Revisiting Environmental Sustainability in Ruminants: A Comprehensive Review

Abstract

Ruminant livestock production faces increasing pressure to reduce environmental impacts while maintaining productivity and food security. This comprehensive review examines current strategies and emerging technologies for enhancing environmental sustainability in ruminant systems. The review synthesizes recent advances across four interconnected domains: genetic and genomic approaches for breeding environmentally efficient animals, rumen microbiome manipulation, nutritional strategies for emission reduction, and precision management practices. Specifically, genetic and genomic strategies demonstrate significant potential for long-term sustainability improvements through selective breeding for feed efficiency, methane reduction, and enhanced longevity. Understanding host–microbe interactions and developing targeted interventions have also shown promising effects on optimizing fermentation efficiency and reducing methane production. Key nutritional interventions include dietary optimization strategies that improve feed efficiency, feed additives, and precision feeding systems that minimize nutrient waste. Furthermore, management approaches encompass precision livestock farming technologies including sensor-based monitoring systems, automated feeding platforms, and real-time emission measurement tools that enable data-driven decision making. Integration of these approaches through system-based frameworks offers the greatest potential for achieving substantial environmental improvements while maintaining economic viability. In addition, this review identifies key research gaps including the need for standardized measurement protocols, long-term sustainability assessments, and economic evaluation frameworks. Future directions emphasize the importance of interdisciplinary collaboration, policy support, and technology transfer to accelerate adoption of sustainable practices across diverse production systems.

1. Introduction

Environmental sustainability has become a critical global concern, influencing nearly every sector of human activities [1]. Among the major contributors, the livestock industry, particularly ruminant production, has drawn increasing attention due to its wide-ranging effects on ecosystems. Ruminants such as cattle, sheep, and goats are central to global food security, supplying protein, dairy products, and other essential commodities that sustain the livelihoods of millions worldwide [2]. However, the intensification of ruminant production systems to meet the escalating global demand for animal products has also led to substantial environmental pressures. These pressures arise primarily from significant contributions to anthropogenic greenhouse gas (GHG) emissions [3], nutrient imbalances that drive issues such as eutrophication, and extensive use of natural resources, notably land for grazing and feed cultivation, as well as freshwater resources [2,4]. Assessing these interactions requires a holistic perspective that goes beyond emissions alone, encompassing resource efficiency and land suitability [4].

The major GHGs from ruminant systems include methane (CH₄), primarily from enteric fermentation and manure management, nitrous oxide (N₂O) from manure, soils, and atmospheric deposition, and carbon dioxide (CO₂) from farm operations and transportation [2]. Ambitious commitments have been made by global organizations and national governments to achieve net-zero GHG emissions or significant reduction targets within the livestock sector by the mid-21st century [5]. Meeting such transformative goals requires coordinated effort across multiple interconnected disciplines and sectors, including genetics, animal nutrition, farm and nutrient management, and microbiome research [6], all supported by a rapidly evolving technological landscape. The range of tools available to stakeholders has expanded dramatically with technological advancements supporting improved monitoring and breeding for sustainability [7,8,9]. These innovations, while varied, can be understood as complementary components of a broader systems-based approach to improving the environmental sustainability of ruminant production [9].

Given the accelerated pace of technological advancement and the continuous emergence of new research findings across these diverse fields, a contemporary and comprehensive review paper is not merely beneficial but imperative. Such a review is needed to critically evaluate, synthesize, and contextualize the most recent research findings, pinpoint persistent knowledge gaps, and propose integrated, systems-based approaches to further enhance the environmental sustainability of ruminant production. Therefore, this review aims to fulfil this critical need by adopting an animal science perspective, focusing specifically on the pivotal and interconnected aspects of animal genetics and genomics, nutrition, rumen microbiome manipulation, and management practices. Ultimately, this review aims to serve as a valuable resource for researchers, policymakers, industry stakeholders, and students dedicated to advancing the environmental sustainability of ruminant production.

2. Genetic and Genomic Approaches for Greener Ruminants

The growing global demand for animal-derived products has intensified concerns regarding the environmental footprint of livestock production. In ruminants, methane emissions, nitrogen excretion, and inefficient feed conversion are major sustainability challenges [1,2]. Genetic and genomic approaches have emerged as powerful tools to develop “greener” ruminants—animals that maintain high productivity while minimizing environmental impacts. Through the identification and selection of individuals with superior genetic merit for traits related to resource efficiency and reduced emissions, it is possible to promote cumulative, long-term improvements toward sustainability [10].

Traditional genetic selection based on Best Linear Unbiased Prediction (BLUP) revolutionized livestock breeding by providing a reliable framework for estimating breeding values (EBVs) [11]. BLUP incorporates pedigree and phenotypic information to predict the genetic potential of animals for various traits, allowing breeders to select the best individuals to become parents of the next generation [12]. This approach has been remarkably successful in improving production traits such as milk yield, growth rate, and carcass quality. However, while BLUP remains a cornerstone of animal breeding, it is limited by the accuracy of pedigree information and the difficulty of measuring traits associated with environmental efficiency, which are often expensive or complex to record.

The development of single-nucleotide polymorphism (SNP) markers and the introduction of genomic selection have transformed the potential for sustainable breeding. By using high-density SNP panels, it is possible to estimate genomic breeding values (GEBVs) that capture the genetic contribution of thousands of markers distributed across the genome [13]. This method increases the accuracy of selection, particularly for traits with low heritability or that are difficult to measure directly, such as methane emission or feed efficiency [14]. Genomic selection also reduces generation intervals, allowing for faster genetic progress and the integration of sustainability traits into multi-trait selection indices without compromising productivity [15].

Recent advances in high-throughput phenotyping (HTP) and precision livestock technologies have further enhanced the potential for selecting greener ruminants. Automated systems such as infrared thermography, respiration chambers, wearable sensors, and feeding behavior monitors now provide detailed, real-time phenotypic data at large scales [16]. These technologies enable the identification of new phenotypes related to sustainability, including feed intake patterns, methane output, water use efficiency, and animal resilience [17]. Integrating HTP data with genomic information strengthens the predictive accuracy of selection models and facilitates the discovery of biological pathways underlying environmentally relevant traits.

Finally, focusing on phenotypes for sustainability—such as reduced methane production, improved feed conversion efficiency, and lower nitrogen excretion—can lead to measurable genetic progress in mitigating livestock’s environmental impact [17]. Combined with genomic data and HTP tools, breeders can identify animals that achieve optimal productivity while contributing to reduced greenhouse gas emissions and resource use. Overall, the integration of genetic, genomic, and phenotyping technologies provides a roadmap for achieving long-term, sustainable improvements in ruminant production systems, aligning animal breeding with global climate and environmental goals.

2.1. Genetic Architecture and Breeding Strategies for Environmental Traits

Sustainability-related traits—sometimes referred to as environmentally relevant or eco-efficiency traits—are those that influence how efficiently animals use resources and how their biological processes affect the environment [18,19]. These traits include feed efficiency, methane production, production efficiency and dilution of maintenance, body size optimization, and longevity enhancement. Unlike traditional production traits such as growth rate or milk yield, sustainability-related traits are often more difficult to measure and record accurately in commercial settings [18]. The measurement of these traits is typically labor-intensive, costly, and sometimes invasive, which limits the availability of large phenotypic datasets necessary for traditional genetic evaluations. They are also governed by multiple genes and influenced by interactions between metabolism, behavior, and microbiome composition [19].

The development of genomic technologies has significantly advanced the genetic improvement of sustainability-related traits. Single-step genomic evaluation systems (ssGBLUP), which integrate pedigree, phenotypic, and genomic information, have improved the accuracy of estimated breeding values (EBVs), particularly for traits that are difficult or expensive to measure, such as feed efficiency and methane emission [20]. These systems enable breeders to utilize genotyped and non-genotyped animals simultaneously, improving the connectedness of reference populations and allowing for more precise genetic predictions. Genomic prediction accuracies for sustainability-related traits generally range from 0.35 to 0.65 [21]—sufficient to drive meaningful genetic progress even when direct phenotyping is limited. Furthermore, the use of genomic data in genome-wide association studies (GWASs) allows for the identification of genomic regions and candidate genes associated with traits related to metabolic efficiency, digestive microbiota composition, and energy partitioning, contributing to a deeper understanding of the underlying biological mechanisms controlling sustainability traits [15,19].

Breeding strategies targeting sustainability-related traits are now focusing on the joint optimization of production and environmental efficiency. Selecting for feed efficiency not only reduces feed costs but also lowers methane emissions per unit of product through improved nutrient utilization [19]. Similarly, optimizing body size contributes to reducing maintenance energy requirements, thereby improving the dilution of maintenance and enhancing lifetime production efficiency [19]. Longevity, another key sustainability trait, reduces replacement rates and the overall environmental footprint of the herd. The incorporation of these traits into multi-trait selection indices supports a balanced breeding approach that simultaneously promotes productivity, animal robustness, and environmental resilience. As recording technologies and genomic resources continue to evolve, these approaches will play a central role in developing livestock populations that are both productive and environmentally sustainable. Below, we bring a brief overview and some of the genetic parameters for each of these traits.

2.1.1. Feed Efficiency

Feed efficiency is a key sustainability-related trait in livestock production, as it determines how effectively animals convert feed into productive outputs such as meat or milk [22]. Improving this trait has direct implications for economic profitability and environmental sustainability, as feed represents the largest input cost in most production systems and is tightly linked to greenhouse gas emissions and land use [22]. For detailed information on the genetic correlation between methane and feed efficiency and studies up to date on this topic, please refer to Supplementary Material Table S1. Among various indicators of feed efficiency, residual feed intake (RFI) has become the preferred measure for genetic evaluation because it accounts for differences in feed intake that are not explained by maintenance or production requirements [23]. Animals with lower RFI values consume less feed than expected for their growth or production level, thereby using nutrients more efficiently and producing fewer emissions per unit of output.

