Quantification of methane emitted by ruminants: a review of methods

Feed additives for methane mitigation: Recommendations for testing enteric methane-mitigating feed additives in ruminant studies

There is a need for rigorous and scientifically-based testing standards for existing and new enteric methane mitigation technologies, including antimethanogenic feed additives (AMFA). The current review provides guidelines for conducting and analyzing data from experiments with ruminants intended to test the antimethanogenic and production effects of feed additives. Recommendations include study design and statistical analysis of the data, dietary effects, associative effect of AMFA with other mitigation strategies, appropriate methods for measuring methane emissions, production and physiological responses to AMFA, and their effects on animal health and product quality. Animal experiments should be planned based on clear hypotheses, and experimental designs must be chosen to best answer the scientific questions asked, with pre-experimental power analysis and robust post-experimental statistical analyses being important requisites. Long-term studies for evaluating AMFA are currently lacking and are highly needed. Experimental conditions should be representative of the production system of interest, so results and conclusions are applicable and practical. Methane-mitigating effects of AMFA may be combined with other mitigation strategies to explore additivity and synergism, as well as trade-offs, including relevant manure emissions, and these need to be studied in appropriately designed experiments. Methane emissions can be successfully measured, and efficacy of AMFA determined, using respiration chambers, the sulfur hexafluoride method, and the GreenFeed system. Other techniques, such as hood and face masks, can also be used in short-term studies, ensuring they do not significantly affect feed intake, feeding behavior, and animal production. For the success of an AMFA, it is critically important that representative animal production data are collected, analyzed, and reported. In addition, evaluating the effects of AMFA on nutrient digestibility, animal physiology, animal health and reproduction, product quality, and how AMFA interact with nutrient composition of the diet is necessary and should be conducted at various stages of the evaluation process. The authors emphasize that enteric methane mitigation claims should not be made until the efficacy of AMFA is confirmed in animal studies designed and conducted considering the guidelines provided herein.

Feed additives for methane mitigation: Modeling the impact of feed additives on enteric methane emission of ruminants-Approaches and recommendations

Over the past decade, there has been considerable attention on mitigating enteric methane (CH4) emissions from ruminants through the utilization of antimethanogenic feed additives (AMFA). Administered in small quantities, these additives demonstrate potential for substantial reductions of methanogenesis. Mathematical models play a crucial role in comprehending and predicting the quantitative impact of AMFA on enteric CH4 emissions across diverse diets and production systems. This study provides a comprehensive overview of methodologies for modeling the impact of AMFA on enteric CH4 emissions in ruminants, culminating in a set of recommendations for modeling approaches to quantify the impact of AMFA on CH4 emissions. Key considerations encompass the type of models employed (i.e., empirical models including meta-analyses, machine learning models, and mechanistic models), the modeling objectives, data availability, modeling synergies and trade-offs associated with using AMFA, and model applications for enhanced understanding, prediction, and integration into higher levels of aggregation. Based on an evaluation of these critical aspects, a set of recommendations is presented concerning modeling approaches for quantifying the impact of AMFA on CH4 emissions and in support of farm-level, national, regional, and global inventories for accounting greenhouse gas emissions in ruminant production systems.

Feed additives for methane mitigation: Assessment of feed additives as a strategy to mitigate enteric methane from ruminants-Accounting; How to quantify the mitigating potential of using antimethanogenic feed additives

Recent advances in our understanding of methanogenesis have led to the development of antimethanogenic feed additives (AMFA) that can reduce enteric methane (CH4) emissions to varying extents, via direct targeting of methanogens, alternative electron acceptors, or altering the rumen environment. Here we examine current and new approaches used for the accounting (i.e., quantification) of enteric CH4 abatement by the use of AMFA in the livestock sector from the individual animal to the global scale. Along with this process, recommendations are provided on how to account for the mitigation potential at the animal level, as well as in farm-scale models, emissions trading schemes, life cycle assessment, and carbon (C) footprinting tools, and in regional and national inventories. In addition, an assessment of uncertainties and potential trade-offs and off-setting with the use of AMFA (i.e., efficacy vs. effectiveness, upstream and downstream emissions) is provided. The accounting of on-farm enteric CH4 emissions and benefits from the use of AMFA starts with the ruminant animal (with estimates obtained from a range of approaches, from simple empirical emission factors or equations to complex process-based models) and goes all the way to national and supranational accounting. The choice of methodologies and levels of complexity to account for mitigation of enteric CH4 (or total GHG) emissions in livestock systems must be tailored to the scale of analysis aimed, the availability of input data to represent contextualized conditions, and the accounting objectives (e.g., academic exercise vs. producer's GHG certification vs. national GHG inventory). The accounting of enteric CH4 mitigating effects needs to consider the AMFA delivery methods and synergies and trade-offs of GHG emissions at levels before and beyond (upstream and downstream) the animal to fully assess the impact of AMFA use. At large, the accounting of methane abatement by feed additives remains to be fully assessed beyond experimental results (efficacy) to address pragmatism (effectiveness), potential for adoption, and societal acceptance.

