# Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification

T.F. Akinropo, P. Ricci, C. Faverin, V. Ciganda, C. Muñoz, E. Ungerfeld, N.

Urrutia, R. Rodriguez, D.P. Morgavi, Maguy Eugène

► To cite this version:

T.F. Akinropo, P. Ricci, C. Faverin, V. Ciganda, C. Muñoz, et al. Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification. Animal, 2025, 19 (11), pp.101665. 10.1016/j.animal.2025.101665. hal-05349869

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# Improved prediction by enteric methane emission models in ruminant production systems by integrating climate classification

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

T.F. Akinropo<sup>a</sup>, P. Ricci<sup>b</sup>, C. Faverin<sup>b</sup>, V. Ciganda<sup>c</sup>, C. Muñoz<sup>d</sup>, E. Ungerfeld<sup>e</sup>, N. Urrutia<sup>d</sup>, R. Rodriguez<sup>d</sup>, D.P. Morgavi<sup>a</sup>, M. Eugène<sup>a,f,*</sup>

## ARTICLE INFO

**Article history:**

Received 13 March 2025

Revised 8 September 2025

Accepted 16 September 2025

Available online 23 September 2025

**Keywords:**

Agroclimatic zone

Animal species

Greenhouse gas

Mitigation strategies

Model evaluation

## ABSTRACT

Several mathematical models predict enteric methane (CH₄) production (g/d) from ruminants. These models were developed from data originated from different regions and production systems and are not always applicable elsewhere. This study evaluated the performance of selected existing models for predicting enteric CH₄ emissions of ruminants raised in diverse production systems in Argentina, Brazil, Chile and Uruguay. Climate was classified using the Köppen climate classification, while diets were classified by composition, such as high- or low-NDF, ether extract (EE), starch content and NDF digestibility. These classifications were applied to the diets fed to cattle (dairy, beef) and sheep. Models were evaluated and ranked by the lowest root mean square prediction error (RMSPE) and the ratio of RMSPE to SD of observed values (RSR). Models from the Intergovernmental Panel on Climate Change and those developed with Latin American data were used as reference. In temperate, no dry season, hot summer climates, all models performed poorly (RSR > 1) for dairy cattle. By diet composition, four models performed well (RSR < 1) for high-NDF diets. For beef cattle, the model by Yan et al. (2009) developed for forage-fed beef cattle performed best (RSR = 0.85) as all diets had high NDF. For sheep, the model by Congio et al. (2022a), which incorporates DM intake (DMI) and feeding level, performed best (RSR = 0.63); however, for high-NDF diets, the model from Belanche et al. (2023) performed best. In temperate, no dry season, warm summer (Cfb) climates, models by Mills et al. (2003), which included DMI, performed best for dairy cattle (RSR = 0.78), including when assessed by diet composition. For low-EE diets, CH₄ production (g/d) was best predicted using models with NDF intake (NDFI) and EE. For beef cattle, van Lingen et al. (2019), which included DMI, had the lowest RSR (0.91). By diet composition, models integrating fatty acids (FA), DMI, and NDFI performed best for low-NDF diets, while FA- and DMI-based models were most accurate for high-NDF diets. For sheep in Cfb climate, all models performed poorly. In tropical savanna, dry winter climates, all models performed poorly for beef cattle; similarly, poor performance was obtained when assessed by diet compositions. In conclusion, to improve CH₄ emission prediction, model development should consider the potential effect of climate zones, together with ruminant category, feed intake, dietary composition (including potential of mitigation strategies), and feed digestibility parameters.

© 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/).

## Implications

Enteric methane from ruminants is a major contributor to agricultural greenhouse gas emissions; reliable prediction models are essential for inventories and mitigation planning. This study evaluated existing models for predicting enteric methane emissions in selected Latin American countries to identify predictors that improve applicability across diverse climates and production systems. Models' performance varied with climate classification, ruminant category, and mitigation strategies. Models including intake and dietary strategies had good predictive ability. Climate effects were confounded but strongly associated with diet composition. Results emphasise that integrating climate, diets and intake

<footnote><sup>a</sup>INRAE, Université Clermont, VetAgroSup, UMR1213 Herbivores, 63122 Saint-Genès-Champanelle, France</footnote>

<footnote><sup>b</sup>Instituto Nacional de Tecnología Agropecuaria, Estación Experimental Balcarce, Ruta 226 km 73.5, Balcarce 7620, Buenos Aires, Argentina</footnote>

<footnote><sup>c</sup>Instituto Nacional de Investigación Agropecuaria, Estación Experimental La Estanzuela, Ruta 50km 11, Semillero, Colonia 70006, Uruguay</footnote>

<footnote><sup>d</sup>Instituto de Investigaciones Agropecuarias, INIA Remehue, Ruta 5 km 8 norte, Osorno, Chile</footnote>

<footnote><sup>e</sup>Instituto de Investigaciones Agropecuarias, Centro Regional de Investigación Carillanca, Vilcún, La Araucanía 4880000, Chile</footnote>

<footnote><sup>f</sup>INRAE, ASSET, Centre Antilles-Guyane, Domaine Duclos, Prise d'Eau, 97170 Petit Bourg, Guadeloupe, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: maguy.eugene@inrae.fr (M. Eugène).</footnote>
improves methane predictions, supporting more effective mitigation strategies in ruminant production.

## Introduction

The agricultural sector is a large source of greenhouse gas (GHG) emissions, with the livestock sector contributing 6.2 Gt CO₂eq/year (about 12% of global emissions) (FAO, 2023). Enteric CH₄ from ruminants is a large source of GHG in Latin America, which accounts for 28% of the global cattle population (Congio et al. 2022a and 2022b). Argentina, Brazil, Chile, and Uruguay have targets to reduce GHG emissions by 2030, in line with the Paris agreement. To achieve these goals, these countries are testing and preconising diverse strategies to mitigate CH₄ emissions from livestock. The development, evaluation, and application of practices/ strategies for enteric CH₄ mitigation require quantifying CH₄ emissions from individual animals. Although several CH₄ mitigation strategies have been identified and intensively reviewed (Arndt et al., 2022), the *in vivo* verification of some of these strategies is expensive and laborious. This has led to an increasing reliance on the use of prediction models, including those recommended by the Intergovernmental Panel on Climate Change (IPCC), to estimate the CH₄ mitigation potential of dietary strategies for ruminants. There is a need to use accurate models to predict the emissions of enteric CH₄ from ruminant production systems under their local (or regional/country) conditions. Most extant models were developed from studies with ruminants in intensive production systems in North America and Europe. (Niu et al., 2018; van Lingen et al., 2019; Belanche et al., 2023). Recently, models for estimating enteric CH₄ production have been developed using experimental results from the tropical and subtropical regions of South America (Benaouda et al., 2020; Congio et al., 2022a and 2022b). It is unknown whether these extant models can adequately predict enteric CH₄ production of ruminant livestock in Argentina, Brazil, Chile, and Uruguay, whose production systems are predominantly based on pastures.

We set out this study to identify extant models for predicting enteric CH₄ that can be useful to estimate enteric CH₄ emissions in Argentina, Brazil, Chile, and Uruguay. We hypothesised that in these countries, where temperature and precipitation vary widely among ruminant production areas, grouping dataset based on the Köppen climate classification, a widely used agroclimatic zoning based on rainfall distribution and seasonal temperature variation (Alvares et al., 2014), would improve the accuracy and robustness of existing models. The objective of this study was to evaluate the performance of existing models using treatment means of CH₄ emissions from a range of pasture-based production systems in Argentina, Brazil, Chile, and Uruguay. Data were grouped according to Köppen climate classification, ruminant species, and the use of dietary mitigation strategies. Whilst our results provide a region- and country-specific evaluation, our approach can be used by scientists elsewhere where enough measurements exist to select the best extant models for predicting enteric CH₄ emissions.

## Material and methods

### Data collection and database Construction

The datasets employed in this study were collected from South American partners in the CLIMAT AMSUD Regional project titled "Identifying enteric methane emission factors from pasture-based ruminant livestock in contrasting agro-climatic regions for mitigation of climate change" [(GHG for MACC) 2024/2025 ID: CLIMAT-AmSud230060]. We collected a total of 109 treatment means from 34 trials conducted between 2011 and 2023, where

enteric CH₄ emissions were quantified in ruminants fed different diets with the aim of mitigating CH₄ emissions. Treatments in the database investigated the effect of dietary composition and feeding level (see details in Supplementary Table S1 and references in Supplementary Material S1). Each dietary treatment within a trial represents a record, with animal characteristics, diets' chemical composition, daily dry matter intake (DMI) (kg/d), daily CH₄ emissions, digestibility parameters, and animal productivity indices. The experiments were conducted in Argentina (7 records from 3 trials), Brazil (53 records from 15 trials), Chile (36 records from 11 trials), and Uruguay (13 records from 5 trials). Two ruminant species were represented in the database: cattle (dairy and beef) and sheep.

For dairy cattle, 80% of the trials were with Holstein, 13% cross-breed Holstein × Jersey, and 7% did not report the breed of dairy cattle used. For beef cattle, breeds include Nellore (33.3%); Hereford (16.7%), Angus × Hereford crosses (16.7%); Angus (8.3%), a cross between Angus × Hereford × Nellore (8.3%), and 16.7% were not reported in their publications. Only 10% of sheep records reported the breed, which was a cross between Texel and Polwarth. All trials captured in our database used the SF₆ tracer technique for CH₄ quantification except for one study with sheep, where respiratory chambers were used (Gualdrón-Duarte et al., 2023). Most of the studies were pasture based (82.4%) while others were carried out under confinement (17.6%).

### Data processing and gap-filling

Enteric CH₄ emissions reported in different units were converted to the standard unit (g/d) using the equivalence of CH₄ = 0.714 g/L; 39.54 kJ/L (Brouwer, 1965). When missing, the gross energy content of feeds was estimated according to the equation described by Weiss and Tebbe (2019). Outliers were identified and screened as described by van Lingen et al. (2019), using the interquartile range method with a factor of 2.5 to define the extremes. As a result, no records were removed from the refined dataset. For studies where either pure chemical feed additives or plant extracts were fed to animals, only the non-supplemented treatments (e.g., control treatment) were retained and evaluated in the refined database (106 records from 34 trials, 27 studies) to prevent bias.

### Selection of extant models

Extant models were selected through an extensive literature search conducted through online databases (Science Direct, Scopus and Web of Science). The search included publications written in English published up to 2023, based on the keywords "methane", "in vivo", "prediction", "model", "equation", and "ruminant" ("cattle", "dairy", "beef", "sheep"). A total of 96 models were found from 34 publications. Extant models with predictors applicable to all or specific ruminant categories, dietary mitigation strategies, and Köppen Climate classification captured in our database were selected for evaluation (Table 1). This resulted in 52 models from 23 publications. All models were used in their original version except one model from Grainger and Beauchemin (2011), where fatty acid content in the diet was estimated using the adapted model of Giger-Reverdin et al. (2003).

