Models to predict nitrogen excretion from beef cattle fed a wide range of diets compiled from South America.

The objective of this meta-analysis was to develop and evaluate models for predicting nitrogen (N) excretion in feces, urine, and manure in beef cattle in South America. The study incorporated a total of 1,116 individual observations of N excretion in feces and 939 individual observations of N excretion in feces and in urine (g/d), representing a diverse range of diets, animal genotypes, and management conditions in South America. The dataset also included data on dry matter intake (DMI; kg/d) and nitrogen intake (NI; g/d), concentrations of dietary components, as well as average daily gain (ADG; g/d) and average body weight (BW; kg). Models were derived using linear mixed-effects regression with a random intercept for the study. Fecal N excretion was positively associated with DMI, NI, nonfibrous carbohydrates, average BW, and ADG and negatively associated with EE and CP concentration in the diet. The univariate model predicting fecal N excretion based on DMI (model 1) performed slightly better than the univariate model, which used NI as a predictor variable (model 2) with a root mean square error (RMSE) of 38.0 vs. 39.2%, the RMSE-observations SD ratio (RSR) of 0.81 vs. 0.84, and concordance correlation coefficient (CCC) of 0.53 vs. 0.50, respectively. Models predicting urinary N excretion were less accurate than those derived to predict fecal N excretion, with an average RMSE of 43.7% vs. 37.0%, respectively. Urinary and manure N excretion were positively associated with DMI, NI, CP, average BW, and ADG and negatively associated with neutral detergent fiber concentration in the diet. As opposed to fecal N excretion, the univariate model predicting urinary N excretion using NI (model 10) performed slightly better than the univariate model using DMI (model 9) as predictor variable with an RMSE of 36.0% vs. 39.7%, RSR 0.85 vs. 0.93, and CCC of 0.43 vs. 0.29, respectively. The models developed in this study are applicable for predicting N excretion in beef cattle across a broad spectrum of dietary compositions and animal genotypes in South America. The univariate model using DMI as a predictor is recommended for fecal N prediction, while the univariate model using NI is recommended for predicting urinary and manure N excretion because the use of more complex models resulted in little to no benefits. However, it may be more useful to consider more complex models that incorporate nutrient intakes and diet composition for decision-making when N excretion is a factor to be considered. Three extant equations evaluated in this study have the potential to be used in tropical conditions typical of South America to predict fecal N excretion with good precision and accuracy. However, none of the extant equations are recommended for predicting urine or manure N excretion because of their high RMSE, and low precision and accuracy.

Alterations of rumen and fecal microbiome in growing beef and dairy steers fed rumen-protected Capsicum oleoresin.

The microbiome has been linked to animal health and productivity, and thus, modulating animal microbiomes is becoming of increasing interest. Antimicrobial growth promoters (AGP) were once a common technology used to modulate the microbiome, but regulation and consumer pressure have decreased AGP use in food animals. One alternative to antimicrobial growth promoters are phytotherapeutics, compounds derived from plants. Capsaicin is a compound from the Capsicum genus, which includes chili peppers. Capsaicin has antimicrobial properties and could be used to manipulate the gastrointestinal microbiome of cattle. Both the rumen and fecal microbiomes are essential to cattle health and production, and modulation of either microbiome can affect both cattle health and productivity. We hypothesized that the addition of rumen-protected capsaicin to the diet of cattle would alter the composition of the fecal microbiome, but not the rumen microbiome. To determine the impact of rumen-protected capsaicin in cattle, four Holstein and four Angus steers were fed rumen-protected Capsicum oleoresin at 0 (Control), 5, 10, or 15 mg kg-1 diet dry matter. Cattle were fed in treatment groups in a 4 × 4 Latin Square design with a 21-d adaptation phase and a 7-d sample collection phase. Rumen samples were collected on day 22 at 0-, 2-, 6-, 12-, and 18-h post-feeding, and fecal swabs were collected on the last day of sample collection, day 28, within 1 h of feeding. Sequencing data of the 16s rRNA gene was analyzed using the dada2 pipeline and taxa were assigned using the SILVA database. No differences were observed in alpha diversity among fecal or rumen samples for either breed (P > 0.08) and no difference between groups was detected for either breed in rumen samples or for Angus steers in fecal samples (P > 0.42). There was a difference in beta diversity between treatments in fecal samples of Holstein steers (P < 0.01), however, a pairwise comparison of the treatment groups suggests no difference between treatments after adjusting for multiple comparisons. Therefore, we were unable to observe substantial overall variation in the rumen or fecal microbiomes of steers due to increasing concentrations of rumen-protected capsaicin. We do, however, see a trend toward increased concentrations of capsaicin influencing the fecal microbiome structure of Holstein steers despite this lack of significance.

