References of "Soyeurt, Hélène"
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See detailHow to use ICT to help students to gain in confidence and efficiency in an algorithmic and computer programming course ?
Colaux, Catherine ULg; Soyeurt, Hélène ULg

in INTED2015 Proceedings (2015)

Algorithmic and computer programming in the bachelor’s degree is a course that demands a large involvement of students in performing non-standard exercises. This practical aspect is incompatible with ... [more ▼]

Algorithmic and computer programming in the bachelor’s degree is a course that demands a large involvement of students in performing non-standard exercises. This practical aspect is incompatible with classical ex cathedra course. It is the reason why we implement a blended learning approach much more responsive to students in a bachelor class of Bio Engineering at the Gembloux Agro Bio Tech Faculty (University of Liege, Belgium). This course alternates theoretical classes, take-home lessons with the help of online pedagogical resources and video and debriefing sessions where students have the possibility to benefit from teacher’ support. In doing so, the students are better prepared for the examination. They also gain in confidence and motivation. The teacher no longer simply transmits the knowledge but assists the students in their reflection process and their mastering of programming tools [less ▲]

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See detailGenetics of beef and milk fatty acid composition
Soyeurt, Hélène ULg; Beitz, Donald

in Garrick, D; Ruvinsky, A (Eds.) The genetics of cattle (2014)

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See detailInnovative lactation stage specific prediction of CH4 from milk MIR spectra
Vanlierde, Amélie; Vanrobays, Marie-Laure ULg; Dehareng, Frédéric et al

Conference (2014, August 28)

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See detailUsing milk spectral data for large-scale phenotypes linked to mitigation and efficiency
Soyeurt, Hélène ULg; Vanlierde, Amélie; Vanrobays, Marie-Laure ULg et al

Conference (2014, August 26)

Even if producing milk efficiently has always been a major concern for producers, the direct environmental impact of their cows is becoming a novel one. Traits linked to this issue were identified as ... [more ▼]

Even if producing milk efficiently has always been a major concern for producers, the direct environmental impact of their cows is becoming a novel one. Traits linked to this issue were identified as methane emission (CH4), dry matter intake (DMI) and feed efficiency (FE); however they are available on a small scale. Researches showed that CH4 could be predicted from milk mid-infrared (MIR) spectra, allowing large-scale recording at low cost. The main objective of this study was to show, using a modelling approach, that DMI and FE could be derived from milk MIR spectra. For that, knowledge of body weight (BW) is required; however it was unknown in this study. Derived procedure was based on milk yield and composition, MIR CH4, and modelled standard animal requirements, allowing the prediction of expected BW. An external validation was conducted based on 91 actual records. 95% confidence limit for the difference ranged between -0.66 and 18.84 kg for BW, from -0.02 to 0.26 kg/day for DMI, and from -0.02 to 0.002 kg of fat corrected milk/kg DM for FE. Root mean square errors were 39.66 kg, 0.56 kg/d, and 0.03 kg/DM for the 3 studied traits. P-value for the t-test was not significant for BW and DMI. This suggests the possibility to obtain expected BW and therefore DMI from MIR spectra. Single trait animal test-day models used 1,291,850 records to assess the variability of studied traits. Significant variations were observed for the lactation stage, parity, genetics, and age. These findings were in agreement with the literature except for early lactation. This suggests in conclusion that the MIR information gave similar results for DMI and CH4 for the major part of lactation. The use of this novel method to predict expected BW offers new possibilities interesting for the development of genomic and genetic tools. [less ▲]

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See detailCreation of universal MIR calibration by standardization of milk spectra: example of fatty acids
Grelet, Clément ULg; Fernandez Pierna, Juan; Soyeurt, Hélène ULg et al

Poster (2014, August 25)

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See detailCreation of universal MIR calibration by standardization of milk spectra: example of fatty acids
Grelet, Clément ULg; Fernandez Pierna, Juan; Soyeurt, Hélène ULg et al

in Book of abstracts of the 65th annual meeting of the European Federation of Animal Science (2014, August)

