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Breeding for a Greener Future: Selective Breeding and
Crossbreeding Approaches to Minimize Methane Emissions in
Ruminant Livestock
Assan, Never
Faculty of Agriculture, Department of Agriculture Management, Zimbabwe Open University,
Bulawayo Regional Campus, Bulawayo, Zimbabwe
ABSTRACT
Methane emissions from ruminant livestock systems are a major contributor to agricultural greenhouse gases,
intensifying global climate change. To mitigate these emissions, breeding strategies that reduce enteric methane
output without compromising productivity must be developed. This review synthesizes the current research on
the role of selective breeding and strategic crossbreeding in lowering methane emissions through enhanced feed
conversion efficiency, rumen function, and incorporation of low-emission genotypes. The success of such
breeding programs hinges on the precision of methane phenotyping techniques, including both direct (respiration
chambers and tracer gas methods) and indirect (infrared sensors and milk or fecal biomarkers) measures,
alongside the application of advanced quantitative genetic models, such as random regression and reaction norm
models. The integration of genomic selection, high-throughput phenomics, and environmental covariates enables
the identification of heritable variations in methane traits and facilitates genotype-by-environment interaction
(GxE) modeling. Effective mitigation through genetic improvement requires a holistic understanding of the
genetic architecture of methane production and its interactions with dietary, microbial, and management factors.
Ultimately, incorporating both additive genetic effects and non-genetic influences into selection decisions can
significantly accelerate progress toward low-emission ruminant populations with low methane emissions.
Keywords: Selective Breeding, Crossbreeding, Methane, GxE Interactions, Epigenetics, Genomics, Phenomics,
Ruminants.
INTRODUCTION
The significant impact of methane on climate change has been underscored by the International Energy Agency
(IEA, 2021), with atmospheric levels now increasing to 250% of pre-industrial baselines (Nature, 2021).
Globally, atmospheric CH₄ concentrations total 570 Mt annually, with human activities, including agriculture,
accounting for 60% of the emissions (Jackson et al., 2020). Research suggests that animal breeding is a viable
strategy for mitigating methane emissions (López-Paredes et al., 2020; Manzanilla-Pech et al., 2021). In the
context of the European Union, beef and dairy cattle contribute substantially to methane emissions, with
heritability of methane emissions ranging between 0.12 and 0.45, accompanied by a genetic coefficient of
variation close to 20%, indicating the potential for selection (Danielsson et al., 2017; López-Paredes et al., 2020;
Manzanilla-Pech et al., 2021). Furthermore, livestock generates a notable 31% of global methane emissions
(Nature, 2021).
To mitigate the significant proportion of global greenhouse gas emissions attributable to ruminant livestock,
selective breeding complemented by strategic crossbreeding initiatives has been investigated (Van Marle-Köster
and Visser, 2021; González-Recio et al., 2020; Pinares-Patio et al., 2013; Gerber et al., 2013). Ruminant
livestock, particularly cattle and sheep, account for a substantial 48% of GHG emissions, whereas small
ruminants, such as goats and buffaloes, emit less enteric methane (Søren et al., 2017). The livestock industry,
encompassing beef and dairy production, generates approximately 6.3 Gt CO2-eq annually, accounting for 14–
18% of emissions linked to human activity (Cusack et al., 2021; Gerber et al., 2013; Herrero et al., 2016;
Friedlingstein et al., 2019). This review examines the potential of selective breeding and crossbreeding to
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develop sustainable ruminant systems, reduce methane emissions, and promote environmentally friendly
practices in the livestock industry. The findings highlight the essential contribution of sustainable ruminant
production systems in mitigating global warming.
Selective breeding offers a promising approach for the abatement of methane released by ruminant species by
improving the efficiency of suppressing methane yield from dietary intake (Króliczewska et al., 2023). By
selecting animals with inherently lower methane production, overall emissions can be decreased. This method
leverages the genetic variation in methane emissions, providing a long-term solution (Zetouni et al., 2018).
Selective breeding not only enhances sustainability and reduces methane emissions but also improves the
production efficiency (Brito et al., 2018; Pickering et al., 2015; Kandel et al., 2017). This approach combines
multiple disciplines, including molecular genetics, computer science, reproductive physiology, and genetics.
Selective and crossbreeding can optimize breed traits, leading to improved production and reduced CH
4
generation (Quinton et al., 2018). Genomic selection, a specialized form of selective breeding, has been
advocated as a cost-efficient strategy for mitigating methane emissions (Hayes et al., 2016). However, it is vital
to appreciate and manage the challenges and shortcomings of these approaches to ensure successful outcomes.
Crossbreeding involves mating two distinct animal breeds to produce offspring with preferred characteristics
(Assan et al., 2024). By leveraging complementarity, this approach increases the commercial value of the
offspring. Although crossbreeding improves beef cattle performance, its impact on methane production in
tropical systems remains unclear (Maciel et al., 2019). However, studies suggest that crossbreeding can reduce
methane emissions, with SimHerd data indicating a 6% decrease in emissions from crossbred cows compared to
pure Holstein cows (VikingGenetic, 2021). The benefits of crossbreeding lie in hybrid vigor, although its
relationship with low methane production remains unknown. Crossbreeding is a viable strategy for traits with
low heritability and can also contribute to reducing methane emissions at the farm level, thereby mitigating
climate change and global warming. Additionally, two- and three-way crosses have exhibited the ability to
diminish CH
4
emissions per kilogram of ECM, resulting in healthier animals, fewer replacement heifers, and
longer lifespans (VikingGenetic, 2021).
To quantify methane emissions, researchers have developed various methods, including respiration chambers,
SF6 tracer techniques, breath sampling, GreenFeed systems and laser methane detectors (Johnson et al., 2022).
However, some of these methods are impractical for population-wide genetic evaluations because of limitations
in scalability and practicality (Garnworthy et al., 2019; Hill et al., 2017; Huhtanen et al., 2015; Chagunda et al.,
2013). While respiration chambers measure total animal emissions, other methods focus on methane emitted in
the breath (Goopy et al., 2016; Lin et al., 2010). Recent advancements have led to the development of more
practical and cost-effective technologies for measuring CH4 emissions in farm settings, which may potentially
surpass existing methods (Hammond et al., 2016; Storm et al., 2012; Grainger et al., 2007). A comprehensive
life cycle impact analysis strategy is necessary to evaluate methane emissions throughout the bovine production
chain and to minimize emissions from ruminant species.
Genomics is a potential tool for reducing methane output from ruminants by identifying key genes, biomarkers,
and rumen microbial genes linked to CH
4
generation (Asselstine et al., 2021; Mijena and Getiso, 2021; González-
Recio et al., 2020). This method accelerates genetic progress by enhancing the selection accuracy and reducing
the generation intervals. Furthermore, phenomics can inform selective breeding decisions, guiding farm
management and genetic improvement (Prez-Enciso et al., 2021). The integration of automated high-capacity
analysis phenomics and breeding can help mitigate greenhouse gas emissions in agricultural animals (Waseem
et al., 2022). The deployment of high-speed data acquisition characterized by phenomics can optimize ruminant
breeding for low methane emissions (Mondenal and Singh, 2021). Recent advancements in automated high-
capacity analysis sequencing, genome editing, and artificial intelligence offer new opportunities to address
climate change and promote animal welfare. Ultimately, selective and crossbreeding can effectively reduce
methane emissions from ruminants, mitigating the impact of livestock on climate change. This systematic review
examines the effectiveness of selective breeding and crossbreeding in curtailing methane emissions in ruminant
animals and highlights the necessity of effective methane quantification techniques, advanced statistical models,
and the integration of genomics, phenomics, and environmental data.
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MATERIALS AND METHODS
A systematic review was conducted to examine selective breeding and crossbreeding strategies for reducing
methane emissions in ruminant livestock. The review drew on literature from Scopus, Web of Science, PubMed,
and Google Scholar, only peer-reviewed studies published between 2000 and 2024 were considered, with a focus
on ruminant species such as cattle, sheep, and goats. An initial search using keywords such as “methane
emissions,” “livestock breeding,” “ruminant genetics,” and “climate-smart breeding” yielded 183 articles. After
removing duplicates, non-peer-reviewed sources, and studies lacking relevance, 74 articles were selected for in-
depth analysis. Studies were included based on methodological rigor, regional relevance, and a clear focus on
methane as a breeding trait. The search emphasized terms like “methane emissions in ruminants,” “selective
breeding for methane reduction,” “crossbreeding and greenhouse gases,” “genomics and phenomics in livestock
breeding,” and “enteric fermentation mitigation strategies.” Priority was given to research assessing methane
measurement techniques and evaluating genetic traits like heritability, feed efficiency, and productivity.
Exclusion criteria ruled out studies focusing exclusively on monogastric species, non-English publications, and
those lacking empirical data or peer review. To strengthen the review, relevant grey literature from international
organizations (e.g., FAO, IPCC, IEA) and breeding institutions (e.g., Viking-Genetics) was also examined. This
integrative approach offers a comprehensive foundation for subsequent analysis and the formulation of a
conceptual framework.
RESULTS AND DISCUSSION
Conceptual Framework for Methane Emission Reduction in Ruminants through Strategic Breeding and
Crossbreeding Programs
Reducing livestock emissions is key in international efforts to curb climate change, as ruminants are recognized
as major contributors to agricultural greenhouse gas emissions. As a sustainable and effective method, genetic
selection offers potential for reducing enteric methane emissions, as a result of the genetic basis of methane
production traits and the presence of substantial genetic variation among ruminant populations (Pérez-Enciso et
al., 2021). By exploiting this genetic variability, selective breeding and crossbreeding strategies can effectively
target low-emission traits, supporting both environmental sustainability and climate change mitigation. Selective
breeding focuses on enhancing pure-breed lineages by utilizing genetic and genomic markers associated with
rumen microbiota composition, feed efficiency, digestive capacity, and nutrient utilization (Woolliams, 2015).
Figure 1 illustrates the pathway for selective breeding towards low-methane ruminants, emphasizing the critical
role of sufficient population size and structured breeding programs. The initial step in such programs involves
identifying top-performing females exhibiting low-methane traits, which are then mated with high-merit males
proven to produce low emissions. Performance testing of males is essential to ensure the propagation of low-
emission traits across herds or flocks, including in crossbreeding initiatives to maximize heterosis.
Crossbreeding, the mating of individuals from genetically distinct breeds, leverages hybrid vigor (heterosis) to
enhance performance traits, including methane reduction (Tomar, 2010). This approach enhances fertility,
growth rate, viability, and maternal abilities, all of which contribute to overall production efficiency and
sustainability. Crossbreeding can be particularly effective when breeds with complementary traits—such as low
methane emissions and high productivity—are combined. The resulting progeny may exhibit superior
environmental and production characteristics compared to their parents. Nevertheless, crossbreeding presents
challenges, such as trait variability and potential genetic incompatibilities between parent breeds, which may
result in inconsistent performance outcomes.
Methane production in ruminants is influenced by both genetic and environmental factors. Non-genetic variables
such as diet, housing, and management practices also play a significant role in shaping emission levels,
underscoring the importance of integrated approaches. To enhance the precision and effectiveness of genetic
improvement programs, selection indices incorporating multiple traits—methane output, feed conversion
efficiency, animal welfare, and productivity—can be employed. to sustainable animal agriculture and long-term
climate resilience.