The microbiome is the collection of microbes present in an animal's body and has been discovered to be directly connected to animal health and productivity. In production animals, such as feedlot cattle, the microbiome can be modulated by antimicrobials to promote growth, but increasing consumer pressure to reduce antimicrobial use has producers seeking alternatives. Capsaicin is a phytotherapeutic derived from chili peppers that can be used to modulate the microbiome due to its antimicrobial properties. Eight steers were fed rumen-protected Capsicum oleoresin to determine its effect on average daily gain. In addition, rumen and fecal samples were collected for microbiome testing. No differences were detected in the rumen microbiomes between cattle fed capsaicin (treatment) or those that received no capsaicin (control). While no overall effect was observed on the fecal microbiome of cattle fed different doses of capsaicin or control, we did observe changes in fecal beta diversity due to capsaicin treatment in Holstein steers fed greater doses. The fecal microbiome structure of Holsteins fed greater dosages of capsaicin differed from those fed control or low doses, as observed by the presence of two distinct clusters. This observation suggests an impact of greater doses of capsaicin treatment on microbiome structure.

Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy.

Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.

A global dataset of enteric methane mitigation experiments with lactating and non-lactating dairy cows conducted from 1963 to 2022.

A dataset of descriptive information was compiled from 213 peer-reviewed scientific publications that focused on dairy cow experiments and measured enteric methane emissions. This dataset was primarily based on the bibliography used by Arndt et al. (2022), with the addition of studies conducted from 2019 to 2022. The articles were identified for inclusion in the dataset using the "Web of Science Core Collection" database, using various combinations of search terms related to methane, dairy, cattle, rumen, ruminant, energy balance, energy metabolism, energy partitioning, and enteric emissions. For inclusion in the dataset, studies had to be written in English and provide information on enteric methane emission, as well as report feed dry matter intake along with measures of variance. Both continuous and crossover design studies were included, resulting in a comprehensive dataset with 797 records (rows) and 162 variables (columns). The variables cover various aspects such as publication information, experimental design, animal description, methane measurement method, and diet nutrient composition. Additionally, when available, the dataset includes treatment means and measures of variance for feed dry matter intake, rumen fermentation parameters, nutrient digestibility, nitrogen excretion, milk yield, milk components, as well as enteric methane, carbon dioxide, and hydrogen emissions. Researchers can use this dataset to assess the effectiveness of different enteric methane mitigation strategies and their impact on milk yield and other essential dairy cow nutrition and performance variables. Furthermore, it offers the opportunity to explore potential interactions between nutrients and feed additives.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries.

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 °C target by 2030 but not 2050.

To meet the 1.5 °C target, methane (CH4) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute (ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies­namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio­decreased CH4 per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies­namely CH4 inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds­decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH4 emissions.

China's low-emission pathways toward climate-neutral livestock production for animal-derived foods.

Animal-derived food production accounts for one-third of global anthropogenic greenhouse gas (GHG) emissions. Diet followed in China is ranked as low-carbon emitting (i.e., 0.21 t CO2-eq per capita in 2018, ranking at 145th of 168 countries) due to the low average animal-derived food consumption rate, and preferential consumption of animal-derived foods with lower GHG emissions (i.e., pork and eggs versus beef and milk). However, the projected increase in GHG emissions from livestock production poses great challenges for achieving China's "carbon neutrality" pledge. We propose that the livestock sector in China may achieve "climate neutrality" with net-zero warming around 2050 by implementing healthy diet and mitigation strategies to control enteric methane emissions.

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database.

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d-1) and yield [g kg-1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.

Key Considerations for the Use of Seaweed to Reduce Enteric Methane Emissions From Cattle.