Feed efficiency exhibits moderate heritability, with residual feed intake (RFI) estimates ranging from 0.15 to 0.39 across cattle populations [22,23,24]. This level of heritability indicates that meaningful genetic progress can be achieved through selection. Importantly, RFI shows low to near-zero genetic correlations with major production traits such as milk yield or growth rate. This independence allows breeders to improve feed efficiency without negatively affecting productivity, a crucial advantage over traditional feed conversion ratio (FCR), which tends to be correlated with growth and body size. Additionally, the moderate heritability of RFI suggests that genomic selection can further accelerate improvement, as genomic evaluations provide higher accuracy than pedigree-based predictions, especially in young selection candidates without phenotypic records

The biological mechanisms underlying variation in RFI are complex, encompassing differences in digestion, nutrient absorption, metabolism, activity level, and thermoregulation [22]. Recent genomic studies have identified several candidate genes and pathways associated with energy metabolism, mitochondrial function, and gut microbiota interactions, which help explain part of the genetic variance in RFI [24]. Moreover, advances in HTP technologies—such as automated feed intake systems, methane sensors, and wearable activity monitors—are enabling the collection of large-scale data on feed intake and behavior, facilitating more accurate and cost-effective evaluations. A comprehensive overview of feed efficiency measurement methodologies and their integration into genetic and genomic evaluations is provided by Ojo et al. [22]. As these technologies and datasets expand, they will continue to refine the selection for feed efficiency, contributing to livestock systems that are both economically viable and environmentally sustainable.

2.1.2. Methane Production

Enteric methane production represents one of the most significant environmental challenges in ruminant agriculture, as methane (CH₄) contributes substantially to the livestock sector’s greenhouse gas emissions [25]. Methane is generated during the microbial fermentation of feed in the rumen, primarily by methanogenic archaea, and is expelled through eructation [25]. This process not only impacts the environment but also represents an energy loss equivalent to 2–12% of the gross energy consumed by the animal [26]. Consequently, reducing methane emissions improves both environmental sustainability and feed energy utilization efficiency. The recognition that methane output exhibits heritable variation provides opportunities for genetic improvement toward lower-emitting and more energetically efficient animals.

Heritability estimates for methane production range from 0.12 to 0.45. This wide range reflects both biological variation and methodological differences in measurement precision, with higher estimates typically observed when using more controlled measurement systems that minimize environmental and technical variation [27]. The sulfur hexafluoride (SF₆) tracer technique—a precise but labor-intensive method—yields heritability estimates around 0.33 ± 0.15, while breath sampling systems and respiration chambers provide estimates between 0.12 and 0.45 [27]. Respiration chambers tend to produce higher heritability estimates (0.21–0.45) due to their superior measurement precision under controlled conditions, whereas automated breath sampling systems often yield lower estimates (0.12–0.25) when sampling frequency is limited or animal visit patterns are inconsistent, introducing additional measurement error that reduces the proportion of variance attributable to genetic effects [27]. These values confirm that methane output is a genetically influenced trait, capable of responding to selection. Methane production shows positive genetic correlations with feed intake (0.40–0.70) [19] and milk yield (0.20–0.40), as higher-producing and larger animals naturally consume more feed and thus generate more methane [18,25]. However, when methane production is adjusted for feed intake—expressed as residual methane production (RMP)—the correlations with productivity traits are near-zero or even negative. This indicates that it is feasible to select animals that are both high-yielding and low-emitting, thereby improving environmental performance without sacrificing production [20]. A recent milestone in the integration of methane traits into breeding programs occurred in Canada, which launched the first national genomic evaluation for methane efficiency in April 2023. This innovation allows large-scale, cost-effective phenotyping and has set a global precedent for environmentally conscious breeding programs. Simulations suggest that consistent selection for methane efficiency could achieve 20–30% reductions in methane emissions per unit of product by 2050 [20]. In parallel, ongoing research explores genomic regions and candidate genes related to rumen function, feed digestion, and microbial composition [20], while HTP technologies—such as portable gas analyzers and integrated feeding systems—are improving methane measurement in real time [27].

Despite the advances, genetic selection for reduced methane emissions must be approached with careful consideration of potential trade-offs and unintended consequences. A primary concern is whether selection for lower enteric methane production might inadvertently reduce fiber digestibility, given that methanogenesis serves as an essential hydrogen sink during ruminal fermentation of structural carbohydrates. If animals with lower methane emissions achieve this through reduced capacity to digest fibrous feeds, the environmental benefit could be offset by decreased productivity, as methane produced during digestion is intrinsically linked to the conversion of feed into meat and milk. In contrast, methane emissions from manure storage represent a loss of carbon that provides no productive output, making the source of methane reduction an important consideration. Additional concerns include the potential for selection to alter rumen microbial community structure in ways that affect correlated traits such as feed efficiency, immune function, or metabolic health. The variability in reported heritability estimates and genetic correlations for methane traits, which stems partly from differences in phenotyping methods, measurement definitions, and experimental conditions [28], further underscores the need for standardized protocols and large, well-characterized reference populations. Ultimately, successful implementation of genetic selection for environmental traits requires balanced breeding objectives that simultaneously consider productivity, animal health and welfare, genetic diversity, and environmental impact, supported by ongoing monitoring to detect and address any unfavorable correlated responses that may emerge over time.

2.1.3. Production Efficiency and Dilution of Maintenance

Production efficiency and dilution of maintenance are central principles in sustainable livestock breeding, linking productivity and environmental impact through the biological allocation of energy [29]. The dilution of the maintenance concept states that as animals produce more milk, meat, or offspring per unit of maintenance energy, the environmental burden per unit of product decreases [19,29]. Thus, selecting for higher individual productivity is one of the most effective strategies for lowering the carbon footprint of livestock production. Over the past decades, genetic selection has dramatically increased output: modern dairy cows produce roughly three times more milk than cows from the 1970s while consuming only marginally more feed [29]. This remarkable progress exemplifies how sustained genetic selection, coupled with improved management, can drive both production gains and efficiency improvements simultaneously.

Energetic efficiency encompasses the animal’s ability to convert feed energy into usable products while minimizing losses through heat, methane, and other metabolic inefficiencies [30]. The underlying biological variation arises from multiple factors, including organ size, protein turnover rates, and cellular energy metabolism [31]. Animals with superior energetic efficiency exhibit reduced maintenance requirements, more efficient protein synthesis, and optimized metabolic pathways that minimize energy waste [30,31]. Importantly, these traits often show moderate heritability, suggesting that selection for improved energy conversion can yield cumulative long-term benefits [19]. Modern genomic tools are now enabling researchers to identify genomic regions and biological pathways associated with energy metabolism, contributing to a deeper understanding of the genetic architecture underlying production efficiency [19].

From a breeding standpoint, the incorporation of production efficiency and maintenance-related traits into multi-trait selection indices represents a key step toward balanced genetic improvement [29]. While maximizing yield remains important, overemphasis on output alone can lead to health and fertility issues that ultimately reduce lifetime efficiency. Therefore, breeding goals increasingly consider both production and functional traits to optimize lifetime performance. Advances in genomic selection, sensor-based phenotyping, and metabolic modeling are further refining these evaluations, allowing the identification of animals with optimal energy efficiency profiles suited to specific production systems [30]. By integrating energetic efficiency and dilution of maintenance into selection strategies, the livestock industry can achieve meaningful reductions in greenhouse gas emissions and resource use without compromising productivity.

2.1.4. Body Size Optimization and Longevity Enhancement

Optimizing body size and extending productive life are complementary strategies for enhancing environmental efficiency and overall sustainability in livestock systems. Smaller animals generally require less feed and energy for maintenance, leading to a lower environmental footprint. A 10% reduction in mature body weight can decrease maintenance energy requirements by approximately 8%, translating into significant feed and resource savings over the animal’s lifetime [32]. However, determining the optimal body size requires consideration of the production system, feed availability, and economic goals, as excessively small animals may reduce total output or market value. Thus, the breeding objective is not simply to reduce size but to achieve the ideal balance between body weight, productivity, and resource efficiency.

Modern genomic selection tools have enhanced the ability to fine-tune body size and related traits while controlling correlated responses in production, reproduction, and functional characteristics [33]. Genomic data enable the estimation of breeding values for traits such as stature, body depth, and chest width with high accuracy, facilitating more precise management of herd structure [34]. Moreover, GWASs have identified loci associated with body size that also influence energy metabolism and growth efficiency, providing opportunities to select for metabolically favorable phenotypes [35]. By incorporating body size optimization into selection indices, breeders can tailor animals to specific environmental and management conditions, reducing maintenance energy demands while maintaining robust production performance

Longevity enhancement further amplifies sustainability gains by extending the number of productive lactations or years of use per animal. Longer-lived animals dilute the environmental and economic costs of raising replacement heifers across more productive cycles, thereby reducing greenhouse gas emissions and resource inputs per unit of lifetime output [36]. Life cycle assessments have shown that increasing the average productive lifespan from 2.5 to 4.0 lactations can reduce emissions intensity by 15–20% [35]. However, longevity is a complex trait influenced by fertility, disease resistance, structural soundness, and management conditions. Through genomic selection, breeding values for longevity can now be predicted early in life using correlated traits such as udder health, fertility, and locomotion [34]. These advances allow the simultaneous improvement of survivability, resilience, and productivity—key components of sustainable animal breeding systems. When combined, strategies for body size optimization and longevity enhancement contribute to the development of livestock populations that are more efficient, resilient, and environmentally sustainable across diverse production contexts.

