References of "Soyeurt, Hélène"
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See detailChanges throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra
Vanrobays, Marie-Laure ULiege; Bastin, Catherine; Vandenplas, J. et al

in Journal of Dairy Science (2016), 99(9), 7247-7260

Abstract The aim of this study was to estimate phenotypic and genetic correlations between methane production (Mp) and milk fatty acid contents of first-parity Walloon Holstein cows throughout lactation ... [more ▼]

Abstract The aim of this study was to estimate phenotypic and genetic correlations between methane production (Mp) and milk fatty acid contents of first-parity Walloon Holstein cows throughout lactation. Calibration equations predicting daily Mp (g/d) and milk fatty acid contents (g/100 dL of milk) were applied on milk mid-infrared spectra related to Walloon milk recording. A total of 241,236 predictions of Mp and milk fatty acids were used. These data were collected between 5 and 305 d in milk in 33,555 first-parity Holstein cows from 626 herds. Pedigree data included 109,975 animals. Bivariate (i.e., Mp and a fatty acid trait) random regression test-day models were developed to estimate phenotypic and genetic parameters of Mp and milk fatty acids. Individual short-chain fatty acids (SCFA) and groups of saturated fatty acids, SCFA, and medium-chain fatty acids showed positive phenotypic and genetic correlations with Mp (from 0.10 to 0.16 and from 0.23 to 0.30 for phenotypic and genetic correlations, respectively), whereas individual long-chain fatty acids (LCFA), and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed null to positive phenotypic and genetic correlations with Mp (from −0.03 to 0.13 and from −0.02 to 0.32 for phenotypic and genetic correlations, respectively). However, these correlations changed throughout lactation. First, de novo individual and group fatty acids (i.e., C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, SCFA group) showed low phenotypic or genetic correlations (or both) in early lactation and higher at the end of lactation. In contrast, phenotypic and genetic correlations between Mp and C16:0, which could be de novo synthetized or derived from blood lipids, were more stable during lactation. This fatty acid is the most abundant fatty acid of the saturated fatty acid and medium-chain fatty acid groups of which correlations with Mp showed the same pattern across lactation. Phenotypic and genetic correlations between Mp and C17:0 and C18:0 were low in early lactation and increased afterward. Phenotypic and genetic correlations between Mp and C18:1 cis-9 originating from the blood lipids were negative in early lactation and increased afterward to become null from 18 wk until the end of lactation. Correlations between Mp and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed a similar or intermediate pattern across lactation compared with fatty acids that compose them. Finally, these results indicate that correlations between Mp and milk fatty acids vary following lactation stage of the cow, a fact still often ignored when trying to predict Mp from milk fatty acid profile. [less ▲]

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

in Book of Abstracts of the 67th Annual Meeting of the European Federation of Animal Science (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 detailDevelopment of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network
Grelet, Clément ULiege; Bastin, Catherine ULiege; Gele, M et al

in Journal of Dairy Science (2016), 99(6), 4816-4825

To manage negative energy balance and ketosis in dairy farms, rapid and cost-effective detection is needed. Among the milk biomarkers that could be useful for this purpose, acetone and β-hydroxybutyrate ... [more ▼]

To manage negative energy balance and ketosis in dairy farms, rapid and cost-effective detection is needed. Among the milk biomarkers that could be useful for this purpose, acetone and β-hydroxybutyrate (BHB) have been proved as molecules of interest regarding ketosis and citrate was recently identified as an early indicator of negative energy balance. Because Fourier transform mid-infrared spectrometry can provide rapid and cost-effective predictions of milk composition, the objective of this study was to evaluate the ability of this technology to predict these biomarkers in milk. Milk samples were collected in commercial and experimental farms in Luxembourg, France, and Germany. Acetone, BHB, and citrate contents were determined by flow injection analysis. Milk mid-infrared spectra were recorded and standardized for all samples. After edits, a total of 548 samples were used in the calibration and validation data sets for acetone, 558 for BHB, and 506 for citrate. Acetone content ranged from 0.020 to 3.355 mmol/L with an average of 0.103 mmol/L; BHB content ranged from 0.045 to 1.596 mmol/L with an average of 0.215 mmol/L; and citrate content ranged from 3.88 to 16.12 mmol/L with an average of 9.04 mmol/L. Acetone and BHB contents were log-transformed and a part of the samples with low values was randomly excluded to approach a normal distribution. The 3 edited data sets were then randomly divided into a calibration data set (3/4 of the samples) and a validation data set (1/4 of the samples). Prediction equations were developed using partial least square regression. The coefficient of determination (R2) of cross-validation was 0.73 for acetone, 0.71 for BHB, and 0.90 for citrate with root mean square error of 0.248, 0.109, and 0.70 mmol/L, respectively. Finally, the external validation was performed and R2 obtained were 0.67 for acetone, 0.63 for BHB, and 0.86 for citrate, with respective root mean square error of validation of 0.196, 0.083, and 0.76 mmol/L. Although the practical usefulness of the equations developed should be further verified with other field data, results from this study demonstrated the potential of Fourier transform mid-infrared spectrometry to predict citrate content with good accuracy and to supply indicative contents of BHB and acetone in milk, thereby providing rapid and cost-effective tools to manage ketosis and negative energy balance in dairy farms. [less ▲]