Enteric methane emissions are the single largest source of direct greenhouse gas emissions (GHG) in beef and dairy value chains and a substantial contributor to anthropogenic methane emissions globally. In late 2019, the World Wildlife Fund (WWF), the Advanced Research Projects Agency-Energy (ARPA-E) and the Foundation for Food and Agriculture Research (FFAR) convened approximately 50 stakeholders representing research and production of seaweeds, animal feeds, dairy cattle, and beef and dairy foods to discuss challenges and opportunities associated with the use of seaweed-based ingredients to reduce enteric methane emissions. This Perspective article describes the considerations identified by the workshop participants and suggests next steps for the further development and evaluation of seaweed-based feed ingredients as enteric methane mitigants. Although numerous compounds derived from sources other than seaweed have been identified as having enteric methane mitigation potential, these mitigants are outside the scope of this article.

The value of manure - Manure as co-product in life cycle assessment.

Livestock production is important for food security, nutrition, and landscape maintenance, but it is associated with several environmental impacts. To assess the risk and benefits arising from livestock production, transparent and robust indicators are required, such as those offered by life cycle assessment. A central question in such approaches is how environmental burden is allocated to livestock products and to manure that is re-used for agricultural production. To incentivize sustainable use of manure, it should be considered as a co-product as long as it is not disposed of, or wasted, or applied in excess of crop nutrient needs, in which case it should be treated as a waste. This paper proposes a theoretical approach to define nutrient requirements based on nutrient response curves to economic and physical optima and a pragmatic approach based on crop nutrient yield adjusted for nutrient losses to atmosphere and water. Allocation of environmental burden to manure and other livestock products is then based on the nutrient value from manure for crop production using the price of fertilizer nutrients. We illustrate and discuss the proposed method with two case studies.

Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.

Enteric methane (CH4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

Discrepancies and Uncertainties in Bottom-up Gridded Inventories of Livestock Methane Emissions for the Contiguous United States.

In this analysis we used a spatially explicit, simplified bottom-up approach, based on animal inventories, feed dry matter intake, and feed intake-based emission factors to estimate county-level enteric methane emissions for cattle and manure methane emissions for cattle, swine, and poultry for the contiguous United States. Overall, this analysis yielded total livestock methane emissions (8916 Gg/yr; lower and upper 95% confidence bounds of ±19.3%) for 2012 (last census of agriculture) that are comparable to the current USEPA estimates for 2012 and to estimates from the global gridded Emission Database for Global Atmospheric Research (EDGAR) inventory. However, the spatial distribution of emissions developed in this analysis differed significantly from that of EDGAR and a recent gridded inventory based on USEPA. Combined enteric and manure methane emissions from livestock in Texas and California (highest contributors to the national total) in this study were 36% lesser and 100% greater, respectively, than estimates by EDGAR. The spatial distribution of emissions in gridded inventories (e.g., EDGAR) likely strongly impacts the conclusions of top-down approaches that use them, especially in the source attribution of resulting (posterior) emissions, and hence conclusions from such studies should be interpreted with caution.

Short communication: Analytical method and amount of preservative added to milk samples may alter milk urea nitrogen measurements.

Milk urea N (MUN) is used by dairy nutritionists and producers to monitor dietary protein intake and is indicative of N utilization in lactating dairy cows. Two experiments were conducted to explore discrepancies in MUN results provided by 3 milk processing laboratories using different methods. An additional experiment was conducted to evaluate the effect of 2-bromo-2-nitropropane-1, 3-diol (bronopol) on MUN analysis. In experiment 1, 10 replicates of bulk tank milk samples, collected from the Pennsylvania State University's Dairy Center over 5 consecutive days, were sent to 3 milk processing laboratories in Pennsylvania. Average MUN differed between laboratory A (14.9 ± 0.40 mg/dL; analyzed on MilkoScan 4000; Foss, Hillerød, Denmark), laboratory B (6.5 ± 0.17 mg/dL; MilkoScan FT + 6000), and laboratory C (7.4 ± 0.36 mg/dL; MilkoScan 6000). In experiment 2, milk samples were spiked with urea at 0 (7.3 to 15.0 mg/dL, depending on the laboratory analyzing the samples), 17.2, 34.2, and 51.5 mg/dL of milk. Two 35-mL samples from each urea level were sent to the 3 laboratories used in experiment 1. Average analyzed MUN was greater than predicted (calculated for each laboratory based on the control; 0 mg of added urea): for laboratory A (23.2 vs. 21.0 mg/dL), laboratory B (18.0 vs. 13.3 mg/dL), and laboratory C (20.6 vs. 15.2 mg/dL). In experiment 3, replicated milk samples were preserved with 0 to 1.35 mg of bronopol/mL of milk and submitted to one milk processing laboratory that analyzed MUN using 2 different methods. Milk samples with increasing amounts of bronopol ranged in MUN concentration from 7.7 to 11.9 mg/dL and from 9.0 to 9.3 mg/dL when analyzed on MilkoScan 4000 or CL 10 (EuroChem, Moscow, Russia), respectively. In conclusion, measured MUN concentrations varied due to analytical procedure used by milk processing laboratories and were affected by the amount of bronopol used to preserve milk sample, when milk was analyzed using a mid-infrared analyzer. Thus, it is important to maintain consistency in milk sample preservation and analysis to ensure precision of MUN results.