2.2. Host Genetics and the Rumen Microbiome (The Hologenome Concept)

The rumen microbiome is a highly complex microbial ecosystem that plays an essential role in ruminant nutrition and methane production. It consists of bacteria, archaea, protozoa, and fungi that collaborate to degrade fibrous plant materials into volatile fatty acids (VFAs), which serve as primary energy sources for the host. During this fermentation process, hydrogen (H₂) is produced as a byproduct, and methanogenic archaea use this hydrogen to reduce carbon dioxide (CO₂) into methane (CH₄). Recent studies have demonstrated that the host’s genetic makeup can influence the structure and function of the rumen microbiome, affecting both nutrient utilization and methane emissions [8,37]. These findings support the emerging “hologenome concept”, which views the host animal and its associated microbiome as a single evolutionary and functional unit. Heritable variation in microbiome composition suggests that selective breeding could indirectly shape microbial communities toward improved environmental and production outcomes [28].

The interplay between host genetics and the microbiome involves genes related to immunity, metabolism, and rumen epithelial function that influence microbial colonization and stability. Host genotypes may create rumen environments that favor specific microbial taxa—such as bacteria that enhance propionate formation or suppress methanogenesis [8,37]. For instance, genes like AMY2B, PYGB, GYG1, PYGM, and MGAT4A are associated with carbohydrate metabolism and may shift fermentation pathways toward propionate production, which consumes hydrogen and thereby reduces methane output [37]. Similarly, genes involved in oxidative stress regulation (GPX3 and PRDX6) and in energy metabolism (COQ2, COX5A, COX6B1, ATP5ME, ATP6V0A4, ATP5F1E, and ATP6V1B1) influence hydrogen availability and electron flow, shaping the redox balance of the rumen ecosystem [37]. This complex network of host–microbe interactions illustrates how host genetic architecture can modulate microbial activity and methane formation, providing an indirect but powerful route for environmental mitigation through selective breeding.

Genome-wide association studies have identified specific SNPs and host genes linked to microbial taxa associated with methane metabolism. For example, Lentisphaerae—a bacterial phylum linked to fiber degradation and higher methane intensity—is influenced by 78 host genes, while Neocallimastix (a fungal genus breaking down lignocellulose) is associated with RNF186, TMCO4, and PAX7. Dialister, another rumen bacterium, is linked to FBRSL1 and PRP2 [8,38,39]. These findings support the feasibility of identifying host genetic markers predictive of microbiome structure and function. Future research integrating multi-omics data (e.g., genomics, metagenomics, transcriptomics) and AI-driven predictive models could enhance our capacity to select animals that naturally harbor microbial communities with lower methane output and improved feed efficiency. As genetic improvements are cumulative and permanent, selecting for host genotypes that foster an environmentally favorable microbiome represents a promising long-term strategy for sustainable ruminant production, but that needs to be further studied.

2.3. Gene Editing and Advanced Genetic Technologies

Recent advances in gene editing technologies, particularly CRISPR-Cas9, have opened new possibilities for precise genetic modifications aimed at improving environmental sustainability in livestock [40]. These tools allow targeted edits at specific genomic loci, potentially enabling the alteration of genes associated with methane emissions, feed efficiency, or nitrogen utilization. For example, CRISPR-Cas9 can be used to introduce beneficial alleles, silence unfavorable variants, or modify gene regulatory elements that influence rumen fermentation and host–microbiome interactions [41]. Early experimental studies have demonstrated the feasibility of using CRISPR-based systems to disrupt key genes in rumen methanogens, thereby reducing their methane-producing capability [40]. Despite these advances, the application of gene editing in livestock remains limited by regulatory frameworks, ethical considerations, and public perception, which must be addressed through transparent governance, risk assessment, and communication [42].

Beyond gene editing, other advanced genetic tools such as transcriptomics and epigenomics provide complementary insights into the biological mechanisms controlling environmental traits [37]. Transcriptomics—through the study of gene expression profiles—has revealed differential expression patterns between high- and low-emitting animals, identifying genes and pathways related to rumen function, oxidative metabolism, and methanogenesis [37]. Meanwhile, epigenetic modifications, such as DNA methylation and histone acetylation, can regulate gene activity without altering the underlying DNA sequence, contributing to phenotypic variability in traits like methane production and feed efficiency [43]. These molecular layers highlight how environmental conditions and management practices can influence gene expression across generations, shaping both short-term adaptability and long-term genetic trends.

3. The Ruminant Microbiome and Its Environmental Implications

The rumen, the largest forestomach compartment of ruminant animals, harbors a dense and diverse microbial ecosystem comprising bacteria, archaea, protozoa, fungi, and viruses [44]. This complex microbiome plays a crucial role in the digestion of plant fibers, synthesis of microbial protein, and production of volatile fatty acids (VFAs), which are the primary energy source for the host [44]. However, the rumen microbiome is also central to several environmental challenges associated with ruminant production, most notably the emission of enteric CH₄, and the efficiency of N utilization [28].

3.1. Microbial Ecology of the Rumen, Methane and Nitrogen Production

Enteric CH₄ is primarily produced by methanogenic archaea (methanogens) in the rumen as a terminal step in the anaerobic fermentation of carbohydrates [45]. These microorganisms utilize H₂ and CO₂, or other simple carbon compounds like formate and acetate, produced by other rumen microbes (bacteria, fungi, protozoa) during the breakdown of plant cell walls and other feed components, to generate CH₄ [45]. During methanogenesis, hydrogen acts as the primary electron donor and carbon dioxide serves as the terminal electron acceptor, leading to methane formation; meanwhile, certain rumen microorganisms function as alternative hydrogen sinks, utilizing H₂ for acetogenesis or other reductive pathways instead of methanogenesis [46]. Key methanogen genera commonly found in the rumen include Methanobrevibacter, Methanomicrobium, and Methanosphaera [47].

The composition and activity of the entire rumen microbial community influence the amount of CH₄ produced. For instance, cellulolytic bacteria (e.g., Ruminococcus, Fibrobacter), hemicellulolytic bacteria (e.g., Butyrivibrio), and amylolytic bacteria (e.g., Streptococcus, Prevotella) degrade different carbohydrate fractions, producing H₂ as a major byproduct [48,49]. Rumen protozoa, particularly ciliate protozoa, are also significant H₂ producers and often live in symbiotic relationships with methanogens, with some methanogens residing directly on or within protozoal cells [50,51]. Defaunation (removal of protozoa) has been shown to reduce CH₄ emissions, although its effects on overall animal productivity can be variable [52,53]. In addition, rumen fungi (anaerobic Chytridiomycota, e.g., Neocallimastix, Piromyces) play a role in fiber degradation by physically disrupting plant tissues and producing potent fibrolytic enzymes, which can also influence H₂ availability for methanogenesis [54].

In addition to CH₄ production, the rumen microbiome plays a crucial role in N utilization. Rumen microbes can degrade dietary protein into peptides, amino acids, and ammonia, which can be re-utilized by microbes to synthesize microbial protein, serving as a high-quality protein source for the host animal [55]. However, when ammonia production exceeds microbial uptake capacity due to excess dietary protein or inadequate fermentable energy, it is absorbed across the rumen wall, converted into urea, and excreted, resulting in nitrogen losses and environmental N pollution [56].

3.2. Factors Influencing Rumen Microbiome Composition and Function

The rumen microbiome is highly dynamic and influenced by both host (as previously mentioned) and environmental factors. Among these, diet composition is a major driver, as the type and amount of carbohydrates (fiber vs. starch), protein levels, secondary plant compounds, and feed additives can dramatically shift microbial populations and their metabolic activities, thereby affecting VFA profiles, H₂ production, and CH₄ emissions [57,58]. Additionally, the microbiome develops from birth to a mature, stable community in adulthood, and early-life colonization patterns can have lasting impacts on rumen function and animal performance [44,59]. Environmental influences, including geographic location, climate, and farm management, further shape the microbiome through their effects on feed availability, quality, and animal stress levels [47,60].

3.3. Microbiome-Targeted Strategies for Environmental Sustainability

The rumen microbiome plays a major role in enteric methane production and nutrient metabolism in ruminants. Targeting specific microbial groups has emerged as a useful strategy for reducing enteric methane emissions and enhancing N utilization. In this section, we briefly introduce microbiome-related mechanisms relevant to methane mitigation to provide a concept for subsequent discussion.

3.3.1. Dietary Interventions

Many dietary strategies influence the rumen microbiome and fermentation processes, thereby affecting methane production and nutrient utilization. For example, dietary changes can influence methane production through microbiome- and fermentation-mediated pathways [58], which provide a concept for the nutritional strategies shown in Section 4.

3.3.2. Probiotics, Prebiotics, and Synbiotics

Probiotics, prebiotics, and synbiotics are distinct dietary strategies that influence the rumen microbiota through different mechanisms. [61,62]. Further details on nutritional strategies based on them are provided in Section 4.1.4.

3.3.3. Bacteriophages and Bacteriocins

Bacteriophages are viruses that specifically infect and lyse bacteria. The use of phages targeting methanogens or H₂-producing bacteria has been explored as a highly specific way to reduce CH₄ emissions [63]. Similarly, bacteriocins are antimicrobial peptides produced by bacteria that can inhibit other, often closely related bacterial species [64]. Targeting key rumen microbes with these agents is an area of active research, but challenges related to delivery, stability in the rumen environment, and potential for resistance development need to be addressed [63,65].