Systematic review for optimizing sample size in dairy cow methane emission studies in temperate regions: A comprehensive methodological approach

This research introduces a systematic framework for calculating sample size in studies focusing on enteric methane (CH4, g/kg of DMI) yield reduction in dairy cows. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search across the Web of Science, Scopus, and PubMed Central databases for studies published from 2012 to 2023. The inclusion criteria were as follows: studies reporting CH4 yield and its variability in dairy cows, employing specific experimental designs (Latin square design [LSqD], crossover design, randomized complete block design [RCBD], and repeated measures design) and measurement methods (open-circuit respirometry chambers [RC], the GreenFeed system, and the sulfur hexafluoride tracer technique), conducted in Canada, the United States, and Europe. A total of 150 studies, comprising 177 reports, met our criteria and were included in the database. Our methodology for using the database for sample size calculations began by defining 6 CH4 yield reduction levels (5%, 10%, 15%, 20%, 30%, and 50%). Using an adjusted Cohen's f formula and conducting power analysis, we calculated the sample sizes required for these reductions in balanced LSqD and RCBD reports from studies involving 3 or 4 treatments. The results indicate that within-subject studies (i.e., LSqD) require smaller sample sizes to detect CH4 yield reductions compared with between-subject studies (i.e., RCBD). Although experiments using RC typically require fewer individuals due to their higher accuracy, our results demonstrate that this expected advantage is not evident in reports from RCBD studies with 4 treatments. A key innovation of this research is the development of a web-based tool that simplifies the process of sample size calculation (https://samplesizecalculator.ucdavis.edu/). Developed using Python, this tool leverages the extensive database to provide tailored sample size recommendations for specific experimental scenarios. It ensures that experiments are adequately powered to detect meaningful differences in CH4 emissions, thereby contributing to the scientific rigor of studies in this critical area of environmental and agricultural research. With its user-friendly interface and robust back-end calculations, this tool represents an important advancement in the methodology for planning and executing CH4 emission studies in dairy cows, aligning with global efforts toward sustainable agricultural practices and environmental conservation.

Effect of High-Tannin and -Polyphenol Plant Material Supplement on Rumen Fermentation, Nitrogen Partitioning and Nutrient Utilization in Beef Cattle

The current issue of ruminant methane emissions is still being researched by animal nutritionists in an effort to find new approaches. In this study, five beef cattle were randomly assigned in a 5 × 5 Latin square design to examine the effects of supplementation with high-tannin and -polyphenol plant materials on nutrient utilization, rumen fermentation, and nitrogen partitioning. Cattle offered total mixed ration (TMR) silage diets with or without tannin-rich tree leaf or plant herbs, such as Piper sarmentosum Roxb., Cymbopogon citratus (DC.) Stapf, Anacardium occidentale L., and Careya arborea Roxb., were supplemented at a dose of 10 g/d. Prior to TMR feeding, the animals' meals were supplemented with 10 g of fortified plant materials twice a day, along with 100 g of rice bran. The animals in the control group received only 100 g of rice bran and no other plant materials. The result showed that there was no difference in nutrient intake or digestibility between the supplemented and control groups. Although the effect of ruminal pH, NH3-N, Total VFA, acetate (C2), and butyrate (C4) was not significant (p > 0.05), the proportion of propionate (C3) tended to increase with supplementation (p = 0.07). There was no difference in the excretion of purine derivatives or the amount of microbial nitrogen supply, even though supplemented animals had significantly lower protozoal populations than the control group (p < 0.05). Moreover, when A. occidentale or C. arborea was added to the TMR silage diet, the nitrogen intake and retention increased considerably, although total nitrogen excretion decreased. In this approach, the leaves of Anacardium occidentale L. and Careya arborea Roxb. were particularly promising for strategic supplementation.