For each animal category (beef or dairy cattle or sheep), two types of models were selected as a standard reference to compare against the selected extant models under evaluation. One standard reference was the generic models from the IPCC, whilst the second standard reference was Congio et al. (2022a, b) models developed specifically for dairy cattle and sheep in Latin America. The IPCC models and Benaouda et al. (2020) were used as a reference for beef cattle. These selected reference models are presented in the
<table><caption>**Table 1**
List of Extant models evaluated in this study for all ruminant categories.</caption><thead><tr><th>Model code</th><th>Source</th><th>Model:CH<sub>4</sub>(g/d) =</th><th>Mitigating strategies</th><th>KCC</th></tr></thead><tbody><tr><td>A1</td><td>All ruminant categories<br>IPCC (1997)</td><td>(0.060 × GEI)/0.05565</td><td>All diets</td><td>A, B, C,<br>D, E</td></tr><tr><td>A2</td><td>IPCC (2006)</td><td>(0.065 × GEI)/0.05565</td><td>All diets</td><td>A, B, C,<br>D, E</td></tr><tr><td>A3</td><td>IPCC (2019)_var</td><td>19.0 × DMI (for dairy cows, if dGE ≥ 70% and NDF ≤ 350)<br>20.0 × DMI (for dairy cows, if dGE ≥ 70% and NDF ≥ 350)<br>21.0 × DMI (for dairy cows, if dGE [63 – 70%] and NDF > 370)<br>23.3 × DMI (for non-dairy cattle, if forage > 75% and dGE ≤ 62%)<br>21.0 × DMI (for non-dairy cattle, if forage > 15% and dGE [62–71%])<br>13.6 × DMI (for feedlot where dGE ≥ 72%)<br>0.070(0.067 or 0.065 × GEI)/0.05565 when DMI less 0.6(or 0.6 – 0.8,<br>or > 0.8) kg/d for sheep</td><td>All diets</td><td>A, B, C,<br>D, E</td></tr><tr><td>A4</td><td>IPCC (2019)_Fix</td><td>(0.057 × GEI)/0.05565 (for dairy cows, if dGE ≥ 70% and NDF ≤ 350)<br>(0.060 × GEI)/0.05565 (for dairy cows, if dGE ≥ 70% and NDF ≥ 350)<br>(0.063 × GEI)/0.05565 × DMI (for dairy cows, if dGE [63 – 70%] and NDF<br>> 370)<br>(0.070 × GEI)/0.05565 (for non-dairy cattle, if forage > 75% and<br>dGE ≤ 62%)<br>(0.063 × GEI)/0.05565 (for non-dairy cattle, if forage > 15% and dGE62–<br>71%)<br>(0.040 × GEI)/0.05565 (for feedlot where dGE ≥ 72%)</td><td>All diets</td><td>A, B, C,<br>D, E</td></tr><tr><td>A5</td><td>Blaxter and Clapperton<br>(1965)<br>Note: I = intake</td><td>18 + 22.5l</td><td>All diets</td><td>A, C, D, E</td></tr><tr><td>A6</td><td>Ramin and Huhtanen<br>(2013)</td><td>(20 + 35.8 × DMI – 0.5 × DMI^2) × 0.714</td><td>All diets</td><td>A, C, D, E</td></tr><tr><td>A7</td><td>Sauvant and Nozière (2016)</td><td>(7.14 + 0.22 × OMD) × DMI</td><td>All diet + diet quality</td><td>A, C, D, E</td></tr><tr><td>A8</td><td>Grainger and Beauchemin<br>(2011)_1</td><td>(24.65 – 0.103*FA) × DMI</td><td>Lipid supplementation</td><td>A, C, D, E</td></tr><tr><td>A9</td><td>Grainger and Beauchemin<br>(2011)_2</td><td>(26.5 – (0.187 × FA) + (0.0007 × FA<sup>2</sup>)) × DMI</td><td>Lipid supplementation</td><td>A, C, D, E</td></tr><tr><td>DB1</td><td>Dairy and Beef cattle<br>Charmley et al. (2016)_2</td><td>20.7 × DMI</td><td>All diets</td><td>C, D, E</td></tr><tr><td>DB2</td><td>Benaouda et al. (2020)_1</td><td>19.4 + 16.7 × DMI</td><td>All diets</td><td>A, B, C</td></tr><tr><td>DB3</td><td>Benaouda et al. (2020)_2</td><td>63.8 + 0.96 × GEI for dairy cattle<br>63.8 + 0.72 × GEI for mature cattle<br>63.8 + 0.57 × GEI for growing cattle</td><td>All diets</td><td>A, B, C</td></tr><tr><td>DB4</td><td>Benaouda et al. (2020)_4</td><td>68.1 + 12.4 × DMI – 0.53 × EE</td><td>Lipid supplementation</td><td>A, B, C</td></tr><tr><td>DB5</td><td>Moate et al. (2011)_1</td><td>(Exp (3.15 – 0.0035 × FA)) × DMI,</td><td>Lipid supplementation</td><td>A, C, D</td></tr><tr><td>DB6</td><td>Benaouda et al. (2020)_3</td><td>17 × DMI + 0.03 × NDF</td><td>Diet quality</td><td>A, B, C</td></tr><tr><td>D1</td><td>Dairy cattle<br>Charmley et al. (2016)_1</td><td>38 + 19.22 × DMI</td><td>All diets</td><td>C, D, E</td></tr><tr><td>D2</td><td>Charmley et al. (2016)_3</td><td>(2.14 + 0.058 × GEI)/0.05565</td><td>All diets</td><td>C, D, E</td></tr><tr><td>D3</td><td>Niu et al. (2018)_1</td><td>(7.13 + 0.00391 × GEI)/0.05565</td><td>All diets</td><td>A, C, D, E</td></tr><tr><td>D4</td><td>Niu et al. (2018)_2</td><td>124 + 13.3 × DMI</td><td>All diets</td><td>A, C, D, E</td></tr><tr><td>D5</td><td>Congio et al. (2022b)_3</td><td>42.1 + 1.0 × GEI</td><td>All diets</td><td>C, D, E</td></tr><tr><td>D6</td><td>Congio et al. (2022b)_1</td><td>40.7 + 18.0 × DMI</td><td>All diets</td><td>A, B, C</td></tr><tr><td>D7</td><td>Mills et al. (2003)_1</td><td>(5.93 + 0.92 × DMI)/0.05565,</td><td>All diets</td><td>C, D, E</td></tr><tr><td>D8</td><td>Mills et al. (2003)_2</td><td>(56.27 × (1-exp (-0.028 × DMI)))/0.05565,</td><td>All diets</td><td>C, D, E</td></tr><tr><td>D9</td><td>Hristov et al. (2013)</td><td>2.54 + 19.14 × DMI,</td><td>All diets</td><td>C, D, E</td></tr><tr><td>D10</td><td>Ellis et al. (2007)_1</td><td>(3.23 + 0.809 × DMI)/0.05565</td><td>All diets</td><td>C, D</td></tr><tr><td>D11</td><td>Moraes et al. (2014)</td><td>(0.225 + 0.042 × GEI + 0.0125 × NDF –0.0329 × EE)/0.05565</td><td>Lipid supplementation, Diet<br>quality</td><td>C, D, E</td></tr><tr><td>D12</td><td>Nielsen et al. (2013)</td><td>(1.23 × DMI – 0.145 × FA + 0.012 × NDF)/0.05565</td><td>Diet quality</td><td>C, D, E</td></tr><tr><td>D13</td><td>Ellis et al. (2007)_2</td><td>(3.14 + 2.11 × NDFI)/0.05565</td><td>Diet quality</td><td>C, D</td></tr><tr><td>D14</td><td>Niu et al. (2018)_3</td><td>299 + 2.73 × milk yield</td><td>Animal performance</td><td>A, C, D, E</td></tr><tr><td>D15</td><td>Congio et al. (2022b)_2</td><td>178 + 7.21 × milk yield</td><td>Animal performance</td><td>A, C, D, E</td></tr><tr><td>D16</td><td>Niu et al. (2018)_4</td><td>163 + 13.3 × DMI –11.0 × EE</td><td>Lipid supplementation</td><td>A, C, D, E</td></tr><tr><td>D17</td><td>Niu et al. (2018)_5</td><td>33.2 + 13.6 × DMI + 2.43 × NDF</td><td>Diet quality</td><td>A, C, D, E</td></tr><tr><td>B1</td><td>Beef cattle<br>Yan et al. (2009)_1</td><td>((35.1 × DMI) + 14.7) × 0.714</td><td>All diets</td><td>C, D, E</td></tr><tr><td>B2</td><td>Yan et al. (2009)_2</td><td>((1.959 × GEI) + 8.8) × 0.714</td><td>All diets</td><td>C, D, E</td></tr><tr><td>B3</td><td>van Lingen et al. (2019)_1</td><td>54.2 + 12.6 × DMI</td><td>All diets</td><td>A, B, C,<br>D, E</td></tr><tr><td>B4</td><td>van Lingen et al. (2019)_2</td><td>-16.4 + 12.1 × DMI + 2.10 × NDF</td><td>Diet quality</td><td>A, B, C,<br>D, E</td></tr><tr><td>B5</td><td>Ellis et al. (2007)_3</td><td>(5.58 + 0.848 × NDFI)/ 0. 05565</td><td>Diet quality</td><td>C, D, E</td></tr><tr><td>B6</td><td>Ellis et al. (2007)_4</td><td>(-1.02 + 0.681 × DMI + 4.81 × forage) /0.05565</td><td>Diet quality</td><td>C, D, E</td></tr><tr><td>B7</td><td>van Lingen et al. (2019)_4</td><td>-0.767 + 12.0 × DMI + 1.12 × forage</td><td>Diet quality</td><td>A, B, C,<br>D, E</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Model code</th><th>Source</th><th>Model:CH<sub>4</sub>(g/d) =</th><th>Mitigating strategies</th><th>KCC</th></tr></thead><tbody><tr><td>B8</td><td>Escobar-Bahamondes et al. (2017)</td><td>-35.0 + 0.08 × BW + 120 × Forage-69.8 × EEI<sup>3</sup> + 3.14 × GEI</td><td>Diet quality + lipid supplementation</td><td>C, D, E</td></tr><tr><td>B9</td><td>van Lingen et al. (2019)<sub>3</sub></td><td>83 + 12.1 × DMI - 7.31 × EE</td><td>Lipid supplementation</td><td>A, B, C, D, E</td></tr><tr><td>S1</td><td>Sheep<br>Belanche et al. (2023)<sub>1</sub></td><td>6.29 + 12.6 × DMI</td><td>All diets</td><td>A, B, C, D, E</td></tr><tr><td>S2</td><td>Belanche et al. (2023)<sub>2</sub></td><td>(0.358 + 0.0393 × GEI)/0.05565,</td><td>All diets</td><td>A, B, C, D, E</td></tr><tr><td>S3</td><td>Congio et al. (2022a)<sub>1</sub></td><td>5.53 + 1.16 × GEI - 2.60 × FL</td><td>All diets</td><td>A, B, C</td></tr><tr><td>S4</td><td>Congio et al. (2022a)<sub>2</sub></td><td>7.12 + 20.4 × DMI - 3.18 × FL</td><td>All diets</td><td>A, B, C</td></tr><tr><td>S5</td><td>Congio et al. (2022a)<sub>3</sub></td><td>1.66 + 16.6 × DMI</td><td>All diets</td><td>A, B, C</td></tr><tr><td>S6</td><td>Congio et al. (2022a)<sub>4</sub></td><td>0.918 + 0.993 × GEI</td><td>All diets</td><td>A, B, C</td></tr><tr><td>S7</td><td>Patra et al. (2017)<sub>1</sub></td><td>(0.223 + 0.876 × DMI)/0.05565</td><td>All diets</td><td>C, D, E</td></tr><tr><td>S8</td><td>Patra et al. (2017)<sub>2</sub></td><td>(0.208 + 0.049 × GEI)/0.05565</td><td>All diets</td><td>C, D, E</td></tr><tr><td>S9</td><td>Belanche et al. (2023)<sub>3</sub></td><td>2.47 + 10.2 × DMI + 0.14 × BW</td><td>All diets</td><td>A, B, C, D, E</td></tr><tr><td>S10</td><td>Belanche et al. (2023)<sub>4</sub></td><td>4.10 + 12.57 × DMI + 0.261 × ASH</td><td>Diet quality</td><td>A, B, C, D, E</td></tr><tr><td>S11</td><td>Belanche et al. (2023)<sub>5</sub></td><td>0.432 + 10.2 × DMI + 0.244 × ASH + 0.138 × BW</td><td>Diet quality</td><td>A, B, C, D, E</td></tr></tbody></table>

Abbreviations: dGE = digestibility of gross energy (coefficient), DMI = DM intake (kg/d), OMD = digestibility of organic matter (coefficient) EE = dietary extract ether (g/kg DM), EEI = EE intake (kg/d), FA = dietary fatty acids (g/kg DM), FL = feeding level, forage = forage proportion in the diet (% for van Lingen et al. (2019)), GEI = gross energy intake (MJ/d), NDFI = NDF intake (kg/d), (kg)), (KCC = Köppen Climate Classification: A = Tropical, B = Dry, C = Temperate, D =Continental, E = Polar).

Results section irrespective of their evaluation performance. The database included classificatory variables that allowed for dividing the data to test the extant models within different types of animals and diet composition across climatic zones. Two animal species (cattle and sheep) and three ruminant categories (dairy, beef, and sheep) were thus considered.

