter for fat and protein contents. The minimum corresponded to the month of November for milk yield and to the summer for the other traits. For MIR CH4 (g/d), monthly HTD means showed similar patterns as fat and protein content within year. MIR CH4 (g/kg of milk) presented maximum values of monthly HTD means in November and minimum values in May. Finally, results of this study showed that HTD effects on milk production traits and on MIR CH4 emissions varied through herds and seasons. [less ▲]

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See detailGenetic effects of heat stress on milk yield and MIR predicted methane emissions of Holstein cows
Vanrobays, Marie-Laure ULg; Gengler, Nicolas ULg; Kandel, Purna Bhadra ULg et al

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

Dairy cows both contribute to and are affected by climate change. Breeding for heat tolerance and reduced methane (CH4) emissions is a key requirement to mitigate interactions between dairy cows and ... [more ▼]

Dairy cows both contribute to and are affected by climate change. Breeding for heat tolerance and reduced methane (CH4) emissions is a key requirement to mitigate interactions between dairy cows and climate change. This study was aimed to estimate genetic variation of milk yield and CH4 emissions over the whole trajectory of temperature humidity index (THI) using a reaction norm approach. A total of 257,635 milk test-day (TD) records and milk mid-infrared (MIR) spectra from 51,782 Holstein cows were used. Data were collected between January 2007 and December 2010 in 983 herds by the Walloon Breeding Association (Ciney, Belgium). The calibration equation developed by Vanlierde et al. (Abstract submitted to EAAP 2013; R² of cross-validation=0.70) was applied on the spectral data in order to predict CH4 emissions values (g CH4/d). These values were divided by fat and protein corrected milk yield (FPCM) defining a new CH4 trait (g CH4/kg of FPCM). Daily THI values were calculated using the mean of daily values of dry bulb temperature and relative humidity from meteorological data. Mean daily THI of the previous 3 days before each TD record was used as the THI of reference for that TD. Bivariate (milk yield and a CH4 trait) random regression TD mixed models with random linear regressions on THI values were used. Estimated average daily heritability for milk yield was 0.17 and decreased slightly at extreme THI values. However, heritabilities of MIR CH4 traits increased as THI values increase: from 0.10 (THI=28) to 0.14 (THI=75) for MIR CH4 (g/d) and from 0.14 (THI=28) to 0.21 (THI=75) for MIR CH4 (g/kg of FCPM). Genetic correlations between milk yield and MIR CH4 (g/d) ranged from -0.09 (THI=28) to -0.12 (THI=75) and those between milk yield and MIR CH4 (g/kg of FPCM) from -0.75 (THI=28) to -0.71 (THI=75). These results showed that milk production and CH4 emissions of dairy cows seemed to be influenced by THI. [less ▲]

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See detailHerd-test-day variability of methane emissions predicted from milk MIR spectra in Holstein cows
Vanrobays, Marie-Laure ULg; Kandel, Purna Bhadra ULg; Soyeurt, Hélène ULg et al

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

The aim of this study was to estimate the herd-test-day (HTD) effect on milk yield, fat and protein content, and methane (CH4) emissions of Walloon Holstein first-parity cows. A total of 412,520 test-day ... [more ▼]

The aim of this study was to estimate the herd-test-day (HTD) effect on milk yield, fat and protein content, and methane (CH4) emissions of Walloon Holstein first-parity cows. A total of 412,520 test-day records and milk mid-infrared (MIR) spectra of 69,223 cows in 1,104 herds were included in the data set. The prediction equation developed by Vanlierde et al. (Abstract submitted to EAAP 2013; R² of cross-validation=0.70) was applied on the recorded spectral data to predict CH4 emissions (g/d). Daily CH4 emissions expressed in g/kg of milk were computed by dividing CH4 emissions (g/d) by daily milk yield of cows. Several bivariate (a CH4 trait with a production trait) random regression test-day models including HTD and classes of days in milk and age at calving as fixed effects and permanent environment and genetic as random effects were used. HTD solutions of studied traits obtained from these models were studied and presented large deviations (CV=17.54%, 8.93%, 4.68%, 15.51%, and 23.18% for milk yield, fat and protein content, MIR CH4 (g/d), and MIR CH4 (g/kg of milk), respectively) indicating differences among herds, especially for milk yield and CH4 traits. HTD means per month of milk yield and fat and protein contents presented similar patterns within year. The maximum of monthly HTD means corresponded to the spring (pastern release) for milk yield and to the winter for fat and protein contents. The minimum corresponded to the month of November for milk yield and to the summer for the other traits. For MIR CH4 (g/d), monthly HTD means showed similar patterns as fat and protein content within year. MIR CH4 (g/kg of milk) presented maximum values of monthly HTD means in November and minimum values in May. Finally, results of this study showed that HTD effects on milk production traits and on MIR CH4 emissions varied through herds and seasons. [less ▲]

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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 et al

Scientific conference (2013, March 25)

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

in Advances in Animal Biosciences (2013)

N/A

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

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

N/A

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

Scientific conference (2012, December 03)

N/A

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See detailEstimation of Myostatin gene effects on production traits and fatty acid contents in bovine milk
Vanrobays, Marie-Laure ULg; Bastin, Catherine ULg; Colinet, Frédéric ULg et al

in Book of Abstracts of the 63rd Annual Meeting of the European Federation of Animal Science (2012, August)