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See detailCapitalizing on fine milk composition for breeding and management of dairy cows
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; Dehareng, Fréderic et al

in Journal of Dairy Science (2016), 99(5), 4071-4079

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of ... [more ▼]

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest. [less ▲]

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See detailImpact of wheat bran supplementation to sows on their milk quality, their performances and their progeny’s
Leblois, Julie ULiege; Bindelle, Jérôme ULiege; Dehareng, Frédéric et al

Conference (2016, April 15)

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See detailMilk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes
Vanlierde, Amélie ULiege; Vanrobays, Marie-Laure ULiege; Gengler, Nicolas ULiege et al

in Animal Production Science (2016), 56(3), 258-264

Mitigating the proportion of energy intake lost as methane could improve the sustainability and profitability of dairy production. As widespread measurement of methane emissions is precluded by current in ... [more ▼]

Mitigating the proportion of energy intake lost as methane could improve the sustainability and profitability of dairy production. As widespread measurement of methane emissions is precluded by current in vivo methods, the development of an easily measured proxy is desirable. An equation has been developed to predict methane from the mid-infrared (MIR) spectra of milk within routine milk-recording programs. The main goals of this study were to improve the prediction equation for methane emissions from milk MIR spectra and to illustrate its already available usefulness as a high throughput phenotypic screening tool. A total of 532 methane measurements considered as reference data (430 ± 129 g of methane/day) linked with milk MIR spectra were obtained from 165 cows using the SF6 technique. A first derivative was applied to the MIR spectra. Constant (P0), linear (P1) and quadratic (P2) modified Legendre polynomials were computed from each cows stage of lactation (days in milk), at the day of SF6 methane measurement. The calibration model was developed using a modified partial least-squares regression on first derivative MIR data points × P0, first derivative MIR data points × P1, and first derivative MIR data points × P2 as variables. The MIR-predicted methane emissions (g/day) showed a calibration coefficient of determination of 0.74, a cross-validation coefficient of determination of 0.70 and a standard error of calibration of 66 g/day. When applied to milk MIR spectra recorded in the Walloon Region of Belgium (≈2 000 000 records), this equation was useful to study lactational, annual, seasonal, and regional methane emissions. We conclude that milk MIR spectra has potential to be used to conduct high throughput screening of lactating dairy cattle for methane emissions. The data generated enable monitoring of methane emissions and production characteristics across and within herds. Milk MIR spectra could now be used for widespread screening of dairy herds in order to develop management and genetic selection tools to reduce methane emissions. [less ▲]

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See detailImprovement of robustness of forward selection of variables using split datasets: Mid-infrared methane equation
Soyeurt, Hélène ULiege; Vanlierde, Amélie ULiege; Grelet, Clément ULiege et al

in CHIMIOMETRIE XVII - Session 1: Sprectrométrie et prétraitement (2016, January 18)

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See detailHeritability of milk fat composition is considerably lower for Meuse-Rhine-Yssel compared to Holstein Friesian cattle
Maurice-Van Eijndhoven, Myrthe; Veerkamp, Roel; Soyeurt, Hélène ULiege et al

in Livestock Science (2015), 180

The aim of this paper is to identify differences in genetic variation of fatty acid (FA) composition in milk in different breeds. Data used included Meuse-Rhine-Yssel (MRY) and Holstein Friesian (HF ... [more ▼]

