# Genetic parameters for methane production, intensity, and yield predicted from milk mid-infrared spectra throughout lactation in Holstein dairy cows

S. Fresco, D. Boichard, S. Fritz, P. Martin

► To cite this version:

S. Fresco, D. Boichard, S. Fritz, P. Martin. Genetic parameters for methane production, intensity, and yield predicted from milk mid-infrared spectra throughout lactation in Holstein dairy cows. Journal of Dairy Science, 2024, 107 (12), pp.11311-11323. 10.3168/jds.2024-25231. hal-04885713

HAL Id: hal-04885713

https://hal.inrae.fr/hal-04885713v1

Submitted on 14 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

# Genetic parameters for methane production, intensity, and yield predicted from milk mid-infrared spectra throughout lactation in Holstein dairy cows

S. Fresco,<fnref n="1" />,<fnref n="2" />* D. Boichard,<fnref n="2" /> S. Fritz,<fnref n="1" />,<fnref n="2" /> and P. Martin<fnref n="2" />

## ABSTRACT

Genetic selection to reduce CH₄ emissions is a promising solution for reducing the environmental impact of dairy cattle production. Before such a selection program can be implemented, however, it is necessary to have a better understanding of the genetic determinism of CH₄ emissions and how this might influence other traits of interest. In this study, we performed a genetic analysis of 6 CH₄ traits predicted from milk mid-infrared spectra. We predicted 4 CH₄ traits in g/d (methane production [MeP], calculated using different prediction equations), 1 trait in g/kg of fat- and protein-corrected milk (methane intensity [MeI]), and 1 trait in g/kg of DMI. Using an external dataset, we determined these prediction equations to be applicable in the range of 70 to 200 DIM. We then estimated genetic parameters in this DIM range using random regression models on a large dataset of 829,025 spectra collected between January 2013 and February 2023 from 167,514 first- and second-parity Holstein cows. The 6 CH₄ traits were found to be genetically stable throughout and across lactations, with average genetic correlations within a lactation ranging from 0.93 to 0.98, and those between lactations ranging from 0.92 to 0.98. All 6 CH₄ traits were also found to be heritable, with average heritability ranging from 0.24 to 0.45. The average pairwise genetic correlations between the 6 CH₄ traits ranged from -0.15 to 0.77, revealing that they are genetically distinct, including the 4 measurements of MeP. Of the 6 traits, 2 measures of MeP and MeI did not present antagonistic genetic correlations with milk yield, fat and protein contents, and SCS, and can probably be included in breeding goals with limited impact on other traits of interest.

**Key words:** genetic correlation, methane prediction, random regression model, heritability

## INTRODUCTION

For countries to meet their international commitments to cut greenhouse gas emissions and limit global warming, it is essential to reduce CH₄ emissions from agriculture, especially from cattle production. For this, multiple strategies are possible, including modifications of herd and feeding management, as well as genetic selection (Cottle et al., 2011; Beauchemin et al., 2022). Of the options available, genetic selection is relatively slow but provides permanent and cumulative gain over time. The possibility of reducing methane emissions through genetic selection has already been demonstrated in multiple studies (González-Recio et al., 2020; Pryce and Haile-Mariam, 2020; Manzanilla-Pech et al., 2021; Oliveira et al., 2024).

One challenge of this approach is the difficulty of directly measuring CH₄ emissions at a large scale, due to the cost and technical requirements involved (Hammond et al., 2016; Tedeschi et al., 2022). Existing methods are not adapted for large-scale phenotyping on commercial farms, which is a prerequisite for creating and maintaining a reference population for genomic evaluation. One solution is to use proxies to estimate CH₄ emissions based on another phenotype that is more easily accessed (Negussie et al., 2017). In particular, efforts have been made to predict CH₄ emissions from milk mid-infrared (MIR) spectra (Dehareng et al., 2012), which are obtained from routine milk samples and which, progressively since 2012, have been subject to standardized procedures for storage and analysis (Grelet et al., 2015).

The prediction of CH₄ emissions from milk MIR spectra has the potential to be very useful, and various equations have been developed for this purpose (Vanlierde et al., 2021; Coppa et al., 2022; Shadpour et al., 2022; Fresco et al., 2024). Most predict CH₄ in g/d (methane production [MeP]), but some predict CH₄ in g/kg of milk (methane intensity [MeI]) or in g/kg of DMI (methane yield [MeY]). Some of these equations are already in routine use for counseling purposes, for example in France (Vanlierde et al., 2021), or for genomic evaluation in Canada (Malchiodi et al., 2023; Oliveira et al., 2024).

<footnote n="1">¹Eliance, 75595 Paris Cedex 12, France</footnote>

<footnote n="2">²Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France</footnote>
Several recent studies have examined the genetic relationships of direct or predicted CH₄ emissions (Kandel et al., 2017; Lassen and Difford, 2020; Manzanilla-Pech et al., 2022a; van Breukelen et al., 2023). Such studies are important because before any program of genetic selection can be implemented, it is essential to thoroughly understand the trait in question and make sure it is suited for selection. Moreover, genetic correlations of CH₄ emissions with other traits of interest must be fully characterized in order to ensure that any future selection on CH₄ does not have deleterious effects.

The aim of this study was thus 2-fold: first, to estimate the genetic parameters of CH₄ emissions using measures of MeP, MeI, and MeY predicted from MIR spectra of first- and second-parity Holstein cows; and second, to estimate the genetic correlations between these CH₄ predictions and milk production traits.

# MATERIALS AND METHODS

## Animal and Milk Production Data

The original dataset comprised 13,721,855 milk MIR spectra obtained in commercial farms by the French milk recording companies between 5 and 305 DIM from 1,306,268 mixed-parity Holstein cows. Spectra were standardized according to the method of Grelet et al. (2017) and 6 methane predictions were obtained by applying 4 equations developed by the authors (Fresco et al., 2024) and 2 other prediction equations (Chilliard et al., 2009; Vanlierde et al., 2021) available in France. Due to contractual limitations, MIR spectra from French commercial farms were not directly available to us. Therefore, the equations developed by Fresco et al. (2024) were

transferred to ESTEL Numérique (Nancy, France), the company that performs all routine predictions from milk MIR spectra for the French milk recording companies. The corresponding predictions and those derived from the equations developed by Chilliard et al. (2009) and Vanlierde et al. (2021), that were already implemented in the calculation system, were provided to us. The predictions corresponded to spectra collected between one and 11 years ago (from January 2013 to February 2023) depending on the milk recording company from which they were obtained.

