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See detailConsequences of genetic selection for environmental impact traits on economically important traits in dairy cows
Kandel, Purna ULiege; Vanderick, Sylvie ULiege; Vanrobays, Marie-Laure ULiege et al

in Animal Production Science (2017)

Methane (CH4) emission is an important environmental trait in dairy cows. Breeding aiming to mitigate CH4 emissions require the estimation of genetic correlations with other economically important traits ... [more ▼]

Methane (CH4) emission is an important environmental trait in dairy cows. Breeding aiming to mitigate CH4 emissions require the estimation of genetic correlations with other economically important traits and the prediction of their selection response. In this study, test-day CH4 emissions were predicted from milk mid-infrared spectra of Holstein cows. Predicted CH4 emissions (PME) and log-transformed CH4 intensity (LMI) computed as the natural logarithm of PME divided by milk yield (MY). Genetic correlations of PME and LMI with traits used currently were approximated from correlations between estimated breeding values of sires. Values were for PME with MY 0.06, fat yield (FY) 0.09, protein yield (PY) 0.13, fertility 0.17; body condition score (BCS) –0.02; udder health (UDH) 0.22; and longevity 0.22. As expected by its definition, values were negative for LMI with production traits (MY –0.61; FY –0.15 and PY –0.40) and positive with fertility (0.36); BCS (0.20); UDH (0.08) and longevity (0.06). The genetic correlations of 33 type traits with PME ranged from –0.12 to 0.25 and for LMI ranged from –0.22 to 0.18. Without selecting PME and LMI (status quo) the relative genetic change through correlated responses of other traits were in PME by 2% and in LMI by –15%, but only due to the correlated response to MY. Results showed for PME that direct selection of this environmental trait would reduce milk carbon foot print but would also affect negatively fertility. Therefore, more profound changes in current indexes will be required than simply adding environmental traits as these traits also affect the expected progress of other traits. [less ▲]

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See detailGenetic parameters of mid-infrared methane predictions and their relationships with milk production traits in Holstein cattle
Kandel, Purna ULiege; Vanrobays, Marie-Laure ULiege; Vanlierde, Amélie ULiege et al

in Journal of Dairy Science (2017), 100(7), 5578-5591

Many countries have pledged to reduce greenhouse gases. In this context, the dairy sector is one of the identified sectors to adapt production circumstances to address socio-environmental constraints due ... [more ▼]

Many countries have pledged to reduce greenhouse gases. In this context, the dairy sector is one of the identified sectors to adapt production circumstances to address socio-environmental constraints due to its large carbon footprint related to CH4 emission. This study aimed mainly to estimate (1) the genetic parameters of 2 milk mid-infrared-based CH4 proxies [predicted daily CH4 emission (PME, g/d), and log-transformed predicted CH4 intensity (LMI)] and (2) their genetic correlations with milk production traits [milk (MY), fat (FY), and protein (PY) yields] from first- and second-parity Holstein cows. A total of 336,126 and 231,400 mid-infrared CH4 phenotypes were collected from 56,957 and 34,992 first- and second-parity cows, respectively. The PME increased from the first to the second lactation (433 vs. 453 g/d) and the LMI decreased (2.93 vs. 2.86). We used 20 bivariate random regression test-day models to estimate the variance components. Moderate heritability values were observed for both CH4 traits, and those values decreased slightly from the first to the second lactation (0.25 ± 0.01 and 0.22 ± 0.01 for PME; 0.18 ± 0.01 and 0.17 ± 0.02 for LMI). Lactation phenotypic and genetic correlations were negative between PME and MY in both first and second lactations (−0.07 vs. −0.07 and −0.19 vs. −0.24, respectively). More close scrutiny revealed that relative increase of PME was lower with high MY levels even reverting to decrease, and therefore explaining the negative correlations, indicating that higher producing cows could be a mitigation option for CH4 emission. The PME phenotypic correlations were almost equal to 0 with FY and PY for both lactations. However, the genetic correlations between PME and FY were slightly positive (0.11 and 0.12), whereas with PY the correlations were slightly negative (−0.05 and −0.04). Both phenotypic and genetic correlations between LMI and MY or PY or FY were always relatively highly negative (from −0.21 to −0.88). As the genetic correlations between PME and LMI were strong (0.71 and 0.72 in first and second lactation), the selection of one trait would also strongly influence the other trait. However, in animal breeding context, PME, as a direct quantity CH4 proxy, would be preferred to LMI, which is a ratio trait of PME with a trait already in the index. The range of PME sire estimated breeding values were 22.1 and 29.41 kg per lactation in first and second parity, respectively. Further studies must be conducted to evaluate the effect of the introduction of PME in a selection index on the other traits already included in this index, such as, for instance, fertility or longevity. [less ▲]

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See detailChallenges and priorities for modelling livestock health and pathogens in the context of climate change
Özkan, Şeyda; Vitali, Andrea; Lacetera, Nicola et al

in Environmental Research (2016), 151(Supplement C), 130-144

Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role ... [more ▼]

Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role in assessing the impacts of climate change on livestock systems and the efficacy of potential adaptation strategies, to support decision making for more efficient, resilient and sustainable production. However, a coherent set of challenges and research priorities for modelling livestock health and pathogens under climate change has not previously been available. To identify such challenges and priorities, researchers from across Europe were engaged in a horizon-scanning study, involving workshop and questionnaire based exercises and focussed literature reviews. Eighteen key challenges were identified and grouped into six categories based on subject-specific and capacity building requirements. Across a number of challenges, the need for inventories relating model types to different applications (e.g. the pathogen species, region, scale of focus and purpose to which they can be applied) was identified, in order to identify gaps in capability in relation to the impacts of climate change on animal health. The need for collaboration and learning across disciplines was highlighted in several challenges, e.g. to better understand and model complex ecological interactions between pathogens, vectors, wildlife hosts and livestock in the context of climate change. Collaboration between socio-economic and biophysical disciplines was seen as important for better engagement with stakeholders and for improved modelling of the costs and benefits of poor livestock health. The need for more comprehensive validation of empirical relationships, for harmonising terminology and measurements, and for building capacity for under-researched nations, systems and health problems indicated the importance of joined up approaches across nations. The challenges and priorities identified can help focus the development of modelling capacity and future research structures in this vital field. Well-funded networks capable of managing the long-term development of shared resources are required in order to create a cohesive modelling community equipped to tackle the complex challenges of climate change. [less ▲]

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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 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 detailModeling heat stress under different environmental conditions
Carabano, Maria-Jesus; Logar, Betka; Bormann, Jeanne et al

in Journal of Dairy Science (2016), 99(5), 37983814

Renewed interest in heat stress effects on livestock productivity derives from climate change, which is expected to increase temperatures and the frequency of extreme weather events. This study aimed at ... [more ▼]

Renewed interest in heat stress effects on livestock productivity derives from climate change, which is expected to increase temperatures and the frequency of extreme weather events. This study aimed at evaluating the effect of temperature and humidity on milk production in highly selected dairy cattle populations across three European regions differing in climate and production systems to detect differences and similarities that can be used to optimize heat stress (HS) effect modeling. Milk, fat and protein test day data from official milk recording for years 1999 to 2010 in four Holstein populations located in the Walloon Region of Belgium (BEL), Luxembourg (LUX), Slovenia (SLO) and Southern Spain (SPA) were merged with temperature and humidity data provided by the state meteorological agencies. After merging, the number of test day records/cows per trait ranged from 686,726/49,655 in SLO to 1,982,047/136,746 in BEL. Values for the daily average and maximum temperature and humidity index (THIavg and THImax) ranges for THIavg/THImax were largest in SLO (22-74/28-84) in SLO and shortest in SPA (39-76/46-83). Change point techniques were used to determine comfort thresholds, which differed across traits and climatic regions. Milk yield showed an inverted U shaped pattern of response across the THI scale with a HS threshold around 73 THImax units. For fat and protein, thresholds were lower than for milk yield and were shifted around 6 THI units towards larger values in SPA compared with the other countries. Fat showed lower HS thresholds than protein traits in all countries. The traditional broken line model was compared to quadratic and cubic fits of the pattern of response in production to increasing heat loads. A cubic polynomial model allowing for individual variation in patterns of response and THIavg as heat load measure showed the best statistical features. Higher/lower producing animals showed less/more persistent production (quantity and quality) across the THI scale. The estimated correlations between comfort and THIavg values of 70 (which represents the upper end of the THIavg scale in BEL-LUX) were lower for BEL-LUX (0.70 - 0.80) than for SPA (0.83 - 0.85). Overall, animals producing in the more temperate climates and semi-extensive grazing systems of BEL and LUX showed HS at lower heat loads and more re-ranking across the THI scale than animals producing in the warmer climate and intensive indoor system of SPA. [less ▲]

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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 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 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 ▲]

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See detailGenetic analysis of heat stress effects on yield traits, udder health, and fatty acids of Walloon Holstein cows
Hammami, Hedi ULiege; Vandenplas, Jérémie; Vanrobays, Marie-Laure ULiege et al

in Journal of Dairy Science (2015), 98(7), 4956-4968

Genetic parameters that considered tolerance for heat stress were estimated for production, udder health, and milk composition traits. Data included 202,733 test-day records for milk, fat, and protein ... [more ▼]

Genetic parameters that considered tolerance for heat stress were estimated for production, udder health, and milk composition traits. Data included 202,733 test-day records for milk, fat, and protein yields, fat and protein percentages, somatic cell score (SCS), 10 individual milk fatty acids (FA) predicted by mid-infrared spectrometry, and 7 FA groups. Data were from 34,468 first-lactation Holstein cows in 862 herds in the Walloon region of Belgium and were collected between 2007 and 2010. Test-day records were merged with daily temperature-humidity index (THI) values based on meteorological records from public weather stations. The maximum distance between each farm and its corresponding weather station was 21km. Linear reaction norm models were used to estimate the intercept and slope responses of 23 traits to increasing THI values. Most yield and FA traits had phenotypic and genetic declines as THI increased, whereas SCS, C18:0, C18:1 cis-9, and 4 FA groups (unsaturated FA, monounsaturated FA, polyunsaturated FA, and long-chain FA) increased with THI. Moreover, the latter traits had the largest slope-to-intercept genetic variance ratios, which indicate that they are more affected by heat stress at high THI levels. Estimates of genetic correlations within trait between cold and hot environments were generally high (>0.80). However, lower estimates (< = 0.67) were found for SCS, fat yield, and C18:1 cis-9, indicating that animals with the highest genetic merit for those traits in cold environments do not necessarily have the highest genetic merit for the same traits in hot environments. Among all traits, C18:1 cis-9 was the most sensitive to heat stress. As this trait is known to reflect body reserve mobilization, using its variations under hot conditions could be a very affordable milk biomarker of heat stress for dairy cattle expressing the equilibrium between intake and mobilization under warm conditions. [less ▲]

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