Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand

Predicting CO2 production of lactating dairy cows from animal, dietary, and production traits using an international dataset

Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called "sniffer technique") and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 ("best model"), where all significant traits were included; model 2 ("on-farm model"), where DMI was excluded; and model 3 ("reduced on-farm model"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

Industrialization Mitigates Greenhouse Gas Intensity in China's Dairy Sector yet May Prove Insufficient to Offset Emissions from Future Production Expansion

China's dairy farming is undergoing a critical transition from extensive to industrial systems. To achieve sustainable milk production within China's dual-carbon goals, understanding the multidimensional impacts of industrialization on greenhouse gas (GHG) emissions is imperative. This study comprehensively analyzed the implications of China's dairy industrialization on GHG emissions and explored future mitigation potential. Results indicated that industrial systems exhibited lower methane but higher carbon dioxide intensities, with net GHG intensity lower than other systems. During 2002-2020, China's milk production increased by 165%, while GHG emissions increased by 105% to 50.27 Tg CO2eq, accompanying an industrialization rate increased from 16% to 75%. The industrialization progress played a mitigating effect on GHG primarily through intensification within individual production systems before 2008 and transformation between systems post-2008. However, the industrialization's effect was relatively modest compared to other socio-economic factors. By 2030, 11.8 Tg CO2eq will be triggered by predicted milk production growth, but only 0.6 Tg can be offset by system transformation. Integrating measures to improve feed, herd, and manure management on industrial farms could decouple GHG emissions from milk production and achieve a carbon peak before 2030. We suggest transforming to improved industrial systems as a necessary step toward sustainable livestock production.

Dietary fat and carbohydrate-balancing the lactation performance and methane emissions in the dairy cow industry: A meta-analysis

For the agroecosystems of the dairy cow industry, dietary carbohydrate (starch, neutral detergent fiber [NDF]) and fat could directly affect rumen methane emissions and host energy utilization. However, the relationships among diet, lactation performance, and methane emissions need to be further determined to assist dairy farms to adjust diet formulations and feeding strategies for environmental and production management. A meta-analysis was conducted in the current study to explore quantitative patterns of dietary fat and carbohydrate at different levels in balancing lactation performance and environment sustainability of dairy cows, and to establish a methane emission prediction model using the artificial neural network (ANN) model. The results showed that the regression relationship between dietary fat, carbohydrate and methane emissions could be shown by the following models: methane = 106.78 + (14.86 × DMI), R2 = 0.80; methane = 443.17 - (46.41 × starch/NDF), R2 = 0.76; and methane = 388.91 + (31.40 × fat) - (5.42 × fat2), R2 = 0.80. The regression relationships between dietary fat, carbohydrate and lactation performance could be shown by the following models: milk fat yield = 1.08 + (0.43 × starch/NDF) - [0.34 × (starch/NDF)2], R2 = 0.79; milk protein yield = 0.68 + (0.15 × fat) - (0.016 × fat2), R2 = 0.82. In the structural equation model, we found that when formulating dietary carbohydrates and fats, it was necessary to balance the relationship between methane emissions and lactation performance. Specifically, dietary starch/NDF was lower than 0.63 (extremum point) and dietary fat was between 2.89% and 4.69% (extremum point), it could ensure that the aim of methane emission reduction (methane emissions decrease with increasing dietary starch/NDF and fat) was achieved without losing lactation performance of dairy cows (lactation performance increase with increasing dietary starch/NDF and fat). Finally, we established the ANN model to predict methane emissions (training set: R2 = 0.62; validation set: R2 = 0.61).

Principal component analysis of phenotypic and breeding value data for semen traits in Egyptian buffalo bulls

Buffalo bull semen traits are economically important traits that influence farm fertility and profitability. Genetic improvement of semen characteristics is an important detail of the genetic improvement. This study was conducted to assess the relationship between the breeding values as well as the phenotypic values for semen traits (VOL, MM, LS, AS and CONC) of the Egyptian buffalo bulls. A total of 7761 normal semen ejaculates were collected and characterized at ILMTC laboratory from 26 bulls from 2009 to 2019. For VOL, MM, LS, AS, and CONC, the actual means were 3.89 mL, 62.37%, 60.64%, 3.94%, and 0.67 × 109 sperm/mL, respectively. The prediction of breeding values for semen traits was estimated using a Bayesian procedure. The estimated standardized EBVs and phenotypic values were used in the principal component analysis (PCA). Of five PCs, one PC (PC1) had > 1 eigenvalues that was responsible for 87.19% of the total variation of SEBV, and two PCs had > 1 eigenvalues that were responsible for 59.61% and 21.35% of the total variation of the phenotypic values. Together, PC1 and PC2 accounted for 97.97% of the total variance of SEBV and 80.96% of the total variance of phenotypic values. A graphs of the first two components showed the traits separated into two different directions by group. This indicates each group was under similar genetic influence. Therefore, selection can be done separately for each group without influencing the other. Principal component analysis reduced variables to describe the key information in buffalo semen data.