For each ruminant category, models were evaluated by diet composition within each climatic zone defined based on the Köppen climate classification system. The Köppen climate classification system categorises climates based on temperature and precipitation (Köppen, 1884 and updated by Beck et al., 2018). Each Köppen climate classification climate has peculiar seasonal patterns influencing vegetation (forages) and the type of livestock production system that can predominate. In this study, three climate classifications were identified and used to classify the data according to the climate in which they originated in Argentina, Brazil, Chile, and Uruguay. The climatic zones were *Aw*, a tropical savanna climate with distinct wet and dry seasons, typical of northern region in Brazil; *Cfa*, a temperate climate with hot summers and year-round precipitation, comprising data from Chile and Uruguay, and *Cfb* which represents temperate oceanic climates with warm summers and consistent rainfall, characteristic of Argentina, Chile and Brazil. The mean-median split method was used to assess the models' ability to predict differences in emissions caused by each dietary mitigation strategy. If the data in the dataset had a normal distribution, the mean was used; otherwise, the median was used. Different criteria to divide the dataset were applied to each animal type. Dairy cow data were divided into subsets based on four dietary characteristics: low- and high-NDF content (under and over 450 g/kg DM), low- and high-ether extract (EE) content (under and over 31.5 g of EE/kg DM, respectively), low- and high-starch (STA) content (under and over 134 g of STA/kg DM, respectively), and low and high digestibility of NDF (NDFD) (under and over 0.59 of NDFD, respectively). The beef cattle dataset was divided into subsets based on three dietary characteristics: low- and high-NDF content (under and over 472 g/kg DM), low- and high-lipid content (under and over 32.8 g of EE/kg DM, respectively), and low- and high-NDFD (under and over 0.57 of NDFD,

respectively). The sheep dataset was divided into subsets based on three dietary characteristics; low- and high-NDF content (under and over 483 g/kg DM), low- and high-lipid content (under and over 31.5 g of EE/kg DM, respectively), and low and high starch content (under and over 272 g of STA/kg DM, respectively). The descriptive statistics of the datasets are presented in Supplementary Tables S2-S10.

The model evaluation was done following the methodology described by Niu et al. (2018) and van Lingen et al. (2019). CH₄ prediction models' performance was evaluated based on the root mean square prediction error and the root mean square prediction error to SD of observed values ratio (RSR). The mean square prediction error was calculated according to Bibby and Toutenburg (1977). The ranking of the performance of existing model was performed as described by Benaouda et al. (2019).

## Results

### Descriptive statistics of the datasets

For dairy cows, 80% of the data were from *Cfa* and the remaining from *Cfb* climate. Their average CH₄ emissions and methane emission factor were numerically higher (434 vs 365 g/d and 10.7 vs 7.91%) for *Cfa* than *Cfb*, respectively. All records had forage content > 27% for all diets. The beef cattle dataset had 80.5% (Aw = 46.4%, Cfa = 14.6% and Cfb = 19.5%) growing cattle while 19.5% (Aw = 9.75%, Cfb = 9.75%) were beef cows. Both sub-categories produced 161 g CH₄ g/d on average and CH₄ yield of 17.2 g/kg DMI. Their diet consisted of 88% forage with a DMI of 10.9 kg/d. The EE content (42 vs 30 g/kg DM) and NDF content (547 vs 454 g/kg DM) for beef cows were higher than those of growing beef cattle, respectively. For sheep, the average CH₄ emissions and methane emission factor were 22.5 g/d and 7.68%, respectively. Data from two Köppen climate classification (Cfa and Cfb) were available for sheep. Sheep diets in *Cfb* had higher EE content and were fed a higher content of by-products than in *Cfa*. One study was available for the Cfb classification with methane emission factor (1.86%) reported out of range; this incapacitated all evaluated
models to fit this Köppen climate classification with a concordance correlation coefficient of zero. This study is characterised by high ether extract content, high STA and low NDF consequentially responsible for low CH₄ emission by the animals.

## Performance of the selected extant models

### Performance of extant models on dairy dataset by dietary strategies and climate zones

The unbalanced meta-design, characterised by the number of observations used for the evaluation within each Köppen climate classification and the unique characteristics of each classification, guided our decision to rank models' performance within Köppen climate classification (Supplementary Table S11). For the two climatic zones obtained for the dairy dataset, the evaluated extant models performed differently. In Cfa, all models evaluated performed poorly (RSR > 1 & root mean square prediction error% (32.2 – 38.5%)). Decomposition of the error indicated high mean bias (Supplementary Table S12 and Supplementary Figure S1). Performance of the best performing reference models A3 and D6 slightly had better performance when evaluated on the high-NDF subset than low-NDF subset data (Table 2, Fig. 1). The high- and low-NDF subsets had mean CH₄ yields and intensities of 29.1 vs 28.5 g/kg DMI, and 17.7 vs 8.3 g/kg of milk, respectively (Supplementary Table S5).

For Cfb, models had lower RSR, which was an improvement in performance compared to the evaluation of the whole dairy dataset (Supplementary Table S11). The best reference models, A3 and D6, were included (Supplementary Table S12 and Supplementary Figure S1). All good performing models included intake as a predictor and had a positive relationship with CH₄ emissions (g/d). In this climate zone, four identified diet qualities were evaluated. Firstly, diets supplemented with lipids had mean CH₄ yields 22.9 vs 23.2 g/kg DMI for high- and low-EE subsets, respectively (Supplementary Table S7). Model D7, from Ellis et al. (2007), had good performance for both subsets. Furthermore, models D7 and D1 had good performance for high- and low-NDF subsets, respectively (Fig. 1). Our best reference models, A3 and D6, had lower RSR for high-STA when compared to low-STA (Fig. 2). Lastly, subsets were created to evaluate the effect of diet quality related to NDFD. The high- and low-NDFD subsets had mean NDFD contents of 0.70 vs 0.49 with methane yield (23.5 g/kg DMI vs 21.8 g/kg DMI), methane emission factor (8.69 vs 6.35%), respectively. All models evaluated for high-NDFD had RSR > 1. Our reference models, A2, A4 and A3, ranked 1st, 2nd and 3rd respectively for low-NDFD subset (Fig. 3).

### Performance of extant models on beef dataset by dietary strategies and climate zones

Three climatic zones were obtained for the beef cattle, twenty-four extant models were evaluated, 37.5% of the models were specific to beef cattle, 25% for dairy and beef cattle while multispecies model accounted for 37.5%. Generally, models specific to beef cattle only had lower RSR when compared to multispecies models (Supplementary Table S13). For the tropical savanna (Aw), all extant models evaluated performed poorly (RSR > 1). When the dataset was evaluated based on diet quality, the EE content for the high-NDF subset was lower than that of the low-NDF content by 50%, and this resulted in the mean CH₄ yields of 13.3 g/kg DMI vs 10.6 g/kg DMI, respectively (Supplementary Table S2 and S3). All models evaluated for the NDF subsets had RSR > 1. Similar poor performance of models was observed for the subsets based on lipid supplementation and diet quality by NDFD; all evaluated extant models had RSR > 1 (Table 3). For Cfa, three models had RSR < 1, and these models had DMI as a predictor, while all the reference models had RSR > 1. Only diet quality by

NDF was evaluated (Table 3, Fig. 4), five and one record(s) for the high- and low-NDF, respectively; hence, the low-NDF subset was not evaluated. For Cfb, four models performed well (RSR < 1) while all the reference models performed poorly (Supplementary Table S14). Model B3, a model from van Lingen et al. (2019), had DMI ranked 1st (Supplementary Figure S2). When evaluated based on diet quality, the high- and low-NDF subsets had mean CH₄ yields and methane emission factor of 20.3 g/kg DMI vs 16.3 g/kg DMI, and 7.02% vs 5.28%, respectively (Supplementary Table S2 and S3). Twelve models had RSR < 1 for the high-NDF subset, model A3 (IPCC, 2019) ranked 1st among the IPCC models, while model DB2 (Benaouda et al., 2020) performed best for the region-specific models. All models performed poorly when the low-NDF subset was evaluated. For Cfb, nine models had RSR < 1 when evaluated on the low-EE subset. Model A2 (IPCC, 2006) ranked 1st among the IPCC models, and DB6 (Benaouda et al., 2020) including DMI and NDF ranked first in the region-specific category.

### Performance of extant models on sheep dataset by dietary strategies and climate zones

Twenty extant models were evaluated for the sheep category (Supplementary Table S15). For Cfa, 10 evaluated models had RSR < 1 ranked by RSR (Supplementary Table S16). Model S4, from Congio et al. (2022a) including DMI and feeding level, had the smallest RSR (0.63) with the root mean square prediction error% being 26.5%. When diet quality was considered (Table 4), high- and low-NDF diets had mean NDF contents of 528 vs 290 g/kg DM, while the CP content for the high-NDF subset was numerically lower than the low-NDF content. The Gross energy intake was 22.2% higher for the low-NDF than the high-NDF subset (Fig. 5). Out of 18 evaluated models for high-NDF, 11 models had RSR < 1. All IPCC models had RSR < 1. IPCC (2019) model ranked 1st overall with RSR (0.35) & concordance correlation coefficient = 0.93, and most of the error emanated from random sources. Model S5 from the region-specific reference models (Congio et al., 2022a) ranked 3rd overall with RSR of 0.45, root mean square prediction error of 13.0 and 81.0% of the error from random sources.

## Discussion

### Variability in model accuracy across dietary strategies and climatic zones

#### Dietary NDF content

The evaluated models showed poor performance in Cfa climate for dairy cows fed high-quality diets characterised by low NDF contents. Conversely, the performance of four models that included DMI better predicted CH₄ production (g/d) for the high-NDF subset. This is in agreement with feed intake being the key factor driving CH₄ emissions (Hristov et al., 2013; Niu et al., 2018) that can explain up to 70% of the variation in CH₄ emissions from cattle (Ricci et al., 2013).

Digestibility was not affected by higher inclusion level of forages in high-NDF diets in Cfa and Cfb climates. In terms of CH₄ emissions (g/d), yield (g/kg DMI), and intensity (g/kg milk), similar values were found between the high- and low-NDF for Cfa. This suggests that other dietary components in NDF subsets would be better predictors for enteric CH₄ emissions (g/d) in dairy cattle in Cfa.

For Cfb, the low-NDF subset had higher DMI and lower CH₄ yield than the high-NDF subset, which might explain the improvement in the performance of models, especially with DMI as a predictor for this climate zone for the NDF subsets. The existing models developed for dairy and beef cows often show a positive
<caption>**Table 2**</caption>

Evaluation of methane emission (g/d) prediction model performance for dairy cattle according to diet quality based on neutral detergent fibre content, and lipid or starch supplementation, and by the Köppen climate classification.