The aim of this paper is to identify differences in genetic variation of fatty acid (FA) composition in milk in different breeds. Data used included Meuse-Rhine-Yssel (MRY) and Holstein Friesian (HF) cattle breeds which were raised in the Netherlands. Both populations participated in the same milk recording system, but differed in selection history, where in the MRY there has been relatively very little emphasis on selection for high-input high-output production systems compared to HF. Differences in genetic variation were investigated by estimating breed specific additive genetic variances and heritabilities for FA contents in milk of MRY and HF. Mid Infrared Spectrometry spectra were used to predict total fat percentage and detailed FA contents in milk (14 individual FA and 14 groups of FA in g of fat/dL of milk). The dataset for MRY contained 2916 records from 2049 registered cows having at least 50% genes of MRY origin and the dataset used for HF contained 155,319 records from 96,315 registered cows having at least 50% genes of HF origin. Variance components of individual FA content in milk for the different breeds were estimated using a single trait animal model. Additive genetic variances for FA produced through de novo synthesis (short chain FA, C12:0, C14:0, and partly C16:0), C14:1 c-9 and C16:1 c-9 were significantly higher (. P<0.001) for HF compared to MRY. Heritabilities of the individual FA, C4:0 to C18:0, for HF ranged from 0.28 to 0.52 and for MRY from 0.17 to 0.34. Heritabilities of the individual C18 unsaturated FA for HF ranged from 0.11 to 0.34 and for MRY from 0.10 to 0.26. Although the mean content in milk for the FA C18:2 c-9, t-11 was low in both breeds, the additive genetic variance in our dataset was significantly higher for MRY (P<0.05) compared to HF. Heritabilities of the groups of FA for HF ranged from 0.19 to 0.53 and for MRY from 0.11 to 0.28. For the majority of the FA, the additive genetic variances for HF were significantly higher compared to MRY, except for most of the poly-unsaturated FA. The results for the poly-unsaturated FA, however, may be affected by the lower accuracy of the predictions for these FA. In conclusion, our results show that the HF breed has substantially larger genetic variance for most FA compared to MRY. © 2015 Elsevier B.V. [less ▲]

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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 detailHot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra
Vanlierde, Amélie ULiege; Vanrobays, Marie-Laure ULiege; Dehareng, Frédéric et al

in Journal of Dairy Science (2015), 98(8), 5740-5747

The main goal of this study was to develop, apply, and validate a new method to predict an indicator for CH4 eructed by dairy cows using milk mid-infrared (MIR) spectra. A novel feature of this model was ... [more ▼]

The main goal of this study was to develop, apply, and validate a new method to predict an indicator for CH4 eructed by dairy cows using milk mid-infrared (MIR) spectra. A novel feature of this model was the consideration of lactation stage to reflect changes in the metabolic status of the cow. A total of 446 daily CH4 measurements were obtained using the SF6 method on 142 Jersey, Holstein, and Holstein-Jersey cows. The corresponding milk samples were collected during these CH4 measurements and were analyzed using MIR spectroscopy. A first derivative was applied to the milk MIR spectra. To validate the novel calibration equation incorporating days in milk (DIM), 2 calibration processes were developed: the first was based only on CH4 measurements and milk MIR spectra (independent of lactation stage; ILS); the second included milk MIR spectra and DIM information (dependent on lactation stage; DLS) by using linear and quadratic modified Legendre polynomials. The coefficients of determination of ILS and DLS equations were 0.77 and 0.75, respectively, with standard error of calibration of 63 g/d of CH4 for both calibration equations. These equations were applied to 1,674,763 milk MIR spectra from Holstein cows in the first 3 parities and between 5 and 365 DIM. The average CH4 indicators were 428, 444, and 448 g/d by ILS and 444, 467, and 471 g/d by DLS for cows in first, second, and third lactation, respectively. Behavior of the DLS indicator throughout the lactations was in agreement with the literature with values increasing between 0 and 100 DIM and decreasing thereafter. Conversely, the ILS indicator of CH4 emission decreased at the beginning of the lactation and increased until the end of the lactation, which differs from the literature. Therefore, the DLS indicator seems to better reflect biological processes that drive CH4 emissions than the ILS indicator. The ILS and DLS equations were applied to an independent data set, which included 59 respiration chamber measurements of CH4 obtained from animals of a different breed across a different production system. Results indicated that the DLS equation was much more robust than the ILS equation allowing development of indicators of CH4 emissions by dairy cows. Integration of DIM information into the prediction equation was found to be a good strategy to obtain biologically meaningful CH4 values from lactating cows by accounting for biological changes that occur throughout the lactation. [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 ULiege; Colinet, Frédéric ULiege; 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 detailOverview of possibilities and challenges of the use of infrared spectrometry in cattle breeding
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; 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 detailPredictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval
Arnould, Valérie ULiege; Reding, Romain; Bormann, Jeanne et al

