# Short communication: First insights into response to selection for reducing methane emissions in dairy cows

S. Fresco, D. Boichard, S. Aguerre, S. Fritz, Pauline Martin

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S. Fresco, D. Boichard, S. Aguerre, S. Fritz, Pauline Martin. Short communication: First insights into response to selection for reducing methane emissions in dairy cows. Animal, 2025, 19 (12), pp.101700. 10.1016/j.animal.2025.101700. hal-05388818

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# Short communication: First insights into response to selection for reducing methane emissions in dairy cows

<figure><img src="image_2.png" /></figure>

S. Fresco<sup>a,b,*</sup>, D. Boichard<sup>b</sup>, S. Aguerre<sup>b,c</sup>, S. Fritz<sup>a,b</sup>, P. Martin<sup>b</sup>

## ARTICLE INFO

**Article history:**

Received 18 June 2025
Revised 29 October 2025
Accepted 30 October 2025
Available online 7 November 2025

**Keywords:**

Cattle
Environmental footprint
Expected genetic gain
Milk mid-infrared prediction
Total merit index

## ABSTRACT

Genetic selection against methane emissions is one of the levers to reduce the environmental impact of dairy production. Once genetic evaluation of methane emissions is available, the next step is to include this trait in breeding objectives. In this study, we investigated the consequences of three selection scenarios on methane emission and traits currently under selection for the Montbéliarde dairy cattle breed. We estimated the expected genetic progress based on genetic (co)variances between traits currently under selection and two methane traits predicted from milk mid-infrared spectra. The first scenario, representing the current total merit index, revealed the necessity of incorporating a methane trait to reduce methane emissions. The second scenario demonstrated the feasibility of achieving –1% annual methane emissions, but at the expense of a reduced genetic progress on the other selected traits, in particular milk production and fertility, which reached only 66 and 65%, respectively, of the progress achieved in the first scenario. The third scenario, maintaining 90% of the current genetic gain on existing traits while incorporating methane traits, achieved approximately –0.38% annual methane emissions. These preliminary results illustrate the feasibility of genetically reducing methane emissions through including methane traits into breeding objectives.

© 2025 The Author(s). Published by Elsevier B.V. on behalf of The animal Consortium. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

## Implications

Genetic selection to reduce methane emissions from dairy cows has the potential to mitigate climate change. However, the impact of such a selection on the genetic progress of other traits of interest must first be evaluated. This study estimates the impact of the current total merit index for Montbéliarde cows on methane emissions. It shows that achieving –1% annual methane emissions is feasible albeit at the cost of somewhat reduced genetic progress on other traits of interest, particularly traits with an economic value to breeders.

## Introduction

To meet international climate targets and reduce the environmental footprint of the agricultural sector, it is essential to decrease methane (CH₄) emissions from dairy cattle. Genetic selection is of particular interest due to its cumulative and permanent

effects, along with other main mitigation strategies, including herd management or feed additives. Several countries are implementing national genetic evaluations aimed at reducing CH₄ emissions in dairy cattle. Some of these rely on CH₄ predictions derived from milk mid-infrared (MIR) spectra, whose genetic determinism has been documented (Oliveira et al., 2024; Fresco et al., 2024a). In France, a genomic evaluation of predicted CH₄ emissions will be available for the Holstein, Montbéliarde, Normande, Abondance, Tarentaise, Simmental, Brown Swiss, and Vosgienne breeds from 2026 onwards. The next step is to evaluate the impact of incorporating CH₄-related traits into selection objectives while maintaining genetic progress on other economically important traits. This study focuses on the Montbéliarde breed, the leading non-Holstein dairy breed in France with 559 000 lactating cows in 2024 ([https://idele.fr/detail-article/chiffres-cles-bovins-2024-productions-lait-et-viande](https://idele.fr/detail-article/chiffres-cles-bovins-2024-productions-lait-et-viande)). This breed is characterised by an extensive historical dataset, wide national distribution, and high genotyping rates. Our aim was to estimate the effects of (1) the current total merit index (TMI) and (2) alternative aggregate genotypes incorporating CH₄ traits on the genetic progress on predicted CH₄ (pCH₄) and other selected traits.