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Future breeding efforts are likely to benefit from advanced genomic tools, including genome-wide association
studies (GWAS) and marker-assisted selection, which can accelerate the identification of low-emission traits
and improve breeding accuracy. In sum, genetic selection and crossbreeding provide viable and impactful
strategies for reducing methane emissions in livestock systems, thereby contributing
Figure 1. Integrated Breeding Program for Reduced Methane Footprint
Towards a Low-Methane Future: Evaluating the Effects of Crossbreeding on Methane Emissions in
Ruminant Livestock
Crossbreeding—mating two distinct breeds to produce high-performing offspring—has proven effective in
improving livestock productivity and reducing enteric methane emissions in tropical regions (Thakur, 2022). By
enhancing feed conversion and growth rates, crossbreeding boosts production efficiency in ruminants, leading
to lower methane emissions per unit of meat or milk (Maciel et al., 2019). Strategic mating of locally adapted
breeds with high-yielding commercial breeds can introduce low-emission traits while maintaining fertility and
productivity (Theunissen, 2011). Genetic improvement in beef cattle thus presents a promising strategy for
reducing greenhouse gas emissions (Donoghue et al., 2016; Hayes et al., 2016).
In tropical climates, crossbred cattle offer a viable path to higher production rates, as faster-growing animals
tend to be more feed-efficient and produce less waste (Ducatti et al., 2009). This intensive approach can
significantly reduce methane emissions per kilogram of meat produced (Fraser et al., 2014). As global demand
for meat grows, identifying efficient cattle breeds and adopting appropriate production systems becomes
increasingly important for sustainable livestock development (Rowntree et al., 2016).
Well-designed crossbreeding systems can increase productivity by up to 21% and substantially lower the carbon
footprint of beef production (Mokolobate et al., 2014). The success of such systems depends on breed
compatibility, with complementary traits offering a competitive advantage (Huhtanen et al., 2021). Advanced
strategies like composite breed development and rotational crossing can further reduce emissions, particularly
in resource-constrained settings. Community-based crossbreeding initiatives also show promise, as they improve
resilience traits such as heat tolerance and disease resistance while enhancing production efficiency. However,
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these benefits come with challenges. Over-reliance on local genetic resources can lead to unsustainable hybrids
and the erosion of purebred lines (Solomon et al., 2010). Breed-specific plans, while offering targeted
improvements, may also face limitations due to environmental variability and genetic dilution (Leroy et al.,
2015).
Despite these concerns, crossbreeding remains a key tool in climate-smart livestock systems. By leveraging
genetic variation and hybrid vigor (heterosis), breeding programs can select animals with lower methane outputs,
optimizing both environmental performance and productivity. However, the broader success of crossbreeding
efforts depends on robust environmental impact assessments, coordinated stakeholder involvement, reliable
production data, and the inclusion of smallholder farmer needs in breeding strategies.
Cross-Species Evaluation of Selective Breeding as a Strategy to Reduce Enteric Emissions in Ruminants
Selective breeding of ruminant species to reduce enteric methane emissions is gaining attention as a sustainable
strategy to mitigate greenhouse gas outputs from livestock. This approach involves identifying and promoting
individuals with genetic traits that inherently result in lower methane production (López-Paredes et al., 2020;
Manzanilla-Pech et al., 2021). However, any breeding strategy must be carefully balanced to avoid unintended
trade-offs, such as reduced productivity, impaired animal welfare, or diminished genetic diversity.
Genetic variation within and among ruminant breeds presents opportunities for selection based on specific trait
ratios that indicate an animal’s potential for reduced emissions (Crew, 2013). Advances in genomic selection
have enhanced the precision of these efforts, allowing for the identification of low-emission individuals across
multiple breeds and species through the use of molecular markers (Calus, 2010; Pickerig et al., 2015).
To ensure long-term success, breeding programs must integrate methane reduction goals with the maintenance
of other economically and biologically important traits, while also mitigating risks such as inbreeding depression
(Brito et al., 2021). Moreover, the development of effective breeding strategies requires accurate, up-to-date data
on the distribution and abundance of cattle populations. Such information is essential for tailoring breeding
efforts to specific regional and global contexts (Guo et al., 2022; Pulina et al., 2021; Toorn et al., 2016). This
section explores the comparative potential for enteric methane reduction through selective breeding across
different ruminant species, highlighting the genetic, environmental, and management factors that shape breeding
outcomes.
Reducing Enteric Methane in Cattle: A Selective Breeding Approach for Low-Carbon Beef Systems
The beef cattle industry is a substantial contributor to methane production, but targeting the cow population can
help reduce emissions due to its numerical advantage (Guo et al., 2022; Pulina et al., 2021). Cattle produce 250-
500 L of CH4 daily, influenced by genetics and diet, with heritability estimates ranging from 0.19 to 0.29
(Dressler et al., 2024). Accurate estimates of CH4 production heritability are vital for the beef industry, offering
potential for reduced methane emissions and mitigating global warming. Research by Donoghue et al. (2016)
provided the first heritability estimates for methane traits in beef cattle, using data from Angus bulls and heifers.
The study revealed low to moderate heritabilities (0.21, 0.19, and 0.23) for daily methane production, methane
yield, and methane intensity, respectively. Notably, no phenotypic or genetic correlation was found between
methane characteristics and body composition traits, highlighting the potential for selective breeding to reduce
methane emissions without compromising animal productivity.
Studies on Angus heifers and bull progeny revealed high consistency and strong phenotypic dependence in
methane measurements over short- and long-term periods, using respiration chamber tests (Donoghue et al.,
2016). The results showed high repeatability (0.75-0.94) and strong phenotypic dependence (0.85-0.95) across
all periods.
Researchers are increasingly focused on broad-sense repeatability, which examines the persistence of individual
differences in traits over time, particularly for physiological traits like methane production (Dohm, 2002). This
concept is crucial for understanding the stability of methane emission traits in livestock.
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The Australian beef industry aims to decrease methane emissions by utilizing bulls with lower residual feed
intake, which are considered more feed-efficient, in both southern and national herds (Alford et al., 2016).
Adopting selection for reduced residual feed intake in grazing beef herds is expected to yield significant and
lasting methane reductions. Genetic correlations play a vital role in the selection index of livestock, particularly
beef cattle (Hayes et al., 2016). Crossbreeding can produce cows with improved roughage consumption,
enhanced feed valuation, faster growth rates, and better meat quality (Gill et al., 2010). By incorporating
multiple-trait selection, emissions can be reduced while improving economic performance, especially when feed
costs are a significant factor in the breeding objective.
Breeding for a Greener Dairy: Selective Breeding Strategies to Minimize Enteric Emissions in Dairy
Cattle
Studies on dairy production have shown that genetically selecting against methane (CH4) emissions is possible,
given its moderate genetic variability (CV of approximately 20%) and heritability estimates ranging from 0.12
to 0.45 (López-Paredes et al., 2020; Manzanilla-Pech et al., 2021; Breider et al., 2019; Pszczola et al., 2017).
Research has explored the potential of genetic selection to reduce methane emissions from dairy cows,
investigating the relationships between CH4 and various dairy traits (Fresco et al., 2022). By measuring CH4
emissions, feed intake, milk production, body weight, and body condition in Holstein cows, scientists have
identified opportunities to mitigate climate change impacts through genetic selection.
Genetic selection offers a promising approach to reducing methane emissions from dairy cattle while enhancing
energy efficiency (Bačėninaitė et al., 2022; Richardson et al., 2021). To promote sustainable dairy production,
breeding objectives should prioritize lowering methane emissions without compromising key economic traits
(Pickering et al., 2015). Recent research on sheep and cattle has demonstrated that methane-related traits are
heritable and can be improved through direct selection (Fresco et al., 2023; van Middelaar et al., 2014). While
genetic selection can decrease net methane emissions, it may also have unintended consequences, such as
negatively impacting milk protein and fat content (Fennessy et al., 2019). Variations in dry matter intake (DMI)
drive differences in methane output, with selection decisions influencing expected methane emissions (Amer et
al., 2018). Methane yields predicted from milk fat content are heritable, with heritability estimates ranging from
0.12 to 0.44.
Research suggests that methane reductions of up to 20-26% over ten years are achievable, but this may come at
the cost of a 6-18% decline in genetic gains for production traits (Genesis-Faraday Partnership, 2008; Jones et
al., 2008). Studies have shown that breeding dairy cows can reduce enteric methane production per unit of milk
produced (Olijhoek et al., 2018). However, methane production per kilogram of energy-corrected milk remained
unaffected by breed. Research has investigated the heritability of methane production in dairy cattle, providing
evidence of its genetic basis (Pszczola et al., 2017).
A positive genetic link was found between methane production and milk yield, indicating that reducing methane
emissions may require a decrease in milk yield at the animal level. Breeding strategies for reduced methane
emissions in dairy cows pose challenges.
Breeding for a Lower Environmental Impact: Selective Breeding Strategies to Reduce Enteric Emissions
in Small Ruminants
Sheep have a unique digestive system, with the rumen occupying over 70% of the total stomach capacity and
holding approximately 15 liters (Broucek, 2014). In recent years, sheep breeding programs have focused on
selecting for lower methane emissions, resulting in some flocks producing 10-12% less methane (Rowe et al.,
2019). Research suggests that sheep can be a cost-effective alternative to cattle for studying methane emissions,
as they are less expensive to maintain and also produce methane (Zaman et al., 2021). However, cattle and sheep
are significantly larger methane producers than goats, with the majority of emissions (87-90%) occurring in the
rumen and a smaller proportion (10-13%) in the large intestine (Mebrate et al., 2019). Fortunately, selective
breeding can help reduce methane production in sheep without compromising productivity, leading to improved
feed conversion and lower methane emissions per unit of feed intake (GWA, 2023).
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Table 1. Key Methane-Associated Traits and Heritability Estimates
Trait
Heritability
(h²)
Measurement
Method
Impact on Methane Emissions
References
Residual
Methane
Production
(RMP)
0.20–0.30
SF₆ tracer,
respiration
chamber,
GreenFeed
Direct trait; allows selection for low-
emitting animals without
compromising productivity.
Rowe et al.
(2019); Jonker
et al. (2022)
Feed Conversion
Ratio (FCR)
0.15–0.25
Dry matter intake
vs. weight gain
Indirect; improved FCR reduces
methane per unit of product.
Berry &
Crowley
(2019)
Residual Feed
Intake (RFI)
0.20–0.45
Calorimetry,
automated intake &
growth monitoring
Strong negative genetic correlation
with methane intensity; animals with
low RFI emit less methane.
Oliveira et al.
(2020); Berry
& Crowley
(2019)
Methane Yield
0.10–0.25
Respiration
chamber (g CH₄/kg
DMI)
Selection reduces methane per kg of
feed without necessarily affecting
productivity.
Conington et
al. (2021)
Rumen
Microbial
Efficiency
Low (<0.10)
Metagenomic
sequencing,
microbial profiling
Low heritability; promising for long-
term microbiome-based mitigation
strategies though not yet widely
adopted in breeding programs.
Li et al. (2022)
MIR-Predicted
Methane
0.15–0.25
Mid-infrared
spectroscopy (MIR)
of milk
Non-invasive proxy for methane;
suitable for dairy systems and already
being piloted in smallholder settings
such as Kenya.
Ndung’u et al.
(2024)
Table 1 presents key methane-associated traits with heritability estimates ranging from less than 10% to 45%.
These variations suggest that the estimation of genetic parameters may be influenced by the measurement
methods used, highlighting the need for consistency and validation across different methodologies. Research has
shown that methane production and yield (MY) in sheep are heritable and repeatable traits, with heritability
estimates ranging from 0.13 to 0.29 ± 0.05 for absolute methane emissions and 0.13 ± 0.03 for methane yield
per kg dry matter intake (DMI) (Dressler et al., 2024).