in Animals (2015), 5(3), 643-661

Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem ... [more ▼]

Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for models which included a MI effect. The corresponding correlation values ranged from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings. The simplicity of these proposed models should facilitate their use by breeding and milk recording organizations. [less ▲]

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See detailComparison of 3 different variable selection strategies to improve the predictions of fatty acid profile in bovine milk by mid-infrared spectrometry
Soyeurt, Hélène ULiege; Brostaux, Yves ULiege; Dehareng, Frédéric et al

in Journal of Animal Science (2015, July 15), 93/98(Suppl. s3/ Suppl. 2), 804

Mid-infrared (MIR) spectrometry is used to provide phenotypes related to the milk composition. Foss spectrum contains 1,060 datapoints. The number of reference values required to build a calibration ... [more ▼]

Mid-infrared (MIR) spectrometry is used to provide phenotypes related to the milk composition. Foss spectrum contains 1,060 datapoints. The number of reference values required to build a calibration equation is often lower than the spectral variables mainly due to the cost of chemical analysis. Problems of collinearity and overfitting appear when this high dimensional data set is used. This research will study the interest of using variable selection (VS) approach before the use of partial least square regression (PLS). The data set included 1,236 milk spectra related to their fatty acid (FA) contents. Saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA), short chain (SCFA), medium chain (MCFA), and long chain FA (LCFA) were studied. The data set was randomly divided in 3 groups which were used to create 3 calibration and validation data sets. Three different VS methods were compared. The first strategy was based on the part of trait variability explained by each considered variables (R2VS). The second method was based on the regression coefficient estimated after PLS procedure divided by the standard deviation of the considered spectral variable (BSVS). The third strategy permitted to underline the uninformative variables which were the ones having the lowest ratio of average regression coefficient to their corresponding standard deviation estimated after a leave-one out cross-validation (UVEVS). For UVEVS and BSVS, the cutoff was determined from the known uninformative region of MIR milk spectrum. The cutoff for R2VS was determined by testing different thresholds ranged between 5 and 40%. The most interesting cutoff for R2VS was 25%. The worst results in terms of validation root mean square error of prediction (RMSEPv) were obtained using a full PLS (i.e., without VS). The maximum difference (g/dl of milk) of RMSEPv obtained from the full PLS and from the PLS using selected variables were 0.156 for SFA, 0.139 for MUFA, 0.011 for PUFA, 0.025 for SCFA, 0.164 for MCFA, and 0.188 for LCFA. R2VS gave the best results for all studied traits followed by UVEVS and then BSVS. In conclusion, the use of VS improved significantly the performance of FA MIR equations. [less ▲]

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See detailEffect of calving interval on the economic results of dairy farms based on their typology.
Dalcq, Anne-Catherine ULiege; Beckers, Yves ULiege; Wyzen, Benoit et al

Conference (2015, July 15)

The calving interval (CI) can influence the milk production (MP) and the economic results of a farm. This research aimed to highlight the most economically important CI, on the basis of the accounts of ... [more ▼]