<footnote><sup>a</sup>Eliance, 149 rue de Bercy, 75595 Paris, France</footnote>

<footnote><sup>b</sup>Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France</footnote>

<footnote><sup>c</sup>Institut de l'Elevage, 149 rue de Bercy, 75012 Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: solene.fresco@eliance.fr (S. Fresco).</footnote>
## Material and methods

### *Traits in the current total merit index*

We used a simplified version of the current French Montbéliarde TMI due to some unavailable covariance estimates and to avoid increased uncertainty. It includes 10 traits divided into four sub-indexes:

- Milk production: fat and protein yields and contents;

- Udder health: somatic cell count and mastitis;

- Fertility: cow conception rate;

- Conformation traits: udder front attachment, udder depth, and body depth.

Genetic parameters were estimated using phenotypes adjusted for non-genetic effects from routine evaluation.

### *Methane traits*

The two pCH4 traits in g/d studied were predicted from milk MIR spectra and will contribute equally to the upcoming official index. MeP_direct was predicted using the equation from Fresco et al. (2024b), based on a multibreed (Holstein, Montbéliarde, and Abondance) calibration population of 235 cows phenotyped with GreenFeed (C-Loc Inc., Rapid City, SD, USA). MeP_RC was predicted using the equation from Vanlierde et al. (2021), based on a multibreed (Holstein, Brown Swiss, Jersey, Red Holstein, Swedish Red Crossbred, Norwegian Red, and Holstein Crossbred) calibration population of 299 cows phenotyped with respiration chambers or the SF6 tracer gas method. Cross-validation R² (RMSE) were 0.38 (60 g/d) and 0.60 (61 g/d), respectively.

Spectra were collected from test-day records between 70 and 200 days in milk from A- and B-type protocols (Fresco et al., 2024a). Selected lactations started between 2015 and 2021 included at least two records, for a total of 480 804 MIR spectra from 177 118 Montbéliarde primiparous cows. Mean predicted emissions (SD) were 403 (36) and 422 (34) g/d for MeP_direct and MeP_RC, respectively.

### *Estimation of genetic progress*

The aggregate genotype h is a linear combination of the breeding values of the traits g with weights w such that $h = w \cdot g$. Accordingly, var(h) = w \cdot Gw, with G the genetic variance-covariance matrix between all traits.

Theoretical genetic progress was estimated considering the four selection pathways (Table 1) following Rendel and Robertson (1950). We assumed that all genomic predictions of young candidates had the same high accuracy of 0.9 (reliability Rel = 0.81) and that environmental correlations between them were zero. This strong assumption is realistic in the Montbéliarde breed because of the very large reference population (more than 300 000 cows and 4 000 bulls). Genetic progress was computed with the following formula in R software (version 3.6.2, R Core Team, Vienna, Austria):

<table><caption>**Table 1**
Selection intensity (i) and generation interval in years (t) for the four selection pathways in cattle.</caption><thead><tr><th>Pathways</th><th>i</th><th>t</th></tr></thead><tbody><tr><td>Sire-to-cow</td><td>3</td><td>2.5</td></tr><tr><td>Sire-to-bull</td><td>3</td><td>3.5</td></tr><tr><td>Dam-to-cow</td><td>0</td><td>5</td></tr><tr><td>Dam-to-bull</td><td>3</td><td>5</td></tr></tbody></table>

$$ \Delta G_j = \frac{\sum_i}{\sigma_{\hat{h}} \sum_t} \text{cov}(g_j, \hat{h}) $$

where $\Delta G_j$ is the annual expected genetic progress for trait j, $\sum_i$ the sum of the selection intensities for the four selection pathways, $\sum_t$ the sum of the generation intervals for the four selection pathways, $\sigma_{\hat{h}}$ the genetic standard deviation of the total merit index, and $\text{cov}(g_j, \hat{h})$ the covariance between the breeding value of trait j and the total merit index corresponding to the aggregate genotype.