Genetic selection can be employed to identify animals that produce less methane per unit of feed intake,
contributing to reduced methane emissions from ruminants. For a trait to be responsive to selection, it should
have moderate to high heritability (Pinares-Patiño et al., 2013). Selecting goats with improved feed conversion
efficiency through genetic selection can indirectly increase farmers' earnings without relying on carbon credits.
However, the high cost of detecting methane emissions poses a challenge for selecting animals specifically for
low methane production.
The rumen microbial community (RMC) profile may serve as a reliable surrogate for methane emissions. A
study by Bilton et al. (2022) found moderate to high genetic correlations (0.66 and 0.77) between direct and
indirect methane measurements in an ewe breeding program, highlighting the potential of indirect methods for
selective breeding. Although direct methane measurement is challenging, indirect methods like feed intake and
rumen digesta retention time can be used for selection. Research has shown that sheep bred for low methane
emissions consistently produce less methane than those bred for high methane emissions, regardless of the season
(Jonker et al., 2017). To reduce methane emissions in ruminant animals through breeding, indirect or proxy
measures must be used to estimate methane emissions.
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Methane Production and Economic Traits in Ruminants: Phenotypic and Genetic Relationships
Breeding ruminants for low methane emissions requires a comprehensive understanding of the phenotypic and
genetic relationships between methane-related traits and their economic importance. This knowledge will help
animal breeders develop strategies to enhance multiple traits simultaneously, considering both genetic and
environmental influences on phenotypic correlations. Positive correlations indicate that traits tend to increase
together, meaning improvement in one trait is generally associated with improvement in the other (Roehe et al.,
2016; Rowe et al., 2019). In contrast, negative correlations suggest a potential trade-off; for example, selecting
for higher milk yield may lead to a reduction in methane yield (Lassen & Løvendahl, 2016; van Engelen et al.,
2022). It is important to note that the strength and direction of these correlations can vary depending on factors
such as breed, production system, and the methods used for measurement (Pickering et al., 2015; Difford et al.,
2018). Table 2 presents the genetic and phenotypic correlations between methane-associated and performance
traits in ruminant livestock, which range from negative to strong positive depending on the trait pairing. This
highlights the importance of considering these correlations in breeding strategies, as they can significantly
influence the effectiveness and direction of selection responses.
Selecting for low methane emission traits can have implications for other economically important traits, such as
growth and milk yield. Research by Herd et al. (2014) revealed that methane production is positively correlated
with dry matter intake (DMI), milk yield (MY), and residual methane production (RMP) traits, as well as growth
and body composition traits in cattle. However, methane yield was not correlated with DMI, growth, or body
composition traits. The strong correlations among the three RMP traits indicate that RMP can be an effective
tool for reducing methane production. Gavi (2022) identified genetic links between methane intensity, milk
composition, and methane production, as well as daily milk yield and condensed milk yield.
Table 2. Genetic and Phenotypic Correlations between Methane-Associated and Performance Traits in Ruminant
Livestock
Methane Trait
Performance
Trait
Genetic
Correlation (rG)
Phenotypic
Correlation (rP)
Reference
Residual Methane
Emission
Feed Intake
0.20 – 0.35
0.15 – 0.30
Rowe et al. (2019); Pickering
et al. (2015)
Residual Methane
Emission
Liveweight Gain
−0.10 to −0.20
−0.05 to −0.15
Donoghue et al. (2016);
Lassen & Løvendahl (2016)
Methane Yield (g
CH₄/kg DMI)
Milk Yield
−0.30 to −0.45
−0.25 to −0.35
van Engelen et al. (2022)
Methane Yield
Feed Efficiency
(FCE)
−0.40 to −0.60
−0.30 to −0.50
Roehe et al. (2016)
Rumen Microbial
Efficiency
Dry Matter Intake
(DMI)
Low to moderate
(0.10–0.25)
Low
Difford et al. (2018); Noel et
al. (2023)
Enteric CH₄
Production
Body Weight
0.30 – 0.50
0.25 – 0.40
Pickering et al. (2015)
The strong correlation between residual methane production (RMP) traits and milk yield (MY) suggests that
reducing MY can lower methane production without compromising productivity, providing valuable insights for
methane-mitigating breeding programs. Bird-Gardiner et al. (2017) found a moderate negative correlation
between methane yield (MY) and dry matter intake (DMI) in cattle fed roughage and grain-based diets. A meta-
analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows, which
can be leveraged in genetic selection strategies. Understanding the relationships between traits can reveal new
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biological pathways, providing a deeper understanding of the connections between different traits and informing
the development of effective breeding programs.
Understanding the genetic correlations between traits enables the development of selection indexes, a valuable
tool in animal breeding for selecting animals with specific traits, such as low methane production. This
knowledge can inform breeding strategies, allowing for the simultaneous improvement of multiple traits and the
reduction of methane emissions in livestock. The connection between methane production, energy intake, and
milk yield is likely rooted in genetics, with positive genetic correlations indicating shared underlying
mechanisms controlling these traits. By examining phenotypic and genetic correlations, researchers and
practitioners can better navigate the complex relationships between methane-related traits and production
factors.
Enhancing Feed Efficiency and Reducing Methane Production in Ruminants through Breeding: A Multi-
Trait Approach
Breeding ruminants for low methane emissions requires a deep understanding of the relationships between feed
intake, feed efficiency, and methane production. These factors are closely linked, with increased feed intake
typically leading to higher methane production, reduced feed efficiency, and increased productivity (Li et al.,
2021). Methane production is also influenced by factors such as feed type and quality, with high-fiber feeds, and
ruminant species and breed playing a role (López-Paredes et al., 2020; Pitchford, 2004). Enhancing feed intake
and feed efficiency is critical for reducing methane emissions in ruminants, as these traits directly impact
production efficiency (Difford et al., 2020). Reducing feed intake can be an effective strategy for decreasing
methane production, as less fiber is fermented in the rumen, ultimately leading to lower emissions.
Residual feed intake (RFI), a key indicator of feed efficiency, is commonly used to assess an animal's energy
expenditure for various biological outputs (Difford et al., 2020; Li et al., 2021; López-Paredes et al., 2020).
Indirectly selecting animals with lower RFI could provide a short-term solution for reducing methane emissions
(Manzanilla-Pech et al., 2022; González-Recio, 2020). Studies have reported that the heritability of RFI in cattle
ranges from 0.25 to 0.43, indicating a significant genetic component (Pitchford, 2004). A strong correlation was
found between post-weaning RFI and cow RFI, suggesting that selecting heifers with lower RFI could lead to
reduced feed consumption and improved feed efficiency in adult cows (Berry and Crowley, 2013).
Breeding ruminants for low methane production involves selecting for traits related to feed intake, feed
efficiency, and production efficiency. By reducing feed intake, methane production can be lowered as there is
less substrate available for microbial fermentation. Improving feed efficiency, as measured by the feed
conversion ratio (FCR), can decrease methane production per unit of product, such as milk or meat. Prioritizing
high feed efficiency in breed selection is essential for sustainable ruminant production practices while
minimizing methane emissions (Yulistiani et al., 2021).
Selecting for higher production efficiency, such as increased milk yield or growth rate, can also reduce methane
production per unit of product (Connor, 2015). Enhanced feed efficiency and production efficiency can
significantly mitigate methane emissions by maximizing milk or meat production per unit of feed consumed and
selecting animals with higher production efficiency. Methane Yield and Residual Feed Intake are critical
environmental traits that are challenging to measure in large animal populations. Identifying causal mutations or
indicator traits can facilitate selection, and genomic selection offers promising opportunities. Integrating
quantitative trait loci (QTL) or their associated single nucleotide polymorphisms (SNPs) into current selection
models may enhance the potential of genomics in improving these traits (Rowe et al., 2014).
Breeding programs employ genetic correlations and multi-trait selection indices to identify animals with optimal
production traits, striking a balance between advancements in feed efficiency, production efficiency, and
methane production. Enhancing feed efficiency can result in significant cost savings and increased profitability.
To achieve this, strategies focus on optimizing feed formulation and nutrient management, as well as leveraging
animal breeding and genetics. Additionally, methane mitigation technologies, such as feed additives and methane
capture systems, are being investigated as potential solutions to reduce greenhouse gas emissions. These
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technologies can be integrated into comprehensive methane reduction approaches, offering a holistic solution to
minimize environmental impact.
Beyond Genetics: Understanding the Role of Non-Genetic Factors in Shaping Enteric Methane Emissions
in Ruminants
Non-genetic factors significantly impact methane production in ruminants, making it essential to correct for
these factors to improve the accuracy of direct selection for methane-related traits. The debate surrounding the
relative contributions of genetics and environment to phenotypic variation is a longstanding issue in animal
breeding. Research has shown that both genetic and non-genetic factors contribute to differences in specific traits
among animals (Assan and Makuza, 2005). This study highlights the importance of understanding and
quantifying the impact of non-genetic factors on enteric methane emissions in ruminants, as these factors can
significantly influence breeding programs aimed at reducing methane emissions. Non-genetic factors, such as
diet, feeding management, animal health, and environmental conditions, can substantially impact methane
emissions. Specific factors influencing methane emissions include feed quality, composition, and digestibility,
as well as feeding frequency, amount, and timing. Animal health status, stress levels, and parasite burden also
play a role.
The rumen microbiome is a critical factor in methane production, with its composition varying depending on
age, lactation status, and production stage. Management practices, such as grazing, confinement, bedding, and
housing conditions, also affect methane emissions. Additionally, regional and climatic differences, as well as
interactions between genetic and non-genetic factors, can influence methane emissions. To enhance the accuracy
of breeding value estimates, producers should ensure equal treatment of animals, maintain precise records, and
adjust these records to account for non-genetic factors that influence variation, such as nutrition, microbial
profiles, management, animal health, and other environmental factors.
Methane emissions in ruminants are influenced by both hereditary and non-genetic factors, making breeding
progress in methane production traits crucial. Factors such as methane production, yield, residual methane
production, methane emission rate, feed conversion efficiency, dry matter intake, ruminative efficiency,
microbial protein production, acetate-to-propionate ratio, and rumen pH play a significant role in determining
methane production and microbial populations (Islam and Lee, 2019; Hill et al 2016; Hammond et al
2015).Enteric methane emissions vary between animals due to both hereditary and non-genetic factors, making
the process complex and site-specific. The complexity of the methane bioenergetics process, differences between
populations and measurement periods, and host genetics, voluntary feed intake, dietary composition, the rumen
microbiome, and digestive tract physiology likely influence these differences(Zaman et al 2021).
Heritability, which represents the ratio of genetic to phenotypic variation, measures the similarity between
parents and offspring (Wray and Visscher, 2008). A high heritability indicates a strong genetic influence, while
a low heritability suggests a weaker genetic component. Although the genetic architecture of methane (CH4)
emissions is not well understood, research suggests that genetics account for approximately 20-30% of the
variability in methane emissions (Pszczola et al., 2018; Difford et al., 2018). Methane output, a heritable trait, is
influenced by host genetics, with heritability estimates ranging from 0.19 to 0.30 in cattle (Pinares-Patiño et al.,
2011, 2013).