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals.

Ruminant animals (domesticated or wild) emit methane (CH4) through enteric fermentation in their digestive tract and from decomposition of manure during storage. These processes are the major sources of greenhouse gas (GHG) emissions from animal production systems. Techniques for measuring enteric CH4 vary from direct measurements (respiration chambers, which are highly accurate, but with limited applicability) to various indirect methods (sniffers, laser technology, which are practical, but with variable accuracy). The sulfur hexafluoride (SF6) tracer gas method is commonly used to measure enteric CH4 production by animal scientists and more recently, application of an Automated Head-Chamber System (AHCS) (GreenFeed, C-Lock, Inc., Rapid City, SD), which is the focus of this experiment, has been growing. AHCS is an automated system to monitor CH4 and carbon dioxide (CO2) mass fluxes from the breath of ruminant animals. In a typical AHCS operation, small quantities of baiting feed are dispensed to individual animals to lure them to AHCS multiple times daily. As the animal visits AHCS, a fan system pulls air past the animal's muzzle into an intake manifold, and through an air collection pipe where continuous airflow rates are measured. A sub-sample of air is pumped out of the pipe into non-dispersive infra-red sensors for continuous measurement of CH4 and CO2 concentrations. Field comparisons of AHCS to respiration chambers or SF6 have demonstrated that AHCS produces repeatable and accurate CH4 emission results, provided that animal visits to AHCS are sufficient so emission estimates are representative of the diurnal rhythm of rumen gas production. Here, we demonstrate the use of AHCS to measure CO2 and CH4 fluxes from dairy cows given a control diet or a diet supplemented with technical-grade cashew nut shell liquid.

An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production.

A quarter of all anthropogenic methane emissions in the United States are from enteric fermentation, primarily from ruminant livestock. This study was undertaken to test the effect of a methane inhibitor, 3-nitrooxypropanol (3NOP), on enteric methane emission in lactating Holstein cows. An experiment was conducted using 48 cows in a randomized block design with a 2-wk covariate period and a 12-wk data collection period. Feed intake, milk production, and fiber digestibility were not affected by the inhibitor. Milk protein and lactose yields were increased by 3NOP. Rumen methane emission was linearly decreased by 3NOP, averaging about 30% lower than the control. Methane emission per unit of feed dry matter intake or per unit of energy-corrected milk were also about 30% less for the 3NOP-treated cows. On average, the body weight gain of 3NOP-treated cows was 80% greater than control cows during the 12-wk experiment. The experiment demonstrated that the methane inhibitor 3NOP, applied at 40 to 80 mg/kg feed dry matter, decreased methane emissions from high-producing dairy cows by 30% and increased body weight gain without negatively affecting feed intake or milk production and composition. The inhibitory effect persisted over 12 wk of treatment, thus offering an effective methane mitigation practice for the livestock industries.

Methane cycling. Nonequilibrium clumped isotope signals in microbial methane.

Methane is a key component in the global carbon cycle, with a wide range of anthropogenic and natural sources. Although isotopic compositions of methane have traditionally aided source identification, the abundance of its multiply substituted "clumped" isotopologues (for example, (13)CH3D) has recently emerged as a proxy for determining methane-formation temperatures. However, the effect of biological processes on methane's clumped isotopologue signature is poorly constrained. We show that methanogenesis proceeding at relatively high rates in cattle, surface environments, and laboratory cultures exerts kinetic control on (13)CH3D abundances and results in anomalously elevated formation-temperature estimates. We demonstrate quantitatively that H2 availability accounts for this effect. Clumped methane thermometry can therefore provide constraints on the generation of methane in diverse settings, including continental serpentinization sites and ancient, deep groundwaters.