An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows

Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.

Use of methane production data for genetic prediction in beef cattle: A review

Methane (CH4) is a greenhouse gas that is produced and emitted from ruminant animals through enteric fermentation. Methane production from cattle has an environmental impact and is an energetic inefficiency. In the beef industry, CH4 production from enteric fermentation impacts all three pillars of sustainability: environmental, social, and economic. A variety of factors influence the quantity of CH4 produced during enteric fermentation, including characteristics of the rumen and feed composition. There are several methodologies available to either quantify or estimate CH4 production from cattle, all with distinct advantages and disadvantages. Methodologies include respiration calorimetry, the sulfur-hexafluoride tracer technique, infrared spectroscopy, prediction models, and the GreenFeed system. Published studies assess the accuracy of the various methodologies and compare estimates from different methods. There are advantages and disadvantages of each technology as they relate to the use of these phenotypes in genetic evaluation systems. Heritability and variance components of CH4 production have been estimated using the different CH4 quantification methods. Agreement in both the amounts of CH4 emitted and heritability estimates of CH4 emissions between various measurement methodologies varies in the literature. Using greenhouse gas traits in selection indices along with relevant output traits could provide producers with a tool to make selection decisions on environmental sustainability while also considering productivity. The objective of this review was to discuss factors that influence CH4 production, methods to quantify CH4 production for genetic evaluation, and genetic parameters of CH4 production in beef cattle.

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.

Approaches for predicting dairy cattle methane emissions: from traditional methods to machine learning

Measuring dairy cattle methane (CH4) emissions using traditional recording technologies is complicated and expensive. Prediction models, which estimate CH4 emissions based on proxy information, provide an accessible alternative. This review covers the different modeling approaches taken in the prediction of dairy cattle CH4 emissions and highlights their individual strengths and limitations. Following the guidelines set out by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA); Scopus, EBSCO, Web of Science, PubMed and PubAg were each queried for papers with titles that contained search terms related to a population of "Bovine," exposure of "Statistical Analysis or Machine Learning," and outcome of "Methane Emissions". The search was executed in December 2022 with no publication date range set. Eligible papers were those that investigated the prediction of CH4 emissions in dairy cattle via statistical or machine learning (ML) methods and were available in English. 299 papers were returned from the initial search, 55 of which, were eligible for inclusion in the discussion. Data from the 55 papers was synthesized by the CH4 emission prediction approach explored, including mechanistic modeling, empirical modeling, and machine learning. Mechanistic models were found to be highly accurate, yet they require difficult-to-obtain input data, which, if imprecise, can produce misleading results. Empirical models remain more versatile by comparison, yet suffer greatly when applied outside of their original developmental range. The prediction of CH4 emissions on commercial dairy farms can utilize any approach, however, the traits they use must be procurable in a commercial farm setting. Milk fatty acids (MFA) appear to be the most popular commercially accessible trait under investigation, however, MFA-based models have produced ambivalent results and should be consolidated before robust accuracies can be achieved. ML models provide a novel methodology for the prediction of dairy cattle CH4 emissions through a diverse range of advanced algorithms, and can facilitate the combination of heterogenous data types via hybridization or stacking techniques. In addition to this, they also offer the ability to improve dataset complexity through imputation strategies. These opportunities allow ML models to address the limitations faced by traditional prediction approaches, as well as enhance prediction on commercial farms.

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.

Dynamic changes of rumen bacteria and their fermentative ability in high-producing dairy cows during the late perinatal period

Background

High-producing dairy cows face varying degrees of metabolic stress and challenges during the late perinatal period, resulting in ruminal bacteria abundance and their fermentative ability occurring as a series of changes. However, the dynamic changes are still not clear.

Aims/methods

Ten healthy, high-producing Holstein dairy cows with similar body conditions and the same parity were selected, and ruminal fluid from the dairy cows at postpartum 0, 7, 14, and 21 d was collected before morning feeding. 16S rRNA high-throughput sequencing, GC-MS/MS targeted metabolomics, and UPLC-MS/MS untargeted metabolomics were applied in the study to investigate the dynamic changes within 21 d postpartum.