Understanding the genetic mechanisms and interactions between genetic and non-genetic factors can lead to
increased genetic progress and reduced CH4 emissions. Selecting for low-CH4-emitting cows can sustainably
reduce greenhouse gas production from dairy cattle through cumulative genetic progress over generations
(Lassen and Difford, 2020; Manzanilla-Pech et al., 2022). Identifying non-genetic factors that significantly
influence enteric methane emissions in ruminants will optimize genetic gain and improve the accuracy of
breeding values for methane breeding. Understanding the interactions between non-genetic factors and genetic
merit will inform the development of models to predict methane emissions, ultimately guiding breeding
programs to reduce methane emissions.
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Nutritional Factors: The Primary Non-Genetic Determinants of Enteric Methane Emissions in Ruminants
The diet of ruminants plays a crucial role in determining the intensity and yield of enteric methane emissions
(Bosher et al, 2024; Tseten et al, 2022; Getiso and Mijena, 2021). Variations in methane production among
animal populations or individuals can be attributed to dietary factors, including feed quality, fiber content,
forage-to-concentrate ratio, starch, fat, and protein content (Lileikis et al, 2023). Understanding the impact of
these dietary factors can inform strategies to mitigate methane production in ruminants, such as optimizing
ingredient combinations and nutritional management approaches (Beauchemin et al., 2019). The composition
and digestibility of the rumen significantly influence methanogenesis, affecting hydrogen concentrations and
substrate availability for fermentation.
Diets with enhanced energy availability or digestibility can lead to reduced methane emissions per unit of energy-
corrected milk yield (Patra, 2013). The type of dietary carbohydrate also plays a role, with high-starch diets
potentially increasing methane production due to starch fermentation by rumen microbes. In contrast, high-fat
diets may decrease methane production, as fat can inhibit methanogenesis. Conversely, poor-quality feed can
result in increased methane production due to reduced digestibility and fermentation (Huang et al, 2021). Certain
feed additives, such as ionophores, have been shown to reduce methane production by inhibiting the growth of
methanogenic microbes (Tseten et al, 2022). These findings highlight the potential for dietary interventions to
mitigate methane emissions in ruminant livestock.
Grazing animals tend to produce more methane than those fed indoors, primarily due to differences in feed
quality and composition (Danielsson et al., 2017). Certain dietary supplements, such as essential oils and plant
extracts, have been shown to reduce methane production by modifying rumen fermentation patterns
(Beauchemin et al, 2008). Factors influencing methane production from enteric fermentation include feed intake,
feed composition, and energy consumption. Enhancing the nutritional quality of grazed forage can lead to
improved animal growth rates and reduced lifetime emissions (Quninton et al., 2018).
An animal's digestive physiology plays a significant role in determining its methanogenic output (Smith et al.,
2022). The availability of substrates for methanogenesis is crucial for ruminant metabolism, as the fermentation
of carbohydrates into volatile fatty acids and microbial protein synthesis releases methane (Goopy et al., 2013,
2014). Research has identified physiological differences in livestock with low methane emissions, including
smaller rumens, altered microbial fermentation profiles, and changes in volatile fatty acid ratios, such as a higher
propionate-to-butyrate ratio (Bain et al., 2014; Pinares-Patiño et al., 2011; Jonker et al., 2018).
Gut Microbes and Methane Emissions: A Complex Interplay in Ruminant Digestion
Methane production in ruminants is substantially influenced by microbial profiles, particularly ruminal
microorganisms involved in hydrogen metabolism (Zhong et al., 2024; Smith et al., 2022; Mao et al., 2010). The
intricate relationship between the host and rumen microbiota plays a critical role in enteric methane production.
Microbial viruses contribute to climate change by cycling methane through the environment, while plants harbor
auxiliary metabolic genes (AMGs) that regulate methane processes (Zhong et al., 2024). Predicting methane
phenotypes relies on the rumen microbiome, assuming that similar microbiomes in different animals will result
in similar methane production levels (Ross et al., 2013a; Wallace et al., 2019).
Wang et al. (2015) leveraged a relationship matrix based on rumen microbiota and genomic relationships to
enhance the accuracy of predictions for feed conversion efficiency, which is positively correlated with methane
production. This approach highlights the potential for integrating microbiome data into predictive models for
methane emission traits.
Methane production in sheep is shaped by the rumen microbial population and protozoa activity, but is not
influenced by the proportion of volatile fatty acids (VFAs) when tea saponin or fat supplements are added (Mao
et al., 2010). Methods to manipulate rumen microorganisms are still in their infancy, and vaccines aimed at
inhibiting methanogenesis have yielded inconsistent results.
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Further research is necessary to assess the effectiveness of microbiome-targeted CH4 reduction strategies in
breeding programs (Ross et al., 2013a; Wallace et al., 2019). Various biological approaches are being explored
to decrease CH4 production in the rumen, including:
i. Viruses that target CH4-producing microbes
ii. Specialized proteins that target methanogens
iii. Methanotrophs that break down CH4 in the rumen
iv. Breeding low-emission animals
Some of these methods could potentially be administered through vaccines (Zhong et al., 2024; Beauchemin et
al., 2020; Clark et al., 2011).
Protocols for Methane Quantification and Breeding Program Assessment in Low-Enteric-Emission
Ruminants
Methane emissions measurement is crucial for breeding ruminants with low enteric emissions, as it directly
impacts breeding values and genetic parameter estimates. Various methods have been developed to measure
methane in animal breeding, including respiration chambers, GreenFeed System, SF6 Tracer Technique, open-
circuit and closed-circuit respiration chambers, infrared spectroscopy, portable methane analyzers, whole-room
calorimeters, automated methane measuring systems, and modeling and simulation. (EPA, 2012; FAOSTAT,
2020; Janssens-Maenhout et al., 2019; Wolf et al., 2017). However, the accuracy of these methods varies, and
estimates can differ based on methodological tier, emission factors, and livestock activity data. To address this,
efforts have been made to categorize data by livestock type, significant category, or major categories (Crippa et
al., 2020; EPA, 2012; FAOSTAT, 2020; Janssens-Maenhout et al., 2019; Wolf et al., 2017), or by major
categories (Chang et al., 2019; Dangal et al., 2017; Gerber, Steinfeld, et al., 2013; Herrero et al., 2013).
Although various methane measurement methods have been developed, further research is necessary to create
accurate techniques for quantifying enteric CH4 emissions, a crucial step in genetically evaluating low methane
emissions (Clark et al., 2011). The accuracy of methane quantification is vital for breeding ruminants with low
enteric emissions, as inadequate methods can compromise the estimation of genetic parameters for methane traits
(Garnsworthy et al., 2019).
Researchers such as Hammond et al. (2016) and Hardan et al. (2022) have made significant contributions to the
development of various techniques for quantifying methane emissions under diverse environmental conditions.
One approach is the sulfur hexafluoride (SF6) method, which involves daily handling, rumen bolus insertion,
and laboratory gas monitoring. Alternatively, non-invasive methods have emerged, including laser methane
detectors, infrared, and photoacoustic gas analyzers, which provide rapid measurements over short periods
(minutes to hours). These advancements aim to improve the accuracy and efficiency of methane measurement,
ultimately supporting the genetic selection of low-methane-emitting ruminants.
Dairy cows offer a unique opportunity for monitoring enteric CH4 emissions, as they can be easily and non-
invasively monitored, particularly when integrated with automatic milking systems (Garnsworthy et al., 2012).
This integration can provide accurate, repeatable information on CH4 emissions, supporting informed breeding
decisions. To support greenhouse gas-focused breeding, it is essential to develop precise techniques for detecting
gas emissions, estimating breeding values, and determining variance components in genetic models. This will
enable accurate determination and estimation of CH4 emissions from ruminants. Accounting for variables such
as feed intake, feed type, animal health, environmental temperature, and rumen microbial population is critical
for genetically evaluating low CH4 emissions. A combination of a selection index and a repeatable gas
measurement procedure is recommended to reduce gas emissions.
The International Panel on Climate Change (IPCC) stresses the need for a standardized technique to calculate
genetic parameters for methane-associated traits, as estimates derived from varying methods may be unreliable
(IPCC, 1997, 2000, 2003, 2006, 2019). Livestock emissions reported to the United Nations Framework
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Convention on Climate Change (UNFCCC) rely on techniques outlined in the IPCC Guidelines. This enables
countries to develop inventories with varying levels of detail and update them regularly to reflect the latest
scientific knowledge (Chang et al., 2019; Dangal et al., 2017). Research on methane measurements has identified
heritable and repeatable variation among individual animals, suggesting that genetic selection can be used to
reduce methane production (Steinfeld et al., 2013; Herrero et al., 2013). This finding implies that breeding
programs can be designed to favor low-methane-emitting animals.
Measuring enteric methane emissions from individual animals using portable gas analysis apparatuses and
regular methane emission measurements during robotic milking has shown significant promise (Garnsworthy et
al., 2019; Lassen and Løvendahl, 2015). However, it is crucial to minimize errors, such as those related to
measurement collection and animal head posture. There is a growing interest in non-invasive, portable
techniques that do not disrupt the animal's environment or daily routine. Despite progress, determining the
accuracy of methane monitoring methods across diverse production systems remains an ongoing challenge.
While methane assessment has been extensively studied in commercial dairy systems, it has received limited
attention in smallholder dairy systems in Africa. Accurate methane measurement methods are vital for breeding
low-methane ruminants, enabling precise selection, reliable data, consistency, precision, repeatability, cost-
effectiveness, and animal welfare. When developing methane measurement protocols, it is essential to prioritize
animal welfare, minimize stress, and ensure the feasibility of breeding programs. Standardized methods facilitate
collaboration, comparison, and the integration of data across research studies and breeding programs.
Genetic-Environmental Interactions in Methane Emission Breeding: Which Comes First?
The interplay between genetic and environmental factors (G-E) plays a critical role in shaping methane
production outcomes (Boyce et al., 2020). Individuals exhibit varying responses to environmental factors, and
G-E interactions occur when genetically distinct individuals react differently to environmental changes. G-E
correlations arise when an individual's genetic predisposition influences their choice or modification of the
production environment. The concept of gene-environment interplay encompasses these interactions and
correlations, which are essential considerations in animal breeding research. Gene-environment interaction
(GEI) is vital for accurately assessing the impact of genetic and environmental factors on traits. GEI determines
how environmental factors affect a trait differently in individuals with distinct genotypes (Orgogozo et al., 2015).
Various biologically plausible models can describe the relationship between genotypes and environmental
factors, leading to differing predictions about traits in individuals. These models highlight the complexity of G-
E interactions and the need for nuanced approaches to understanding their influence on methane production and
other traits.
The traditional nature-nurture debate in animal breeding has given way to a more nuanced understanding,
recognizing that both genetic and environmental factors contribute additively to individual differences in
production traits. To develop effective breeding programs for low methane production, it is essential to
understand the genetic makeup of a population and its interaction with the environment (Gibson and Cundiff,
1975). The discovery by Garrod (1909) that environmental factors can modify the effect of genes on phenotype
has far-reaching implications for all living organisms. In animal breeding, phenotype is determined by the
interplay between genotype (including individual animal genes) and environmental influences, applying to all
traits, including methane production.
Genotype-by-environment interactions (GEI) significantly impact various aspects of animal breeding, such as
production efficiency, health, animal welfare, longevity, and overall productivity (Falconer, 1996; Badu-Apraku
et al., 2003). Climate change has affected animal breeding globally, and assessing gas emissions for selection
purposes can provide valuable insights into how an animal's genetic makeup affects its production efficiency
rankings in different environments, particularly regarding CH4 emissions. Research by Kilplagat et al. (2012)
suggests that genotype-by-environment interactions (GEI) may compromise the effectiveness of genetic
improvement efforts aimed at selecting for low CH4 emissions. This highlights the need for a more
comprehensive understanding of GEI in animal breeding programs focused on reducing methane emissions.