<table><thead><tr><th>Mitigation strategies</th><th>Levels</th><th>Rank</th><th>Model</th><th>n</th><th>RMSPE (g/d)</th><th>RMSPE (%)</th><th>ECT (%)</th><th>ER (%)</th><th>ED (%)</th><th>CCC</th><th>r</th><th>RSR</th></tr></thead><tbody><tr><td rowspan="12">Cfa<br>Diet quality by NDF</td><td rowspan="12">High NDF<br>(mean 552 g/kg DM)</td><td>8</td><td>A3</td><td>6</td><td>165</td><td>38.7</td><td>29.1</td><td>0.59</td><td>70.3</td><td>0.09</td><td>0.21</td><td>1.06</td></tr><tr><td>10</td><td>D6</td><td>6</td><td>169</td><td>39.6</td><td>32.7</td><td>0.16</td><td>67.1</td><td>0.07</td><td>0.21</td><td>1.09</td></tr><tr><td>1</td><td>A5</td><td>6</td><td>147</td><td>34.5</td><td>10.2</td><td>1.15</td><td>88.7</td><td>0.11</td><td>0.21</td><td>0.95</td></tr><tr><td>2</td><td>D7</td><td>6</td><td>149</td><td>34.9</td><td>13.4</td><td>0.06</td><td>86.5</td><td>0.08</td><td>0.21</td><td>0.96</td></tr><tr><td>3</td><td>D8</td><td>6</td><td>152</td><td>35.5</td><td>16.4</td><td>0.21</td><td>83.4</td><td>0.09</td><td>0.22</td><td>0.98</td></tr><tr><td>4</td><td>D17</td><td>6</td><td>152</td><td>35.7</td><td>7.21</td><td>6.20</td><td>86.6</td><td>0.00</td><td>0.01</td><td>0.98</td></tr><tr><td>5</td><td>D5</td><td>6</td><td>160</td><td>37.5</td><td>23.1</td><td>4.89</td><td>72.0</td><td>0.02</td><td>0.28</td><td>1.03</td></tr><tr><td>6</td><td>D1</td><td>6</td><td>160</td><td>37.5</td><td>24.8</td><td>0.33</td><td>74.9</td><td>0.09</td><td>0.21</td><td>1.03</td></tr><tr><td>7</td><td>A3</td><td>4</td><td>158</td><td>35.8</td><td>65.2</td><td>11.6</td><td>23.2</td><td>0.15</td><td>0.71</td><td>1.26</td></tr><tr><td>9</td><td>D15</td><td>4</td><td>162</td><td>36.6</td><td>47.1</td><td>47.9</td><td>5.04</td><td>-0.08</td><td>-0.94</td><td>1.28</td></tr><tr><td>1</td><td>A5</td><td>4</td><td>127</td><td>28.8</td><td>47.2</td><td>16.8</td><td>35.9</td><td>0.22</td><td>0.71</td><td>1.01</td></tr><tr><td>2</td><td>D7</td><td>4</td><td>131</td><td>29.6</td><td>45.6</td><td>20.4</td><td>34.0</td><td>0.16</td><td>0.71</td><td>1.04</td></tr><tr><td>3</td><td>D8</td><td>4</td><td>136</td><td>30.6</td><td>50.7</td><td>17.2</td><td>32.1</td><td>0.17</td><td>0.71</td><td>1.08</td></tr><tr><td rowspan="12">Cfb<br>Lipid supplementation</td><td rowspan="12">High EE<br>(mean 43.7 g/kg DM)</td><td>5</td><td>A3</td><td>12</td><td>54.1</td><td>13.5</td><td>37.5</td><td>1.49</td><td>61.1</td><td>0.43</td><td>0.65</td><td>0.93</td></tr><tr><td>7</td><td>D6</td><td>12</td><td>62.7</td><td>15.6</td><td>51.7</td><td>2.96</td><td>45.4</td><td>0.32</td><td>0.64</td><td>1.08</td></tr><tr><td>1</td><td>A5</td><td>12</td><td>44.0</td><td>10.9</td><td>6.55</td><td>1.05</td><td>92.4</td><td>0.54</td><td>0.65</td><td>0.76</td></tr><tr><td>2</td><td>D7</td><td>12</td><td>44.5</td><td>11.1</td><td>1.28</td><td>8.35</td><td>90.4</td><td>0.46</td><td>0.65</td><td>0.77</td></tr><tr><td>3</td><td>D8</td><td>12</td><td>44.6</td><td>11.1</td><td>4.66</td><td>6.48</td><td>88.9</td><td>0.48</td><td>0.65</td><td>0.77</td></tr><tr><td>4</td><td>D1</td><td>12</td><td>50.6</td><td>12.6</td><td>27.1</td><td>3.22</td><td>69.7</td><td>0.44</td><td>0.65</td><td>0.87</td></tr><tr><td>6</td><td>D2</td><td>12</td><td>57.5</td><td>14.3</td><td>44.5</td><td>1.49</td><td>54.0</td><td>0.40</td><td>0.65</td><td>0.99</td></tr><tr><td>9</td><td>A3</td><td>12</td><td>64.1</td><td>18.4</td><td>14.8</td><td>12.6</td><td>72.6</td><td>0.56</td><td>0.61</td><td>0.91</td></tr><tr><td>8</td><td>D6</td><td>12</td><td>63.9</td><td>18.3</td><td>22.4</td><td>4.47</td><td>73.1</td><td>0.53</td><td>0.61</td><td>0.91</td></tr><tr><td>1</td><td>D13</td><td>21</td><td>56.3</td><td>16.1</td><td>13.1</td><td>0.38</td><td>86.6</td><td>0.54</td><td>0.65</td><td>0.80</td></tr><tr><td>2</td><td>D7</td><td>21</td><td>56.8</td><td>16.3</td><td>5.17</td><td>2.51</td><td>92.3</td><td>0.57</td><td>0.61</td><td>0.81</td></tr><tr><td>3</td><td>D8</td><td>21</td><td>57.0</td><td>16.3</td><td>0.39</td><td>8.09</td><td>91.5</td><td>0.60</td><td>0.61</td><td>0.81</td></tr><tr><td>4</td><td>D4</td><td>21</td><td>58.0</td><td>16.6</td><td>11.3</td><td>0.02</td><td>88.6</td><td>0.50</td><td>0.61</td><td>0.82</td></tr><tr><td>5</td><td>D1</td><td>21</td><td>59.0</td><td>16.9</td><td>5.74</td><td>8.55</td><td>85.7</td><td>0.59</td><td>0.61</td><td>0.84</td></tr><tr><td>6</td><td>A6</td><td>21</td><td>61.6</td><td>17.7</td><td>21.2</td><td>0.62</td><td>78.1</td><td>0.51</td><td>0.61</td><td>0.88</td></tr><tr><td rowspan="12">Diet Quality by NDF</td><td rowspan="12">High NDF<br>(mean 487 g/kg DM)</td><td>8</td><td>A3</td><td>22</td><td>61.8</td><td>17.6</td><td>25.9</td><td>4.39</td><td>69.6</td><td>0.59</td><td>0.66</td><td>0.87</td></tr><tr><td>9</td><td>D6</td><td>22</td><td>63.2</td><td>18.0</td><td>33.0</td><td>0.52</td><td>66.4</td><td>0.53</td><td>0.66</td><td>0.90</td></tr><tr><td>1</td><td>D7</td><td>22</td><td>52.0</td><td>14.9</td><td>1.92</td><td>0.01</td><td>98.1</td><td>0.61</td><td>0.66</td><td>0.74</td></tr><tr><td>2</td><td>D8</td><td>22</td><td>52.5</td><td>15.0</td><td>0.18</td><td>2.44</td><td>97.4</td><td>0.64</td><td>0.66</td><td>0.75</td></tr><tr><td>3</td><td>D16</td><td>18</td><td>59.4</td><td>17.0</td><td>7.90</td><td>6.05</td><td>86.0</td><td>0.52</td><td>0.69</td><td>0.76</td></tr><tr><td>4</td><td>D17</td><td>22</td><td>59.9</td><td>15.4</td><td>2.10</td><td>6.24</td><td>91.6</td><td>0.50</td><td>0.66</td><td>0.77</td></tr><tr><td>5</td><td>D13</td><td>22</td><td>54.7</td><td>15.6</td><td>8.30</td><td>0.39</td><td>91.3</td><td>0.55</td><td>0.65</td><td>0.78</td></tr><tr><td>6</td><td>A6</td><td>22</td><td>55.1</td><td>15.7</td><td>2.85</td><td>9.66</td><td>87.5</td><td>0.65</td><td>0.66</td><td>0.78</td></tr><tr><td>2</td><td>A3</td><td>19</td><td>73.1</td><td>19.0</td><td>4.90</td><td>8.57</td><td>86.5</td><td>0.41</td><td>0.63</td><td>0.81</td></tr><tr><td>8</td><td>D6</td><td>19</td><td>78.0</td><td>20.3</td><td>12.9</td><td>11.2</td><td>75.9</td><td>0.34</td><td>0.63</td><td>0.87</td></tr><tr><td>1</td><td>D1</td><td>19</td><td>72.8</td><td>19.0</td><td>1.64</td><td>11.1</td><td>87.3</td><td>0.39</td><td>0.63</td><td>0.81</td></tr><tr><td>3</td><td>D8</td><td>19</td><td>73.1</td><td>19.1</td><td>1.02</td><td>14.8</td><td>84.2</td><td>0.37</td><td>0.64</td><td>0.81</td></tr><tr><td>4</td><td>D2</td><td>19</td><td>74.6</td><td>19.4</td><td>8.26</td><td>8.61</td><td>83.1</td><td>0.40</td><td>0.63</td><td>0.83</td></tr><tr><td>5</td><td>D7</td><td>19</td><td>74.7</td><td>19.5</td><td>2.66</td><td>14.6</td><td>82.7</td><td>0.34</td><td>0.63</td><td>0.83</td></tr><tr><td rowspan="12">Starch supplementation</td><td rowspan="12">High STA<br>(mean 209 g/kg DM)</td><td>6</td><td>A5</td><td>19</td><td>76.0</td><td>19.8</td><td>13.7</td><td>6.26</td><td>80.1</td><td>0.41</td><td>0.63</td><td>0.85</td></tr><tr><td>2</td><td>A3</td><td>13</td><td>35.1</td><td>9.39</td><td>2.34</td><td>0.83</td><td>96.8</td><td>0.69</td><td>0.72</td><td>0.68</td></tr><tr><td>4</td><td>D6</td><td>13</td><td>38.7</td><td>10.4</td><td>20.0</td><td>0.36</td><td>79.6</td><td>0.62</td><td>0.72</td><td>0.75</td></tr><tr><td>1</td><td>D1</td><td>13</td><td>34.6</td><td>9.24</td><td>0.16</td><td>0.00</td><td>99.8</td><td>0.68</td><td>0.72</td><td>0.67</td></tr><tr><td>3</td><td>D2</td><td>13</td><td>36.2</td><td>9.69</td><td>8.61</td><td>0.54</td><td>90.9</td><td>0.68</td><td>0.72</td><td>0.70</td></tr><tr><td>5</td><td>D8</td><td>13</td><td>39.2</td><td>10.5</td><td>20.6</td><td>0.23</td><td>79.2</td><td>0.61</td><td>0.71</td><td>0.76</td></tr><tr><td>6</td><td>D4</td><td>13</td><td>39.8</td><td>10.7</td><td>17.2</td><td>7.64</td><td>75.1</td><td>0.53</td><td>0.72</td><td>0.77</td></tr><tr><td>5</td><td>A3</td><td>16</td><td>74.6</td><td>19.3</td><td>25.2</td><td>0.06</td><td>74.8</td><td>0.32</td><td>0.50</td><td>0.97</td></tr><tr><td>8</td><td>D6</td><td>16</td><td>79.8</td><td>20.7</td><td>33.9</td><td>0.74</td><td>65.3</td><td>0.26</td><td>0.50</td><td>1.04</td></tr><tr><td>1</td><td>A5</td><td>16</td><td>64.7</td><td>16.8</td><td>0.71</td><td>0.01</td><td>99.3</td><td>0.40</td><td>0.50</td><td>0.84</td></tr><tr><td>2</td><td>D7</td><td>16</td><td>65.3</td><td>16.9</td><td>0.57</td><td>2.05</td><td>97.4</td><td>0.33</td><td>0.50</td><td>0.85</td></tr><tr><td>3</td><td>D8</td><td>16</td><td>65.4</td><td>17.0</td><td>2.72</td><td>1.43</td><td>95.8</td><td>0.35</td><td>0.51</td><td>0.85</td></tr><tr><td>4</td><td>D1</td><td>16</td><td>70.8</td><td>18.4</td><td>16.7</td><td>0.47</td><td>82.8</td><td>0.33</td><td>0.50</td><td>0.92</td></tr><tr><td rowspan="12">Diet quality by NDFD</td><td rowspan="12">High NDFD<br>(mean 0.70)</td><td>12</td><td>A3</td><td>7</td><td>65.2</td><td>23.2</td><td>3.43</td><td>94.6</td><td>1.96</td><td>0.02</td><td>0.06</td><td>6.60</td></tr><tr><td>5</td><td>D6</td><td>7</td><td>55.9</td><td>19.9</td><td>3.00</td><td>94.3</td><td>2.66</td><td>0.02</td><td>0.06</td><td>5.66</td></tr><tr><td>1</td><td>D4</td><td>7</td><td>43.2</td><td>15.4</td><td>9.88</td><td>85.7</td><td>4.47</td><td>0.02</td><td>0.06</td><td>4.37</td></tr><tr><td>2</td><td>D11</td><td>7</td><td>47.5</td><td>16.9</td><td>35.8</td><td>60.5</td><td>3.64</td><td>-0.04</td><td>-0.14</td><td>4.81</td></tr><tr><td>3</td><td>A6</td><td>7</td><td>49.0</td><td>17.5</td><td>0.05</td><td>96.5</td><td>3.46</td><td>0.03</td><td>0.09</td><td>4.96</td></tr><tr><td>1</td><td>A2</td><td>8</td><td>88.7</td><td>23.1</td><td>0.04</td><td>37.7</td><td>62.3</td><td>0.51</td><td>0.81</td><td>0.70</td></tr><tr><td>8</td><td>D5</td><td>8</td><td>93.2</td><td>24.3</td><td>1.57</td><td>42.0</td><td>56.4</td><td>0.44</td><td>0.81</td><td>0.73</td></tr><tr><td>2</td><td>A4</td><td>8</td><td>88.9</td><td>23.1</td><td>2.35</td><td>35.7</td><td>62.0</td><td>0.51</td><td>0.81</td><td>0.70</td></tr><tr><td>3</td><td>A3</td><td>8</td><td>90.8</td><td>23.6</td><td>3.89</td><td>55.1</td><td>41.0</td><td>0.48</td><td>0.87</td><td>0.71</td></tr><tr><td>4</td><td>A5</td><td>8</td><td>91.0</td><td>23.7</td><td>8.33</td><td>50.8</td><td>40.9</td><td>0.50</td><td>0.87</td><td>0.71</td></tr><tr><td>5</td><td>D2</td><td>8</td><td>91.5</td><td>23.8</td><td>0.02</td><td>41.5</td><td>58.5</td><td>0.46</td><td>0.81</td><td>0.72</td></tr><tr><td>6</td><td>D1</td><td>8</td><td>92.1</td><td>24.0</td><td>1.42</td><td>58.7</td><td>39.9</td><td>0.45</td><td>0.87</td><td>0.72</td></tr></tbody></table>