The calving interval (CI) can influence the milk production (MP) and the economic results of a farm. This research aimed to highlight the most economically important CI, on the basis of the accounts of breeders. The data set contained 1,318 accounts spread between 2007 and 2012. Technical information such as mean CI of the herd, percent of cows with a CI of less than 380 d (m380), between 380 and 419 d (e380419), between 420 and 459 d (e420459) and more than 459 d (p459), mean MP of the herd; as well as typological information such as quantity of equivalent concentrate (CC), number of ares of grass (GR) and of corn silage (CS) per livestock unit (LU); and economic information such as mean gross margin per cow were available. The relation between CI and the gross margin showed that if a single economic optimum of CI cannot be determined, this optimum could depend on the typology of the farm. Therefore, 4 groups were created by using a multiple correspondence analysis, including quantity of equivalent CC, number of ares of GR and of CS per LU as variables. The first group was the most intensive one with a feeding based mostly on CC and CS; the second group was similar but less intensive. The third group was the most extensive with high GR consumption. The fourth group was characterized by a near absence of CS but more CC. Moreover, m380, e380420, e420459, p459 were transformed from quantitative to qualitative variables by using numerical classification. A qualitative variable CI profile was created as a summary of all these variables. In each group, MP was modeled using the different CI variables. The assumption behind this modeling was that for a typological profile, the breeder must have the highest MP to maximize the gross margin. These models showed that MP is maximized when p459 is lower than 26%, lower than 37%, above 27% for the group 1, 2, 3 respectively. For the group 4, the model with the variable CI profile suggested that the economic optimum of CI is intermediate. These results underlined that the economic optimum of CI is related to the typology of the considered farm. Studying individual data is a perspective to determine more precisely CI with the best economic results. [less ▲]

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See detailCapitalizing on fine milk composition for breeding and management of dairy cows
Gengler, Nicolas ULiege; Soyeurt, Hélène ULiege; Dehareng, Frédéric et al

in Journal of Animal Science (2015, July 12), 93/ 98(Suppl. s3/ Suppl. 2), 4

Management and breeding of dairy cows face the challenge of permanently adapting to changing production circumstances under socioeconomic constraints. If management and breeding addresses different ... [more ▼]

Management and breeding of dairy cows face the challenge of permanently adapting to changing production circumstances under socioeconomic constraints. If management and breeding addresses different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, their products (i.e., milk and subsequently dairy products), their behavior and their environmental impact. Milk composition has been identified as an important source of information since it could reflect, at least partially, all these elements. Major milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other components might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using classical prediction equation based techniques. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate), that can then be useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., ketosis) are predicted directly from MIR spectra. In a second approach, in an innovative manner, patterns detected by comparing observed from expected MIR spectra can be used directly. All these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality and limit environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlation with other traits of interest. [less ▲]

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

Conference (2015, June 10)

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See detailGenetic correlations between methane production and milk fatty acid contents of Walloon Holstein cattle throughout the lactation
Vanrobays, Marie-Laure ULiege; Vandenplas, Jérémie; Bastin, Catherine ULiege et al

in Biotechnologie, Agronomie, Société et Environnement = Biotechnology, Agronomy, Society and Environment (2015, April 16), 19(2), 117

Methane (CH4) from ruminal fermentation is the major greenhouse gas produced by dairy cattle, which contributes largely to climate change. Production of CH4 also represents losses of gross energy intake ... [more ▼]

Methane (CH4) from ruminal fermentation is the major greenhouse gas produced by dairy cattle, which contributes largely to climate change. Production of CH4 also represents losses of gross energy intake. Therefore, there is a growing interest in mitigating these emissions. Acetate and butyrate have common bio-chemical pathways with CH4. Because some milk fatty acids (FA) arise from acetate and butyrate, milk FA are often considered as potential predictors of CH4. However, relationships between these traits remain unclear. Moreover, the evolution of the phenotypic and genetic correlations of CH4 and milk FA across days in milk (DIM) has not been evaluated. The main goal of this study was to estimate genetic correlations between CH4 and milk FA contents throughout the lactation. Calibration equations predicting daily CH4 production (g.d-1) and milk FA contents (g.100 dl-1 of milk) from milk mid-infrared (MIR) spectra were applied on MIR spectra related to Walloon milk recording. Data included 243,260 test-day records (between 5 and 365 DIM) from 33,850 first-parity Holstein cows collected in 630 herds. Pedigree included 109,975 animals. Bivariate (i.e., CH4 production and one of the FA traits) random regression test-day models were used to estimate genetic parameters of CH4 production and seven groups of FA contents in milk. Saturated (SFA), short-chain (SCFA), and medium-chain FA (MCFA) showed positive averaged daily genetic correlations with CH4 production (from 0.25 to 0.29). Throughout the lactation, genetic correlations between SCFA and CH4 were low in the beginning of the lactation (0.11 at 5 DIM) and higher at the end of the lactation (0.54 at 365 DIM). Regarding SFA and MCFA, genetic correlations between these groups of FA and CH4 were more stable during the lactation with a slight increase (from 0.23 to 0.31 for SFA and from 0.23 to 0.29 for MCFA, at 5 and 365 DIM respectively). Furthermore, averaged daily genetic correlations between CH4 production and monounsaturated (MUFA), polyunsaturated (PUFA), unsaturated (UFA), and long-chain FA (LCFA) were low (from 0.00 to 0.15). However, these genetic correlations varied across DIM. Genetic correlations between CH4 and MUFA, PUFA, UFA, and LCFA were negative in early lactation (from -0.24 to -0.34 at 5 DIM) and increased afterward to become positive from 15 weeks till the end of the lactation (from 0.14 to 0.25 at 365 DIM). Finally, these results indicate that genetic and, therefore, phenotypic correlations between CH4 production and milk FA vary following lactation stage of the cow, a fact still often ignored when trying to predict CH4 production from FA composition. [less ▲]