Implications of seasonal and daily variation on methane and ammonia emissions from naturally ventilated dairy cattle barns in a Mediterranean climate: A two-year study

Seasonal and daily variations of gaseous emissions from naturally ventilated dairy cattle barns are important figures for the establishment of effective and specific mitigation plans. The present study aimed to measure methane (CH4) and ammonia (NH3) emissions in three naturally ventilated dairy cattle barns covering the four seasons for two consecutive years. In each barn, air samples from five indoor locations were drawn by a multipoint sampler to a photoacoustic infrared multigas monitor, along with temperature and relative humidity. Milk production data were also recorded. Results showed seasonal differences for CH4 and NH3 emissions in the three barns with no clear trends within years. Globally, diel CH4 emissions increased in the daytime with high intra-hour variability. The average hourly CH4 emissions (g h-1 livestock unit-1 (LU)) varied from 8.1 to 11.2 and 6.2 to 20.3 in the dairy barn 1, from 10.1 to 31.4 and 10.9 to 22.8 in the dairy barn 2, and from 1.5 to 8.2 and 13.1 to 22.1 in the dairy barn 3, respectively, in years 1 and 2. Diel NH3 emissions highly varied within hours and increased in the daytime. The average hourly NH3 emissions (g h-1 LU-1) varied from 0.78 to 1.56 and 0.50 to 1.38 in the dairy barn 1, from 1.04 to 3.40 and 0.93 to 1.98 in the dairy barn 2, and from 0.66 to 1.32 and 1.67 to 1.73 in the dairy barn 3, respectively, in years 1 and 2. Moreover, the emission factors of CH4 and NH3 were 309.5 and 30.6 (g day-1 LU-1), respectively, for naturally ventilated dairy cattle barns. Overall, this study provided a detailed characterization of seasonal and daily gaseous emissions variations highlighting the need for future longitudinal emission studies and identifying an opportunity to better adequate the existing mitigation strategies according to season and daytime.

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

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

Evaluating GreenFeed and respiration chambers for daily and intraday measurements of enteric gaseous exchange in dairy cows housed in tie-stalls

The objective of this study was to evaluate the GreenFeed (GF) and respiration chambers (RC) for daily and intraday measurements of the enteric gaseous exchange, as well as the metabolic heat production, lying behavior, and feed intake (FI) rate of dairy cows at these 2 respective housing conditions [tie-stall barn (TS) vs. RC] during the summer periods. Sixteen multiparous lactating dairy cows were recruited and arranged in a randomized complete block design with a baseline period established for each cow. Cows were given a basal diet (CON) for a baseline period of 7 d and were then fed a 3-nitrooxypropanol (3-NOP)-containing feed for the subsequent 26 d as experimental period. During both the baseline and the last 7 d of treatment period, gaseous exchanges of each animal were measured in the TS using GF for 8 6-hourly staggered measurements over 3 d, immediately followed by the measurement in RC for 2 d. Corresponding DMI, milk yield, and behavior parameters (e.g., lying behavior and FI rate) in TS and RC were recorded. The correlation coefficients of CH4 and H2 using raw data were 0.84 and 0.85, respectively. For all gases, correlation coefficients between GF and RC on individual cow level decreased when the marginal fixed effects (e.g., inhibitor and breed) were corrected by a mixed model. There were no differences in daily CH4 production or intensity between GF and RC (442 vs. 443 g CH4/d or 16.6 vs. 16.2 g CH4 /kg MY). However, greater CH4 yield was measured by GF than RC (19.0 vs. 17.8 g CH4/kg DMI), driven by a lower DMI (23.3 vs. 24.6 kg/d) when cows were housed in TS sampled by GF compared with cows being housed and sampled in RC. The correlations for CO2 production and O2 consumption were moderate and expected due to the variation associated with the mild heat stress condition during GF measurements in the TS (Thermal humidity index (THI) 56 vs. 68), as indicated by the reduced lying time (-2.1 h/d). At the intraday level, there was an interaction between techniques and hour-of-day for CH4 production, as indicated by the discrepancies in post-prandial CH4 emissions between techniques. In summary, this set of results showed that there were strong positive correlations for CH4 and H2 emissions between GF and RC based on individual cow data. However, such relationship should be interpreted with caution, given the data clustering resulting from the use of inhibitor 3-NOP. On treatment level, these 2 techniques detected similar inhibitor effect on the estimated daily CH4 emissions. The intraday patterns of CH4 and H2 production captured by GF provided a close approximation for those measured by RC. Nevertheless, potential underestimation may occur, especially following fresh feed delivery. For measuring CO2 production and O2 consumption, the GF captured similar intraday variations to those in the RC. However, the estimated daily production and consumption were not directly comparable, which was expected due to the variable thermal conditions during the summer. Further evaluations under the same weather conditions are warranted.