3.3.4. Early-Life Microbial Programming

Influencing the initial colonization of the rumen in young animals could potentially establish a more environmentally favorable microbiome that persists into adulthood. For example, early life manipulation of the rumen microbiota can be achieved through direct inoculation with adult fluid, which accelerates microbial maturation and improves rumen later in life [66]. Feeding management during pre-weaning stage can shape distinct microbial colonization pathways and long-term fermentation efficiency [67]. This concept of “microbial programming” is gaining popularity, with studies suggesting that early-life interventions can have long-term impacts on rumen fermentation and CH₄ emissions [66,67].

3.3.5. Vaccines Against Rumen Microbes

Vaccination strategies targeting methanogens have been explored as a means to reduce their populations or activity [68]. While some studies have shown promise in inducing an immune response and reducing CH₄ emissions, developing a broadly effective and long-lasting vaccine remains a significant challenge due to the diversity of methanogen species and the complexity of the rumen environment [68,69].

4. Role of Nutrition in Sustainable Ruminant Production

Nutrition plays a key role in shaping the environmental sustainability of ruminant production systems. Dietary strategies can significantly influence GHG emissions, particularly enteric CH₄, nutrient utilization efficiency, manure composition, and overall animal health and productivity. Therefore, optimizing ruminant nutrition is a cornerstone of efforts to reduce the environmental footprint of this sector while maintaining or enhancing its contribution to global food security [2,70]. In this review, we are focusing on environmental impact due to the ruminant production; any upstream life cycle impacts related to the strategies to reduce the environmental impact of ruminant production are not discussed here. This includes sustainability assessment of chemical manufacturing involved in the strategies and agricultural practices related to the feed input to the ruminant.

4.1. Dietary Strategies to Mitigate Enteric Methane Emissions

Nutritional interventions aimed at reducing enteric CH₄ focus on several key mechanisms: altering rumen fermentation pathways to favor less CH₄ production, directly inhibiting methanogenic archaea, or providing alternative H₂ sinks in the rumen [71,72].

4.1.1. Improving Forage Quality and Digestibility

Improving the quality and digestibility of forages can play a key role in reducing enteric methane emissions from ruminants. High-quality forages, characterized by higher digestibility and nutrient density (e.g., immature grasses, legumes), generally lead to lower CH₄ emissions per unit of animal product compared to low-quality, highly fibrous forages [56,73]. This is caused by their improved fermentability in the rumen, which supports more efficient microbial activities and shifts volatile fatty acid (VFA) profiles toward propionate. As feed is digested, when its particle size decreases, the passage rate increases [74]. However, it is primarily the fermentation characteristic of the forage that influences the VFA profile, not only the rate of passage [74]. Since propionate formation acts as a hydrogen sink, this shift reduces hydrogen availability for methanogenesis, ultimately lowering CH₄ emissions per unit of animal product [56,73]. Incorporating legumes (e.g., alfalfa, clover) into grazing systems or as conserved forage can also reduce CH₄ emissions compared to traditional grass-based pastures (e.g., perennial ryegrass and white clover), due to their higher digestibility, faster passage rate, and presence of condensed tannins in some species, which can have a direct inhibitory effect on methanogens [75,76].

4.1.2. Increasing Concentrate Supplementation

Supplementing forage-based diets with concentrates (e.g., grains, protein meals) typically reduces CH₄ emissions per unit of feed intake and per unit of product. Concentrates are generally more digestible than forages and promote a shift in rumen fermentation towards propionate production [26,77]. However, high levels of concentrate feeding can lead to ruminal acidosis if not managed carefully, which may negatively affect animal health and productivity, and increasing the environmental footprint of concentrate production (i.e., land use, fertilizer, energy) must also be considered in a life-cycle assessment [78]. High concentrations of grain sources such as wheat, corn, and milo, especially those that are processed by heat, pressure, and fine grinding, can increase the risk of acidosis [79]. Both the amount of starch and its processing method can increase starch hydrolysis and glucose production, which will later increase lactic acid and lower rumen pH [80]. Regarding the environmental footprint, many studies show that feed contributes most to carbon footprint variation in different livestock systems [81,82]. In this context, biofuel crops for livestock feed, including corn and soybeans, have been expected to account for 48% of land use and 23% of carbon emissions by 2050 [83,84]. However, determining the appropriate level of concentrate supplementation requires careful consideration, as the optimal level will vary depending on the basal forage quality, animal requirements, and production goals.

4.1.3. Dietary Lipids

Supplementing ruminant diets with lipids (fats and oils) can be an effective strategy for reducing enteric CH₄ emissions. Lipids can reduce CH₄ production through several mechanisms: direct toxicity to methanogens and protozoa, reduction in organic matter fermentation in the rumen due to decreased fiber digestion by unsaturated fatty acids, and by acting as an H₂ sink during biohydrogenation of unsaturated fatty acids [73,85]. The extent of CH₄ reduction depends on the type and level of lipid supplementation. For example, medium-chain fatty acids (e.g., lauric and myristic acid found in coconut or palm kernel oil) have shown strong anti-methanogenic effects [86,87]. However, high levels of unsaturated dietary lipids can negatively impact feed intake and milk fat yield, so careful formulation is required [88].

4.1.4. Feed Additives

Various feed additives have been evaluated for their potential to suppress methanogenesis. These can be broadly categorized as chemical inhibitors, ionophores, plant-derived compounds (e.g., tannins, saponins, essential oils), diet-fed microbials, and prebiotics [6,72].

3-nitrooxypropanol (3-NOP): Compounds 3-NOP have demonstrated consistent and significant reductions in enteric CH₄ emissions (often >30%) across various ruminant species and diets by specifically inhibiting the enzyme methyl-coenzyme M reductase, which is crucial for the final step of methanogenesis [89,90]. This direct inhibition mechanism differs fundamentally from dietary strategies that indirectly affect methanogenesis through alterations in fermentation patterns. However, the regulatory landscape for 3-NOP in Europe presents important practical considerations. In the European Union, 3-NOP (marketed as Bovaer, DSM-Firmenich, Kaiseraugst, Switzerland) received conditional authorization in 2022 under Commission Implementing Regulation (EU) 2022/565, but this authorization is currently limited to dairy cows and cows for reproduction, and does not extend to beef cattle, sheep, or other ruminant categories [91]. The authorization process highlighted specific safety concerns related to human exposure during handling, requiring protective measures to minimize inhalation and skin contact by farm workers. Additionally, recent public concerns and misinformation regarding 3-NOP safety have emerged in some European countries, despite regulatory agencies’ conclusions that the additive is safe for animals, consumers, and the environment when used according to approved conditions. These regulatory restrictions and social acceptance challenges may limit near-term adoption in certain markets, even as the technology continues to gain approval in other regions globally.

Nitrate: Nitrate supplementation offers an alternative approach to methane mitigation through a fundamentally different mechanism than direct methanogen inhibitors. When added to ruminant diets, nitrate serves as an alternative electron acceptor in the rumen, competing with carbon dioxide for hydrogen that would otherwise be used in methanogenesis. Nitrate is reduced to nitrite and subsequently to ammonia by rumen microorganisms, a process that consumes hydrogen and can result in methane reductions of 10–30%, depending on inclusion level, diet composition, and adaptation period [92,93]. The ammonia produced can be utilized by rumen microbes for protein synthesis, potentially improving nitrogen utilization efficiency. The primary concern associated with nitrate feeding has historically been the risk of nitrite toxicity, as nitrite is an intermediate that can accumulate if reduction rates are imbalanced. However, substantial research has demonstrated that the risk of nitrite toxicity is highly dose-dependent and manageable through proper feeding practices. Toxic levels of nitrate are difficult to reach under controlled feeding conditions, especially when animals are gradually adapted over 2–3 weeks, allowing rumen microbial populations to develop enhanced nitrite-reducing capacity [92,93]. Practical guidelines for safe use include limiting dietary nitrate to no more than 2–3% of diet dry matter, ensuring adequate readily fermentable carbohydrates, and avoiding situations where animals might consume large amounts after feed restriction. Rather than focusing primarily on toxicity concerns, it is important to recognize the practical potential of nitrate as an accessible and cost-effective mitigation tool. Nitrate sources are widely available and relatively inexpensive compared to specialized feed additives and can be successfully implemented in both intensive and extensive production systems, including grazing scenarios. With appropriate management and adherence to established safety guidelines, nitrate supplementation represents a viable and underutilized option in the portfolio of methane mitigation strategies for ruminant production systems worldwide.

Ionophores: Ionophores such as monensin are antimicrobials that alter rumen microbial populations, typically increasing propionate production and decreasing CH₄ emissions by 5–15% on average, although the effect is highly variable depending on diet composition (more effective in high-forage diets) and frequently diminishes within 2–4 weeks due to microbial adaptation [94,95]. Their use is restricted in some regions due to concerns about antimicrobial resistance. For example, New Zealand’s farmers who want to use ionophores will be monitored by veterinarians with a prescription due to antimicrobial resistant and public health implications, such as shifting microbiome to be more resistant to ionophores [96]. Given these limitations (i.e., modest and transient methane reduction, regulatory restrictions, and antimicrobial resistance concerns), ionophores are best viewed as a mature technology providing supplementary rather than primary methane mitigation benefits in specific production contexts.