Assuming zero environmental correlations between estimated genomic breeding values – a reasonable approximation with a large reference population – we applied this approximation:

$$ \text{cov}(g_j, \hat{h}) \approx \text{cov}(g_j, w'\hat{g}) \approx \text{Rel cov}(g_j, w'g). $$

The (co)variances of traits in the current TMI were those routinely used for genetic evaluation (Table 2). Variance components for the two pCH4 traits were estimated with a univariate repeatability model in Wombat (Meyer, 2007), following Fresco et al. (2025) and the covariances between pCH4 and the other traits were estimated as follows. The pCH4 phenotypes were corrected for environmental effects – including herd × test-day, year × month of calving, age at calving, milk recording protocol, and days in milk modelled with third-order Legendre polynomials – and averaged per cow to obtain a single value per individual. These values were used in a bivariate linear model to estimate genetic covariances with the other traits using Wombat. The latter models included only birth year as a fixed effect, as phenotypes were already adjusted for other non-genetic effects.

### *Scenarios of response to selection*

This study compared expected genetic progress across three scenarios. The first scenario ($S_{ref}$), a simplified version of the current TMI, comprised four sub-indexes: milk production, udder health, fertility, and conformation traits. In the second scenario ($S_{CH4}$), the aggregate genotype included the same four sub-indexes with proportionally reduced weights to incorporate the CH4 sub-index, with a weight enabling –1% annual pCH4 reduction (–4.1 g/d). In the third scenario ($S_{90}$), the aggregate genotype incorporates the CH4 sub-index while maintaining at least 90% of the expected progress for the $S_{ref}$ scenario sub-indexes.

## Results and discussion

The results of the selection scenarios were expressed as the percentage of expected annual genetic progress averaged per sub-index. For milk production, udder health, fertility, and conformation traits, a genetic progress of 100% corresponded to the progress obtained in the $S_{ref}$ scenario. For the CH4 sub-index, a 100% corresponded to –1% annual pCH4 reduction.

Selection with the current TMI ($S_{ref}$) resulted in a 0.57 g/d increase in pCH4 emissions (208 g/year; Table 3), lower than the increases of around 7.4, 1.7, and 5.8 g/d reported in Manzanilla-Pech et al. (2022), de Haas et al. (2021), and González-Recio et al. (2020), respectively, for selection scenarios that did not include a CH4 trait. Our lower value is likely optimistic, probably reflecting the use of pCH4, as MIR prediction involves loss of information, in particular, genetic variance. Moreover, it may underestimate genetic correlations with traits in the aggregate genotype, in particular milk yield. Nevertheless, both these previous studies and our results emphasised the importance of incorporating a CH4-related trait into the breeding objective to achieve a reduction of CH4 emissions.
<caption>**Table 2**</caption>

Genetic variances (diagonal) and covariances (above the diagonal) for the two methane traits in g/d predicted from milk mid-infrared spectra and the traits under selection in the French Montbéliarde cows.

<table><thead><tr><th rowspan="3">Traits</th><th colspan="10">Sub-indexes</th></tr><tr><th colspan="2">Methane traits</th><th colspan="4">Milk production traits</th><th colspan="2">Udder health</th><th colspan="3">Fertility</th><th colspan="2">Conformation traits</th></tr><tr><th>MeP_direct</th><th>MeP_RC</th><th>FY</th><th>PY</th><th>FC</th><th>PC</th><th>SCC</th><th>Mastitis</th><th>CCR</th><th>UFA</th><th>UD</th><th>BD</th></tr></thead><tbody><tr><td>MeP_direct<fnref n="1" /></td><td>199.0</td><td>151.4</td><td>55.44</td><td>11.96</td><td>3.940</td><td>1.321</td><td>0.742</td><td>0.104</td><td>0.142</td><td>0.823</td><td>1.179</td><td>1.273</td></tr><tr><td>MeP_RC<fnref n="2" /></td><td></td><td>330.9</td><td>14.62</td><td>3.655</td><td>2.042</td><td>-0.481</td><td>0.521</td><td>0.062</td><td>0.081</td><td>0.318</td><td>0.325</td><td>-1.152</td></tr><tr><td>Fat yield (FY)</td><td></td><td></td><td>733.0</td><td>492.4</td><td>6.567</td><td>0.000</td><td>1.354</td><td>0.531</td><td>-0.586</td><td>0.000</td><td>-3.377</td><td>8.731</td></tr><tr><td>Protein yield (PY)</td><td></td><td></td><td></td><td>457.7</td><td>0.000</td><td>0.000</td><td>2.139</td><td>0.157</td><td>-0.387</td><td>0.000</td><td>-2.669</td><td>6.899</td></tr><tr><td>Fat content (FC)</td><td></td><td></td><td></td><td></td><td>5.883</td><td>2.017</td><td>0.000</td><td>-0.030</td><td>-0.018</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Protein content (PC)</td><td></td><td></td><td></td><td></td><td></td><td>1.921</td><td>0.173</td><td>-0.017</td><td>0.015</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Somatic cell count (SCC)</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.250</td><td>0.015</td><td>-0.011</td><td>-0.020</td><td>0.143</td><td>0.091</td></tr><tr><td>Mastitis</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.002</td><td>-0.004</td><td>-0.002</td><td>0.015</td><td>0.005</td></tr><tr><td>Cow conception rate (CCR)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.005</td><td>0.004</td><td>-0.009</td><td>-0.021</td></tr><tr><td>Udder front attachment (UFA)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.372</td><td>-0.087</td><td>-0.093</td></tr><tr><td>Udder depth (UD)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.389</td><td>0.086</td></tr><tr><td>Body depth (BD)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>2.600</td></tr></tbody></table>

