References of "Vanlierde, Amélie"
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See detailA simple method to predict methane emissions based on milk mid infrared spectra
Vanlierde, Amélie ULg; Dehareng, Frédéric; Froidmont, Eric et al

Poster (2016, October)

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See detailNovel innovative possibilities for the dairy industry opened by common format of FT-MIR instruments
Grelet, Clément ULg; Fernandez Pierna, Juan Antonio; Dardenne, Pierre et al

Poster (2016, October)

FT-MIR technology is worldwide used for fast and cost effective determination of major milk components. However, due to the different individual response of each instrument the potential of this ... [more ▼]

FT-MIR technology is worldwide used for fast and cost effective determination of major milk components. However, due to the different individual response of each instrument the potential of this technology is currently underexploited as new tools cannot be easily ported to other instruments. Recently a standardization method was developed in order to harmonize the spectral response format between instruments of different brands and models but also across time for each spectrometer. The method matches monthly the infrared response of all spectrometers on the response of a reference instrument, making all machines talking a common language. The objective is to allow the creation and the use of common, new and innovative concepts by pooling resources and sharing data. Using this method, new tools for analysis of milk quality and milk technological properties have been created and shared within the network, as fatty acids and minerals predictions or milk coagulation properties. New concepts requiring a common spectral format have been developed like the untargeted detection of milk contaminant and abnormal milk or the determination of milk geographic origin. Models in relation with the status of the dairy cow were also created and shared as to predict ketosis, negative energy balance or methane emissions. Therefore models can be developed at one place and deployed within the entire network, in which 90 instruments are currently monthly standardized. [less ▲]

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See detailOptiMIR: Use of MIR spectra to predict multiple cow status as advisory tools for dairy farms
Grelet, Clément ULg; Gengler, Nicolas ULg; Bastin, Catherine et al

Conference (2016, August)

Considering the current increasing of herd size, there is a need for precise and rapid information on individual cow state. Mid infrared (MIR) technology is already used worldwide for milk analysis; it ... [more ▼]

Considering the current increasing of herd size, there is a need for precise and rapid information on individual cow state. Mid infrared (MIR) technology is already used worldwide for milk analysis; it allows rapid and cost effective determination of milk composition. The objective of OptiMIR project was to optimize the use of MIR spectra in order to produce indications on cow status thereby providing advisory tools to dairy farmers. Hence phenotypes of interest were collected in several countries and linked to MIR spectra. Since the OptiMIR network comprised 65 MIR instruments in 6 countries, standardisation of MIR data was necessary, allowing the collation of spectral databases and the use by all milk recording organizations (MRO) of the models developed. Using chemometric tools (like PLS regression), predictive models were developed to provide indicators on fine milk composition, on milk biomarkers of physiological imbalance, and directly on status of the cows. Equations predicting fine milk composition such as fatty acids and minerals were consolidated through the OptiMIR network, providing indirectly information on technological properties of milk and cow status. As biomarker of early physiological imbalance, an equation predicting citrate in milk was developed with good accuracy (R²cv=0.86); and as milk biomarkers of ketosis, BHB and acetone were calibrated with fair results (R²cv=0.63 and 0.67 respectively). Direct classification of spectra regarding low vs high risk of ketosis was also performed (84.5% sensitivity and 84.2% specificity). Direct regressions were realized for various negative energy balance criteria (r from 0.43 to 0.57) and enteric methane (R²cv=0.7). All equations are available to be used by MRO on field and converted into advisory tools for the dairy sector. [less ▲]

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See detailOn the use of novel milk phenotypes as predictors of difficult-to-record traits in breeding programs
Bastin, Catherine ULg; Colinet, Frédéric ULg; Dehareng, Frédéric et al

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

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See detailQuality Assurance for new analytical parameters, Optimir standardisation of MIR instruments
Grelet, Clément ULg; Fernandez Pierna, Juan; Dardenne, Pierre et al

Conference (2015, June 10)

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See detailStandardisation of milk MIR spectra, Development of common MIR equations
Grelet, Clément ULg; Fernandez Pierna, Juan Antonio; Dardenne, Pierre et al

Conference (2015)

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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 detailNew tools for the dairy sector based on MIR and NIR spectroscopy
Grelet, Clément ULg; Dehareng, Frédéric; Vanlierde, Amélie ULg et al

Conference (2013, November 05)

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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 detailDosage du méthane dans les gaz produits par les bovins : évaluation de la méthode d’analyse.
Romnée, Jean-Michel; Vanlierde, Amélie ULg; Surdu, Irina et al

Poster (2013, June)

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See detailElevage et gaz à effet de serre : le bilan des émissions de l'animal à la filière
Dumortier, Pierre ULg; Rabier, Fabienne; Beckers, Yves ULg et al

Scientific conference (2013, February 20)

Detailed reference viewed: 47 (11 ULg)
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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 (2013, February 07)

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 detailDevelopment of a method to predict individual enteric methane emissions from cows based on milk mid-infrared spectra
Vanlierde, Amélie ULg; Froidmont, Eric; Soyeurt, Hélène ULg et al

in Hassouna, Mélynda; Guingand, Nadine (Eds.) Emissions of Gas and Dust from Livestock (2013)

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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 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)

N/A

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See detailMid-infrared prediction of milk titratable acidity and its genetic variability in first-parity cows
Colinet, Frédéric ULg; Vanlierde, Amélie ULg; Vanden Bossche, sandrine ULg et al

Conference (2012, August 27)

Coagulation of milkhas a direct effect on cheese yield. Among several parameters, titratable acidity of milk (TA) influences all the phases of milk coagulation. In order to study the genetic variability ... [more ▼]

Coagulation of milkhas a direct effect on cheese yield. Among several parameters, titratable acidity of milk (TA) influences all the phases of milk coagulation. In order to study the genetic variability of this trait on a large scale, mid-infrared (MIR) chemometric methods were used to predict TA. A total of 507 milk samples collected in the Walloon Region of Belgium from individual cows were analyzed using a MIR spectrometer. TA was recorded as Dornic degree. An equation to predict TA from milk MIR spectrum was developed using partial least squared regression after a first derivative pre-treatment applied to the spectra to correct the baseline drift. During the calibration process, 45 outliers were detected and removed from the calibration set. The TA mean of the final calibration set was 16.62 (standard deviation (SD) = 1.80). The coefficient of determination (R²) was 0.82 for the calibration with a standard error (SE) of 0.76. A cross-validation (cv) was performed (R²cv = 0.81 with SEcv = 0.80). This equation was then applied on the spectral database generated during the Walloon routine milk recording. The variances components were estimated by REML using single-trait random regression animal test-day model. The dataset used included 33,717 records from 9,191 Holstein first-parity cows; the TA mean was 17.05 (SD = 1.35) and TA ranged from 12.83 to 20.87. Estimated daily heritabilities ranged from 0.43 at 5th day in milk to 0.59 at 215th day in milk indicating potential of selection. Further research will study phenotypic and genetic correlations between TA and milk production traits. [less ▲]

Detailed reference viewed: 117 (40 ULg)