Additional data (pedigree, dates of birth and calving, test-day information, herd, date, milk production, fat and protein contents, somatic cell count) were extracted from the French National Bovine Database (INRAE-CTIG). Descriptive statistics of 7 milk production traits for first-parity Holstein cows are presented in Table 1. Milk yield (MY), fat and protein contents (FC and PC), and SCC were obtained from morning and evening milkings. We

$$ \text{derived SCS from SCC as SCS} = \log_2 \left( \frac{\text{SCC}}{100,000} \right) + 3. $$

## Equations for Predicting CH₄ Emissions from Milk MIR Spectra

In this study, 6 CH₄ traits predicted from milk MIR spectra were compared. Those traits were predicted from 5 different equations (Chilliard et al., 2009; Vanlierde et al., 2021; Fresco et al., 2024). First, 3 different equations were used to predict CH₄ emissions in g/d (MeP). The first equation, developed by Fresco et al. (2024) using partial least square regression and referred to here as MeP_direct, is based on 2-wk averages of measurements (minimum 20 measurements, n = 694) taken using

<table><caption>**Table 1.** Descriptive statistics of all methane traits and milk production traits studied for 156,381 first-parity Holstein cows (579,753 test-day records)</caption><thead><tr><th>Trait<sup>1</sup></th><th>Unit</th><th>Mean</th><th>SD</th><th>Minimum</th><th>Maximum</th></tr></thead><tbody><tr><td>MeP_direct</td><td>g/d</td><td>420</td><td>41.2</td><td>227</td><td>572</td></tr><tr><td>MeI</td><td>g/kg FPCM</td><td>14.0</td><td>1.82</td><td>7.40</td><td>19.7</td></tr><tr><td>MeY</td><td>g/kg DMI</td><td>17.8</td><td>1.40</td><td>13.4</td><td>22.9</td></tr><tr><td>MeP_Indirect</td><td>g/d</td><td>367</td><td>80.2</td><td>95</td><td>630</td></tr><tr><td>MeP_FA</td><td>g/d</td><td>448</td><td>43.6</td><td>295</td><td>570</td></tr><tr><td>MeP_RC</td><td>g/d</td><td>427</td><td>41.8</td><td>292</td><td>577</td></tr><tr><td>SCS</td><td>NA<sup>2</sup></td><td>2.13</td><td>1.41</td><td>-0.64</td><td>7.10</td></tr><tr><td>Milk yield</td><td>kg</td><td>26.9</td><td>4.98</td><td>9.90</td><td>43.5</td></tr><tr><td>Fat content</td><td>g/kg</td><td>38.8</td><td>5.04</td><td>24.2</td><td>56.4</td></tr><tr><td>Protein content</td><td>g/kg</td><td>32.0</td><td>2.54</td><td>24.6</td><td>40.6</td></tr><tr><td>FPCM</td><td>kg</td><td>26.2</td><td>4.60</td><td>10.1</td><td>41.1</td></tr><tr><td>Fat yield</td><td>g</td><td>1,035</td><td>194.6</td><td>360.8</td><td>1,662.7</td></tr><tr><td>Protein yield</td><td>g</td><td>859</td><td>161.8</td><td>298</td><td>1,372</td></tr></tbody></table>

<footnote n="1">¹MeP_direct = prediction of methane production; MeI = prediction of methane intensity; MeY = prediction of methane yield; MeP_Indirect = prediction of methane intensity multiplied by the recorded fat- and protein-corrected milk (FPCM), using the equations developed by Fresco et al. (2024); MeP_FA = prediction of methane production using the equation developed by Chilliard et al. (2009); MeP_RC = prediction of methane emissions in g/d using the equation developed by Vanlierde et al. (2021).</footnote>

<footnote n="2">²NA = not applicable.</footnote>
a GreenFeed device (C-Lock Inc., Rapid City, SD) from 235 Holstein, Montbéliarde, and Abondance dairy cows. Its coefficient of determination in cross-validation (R²cv) is 0.38 and its standard error in cross-validation (SEcv) is 60 g/d. The second equation was developed by Chilliard et al. (2009) using linear regression and will be called MeP_FA (R² = 0.88). It was calibrated with fatty acid composition predicted from milk MIR spectra and CH4 emissions measured with the SF6 tracer gas method from 8 dairy cows in a 4 × 4 Latin square with 4 different diets. The third equation was developed by Vanlierde et al. (2021) using partial least square regression and will be referred to as MeP_RC (R²cv = 0.60 and SEcv = 61 g/d). This equation was calibrated with CH4 measurements recorded using either the SF6 tracer technique (n = 513) or respiration chambers (n = 576) from 299 cows from 7 breeds. Then, 2 equations predicting MeI and MeY were developed using the same dataset and the same methods as MeP_direct by Fresco et al. (2024). For the MeI equation, the calibration population contained 681 spectra from 201 cows (Holstein and Montbéliarde), and the R²cv (SEcv) of the equation was 0.47 (2.60 g/kg of fat- and protein-corrected milk [FPCM]). For MeY, only the animals with DMI records were used for calibration, reducing the dataset to 422 milk MIR spectra from 57 Holstein cows. The R²cv (SEcv) of this equation was 0.33 (2.01 g/kg of DMI). Finally, we defined a sixth CH4 trait, referred to as MeP_indirect, by multiplying the predicted MeI by the observed FPCM values associated with the spectra.

## Applicability of CH4 Prediction Equations

As noted above, we did not have direct access to the milk spectra, only to the predictions resulting from them. Therefore, before going any further, it was necessary to evaluate the applicability of the prediction equations. To do so, we used data provided by CEL25-90 (Roulans, France), a milk recording company that agreed to share its spectra for this study. This dataset contained 2,049,006 MIR spectra collected between 5 and 305 DIM from 150,712 mixed-parity Montbéliarde cows (average MY = 23.4 kg/d, average FC = 38.2 g/kg of milk, and average PY = 33.1 g/kg of milk). Spectra were collected between January 2012 and June 2017 from a total of 302,355 lactations. We then calculated the standardized Mahalanobis distance (GH) between each spectrum in this dataset and the spectra used to calibrate the equations for MeP_direct, MeI, and MeY (described in the previous paragraph; Fresco et al., 2024). Spectra with a GH value greater than 3 were classified as too distant from (i.e., dissimilar to) the calibration population to be used further (Vanlierde et al., 2015). To identify the potential factors that could influence the proportions of high-GH spectra, we analyzed

the distribution of 2 categories of spectra (similar or distant) depending on the following factors: DIM, parity, herd, date of milking, date of spectral analysis, MY, FC, PC, and SCS. The results from this preliminary step allowed us to determine which filters to apply to our overall dataset for data curation.

## Data Curation

The following conditions were applied successively for data curation. For a given cow, records from a second lactation were included only if records from the first lactation were available. Based on the results of our analysis of the applicability of the equations, records before 70 or after 200 DIM were excluded. We selected lactations for which there were at least 3 records classified in milk recording protocols, including both morning and evening milkings. All lactations started between 2015 and 2021. For each trait, outlier values (± 3 SD outside the mean) were discarded.

For the estimation of variance components, the contemporary groups were defined as the combination of herd × test-day, with a minimum of 3 records. The age at calving for the cows ranged from 20 to 38 mo for first-parity cows and from 34 to 52 mo for second-parity cows. The final dataset included 829,025 CH4 predictions between 70 and 200 DIM from 167,514 cows belonging to 1,508 herds.

The pedigree was traced back 3 generations and contained 326,387 animals, including 7,913 sires and 178,771 dams. The average (SD) number of daughters per sire was 38 (128).

## Genetic Analyses

First, bivariate random regression models were constructed for the 6 CH4 traits measured in first- and second-parity cows. Then, for first-parity data, 30 bivariate random regression models were performed that examined the relationships between the different CH4 traits and between each CH4 trait and each milk production trait. Variance components of the random regression models were estimated using the AI-REML algorithm as implemented in Wombat software (Meyer, 2007) using the following model:

$$y = X\beta + Za + Wpe + e,$$

where y is the vector of CH4 predictions or milk production phenotypes, β is the vector including the fixed effects of herd × test-day [69,116 classes] and 3 fixed effects nested within parity (year × month of calving [84 and 74 classes for first- and second-parity, respectively], age at calving [16 and 18 2-mo classes within first- and
second-parity, respectively], and DIM modeled by a 6-knot spline). **X** is the corresponding incidence matrix; **a** ~ N(0, **A** ⊗ **G**₀) is the vector of random regression additive genetic coefficients, with **A** the relationship matrix among individuals and **G**₀ the matrix of additive genetic variances; and **pe** ~ N(0, **I** ⊗ **P**₀) is the vector of random regression permanent environmental coefficients, with **I** being the identity matrix and **P**₀ the matrix of permanent environmental variances. Both **a** and **pe** were modeled over DIM by second-order Legendre polynomials, and **Z** and **W** were the incidence matrices. Residual variance was assumed to be heterogeneous with 7-d classes and was smoothed using the method of Druet et al. (2003).

