Quantification of methane emitted by ruminants: A review of methods

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. Adhering to 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: studies reporting CH4 yield and its variability in dairy cows, employing specific experimental designs (Latin Square Design (LSD), 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, which included 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%). Utilizing an adjusted Cohen's f formula and a power analysis we calculated the sample sizes required for these reductions in balanced LSD and RCBD reports from studies involving 3 or 4 treatments. The results indicate that within-subject studies (i.e., LSD) 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 (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 backend calculations, this tool represents a significant 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.

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>0.1), however ASP1 reduced CH4 mmol/g DOM by 49% (P<0.01) relative to the control. Both ASC and ASP1 tended to increase TGP (P<0.1) relative to the control. In addition to this, the inclusion of seaweed in experiment 1 reduced the production of NH3-N (P<.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<0.01) but only ASP2 significantly reduced CH4 mmol/d, CH4 L/d and CH4 mmol/g DOM (P<0.05). Daily mMol butyrate was reduced by ASP2 relative to the control and most other seaweeds (P<.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).