Genotype-environment interactions (GEI) play a crucial role in methane production, with high variance
components contributing to low heritability (Chang et al., 2019). Understanding the production environment is
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vital for informed management decisions, such as selecting breeds in crossbreeding systems (Dickerson, 1962).
Estimating the genetic correlation of a trait between environments helps determine the impact of GEI.
Inconsistent environmental sensitivity can lead to genotype × environmental interactions, emphasizing the
importance of considering GEI for optimal performance. This approach focuses on the specific region where the
animal will produce progeny. Baye's model of genotype-environment relationships can help reduce noise in
genomic research and quantify relationships between genotype, environment, and phenotype (Baye et al., 2011).
Breeding programs for low CH4 emissions can incorporate GEI through methods like multi-environment trials,
reaction norm models, genomic selection, environmental covariates, robustness selection, and accounting for
GxE variance (McCarter et al., 1991).
To enhance breeding programs for low methane production, environmental factors such as temperature and feed
quality should be considered to ensure accurate selection and improved outcomes. By modifying the
environment, breeders can optimize genotype expression, particularly in pasture-based systems where methane
emissions are influenced by various factors (Hammami et al., 2008). Methane emissions are affected by a range
of environmental stimuli, including chemical, physical, climate-dependent, and biological factors. However,
breeding for low methane emissions in different production systems can yield varying results, which are not
solely influenced by genetics.
Enteric gas emissions pose a significant challenge to genetic evaluation of methane, particularly in livestock
production, where human actions can impact both genotypes and the environment (Corris, 2020). While genome
sequencing has advanced, resolving the influence of environmental factors (E) remains an ongoing challenge.
To address this, Dempfle et al. (2008) developed multitrait models and genomic estimated breeding values
(GEBV) for different environments. These approaches can better accommodate reduced replication of
individuals across environments, leading to more accurate breeding outcomes and improved selection for low
methane-emitting animals.
Genotype-environment interactions (GEI) play a vital role in breeding programs, as they can reduce selection
responses and efficiency in germplasm programs or importations (Robertson, 1959). Estimating GEI involves
calculating the genetic correlation between traits expressed in different environments. However, identifying
specific environmental factors influencing GEI can be challenging. Breeding dairy cattle for low enteric gas
emissions in tropical and subtropical regions can be complicated due to genotype-by-environment interactions
(Endris et al., 2023). GxE interactions are crucial in breeding for low methane emissions, as they help identify
genetic variations that respond differently to environmental conditions, develop breeding strategies, select
animals producing less methane in specific production systems, improve genetic evaluation accuracy, enhance
adaptability to diverse farming systems and climates, reduce unintended consequences, optimize methane
mitigation strategies, and increase breeding program efficiency by targeting effective genetic improvements
across multiple environments.
Leveraging Adaptive Genetic Traits in Ruminants to Facilitate Low-Methane Breeding
Understanding how animals adapt to their environment is essential, as it significantly impacts methane
production processes and biology. Adaptation refers to a population's gradual shift towards an optimal state,
characterized by multiple favorable traits, which enhances fitness (Orr, 2000). Two distinct models elucidate the
genetic mechanisms underlying adaptation: the infinitesimal model, which involves numerous factors with small
effects, and an alternative model, which includes a smaller number of factors with large effects. Improving
ruminant production efficiency and reducing greenhouse gas emissions are critical research priorities in animal
adaptability. To achieve this, breeding programs should focus on developing animals that can thrive in diverse
environments, even under suboptimal conditions (Gaughan et al., 2019). Fixing specific genes in ruminant
populations can enhance adaptability, production efficiency, and reduce enteric gas emissions. Rearing ruminant
populations in suitable environmental conditions is vital for increasing production and mitigating GHG
emissions. Animals adapted to local conditions are more likely to flourish, reducing stress and methane
production. Moreover, animals adapted to local feed sources will be more efficient in converting feed to energy,
resulting in lower methane emissions.
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Breeding programs aimed at reducing methane production should prioritize the adaptation of animal genetic
resources to their environment. Adaptability pertains to an animal’s capability to survive, reproduce, and thrive
in a specific environment, influenced by both physiological and genetic factors (Gaughan et al., 2022). Ruminant
breeds, such as cattle, goats, and sheep, have evolved in diverse, challenging tropical environments, developing
unique adaptive traits. Such characteristics encompass resistance to diseases and heat stress, adaptability to
limited water availability, and the capacity to efficiently use low-quality feed (Barker, 2009). Studies have
demonstrated that these adaptive traits enable ruminants to survive and maintain productivity in harsh
environments (Joy et al., 2020). When selecting breeds for challenging environmental conditions, it is essential
to consider their physiological characteristics and breed-specific adaptations. Moreover, morphological changes
across animal generations can lead to physiological changes, which, in turn, can impact methane production.
Therefore, a comprehensive understanding of these factors is crucial for developing effective breeding strategies
that balance productivity with environmental sustainability.
In addition to crossbreeding for low methane production, tropical and subtropical regions should maintain
parallel programs focused on evaluating, improving, and conserving indigenous parental breeds that are well-
adapted to local environmental conditions. This approach will help breed animals resilient to environmental
stressors, which can influence methane production. Identifying and characterizing ruminant breeds or individuals
with natural tolerance to high-fiber diets is crucial for facilitating low methane production. The adaptation of
animals to their environment can result in varying physiological functions, such as rumen function, which may
impact methane production levels. Conserving and leveraging indigenous breeds' genetic diversity can provide
valuable insights into breeding for low methane production. By understanding the unique physiological
characteristics of these breeds, researchers can develop targeted breeding strategies that prioritize both
environmental sustainability and animal productivity.
According to Colditz and Hine (2016), reducing methane (CH4) emissions from ruminants must be accompanied
by efforts to enhance the resilience of livestock production systems to stressors. Animals that are not adapted to
production conditions may fail to efficiently utilize feed, potentially leading to increased CH4 emissions. Feed
efficiency is a critical factor influencing the profitability of the beef production industry, as it helps minimize
the environmental footprint (Knap and Wang, 2012). Both genetic and environmental factors contribute to
variability in animal feed efficiency, resulting in phenotypic differences. To optimize animal adaptation, it is
essential to investigate all factors that enhance or impede adaptation. Breeding programs aimed at reducing CH4
emissions in ruminants should prioritize the promotion of genetic resources from adaptive ruminant species,
particularly in challenging environments such as semi-arid tropical climates.
Environmental stressors can significantly impact methane production, particularly in animal genetic resources
that are not well-adapted to their environment. When breeding for reduced methane emissions, it may be
beneficial to select or crossbreed suited breeds that are resilient to local conditions. Research by Ayalew et al.
(2023) highlights the value of African cattle breeds, which have undergone long-term natural selection, resulting
in high genetic differentiation and unique adaptive traits. These traits enable them to thrive in challenging
environments characterized by limited feed, high temperatures, parasites, and diseases. However, these valuable
genetic resources are under threat from indiscriminate crossbreeding, replacement with exotic breeds, and
climate change pressures. To mitigate methane emissions, breeding programs on continents like Africa should
prioritize local adapted ruminant species, leveraging their natural resilience and adaptive abilities to reduce
environmental impact.
According to Fu and Yuna (2022), integrating genomics and phenomics is crucial for breeding programs focused
on adaptation and animal welfare traits. A study by Bayer and Feldmann (2003) found that livestock adapted to
semi-arid tropical regions can slow down their metabolism during weight loss and recycle nutrients more
efficiently than improved temperate breeds. Research by Mirkena et al. (2010) suggests that imported temperate
breeds may produce more when fed high-quality feed, but their performance declines when given low-quality
grass or forage. In contrast, adapted local animal genetic resources are more suitable for breeding programs
aimed at reducing methane production. When developing breeding programs, it is essential to consider genetic
adaptations to the local environment, such as disease resistance or heat tolerance, as these traits may influence
methane production. Furthermore, breeding programs must account for the potential impacts of climate change
on methane emissions to produce animals that are resilient to these changes.
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Preserving and promoting adaptive animal genetic resources is vital for successful low-methane breeding
programs in ruminants. These resources provide numerous advantages such as genetic diversity, improved
performance and productivity, and reduced environmental impact. Breeding for low methane emissions should
not compromise the preservation of animal genetic heritage. Instead, it should support smallholder farmers,
enhance food security, and prioritize the use of adaptive genetic resources. These resources provide a broader
genetic base, enabling the identification of genes associated with low methane production. They also reduce the
need for costly inputs, improve overall sustainability, and promote thriving animals in local conditions,
minimizing the risk of unintended consequences. Breeding programs using adaptive genetic resources can reduce
environmental impact of livestock production, improve animal performance, and enhance food security,
especially in developing countries, by enhancing growth rate, fertility, and milk production. Ultimately,
promoting adaptive animal genetic resources can lead to more sustainable, resilient, and productive livestock
production systems.
Breeding for Low-Methane Ruminants: The Role of Non-Genetic Inheritance and Epigenetic Regulation
The complex interplay of non-genetic inheritance (NGI), epigenetics, and environmental variables highlights the
dynamic control of gene expression and its significant impact on cattle methane emissions. Recognizing and
utilizing these factors enables more focused and effective measures for reducing methane emissions. Recent
improvements have accelerated the use of epigenetic and NGI frameworks in cattle productivity and welfare
programs (Dunislawska et al., 2021; Ibeagha-Awemu & Yu, 2021). Environmental factors experienced during
an animal's life impact epigenetic changes, which significantly influence important biological mechanisms like
methanogenesis. These adjustments have the potential to substantially affectgrowth, reproductive performance,
health status of animals, and overall output. As a result, understanding epigenetic dynamics provides significant
opportunity for improving breeding programs and livestock management systems (Schenkel, 2021).
NGI refers to the transmission of traits or phenotypes through mechanisms other than DNA sequence changes,
such as epigenetic marks, gene regulation, and environmental factors (Danchin et al 2011). Understanding these
processes is critical in developing effective breeding strategies to mitigate methane output in ruminants and
accelerate genetic improvement. NGI and inherited gene regulation are two mechanisms that shape gene
expression, with NGI involving various mechanisms and IGR describing a unified range of heritable factors
(Gibney and Nolan, 2010). Non-genetic inheritance and epigenetics substantially influence the ruminant
phenotype, including methane production. Incorporating NGI and epigenetics into breeding programs can
facilitate the identification of markers associated with low methane production. Furthermore, it can enhance
understanding of environmental factors that influence methane production through epigenetic modifications.
The expression of a phenotype is a result of the interplay between the genome and epigenome, with epigenetic
variation contributing significantly to phenotypic variation and improving predictive accuracy (Britannica, 2024;
de Vienne, 2022). A phenotypic trait refers to a specific variation of an organism's characteristic, which can be
inherited, influenced by external factors, or a combination of both. Genomic imprinting governs a range of
biological functions, such as fetal development, metabolic regulation, and behavioural traits (Jiang et al., 2007).
Epigenetic modifications are also key regulators of lipid metabolism, fat cell formation, and milk synthesis
(Eveline et al., 2017; Singh et al., 2010). Furthermore, epigenetics is essential for genome reprogramming and
gene expression, controlling growth, development, and biochemical processes, including methanogenesis
(Crouch et al., 2022; Schenkel, 2021). In essence, epigenetics plays a vital role in shaping an organism's
phenotype and influencing various biological processes, making it a critical area of study in understanding
complex traits like methanogenesis.