Plant-Derived Compounds: Many plants produce secondary metabolites with anti-methanogenic properties. Condensed tannins, found in certain legumes and tree fodders, can reduce CH₄ by directly inhibiting methanogens, reducing protein degradation in the rumen (which reduces H₂ availability), and binding to dietary fiber, thereby reducing its fermentability [97,98]. Saponins, found in plants like Yucca schidigera and Quillaja saponaria, can reduce CH₄ by suppressing protozoa and directly inhibiting methanogens [99]. Essential oils, which are volatile components of aromatic plants, have also shown promise in modulating rumen fermentation and reducing CH₄, though their effects can be inconsistent [73,100].

Seaweeds: Certain species of seaweed, particularly the red macroalga Asparagopsis taxiformis and Asparagopsis armata, have garnered significant attention for their potent CH₄-reducing capabilities, often exceeding 80–90% reduction in vitro. However, it is important to note that in vivo studies typically report more modest reductions of 40–60%, depending on inclusion rate and diet composition, as in vitro batch culture systems lack the dynamic rumen environment, passage rates, and host physiological responses present in live animals [101,102]. Regardless, this effect is attributed to bromoform and other halogenated compounds present in the seaweed, which are powerful inhibitors of methanogenesis [101,102]. There are typically three mechanisms for seaweed to reduce methane: inhibition of methanogenic archaea, reducing the H₂ levels, and altering the rumen fermentation pathways. The inhibition works with the presence of halogenated compounds, such as bromoform, at the methyl-coenzyme M reductase site, blocking the methane synthesis’s final step [103,104]. For alternating, it works by shifting pathways to increase propionate production and reduce acetate production [105,106]. It also indirectly limits methane emission by reducing H₂, which is required for methanogenic archaea [107,108]. Additionally, seaweed bioactivity can vary substantially depending on harvest location, season, and preservation method, and some studies have observed diminished methane reduction over extended feeding periods, potentially due to microbial adaptation or degradation of active compounds [109,110]. However, challenges related to sustaining large-scale cultivation, bromoform residues in animal products and the environment, and long-term effects on animal health and rumen function are still being actively researched [109,110]. Given these uncertainties and the current experimental stage of development, realistic expectations for commercial application should be based on in vivo evidence (40–60% reduction under optimal conditions) rather than the higher reductions frequently cited from in vitro studies.

Direct-fed microbials (DFMs): Direct-fed microbials (DFMs), also known as probiotics, are live microbial feed supplements (e.g., specific strains of bacteria or yeast) that can beneficially affect the host animal by modulating rumen microbial communities. In ruminants, DFMs such as Saccharomyces cerevisiae and Lactobacillus spp. have been shown to stimulate beneficial microbial populations that enhance fiber digestion, improve volatile-fatty-acid (VFA) profiles, and in some cases outcompete methanogens for hydrogen (H₂) [62,111].

Prebiotics: Prebiotics, such as oligosaccharides, are non-digestible feed components that selectively stimulate the growth or activity of beneficial microbes, and their inclusion alongside DFM can further stabilize fermentation. While these microbial approaches are promising, their effectiveness in consistently reducing enteric CH₄ emissions remains variable and warrants further research [61,112]. Additionally, interventions that favor microbial populations more efficient in ammonia assimilation can enhance nitrogen retention and reduce excretion losses.

For detailed information on feed additives and studies to date on this topic, please refer to Supplementary Material Table S2.

4.2. Enhancing Nutrient Utilization Efficiency

While mitigating enteric methane is a key objective, nutritional strategies must also address the efficiency with which animals utilize dietary nutrients. Improving nutrient utilization not only supports animal productivity but also reduces nutrient excretion and its associated environmental impacts. This involves minimizing nutrient losses, particularly N and P, which contribute to GHG emissions and eutrophication. Several nutritional strategies have been developed to improve N and P utilization efficiency, reduce nutrient excretion, and enhance overall feed conversion in ruminant systems.

Feeding animals according to their precise nutrient requirements, avoiding both deficiencies and excesses, is fundamental to enhancing nutrient utilization efficiency [74]. Overfeeding protein leads to increased N excretion in urine and feces, which can then be volatilized as ammonia or converted to N₂O [113]. Precision feeding techniques, often supported by nutritional models and real-time monitoring, allow for the formulation of diets that closely match animals’ needs for energy, protein, minerals, and vitamins at different physiological stages (e.g., growth, lactation, pregnancy) [114,115]. This not only reduces nutrient excretion but can also improve animal performance and reduce feed costs.

Strategies to improve N utilization efficiency include optimizing the balance of rumen degradable protein and rumen undegradable protein, supplementing diets to meet limiting amino acid requirements more precisely, and using feed additives that reduce protein degradation in the rumen such as tannins [116,117]. Reducing crude protein levels in diets, while ensuring that metabolizable protein and essential amino acid requirements are met, is an effective way to decrease excess N intake and urinary urea excretion when rumen degradable protein is aligned with microbial demand for fermentable carbohydrates [118]. Many additives like monensin can reduce ammonia, but they cannot improve urea-N cycling consistently unless nitrogen and energy supply balancing is achieved [118].

Over-supplementation of P can lead to excessive P excretion and associated environmental risks. Strategies to improve P utilization include accurately assessing P requirements using feed ingredients with higher bioavailable P, and supplementing with phytase enzymes to improve digestion of phytate-bound P in plant-based feedstuffs, thereby reducing the need for inorganic P supplementation [119,120].

4.3. Impact of Nutrition on Manure Composition and Emissions

Dietary choices directly influence the quantity and composition of manure produced, which in turn affects emissions from manure storage and application. Diets that are highly digestible and efficiently utilized by the animal generally result in less manure production and lower concentrations of undigested nutrients in the manure [121]. Therefore, reducing dietary N and P to meet, but not exceed, animal requirements is key to minimizing N and P concentrations in manure, thereby reducing the potential for NH₃ volatilization, N₂O emissions, and P runoff [113]. The form of excreted N (urea vs. fecal N) is also influenced by diet; for example, diets high in fermentable carbohydrates can increase microbial protein synthesis in the rumen, leading to a greater proportion of N being excreted in feces (as microbial protein) rather than in urine (as urea), which is generally less prone to volatilization [122].

5. Main Environmental Impacts of Ruminant Production

Ruminant livestock production, while indispensable for global food security, places considerable pressure on the environment [2]. The primary environmental concerns associated with this sector are GHG emissions, eutrophication of aquatic ecosystems, and extensive use of land and water resources [2]. Understanding the scale and mechanisms of these impacts is crucial for developing effective mitigation strategies and transitioning towards more sustainable production systems [2,4,123].

5.1. GHG Emissions

As outlined earlier, the major GHGs from ruminant systems include CH₄, N₂O, and CO₂, with CH₄ being the dominant contributor. Ruminant animals are significant contributors to anthropogenic GHG emissions, accounting for the majority of the 14.5% of total human-induced emissions, primarily through the release of CH₄ [2,124]. Specifically, CH₄ is the largest contributor from ruminant systems, which has a global warming potential approximately 27–30 times that of CO₂ over 100 years [125]. N₂O is primarily produced through microbial nitrification and denitrification processes in soils where manure is applied or deposited during grazing, and also during manure storage [126]. In addition, CO₂ emissions from ruminant production are mainly associated with fossil fuel consumption for on-farm machinery, electricity use, production of fertilizers and pesticides for feed crops, and transportation of inputs and outputs [2]. Land-use change, particularly deforestation for pasture expansion or feed cultivation, also contributes significantly to CO₂ emissions through the loss of carbon stored in forests and soils [127].

It is important to note that emission intensities (GHG emissions per unit of product, e.g., kg of meat or milk) vary widely across regions and production systems, reflecting differences in animal productivity, feed quality, manure management practices, and land use [123]. For instance, extensive, low-input grazing systems in some developing regions may have higher emission intensities compared to more intensive, higher-productivity systems in developed countries, although the latter may have higher absolute emissions due to larger animal numbers or reliance on fossil fuel-intensive inputs [128]. Different types of ruminants also show significant GHG emission variations. For example, mature ewes can emit around 10–14 kg per head per year, while beef cattle can emit 50–90 kg, and lactating cows around 91–146 kg [70]. Factors such as the age of animals also influence GHG emissions [128]. For example, young cattle produce less total CH₄ because they consume less feed. However, they emit more CH₄ per unit of weight gain compared to mature cattle, as they must support both basal metabolism and growth simultaneously [70]. This highlights the complexity of assessing and mitigating GHG emissions from diverse ruminant production landscapes.

5.2. Eutrophication and Water Quality Degradation

Eutrophication, the enrichment of water bodies with excess nutrients, primarily nitrogen (N) and phosphorus (P), is a major environmental consequence of intensive livestock production. These excess nutrients originate from manure and fertilizers applied to feed crops and pastures, which can then be transported into rivers, lakes, and coastal waters via surface runoff, leaching, and atmospheric deposition [129]. The accumulation of N and P in aquatic ecosystems stimulates excessive growth of algae and aquatic plants. When these blooms die and decompose, they consume dissolved oxygen in the water, leading to hypoxic or anoxic conditions that can kill fish and other aquatic organisms, degrade water quality, and impair the ecological health and recreational value of water bodies [130]. The inefficient use or improper handling of manure can lead to these significant losses to the environment. A study performed in Northern Ireland found that 50% of dairy farms had soil P concentrations above target levels, compared to 25% of non-dairy ruminant farms, which was attributed to the intensive use of fertilizers and manure in dairy systems [131]. In this context, applying manure to saturated or frozen soils or over crop nutrient requirements increases the risk of runoff and leaching [132].