<caption>**Table 3**</caption>

Weights and expected percent of genetic progress (%ΔG) associated with each trait in the current total merit index and the alternative scenarios including methane in French Montbéliarde cows.

<table><thead><tr><th rowspan="3">Sub-indexes</th><th colspan="4">Scenarios<fnref n="1" /></th></tr><tr><th colspan="2">S<sub>ref</sub></th><th colspan="2">S<sub>CH4</sub></th><th colspan="2">S<sub>90</sub></th></tr><tr><th>Weights</th><th>%ΔG</th><th>Weights</th><th>%ΔG</th><th>Weights</th><th>%ΔG</th></tr></thead><tbody><tr><td>Milk production</td><td>0.41</td><td>100</td><td>0.31</td><td>66.5</td><td>0.37</td><td>93.5</td></tr><tr><td>Udder health</td><td>0.27</td><td>100</td><td>0.20</td><td>103.6</td><td>0.24</td><td>102.6</td></tr><tr><td>Fertility</td><td>0.15</td><td>100</td><td>0.11</td><td>64.8</td><td>0.14</td><td>90.8</td></tr><tr><td>Conformation traits</td><td>0.17</td><td>100</td><td>0.13</td><td>86.2</td><td>0.15</td><td>95.6</td></tr><tr><td>Methane</td><td>0.00</td><td>-13.9</td><td>0.25</td><td>100.0</td><td>0.10</td><td>37.9</td></tr></tbody></table>

A 100% genetic progress corresponds to 1% annual reduction in methane emissions and to the S<sub>ref</sub> progress for the other traits.

¹ S<sub>ref</sub> = simplified current total merit index, S<sub>CH4</sub> = scenario with a methane sub-index enabling -1% annual methane emission reduction, and S<sub>90</sub> = scenario with a methane sub-index allowing ≥90% of expected genetic progress on other sub-indexes.

We modelled two alternative scenarios, including a CH₄ sub-index, S<sub>CH4</sub> and S<sub>90</sub>, with respective weights of 25 and 10% in the aggregate genotype. It should be emphasised that these weights are very high compared to the market value of an eqCO₂ tonne. Indeed, under French conditions, where one genetic standard deviation for milk production is valued around €70, the weight for CH₄ emissions corresponds to around €280 and €140/tonne of CO₂ for S<sub>CH4</sub> and S<sub>90</sub> scenarios. Therefore, these weights anticipate more constrained future conditions (e.g., future high CO₂ value, regulations, methane quotas, incentives, or penalties), which strongly increase the economic value of methane.

S<sub>CH4</sub> and S<sub>90</sub> yielded net reductions in pCH₄ emissions of 4.1 and 1.6 g/d, respectively, consistent with previous studies reporting reductions ranging from around 0.3 to 1.8 g/d under active selection against CH₄ emissions (Richardson et al., 2025; Manzanilla-Pech et al., 2022; de Haas et al., 2021; González-Recio et al., 2020).

