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See detailRelationships between methane emissions and technico-economic data from commercial dairy herds
Delhez, Pauline ULiege; Wyzen, Benoit; Dalcq, Anne-Catherine ULiege et al

Conference (2017, September 01)

Considering economic and environmental issues is important for the sustainability of dairy farms. Regarding environment, direct methane (CH4) emissions from cows are of increasing concern. Many studies ... [more ▼]

Considering economic and environmental issues is important for the sustainability of dairy farms. Regarding environment, direct methane (CH4) emissions from cows are of increasing concern. Many studies examined CH4 variation factors but often on a low number of experimental cows. Also, few studies linked CH4 to economic aspects of dairy farms. The innovative aim of this study was to highlight technical factors associated with dairy cow CH4 emissions and gain insight into the relationships between CH4 and herd economic results by the use of large scale and on-farm data. A total of 525,697 individual CH4 predictions from milk mid-infrared (MIR) spectra [MIR-CH4 (g/day)] of milk samples collected on 206 farms during the Walloon milk recording were used to create a CH4 proxy at the herd by year (herd*year) level. This proxy was merged with accounting data. This allowed a simultaneous study of CH4 emissions and 56 technico-economic variables for 1,024 herd*year records from 2007 to 2014. Significant effects were detected from ANOVA analyses and correlations (r). MIR-CH4 was weakly linked to technical variables considered individually (r < 0.38), suggesting complex associations between variables. Lower MIR-CH4 was associated with lower fat and protein corrected milk (FPCM) yield (r=0.18), lower milk fat and protein content (r=0.38 and 0.33, respectively), lower quantity of milk produced from forages (r=0.12) and suboptimal reproduction and health performances (e.g. higher calving interval (r=-0.21), higher culling rate (r=-0.15)). On an economic point of view, lower MIR-CH4 was associated with lower gross margin per cow (r=0.19) and per litre FPCM (r=0.09). To conclude, this study suggested that low dairy cow CH4 emissions tended to be associated with suboptimal and also less profitable herd management practices. Further research is needed to confirm and expand on these results. [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 detailRelationships between methane emissions from dairy cows and farm technico-economic results
Delhez, Pauline ULiege; Wyzen, Benoit; Dalcq, Anne-Catherine ULiege et al

Poster (2017, February 07)

Considering economic and environmental issues is important for the sustainability of dairy farms. Regarding environment, direct methane (CH4) emissions from cows are of increasing concern. Many studies ... [more ▼]

Considering economic and environmental issues is important for the sustainability of dairy farms. Regarding environment, direct methane (CH4) emissions from cows are of increasing concern. Many studies examined CH4 variation factors but often on a low number of experimental cows. Also, few studies linked CH4 to economic aspects of dairy farms. The innovative aim of this study was to highlight technical factors associated with dairy cow CH4 emissions and gain insight into the relationships between CH4 and herd economic results by the use of large scale and on-farm data. A total of 525,697 individual CH4 predictions from milk mid-infrared (MIR) spectra [MIR-CH4 (g/day)] of milk samples collected on 206 farms during the Walloon milk recording were used to create a CH4 proxy at the herd by year (herd*year) level. This proxy was merged with accounting data. This allowed a simultaneous study of CH4 emissions and 56 technico-economic variables for 1,024 herd*year records from 2007 to 2014. Significant effects were detected from ANOVA analyses and correlations (r). MIR-CH4 was weakly linked to technical variables considered individually (r < 0.38), suggesting complex associations between variables. Lower MIR-CH4 was associated with lower fat and protein corrected milk (FPCM) yield (r=0.18), lower milk fat and protein content (r=0.38 and 0.33, respectively), lower quantity of milk produced from forages (r=0.12) and suboptimal reproduction and health performances (e.g. higher calving interval (r=-0.21), higher culling rate (r=-0.15)). On an economic point of view, lower MIR-CH4 was associated with lower gross margin per cow (r=0.19) and per litre FPCM (r=0.09). To conclude, this study suggested that low dairy cow CH4 emissions tended to be associated with suboptimal and also less profitable herd management practices. Further research is needed to confirm and expand on these results. [less ▲]