Editing microbes to mitigate enteric methane emissions in livestock

Livestock production significantly contributes to greenhouse gas (GHG) emissions particularly methane (CH4) emissions thereby influencing climate change. To address this issue further, it is crucial to establish strategies that simultaneously increase ruminant productivity while minimizing GHG emissions, particularly from cattle, sheep, and goats. Recent advancements have revealed the potential for modulating the rumen microbial ecosystem through genetic selection to reduce methane (CH4) production, and by microbial genome editing including CRISPR/Cas9, TALENs (Transcription Activator-Like Effector Nucleases), ZFNs (Zinc Finger Nucleases), RNA interference (RNAi), Pime editing, Base editing and double-stranded break-free (DSB-free). These technologies enable precise genetic modifications, offering opportunities to enhance traits that reduce environmental impact and optimize metabolic pathways. Additionally, various nutrition-related measures have shown promise in mitigating methane emissions to varying extents. This review aims to present a future-oriented viewpoint on reducing methane emissions from ruminants by leveraging CRISPR/Cas9 technology to engineer the microbial consortia within the rumen. The ultimate objective is to develop sustainable livestock production methods that effectively decrease methane emissions, while maintaining animal health and productivity.

Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11-17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments.

Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra

Predicting methane (CH4) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R2v = 0.22-0.36, RMSE = 57-70 g/d). Equations using CH4 averages that had been pre-corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre-correcting CH4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population.

City-level livestock methane emissions in China from 2010 to 2020

Livestock constitute the world's largest anthropogenic source of methane (CH4), providing high-protein food to humans but also causing notable climate risks. With rapid urbanization and increasing income levels in China, the livestock sector will face even higher emission pressures, which could jeopardize China's carbon neutrality target. To formulate targeted methane reduction measures, it is crucial to estimate historical and current emissions on fine geographical scales, considering the high spatial heterogeneity and temporal variability of livestock emissions. However, there is currently a lack of time-series data on city-level livestock methane emissions in China, despite the flourishing livestock industry and large amount of meat consumed. In this study, we constructed a city-level livestock methane emission inventory with dynamic spatial-temporal emission factors considering biological, management, and environmental factors from 2010 to 2020 in China. This inventory could serve as a basic database for related research and future methane mitigation policy formulation, given the population boom and dietary changes.

Exploring indicators of genetic selection using the sniffer method to reduce methane emissions from Holstein cows

OBJECTIVE

This study aimed to evaluate whether the methane (CH4) to carbon dioxide (CO2) ratio (CH4/CO2) and methane-related traits obtained by the sniffer method can be used as indicators for genetic selection of Holstein cows with lower CH4 emissions.

METHODS

The sniffer method was used to simultaneously measure the concentrations of CH4 and CO2 during milking in each milking box of the automatic milking system to obtain CH4/CO2. Methane-related traits, which included CH4 emissions, CH4 per energy-corrected milk, methane conversion factor (MCF), and residual CH4, were calculated. First, we investigated the impact of the model with and without body weight (BW) on the lactation stage and parity for predicting methane-related traits using a first on-farm dataset (Farm 1; 400 records for 74 Holstein cows). Second, we estimated the genetic parameters for CH4/CO2 and methane-related traits using a second on-farm dataset (Farm 2; 520 records for 182 Holstein cows). Third, we compared the repeatability and environmental effects on these traits in both farm datasets.