Adding a new sub-index slowed down genetic progress on currently selected sub-indexes (Table 3) due to reduced relative weights and unfavourable genetic correlations with the new trait. In the S<sub>90</sub> scenario, genetic progress on current sub-indexes was maintained at ≥ 90%, resulting in moderate progress on the CH₄ sub-index (-1.6 g/d). In the S<sub>CH4</sub> scenario, the largest reductions were observed for milk production and fertility (66.5 and 64.8% of S<sub>ref</sub> progress, respectively) while conformation traits were moderately affected (86.2%). In contrast, the udder health increased (103.6%), as the sub-index is favourably correlated with both MeP_RC and MeP_direct but unfavourably correlated with milk production traits. Similarly, González-Recio et al. (2020) observed

a reduced genetic progress for milk production and fertility, stable conformation traits, and increased udder health with a -1.1 g/d CH₄ reduction. Conversely, Manzanilla-Pech et al. (2022) observed only slight reductions in milk production progress (95-99% of genetic progress achieved), because the economic value associated with CH₄ emissions was very low compared to the one for milk production. In de Haas et al. (2021), a -1.5 g/d CH₄ reduction resulted in a genetic progress on milk yield of only 8.7 to 46.0% of their reference progress.

Our study provides a theoretical estimate of expected genetic progress, based on genetic (co)variances, selection intensity, and generation interval. It is not intended as a decision-making tool, as some parameters could be further refined, and the aggregate genotype considered here is a simplified version of that currently applied by breeders. In addition, estimating the genetic correlation between pCH₄ and true CH₄ emissions would be valuable, as it could lead to more accurate – and potentially less favourable – predictions of genetic trends. This study aimed to provide initial insights and highlight trends in incorporating a CH₄ trait into the Montbéliarde TMI.

The weight of the CH₄ sub-index is determined by the breeding organisations which define the Montbéliarde TMI. They will need to establish the minimum genetic progress required on the priority traits. This requires adjusting individual trait weights, whereas our study applied a proportional reduction.

Indirect traits, including herd life, feed efficiency, and body maintenance requirements, also influence CH₄ emissions (Richardson et al., 2025). Including these indirect traits could

<footnote n="1">¹ MeP_direct = predicted methane emissions in g/d (Fresco et al., 2024a, b).</footnote>

<footnote n="2">² MeP_RC = predicted methane emissions in g/d (Vanlierde et al., 2021).</footnote>
reduce the weight needed for the direct CH₄ trait, minimising the reduction in genetic progress for economically important traits. This approach is currently explored in the Methane 2030 project for dairy and beef cattle breeds in France.

## Conclusion

The current French Montbéliarde TMI slightly increases predicted CH₄ emissions. Achieving a 1% annual reduction would require a substantial weighting of the CH₄ sub-index, which would significantly slow down genetic progress on milk production and fertility traits. As CH₄ emissions do not yet have economic value at the farm level, reducing them will require appropriate public policies and a concerted effort across the entire sector.

## Ethics approval

Not applicable.

## Data and model availability statement

Data are owned by the French farmers and cannot be publicly disclosed. The data that support the study are available from the authors upon reasonable request.

## Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) did not use any AI and AI-assisted technologies.

## Author ORCIDs

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

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

S. Aguerre: https://orcid.org/0009-0008-2983-3782.

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

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

## CRediT authorship contribution statement

S. Fresco: Writing – original draft, Visualisation, Methodology, Formal analysis. D. Boichard: Writing – review & editing, Supervision, Methodology. S. Aguerre: Writing – review & editing, Formal analysis. S. Fritz: Writing – review & editing, Supervision, Project administration. P. Martin: Writing – review & editing, Supervision, Methodology.

## Declaration of interest

None.

## Acknowledgements

The authors thank the French milk recording companies for providing the milk MIR spectra, and Jean-Pierre Allard and Guil-laume Courtois (ESTEL Numérique, France) for calculating the methane phenotypes.

## Financial support statement

This work was part of the Methabreed project, funded by APIS-GENE (Paris, France). S. Fresco is a recipient of a CIFRE PhD grant from Eliance, with the financial support of the Association Nationale de la Recherche et de la Technologie (grant number CIFRE n°2021/1241) and APIS-GENE (Paris, France).

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