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See detailUsing milk spectral data for large-scale phenotypes linked to mitigation and efficiency
Soyeurt, Hélène ULg; Vanlierde, Amélie; Vanrobays, Marie-Laure ULg et al

in Book of abstracts of the 65th annual meeting of the European Federation of Animal Science (2014, August)

Even if producing milk efficiently has always been a major concern for producers, the direct environmental impact of their cows is becoming a novel one. Traits linked to this issue were identified as ... [more ▼]

Even if producing milk efficiently has always been a major concern for producers, the direct environmental impact of their cows is becoming a novel one. Traits linked to this issue were identified as methane emission (CH4), dry matter intake (DMI) and feed efficiency (FE); however they are available on a small scale. Researches showed that CH4 could be predicted from milk mid-infrared (MIR) spectra, allowing large-scale recording at low cost. The main objective of this study was to show, using a modelling approach, that DMI and FE could be derived from milk MIR spectra. For that, knowledge of body weight (BW) is required; however it was unknown in this study. Derived procedure was based on milk yield and composition, MIR CH4, and modelled standard animal requirements, allowing the prediction of expected BW. An external validation was conducted based on 91 actual records. 95% confidence limit for the difference ranged between -0.66 and 18.84 kg for BW, from -0.02 to 0.26 kg/day for DMI, and from -0.02 to 0.002 kg of fat corrected milk/kg DM for FE. Root mean square errors were 39.66 kg, 0.56 kg/d, and 0.03 kg/DM for the 3 studied traits. P-value for the t-test was not significant for BW and DMI. This suggests the possibility to obtain expected BW and therefore DMI from MIR spectra. Single trait animal test-day models used 1,291,850 records to assess the variability of studied traits. Significant variations were observed for the lactation stage, parity, genetics, and age. These findings were in agreement with the literature except for early lactation. This suggests in conclusion that the MIR information gave similar results for DMI and CH4 for the major part of lactation. The use of this novel method to predict expected BW offers new possibilities interesting for the development of genomic and genetic tools. [less ▲]

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See detailInnovative lactation stage specific prediction of CH4 from milk MIR spectra
Vanlierde, Amélie; Vanrobays, Marie-Laure ULg; Dehareng, Frédéric et al

in Book of abstracts of the 65th annual meeting of the European Federation of Animal Science (2014, August)

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

in Proceedings, 10th World Congress of Genetics Applied to Livestock Production (2014, August)

Genetic selection programs aiming to mitigate methane (CH4) emissions require the estimation of genetic correlations with other production and economical traits and predicted selection response. CH4 ... [more ▼]

Genetic selection programs aiming to mitigate methane (CH4) emissions require the estimation of genetic correlations with other production and economical traits and predicted selection response. CH4 intensity was predicted from Mid-infrared spectra of milk samples from Holstein cows. Genetic correlations between CH4 intensity and milk yield (MY) was -0.68, fat yield (FY) -0.13, protein yield (PY) -0.47, somatic cell score (SCS) 0.07, longevity 0.05, fertility 0.31, body condition score (BCS) 0.17. Adding 25% relative weight on CH4 intensity to the current Walloon selection index, the response to selection would reduce CH4 intensity by 24%, increase MY by 30%, FY by 17%, PY by 29%, SCS by -14%, longevity by 24% but also reduce fertility by 11% and BCS by 13%. In conclusion, environmental traits can be added without jeopardizing production traits, but energy balance related traits have to be protected. [less ▲]

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See detailPhenotypic and genetic variability of methane emissions and milk fatty acid contents of Walloon Holstein dairy cows
Vanrobays, Marie-Laure ULg; Kandel, Purna Bhadra ULg; Soyeurt, Hélène ULg et al

Poster (2014, February 17)

There is a growing interest in reducing methane (CH4) emissions from enteric fermentation of dairy cows because these emissions contribute to climate change and represent losses of gross energy intake for ... [more ▼]