RESULTS

The data from Farm 1 revealed that MCF can be reliably evaluated during the lactation stage and parity, even when BW is excluded from the model. Farm 2 data revealed low heritability and moderate repeatability for CH4/CO2 (0.12 and 0.46, respectively) and MCF (0.13 and 0.38, respectively). In addition, the estimated genetic correlation of milk yield with CH4/CO2 was low (0.07) and that with MCF was moderate (-0.53). The on-farm data indicated that CH4/CO2 and MCF could be evaluated consistently during the lactation stage and parity with moderate repeatability on both farms.

CONCLUSION

This study demonstrated the on-farm applicability of the sniffer method for selecting cows with low CH4 emissions.

Advances in Methane Emission Estimation in Livestock: A Review of Data Collection Methods, Model Development and the Role of AI Technologies

This review examines the significant role of methane emissions in the livestock industry, with a focus on cattle and their substantial impact on climate change. It highlights the importance of accurate measurement and management techniques for methane, a potent greenhouse gas accounting for 14-16% of global emissions. The study evaluates both conventional and AI-driven methods for detecting methane emissions from livestock, particularly emphasizing cattle contributions, and the need for region-specific formulas. Sections cover livestock methane emissions, the potential of AI technology, data collection issues, methane's significance in carbon credit schemes, and current research and innovation. The review emphasizes the critical role of accurate measurement and estimation methods for effective climate change mitigation and reducing methane emissions from livestock operations. Overall, it provides a comprehensive overview of methane emissions in the livestock industry by synthesizing existing research and literature, aiming to improve knowledge and methods for mitigating climate change. Livestock-generated methane, especially from cattle, is highlighted as a crucial factor in climate change, and the review underscores the importance of integrating precise measurement and estimation techniques for effective mitigation.

Review: The prevailing mathematical modeling classifications and paradigms to support the advancement of sustainable animal production

Mathematical modeling is typically framed as the art of reductionism of scientific knowledge into an arithmetical layout. However, most untrained people get the art of modeling wrong and end up neglecting it because modeling is not simply about writing equations and generating numbers through simulations. Models tell not only about a story; they are spoken to by the circumstances under which they are envisioned. They guide apprentice and experienced modelers to build better models by preventing known pitfalls and invalid assumptions in the virtual world and, most importantly, learn from them through simulation and identify gaps in pushing scientific knowledge further. The power of the human mind is well-documented for idealizing concepts and creating virtual reality models, and as our hypotheses grow more complicated and more complex data become available, modeling earns more noticeable footing in biological sciences. The fundamental modeling paradigms include discrete-events, dynamic systems, agent-based (AB), and system dynamics (SD). The source of knowledge is the most critical step in the model-building process regardless of the paradigm, and the necessary expertise includes (a) clear and concise mental concepts acquired through different ways that provide the fundamental structure and expected behaviors of the model and (b) numerical data necessary for statistical analysis, not for building the model. The unreasonable effectiveness of models to grow scientific learning and knowledge in sciences arise because different researchers would model the same problem differently, given their knowledge and experiential background, leading to choosing different variables and model structures. Secondly, different researchers might use different paradigms and even unalike mathematics to resolve the same problem; thus, model needs are intrinsic to their perceived assumptions and structures. Thirdly, models evolve as the scientific community knowledge accumulates and matures over time, hopefully resulting in improved modeling efforts; thus, the perfect model is fictional. Some paradigms are most appropriate for macro, high abstraction with less detailed-oriented scenarios, while others are most suitable for micro, low abstraction with higher detailed-oriented strategies. Modern hybridization aggregating artificial intelligence (AI) to mathematical models can become the next technological wave in modeling. AI can be an integral part of the SD/AB models and, before long, write the model code by itself. Success and failures in model building are more related to the ability of the researcher to interpret the data and understand the underlying principles and mechanisms to formulate the correct relationship among variables rather than profound mathematical knowledge.