Abbreviations: Cfa = temperate, no dry season hot summer; Cfb = temperate, no dry season, warm summer; EE = dietary ether extract (g/kg DM); STA = dietary starch (g/kg DM); NDFD = digestibility of NDF (coefficient); Rank = rank of the performance based on the RSR; Model = code assigned to each evaluated extant model; RMSPE = square root of the mean square prediction error, expressed in g/d and RMSPE% as a percentage of CH₄ emissions mean; ECT% = error due to central tendency expressed as a percentage of RMSPE; ER% = error due to deviation of the regression slope expressed as a percentage of RMSPE; ED% = error due to the disturbance expressed as percentage of RMSPE; CCC = concordance correlation coefficient; r = correlation coefficient; RSR = RMSPE to SD of observed values ratio.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Observed vs predicted values plots, using dairy cattle dataset for Cfa (temperate, no dry season, hot summer) and Cfb (temperate, no dry season, warm summer), the reference models (A3 and D6) and other models (A5, D7, D8, D1) with the smallest RSR (square root of the mean square prediction error to SD of observed values ratio). The grey solid (—) is the identity line y = x, red solid (—) is the fitted regression line for high-NDF diets, black solid (—) is the fitted regression line for low-NDF diets, and black dashed (—) is the fitted regression line for all diets.</figcaption></figure>

correlation between CH₄ production (g/d) and the level of NDF, ADF or forage content, supporting the notion that structural carbohydrates promote acetate production in the rumen, leading to increased production of hydrogen molecules and ultimately CH₄ emissions (g/d) (Johnson and Johnson, 1995; Bannink et al., 2011). A different trend was observed in beef cattle, in which a model from van Lingen et al. (2019) outperformed other models for the Aw climate classification, and the inclusion of the forage composition improved the predictive ability of this model. For beef cattle fed with low-NDF diets in Cfb, models including fatty acid, DMI and NDFI had lower predictive error than for high-NDF diets. This effect may not be limited to structural carbohydrates, as it can often be confounded by the effects of DMI and the negative role of dietary lipids on the digestibility of NDF, as well as the ratio of structural to non-structural carbohydrates in the diet (Moe and Tyrrell, 1979). In the sheep category of Cfa, models including intake had better predictive performance than other models for the high-NDF subset, particularly for models including intake. Notably, DMI (kg/d) was reduced in the high-NDF subset. As in cattle, DMI is also the most important predictor of enteric CH₄ production (g/d) in sheep (Patra et al., 2017; Swainson et al., 2018; Belanche et al., 2023). However, these findings are inconsistent with our dataset as the low-NDF subset had higher CH₄ emissions (g/d), yield (g/kg DMI) and methane emission factor (%) than the high-NDF subset. This inconsistency might be explained by the higher BW and DMI of animals in the low-NDF subset when compared to those in the high-NDF. Belanche et al. (2023) reported a positive relationship between BW and methane production. Furthermore, Van Gastelen et al. (2019) evaluated the effect of dietary mitigation strategies across dairy cattle, beef cattle and sheep, and reported that CH₄ mitigation strategies that related to forage characteristics (e.g., differences in NDF content or digestibility) were effective for dairy cows but had no or minor effects in sheep.

## Lipid supplementation

Benaouda et al. (2019) proposed that the effect of lipid supplementation is better assessed based on CH₄ yield (g/kg DMI) because of the countereffect of DMI (g/d). Dietary lipids have been reported to decrease CH₄ emissions (g/d) (Beauchemin et al., 2008; Moate et al., 2011). According to some authors, lipid sources (Knapp et al., 2014) or fatty acid profile (Giger-Reverdin et al., 2003) also have an impact, but this has not been a major source of variation, according to the meta-analysis carried out by Beauchemin et al. (2008). Lipids have the ability to inhibit cellulolytic bacteria, protozoa and archaea, decrease NDF digestibility, and are non-fermentable in the rumen, which can reduce CH₄ production (g/d) (Maia et al., 2007; Guyader et al., 2014). For dairy cattle, the negative effect of dietary lipids on daily CH₄ emissions (g/d) was also reported in the meta-analysis of Eugène et al. (2008), where the mean EE (g/kg DM) levels in the low and high lipid subsets were 25 and 64 g/kg DM, respectively. In the current study, the high-EE diets had 78.7% higher EE concentration than the low-EE diets. For dairy cows in cfb climates, extant models including NDFI (g/d) and EE content (g/kg DM) as predictors performed better than other models for the low-EE subset. The countereffect of high EE (g/kg DM) on DMI was clearly observed. Dry matter intake (kg/d) can decrease with increasing levels of EE (g/kg DM) in the diets of dairy cattle, which could lead to less CH₄ production (g/d) (Allen, 2000; Hollmann et al., 2012). In this study, DMI (kg/d) and Gross energy intake (MJ/d) obtained for the high-EE subset were higher than the low-EE diets; however, a lower methane emission factor % was obtained, which is in agreement with Warner et al. (2017), with a 2.1% reduction in methane emission factor per kg of DMI increase in dairy cows. For beef cattle, the evaluated extant models could not sufficiently predict data based on the division by EE (g/kg DM), and this may be attributable to the higher percentage (70%) of the beef dataset having low EE content (g/kg DM) with a low range.
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Observed vs predicted values plots, using dairy cattle data for Cfb (temperate, no dry season, warm summer), the reference models (A3 and D6) and other models (A3, A5, D1, D2, D6, D7, and D8) with the smallest RSR (square root of the mean square prediction error to SD of observed values ratio) for each dietary strategy. The grey solid (—) is the identity line $y = x$, red solid (—) is the fitted regression line for high-ether extract/high-starch diets, black solid (—) is the fitted regression line for low-ether extract/low-starch diets, black dashed (—) is the fitted regression line for all diets.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Observed vs predicted values plots, using dairy cows dataset for Cfb (temperate, no dry season, warm summer), the reference models (A2, A3, A4) and other models (A5, D2, D1, D5, and D6) with the smallest RSR (square root of the mean square prediction error to SD of observed values ratio). The grey solid (—) is the identity line $y = x$, red solid (—) is the fitted regression line for high-NDF digestibility diets, black solid (—) is the fitted regression line for low-NDF digestibility diets, and black dashed (—) is the fitted regression line for all diets.</figcaption></figure>
<caption>**Table 3**</caption>

Evaluation of methane emission (g/d) prediction model performance for beef cattle according to diet quality based on neutral detergent fibre content, and lipid or starch supplementation, and by the Köppen climate classification.