There is a growing interest in reducing methane (CH4) emissions from enteric fermentation of dairy cows because these emissions contribute to climate change and represent losses of gross energy intake for cows. Milk fatty acid (FA) profile is influenced by rumen fermentations. The aim of this study was to estimate phenotypic and genetic variability of enteric CH4 emissions of dairy cows and FA contents of milk. CH4 emissions (g/d) and milk FA contents are predicted from milk mid-infrared (MIR) spectra based on calibration equations developed by Vanlierde et al. (2013) and Soyeurt et al. (2011), respectively. Data included 161,681 records from 22,642 cows in 489 herds. Genetic parameters of MIR CH4 emissions and 7 groups of FA contents in milk were estimated for Walloon Holstein cows in first parity using bivariate (CH4 emission with a FA trait) random regression test-day models. Saturated FA presented higher genetic correlations with MIR CH4 production than unsaturated FA (0.25 vs. 0.10). Genetic correlations with MIR CH4 emissions were higher for short- (SC) and medium-chain (MC) FA (0.24 and 0.23, respectively) than for long-chain (LC) FA (0.13). Phenotypic correlations between MIR CH4 emissions and SC and MC FA were also higher than those between MIR CH4 emissions and LC FA (0.20 vs. -0.08). Finally, results showed that MIR milk FA profile and MIR CH4 emissions are correlated emphasizing indirect link between milk FA and CH4 emissions through rumen metabolism. [less ▲]

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See detailEstimating daily yield and content of major fatty acids from single milking
Arnould, Valérie ULg; Reding, Romain; Delvaux, Charles et al

Poster (2014, February 07)

Reducing the frequency of milk recording and the number of recorded samples per test-day could be a solution in order to reduce costs of official milk recording. However, fewer samples lead to a decrease ... [more ▼]

Reducing the frequency of milk recording and the number of recorded samples per test-day could be a solution in order to reduce costs of official milk recording. However, fewer samples lead to a decrease in the accuracy of predicted daily yields. Unfortunately, the current published equations use the milking interval that is often not available and/or reliable in practice. The first objective of this study was to propose models using easily available traits. Therefore the milking interval was replaced by a combination of data easily recorded by milk recording. The second objective of this study was to enlarge the previous investigations to milk fatty acids (FA) in order to propose a practical method for estimating accurate daily milk, fat and major FA yields from single milking. The fit goodness of proposed models was evaluated based on the correlation values between the estimated and observed daily yields in addition to the calculation of the mean square error. Obtained results are promising. Correlation values were comprised between 96.4% and 97.6% when daily yield were estimated from morning milking, and from 96.9% to 98.3% when daily yield were estimated from evening milking. The combination of records related to lactation stage, month of test, milk yield, and fat could replace the milking interval effect. Because of their simplicity, proposed models would be easy to implement. [less ▲]

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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 detailPotentiel d'utilisation de la spectrometrie moyen infrarouge pour prédire le rendement fromager du lait et étudier sa variabilité génétique
Colinet, Frédéric ULg; Troch, Thibault ULg; Abbas, O. et al

Conference (2013, December 04)

Providing a quick, reliable and cheap indication of the expected cheese yield for a milk sample by avoiding (empirical or theoretical) formulas based on previously determined milk constituents would be an ... [more ▼]