Non-genetic inheritance mechanisms can facilitate rapid adaptation to environmental changes within a single
generation (Gibney, 2010; Galton, 1876; Lerner, 1950). However, the intricate nature of epigenetic pathways
involved in the biochemical processes limits our comprehension (Jablonka and Lamb, 1995). Gene regulation
through inheritance influences methane-related gene expression. The interactions between ruminants and their
environment can shape their epigenetic landscape, subsequently affecting methane emissions. Understanding
these dynamics can empower breeders to develop targeted strategies, selecting for specific epigenetic marks,
gene regulation patterns, and non-genetic inheritance traits that reduce methane emissions. This knowledge can
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also enable the application of precision breeding techniques, such as epigenetic editing, to mitigate methane
emissions in livestock.
Falk (2009) investigated inheritable modifications in gene activity and genome function that take place
independently of changes to the DNA sequence, emphasizing the significance of epigenetic processes. These
processes control gene expression during transcription as well as after transcription, influencing various
phenotypes in livestock. Research by Karrow et al. (2011) investigated the impact of epigenetic factors on
diseases, reproduction, and milk production. The Price equation provides a valuable framework for
understanding changes in trait averages across generations, shedding light on factors contributing to phenotypic
evolution. Evidence points to an association among diet, management, and epigenetic marks on methane
production. This relationship underscores the need to explore the potential of epigenetic editing and integrate
non-genetic inheritance (NGI) and epigenetics with genetic selection. A comprehensive breeding program that
incorporates these elements can provide a more holistic approach to reducing methane emissions in livestock.
Progress in genome editing tools such as zinc finger nucleases (ZFN), transcription activator-like effector
nucleases (TALEN), and CRISPR/Cas9 systems has enabled efficient gene editing, particularly epigenetic
editing at specific loci (Vojta et al., 2016). Identifying genotypes prone to favorable or unfavorable methylation
patterns can inform breeding strategies for low-methane-emitting animals.
Epigenome-wide association analysis can pinpoint methylation patterns that promote low methane biochemical
processes. Furthermore, understanding relationships between methylation patterns and various methane
production-related traits, such as methane production, yield, residual production, and emission rate – can refine
breeding strategies.
Integrating non-genetic inheritance (NGI) and epigenetics into breeding programs can foster a holistic approach
to reducing methane production in ruminants, extending beyond traditional genetic selection methods. The
review suggests a strategy to enhance genetic selection accuracy, improve adaptability to diverse production
systems and environments, considering NGI and epigenetic effects. By adopting this integrated approach,
breeding programs can more effectively reduce methane production in ruminants.
Despite its potential, the regulatory impact of DNA methylation (DNAm) in genome-wide prediction with
understanding complex traits, such as methane production, remains unclear (Coolen et al., 2011; Richards, 2006).
However, epigenetics holds promise for improving animal breeding, and as research accumulates, its benefits
will become more apparent (Ibeagha-Awemu and Khatib, 2007).
Exploration of epigenetic variation stands as a promising and challenging endeavour for the next ten years, is an
exciting challenge for the next decade, particularly in complex traits like methane production, which involves
intricate biochemical and physiological processes (Gibney, 2010). Epigenetic modifications influence gene
regulation, affecting methane-related traits without altering the DNA sequence.
Non-genetic inheritance (NGI) and epigenetics contribute to phenotypic variation, impacting methane
production beyond genetic factors. Epigenetic marks can influence heritable traits, and gene-environment
interactions help elucidate how genetic and environmental factors interact. Considering NGI and epigenetics can
lead to more effective breeding strategies, novel approaches to methane mitigation, and a systems biology
perspective on methane production. Acknowledging the role of NGI and epigenetics can provide an expanded
and integrated understanding of methane generation, enabling more sustainable breeding strategies. Researching
epigenetic factors in methane breeding can uncover new opportunities to decrease ruminant methane emissions
and enhance livestock production sustainability. Integrating epigenetic information into breeding programs can
improve the accuracy of selecting for low methane production, involving studies on the influence of epigenetic
factors pertaining to the structure and metabolic activity of the rumen microbiota and the development of novel
breeding strategies.
A Synergistic Genomics-Phenomics Approach to Reducing Methane Emissions in Ruminants
Mitigating methane emissions from ruminant livestock represents a pivotal strategy in addressing climate change
and enhancing the long-term sustainability of livestock production systems. Integrating phenomic data with
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genomic selection offers a promising avenue for advancing ruminant breeding strategies. This combined
approach harnesses the strengths of both high-resolution phenotypic data and advanced genetic information to
enhance selection accuracy, accelerate genetic progress, and reduce environmental impact.
Phenomics involves the automated, large-volume, detailed phenotypic data, which enables a deeper
understanding of the genotype-to-phenotype relationship (Pérez-Enciso et al., 2021). Genomic selection, in
contrast, leverages dense genome-wide molecular markers to predict breeding values with enhanced precision
(Das et al., 2021; Johnsson, 2023). When these two technologies are integrated, they offer a powerful toolset for
selecting animals with low methane emissions without compromising productivity, fertility, or overall health
(Kader et al., 2022; Asselstine et al., 2021).
This integrated approach enables multi-trait selection—such as methane reduction, feed efficiency, and disease
resistance—while improving genetic gain and shortening generation intervals. Although genomic technologies
have already reached a mature stage with diminishing returns from increasing marker density (Van der Werf,
2013), their integration with phenomics opens new avenues for improvement. High-throughput phenotyping,
while still emerging in its impact, shows great promise in supporting sustainable breeding objectives (Steibel,
2023; Mansoor et al., 2023).
Contemporary findings point to the growing potential of this synergy revolutionize livestock breeding by
facilitating the development of climate-resilient ruminant populations (Kenny et al., 2023; Visser et al., 2023;
Ablondi et al., 2022). By combining genomics and phenomics, breeding programs can make informed, data-
driven decisions that serve to lower methane emissions while simultaneously strengthening the overall
sustainability of livestock production systems (Cortes-Hernández et al., 2021; Dixit et al., 2020; Baes and
Schenkel, 2020; Cole et al., 2020).
In sum, the convergence of phenomics and genomic selection represents a forward-looking approach to ruminant
breeding. Prioritizing this integration can lead to the development of low-emission, high-performance animals
that align with both environmental goals and agricultural productivity demands.
Reducing Methane Emissions in Ruminants via Genomic Selection: A Precise Breeding Approach
Genomic selection has transformed animal breeding via the enhancement of the identification of individuals
characterized by beneficial traits, for example low methane emissions (Das et al., 2021; Van der Werf, 2013).
This technique predicts an animal’s genetic potential using single nucleotide polymorphisms (SNPs), allowing
for improved accuracy and efficiency in selection processes. In ruminant breeding, genomic selection focuses
on identifying genetic variants associated with reduced methane production (Johnsson, 2023).
Recent studies have advanced comprehensive methane mitigation strategies that integrate multiple animal
science disciplines and genetic selection methods (De Haas et al., 2021; Asselstine et al., 2021; González-Recio
et al., 2020; Caruana et al., 2019; Slater et al., 2018). The process involves genotyping animals using SNP chips
or whole-genome sequencing, measuring methane emissions through techniques like gas chromatography or
respiration chambers, and conducting GWAS to pinpoint relevant genetic biomarkers. Integrating genomic
selection and other mitigation strategies contributes to a more sustainable livestock sector.
The benefits of genomic selection include improved accuracy, increased efficiency, and the ability to select for
multiple traits simultaneously, accelerating genetic progress, decreasing methane output (Ren et al., 2021;
Gianola et al., 2020). By combining high-throughput molecular genetics with traditional breeding methods,
breeding programs can be optimized for productivity, sustainability, and early trait assessment (Das et al., 2021).
Nonetheless, future research must focus on developing cost-effective technologies and advanced data analysis
tools to further enhance the impact of genomic selection (Xiao et al., 2022).
Genomic selection reduces methane (CH₄) emissions by using dense SNP marker data to predict breeding values
with significantly higher accuracy—up to 0.31-fold better—than traditional pedigree-based methods (Pickerig
et al., 2015). However, robust datasets are essential, requiring large, representative populations across different
production systems, even though emissions are often driven by a limited number of influential individuals (Black
et al., 2021).
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A key tool in this process is GBLUP, an analytical technique that uses genomic relationship matrices derived
from SNP data to estimate breeding values (Meuwissen et al., 2001; Calus et al., 2007, 2011; Ren et al., 2021).
GBLUP enables reliable forecasting of genetic potential for quantitative characteristics, such as methane
emissions, based on both genotypic and phenotypic data. visual flowchart based on this description?
Various approaches support genomic selection for methane reduction, including the Predicted Methane Emission
(PME) trait, Laser Methane Detector (LMD) data, genotyping, genomic prediction, Estimated Breeding Value
(EBV), and GWAS. These tools contribute to improved feed efficiency, reduced environmental impact, and
greater sustainability in livestock systems. However, the uptake in low- and middle-income countries (LMICs)
is hindered by challenges like limited accessibility to genetic resources, high costs, inadequate infrastructure,
and small breeding populations (Alemu, 2024). Addressing these barriers will require coordinated efforts among
public, private, academic, and international stakeholders (Akdemir & Isidro-Sánchez, 2019).
Future efforts should adopt a holistic approach that considers multi-trait selection, genetic correlations, and gene-
environment interactions (Lassen & Difford, 2020). This includes developing accurate, cost-effective
phenotyping techniques and refining genomic prediction models. Understanding the genetic architecture of
methane production, including its complexity and environmental interactions, is essential. Integrating genomic
selection with technologies such as phenomics can enhance selection accuracy, increase genetic gain, and
improve environmental sustainability.
Ultimately, genomic selection provides a powerful tool for developing ruminants with reduced methane
emissions without compromising productivity. The process involves identifying relevant genes, constructing
selection indices, breeding animals with favorable genotypes, and continuously validating outcomes through
measurement and genetic evaluation. By doing so, genomic selection supports the advancement of climate-
resilient and environmentally responsible livestock production.
Breeding for Low-Methane Ruminants: The Role of Phenomics
The pursuit of low methane emissions is driving innovation in animal genetics and genomics, particularly
through advances in phenomics and selective breeding. Although identifying relevant phenotypes remains a core
challenge in animal breeding programs (Lush, 1994), recent technological developments have enabled the
collection of high-dimensional data on individual animal traits. Progress in genomics, environmental monitoring,
and cost-effective phenotyping methods has further accelerated this field (Houla et al., 2010; Grossi et al., 2019;
Halachmi et al., 2019).
Phenomics is the large-scale study of organismal traits, has introduced new trait dimensions and enhanced
understanding of traditional characteristics in livestock populations (Houla et al., 2010). According to Pérez-
Enciso et al. (2021), phenomics improves the efficiency of breeding programs by providing detailed, quantitative
trait data, which supports genetic selection aimed at reducing greenhouse gas (GHG) emissions.
Characterizing phenotypes is critical for mapping genotype-phenotype relationships, especially for traits linked
to GHG emissions in ruminants. Phenomics enables the identification of animals with inherently lower emissions
and clarifies the genetic underpinnings of these traits. Advances in statistical genetics and genomic technologies
can enhance low-emission breeding efforts by preserving genetic diversity and improving the likelihood of
success. However, these approaches are data-intensive, requiring vast datasets—often comprising hundreds of
thousands of data points—to ensure accurate predictions and effective breeding decisions.