Review: Harnessing extant energy and protein requirement modeling for sustainable beef production

Numerous mathematical nutrition models have been developed in the last sixty years to predict the dietary supply and requirement of farm animals' energy and protein. Although these models, usually developed by different groups, share similar concepts and data, their calculation routines (i.e., submodels) have rarely been combined into generalized models. This lack of mixing submodels is partly because different models have different attributes, including paradigms, structural decisions, inputs/outputs, and parameterization processes that could render them incompatible for merging. Another reason is that predictability might increase due to offsetting errors that cannot be thoroughly studied. Alternatively, combining concepts might be more accessible and safer than combining models' calculation routines because concepts can be incorporated into existing models without changing the modeling structure and calculation logic, though additional inputs might be needed. Instead of developing new models, improving the merging of extant models' concepts might curtail the time and effort needed to develop models capable of evaluating aspects of sustainability. Two areas of beef production research that are needed to ensure adequate diet formulation include accurate energy requirements of grazing animals (decrease methane emissions) and efficiency of energy use (reduce carcass waste and resource use) by growing cattle. A revised model for energy expenditure of grazing animals was proposed to incorporate the energy needed for physical activity, as the British feeding system recommended, and eating and rumination (HjEer) into the total energy requirement. Unfortunately, the proposed equation can only be solved iteratively through optimization because HjEer requires metabolizable energy (ME) intake. The other revised model expanded an existing model to estimate the partial efficiency of using ME for growth (kg) from protein proportion in the retained energy by including an animal degree of maturity and average daily gain (ADG) as used in the Australian feeding system. The revised kg model uses carcass composition, and it is less dependent on dietary ME content, but still requires an accurate assessment of the degree of maturity and ADG, which in turn depends on the kg. Therefore, it needs to be solved iteratively or using one-step delayed continuous calculation (i.e., use the previous day's ADG to compute the current day's kg). We believe that generalized models developed by merging different models' concepts might improve our understanding of the relationships of existing variables that were known for their importance but not included in extant models because of the lack of proper information or confidence at that time.

Factors Affecting Enteric Emission Methane and Predictive Models for Dairy Cows

Enteric methane emission is the main source of greenhouse gas contribution from dairy cattle. Therefore, it is essential to evaluate drivers and develop more accurate predictive models for such emissions. In this study, we built a large and intercontinental experimental dataset to: (1) explain the effect of enteric methane emission yield (g methane/kg diet intake) and feed conversion (kg diet intake/kg milk yield) on enteric methane emission intensity (g methane/kg milk yield); (2) develop six models for predicting enteric methane emissions (g/cow/day) using animal, diet, and dry matter intake as inputs; and to (3) compare these 6 models with 43 models from the literature. Feed conversion contributed more to enteric methane emission (EME) intensity than EME yield. Increasing the milk yield reduced EME intensity, due more to feed conversion enhancement rather than EME yield. Our models predicted methane emissions better than most external models, with the exception of only two other models which had similar adequacy. Improved productivity of dairy cows reduces emission intensity by enhancing feed conversion. Improvement in feed conversion should be prioritized for reducing methane emissions in dairy cattle systems.

Evaluation of a Model (RUMINANT) for Prediction of DMI and CH4 from Tropical Beef Cattle

Simulation models represent a low-cost approach to evaluating agricultural systems. In the current study, the precision and accuracy of the RUMINANT model to predict dry matter intake (DMI) and methane emissions from beef cattle fed tropical diets (characteristic of Colombia) was assessed. Feed intake (DMI) and methane emissions were measured in Brahman steers housed in polytunnels and fed six forage diets. In addition, DMI and methane emissions were predicted by the RUMINANT model. The model's predictive capability was measured on the basis of precision: coefficients of variation (CV%) and determination (R², percentage of variance accounted for by the model), and model efficiency (ME) and accuracy: the simulated/observed ratio (S/O ratio) and slope and mean bias (MB%). In addition, combined measurements of accuracy and precision were carried out by means of mean square prediction error (MSPE) and correlation correspondence coefficient (CCC) and their components. The predictive capability of the RUMINANT model to simulate DMI resulted as valuable for mean S/O ratio (1.07), MB% (2.23%), CV% (17%), R² (0.886), ME (0.809), CCC (0.869). However, for methane emission simulations, the model substantially underestimated methane emissions (mean S/O ratio = 0.697, MB% = -30.5%), and ME and CCC were -0.431 and 0.485, respectively. In addition, a subset of data corresponding to diets with Leucaena was not observed to have a linear relationship between the observed and simulated values. It is suggested that this may be related to anti-methanogenic factors characteristic of Leucaena, which were not accounted for by the model. This study contributes to improving national inventories of greenhouse gases from the livestock of tropical countries.