Heritability was estimated for all traits at DIM *t* as

$$h_{(t)}^2 = \frac{\hat{\sigma}_{a_{(t)}}^2}{\hat{\sigma}_{a_{(t)}}^2 + \hat{\sigma}_{pe_{(t)}}^2 + \hat{\sigma}_e^2}, \quad \text{with } \hat{\sigma}_{a_{(t)}}^2 = L_{(t)}G_0L'_{(t)}, \text{ and } L_{(t)} \text{ being the coefficients of the Legendre polynomials at DIM } t.$$

The additive genetic correlations were computed as

$$r_{a(t)} = \frac{\hat{\sigma}_{a1,a2(t)}}{\hat{\sigma}_{a1(t)} \hat{\sigma}_{a2(t)}}. \quad \text{For bivariate analyses between first- and second-parity data, } \hat{\sigma}_{a1,a2(t)} \text{ was the estimate of additive genetic covariance for a given trait between the first and second parity at DIM } t, \text{ and } \hat{\sigma}_{a1(t)} \text{ and } \hat{\sigma}_{a2(t)} \text{ were the additive genetic SD estimates for a given trait at DIM } t \text{ for the first and second parity, respectively. For bivariate analyses between traits for first-parity data, } \hat{\sigma}_{a1,a2(t)} \text{ was the estimate of additive genetic covariance between trait 1 and trait 2 at DIM } t, \text{ and } \hat{\sigma}_{a1(t)} \text{ and } \hat{\sigma}_{a2(t)} \text{ were the additive genetic SD estimates at DIM } t \text{ for traits 1 and 2, respectively.}}$$

<figure><img src="image_3.png" /><figcaption>**Figure 1.** Percentage of milk mid-infrared spectra classified as too distant from the calibration population of the mid-infrared prediction equation in g/d (A), in g/kg of fat and protein-corrected milk (B), and in g/kg of DMI (C) developed by Fresco et al. (2024).</figcaption></figure>

# RESULTS

## Applicability of the CH₄ Prediction Equations

To determine the conditions under which the CH₄ prediction equations were most appropriate, we assessed the dissimilarity (standardized GH) between the MIR spectra provided by CEL25-90 and the spectra of the calibration population used to create the equations MeP_direct, MeI, and MeY. Figure 1 shows the percentage of spectra for which the GH value was greater than 3 when compared with the spectra of the calibration populations for MeP_direct. Overall, 7.3%, 8.5%, and 25.2% of spectra were determined to be too dissimilar from the calibration populations used for MeP_direct, MeI, and MeY, respectively. The percentage of distant spectra (GH >3) at each DIM varied throughout lactation but exhibited the same pattern for all 3 traits, with lower values in mid-lactation and higher values in early and late lactation. Considering

all parities, mid-lactation presented the lowest proportions of distant spectra for MeP_direct, MeI, and MeY (Table 2). Considering all DIM, the proportions of distant spectra increased with parity. No other factor appeared to have an effect on the proportion of distant spectra. Based on these results, all subsequent analyses were restricted to the period from 70 to 200 DIM.

## Methane Predictions from Milk MIR Spectra

Descriptive statistics of the 6 CH₄ traits and the 7 milk production traits are presented in Table 1. For the 156,381 first-parity cows, there was a total of 579,753 spectra between 70 and 200 DIM. The number of spectra per cow varied from 3 to 7, with a mean of 3.7 and an SD of 0.66. For the 84,349 second-parity cows, there were 312,272 spectra. The number of spectra per cow varied from 3 to 6, with a mean of 3.7 and an SD of 0.66.
<table><caption>**Table 2.** Descriptive statistics of the percentage of spectra with a Mahalanobis distance >3 when compared with the calibration populations used to develop equations predicting methane emissions in g/d (MeP), in g/kg of fat- and protein-corrected milk (MeI), and in g/kg of DMI (MeY; Fresco et al., 2024)</caption><thead><tr><th>Class</th><th>MeP</th><th>MeI</th><th>MeY</th></tr></thead><tbody><tr><td>All parities</td><td></td><td></td><td></td></tr><tr><td>Average over 7–69 DIM (%)</td><td>12.3</td><td>13.2</td><td>34.0</td></tr><tr><td>Average over 70–200 DIM (%)</td><td>3.5</td><td>2.9</td><td>13.8</td></tr><tr><td>Average over 201–350 DIM (%)</td><td>28.1</td><td>20.6</td><td>45.2</td></tr><tr><td>All DIM</td><td></td><td></td><td></td></tr><tr><td>Average in first parity (%)</td><td>3.8</td><td>4.6</td><td>14.1</td></tr><tr><td>Average in second parity (%)</td><td>6.9</td><td>8.1</td><td>25.5</td></tr><tr><td>Average in parities 3 to 10 (%)</td><td>>8</td><td>>10</td><td>>30</td></tr></tbody></table>

## Stability of CH₄ Traits Between Lactations

Genetic correlations between the first and second lactation are presented in Figure 2 for the 6 CH₄ traits each day between 70 and 200 DIM. The average correlations throughout lactation were higher than 0.96 for all traits except MeP_indirect (0.92).

## Stability of CH₄ Traits Throughout Lactation

We then estimated cross-correlations in the first lactation between each pairwise combination of DIM

<table><caption>**Table 3.** Descriptive statistics of the genetic correlation estimates between DIM of first lactation Holstein cows for the 6 methane traits (70–200 DIM)</caption><thead><tr><th>Trait<fnref n="1" /></th><th>Mean</th><th>SD</th><th>Minimum</th><th>Maximum</th></tr></thead><tbody><tr><td>MeP_direct</td><td>0.93</td><td>0.07</td><td>0.68</td><td>1</td></tr><tr><td>MeI</td><td>0.96</td><td>0.04</td><td>0.82</td><td>1</td></tr><tr><td>MeY</td><td>0.95</td><td>0.05</td><td>0.76</td><td>1</td></tr><tr><td>MeP_indirect</td><td>0.98</td><td>0.03</td><td>0.87</td><td>1</td></tr><tr><td>MeP_FA</td><td>0.96</td><td>0.05</td><td>0.79</td><td>1</td></tr><tr><td>MeP_RC</td><td>0.94</td><td>0.07</td><td>0.64</td><td>1</td></tr></tbody></table>

for each trait. The mean, SD, minimum, and maximum values are given in Table 3. The minimum correlation values were always observed between the most distant DIM. The lowest cross-correlation value was 0.64 (MeP_RC) but the lowest mean was 0.93 (MeP_direct). We found that MeP_indirect presented the highest mean (0.98), lowest SD (0.03), and highest minimum values (0.87).