In addition to N and P, manure can also contain pathogens (bacteria, viruses, protozoa), heavy metals, and veterinary drug residues, which can further contaminate surface and groundwater if not managed properly, posing risks to human and animal health [133]. For example, the leaching of nitrates from agricultural soils into groundwater is a particular concern, as high nitrate levels in drinking water can be harmful, especially to infants [134]. Improvements in manure storage, strategic timing of application, and the use of buffer zones have proven effective in reducing nutrient runoff. These practices contributed to the observed declines in phosphorus pollution in water bodies following policy changes in the 1990s [135].

5.3. Land Use and Resource Allocation

Ruminant production is a major user of global land resources, as approximately 26% of the Earth’s ice-free terrestrial surface is used for grazing, and an additional portion of cropland is dedicated to producing feed for ruminants [4]. The expansion of pasture and feed crop areas is a primary driver of deforestation and habitat loss in many regions, particularly in the tropics, at the cost of forests, grasslands, and wetlands [136]. This conversion of natural ecosystems leads to a decline in biodiversity and ecological degradation by destroying habitats and fragmenting landscapes [61]. However, only 26% of the ice-free surface of the Earth is pasture, with some of them being too dry or too cold to support cropping [137]. Most of this pasture is in marginal environments with limited cropping potential. Vast areas of rangelands, grasslands, and savannahs are characterized by arid or semi-arid climates, poor soil quality, or steep topography, making them unsuitable for cultivation but capable of supporting grazing animals. In these systems, ruminants can convert human-inedible plant biomass into high-quality protein, contributing to food security and livelihoods in regions where other agricultural options are limited [56,138]. Well-managed grazing systems can also provide ecosystem services, such as maintaining grassland biodiversity, enhancing soil carbon sequestration, and improving water infiltration [139,140], although these benefits are highly dependent on grazing intensity, management practices, and local environmental conditions.

Water use is another critical resource consideration as livestock production accounts for a significant portion of global freshwater withdrawals, primarily for irrigating feed crops and for animal drinking water [141]. The water footprint of ruminant products can be substantial, although, as with GHG emissions, it varies greatly depending on the production system, climate, and feed sourcing [142]. Regions facing water scarcity may experience increased competition for water resources between agriculture, including livestock production, and other human and environmental needs.

6. Sustainable Management Practices in Ruminant Production

Sustainable management practices are integral to minimizing the environmental footprint of ruminant production systems. These practices encompass a wide range of on-farm strategies, including improved manure management, optimized grazing systems, enhanced animal health and welfare, and efficient resource utilization. Adopting these practices can lead to significant reductions in GHG emissions, nutrient losses, and land degradation, while also potentially improving farm profitability and resilience [2].

6.1. Manure Management Strategies

Manure is a valuable resource containing essential nutrients for crop production, but its mismanagement can lead to substantial environmental pollution, including GHG emissions, NH₃ volatilization, and nutrient leaching into water bodies. During manure storage, there are also chemical additives used, which can be classified into four groups: acidifiers, oxidizers, antimicrobials, and coagulants [143]. Acidifiers reduce slurry pH and help suppress methane and ammonia emissions; oxidizers and antimicrobials inhibit microbial pathways responsible for methane production; and coagulants promote solid separation and nutrient stabilization [143]. These approaches improve nutrient retention and support sustainable manure recycling and fertilizer use [126].

Physical and biological management practices are applied combined with chemical approaches: covering liquid manure storages (e.g., lagoons, slurry tanks) with impermeable or floating materials limits gas exchange and reduces CH₄ and NH₃ losses [144,145]; cooling manure during storage suppresses microbial activity and reduces CH₄ [146]; solid manure storage, such as composting or well-managed stockpiles, generally results in lower CH₄ emissions than liquid systems, but can lead to higher N₂O emissions if not properly aerated and managed [147]. Among biological techniques, anaerobic digestion integrates effectively into manure management as an advanced process that decomposes organic matter in the absence of oxygen, capturing CH₄ for renewable energy while reducing odor and pathogens; the resulting digestate can be separated into solid and liquid fractions for use as fertilizer, bedding, or soil amendment, and co-digestion with organic wastes such as food residues further enhances biogas yield and nutrient recovery [148,149].

In addition to storage management, manure application practices also play a critical role in nutrient efficiency and emission control. Applying manure when crops have the highest nutrient demand in spring and early summer, without snow and frozen soil and heavy rain, and when soil conditions are suitable (e.g., not waterlogged or frozen) maximizes nutrient uptake and minimizes runoff and leaching [132]. Precision application techniques, such as slurry injection or band spreading, place manure directly into the soil or close to the plant roots, reducing NH₃ volatilization and odor emissions compared to broadcast spreading [150]. Incorporating manure into the soil during or shortly after application also helps to reduce NH₃ losses.

Following storage and field application, further treatment and processing technologies—including solid–liquid separation, composting, and anaerobic digestion—enhance fertilizer value while reducing environmental impact [151]. Solid–liquid separation can concentrate nutrients into different fractions, allowing for more targeted application or export of excess nutrients from a region [152]. Composting solid manure stabilizes organic matter, reduces pathogens, and creates a more uniform fertilizer product, though it requires careful management to control N losses and GHG emissions [153]. One of the examples is from the Windy Ridge Dairy in Fair Oaks, Indiana; an earlier manure-processing system based solely on roller pressing has been replaced by an integrated system combining an anaerobic digester with separations [154]. The current system produces solids with approximately 33–34% dry matter that are reused as bedding material. Compared with the previous roller-press-only system, the integrated setup achieves greater reduction in volatile solids and odor, lowers methane emissions from storage, and improves nutrient-recycling efficiency.

6.2. Optimized Grazing Management

Well-managed grazing systems can contribute to environmental sustainability by enhancing soil carbon sequestration, improving pasture productivity, reducing soil erosion, and supporting biodiversity [155]. Conversely, poorly managed grazing (e.g., overgrazing) can lead to land degradation, soil compaction, reduced water infiltration, and increased GHG emissions [155].

6.2.1. Rotational and Adaptive Multi-Paddock Grazing

Rotational grazing, where livestock are moved systematically through a series of paddocks, allowing for periods of rest and regrowth for the pasture, can improve forage utilization, enhance plant vigor, and distribute manure more evenly compared to continuous grazing [156,157]. Adaptive multi-paddock grazing, a more intensive form of rotational grazing that involves frequent animal movements and longer rest periods, has been suggested to further enhance soil health, carbon sequestration, and water infiltration, although research findings on its benefits compared to other well-managed grazing systems are still evolving and can be site-specific [158,159].

6.2.2. Integrating Legumes and Diverse Forage Species

Incorporating N-fixing legumes (e.g., clover, alfalfa, vetch) into pasture swards can reduce the need for synthetic N fertilizers, thereby lowering N₂O emissions associated with fertilizer production and application [160]. Diverse pasture mixtures, including various grasses, legumes, and forbs, can also improve soil health, enhance resilience to climate variability, and provide better nutrition for grazing animals [161,162].

6.2.3. Silvopastoral Systems

Silvopasture, the intentional integration of trees and shrubs with livestock grazing on pasture, is an agroforestry practice that offers multiple environmental benefits. Trees can provide shade and shelter for animals, improve animal welfare, enhance soil carbon sequestration (both above and below ground), reduce soil erosion, improve water quality by filtering runoff, and provide additional income streams from timber or non-timber forest products [163,164]. The presence of trees can also modify the microclimate, potentially reducing heat stress on animals [164].

6.3. Animal Health and Welfare Management

Improving animal health and welfare is not only an ethical imperative but also contributes to environmental sustainability. Healthy and well-cared-for animals are generally more productive, have better feed conversion efficiency, and may have lower GHG emission intensities [165]. Effective disease prevention and control programs, effective biosecurity measures, and management practices that minimize stress can reduce morbidity and mortality, leading to more efficient resource use. For example, researchers demonstrated dairy cows with foot lesions produce more GHGs per unit of milk due to decreased milk yield and long-lasting recovery compared with their healthy counterparts [166]. Another study found that subclinical mastitis increases greenhouse gas (GHG) emissions intensity because affected cows produce less milk without a proportional reduction in feed intake or maintenance needs [167]. As a result, CH₄ and N₂O emissions per kilogram of milk are higher compared to healthy cows.

6.4. Water Resource Management

Efficient water use is beneficial, especially in water-scarce regions. Management practices that improve water use efficiency include selecting drought-tolerant forage species, implementing efficient irrigation systems for feed crops (if irrigation is used), providing clean and accessible drinking water for animals to optimize intake and performance, and managing grazing to enhance water infiltration and reduce runoff [141,168]. For example, researchers have already implemented the drought-tolerant breeds for enhancing resilience in water-scarce regions such as Botswana, Texas [169]. Another way is the mixed crop–livestock system, which establishes synergies between crops and livestock and can efficiently reduce overall water footprints [170]. Rainwater harvesting and water recycling systems can also contribute to more sustainable water management on farms [141].

6.5. Synergistic Integration

Achieving a substantial reduction in the environmental footprint of ruminant production necessitates moving beyond isolated interventions to embrace integrated strategies. These strategies involve synergistically combining multiple approaches across genetics, nutrition, management, and technology to address the multifaceted environmental challenges associated with livestock. An integrated approach recognizes that the effectiveness of individual mitigation measures can be significantly enhanced when implemented as part of a cohesive, whole-farm or even regional system [2]. Key principles guiding such strategies include a holistic perspective that considers the entire production system, the identification and leveraging of synergy and complementarity between different measures, context specificity tailored to diverse agroecological zones and farming systems, the use of Life-Cycle Assessment to evaluate overall environmental impacts and potential trade-offs [171], and an adaptive management approach involving continuous monitoring, evaluation, and refinement.