<table><thead><tr><th>Mitigation strategies</th><th>Levels</th><th>Rank</th><th>Model</th><th>n</th><th>RMSPE (g/d)</th><th>RMSPE (%)</th><th>ECT (%)</th><th>ER (%)</th><th>ED (%)</th><th>CCC</th><th>r</th><th>RSR</th></tr></thead><tbody><tr><td rowspan="12">Aw<br>Diet quality by NDF</td><td rowspan="4">High NDF<br>(mean 575 g/kg DM)</td><td>4</td><td>A1</td><td>11</td><td>57.0</td><td>42.8</td><td>42.1</td><td>46.1</td><td>11.9</td><td>-0.09</td><td>-0.19</td><td>2.71</td></tr><tr><td>7</td><td>DB2</td><td>11</td><td>63.7</td><td>47.9</td><td>75.8</td><td>14.4</td><td>9.73</td><td>-0.03</td><td>-0.11</td><td>3.04</td></tr><tr><td>1</td><td>B7</td><td>11</td><td>29.0</td><td>21.8</td><td>15.0</td><td>37.9</td><td>47.1</td><td>-0.09</td><td>-0.10</td><td>1.38</td></tr><tr><td>2</td><td>A7</td><td>8</td><td>63.2</td><td>46.9</td><td>82.6</td><td>5.73</td><td>11.7</td><td>-0.03</td><td>-0.26</td><td>2.64</td></tr><tr><td rowspan="4">Low NDF<br>(mean 290 g/kg DM)</td><td>3</td><td>B3</td><td>11</td><td>55.9</td><td>42.0</td><td>76.1</td><td>11.3</td><td>12.6</td><td>-0.02</td><td>-0.11</td><td>2.67</td></tr><tr><td>12</td><td>A1</td><td>12</td><td>107</td><td>91.1</td><td>69.6</td><td>27.6</td><td>2.82</td><td>-0.12</td><td>-0.62</td><td>4.46</td></tr><tr><td>8</td><td>DB4</td><td>12</td><td>93.6</td><td>79.7</td><td>86.9</td><td>7.75</td><td>5.25</td><td>3.91</td><td>-0.04</td><td>3.91</td></tr><tr><td>1</td><td>B7</td><td>12</td><td>34.4</td><td>29.3</td><td>37.0</td><td>21.8</td><td>41.2</td><td>-0.12</td><td>-0.27</td><td>1.44</td></tr><tr><td rowspan="4">Lipid supplementation</td><td rowspan="4">High EE<br>(mean 57.7 g/kg DM)</td><td>2</td><td>A7</td><td>12</td><td>42.0</td><td>35.7</td><td>63.6</td><td>8.70</td><td>27.7</td><td>-0.05</td><td>-0.27</td><td>1.75</td></tr><tr><td>3</td><td>B5</td><td>12</td><td>66.7</td><td>56.8</td><td>92.8</td><td>1.14</td><td>6.08</td><td>0.06</td><td>0.70</td><td>2.78</td></tr><tr><td>12</td><td>A1</td><td>7</td><td>144</td><td>142</td><td>96.9</td><td>1.69</td><td>1.36</td><td>0.00</td><td>0.08</td><td>7.91</td></tr><tr><td>8</td><td>DB6</td><td>7</td><td>116</td><td>115</td><td>97.8</td><td>0.28</td><td>1.88</td><td>0.01</td><td>0.32</td><td>6.39</td></tr><tr><td rowspan="4">Low EE<br>mean 23.8 g/kg DM)</td><td>1</td><td>B7</td><td>7</td><td>45.7</td><td>45.0</td><td>87.4</td><td>0.29</td><td>12.4</td><td>0.04</td><td>0.31</td><td>2.50</td></tr><tr><td>2</td><td>A1</td><td>10</td><td>44.8</td><td>33.4</td><td>69.2</td><td>26.9</td><td>3.85</td><td>0.12</td><td>0.45</td><td>4.31</td></tr><tr><td>9</td><td>DB6</td><td>10</td><td>59.6</td><td>44.4</td><td>89.4</td><td>8.48</td><td>2.15</td><td>0.06</td><td>0.46</td><td>5.74</td></tr><tr><td>1</td><td>B7</td><td>10</td><td>15.9</td><td>11.9</td><td>17.1</td><td>52.9</td><td>30.0</td><td>0.37</td><td>0.46</td><td>1.54</td></tr><tr><td rowspan="8">Diet quality by NDFD</td><td rowspan="4">High NDFD<br>(mean 0.62)</td><td>12</td><td>A1</td><td>7</td><td>117</td><td>106</td><td>78.6</td><td>18.8</td><td>2.62</td><td>-0.11</td><td>-0.16</td><td>4.21</td></tr><tr><td>8</td><td>DB6</td><td>7</td><td>103</td><td>93.6</td><td>90.6</td><td>3.32</td><td>6.07</td><td>-0.01</td><td>-0.16</td><td>3.71</td></tr><tr><td>1</td><td>A7</td><td>7</td><td>36.9</td><td>33.2</td><td>47.6</td><td>3.08</td><td>49.3</td><td>-0.01</td><td>-0.05</td><td>1.32</td></tr><tr><td>2</td><td>B7</td><td>7</td><td>40.7</td><td>36.7</td><td>54.0</td><td>5.54</td><td>40.5</td><td>-0.02</td><td>-0.05</td><td>1.45</td></tr><tr><td rowspan="4">Low NDFD<br>(mean 0.54)</td><td>12</td><td>A1</td><td>7</td><td>98.1</td><td>77.2</td><td>67.7</td><td>31.1</td><td>1.19</td><td>-0.09</td><td>-0.57</td><td>6.96</td></tr><tr><td>8</td><td>DB6</td><td>7</td><td>84.9</td><td>66.9</td><td>88.3</td><td>9.67</td><td>2.07</td><td>-0.03</td><td>-0.35</td><td>6.02</td></tr><tr><td>1</td><td>B7</td><td>7</td><td>23.9</td><td>18.9</td><td>27.9</td><td>45.6</td><td>26.5</td><td>-0.23</td><td>-0.33</td><td>1.70</td></tr><tr><td>2</td><td>A7</td><td>7</td><td>45.5</td><td>35.8</td><td>86.1</td><td>6.60</td><td>7.32</td><td>-0.04</td><td>-0.33</td><td>3.23</td></tr><tr><td rowspan="12">Cfa<br>Diet quality by NDF</td><td rowspan="4">High NDF<br>(mean 502 g/kg DM)</td><td>8</td><td>A3</td><td>5</td><td>51.8</td><td>26.7</td><td>69.8</td><td>0.02</td><td>30.2</td><td>0.06</td><td>0.29</td><td>1.56</td></tr><tr><td>4</td><td>DB4</td><td>5</td><td>38.2</td><td>19.8</td><td>60.4</td><td>26.6</td><td>12.9</td><td>0.21</td><td>0.88</td><td>1.14</td></tr><tr><td>1</td><td>B1</td><td>5</td><td>28.7</td><td>14.8</td><td>1.63</td><td>0.07</td><td>98.3</td><td>0.17</td><td>0.29</td><td>0.86</td></tr><tr><td>2</td><td>A6</td><td>5</td><td>32.0</td><td>16.5</td><td>21.0</td><td>0.14</td><td>78.9</td><td>0.12</td><td>0.30</td><td>0.96</td></tr><tr><td rowspan="4">Low NDF<br>(mean 427 g/kg DM)</td><td>3</td><td>A5</td><td>5</td><td>31.9</td><td>16.5</td><td>20.6</td><td>0.01</td><td>79.4</td><td>0.13</td><td>0.29</td><td>0.96</td></tr><tr><td>10</td><td>A3</td><td>9</td><td>38.7</td><td>20.6</td><td>18.2</td><td>13.1</td><td>68.6</td><td>0.56</td><td>0.61</td><td>0.90</td></tr><tr><td>3</td><td>DB2</td><td>9</td><td>33.1</td><td>17.7</td><td>3.18</td><td>3.40</td><td>93.4</td><td>0.58</td><td>0.61</td><td>0.77</td></tr><tr><td>1</td><td>DB5</td><td>4</td><td>29.3</td><td>17.2</td><td>28.2</td><td>0.97</td><td>70.8</td><td>0.72</td><td>0.78</td><td>0.64</td></tr><tr><td rowspan="8">Lipid supplementation</td><td rowspan="4">Low EE<br>mean 18.6 g/kg DM)</td><td>2</td><td>A8</td><td>4</td><td>33.2</td><td>19.5</td><td>44.4</td><td>0.19</td><td>55.4</td><td>0.67</td><td>0.78</td><td>0.73</td></tr><tr><td>4</td><td>B3</td><td>9</td><td>33.9</td><td>18.1</td><td>10.5</td><td>0.17</td><td>89.3</td><td>0.50</td><td>0.61</td><td>0.79</td></tr><tr><td>5</td><td>A9</td><td>4</td><td>36.8</td><td>21.7</td><td>55.0</td><td>0.01</td><td>45.0</td><td>0.62</td><td>0.78</td><td>0.81</td></tr><tr><td>6</td><td>DB6</td><td>9</td><td>35.3</td><td>18.8</td><td>1.82</td><td>6.72</td><td>91.5</td><td>0.52</td><td>0.55</td><td>0.83</td></tr><tr><td rowspan="4">Low NDF<br>(mean 427 g/kg DM)</td><td>6</td><td>A1</td><td>3</td><td>37.9</td><td>22.9</td><td>48.5</td><td>46.5</td><td>5.05</td><td>0.07</td><td>0.18</td><td>3.57</td></tr><tr><td>9</td><td>DB6</td><td>3</td><td>48.6</td><td>29.5</td><td>89.7</td><td>8.31</td><td>2.00</td><td>0.11</td><td>0.61</td><td>4.58</td></tr><tr><td>1</td><td>B5</td><td>3</td><td>14.8</td><td>8.98</td><td>56.6</td><td>11.7</td><td>31.7</td><td>0.17</td><td>0.28</td><td>1.39</td></tr><tr><td>2</td><td>A9</td><td>2</td><td>29.3</td><td>17.4</td><td>76.6</td><td>23.4</td><td>0.00</td><td>0.42</td><td>1.00</td><td>2.44</td></tr><tr><td rowspan="8">Diet quality by NDFD</td><td rowspan="4">low NDFD (mean 0.52)</td><td>3</td><td>B7</td><td>3</td><td>26.9</td><td>16.3</td><td>84.3</td><td>9.36</td><td>6.30</td><td>0.22</td><td>0.63</td><td>2.53</td></tr><tr><td>2</td><td>A2</td><td>6</td><td>28.8</td><td>17.0</td><td>0.94</td><td>28.0</td><td>71.1</td><td>0.66</td><td>0.67</td><td>0.81</td></tr><tr><td>7</td><td>DB6</td><td>6</td><td>32.6</td><td>19.2</td><td>3.94</td><td>26.9</td><td>69.2</td><td>0.55</td><td>0.56</td><td>0.91</td></tr><tr><td>1</td><td>B3</td><td>6</td><td>28.7</td><td>16.9</td><td>1.54</td><td>10.9</td><td>87.6</td><td>0.56</td><td>0.57</td><td>0.80</td></tr><tr><td rowspan="4">low NDFD (mean 0.52)</td><td>3</td><td>A4</td><td>6</td><td>29.4</td><td>17.3</td><td>0.63</td><td>31.0</td><td>68.3</td><td>0.66</td><td>0.67</td><td>0.82</td></tr><tr><td>4</td><td>B5</td><td>6</td><td>29.4</td><td>17.4</td><td>0.73</td><td>6.24</td><td>93.0</td><td>0.24</td><td>0.49</td><td>0.82</td></tr><tr><td>5</td><td>A1</td><td>6</td><td>31.4</td><td>18.5</td><td>24.7</td><td>15.5</td><td>59.7</td><td>0.61</td><td>0.67</td><td>0.88</td></tr><tr><td>6</td><td>B6</td><td>6</td><td>31.5</td><td>18.6</td><td>19.8</td><td>7.56</td><td>72.6</td><td>0.51</td><td>0.57</td><td>0.88</td></tr><tr><td rowspan="4">low NDFD (mean 0.52)</td><td>4</td><td>A3</td><td>2</td><td>15.3</td><td>7.53</td><td>77.0</td><td>23.0</td><td>0.00</td><td>0.72</td><td>1.00</td><td>1.03</td></tr><tr><td>1</td><td>DB3</td><td>2</td><td>3.08</td><td>1.52</td><td>53.4</td><td>46.6</td><td>0.00</td><td>0.97</td><td>1.00</td><td>0.21</td></tr><tr><td>2</td><td>B2</td><td>2</td><td>8.43</td><td>4.14</td><td>13.0</td><td>87.0</td><td>0.00</td><td>0.85</td><td>1.00</td><td>0.57</td></tr><tr><td>3</td><td>A6</td><td>2</td><td>13.9</td><td>6.81</td><td>82.9</td><td>17.1</td><td>0.00</td><td>0.75</td><td>1.00</td><td>0.93</td></tr></tbody></table>

Abbreviations: Aw = tropical savanna, dry winter; Cfa = temperate, no dry season, hot summer; Cfb = temperate, no dry season, warm summer; EE = dietary ether extract (g/kg DM); NDFD = digestibility of NDF (coefficient); Rank = rank of the performance based on the RSR; Model = code assigned to each evaluated extant model; RMSPE = square root of the mean square prediction error, expressed in g/d and RMSPE% as a percentage of CH₄ emissions mean; ECT% = error due to central tendency expressed as a percentage of RMSPE; ER% = error due to deviation of the regression slope expressed as a percentage of RMSPE; ED% = error due to the disturbance expressed as percentage of RMSPE; CCC = concordance correlation coefficient; r = correlation coefficient; RSR = RMSPE to SD of observed values ratio.

## Dietary starch content

Starch-rich diets favour the production of propionate, a pathway that competes for fermentation electrons with methanogenesis (Janssen, 2010). Starch-rich diets increase the number of amylolytic microbes in the rumen, which degrade and ferment starch. Starch is rapidly digested and fermented in the rumen compared

to fibre, resulting in a lower pH that negatively affects methanogenesis. (Popova et al., 2011; Newbold et al., 2015). In the Cfb dairy dataset, the high-STA subset also had higher Gross energy intake (MJ/d), which is expected to capture the effect of starch in the diets (Benaouda et al., 2019). This might explain the improvement of models' performance in this subset. This is compared
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Observed vs predicted values plots, using beef cattle dataset for Aw (tropical savanna, dry winter), Cfa (temperate, no dry season, hot summer) and Cfb (temperate, no dry season, warm summer), the reference models (A1, A3, DB2, DB4) and other models (B7, B1, B3, DB6) with the smallest RSR (square root of the mean square prediction error to SD of observed values ratio). The grey solid (—) is the identity line y = x, red solid (—) is the fitted regression line for high-NDF diets, black solid (—) is the fitted regression line for low-NDF diets, and black dashed (—) is the fitted regression line for all diets.</figcaption></figure>

<caption>**Table 4**</caption>

Evaluation of methane emission (g/d) prediction model performance for sheep according to diet quality based on neutral detergent fibre content, and lipid or starch supplementation, and by the Köppen climate classification.