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See detailRelationships between milk mid-infrared predicted gastro-enteric methane production and the technical and financial performance of commercial dairy herds
Delhez, Pauline ULiege; Wyzen, Benoit; Dalcq, Anne-Catherine ULiege et al

in Animal (2017)

Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy ... [more ▼]

Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy cow gastro-enteric methane (CH4) production predicted from milk mid-infrared spectra and technico-economic variables by the use of large scale and on-farm data. A total of 525 697 individual CH4 predictions from milk mid-infrared spectra [MIR-CH4 (g/day)] of milk samples collected on 206 farms during the Walloon milk recording scheme were used to create a MIR-CH4 prediction for each herd and year (HYMIR-CH4). These predictions were merged with dairy herd accounting data. This allowed a simultaneous study of HYMIR-CH4 and 42 technical and economic variables for 1 024 herd and year records from 2007 to 2014. Pearson correlation coefficients (r) were used to assess significant relationships (P < 0.05). Low HYMIR-CH4 was significantly associated with, amongst others, lower fat and protein corrected milk (FPCM) yield (r = 0.18), lower milk fat and protein content (r = 0.38 and 0.33, respectively), lower quantity of milk produced from forages (r = 0.12) and suboptimal reproduction and health performance (e.g. longer calving interval (r = -0.21) and higher culling rate (r = -0.15)). Concerning economic results, low HYMIR-CH4 was significantly associated with lower gross margin per cow (r = -0.19) and per litre FPCM (r = -0.09). To conclude, this study suggested that low lactating dairy cow gastro-enteric CH4 production tended to be associated with more extensive or suboptimal management practices, which could lead to lower profitability. The observed low correlations suggest complex interactions between variables due to the use of on-farm data with large variability in technical and management practices. [less ▲]

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

Poster (2016, October 17)

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 detailA simple method to predict methane emissions based on milk mid infrared spectra
Vanlierde, Amélie ULiege; Dehareng, Frédéric; Froidmont, Eric et al

Poster (2016, October)

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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 detailPotential of milk MIR spectra to develop new health phenotypes for dairy cows in the GplusE project
Vanlierde, Amélie ULiege; Grelet, Clément ULiege; Gengler, Nicolas ULiege et al

in Book of abstracts of the 67 rd Annual Meeting of the European Association for Animal Production (2016, August 29)

The current context leads to more and more efficient and rational animal productions. The objective of the “Genotype plus Environment” project (G plus E) is to support novel phenotyping approaches to ... [more ▼]

The current context leads to more and more efficient and rational animal productions. The objective of the “Genotype plus Environment” project (G plus E) is to support novel phenotyping approaches to provide large scale phenotypes for a genomic study and contributing to the sustainability of dairy cow production systems. In this framework, 3 European farms (AFBI-UK, UCD-IRL, AU-DK) collected observations (weight, body condition score, uterine health, residual feed intake, lameness,…) and samples (milk, blood, liver, feed,…) on 135 dairy cows, from calving until day 49. Those data constitute a substantial database which permits to link those phenotypes of interest to potential biomarkers, and especially the mid infrared (MIR) spectra of milk. Predicting phenotypes of interest from milk MIR spectra could be very interesting to detect specific status of cows in a cost effective, rapid and routine process, allowing the acquisition of data at large scale. Classification models have been developed from milk MIR spectra. For example a model built on 60 observations permits to distinguish animals with or without lameness with a good predicted classification of 68 and 71% respectively. Otherwise Regression models have been performed to predict molecules of interest from milk MIR spectra. Some of them can be used with a threshold (eg. level of milk NAGase which is associated to an inflammation status) some others present potential to be predicted quantitatively (eg. IGF1 which is linked to uterine health). This database therefore allows developing tools to predict new health indicators from milk MIR spectra that can be easily implemented at large scale. Those observations will be validated through new data collected with the same protocol from 3 other European farms. [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 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 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 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 ▲]

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

Conference (2015)

Detailed reference viewed: 60 (6 ULiège)