Recent breakthroughs in high-throughput phenotyping, sequencing, and breeding technologies—coupled with
artificial intelligence applications in genomic editing—offer significant potential for developing climate-resilient
livestock and poultry breeds (Pérez-Enciso et al., 2021). Genomic selection has progressed, yet identifying causal
mutations remains essential for improving prediction accuracy. Moreover, increasing marker density can yield
further incremental gains in genetic prediction. As Zhao et al. (2019) emphasize, bridging the genome-to-
phenome gap is vital for accelerating genetic improvement, which can be achieved through reliable, automated,
and multipurpose phenotyping technologies. This integrated approach marks the emergence of a new era in
animal breeding, where genomics, phenomics, and artificial intelligence converge.
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Effective breeding programs depend on comprehensive phenotypic assessments that capture the observable traits
of individuals (Rexroad et al., 2019). However, defining and measuring complex phenotypes continues to pose
challenges. Researchers are addressing these through genome-mapping initiatives, environmental monitoring,
and affordable phenotyping strategies (Koltes et al., 2019). These efforts support the collection of
multidimensional data that takes into account a wide spectrum of interrelated production aspects, including:
Microbial population dynamics
Nutrition and diet composition
Feed quality and formulation
Animal production stage and lifecycle
Health status and wellness indicators
These variables are essential for estimating breeding values, as measurements alone do not capture the full
biological context. By integrating selection indices with high-throughput phenomics, scientists can refine
breeding strategies aimed at reducing enteric emissions, using large-scale datasets to support precise, data-driven
decision-making.
Tailoring Genomic and Phenomics Approaches for Climate-Smart Breeding in Smallholder and Tropical
Livestock Systems
There is an urgent need to adapt and deploy genomic and phenomic tools specifically suited to smallholder and
tropical livestock systems. In such contexts, traditional high-cost genomic tools are often financially and
logistically prohibitive. Therefore, emphasis should be placed on developing and implementing low-cost
genotyping platforms, such as customized SNP arrays tailored for indigenous African breeds, which are more
genetically diverse and locally adapted. One notable example is the African Dairy Genomics Program (ADGP),
which has demonstrated the feasibility of developing African-specific SNP chips for dairy cattle to support
genetic improvement in smallholder systems (Marshall et al., 2019; Mrode et al., 2020).
In parallel, phenomics tools must also be reimagined to suit environments where infrastructure for large-scale
measurement is lacking. The use of proxy phenotypes offers a practical and cost-effective alternative. For
instance, mid-infrared (MIR) spectral data from milk samples have shown promise in estimating methane
emissions in dairy cows through correlations with rumen fermentation profiles (de Marchi et al., 2014;
Cecchinato et al., 2019). Similarly, fecal samples can be utilized for microbiome analysis or to indirectly estimate
methane production through indicators like fiber digestibility and volatile fatty acid profiles (Ross et al., 2013;
Roehe et al., 2016).
By combining such low-cost genotyping with easily collectible proxy phenotypes, genomic selection and
methane mitigation breeding strategies can become more accessible and scalable in resource-constrained tropical
environments. Moreover, these tools support the inclusion of climate-resilient and environmentally sustainable
breeding goals in smallholder breeding programs, particularly those using community-based breeding
approaches (Muasa et al., 2023).
The use of miniaturized wearable sensors, such as e-rumen boluses and collar-based feed intake monitors, is
transforming real-time phenomics in extensive and low-input livestock systems. These tools enable the
continuous and automated collection of key physiological and behavioral data, including body temperature, pH,
feeding behavior, activity levels, and in some cases, methane emissions. E-rumen boluses, inserted into the
reticulorumen, can remotely transmit indicators of health and metabolic status, supporting early disease detection
and thermal stress monitoring (Adu et al., 2023). Similarly, collar-mounted devices track feed intake, grazing
patterns, and movement, offering vital insights into animal performance without the need for labor-intensive
manual recording (Wang et al., 2019; Vázquez-Diosdado et al., 2020).
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These technologies are particularly valuable in extensive and pastoral systems, where animals range freely and
traditional phenotyping is logistically difficult. By integrating these sensors with cloud-based platforms and AI
analytics, livestock managers and breeders can generate high-throughput phenotypic data for traits such as feed
efficiency, resilience, and methane output. When adopted within community-based breeding programs (CBBPs),
wearable sensors offer scalable solutions for precision breeding in resource-limited settings, helping to drive
genetic improvement while supporting climate-smart and welfare-conscious livestock management (Muasa et
al., 2023).
Community-Based Breeding Programs (CBBP) for Low-Methane Livestock: A Strategy for Small-Scale
Ruminant Farmers in Africa.
CBBP archetype presents a viable approach for breeding low-methane livestock. Pilot-scale CBBPs have
demonstrated encouraging results, offering an effective and inclusive method for achieving genetic gains while
improving the economic stability of small-scale farming communities (Mueller et al., 2023). This collaborative
model engages farmers, breeders, and local communities in defining breeding objectives, sharing resources,
expertise, and risk (Haile et al., 2023). Haile et al. (2020) emphasize the importance of customized modifications
to breeding strategies are essential for successful implementation. CBBPs unite stakeholders to: i. Conserve
genetic diversity; ii. Enhance livestock productivity; and iii. Empower farmers and local communities to manage
their breeding initiatives. In smallholder agriculture, breeding for low methane emissions requires preserving
genetic diversity while utilizing local animal genetic resources. Effective breeding programs should align with
ecologically responsible and self-sufficient practices. By adopting the CBBP approach, communities can develop
customized breeding strategies that address local needs, improve livelihoods, and preserve animal genetic
diversity (Wurzinger et al., 2011).
CBBPs have proven to be a viable alternative for implementing livestock breeding within smallholder systems.
Successful initiatives have been conducted across various species and regions, including dairy goats in Mexico
and Kenya, sheep populations in Ethiopia and Peru, Angora goats in Argentina, and indigenous pig breeds in
Vietnam. These programs not only improve genetics but also strengthen local capacity, ownership, and
sustainability (Haile et al., 2020; Mueller et al., 2015; Gutu et al., 2015; Peacock, 2008; Kahi et al., 2005; Ahuya
et al., 2003). Key benefits of CBBPs include participatory animal selection, decentralized breeding adapted to
local environments, capacity building for farmers and breeders, collaborative decision-making, and a
community-driven approach tailored to local priorities.
Community-centric livestock breeding programs promote sustainability, empower local stakeholders, and
enhance the resilience of smallholder systems (Wurzinger et al., 2011). However, the smallholder livestock
sector in Africa faces significant challenges in breeding for low methane emissions. These include inadequate
infrastructure, limited genetic resources, and insufficient funding. Two key strategies can help address these
issues: leveraging indigenous genetic resources and implementing crossbreeding programs. Nonetheless,
funding constraints remain a major barrier to progress, often leading to program failures (Endris et al., 2022).
Additional challenges include poor stakeholder coordination—leading to duplication of efforts and inefficient
resource use—and limited capacity among farmers, extension agents, and researchers, which hinders the design
and implementation of effective breeding programs (Getachew, 2018). CBBPs are commonly adopted in low-
input agricultural systems, where farmers collaborate to share genetic resources, improve their breeding
practices, and enhance their livelihoods (Mueller et al., 2021). These programs typically involve farmer-led trait
selection, farmer training, development of diverse flocks, scientific support, and continuous interaction between
farmers and researchers to guide breeding decisions and herd management. While not specifically targeting
methane emission reduction, Table 3 highlights several community-based breeding programs (CBBPs) that have
been established in Sub-Saharan Africa and may offer insights applicable to climate-resilient livestock
development.
Table 3: Community-Based Livestock Breeding Programs in Sub-Saharan Africa
Country
Species/Breeds
Lead Institutions
Key Features
References
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Ethiopia
Sheep (Bonga,
Menz, Horro),
Goats, Cattle
ILRI, ICARDA,
EIAR, Bahir Dar &
Haramaya
Universities
Longest running CBBPs,
genetic gains in growth traits,
strong farmer cooperatives
Haile et al., 2020;
Wurzinger et al., 2021
Malawi
Goats (Small East
African)
LUANAR, ILRI,
ICARDA
Focus on women's inclusion,
participatory selection, and
goat performance recording
Gondwe & Banda,
2018; Haile et al.,
2020
Tanzania
Dairy cattle,
Indigenous goats
Sokoine University
of Agriculture, ILRI
Dairy hubs, farmer-managed
selection, and pilots for local
goats
Gwala et al., 2019;
Wurzinger et al., 2021
Uganda
Goats (Mubende),
Sheep
NaLIRRI, Makerere
University, farmer
groups
Emphasis on local buck
selection, youth participation,
and recordkeeping training
Kugonza et al., 2017;
Ojango et al., 2022
Burkina
Faso
Cattle (Zebu),
Sheep
INERA, ILRI
Trypanotolerance traits; bull
selection by herder groups
Traoré et al., 2016;
Haile et al., 2020
Senegal
Cattle (Ndama,
Gobra), Sheep
(Djallonké)
ISRA, ILRI
Indigenous cattle
improvement; emphasis on
meat and milk yield in pastoral
areas
Ndiaye et al., 2019;
Wurzinger et al., 2021
Zimbabwe
Indigenous goats,
Cattle
University of
Zimbabwe, DR&SS,
Matopos Research
Institute
Buck rotation systems; link to
climate-resilient production;
pilot CBBPs in Matabeleland
Makuza et al., 2021;
Nyoni et al., 2024
Kenya
Dairy goats,
Indigenous
chickens
Egerton University,
ILRI, Ministry of
Agriculture
Performance-based selection;
dual-purpose poultry breeding
trials
Bett et al., 2020;
Okitoi et al., 2023
Nigeria
Goats (WAD),
Cattle
Ahmadu Bello
University, NAPRI
Community ram and bull
stations: focus on dual-purpose
traits
Yakubu et al., 2020;
Bello et al., 2022
Rwanda
Dairy cattle, Goats
RAB, University of
Rwanda
Linked to dairy cooperatives;
data recording and genetic
dissemination through AI and
natural service
Habimana et al., 2023;
RAB, 2021
The table 3 highlights the diversity and scope of community-based livestock breeding programs (CBBPs)
currently operational across Sub-Saharan Africa. These programs span a range of countries—including Ethiopia,
Malawi, Tanzania, Uganda, and Zimbabwe—and focus on locally adapted livestock species such as indigenous
sheep, goats, cattle, and poultry. Ethiopia stands out with the most mature and extensive CBBPs, particularly for
sheep, showing measurable genetic gains in traits such as growth rate and reproductive performance (Haile et
al., 2020; Wurzinger et al., 2021).
Similarly, Malawi and Uganda have made significant strides in goat breeding, with participatory selection
practices and a strong emphasis on gender inclusion and youth engagement (Gondwe & Banda, 2018; Kugonza
et al., 2017). Programs in Kenya and Nigeria have extended CBBP principles to dairy goats, poultry, and dual-
purpose cattle, aligning genetic improvement with farmer-defined priorities such as milk yield, disease
resistance, and adaptation to harsh environments (Bett et al., 2020; Yakubu et al., 2020). Despite differing
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ecological and institutional contexts, all the programs share common features: community ownership, use of
local knowledge in selection, low-cost data recording, and active participation of farmers in decision-making
processes (Ojango et al., 2022; Wurzinger et al., 2021). While these CBBPs are not primarily designed to reduce
methane emissions, they lay a strong foundation for integrating climate-smart traits into future breeding goals,
especially in light of growing concerns over livestock-related greenhouse gas emissions and resilience under
climate change (Makuza et al., 2021; Nyoni et al., 2024).