Examples of effective integrated strategies are numerous and varied. For instance, combining optimized nutritional approaches—such as high-quality forages and methane-inhibiting feed additives—with genetic selection for animals exhibiting lower methane emissions or higher feed efficiency allows the genetic potential for improved performance to be more fully realized, yielding greater overall mitigation impacts [10]. Similarly, in grazing systems, integrating optimized practices like rotational grazing with the incorporation of nitrogen-fixing legumes in pastures can enhance soil carbon sequestration, reduce the need for synthetic nitrogen fertilizers, and improve forage quality. These benefits are further amplified when combined with ruminant breeds genetically adapted to thrive under such conditions [172]. Another powerful integration involves linking advanced manure management technologies, such as anaerobic digestion or slurry acidification, with precision nutrient application techniques. Applying the resulting digestate or treated slurry to croplands at rates and times that match crop needs, guided by variable-rate application and GPS, maximizes nutrient utilization and minimizes environmental losses. Whole-farm nutrient budgeting, tracking all nutrient inputs and outputs, allows for the identification of imbalances and inefficiencies, guiding strategies to improve nutrient cycling and optimize on-farm resource use, thereby fostering closer integration of livestock and crop production [173].

7. Current Measurement Techniques and Artificial Intelligence

Accurate measurement of environmental impacts, particularly GHG emissions and nutrient flows, is fundamental for developing, evaluating, and implementing effective mitigation strategies in ruminant production. Concurrently, a diverse array of technologies is being developed and deployed to reduce these impacts. This section reviews key measurement techniques for quantifying critical environmental parameters and discusses various mitigation technologies beyond direct nutritional or genetic interventions, focusing on technological solutions for emissions reduction and resource management [174].

7.1. Measurement Techniques for Greenhouse Gas Emissions

Quantifying GHG emissions, especially enteric CH₄ and N₂O from manure management, is complex due to the variability influenced by animal type, diet, management, and environmental conditions.

7.1.1. Enteric Methane Measurement

Several measurement techniques are available for quantifying enteric methane emissions from individual animals or groups, each with distinct advantages and limitations. Respiration chambers (also called calorimeters) are considered the gold standard for individual animal CH₄ and CO₂ measurement, involving housing animals in sealed rooms where airflow and gas concentrations are precisely monitored, allowing for accurate, continuous measurement of gas exchange over several days [175]. However, they are expensive, labor-intensive, and may not fully represent animal behavior under normal farm conditions, as animals must be trained to enter and remain calm inside the chambers, and researchers must closely observe for signs of stress that could compromise data accuracy [176,177]. The sulfur hexafluoride (SF₆) tracer technique involves releasing a known quantity of inert tracer gas from a permeation tube placed in the rumen, with air samples collected from around the animal’s mouth and nose and analyzed for CH₄ and SF₆ concentrations; the ratio of CH₄ to SF₆, along with the known SF₆ release rate, allows estimation of CH₄ emissions [178]. This technique is less expensive than chambers and allows animals to be measured under more natural grazing or housing conditions, making it suitable for large-scale applications [27,179], but it has higher variability and lower precision, requiring careful calibration and sample collection, with potential errors when animals interact or share emissions, and large variations associated with different grazing conditions [27,179,180]. Automated head-chamber systems such as GreenFeed (C-Lock Inc., Rapid City, SD, USA) attract individual animals (often using bait feed) to a station where their breath is sampled for short periods (typically a few minutes) multiple times a day, with gas analyzers measuring CH₄ and CO₂ concentrations to estimate daily emissions [181]. These systems are increasingly used for large-scale phenotyping in research and breeding programs due to their ability to measure animals in normal group housing, though accuracy depends on consistent animal visits and proper head orientation for effective gas sample collection [182]. Sniffer systems (e.g., MooLogger, Tecnosens, Brescia, Italy) offer a practical, non-invasive approach using non-dispersive infrared (NDIR) technology to measure enteric methane and CO₂ emissions during routine activities like feeding or milking, integrating into farm infrastructure for automated, large-scale data collection to support genetic selection [183]. Although less precise than respiration chambers and environmentally sensitive, they provide reliable rankings, are cost-effective, and require little labor, though regular calibration, data quality control, and standardized protocols are essential [183]. Laser methane detectors (LMD) are portable devices using infrared laser absorption spectroscopy to measure CH₄ concentration in the air path between the detector and a reflector, enabling rapid, non-invasive spot measurements of CH₄ plumes from individual animals or groups, though accuracy can be influenced by wind conditions, animal movement, and operator skill [184]. For measuring emissions from groups of animals in paddocks or open barns, micrometeorological techniques such as eddy covariance or flux gradient methods measure gas concentrations and atmospheric turbulence at different heights above the source area to calculate emission fluxes [185]; these methods are suitable for larger scales but require specialized equipment and expertise.

Importantly, the field continues to evolve with novel sensor technologies designed to overcome limitations of existing methods, such as the Smart Cannula-mounted Optical Unit for Trace-methane (SCOUT) system, which represents the first robust in vivo sensing technology for continuous, high-resolution monitoring of ruminal methane concentrations [186]. Unlike ambient sampling approaches that suffer from low data retention rates (17%) and environmental interference, SCOUT employs an innovative closed-loop gas recirculation design achieving 82% data retention, capturing methane concentrations 100–1000 times higher than ambient approaches and enabling detection of rapid concentration changes triggered by animal behavior [187]. The advancement of such sensor technologies is crucial for enabling large-scale phenotyping programs essential for genomic selection and precision livestock management. The choice of measurement method should be guided by research objectives, available resources, and production context: respiration chambers and GreenFeed systems are most appropriate for genetic evaluations and breeding programs requiring high precision and repeated measurements on individual animals; SF₆ and micrometeorological techniques are better suited for on-farm emissions inventories under grazing conditions; and sniffer and laser methods are useful for large-scale on-farm screening and ranking of animals. Detailed technical specifications, operational protocols, equipment diagrams, and comparative performance metrics for all methods are provided in Supplementary Materials (Table S3, Figures S1–S4).

7.1.2. Methodological and Data Comparability

The methane measurement methods described above differ in measurement principles, precision, and sampling intensity. These methodological differences have important implications for data comparability across studies, farms, and regions, as estimates derived from different measurement systems may not be directly comparable without appropriate standardization and quality assurance procedures. Moreover, these differences can lead to variation in observed effect sizes and estimated genetic parameters among studies (Table S3, Supplementary Material). For instance, respiration chambers offer high precision and strong experimental control, but their application is limited by low throughput and high operational costs [177]. The SF₆ tracer technique enables methane measurements under grazing conditions and allows larger sample sizes; however, its accuracy depends on tracer release consistency and sampling protocols [187]. Although their use is constrained by high equipment costs and limited portability, automated head-chamber systems, such as GreenFeed, provide precise and standardized multi-gas measurements and can be applied across various farming systems [188]. Sniffer- and laser-based methods allow non-invasive, high-throughput data collection, but are more sensitive to environmental conditions and generally exhibit lower measurement precision [189,190]. Across studies, methane-related traits show moderate heritability, although the magnitude of reported estimates varies across measurement systems (Table S4, Supplementary Material).

Recognizing these challenges, several international initiatives have emerged to promote standardization and improve data comparability. The International Committee for Animal Recording (ICAR) has developed guidelines for recording dairy cattle methane emissions for genetic evaluation, providing standardized protocols and recommendations for comparing different measurement methods (https://www.icar.org/Guidelines/20-Recording-Dairy-Cattle-Methane-Emission-for-Genetic-Evaluation.pdf, accessed on 2 January 2026). The Global Research Alliance on Agricultural Greenhouse Gases has published technical manuals for respiration chamber designs and SF₆ tracer technique protocols to harmonize measurement practices across research groups (https://globalresearchalliance.org/research/livestock/collaborative-activities/livestock-emissions-measurement/, accessed on 2 January 2026). Despite these efforts, complete standardization remains challenging, particularly for on-farm measurements where environmental variability and management diversity create additional complexity [9]. Key areas requiring further standardization include quality assurance procedures, uncertainty quantification methods, and protocols for converting measurements to standardized emission factors for inventory reporting [9]. The choice of measurement method should be guided by the intended application: respiration chambers and GreenFeed systems are most appropriate for genetic evaluation and breeding programs due to their precision and ability to generate repeated measurements on individual animals, whereas SF₆ and micrometeorological techniques are better suited for on-farm emissions inventories under grazing conditions, and in vitro methods are useful for initial screening of feed additives or dietary strategies before conducting costly in vivo trials [9]. Continued development of standardized protocols and quality assurance frameworks will be essential for building more consistent and interoperable datasets that can support both genetic improvement programs and national greenhouse gas inventories.

7.1.3. Measurement of Nitrous Oxide and Ammonia Emissions from Manure

A detailed description of analytical techniques and experimental outcomes for ammonia and nitrogen quantification in ruminant studies is available in the Supplementary Table S5. Closed (non-flow-through) static chambers are commonly used to measure N₂O and NH₃ fluxes from manure storage or land-applied manure. A chamber is placed over the emitting surface, and gas samples are collected from the chamber headspace over time to determine the rate of gas accumulation [191]. They are relatively inexpensive but can alter the microenvironment within the chamber. In contrast, Flow-through dynamic chambers involve a continuous flow of air through the chamber, with gas concentrations measured at the inlet and outlet [192]. This method can provide more realistic conditions but is more complex to operate. For laboratory or controlled studies, wind tunnels can be used to simulate airflow over manure surfaces and measure emissions under defined conditions [193]. Similar to CH₄, micrometeorological methods (Figure S5 in the Supplementary Material) can be used to measure N₂O and NH₃ fluxes from larger areas like fields or manure lagoons [191].