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See detailRelationships between methane emissions of dairy cattle and farm management.
Vanrobays, Marie-Laure ULg; Vanlierde, Amélie; Kandel, Purna Bhadra ULg et al

Poster (2012, February 10)

Livestock is considered as an important contributor to global methane emissions, predominately due to methanogenesis from ruminants. Moreover, these emissions also represent major losses of energy for ... [more ▼]

Livestock is considered as an important contributor to global methane emissions, predominately due to methanogenesis from ruminants. Moreover, these emissions also represent major losses of energy for dairy cows and therefore are linked to production efficiency. The on-going development of predictive equations (e.g., from milk composition) would allow to relate methane emissions to farm management (e.g., nutrition, environment) on a large scale in the Walloon Region of Belgium. Finally, by acquiring improved knowledge of these relationships, contributions to mitigate methane emissions could be based on an improved management of dairy herds. [less ▲]

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See detailInfluence de l'apport de différentes sources lipidiques en fin de gestation sur les résultats techniques des truies allaitantes et de leurs porcelets
Boudry, Christelle ULg; Vanrobays, Marie-Laure ULg; Devos, Sven

in IFIP; INRA (Eds.) 44èmes journées de la Recherche Porcine (2012, February 08)

From day 103 in gestation until farrowing, 4 successive groups of 15 sows received 70 g per day of one of the 3 following oils: 1) coconut oil (CO); 2) fish oil (FO) and 3) shark liver oil (SO). At day ... [more ▼]

From day 103 in gestation until farrowing, 4 successive groups of 15 sows received 70 g per day of one of the 3 following oils: 1) coconut oil (CO); 2) fish oil (FO) and 3) shark liver oil (SO). At day 102 of gestation, at farrowing and at weaning, backfat thickness of the sows was determined. The total number of piglets, the number of piglets born alive, stillborn and dead during the suckling period and the duration of gestation were measured. At birth as well as 24 hours, 14 days and 28 days (weaning) later, the piglets were weighed individually. Colostrum samples were collected at farrowing and 24 hours later for the determination of IgG and IgA contents and the fatty acid profile. Neither performance parameters of the sows nor IgG and IgA concentrations in colostrum were affected by the different dietary treatments. This was probably due to the short duration of trial period in which the oil supplements were administered. However, the colostral fatty acid profiles reflected very well the profiles of the supplemented oils. Furthermore, the different treatments affected the growth performances of the piglets. At weaning, the piglets from the FO treatment were respectively 263 and 329 g heavier than the piglets from the SO and CO treatments (P < 0.05). This corresponded to 4.2 % and 5.8 % increase of the ADG in comparison to the SO and CO treatments, respectively. These results show the importance of the fatty acid composition in colostrum on the performances of piglets and consequently the importance of the feeding of sows at the end of gestation. [less ▲]

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See detailEstimating myostatin gene effect on milk performance traits using estimated gene content for a large number of non-genotyped cows
Buske, Bernd; Vanrobays, Marie-Laure ULg; Szydlowski, Maciej et al

Conference (2011, August 29)

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See detailIs there value in maintaining small populations ? Example of the Dual-Purpose Belgian Blue breed.
Gengler, Nicolas ULg; Soyeurt, Hélène ULg; Bastin, Catherine ULg et al

Conference (2011, July 13)

Current status of thinking on genomic selection in dairy cattle is mostly major breed centric (e.g., Holstein) and only for traditional traits (e.g., milk yields). Once you depart from this, it becomes ... [more ▼]

Current status of thinking on genomic selection in dairy cattle is mostly major breed centric (e.g., Holstein) and only for traditional traits (e.g., milk yields). Once you depart from this, it becomes obvious that different, often related, issues appear (e.g., lack of large training populations, need for expensive recording of new phenotypes). Also, there is an urgent need to rethink issues that are important for sustainability of dairy production (e.g., added value foods, animal robustness). In this context, small populations (breeds/lines) could represent a potential source of extra information to justify their maintenance. As marker densities increase, efficient dissection of different selection histories of divergent breeds or lines, potentially identifying pockets of unexploited variability will increase. A current example from the Belgian (Walloon) perspective is the Dual Purpose (DP) line of the Belgian Blue Breed (BBB), with presently around 4500 breeding females, for historical reason of which only 1500 have good pedigrees, and which is present in Belgium and northern France. Recent research, done on this line, showed its tendency to produce less saturated milk fat and to have better fertility. Results indicated that it could stay competitive in specific markets, especially because of largely increased meat value. Currently, the myostatin mutation is largely used for breeding purposes. To assess the genetic diversity of the breed, recently, over 200 genotypes (SNP50K) for nearly all breeding bulls of the last 20 years became available. HD genotypes should be available in the near future, also allowing to access selection history of this breed as being in between the 2 extreme breeds: Beef BBB (with which it shares a recent history) and Holstein-Friesian (which is related through its geographic proximity over centuries). Finally, genomic selection for DP-BBB will need to consider a single step type approach without the need of reference population and potentially relying heavily on SNP3K of cows, also with the objective to recreate relationships between animals of interest. [less ▲]

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