Effect of brown and green seaweeds on diet digestibility, ruminal fermentation patterns and enteric methane emissions using the rumen simulation technique

Inclusion of the red seaweed Asparagopsis taxiformis as a feed additive, has led to significant reductions in methane (CH4) production from ruminants. However, dietary supplementation with this seaweed is negatively associated with health and environmental concerns mainly due to its bromoform content, a compound with potential carcinogenic properties. Thus, there is renewed focus on ascertaining the anti-methanogenic potential of locally grown brown and green seaweeds, which typically do not contain bromoform. The objective of this study was to investigate the effects of selected brown and green seaweeds on diet digestibility, ruminal fermentation patterns, total gas (TGP) and CH4 production in vitro, using the rumen simulation technique system. In experiment 1, Pelvetia canaliculata (PEC) was examined. In experiment 2, Cystoseira tamariscifolia (CYT), Bifurcaria bifurcata (BIB), Fucus vesiculosus (FUV), Himanthalia elongata (HIM) and Ulva intestinalis (ULI) were analysed. Ascophyllum nodosum (ASC) was included in both experiments. A diet containing A. taxiformis (ASP1; ASP2) and an unsupplemented diet (CON) were included as positive and negative controls, respectively in both experiments. All seaweeds were included at a rate of 10 g/kg dry matter (DM) into a control diet of 50:50 (w:w) forage:concentrate. The seven brown and green seaweeds assessed failed to affect absolute CH4 emissions or alter fermentation patterns. In experiment 1, seaweed treatment had no effect on diet digestibility, CH4%, CH4 mmol/d or CH4 L/d (P&gt;0.1), however ASP1 reduced CH4 mmol/g DOM by 49% (P&lt;0.01) relative to the control. Both ASC and ASP1 tended to increase TGP (P&lt;0.1) relative to the control. In addition to this, the inclusion of seaweed in experiment 1 reduced the production of NH3-N (P&lt;.0001) compared to the control. In experiment 2, seaweed treatment had no effect on diet digestibility or TGP. Both ASP2 and FUV reduced CH4% (P&lt;0.01) but only ASP2 significantly reduced CH4 mmol/d, CH4 L/d and CH4 mmol/g DOM (P&lt;0.05). Daily mMol butyrate was reduced by ASP2 relative to the control and most other seaweeds (P&lt;.0001). In both experiment 1 and 2, seaweed inclusion had no effect on daily total VFA, acetate or propionate production or the acetate:propionate ratio relative to the control. To conclude, including the bromoform-free brown and green seaweeds at 10g/kg DM has no negative effects on diet digestibility or fermentation patterns but also failed to reduce the production of enteric CH4in vitro.

Relationship between Chemical Composition and In Vitro Methane Production of High Andean Grasses

Simple Summary

High Andean grasses have phenological cycles that are influenced by the season of the year (rainy and dry), which could affect their nutritional chemical composition and methane production. Based on this, the in vitro digestibility technique was used to measure this effect. The results of this study show that there is an effect of the chemical composition on methane production and that it changes depending on the season of the year.

Abstract

The present study aims to establish the relationship between chemical composition and in vitro methane (CH₄) production of high Andean grasses. For this purpose, eight species were collected in dry and rainy seasons: Alchemilla pinnata, Distichia muscoides, Carex ecuadorica, Hipochoeris taraxacoides, Mulhenbergia fastigiata, Mulhenbergia peruviana, Stipa brachiphylla and Stipa mucronata. They were chemically analyzed and incubated under an in vitro system. Species such as A. pinnata and H. taraxacoides were characterized by high crude protein (CP. 124 g/kg DM) and low neutral detergent fiber (NDF. 293 g/kg DM) contents in both seasons, contrary to Stipa grasses. This same pattern was obtained for H. taraxacoides, which presented the highest values of gas production, organic matter digestibility (DOM), metabolizable energy (ME) and CH₄ production (241 mL/g DM, 59% DOM, 8.4 MJ ME/kg DM and 37.7 mL CH₄/g DM, on average). For most species, the content of CP, acid detergent fiber (FDA) and ME was higher in the rainy season than in the dry season, which was the opposite for CH₄ production (p ≥ 0.05). In general, the nutritional content that most explained the behavior of CH₄ production was the NDF content (R² = 0.69). Grasses characterized by high NDF content produced less CH₄ (R = −0.85).