## Heritability Estimates of CH₄ Traits Throughout Lactation

Figure 3 presents heritability estimates for the 6 CH₄ traits in the first lactation between 70 and 200 DIM. Overall, heritability estimates varied between 0.20 (MeP_indirect) and 0.50 (MeP_RC), with standard errors lower than 0.01. Heritability increased throughout the lactation for MeP_indirect, MeP_RC, MeP_FA, and MeI, but decreased for MeP_direct and MeY. The average heritability estimates ranged from 0.24 to 0.45 (Table 4). In the second lactation, similar patterns were observed for heritability throughout the lactation but with slightly lower values, with an average difference of 0.03 (ranging from 0 for MeI to 0.05 for MeP_indirect).

## Genetic Correlations Between CH₄ Traits

At a given DIM, the genetic correlations estimated between the different MeP traits were low (<0.40) and varied little throughout the lactation (Figure 4), with the exception of correlations between MeP_direct and MeP_RC, for which the average value (SD) was 0.65 (0.09; Table 4). The genetic correlations between MeI and MeY or MeP were similarly low (<0.50; Figure 4), but the highest and most stable of these were with MeP_indirect, with an average value (SD) of 0.53 (0.02; Table 4). Estimates of genetic correlations between MeI and

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Genetic correlation estimates between first and second lactations for the 6 methane traits (70–200 DIM). Predictions are in g/d for MeP_direct and MeP_indirect (Fresco et al., 2024), MeP_FA (Chilliard et al., 2009), and MeP_RC (Vanlierde et al., 2021); in g/kg of fat- and protein-corrected milk for MeI (Fresco et al., 2024); and in g/kg of DMI for MeY (Fresco et al., 2024).</figcaption></figure>

<footnote n="1">¹MeP_direct = prediction of methane production; MeI = prediction of methane intensity; MeY = prediction of methane yield; MeP_indirect = prediction of methane intensity multiplied by the recorded fat- and protein-corrected milk (FPCM), using the equations developed by Fresco et al. (2024); MeP_FA = prediction of methane production using the equation developed by Chilliard et al. (2009); MeP_RC = prediction of methane emissions in g/d using the equation developed by Vanlierde et al. (2021).</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Heritability estimates for the 6 methane traits in first lactation (70–200 DIM). Predictions are in g/d for MeP_direct and MeP_Indirect (Fresco et al., 2024), MeP_FA (Chilliard et al., 2009), and MeP_RC (Vanlierde et al., 2021); in g/kg of fat- and protein-corrected milk for MeI (Fresco et al., 2024); and in g/kg of DMI for MeY (Fresco et al., 2024).</figcaption></figure>

MeP_FA were negative, with an average value of -0.15, and those between MeI and MeY decreased throughout the lactation from 0.40 to 0.17, with an average value of 0.30 (Table 4). Finally, the genetic correlations estimated between MeY and the 4 measures of MeP were quite variable (Figure 4), with the highest and most stable values obtained with MeP_direct and the lowest found with MeP_FA (Table 4). The genetic correlations between MeY and MeP_RC decreased from 0.69 to 0.31 over the course of the lactation period.

## Genetic Correlations Between CH₄ Traits and Milk Production Traits

Genetic correlations between CH₄ traits and milk production traits are presented in Figure 5 for first-parity Holstein cows between 70 and 200 DIM. Overall, the genetic correlations ranged from -0.67 (MeI and FC) to 0.87 (MeP_indirect and MY). For all 6 predicted CH₄ traits, genetic correlations with SCS were close to zero, with a range of -0.13 to 0.15. With respect to MY, all traits but MeP_FA exhibited positive correlations (average of 0.86, 0.17, 0.34, 0.26, and 0.06 for MeP_indirect, MeP_direct, MeI, MeY, and MeP_RC, respectively). For PC, all traits but MeP_FA presented negative correlations (average of -0.41, -0.45, -0.36, and -0.11 for MeP_direct, MeI, MeP_indirect, and MeP_RC, respectively). Concerning FC, both MeI and MeP_indirect exhibited negative correlations (average of -0.64 and -0.33, respectively), whereas MeP_FA, MeP_RC, and MeP_direct had positive ones (average of 0.56, 0.10, and 0.10, respectively). Correlations between MeY and FC varied from negative (-0.26) to positive (0.17) over the course of lactation. The majority of correlations were stable throughout lactation (SD ≤0.05), with the exception of those between MeP_RC and PC (SD of 0.06), MeY and MY (SD of 0.07), MeY and FC (SD of 0.12), and MeP_direct and FC (SD of 0.06).

## DISCUSSION

### Methane Predictions

We found that MeP_direct, MeP_FA, and MeP_RC, which all measure CH₄ emissions in g/d, were similar in both mean and SD. Their averages were similar to values in the literature, albeit with a lower SD, for both direct (Benaouda et al., 2019; Manzanilla-Pech et al., 2021; Shadpour et al., 2022) and indirect (Vanlierde et

<table><caption>**Table 4.** Average (SD) heritability and genetic correlation estimates between methane traits in first lactation for Holstein dairy cows (70–200 DIM)</caption><thead><tr><th>Trait<sup>1</sup></th><th>MeP_direct</th><th>MeI</th><th>MeY</th><th>MeP_indirect</th><th>MeP_FA</th><th>MeP_RC</th></tr></thead><tbody><tr><td>MeP_direct</td><td>0.42 (0.01)</td><td>0.15 (0.08)</td><td>0.77 (0.04)</td><td>0.26 (0.03)</td><td>0.30 (0.06)</td><td>0.65 (0.09)</td></tr><tr><td>MeI</td><td></td><td>0.35 (0.04)</td><td>0.30 (0.06)</td><td>0.53 (0.02)</td><td>-0.15 (0.03)</td><td>0.28 (0.06)</td></tr><tr><td>MeY</td><td></td><td></td><td>0.37 (0.03)</td><td>0.29 (0.04)</td><td>0.11 (0.08)</td><td>0.53 (0.12)</td></tr><tr><td>MeP_indirect</td><td></td><td></td><td></td><td>0.24 (0.02)</td><td>0.03 (0.03)</td><td>0.27 (0.03)</td></tr><tr><td>MeP_FA</td><td></td><td></td><td></td><td></td><td>0.32 (0.04)</td><td>0.27 (0.03)</td></tr><tr><td>MeP_RC</td><td></td><td></td><td></td><td></td><td></td><td>0.45 (0.05)</td></tr></tbody></table>

<footnote n="1">¹MeP_direct = prediction of methane production, MeI = prediction of methane intensity, MeY = prediction of methane yield, MeP_indirect = prediction of methane intensity multiplied by the recorded fat- and protein-corrected milk (FPCM), using the equations developed by Fresco et al. (2024); MeP_FA = prediction of methane production using the equation developed by Chilliard et al. (2009); MeP_RC = prediction of methane emissions in g/d using the equation developed by Vanlierde et al. (2021).</footnote>
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Genetic correlation estimates (A) among MeP traits, (B) between Mel and MeP traits and MeY, and (C) between MeY and MeP traits in first lactation (70–200 DIM). Predictions are in g/d for MeP_direct and MeP_indirect (Fresco et al., 2024), MeP_FA (Chilliard et al., 2009), and MeP_RC (Vanlierde et al., 2021); in g/kg of fat and protein-corrected milk for Mel (Fresco et al., 2024); and in g/kg of DMI for MeY (Fresco et al., 2024).</figcaption></figure>

al., 2016; Vanrobays et al., 2016) measurements of CH₄ using a variety of recording methods, such as respiration chambers, SF₆, GreenFeed, and sniffer. In contrast, MeP_indirect presented a lower average and a higher SD, closer to SD values reported in the literature, indicating higher variability in this trait. This variability may be related to the variability in FPCM: unlike other methane predictions derived from regression analyses, MeP_indirect is the product of a regressed prediction (MeI) multiplied by an unregressed MY phenotype. However, the difference in absolute values between MeP_indirect and the other predictions in g/d is not an issue for genetic analyses.