Providing a quick, reliable and cheap indication of the expected cheese yield for a milk sample by avoiding (empirical or theoretical) formulas based on previously determined milk constituents would be an economically valuable tool useful for farmers and the dairy industry. In order to study the genetic variability of cheese yield on a large scale, mid-infrared (MIR) chemometric methods were used to predict fresh or dry Individual Laboratory Cheese Yield (RdFF and RdFS, respectively). RdFF and RdFS were determined on a total of 258 milks samples also analyzed by a MIR spectrometer. Equations to predict RdFF and RdFS from milk MIR spectra were developed using partial least square regression (PLS) after first derivative pre-traitment applied to the spectra. The cross-validation coefficients of determination (R²cv) of the two equations were equal to 0.81 for the prediction of RdFF and 0.82 for the prediction RdFS. The ratios of performance to deviation (RPD) of the two equations were both equal to 2.3. Therefore, these results suggest a practical utility of these two equations, i.e. for genetic research. Both equations were applied on the spectral database generated during the Walloon routine milk recording. The variances components were estimated using univariate random regressions animal test-day model. The dataset included 51 537 predicted records from 7 870 Holstein first-parity cows. Estimated daily heritabilities ranged from 0.31 (at 5th day in milk (DIM)) to 0.59 (at 279th DIM) for RdFF and from 0.31 (at 5th DIM) to 0.57 (at 299th DIM) for RdFS. Those moderate to high daily heritabilities indicated potential of selection for both traits. [less ▲]

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See detailPotentiel d'utilisation de la spectrometrie moyen infrarouge pour prédire le rendement fromager du lait et étudier sa variabilité génétique
Colinet, Frédéric ULg; Troch, Thibault ULg; Abbas, O. et al

in 20èmes Rencontres Recherches Ruminants, Paris, les 4 et 5 Décembre 2013 (2013, December)

Fournir une indication rapide, fiable et bon marché du rendement fromager pour un lait donné, sans devoir appliquer des formules (empiriques ou théoriques) à partir des concentrations préalablement ... [more ▼]

Fournir une indication rapide, fiable et bon marché du rendement fromager pour un lait donné, sans devoir appliquer des formules (empiriques ou théoriques) à partir des concentrations préalablement déterminées pour différents constituants du lait, serait un outil utile et économiquement intéressant tant pour les éleveurs que pour l’industrie laitière. En vue d’étudier la variabilité génétique du rendement fromager à l’échelle du cheptel bovin wallon, des méthodes chimiométriques ont été utilisées afin de développer des équations de prédictions basées sur des spectres moyen infrarouge (MIR) pour les rendements fromagers déterminés en laboratoire et exprimés en frais (RdFF) ou en sec (RdFS). Ceux-ci ont été déterminés sur 258 échantillons de lait analysés en spectrométrie MIR. Les équations de prédiction à partir du spectre MIR du lait ont été développées en utilisant la régression des moindres carrés partiels (PLS) avec une validation croisée interne appliquée sur la dérivée première des spectres MIR. Les coefficients de détermination de validation croisée (R²cv) des équations étaient de 0,81 pour les prédictions du RdFF et de 0,82 pour les celles du RdFS. Les rapports des performances sur les variabilités (RPD) étaient égaux à 2,3. Ces résultats peuvent permettre d’envisager une bonne utilité pratique pour leur prédiction respective, notamment dans le cadre de recherches génétiques. Ces équations ont été appliquées sur la base de données spectrales générée dans le cadre du contrôle laitier wallon. Les composantes de la variance ont été estimées séparément pour le RdFF et le RdFS basées sur un modèle animal « contrôles élémentaires » utilisant des régressions aléatoires. Le jeu de données utilisé comportait 51 537 prédictions pour 7 870 vaches primipares Holstein. Les héritabilités journalières moyennes variaient entre 0,31 (au 5ème jour de lactation (JDL)) et 0,59 (au 279ème JDL) pour le RdFF et entre 0,31 (au 5ème JDL) et 0,57 (au 299ème JDL) pour le RdFS. Ces héritabilités journalières modérées à élevées ont indiqué le potentiel de sélection génétique pour ces deux caractères. [less ▲]

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See detailMid-infrared prediction of cheese yield from milk and its genetic variability in first-parity cows
Colinet, Frédéric ULg; Troch, Thibault ULg; Vanden, Bossche et al

Conference (2013, August 29)

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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 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 detailMid-infrared prediction of cheese yield from milk and its genetic variability in first-parity cows
Colinet, Frédéric ULg; Troch, Thibault ULg; Vanden Bossche, Sandrine et al

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

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