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See detailGenetic correlations between methane production and milk fatty acid contents of Walloon Holstein cattle throughout the lactation
Vanrobays, Marie-Laure ULiege; Vandenplas, Jérémie ULiege; Bastin, Catherine ULiege et al

Poster (2015, April 16)

Methane (CH4) from ruminal fermentation is the major greenhouse gas produced by dairy cattle which contributes largely to climate change. Production of CH4 also represents losses of gross energy intake ... [more ▼]

Methane (CH4) from ruminal fermentation is the major greenhouse gas produced by dairy cattle which contributes largely to climate change. Production of CH4 also represents losses of gross energy intake. Therefore, there is a growing interest in mitigating these emissions. Acetate and butyrate have common bio-chemical pathways with CH4. Because some milk fatty acids (FA) arise from acetate and butyrate, milk FA are often considered as potential predictors of CH4. However, relationships between these traits remain unclear. Moreover, the evolution of the phenotypic and genetic correlations of CH4 and milk FA across days in milk (DIM) has not been evaluated. The main goal of this study was to estimate genetic correlations between CH4 and milk FA contents throughout the lactation. Calibration equations predicting daily CH4 production (g/d) and milk FA contents (g/100 dL of milk) from milk mid-infrared (MIR) spectra were applied on MIR spectra related to Walloon milk recording. Data included 243,260 test-day records (between 5 and 365 DIM) from 33,850 first-parity Holstein cows collected in 630 herds. Pedigree included 109,975 animals. Bivariate (i.e., CH4 production and one of the FA traits) random regression test-day models were used to estimate genetic parameters of CH4 production and 7 groups of FA contents in milk. Saturated (SFA), short-chain (SCFA), and medium-chain FA (MCFA) showed positive averaged daily genetic correlations with CH4 production (from 0.25 to 0.29). Throughout the lactation, genetic correlations between SCFA and CH4 were low in the beginning of the lactation (0.11 at 5 DIM) and higher at the end of the lactation (0.54 at 365 DIM). Regarding SFA and MCFA, genetic correlations between these groups of FA and CH4 were more stable during the lactation with a slight increase (from 0.23 to 0.31 for SFA and from 0.23 to 0.29 for MCFA, at 5 and 365 DIM respectively). Furthermore, averaged daily genetic correlations between CH4 production and monounsaturated (MUFA), polyunsaturated (PUFA), unsaturated (UFA), and long-chain FA (LCFA) were low (from 0.00 to 0.15). However, these genetic correlations varied across DIM. Genetic correlations between CH4 and MUFA, PUFA, UFA, and LCFA were negative in early lactation (from -0.24 to -0.34 at 5 DIM) and increased afterward to become positive from 15 weeks till the end of the lactation (from 0.14 to 0.25 at 365 DIM). Finally, these results indicate that genetic and, therefore, phenotypic correlations between CH4 production and milk FA vary following lactation stage of the cow, a fact still often ignored when trying to predict CH4 production from FA composition. [less ▲]

Detailed reference viewed: 104 (13 ULiège)