With regard to MeI and MeY, the average predictions were in the range of averages reported in the literature for direct measurements (Benaouda et al., 2019; Manzanilla-Pech et al., 2021). The main difference observed was that SD values in our study were lower, which could simply reflect differences in variability between regressed predictions and direct measurements. Moreover, we only studied one breed, with all animals coming from the same country, whereas the populations in the earlier studies originated from multiple countries (Benaouda et al., 2019; Manzanilla-Pech et al., 2021).

## Applicability of the CH₄ Prediction Equations

Before using a prediction equation, it is crucial to ensure that the milk MIR spectra to be used as a basis for prediction closely resemble those of the population used to calibrate the equation. In the case of MeP_direct and MeI, the percentages of spectra that were too dissimilar for use (7.2% and 8.5%, respectively) were close to the 5% of discarded spectra reported by Vanlierde et al. (2016) equation on routine spectra from Wallonia, Belgium. However, for MeY there was a notably higher percentage of distant spectra (25.2%), consistent with its smaller and less diverse calibration population. For all 3 CH₄ predictions, the highest proportions of distant spectra were observed during early and late lactation, which are underrepresented in the calibration populations. Early lactation is a physiologically distinct phase in which cows strongly mobilize their body reserves to meet milk production demands while only gradually increasing feed intake. During this period, milk contents decrease dramatically before rebounding, and the fatty acids influencing milk MIR spectra do not originate solely from the diet, resulting in a distortion in the relationship between milk composition and CH₄ production. On the other hand, late lactation is affected by gestation, which has varying effects depending on the DIM at which fertilization occurs. Considering these factors and the variability of GH with DIM, we decided in this study to limit our applications of these equations to milk MIR
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Genetic correlation estimates of the 6 methane traits with milk yield (kg, green dashed line), fat content (g/kg milk, blue dotted line), protein content (g/kg of milk, purple dot-dashed line), and SCS (red solid line) in first lactation (70–200 DIM). Predictions (g/d) are MeP_direct and MeP_indirect (A and B, respectively; Fresco et al., 2024), MeP_FA (C; Chilliard et al., 2009), and MeP_RC (D; Vanlierde et al., 2021). Predictions are in g/kg of fat and protein-corrected milk for MeI (E) and in g/kg of DMI for MeY (F; Fresco et al., 2024).</figcaption></figure>
spectra collected between 70 and 200 DIM. This same
interval should be applied in future genetic evaluations
to ensure their accuracy.

Stability of Predicted CH₄ Traits Throughout
and Between Lactations

The 6 predicted CH₄ traits were stable throughout the lactation, indicating that multiple predictions within a single lactation can be regarded as repetitions of the same trait. This finding is consistent with the results of Pszczola et al. (2017), who observed genetic correlations higher than 0.8 between DIM 200 and all DIM between 70 and 200. Likewise, Manzanilla-Pech et al. (2021) found that CH₄ emissions represented a trait that was consistent throughout lactation, with a genetic correlation of 0.98 (SE of 0.03) between MeP in early (0–100 DIM) and mid-lactation (101–200 DIM).

In addition, each of the 6 predicted CH₄ traits was identified as being the same trait across lactations, with genetic correlations greater than 0.92, on average, at a given DIM. The trait with the lowest correlations was MeP_indirect, which was expected due to the way this trait is calculated. Unlike the other 5 CH₄ predictions, which are independent of parity, MeP_indirect relies on recorded FPCM, which varies between parities, for its derivation from MeI. Despite this, the correlations remained high enough for all traits to consider the second lactation as a repetition of the first for the purpose of genetic evaluation. In this, our results are consistent with genetic correlations reported in the literature: 0.91 (SE of 0.11) between the first and second lactations (Manzanilla-Pech et al., 2021) and 0.8 between the first and second parity throughout lactation (Manzanilla-Pech et al. (2022a).

Heritability Estimates

All 6 predicted CH₄ traits were found to be heritable,
with heritability estimates ranging from 0.20 to 0.50.
Heritability estimates for MeP_FA and MeP_indirect
(averages of 0.32 and 0.24, respectively) were in range
with the averages of 0.25 and 0.27 previously reported by
Kandel et al. (2017) and Vanrobays et al. (2016), whereas
values for MeP_direct and MeP_RC (>0.40 on average)
were higher. These differences may be due in part to
the methodologies used: those earlier studies estimated
genetic parameters using random regression models and
CH₄ predictions obtained using an older version of the
equation we used in this study (Vanlierde et al., 2015)
with routinely recorded spectra from first- and second-
parity Holstein cows in the Walloon region of Belgium.
Two other studies (Pszczola et al., 2017; Breider et al.,
2019) reported heritability estimates ranging from 0.23

to 0.30 and from 0.12 to 0.45, respectively, obtained with
random regression models and direct measurements with
breath analyzers. Unlike MeP_RC and MeP_direct, heri-
tability estimates of MeP_FA and MeP_indirect were in
the range of those derived from repeatability or animal
models based on direct measurements from Sniffers,
GreenFeeds, and SF₆, with values ranging from 0.12 to
0.36 (Lassen and Løvendahl, 2016; Manzanilla-Pech et
al., 2021, 2022b; Richardson et al., 2021; van Breukelen
et al., 2023; Lopes et al., 2024).

Average heritability for MeI and MeY was 0.35 and 0.37, respectively, representing some of the highest values found in the literature. Kandel et al. (2017) estimated the heritability of MeI to be between 0.12 and 0.24 throughout lactation (based on random regression models applied to CH₄ predictions from milk MIR spectra). Other published heritability estimates for MeI range from 0.04 to 0.21 (Lassen and Løvendahl, 2016; Manzanilla-Pech et al., 2022b,c) and from 0.14 to 0.23 for MeY (Richardson et al., 2021; Manzanilla-Pech et al., 2022b,c). However, higher values, 0.33 and 0.30, were obtained for MeI by Richardson et al. (2021) and Manzanilla-Pech et al. (2021), respectively. In the same study, Manzanilla-Pech et al. (2021) estimated a heritability of 0.38 for MeY.

Genetic Correlations Among CH₄ Traits

Overall, the genetic correlations among the predicted CH₄ traits indicate that they represent different traits, with the highest average genetic correlation of 0.77 (0.03) observed between MeP_direct and MeY. These genetic correlations remained relatively stable throughout lactation, with an SD ranging from 0.02 to 0.12 between 70 and 200 DIM.

Likewise, the 4 traits that predicted CH₄ in g/d also
appear to be genetically distinct, with the strongest cor-
relation observed between MeP_direct and MeP_RC,
which are derived from similar methodologies. In this
case, the average correlation of 0.65 suggests that select-
ing the best animals for MeP_direct would also tend to
select good ones for MeP_RC. However, between the
other pairs of traits, the average correlations ranged from
0.03 to 0.30, indicating that the traits are completely dif-
ferent. A combination of several hypotheses may help to
explain these low-to-moderate correlations between MeP
traits. It is possible that the weakness of the correlations
reflects differences between (1) direct prediction in g/d
without information on MY and indirect prediction ob-
tained by multiplying the predicted MeI by the observed
FPCM; (2) prediction from milk MIR spectra and predic-
tion based on fatty acids (which are themselves predicted
from milk MIR spectra—the double prediction does not
extract all of the information from a spectrum and is
instead likely to introduce additional error); and (3) the methods used to measure reference CH₄ for the calibration of the equations (respiration chambers and SF₆ vs. GreenFeeds).