7.2. Artificial Intelligence (AI) in Ruminant Sustainability

Modern technology, particularly AI and big data analytics, plays a crucial role in advancing integrated strategies for sustainable ruminant production. AI-driven innovations have shown potential to enhance resource use and reduce environmental impact [7]. Automated feeding and precision nutrition systems using AI and computer vision monitor individual animals for real-time feed adjustments, improving feed efficiency, lowering methane emissions, and minimizing nitrogen excretion through optimized intake [194,195]. Moreover, AI can simplify data integration by integrating measurements from multiple sensors to create virtual sensors that can estimate unmeasured variables using existing data [196]. For example, temperature sensors, pedometers, and camera-based systems can integrate data to monitor animal health and productivity [196]. These tools complement other sensor technologies that monitor welfare indicators, such as heart rate variability, which has been associated with increased GHG emissions under stress, enabling timely interventions that can improve both welfare and metabolic efficiency [195].

AI can also optimize manure management by integrating data on milk yield, environmental conditions, and breed-specific responses to tailor strategies according to weather and soil factors. It determines ideal manure application times to limit nitrogen losses and directs autonomous cleaning robots [7]. In addition, modeling frameworks such as the Ruminant Farm Systems (RuFAS) model (Cornell University, Ithaca, NY, USA) provide a comprehensive platform that integrates animal, manure, crop/soil, and feed-storage modules to evaluate production, nutrient flows, and GHG emissions. RuFAS is used to assess improvements in feed efficiency, which can lower total feed use and methane emissions at the herd and farm levels [197]. Beyond management optimization, AI and machine learning algorithms—such as Support Vector Machines—accurately predict biogas and energy yields from manure and estimate CH₄ and CO₂ emissions [198]. Research should increasingly adopt system-level design approaches to better capture interations among animals, feeds, management practices, and the environment. Other than methodological advances, design-oriented research strategies which integrate real-world use and management can improve translation from scientific concepts to real outcomes [199]. AI-driven energy systems further support sustainability by forecasting demand, managing renewable energy use, and enabling predictive maintenance to improve overall system efficiency [7,200]. Overall, these digital tools promote circular agriculture by converting organic waste into renewable resources [200].

Despite their potential, integrated technologies face several challenges, including design complexity, high upfront costs, and the need for tailored guidance and policy support. These technologies must avoid inducing stress or unnatural behavior [201]. Machine learning supports early detection of disease, lameness, and stress [194], while off-paddock facilities can reduce heat stress and hoof infections but should still allow natural behaviors [202]. Methane-reducing diets must balance GHG reduction with animal health, a goal that AI can help achieve through ration optimization and precision management [201]. The future of sustainable ruminant production lies in integrated, systems-based approaches that combine diverse tools and scientific knowledge with co-innovation, adaptive learning, and outcome-based policies. Strengthening collaboration among farmers, researchers, and policymakers, together with landscape-level planning, can further enhance strategy effectiveness. Continuous improvement and innovation remain vital for meeting global food demands while protecting planetary health.

8. Knowledge Gaps and Future Directions for Sustainable Ruminant Production

Despite substantial progress in understanding the environmental impacts of ruminant production, critical knowledge gaps persist that must be addressed to achieve meaningful sustainability improvements. Research across genetics, nutrition, microbiome science, and management has generated valuable insights, yet these advances remain largely siloed within disciplinary boundaries. Moving forward requires integrated, context-specific approaches that recognize the inherent complexity of agricultural systems. This complexity demands methodological frameworks capable of handling uncertainty, variability, and the multiple, often conflicting, objectives that characterize real-world farming. Design science offers such a framework, explicitly addressing the particularities of agricultural innovation where biological systems, environmental conditions, and socio-economic constraints interact in ways that defy simple optimization [200]. Unlike traditional research approaches that develop solutions in controlled settings and then attempt technology transfer, design science emphasizes participatory processes where farmers, researchers, and policymakers co-create solutions tailored to specific contexts. This approach is particularly relevant for ruminant systems, where factors such as temporal variability in animal performance, spatial heterogeneity in resource availability, and emergent properties of host–microbiome–environment interactions profoundly influence what strategies will actually work under commercial conditions. Future research must therefore invest in developing and testing participatory design frameworks that can accommodate this complexity while generating actionable knowledge for diverse production systems.

A major challenge lies in applying mitigation strategies across the vast diversity of global agroecological zones, farming systems, and socio-economic contexts. One-size-fits-all solutions have proven inadequate, yet we lack robust frameworks for helping producers select and adapt practices to their specific circumstances. This is especially problematic for resource-constrained smallholder farmers in developing nations, where sustainability interventions must be economically viable and socially equitable rather than exacerbating existing vulnerabilities. Addressing this requires better understanding of how different strategies perform under varying conditions and what trade-offs emerge when multiple objectives are pursued simultaneously. Whole-farm systems models represent essential tools for this work, as they can integrate nutritional, genetic, microbiotic, and management interventions within unified frameworks that capture dynamic interactions across farm components and the broader agroecosystem. Models such as the Ruminant Farm Systems (RuFaS) model and the Integrated Farm System Model (IFSM) exemplify this approach, enabling researchers to evaluate long-term outcomes, quantify trade-offs, assess greenhouse gas mitigation potential, analyze economic performance, and monitor animal welfare implications. However, current modeling capabilities remain limited in several respects. Models need better representation of stochasticity and uncertainty to reflect inherent biological and environmental variability. Multi-scale integration is needed to link molecular-level mechanisms with farm-level sustainability indicators. Biophysical models must be coupled with economic and social components to enable truly comprehensive assessments. User-friendly interfaces and transparent documentation are essential for practitioner adoption. Open-source development frameworks would facilitate collaborative refinement and validation across diverse contexts. Most critically, we need long-term, large-scale field studies on integrated farming systems to validate model predictions and refine our understanding of synergies and trade-offs under real-world conditions. Such studies should be supported by automated Life Cycle Assessment (LCA) methodologies and computational tools capable of capturing complex interactions among environmental, economic, and social outcomes over extended time horizons.

The rumen microbiome and animal genomics represent frontiers with immense potential but also formidable practical challenges. While ‘omics’ technologies have revolutionized our understanding of rumen ecology and host genetics, translating this knowledge into predictable, lasting improvements remains difficult. The rumen microbiome exhibits remarkable resilience, often returning to baseline states after perturbation, which limits the durability of interventions. Microbial responses to dietary and environmental manipulations vary considerably among animals, making it hard to predict outcomes reliably. The complex interactions within the host–microbiome–environment nexus (i.e., the “hologenome” concept) are still poorly understood, particularly regarding how host genetics shape microbial communities and how these communities influence traits like methane emissions and feed efficiency. Future research should prioritize strategies for achieving long-term microbiome modulation, including early-life programming approaches that might establish more stable beneficial communities. Practical, cost-effective delivery methods for microbiome-targeted interventions at commercial scale remain to be developed. On the genomics side, progress in selecting for environmental traits is constrained by inadequate phenotyping infrastructure. Measuring traits like methane emissions and nuanced feed efficiency at the scale needed for genomic selection programs remains expensive and logistically challenging. This bottleneck can only be addressed through substantial investment in innovative phenotyping technologies and international collaborations to build large, well-characterized reference populations. Multi-omics integration offers promise for deepening our understanding of the genetic architecture underlying environmental traits, but this work must proceed carefully to avoid unintended consequences. Selection for reduced emissions or improved efficiency could inadvertently compromise animal health, welfare, or genetic diversity if breeding objectives are not properly balanced. Integrating genomic data with whole-farm systems models would enable in silico evaluation of breeding strategies and their long-term sustainability implications, helping to identify optimal selection approaches before large-scale implementation.

Bridging the gap between scientific discovery and on-farm adoption remains perhaps the most critical challenge. Sophisticated measurement technologies for greenhouse gas emissions and nutrient flows have been developed, but most are too expensive, complex, or fragile for routine farm use. More affordable, robust, and user-friendly sensors and monitoring systems are needed that can provide actionable, real-time data under commercial conditions. Integrating data from diverse sources through advanced analytics and artificial intelligence could create comprehensive decision-support systems, but all such technologies must undergo rigorous LCA to ensure they do not simply shift environmental burdens from one impact category to another. Technology alone will not drive adoption, however. Successful implementation depends on understanding the behavioral, economic, and social factors that shape producer decisions. This requires deeper engagement with the behavioral science of agricultural decision-making, moving beyond the assumption that information provision leads to practice change. More effective knowledge transfer and extension models are needed, particularly co-innovation approaches where farmers are active partners in research and development rather than passive recipients of recommendations. Building adaptive management capacity among producers, supported by continuous learning networks and collaborative platforms, will be essential for long-term evolution of sustainable practices. Policy frameworks play a crucial enabling role by incentivizing sustainable practices, providing technical and financial assistance, and facilitating access to markets for environmentally differentiated products. Ultimately, progress toward sustainable ruminant production demands not only continued scientific innovation but also sustained commitment to transdisciplinary collaboration, participatory research approaches, and institutional arrangements that support the co-evolution of knowledge, technology, and practice.