<table><thead><tr><th>Mitigation strategies</th><th>Levels</th><th>Rank</th><th>Models</th><th>n</th><th>RMSPE (g/d)</th><th>RMSPE (%)</th><th>ECT (%)</th><th>ER (%)</th><th>ED (%)</th><th>CCC</th><th>r</th><th>RSR</th></tr></thead><tbody><tr><td rowspan="14">Cfa<br>Diet quality by NDF</td><td rowspan="14">High NDF<br>(mean 528 g/kg DM)</td><td>1</td><td>A3</td><td>8</td><td>1.88</td><td>10.3</td><td>31.1</td><td>0.85</td><td>68.0</td><td>0.93</td><td>0.95</td><td>0.35</td></tr><tr><td>2</td><td>A2</td><td>8</td><td>2.20</td><td>12.1</td><td>50.3</td><td>0.02</td><td>49.7</td><td>0.91</td><td>0.95</td><td>0.41</td></tr><tr><td>3</td><td>S5</td><td>8</td><td>2.37</td><td>13.0</td><td>4.94</td><td>14.0</td><td>81.1</td><td>0.85</td><td>0.90</td><td>0.45</td></tr><tr><td>4</td><td>S9</td><td>8</td><td>2.59</td><td>14.2</td><td>29.3</td><td>40.7</td><td>30.0</td><td>0.82</td><td>0.96</td><td>0.49</td></tr><tr><td>5</td><td>S8</td><td>8</td><td>2.73</td><td>15.0</td><td>49.9</td><td>17.6</td><td>32.5</td><td>0.83</td><td>0.95</td><td>0.51</td></tr><tr><td>6</td><td>S4</td><td>8</td><td>2.74</td><td>15.1</td><td>74.7</td><td>1.08</td><td>24.2</td><td>0.88</td><td>0.96</td><td>0.52</td></tr><tr><td>7</td><td>S1</td><td>8</td><td>2.96</td><td>16.2</td><td>12.4</td><td>35.3</td><td>52.3</td><td>0.74</td><td>0.90</td><td>0.56</td></tr><tr><td>8</td><td>S2</td><td>8</td><td>2.97</td><td>16.3</td><td>33.5</td><td>39.0</td><td>27.5</td><td>0.76</td><td>0.95</td><td>0.56</td></tr><tr><td>9</td><td>S7</td><td>8</td><td>3.11</td><td>17.1</td><td>40.9</td><td>11.9</td><td>47.2</td><td>0.77</td><td>0.90</td><td>0.58</td></tr><tr><td>10</td><td>A1</td><td>8</td><td>3.26</td><td>17.9</td><td>76.2</td><td>1.08</td><td>22.7</td><td>0.81</td><td>0.95</td><td>0.61</td></tr><tr><td rowspan="4">Low NDF<br>(mean 468 g/kg DM)</td><td>6</td><td>A3</td><td>6</td><td>13.2</td><td>38.2</td><td>94.9</td><td>0.01</td><td>5.08</td><td>0.43</td><td>0.92</td><td>1.51</td></tr><tr><td>1</td><td>S4</td><td>6</td><td>9.73</td><td>28.1</td><td>89.6</td><td>3.77</td><td>6.61</td><td>0.64</td><td>0.95</td><td>1.12</td></tr><tr><td>2</td><td>S7</td><td>6</td><td>9.95</td><td>28.8</td><td>90.3</td><td>0.02</td><td>9.64</td><td>0.56</td><td>0.92</td><td>1.14</td></tr><tr><td>3</td><td>S5</td><td>6</td><td>11.1</td><td>32.1</td><td>92.2</td><td>0.06</td><td>7.75</td><td>0.52</td><td>0.92</td><td>1.28</td></tr></tbody></table>

Abbreviations: Cfa = temperate, no dry season, hot summer; Rank = rank of the performance based on the RSR; Model = code assigned to each evaluated extant model; RMSPE = square root of the mean square prediction error, expressed in g/d and RMSPE% as a percentage of CH₄ emissions mean; ECT% = error due to central tendency expressed as a percentage of RMSPE; ER% = error due to deviation of the regression slope expressed as a percentage of RMSPE; ED% = error due to the disturbance expressed as percentage of RMSPE; CCC = concordance correlation coefficient; r = correlation coefficient; RSR = RMSPE to SD of observed value ratio.

bly the reason why the IPCC (1997, 2006 and 2019) models, based on Gross energy intake, outperform the Latin American-specific models (Congio et al., 2022b) for high-STA diets. In this study, dietary starch comes from the inclusion of concentrates and maize silages in diets. Several missing values were observed for beef cattle because the primary production system was pastoral, with little or no concentrate supplementation.

### Diet quality by NDF digestibility

High-quality diets improve animal performance, thereby reducing CH₄ intensity (g/kg milk; Benaouda et al., 2019). The

quality of the diet can be assessed through the digestibility of DM, organic matter, and/or neutral detergent fibre. In this study, dairy cattle in Cfb with increased NDFD had CH₄ emissions (g/d) and intensity (g/kg milk) reduced by 35.7 and 55.7%, respectively. However, numerically similar organic matter digestibility was found for the high and low NDFD subsets and no extant model fitted the high-NDFD subset. In contrast, model performance increased with DMI as a predictor, having low prediction errors for the low-NDFD category. This may be explained by the high content of NDF, which reduced DMI (when NDFD is low) and consequently decreased the passage rate of ruminal digesta increasing
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Observed vs predicted values plots, using sheep dataset for (Cfa) temperate, no dry season, hot summer, the reference models (A2, A3, S4) and other models (S5, S7) with the smallest RSR (square root of the mean square prediction error to SD of observed values ratio). The grey solid (—) is the identity line $y = x$, red solid (——) is the fitted regression line for high-NDF diets, black solid (—·—) is the fitted regression line for low-NDF diets, and black dashed (—·—) is the fitted regression line for all diets.</figcaption></figure>

CH₄ production. Similarly, lower CH₄ yield (g/kg DMI) and intensity (g/kg ADG) were obtained for beef cattle in Aw with high-NDFD, but this did not improve the performance of extant models.

## Conclusion

None of the extant models, including IPCC and Latin America-specific models, accurately predict CH₄ emissions (g/d) across all the climate zones assessed in this study. The representation of animal categories and dietary strategies across the studied climate zones is incomplete in the literature reviewed to compile our database, and this limited the ability of our database to fully assess the effect of climate zones, and we cannot generalise our conclusions. However, evaluated models had better performance under specific conditions, for dairy cows: in Cfa for the high-NDF subset, and in Cfb for the high- and low-NDF, high- and low-EE, high- and low-STA and low-NDFD subsets. For beef cattle, models had better performance in Cfa for the high-NDF and in Cfb for high-NDF and low-EE subsets. For sheep, models had better performance for the high-NDF subset in Cfa. These disparities could be due to unaccounted independent factors such as breed type, measurement methods, or management systems. Hence, to improve prediction accuracy, model development should consider the potential effect of climate zones, together with ruminant category, feed intake, dietary composition (including potential of mitigation strategies), and feed digestibility parameters.

## Supplementary material

Supplementary Material for this article (https://doi.org/10.1016/j.animal.2025.101665) can be found at the foot of the online page, in the Appendix section.

## Ethics approval

Not applicable.

## Data and model availability statement

Data available upon request and in the publication of De Ondarza, M.B., Hristov, A.N., Tricarico, J.M., 2023. Data Brief. July 28;49:109459. https://doi.org/10.1016/j.dib.2023.109459. PMID: 37577736; PMCID: PMC10412760.

## 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

Tobi Akinropo: https://orcid.org/0000-0003-1961-2077.

Patricia Ricci: https://orcid.org/0000-0003-2463-3833.

Claudia Faverin: https://orcid.org/0000-0002-6951-3029.

Veronica Ciganda: https://orcid.org/0000-0003-2150-3478.

Camila Muñoz: https://orcid.org/0000-0003-4080-9325.

Emilio Ungerfeld: https://orcid.org/0000-0002-5422-5462.

Natalie Urrutia: https://orcid.org/0000-0002-6196-0465.

Romina Rodriguez: https://orcid.org/0000-0001-6702-4689.

Diego Morgavi: https://orcid.org/0000-0002-3883-0937.

Maguy Eugène: https://orcid.org/0000-0002-2111-0597.

## CRediT authorship contribution statement

T.F. Akinropo: Writing – review & editing, Writing – original draft, Visualisation, Validation, Software, Resources, Methodology, Investigation, Formal analysis. P. Ricci: Writing – review & editing, Writing – original draft, Data curation, Conceptualisation. C. Faverin: Writing – review & editing. V. Ciganda: Writing – review & editing, Data curation. C. Muñoz: Writing – review & editing, Funding acquisition, Data curation. E. Ungerfeld: Writing – review & editing. N. Urrutia: Writing – review & editing. R. Rodriguez: Data curation. D.P. Morgavi: Writing – review & editing. M. Eugène: Writing – review & editing, Writing – original draft, Visualisation, Validation, Supervision, Resources, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualisation.

## Declaration of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

None.
## Financial support statement

This work was supported by the project Climat AMSUD regional Project (CLIMAT-AmSud230060) titled "Identifying enteric CH4 emission factors from pasture-based ruminant livestock in contrasting agro-climatic regions for mitigation of climate change" (GHG for MACC) 2024/2025 ID: CLIMAT-AmSud 23-MATH-13; the partners of the Joint Call of the Confund ERA-NET SusCrop (grant no. 771134), FACCE ERA-GAS (grant no. 696356), ICT-AGRI-FOOD (grant no. 862665), and SusAn (grant no. 696231), the French Agence Nationale de la Recherche (project ANR-21-SUGA-0001-04); and the Global Research Alliance through the CLIFF-GRADS (Climate, Food, and Farming Network Global Research Alliance Development Scholarships), Round 5, 2023.

## References

Allen, M.S., 2000. Effects of diet on short-term regulation of feed intake by lactating dairy cattle. Journal of Dairy Science 83, 1598-1624. https://doi.org/10.3168/jds.S0022-0302(00)75030-2.

Alvares, C.A., Stape, J.L., Sentelhas, P.C., de Moraes Gonçalves, J.L., Sparovek, G., 2014. Köppen's climate classification map for Brazil. Meteorologische Zeitschrift 22, 711-728. https://doi.org/10.1127/0941-2948/2013/05070.

Arndt, C., Hristov, A.N., Price, W.J., McClelland, S.C., Pelaez, A.M., Cueva, S.F., Oh, J., Dijkstra, J., Bannink, A., Bayat, A.R., Crompton, L.A., Eugène, M.A., Enahoro, D., Kebreab, E., Kreuzer, M., McGee, M., Martin, C., Newbold, C.J., Reynolds, C.K., Schwarm, A., Shingfield, K.J., Veneman, J.B., Yáñez-Ruiz, D.R., Yu, Z., 2022. Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 °C target by 2030 but not 205. Proceedings of the National Academy of Sciences of the United States of America 119, e2111294119. https://doi.org/10.1073/pnas.2111294119.

Bannink, A., van Schijndel, M.W., Dijkstra, J., 2011. A model of enteric fermentation in dairy cows to estimate methane emission for the Dutch National Inventory Report using the IPCC Tier 3 approach. Animal Feed Science and Technology 166-167, 603-618. https://doi.org/10.1016/j.anifeedsci.2011.04.034.

Beauchemin, K.A., Kreuzer, M., O'Mara, F., McAllister, T.A., 2008. Nutritional management for enteric methane abatement: a review. Australian Journal of Experimental Agriculture 48, 21-27. https://doi.org/10.1071/EA07199.

Beck, E.H., Zimmermann, N.A., McVicar, T.R., Vergopolan, N., Berg, A., Wood, E.F., 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 5, 180214. https://doi.org/10.1038/sdata.2018.214.

Belanche, A., Hristov, A.N., van Lingen, H.J., Denman, S.E., Kebreab, E., Schwarm, A., Kreuzer, M., Niu, M., Eugène, M., Niderkorn, V., Martin, C., Archimède, H., McGee, M., Reynolds, C.K., Crompton, L.A., Bayat, A.R., Yu, Z., Bannink, A., Dijkstra, J., Yáñez-Ruiz, D.R., 2023. Prediction of enteric methane emissions by sheep using an intercontinental database. Journal of Cleaner Production 384, 135523. https://doi.org/10.1016/j.jclepro.2022.135523.

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

Benaouda, M., González-Ronquillo, M., Appuhamy, J.A.D.R.N., Kebreab, E., Molina, L. T., Herrera-Camacho, J., Ku-Vera, J.C., Ángeles-Hernández, J.C., Castelán-Ortega, O.A., 2020. Development of mathematical models to predict enteric methane emission by cattle in Latin America. Livestock Science 241, 104177. https://doi.org/10.1016/j.livsci.2020.104177.

Bibby, J., Toutenburg, H., 1977. Prediction and improved estimation in linear models. John Wiley and Sons, Chichester, UK. https://doi.org/10.1002/bimj.197800029.

Blaxter, K., Clapperton, J., 1965. Prediction of the amount of methane produced by ruminants. British Journal of Nutrition 19, 511-522. https://doi.org/10.1079/BJN19650046.

Brouwer, E., 1965. Report of subcommittee on constants and factors. In: Third symposium on energy metabolism (May 1964, Troon, Scotland), (ed. KL Blaxter), EAAP Publication no. 11. Academic Press, New York, NY, USA and London, UK, 441-443.