Traits that predict CH₄ in different units (g/d, g/kg of FPCM, and g/kg of DMI) are also distinct traits. In our study, the genetic correlations between MeI and the 4 MeP traits ranged from -0.15 to 0.53, whereas previous studies reported a range of 0.41 to 0.94 (Manzanilla-Pech et al., 2021, 2022c; Kamalanathan et al., 2023). Considering MeY and the 4 MeP traits, only the correlations with MeP_direct (average of 0.77) and MeP_RC (average of 0.53) aligned with the values of 0.46, 0.77, and 0.73 reported in the literature (Manzanilla-Pech et al., 2021, 2022c; Kamalanathan et al., 2023). Those earlier studies also reported correlations of 0.70, 0.58, and 0.68, respectively, between MeI and MeY, far exceeding the average genetic correlation of 0.30 obtained in our study. Despite the inconsistencies between our results and the literature, MeP, MeI, and MeY should generally be considered as genetically distinct traits.

## Genetic Correlations Between CH₄ Traits and Milk Production Traits

The genetic correlations estimated in our study can be used to guide the implementation of genetic selection. Both MeP_direct and MeP_RC are particularly promising candidates, as their unfavorable genetic correlations with MY and FC are limited and can be accounted for in the breeding objective without serious effects on selection efficiency. Although it presented the best prediction performance (R² = 0.39 and RMSE = 63 g/d), MeP_indirect is not likely to be included in a breeding goal due to its high and unfavorable genetic correlation (average of 0.86) with MY. Compared with the 3 other MeP traits, MeP_FA presents the opposite correlations with MY and FC. Its unfavorable correlations with both FC and PC likely stem from the method used to build the prediction equation, which takes into account milk fatty acid composition, corresponding to FC, which is itself highly correlated with PC (Boichard and Bonaïti, 1987). In the literature, MeP is generally reported to be positively correlated with MY (Breider et al., 2019; Pszczola et al., 2019; Manzanilla-Pech et al., 2021; Lopes et al., 2024), with the exception of the value of -0.19 reported by Kandel et al. (2017). However, reports on correlations between MeP and FC or PC are scarce, with only one study (Pszczola et al., 2019) giving estimates of 0.04 and 0.07, respectively.

The favorable correlations of MeI with FC and PC mean that the inclusion of MeI in a breeding goal would not degrade these milk traits. However, MeI is unfavorably correlated with MY, meaning that the cows that

produce more CH₄ per kg of FPCM also have higher milk production and a lower FC and PC. The common idea that selection to reduce MeI would result in “diluting” CH₄ production into milk production is not supported by our results. Overall, however, the correlations exhibited by MeI appear to be acceptable for its inclusion in a breeding goal and should not lead to large negative impacts on milk production. Both Kandel et al. (2017) and Manzanilla-Pech et al. (2021) reported highly negative correlations between MeI and MY. The only positive correlation reported in the literature was between MeI and FPCM (0.15, Lassen and Løvendahl, 2016), but Manzanilla-Pech et al. (2021) reported a value of -0.39 for the same correlation. To our knowledge, there have been no previous reports of the correlations between MeI and FC or PC.

We also found that MeY was unfavorably correlated with MY, and its correlation with FC went from slightly favorable to slightly unfavorable throughout the course of lactation. However, because the MeY equation was calibrated on a small and homogeneous population, we believe it is not yet robust enough for large-scale application. The literature contains little information regarding correlations of MeY with milk production traits, with a nonsignificant value of -0.09 reported by Manzanilla-Pech et al. (2021) for the correlation of MeY and MY, and, to our knowledge, no data on correlations with FC and PC.

## Limitations

The authors would like to note some limitations of this study. The accuracy of the prediction equations developed by Fresco et al. (2024) remains limited; the prediction equations require improvement by including additional data in the calibration population. Data from new breeding systems and new breeds would improve the applicability of the equations to large-scale datasets. Validating the equations on independent datasets would provide additional assurance regarding their reliability. The equations developed by Fresco et al. (2024) were validated on data collected from different cows on the same farms as some of the calibration population. Nevertheless, the accurate and numerous direct CH₄ measurements that would be required for external validation are challenging to obtain, and thus most of the equations remain to be validated (Vanlierde et al., 2021; Oliveira et al., 2024). However, the need to provide genetic solutions to the breeders in the context of climate change and political decisions to reduce CH₄ emissions (after the 26th Conference of Parties of the United Nations, more than 150 countries have signed the Global Methane Pledge; CCAC Secretariat, 2021) requires the initiation of genetic studies on traits derived from still evolving
equations. Considering the results of this study, which
demonstrate that the different prediction equations cor-
respond to different traits, we would recommend using
a combination of 2 traits for genetic selection. Indeed,
MeP_direct and MeP_RC appear to be the most corre-
lated equations, due to their similarities in terms of unit
(g/d) and methodology (partial least square regression
and spectral data treatments). By combining the 2 equa-
tions, it is expected that the error associated with each
of the 2 equations taken independently will be reduced.

CONCLUSIONS

Predicting methane emissions from milk MIR spectra provides a larger dataset for genetic studies and is more suitable for genetic selection than direct measurements. The heritability estimates of milk MIR predicted CH4 traits were moderate, indicating that genetic selection could reduce CH4 emissions in dairy cows. The high within-lactation and between-lactation genetic correlation estimates demonstrate the feasibility of using a rather simple model, such as a repeatability model which is currently used for French single-step genomic evaluation. The low to moderate genetic correlation estimates between CH4 traits indicate that they are genetically distinct, highlighting the need for judicious selection of the trait to be included into breeding objectives. One criterion is the genetic correlation with other traits of interest, in particular milk-related traits. The majority of the CH4 traits were unfavorably correlated with milk production and favorably correlated with fat and protein contents. Further studies are required to validate these results before implementing a genetic selection to reduce CH4 emissions of dairy cows.

NOTES

Data were collected within the METHABREED program funded by APIS-GENE (Paris, France). S. Fresco is the recipient of a CIFRE PhD grant from Eliance (Paris, France), with the financial support of the Association Nationale de la Recherche et de la Technologie (Paris, France; grant number CIFRE 2021/1241) and APIS-GENE. The authors gratefully acknowledge the milk recoding companies for providing the MIR spectra. Many thanks to Jean-Pierre Allard and Guillaume Courtois (ESTEL Numérique, Nancy, France) for calculating the CH4 predictions for all the spectra. The authors are especially grateful to CEL 25-90 (Roulans, France) for providing the supplementary spectral data for evaluation of the applicability of the prediction equations. Thanks to Hélène Leclerc (Eliance, Paris, France) for her help in data curation and definition of the genetic model. The data (genotypes and phenotypes) used in this study were

made available by the French milk recording companies.
Restrictions apply to the availability of these commercial
data: they were used under license for the current study
and cannot be made publicly available. The authors can
be contacted for a reasonable request. This study was
based on data routinely collected on dairy farms during
milk recording. We did not perform any experiment on
animals; therefore, no ethical approval was required. The
authors have not stated any conflicts of interest.