For detailed guidance on architecting CBBPs fine-tuned for goats and sheep livestock systems in Africa—and
to learn from successful case studies in other developing countries—refer to Haile and colleagues (2018) and
Mueller and co-authors (2015). Traditional station-based genetic improvement programs have proven
ineffective, largely because they fail to consider socio-cultural, economic, and environmental contexts (Assan,
2023). In contrast, methane mitigation in livestock can be effectively achieved through community-based
strategies, including selective breeding and crossbreeding. Emerging technologies such as genomics and
biotechnologies can further enhance efforts to breed low-methane livestock within CBBPs. Integrating
conventional and genomic data into breeding plans has shown success, especially in medium-scale systems.
CBBPs can also address knowledge gaps in smallholder ruminant farming through stakeholder collaboration.
These programs play a critical role in capacity building and farmer training for sustainable breeding practices
(Lamuno et al., 2018). Empowering local farmers to use surplus males for breeding can help preserve locally
adapted breeds, provide reliable animal multiplication systems, and support access to feed and veterinary
services.
Breeding low-methane ruminants in smallholder systems requires a collaborative, adaptive, and inclusive
approach. This involves quantifying methane emissions and fostering partnerships among stakeholders,
including farmers, researchers, and policymakers. A successful CBBP depends on shared vision, cooperation,
adequate funding, community engagement, training opportunities, and expert support (Mueller et al., 2023;
Mueller et al., 2015).
Wurzinger et al., (2021) reported that implementing CBBPs for low-methane livestock offers several advantages:
higher adoption rates, enhanced genetic diversity, greater community participation, local adaptability, effective
methane reduction, and sustainable livestock production systems. However, as noted by Endris et al. (2022),
several persistent challenges must be resolved to guarantee the enduring success and sustainability of these
efforts.
Participatory data collection models, particularly Community-Based Breeding Programs (CBBPs), have
emerged as effective frameworks for integrating smallholder farmers into genetic improvement initiatives. These
models emphasize local ownership, inclusivity, and capacity building, ensuring that selection decisions are
grounded in farmer preferences and production realities (Haile et al., 2019; Gizaw et al., 2022). One of the key
challenges in scaling CBBPs has been the collection and management of accurate, timely, and cost-effective
performance and pedigree data in dispersed and low-infrastructure settings.
To address these challenges, digital tools are increasingly being deployed to support decentralized and
participatory performance recording. Platforms like WeTrace and the Open Smart Register Platform (OpenSRP)
enable real-time data entry, geo-tagging, and integration with cloud-based data repositories, making them well-
suited for community-level livestock programs in rural areas (Marshall et al., 2021; Mogeni et al., 2020). Digital
tools like WeTrace and OpenSRP enable mobile-based, decentralized collection and integration of livestock
data—ranging from animal performance to health—supporting real-time decision-making in community-based
breeding programs (CBBPs) (Marshall et al., 2021; Mogeni et al., 2020). When combined with participatory
training, these platforms enhance data accuracy, reduce costs, and empower smallholders, making CBBPs more
scalable, sustainable, and climate-resilient.
Addressing Key Research Gaps in Genetic Improvement for Methane Mitigation Toward Sustainable
Livestock Systems
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To advance the breeding of ruminants for reduced methane emissions, several critical research gaps must be
addressed. First, a deeper understanding of the genetic determinants and microbial communities involved in
methanogenesis is essential to inform targeted selection strategies. Second, comprehensive data on the variability
of methane emissions within and among breeds and populations remain limited, impeding the effectiveness of
both selective breeding and crossbreeding programs. Third, the interactions between diet and genetic background
in shaping methane output are not fully understood, particularly regarding the long-term consequences of genetic
improvement on productivity and emission intensity. Fourth, the scalability and affordability of current methane
measurement techniques—often labor-intensive and costly—pose substantial challenges for implementation in
large-scale breeding initiatives, especially in resource-constrained settings. Fifth, the genetic relationships
between methane emission traits and key economic traits like fertility and growth rate need deeper investigation
to prevent unintended negative consequences. Sixth, it is essential to assess the enduring sustainability and
financial feasibility of breeding initiatives centered on low-emission animals, ensuring that environmental goals
align with production efficiency and farmer livelihoods. Lastly, the role of epigenetic mechanisms in regulating
methane emissions is an emerging area of research that warrants further exploration. Addressing these gaps is
pivotal to developing breeding strategies that mitigate methane emissions while maintaining the sustainability
and productivity of ruminant livestock systems.
Methane Trait Improvement: Insights from Existing National Programs and Case Studies
Several countries have initiated targeted breeding programs aimed at reducing enteric methane emissions in
ruminant livestock while maintaining or enhancing productivity. These case studies offer valuable, scalable
strategies, tools, and evidence-based approaches that can inform global efforts toward climate-smart animal
breeding.
In New Zealand, the Low Methane Sheep Program—led by AgResearch and Beef + Lamb NZ Genetics—has
successfully identified and selectively bred sheep with significantly lower residual methane production (RMP)
using direct measurements in respiration chambers. Genetic studies indicate moderate heritability estimates (h²
= 0.2–0.3), demonstrating the feasibility of selection for reduced methane output (Rowe et al., 2019). The
incorporation of genomic tools has further accelerated the identification of low-emitting sires, without
compromising economically important traits such as wool yield and fertility (Jonker et al., 2022).
Similarly, in Scotland, the Climate Smart Sheep Project, coordinated by Scotland’s Rural College (SRUC),
combines genomic prediction with respiration chamber data to estimate breeding values for methane yield—
defined as emissions per unit of feed intake. These methane EBVs are being integrated into commercial sheep
breeding indices to strike a balance between environmental sustainability, carcass quality, and farm profitability
(Conington et al., 2021; Keady et al., 2023).
In Brazil, Embrapa’s Low-Emission Cattle Programs have focused on improving tropical beef cattle breeds,
such as Nelore, by selecting for both feed efficiency and reduced methane emissions. Measurement techniques
like the SF₆ tracer method and open-circuit respiration chambers have been used to collect methane data from
breeding herds. The studies report favorable genetic correlations between residual feed intake and methane
intensity, supporting the feasibility of dual-purpose selection (Oliveira et al., 2020). These efforts are embedded
within the broader ABC+ Program, which aims to promote climate-smart and sustainable livestock systems
(Gonçalves et al., 2022).
In Kenya, pilot programs led by ILRI and the CGIAR Livestock Program are exploring the use of mid-infrared
spectroscopy (MIR) from milk samples to indirectly predict methane emissions in dairy cattle. These initiatives
seek to establish correlations between methane predictions and production traits such as milk yield and fertility
in smallholder systems. The long-term goal is to incorporate low-emission traits into community-based breeding
programs, enhancing climate resilience in resource-limited settings (Ndung’u et al., 2024).
Collectively, these national programs demonstrate diverse yet converging approaches to incorporating methane
traits into breeding strategies, offering adaptable models for climate-smart livestock improvement across both
intensive and smallholder systems.
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IMPLICATIONS AND CONCLUSION
This review emphasizes the critical value of genetic approaches in advancing targeted solutions, specifically,
selective breeding and crossbreeding, as transformative tools for mitigating methane emissions from ruminant
livestock systems. These approaches not only facilitate the gradual incorporation of low-emission traits but also
enhance overall productivity and feed efficiency, positioning them as sustainable strategies to tackle the
interconnected challenges of ensuring food security and combating climate change over the long term.
A key implication is that the successful implementation of breeding strategies for methane reduction hinges on
the integration of reliable methane measurement techniques, standardized emission adjustment protocols, and
advanced genetic evaluation models. Furthermore, the coupling of genomics and phenomics with environmental
data enables a systems-level understanding of the intricate relationship between inherited characteristics and
environmental or management-related influences to methane output. Despite the moderate heritability of
methane-related traits, technological advancements in high-throughput phenotyping, bioinformatics, and multi-
omics are opening new frontiers in the accurate identification and selection of low-emission animals.
The conclusions drawn emphasize that climate-smart breeding programs must be supported by coordinated
research and investment in infrastructure, particularly in the development of universal measurement standards
and robust genetic databases. These will enable scalable and replicable breeding strategies across various
production systems and agro-ecological zones.
Ultimately, integrating methane mitigation into genetic improvement agendas contributes directly to global
sustainability goals, notably Goal 12 (Promoting Sustainable Consumption and Production) and Goal 13
(Combating Climate Change). By aligning livestock productivity with environmental stewardship, selective
breeding and crossbreeding offer a practical, science-based pathway to reducing agriculture’s carbon footprint
while fostering resilient, efficient, and ethical livestock systems.
RECOMMENDATIONS
Based on the findings of this study, the following recommendations are proposed to enhance the role of selective
breeding and crossbreeding in mitigating methane emissions from ruminants, thereby contributing to
environmentally sustainable livestock production
1. Institutionalize Low-Emission Breeding Programs: Stakeholders in the livestock sector—including
governments, research institutions, and breeding organizations—should develop and support long-term
breeding programs focused on selecting and crossbreeding animals with inherently low methane
emissions. These programs must integrate both production traits and environmental traits to ensure
economic and ecological viability.
2. Standardize Methane Measurement Protocols: A universal, species-specific protocol for measuring
methane emissions across diverse production systems should be established. This would ensure
consistency, improve comparability of results, and enable more accurate genetic evaluations of methane-
related traits.
3. Enhance Genetic and Phenotypic Data Collection Systems: National and regional livestock
development programs should invest in building large-scale databases for genetic, phenotypic, and
environmental data. Such databases will facilitate robust genetic analyses, better understanding of
genotype-by-environment interactions, and informed selection decisions.
4. Promote Integration of Genomics and Phenomics: Future breeding strategies should prioritize the
integration of high-throughput phenomics and advanced genomic technologies. This will help identify
key genetic markers, such as traits linked to RFI and metabolic efficiency, to inform the selection of low-
emission animals.
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5. Support Research on Breed Combinations and Crossbreeding Systems: There is a need for continued
investigation into the impact of various breed combinations for methane emissions, particularly under
tropical and sub-Saharan African production systems. This will inform crossbreeding strategies that
optimize both productivity and environmental outcomes.
6. Adopt a Multi-Faceted Mitigation Approach: Breeding efforts should be complemented with other
methane mitigation strategies, such as improving feed quality, managing rumen fermentation, using
dietary additives like 3-nitrooxypropanol (3NOP), optimizing manure management, and integrating
silvo-pastoral systems. A systems-based approach ensures comprehensive emission reductions without
compromising productivity.
7. Account for Non-Genetic Influences: Breeding programs should consider non-genetic factors, such as
maternal effects and epigenetics, which may influence methane emissions. Further research into these
areas will improve breeding accuracy and outcomes.
8. Build Capacity and Awareness: Capacity-building initiatives targeting livestock producers, extension
agents, and researchers are essential. These should highlight the critical role of genetic interventions in
reducing the impacts of climate change and provide practical training in low-emission breeding
techniques.
9. Align with Global Climate and Sustainability Goals: Breeding programs should be designed in
harmony with the United Nations Sustainable Development Goals, with particular emphasis on SDG 13
(Climate Action) and SDG 12 (Responsible Consumption and Production), to ensure that livestock
production contributes positively to global climate resilience.
10. Ensure Ethical and Responsible Genetic Advancement: As genomic tools become more advanced,
ethical considerations must remain at the forefront. Policies should be established to ensure that genetic
modifications or selection practices do not compromise animal welfare, biodiversity, or long-term
ecosystem health.
By implementing these recommendations, stakeholders can harness the power of genetic improvement to
develop climate-smart livestock systems, balancing productivity with sustainability and resilience.
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