Charmley, E., Williams, S.R.O., Moate, P.J., Hegarty, R.S., Herd, R.M., Oddy, V.H., Reyenga, P., Staunton, K.M., Anderson, A., Hannah, M.C., 2016. A universal equation to predict methane production of forage-fed cattle in Australia. Animal Production Science 56, 169-180. https://doi.org/10.1071/AN15365.

Congio, G.F.S., Bannink, A., Mayorga, O.L., Rodrigues, J.P.P., Bougouin, A., Kebreab, E., Carvalho, P.C.F., Abdalla, A.L., Monteiro, A.L.G., Ku-Vera, J.C., Gere, J.L., Gómez, C., Hristov, A.N., 2022a. Prediction of enteric methane production and yield in sheep using a Latin America and Caribbean database. Livestock Science 264, 105036. https://doi.org/10.1016/j.livsci.2022.105036.

Congio, G.F.S., Bannink, A., Mayorga, O.L., Rodrigues, J.P.P., Bougouin, A., Kebreab, E., Silva, R.R., Maurício, R.M., da Silva, S.C., Oliveira, P.P.A., Muñoz, C., Pereira, L.G.R., Gómez, C., Ariza-Nieto, C., Ribeiro-Filho, H.M.N., Castelán-Ortega, O.A., Rosero-Noguera, J.R., Tieri, M.P., Rodrigues, P.H.M., Hristov, A.N., 2022b. Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database. Science of the Total Environment 825, 153982. https://doi.org/10.1016/j.scitotenv.2022.153982.

Ellis, J.L., Kebreab, E., Odongo, N.E., McBride, B.W., Okine, E.K., France, J., 2007. Prediction of methane production from dairy and beef cattle. Journal of Dairy Science 90, 3456-3466. https://doi.org/10.3168/jds.2006-675.

Escobar-Bahamondes, P., Oba, M., Beauchemin, K.A., 2017. An evaluation of the accuracy and precision of methane prediction equations for beef cattle fed high-farage and high-grain diets. Animal 11, 68-77. https://doi.org/10.1017/S175173111600121X.

Eugène, M., Benchaar, C., Chiquette, J., Massé, D., 2008. Meta-analysis on the effects of lipid supplementation on methane production of lactating dairy cows. Canadian Journal of Animal Science 88, 331-337. https://doi.org/10.4141/CJAS07112.

Food and Agriculture Organization (FAO), 2023. Pathways towards lower emissions – A global assessment of the greenhouse gas emissions and mitigation options from livestock agrifood systems. FAO, Rome, Italy. https://doi.org/10.4060/cc9029en.

Giger-Reverdin, S., Morand-Fehr, P., Tran, G., 2003. Literature survey of the influence of dietary fat composition on methane production in dairy cattle. Livestock Production Science 82, 73-79. https://doi.org/10.1016/S0301-6226(03)00002-2.

Grainger, C., Beauchemin, K.A., 2011. Can enteric methane emissions from ruminants be lowered without lowering their production? Animal Feed Science and Technology 166-167, 308-320. https://doi.org/10.1016/j.anifeedsci.2011.04.021.

Gualdrón-Duarte, L.B., Buraschi, L., Cuatrín, A., Villar, L., Ceballos, D., Ricci, P., 2023. Uso de subproductos de trigo, cebada y maíz en la mitigación de emisiones de metano entérico en ovejas. Revista Argentina De Producción Animal 43, 275.

Guyader, J., Eugène, M., Nozière, P., Morgavi, D., Doreau, M., Martin, C., 2014. Influence of rumen protozoa on methane emission in ruminants: a meta-analysis approach. Animal 8, 1816-1825. https://doi.org/10.1017/S1751731114001852.

Hollmann, M., Powers, W.J., Fogiel, A.C., Liesman, J.S., Bello, N.M., Beede, D.K., 2012. Enteric methane emissions and lactational performance of Holstein cows fed different concentrations of coconut oil. Journal of Dairy Science 95, 2602-2615. https://doi.org/10.3168/jds.2011-4896.

Hristov, A.N., Oh, J., Firkins, J.L., Dijkstra, J., Kebreab, E., Waghorn, G., Makkar, H.P., Adesogan, A.T., Yang, W., Lee, C., Gerber, P.J., Henderson, B., Tricarico, J.M., 2013. Special topics-Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. Journal of Animal Science 91, 5045-5069. https://doi.org/10.2527/jas.2013-6583.

IPCC, 1997. Revised 1996 IPCC guidelines for national greenhouse gas inventories. Intergovernmental Panel on Climate Change, IPCC/OECD/IEA, Bracknell, UK.

IPCC, 2006. In: Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.), IPCC Guidelines for National Greenhouse Gas Inventories, chapter 10: emissions from livestock and manure management, prepared by the national greenhouse gas inventories programme. IGES, Japan. Retrieved on 20 October 2024 from: https://www.ipcc-nggip.iges.or.jp/public/2006gl/.

IPCC, 2019. In Gomez, D., Irving, W. (Eds.), 2019 Refinement to the 2006 IPCC guidelines for national greenhouse gas inventories, chapter 10: methane emissions from enteric fermentation, prepared by the task force on national greenhouse gas inventories (TFI), Kyoto, Japan. Retrieved on 28 December 2024 from: https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/.

Janssen, P.H., 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Animal Feed Science and Technology 160, 1-2. https://doi.org/10.1016/j.anifeedsci.2010.07.002. ISSN 0377-8401.

Johnson, K.A., Johnson, D.E., 1995. Methane emissions from cattle. Journal of Animal Science 73, 2483-2492. https://doi.org/10.2527/1995.7382483x.

Knapp, J.R., Laur, G.L., Vadas, P.A., Weiss, W.P., Tricarico, J.M., 2014. Invited review: enteric methane in dairy cattle production: quantifying the opportunities and impact of reducing emissions. Journal of Dairy Science 97, 3231-3261. https://doi.org/10.3168/jds.jds.2013-7234.

Köppen, W., 1884. Die Wärmezonen der Erde, nach der Dauer der heissen, gemässigten und kalten Zeit und nach der Wirkung der Wärme auf die organische Welt betrachtet [The thermal zones of the Earth, according to the duration of hot, moderate, and cold periods and the impact of heat on the organic world]. Meteorologische Zeitschrift 1, 215-226.

Maia, M.R.G., Chaudhary, L.C., Figueres, L., Wallace, R.J., 2007. Metabolism of polyunsaturated fatty acids and their toxicity to the microflora of the rumen. Antonie Van Leeuwenhoek 91, 303-314. https://doi.org/10.1007/s10482-006-9118-2.

Mills, J.A.N., Kebreab, E., Yates, C.M., Crompton, L.A., Cammell, S.B., Dhanoa, M.S., Agnew, R.E., France, J., 2003. Alternative approaches to predicting methane emissions from dairy cows. Journal of Animal Science 81, 3141-3150. https://doi.org/10.2527/2003.81123141x.

Moate, P.J., Williams, S.R.O., Grainger, C., Hannah, M.C., Ponnampalam, E.N., Eckard, R.J., 2011. Influence of cold-pressed canola, brewers' grains and hominy meal as dietary supplements suitable for reducing enteric methane emissions from
lactating dairy cows. Animal Feed Science and Technology 166-167, 254-264.
https://doi.org/10.1016/j.anifeedsci.2011.04.069.

Moe, P.W., Tyrrell, H.F., 1979. Methane production in dairy cows. Journal of Dairy Science 62, 1583-1586. https://doi.org/10.3168/jds.S0022-0302(79)83465-7.

Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P., Kebreab, E., 2014. Prediction of enteric methane emissions from cattle. Global Change Biology 20, 2140-2148.
https://doi.org/10.1111/gcb.12471.

Newbold, C.J., de la Fuente, G., Belanche, A., Ramos-Morales, E., McEwan, N.R., 2015. The role of ciliate protozoa in the rumen. Frontiers in Microbiology 26, 1313.
https://doi.org/10.3389/fmicb.2015.01313.

Nielsen, N.I., Volden, H., Åkerlind, M., Brask, M., Hellwing, A.L.F., Storlien, T., Bertilsson, J., 2013. A prediction equation for enteric methane emission from dairy cows for use in NorFor. Acta Agriculturae Scandinavica Section a: Animal Science 63, 126-130. https://doi.org/10.1080/09064702.2013.851275.

Niu, M., Kebreab, E., Hristov, A.N., Oh, J., Arndt, C., Bannink, A., Bayat, A.R., Brito, A.F., Boland, T., Casper, D., Crompton, L.A., Dijkstra, J., Eugène, M.A., Garnsworthy, P. C., Haque, M.N., Hellwing, A.L.F., Huhtanen, P., Kreuzer, M., Kuhla, B., Lund, P., Madsen, J., Martin, C., McClelland, S.C., McGee, M., Moate, P.J., Muetzel, S., Muñoz, C., O'Kiely, P., Peiren, N., Reynolds, C.K., Schwarm, A., Shingfield, K.J., Storlien, T.M., Weisbjerg, M.R., Yáñez-Ruiz, D.R., Yu, Z., 2018. Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database. Global Change Biology 24, 3368-3389. https://doi.org/10.1111/gcb.14094.

Patra, A.K., Lalhriatpuii, M., Debnath, B.C., 2017. Predicting enteric methane emission in sheep using linear and non-linear statistical models from dietary variables. Animal Production Science 56, 574-584. https://doi.org/10.1071/AN15505.

Popova, M., Morgavi, D., Doreau, M., Martin, C., 2011. Production de méthane et interactions microbienes dans le rumen. INRA Productions Animales 24, 447-460 https://doi.org/10.20870/productions-animales.2011.24.5.3277.

Ramin, M., Huhtanen, P., 2013. Development of equations for predicting methane emissions from ruminants. Journal of Dairy Science 96, 2476-2493. https://doi.org/10.3168/jds.2012-6095.

Ricci, P., Rooke, J.A., Nevison, I., Waterhouse, A., 2013. Methane emissions from beef and dairy cattle: quantifying the effect of physiological stage and diet characteristics. Journal of Animal Science 91, 5379-5389. https://doi.org/10.2527/jas.2013-6544.

Sauvant, D., Nozière, P., 2016. Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems. Animal 10, 755-770. https://doi.org/10.1017/S1751731115002670.

Swainson, N., Muetzel, S., Clark, H., 2018. Updated predictions of enteric methane emissions from sheep suitable for use in the New Zealand national greenhouse gas inventory. Animal Production Science 58, 973-979. https://doi.org/10.1071/AN15766.

van Gastelen, S., Dijkstra, J., Bannink, A., 2019. Are dietary strategies to mitigate entericmethane emission equally effective across dairy cattle, beef cattle, and sheep? Journal of Dairy Science 102, 6109-6130. https://doi.org/10.3168/jds.2018-15785.

van Lingen, H.J., Niu, M., Kebreab, E., Sebastião, C., Filho, V., Rooke, J., Schwarm, A., Kreuzer, M., Hynd, P.I., Caetano, M., Eugène, M., Martin, C., McGee, M., O'Kiely, P., Hünerberg, M., McAllister, T.A., Berchielli, T.T., Messana, J.D., Peiren, N., Chaves, A.V., Charmley, E., Cole, A., Hales, A.K., Lee, S., Berndt, A., Reynolds, C.K., Crompton, L.A., Bayat, A., Yáñez-Ruiz, D.R., Yu, Z., Bannink, A., Dijkstra, J., Casper, D.P., Hristov, A.N., 2019. Prediction of enteric methane production, yield and intensity of beef cattle using an intercontinental database. Agriculture, Ecosystems and Environment 283, 106575. https://doi.org/10.1016/j.agee.2019.106575.

Warner, D., Bannink, A., Hatew, B., van Laar, H., Dijkstra, J., 2017. Effects of grass silage quality and level of feed intake on enteric methane production in lactating dairy cows. Journal of Animal Science 95, 3687-3700.

Weiss, W.P., Tebbe, A.W., 2019. Estimating digestible energy values of feeds and diets and integrating those values into net energy systems. Journal of Dairy Science 102, 3871-3884. https://doi.org/10.1093/tas/txy119.

Yan, T., Porter, M.G., Mayne, C.S., 2009. Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. Animal 3, 1455-1462. https://doi.org/10.1017/S175173110900473X.