**Nonstandard abbreviations used:** FC = fat content; FPCM = fat- and protein-corrected milk; GH = Mahalanobis distance; MeI = methane intensity; MeP = methane production; MeP_direct = prediction of methane production; MeP_FA = prediction of methane production using the equation developed by Chilliard et al. (2009); MeP_indirect = prediction of methane intensity multiplied by the recorded fat- and protein-corrected milk (FPCM), using the equations developed by Fresco et al. (2024); MeP_RC = prediction of methane emissions in g/d using the equation developed by Vanlierde et al. (2021); MeY = methane yield; MIR = mid-infrared; MY = milk yield; PC = protein content; R²cv = coefficient of determination in cross-validation; SEcv = standard error in cross-validation.

REFERENCES

Beauchemin, K. A., E. M. Ungerfeld, A. L. Abdalla, C. Alvarez, C. Arndt, P. Becquet, C. Benchaar, A. Berndt, R. M. Mauricio, T. A. McAllister, W. Oyhantçabal, S. A. Salami, L. Shalloo, Y. Sun, J. Tricarico, A. Uwizeye, C. De Camillis, M. Bernoux, T. Robinson, and E. Kebreab. 2022. Invited review: Current enteric methane mitigation options. J. Dairy Sci. 105:9297-9326. https://doi.org/10.3168/jds.2022-22091.

Benaouda, M., C. Martin, X. Li, E. Kebreab, A. N. Hristov, Z. Yu, D. R. Yáñez-Ruiz, C. K. Reynolds, L. A. Crompton, J. Dijkstra, A. Bannink, A. Schwarm, M. Kreuzer, M. McGee, P. Lund, A. L. F. Hellwing, M. R. Weisbjerg, P. J. Moate, A. R. Bayat, K. J. Shingfield, N. Peiren, and M. Eugène. 2019. Evaluation of the performance of existing mathematical models predicting enteric methane emissions from ruminants: Animal categories and dietary mitigation strategies. Anim. Feed Sci. Technol. 255:114207. https://doi.org/10.1016/j.anifeedsci.2019.114207.

Boichard, D., and B. Bonaïti. 1987. Genetic parameters for first lactation dairy traits in Friesian, Montbéliarde and Normande breeds. Genet. Sel. Evol. 19:337-350. https://doi.org/10.1186/1297-9686-19-3-337.

Breider, I. S., E. Wall, and P. C. Garnsworthy. 2019. Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows. J. Dairy Sci. 102:7277-7281. https://doi.org/10.3168/jds.2018-15909.

Chilliard, Y., C. Martin, J. Rouel, and M. Doreau. 2009. Milk fatty acids in dairy cows fed whole crude linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci. 92:5199-5211. https://doi.org/10.3168/jds.2009-2375.

Secretariat, C. C. A. C. (Climate and Clean Air Coalition Secretariat). 2021. Global Methane Pledge. Accessed Oct. 11, 2024. https://www.globalmethanepledge.org/.

Coppa, M., A. Vanlierde, M. Bouchon, J. Jurquet, M. Musati, F. Dehareng, and C. Martin. 2022. Methodological guidelines: Cow milk mid-infrared spectra to predict reference enteric methane data collected by an automated head-chamber system. J. Dairy Sci. 105:9271-9285. https://doi.org/10.3168/jds.2022-21890.
Cottle, D. J., J. V. Nolan, and S. G. Wiedemann. 2011. Ruminant enteric methane mitigation: A review. Anim. Prod. Sci. 51:491–514. https://doi.org/10.1071/AN10163.

Dehareng, F., C. Delfosse, E. Froidmont, H. Soyeurt, C. Martin, N. Gengler, A. Vanlierde, and P. Dardenne. 2012. Potential use of milk mid-infrared spectra to predict individual methane emission of dairy cows. Animal 6:1694–1701. https://doi.org/10.1017/S1751731112000456.

Druet, T., F. Jaffrézic, D. Boichard, and V. Ducrocq. 2003. Modeling lactation curves and estimation of genetic parameters for first lactation test-day records of French Holstein cows. J. Dairy Sci. 86:2480–2490. https://doi.org/10.3168/jds.S0022-0302(03)73842-9.

Fresco, S., A. Vanlierde, D. Boichard, R. Lefebvre, M. Gaborit, R. Bore, S. Fritz, N. Gengler, and P. Martin. 2024. Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra. Animal 18:101200. https://doi.org/10.1016/j.animal.2024.101200.

González-Recio, O., J. López-Paredes, L. Ouatahar, N. Charfeddine, E. Ugarte, R. Alenda, and J. A. Jiménez-Montero. 2020. Mitigation of greenhouse gases in dairy cattle via genetic selection: 2. Incorporating methane emissions into the breeding goal. J. Dairy Sci. 103:7210–7221. https://doi.org/10.3168/jds.2019-17598.

Grelet, C., J. A. Fernández Pierna, P. Dardenne, V. Baeten, and F. Dehareng. 2015. Standardization of milk mid-infrared spectra from a European dairy network. J. Dairy Sci. 98:2150–2160. https://doi.org/10.3168/jds.2014-8764.

Grelet, C., J. A. F. Pierna, P. Dardenne, H. Soyeurt, A. Vanlierde, F. Colinet, C. Bastin, N. Gengler, V. Baeten, and F. Dehareng. 2017. Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models. J. Dairy Sci. 100:7910–7921. https://doi.org/10.3168/jds.2017-12720.

Hammond, K. J., L. A. Crompton, A. Bannink, J. Dijkstra, D. R. Yáñez-Ruiz, P. O'Kiely, E. Kebreab, M. A. Eugène, Z. Yu, K. J. Shingfield, A. Schwarm, A. N. Hristov, and C. K. Reynolds. 2016. Review of current in vivo measurement techniques for quantifying enteric methane emission from ruminants. Anim. Feed Sci. Technol. 219:13–30. https://doi.org/10.1016/j.anifeedsci.2016.05.018.

Kamalanathan, S., K. Houlahan, F. Miglior, T. C. S. Chud, D. J. Seymour, D. Hailemariam, G. Plastow, H. R. de Oliveira, C. F. Baes, and F. S. Schenkel. 2023. Genetic analysis of methane emission traits in Holstein dairy cattle. Animals (Basel) 13:1308. https://doi.org/10.3390/ani13081308.

Kandel, P. B., M.-L. Vanrobays, A. Vanlierde, F. Dehareng, E. Froidmont, N. Gengler, and H. Soyeurt. 2017. Genetic parameters of mid-infrared methane predictions and their relationships with milk production traits in Holstein cattle. J. Dairy Sci. 100:5578–5591. https://doi.org/10.3168/jds.2016-11954.

Lassen, J., and G. F. Difford. 2020. Review: Genetic and genomic selection as a methane mitigation strategy in dairy cattle. Animal 14:s473–s483. https://doi.org/10.1017/S1751731120001561.

Lassen, J., and P. Løvendahl. 2016. Heritability estimates for enteric methane emissions from Holstein cattle measured using noninvasive methods. J. Dairy Sci. 99:1959–1967. https://doi.org/10.3168/jds.2015-10012.

Lopes, L. S. F., F. S. Schenkel, K. Houlahan, C. M. Rochus, G. A. Oliveira Jr., H. R. Oliveira, F. Miglior, L. M. Alcantara, D. Tulpan, and C. F. Baes. 2024. Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first lactation Holstein cows. J. Dairy Sci. 107:4704–4713. https://doi.org/10.3168/jds.2023-23751.

Malchiodi, F., H. R. Oliveira, S. Narayana, A. Fleming, H. Sweett, S. Shadpour, J. Jamrozik, G. Kistemaker, P. Sullivan, F. S. Schenkel, B. Van Doormaal, C. F. Baes, and F. Miglior. 2023. Genomic evaluation to reduce enteric methane emissions in Holstein cattle. Page 277 in 74th Annual Meeting of the European Federation of Animal Science. Lyon, France. Wageningen Academic Publishers.

Manzanilla-Pech, C. I. V., G. F. Difford, P. Løvendahl, R. B. Stephansen, and J. Lassen. 2022a. Genetic (co-)variation of methane emissions, efficiency, and production traits in Danish Holstein cattle along and across lactations. J. Dairy Sci. 105:9799–9809. https://doi.org/10.3168/jds.2022-22121.

Manzanilla-Pech, C. I. V., G. F. Difford, G. Sahana, H. Romé, P. Løvendahl, and J. Lassen. 2022b. Genome-wide association study for methane emission traits in Danish Holstein cattle. J. Dairy Sci. 105:1357–1368. https://doi.org/10.3168/jds.2021-20410.

Manzanilla-Pech, C. I. V., P. Løvendahl, D. Mansan Gordo, G. F. Difford, J. E. Pryce, F. Schenkel, S. Wegmann, F. Miglior, T. C. Chud, P. J. Moate, S. R. O. Williams, C. M. Richardson, P. Stothard, and J. Lassen. 2021. Breeding for reduced methane emission and feed-efficient Holstein cows: An international response. J. Dairy Sci. 104:8983–9001. https://doi.org/10.3168/jds.2020-19889.

Manzanilla-Pech, C. I. V., R. B. Stephansen, G. F. Difford, P. Løvendahl, and J. Lassen. 2022c. Selecting for feed efficient cows will help to reduce methane gas emissions. Front. Genet. 13:885932. https://doi.org/10.3389/fgene.2022.885932.

Meyer, K. 2007. WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). J. Zhejiang Univ. Sci. B 8:815–821. https://doi.org/10.1631/jzus.2007.B0815.

Negussie, E., Y. de Haas, F. Dehareng, R. J. Dewhurst, J. Dijkstra, N. Gengler, D. P. Morgavi, H. Soyeurt, S. van Gastelen, T. Yan, and F. Biscarini. 2017. Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions. J. Dairy Sci. 100:2433–2453. https://doi.org/10.3168/jds.2016-12030.

Oliveira, H. R., H. Sweett, S. Narayana, A. Fleming, S. Shadpour, F. Malchiodi, J. Jamrozik, G. Kistemaker, P. Sullivan, F. Schenkel, D. Hailemariam, P. Stothard, G. Plastow, B. Van Doormaal, M. Lohuis, J. Shannon, C. Baes, and F. Miglior. 2024. Symposium review: Development of genomic evaluation for methane efficiency in Canadian Holsteins. JDS Commun. 5:756–760. https://doi.org/10.3168/jdsc.2023-0431.

Pryce, J. E., and M. Haile-Mariam. 2020. Symposium review: Genomic selection for reducing environmental impact and adapting to climate change. J. Dairy Sci. 103:5366–5375. https://doi.org/10.3168/jds.2019-17732.

Pszczola, M., M. P. L. Calus, and T. Strabel. 2019. Short communication: Genetic correlations between methane and milk production, conformation, and functional traits. J. Dairy Sci. 102:5342–5346. https://doi.org/10.3168/jds.2018-16066.

Pszczola, M., K. Rzewuska, S. Mucha, and T. Strabel. 2017. Heritability of methane emissions from dairy cows over a lactation measured on commercial farms. J. Anim. Sci. 95:4813–4819. https://doi.org/10.2527/jas2017.1842.

Richardson, C. M., T. T. T. Nguyen, M. Abdelsayed, P. J. Moate, S. R. O. Williams, T. C. S. Chud, F. S. Schenkel, M. E. Goddard, I. van den Berg, B. G. Cocks, L. C. Marett, W. J. Wales, and J. E. Pryce. 2021. Genetic parameters for methane emission traits in Australian dairy cows. J. Dairy Sci. 104:539–549. https://doi.org/10.3168/jds.2020-18565.

Shadpour, S., T. C. S. Chud, D. Hailemariam, G. Plastow, H. R. Oliveira, P. Stothard, J. Lassen, F. Miglior, C. F. Baes, D. Tulpan, and F. S. Schenkel. 2022. Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. J. Dairy Sci. 105:8272–8285. https://doi.org/10.3168/jds.2021-21176.

Tedeschi, L. O., A. L. Abdalla, C. Álvarez, S. W. Anuga, J. Arango, K. A. Beauchemin, P. Becquet, A. Berndt, R. Burns, C. De Camillis, J. Chará, J. M. Echazarreta, M. Hassouna, D. Kenny, M. Mathot, R. M. Mauricio, S. C. McClelland, M. Niu, A. A. Onyango, R. Parajuli, L. G. R. Pereira, A. del Prado, M. Paz Tieri, A. Uwizeye, and E. Kebreab. 2022. Quantification of methane emitted by ruminants: A review of methods. J. Anim. Sci. 100:skac197. https://doi.org/10.1093/jas/ska197.

van Breukelen, A. E., M. N. Aldridge, R. F. Veerkamp, L. Koning, L. B. Sebek, and Y. de Haas. 2023. Heritability and genetic correlations between enteric methane production and concentration recorded by GreenFeed and sniffers on dairy cows. J. Dairy Sci. 106:4121–4132. https://doi.org/10.3168/jds.2022-22735.

Vanlierde, A., F. Dehareng, N. Gengler, E. Froidmont, S. McParland, M. Kreuzer, M. Bell, P. Lund, C. Martin, B. Kuhla, and H. Soyeurt. 2021. Improving robustness and accuracy of predicted daily meth-
ane emissions of dairy cows using milk mid-infrared spectra. J. Sci.
Food Agric. 101:3394-3403. https://doi.org/10.1002/jsfa.10969.

Vanlierde, A., M.-L. Vanrobays, F. Dehareng, E. Froidmont, H. Soyeurt, S. McParland, E. Lewis, M. H. Deighton, F. Grandl, M. Kreuzer, B. Gredler, P. Dardenne, and N. Gengler. 2015. Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. J. Dairy Sci. 98:5740-5747. https://doi.org/10.3168/jds.2014-8436.

Vanlierde, A., M.-L. Vanrobays, N. Gengler, P. Dardenne, E. Froidmont, H. Soyeurt, S. McParland, E. Lewis, M. H. Deighton, M. Mathot, and F. Dehareng. 2016. Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes. Anim. Prod. Sci. 56:258-264. https://doi.org/10.1071/AN15590.

Vanrobays, M.-L., C. Bastin, J. Vandenplas, H. Hammami, H. Soyeurt, A. Vanlierde, F. Dehareng, E. Froidmont, and N. Gengler. 2016. Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra. J. Dairy Sci. 99:7247-7260. https://doi.org/10.3168/jds.2015-10646.

ORCIDS

S. Fresco, https://orcid.org/0000-0002-6091-4328

D. Boichard, https://orcid.org/0000-0003-0361-2961

S. Fritz, https://orcid.org/0000-0002-3378-1048

P. Martin, https://orcid.org/0000-0003-2950-2728