Results

The results displayed that the structures of ruminal bacteria were significantly altered from 0 to 7 d postpartum (R = 0.486, P = 0.002), reflecting the significantly declining abundances of Euryarchaeota and Chloroflexi phyla and Christensenellaceae, Methanobrevibacter, and Flexilinea genera (P < 0.05) and the obviously ascending abundances of Ruminococcaceae, Moryella, Pseudobutyrivibrio, and Prevotellaceae genera at 7 d postpartum (P < 0.05). The structures of ruminal bacteria also varied significantly from 7 to 14 d postpartum (R = 0.125, P = 0.022), reflecting the reducing abundances of Christensenellaceae, Ruminococcaceae, and Moryella genera (P < 0.05), and the elevating abundances of Sharpea and Olsenella genera at 14 d postpartum (P < 0.05). The metabolic profiles of ruminal SCFAs were obviously varied from 0 to 7 d postpartum, resulting in higher levels of propionic acid, butyric acid, and valeric acid at 7 d postpartum (P < 0.05); the metabolic profiles of other ruminal metabolites were significantly shifted from 0 to 7 d postpartum, with 27 significantly elevated metabolites and 35 apparently reduced metabolites (P < 0.05). The correlation analysis indicated that propionic acid was positively correlated with Prevotellaceae and Ruminococcaceae (P < 0.05), negatively correlated with Methanobrevibacter (P < 0.01); butyric acid was positively associated with Prevotellaceae, Ruminococcaceae, and Pseudobutyrivibrio (P < 0.05), negatively associated with Christensenellaceae (P < 0.01); valeric acid was positively linked with Prevotellaceae and Ruminococcaceae (P < 0.05); pyridoxal was positively correlated with Flexilinea and Methanobrevibacter (P < 0.05) and negatively correlated with Ruminococcaceae (P < 0.01); tyramine was negatively linked with Ruminococcaceae (P < 0.01).

Conclusion

The findings contribute to the decision of nutritional management and prevention of metabolic diseases in high-producing dairy cows during the late perinatal period.

Enteric and manure emissions from Holstein-Friesian dairy cattle fed grass silage-based or corn silage-based diets

This study aimed to evaluate trade-offs between enteric and manure CH4 emissions, and the size of synergistic effects for CH4 and nitrogenous emissions (NH3 and N2O). Sixty-four Holstein-Friesian cows were blocked in groups of 4 based on parity, lactation stage, and milk yield. Cows within a block were randomly allocated to a dietary sequence in a crossover design with a grass silage-based diet (GS) and a corn silage-based diet (CS). The GS diet consisted of 50% grass silage and 50% concentrate, and CS consisted of 10% grass silage, 40% corn silage, and 50% concentrate (dry matter basis). The composition of the concentrate was identical for both diets. Cows were housed in groups of 16 animals, in 4 mechanically ventilated barn units for independent emission measurement. Treatment periods were composed of a 2-wk adaptation period followed by a 5-wk measurement period, 1 wk of which was without cows to allow separation of enteric and manure emissions. In each barn unit, ventilation rates and concentrations of CH4, CO2, NH3, and N2O in incoming and outgoing air were measured. Cow excretion of organic matter was higher for CS compared with GS. Enteric CH4 and cow-associated NH3 and N2O emissions (i.e., manure emissions excluded) were lower for CS compared with GS (-11, -40, and -45%, respectively). The CH4 and N2O emissions from stored manure (i.e., in absence of cows) were not affected by diet, whereas that of NH3 emission tended to be lower for CS compared with GS. In conclusion, there was no trade-off between enteric and manure CH4 emissions, and there were synergistic effects for CH4 and nitrogenous emissions when grass silage was exchanged for corn silage, without balancing the diets for crude protein content, in this short-term study.

Evaluation of Intergovernmental Panel on Climate Change (IPCC) equations to predict enteric methane emission from lactating cows fed Mediterranean diets

The study aimed to evaluate Intergovernmental Panel on Climate Change (IPCC) Tier 2 (2006 and 2019) to predict enteric CH4 emissions from lactating cows fed Mediterranean diets. The effects of the CH4 conversion factor (Ym; CH4 energy loss as a percentage of gross energy intake) and digestible energy (DE) of the diet were evaluated as model predictors. A data set was created using individual observations derived from 3 in vivo studies on lactating dairy cows housed in respiration chambers and fed diets typical of the Mediterranean region based on silages and hays. Five models using different Ym and DE were evaluated following a Tier 2 approach: (1) average values of Ym (6.5%) and DE (70%) from IPCC (2006); (2) average value of Ym (5.7%) and DE (70.0%) from IPCC (2019; 1YM); (3) Ym = 5.7% and DE measured in vivo (1YMIV); (4) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE = 70% (2YM); and (5) Ym = 5.7 or 6.0%, depending on dietary NDF, and DE measured in vivo (2YMIV). Finally, a Tier 2 model for Mediterranean diets (MED) was derived from the Italian data set (Ym = 5.58%; DE = 69.9% for silage-based diets and 64.8% for hay-based diets) and validated on an independent data set of cows fed Mediterranean diets. The most accurate models tested were 2YMIV, 2YM, and 1YMIV with predictions of 384, 377, and 377 (g of CH4/d), respectively, versus the in vivo value of 381. The most precise model was 1YM (slope bias = 1.88%; r = 0.63). Overall, 1YM showed the highest concordance correlation coefficient value (0.579), followed by 1YMIV (0.569). Cross-validation on an independent data set of cows fed Mediterranean diets (corn silage and alfalfa hay) resulted in concordance correlation coefficient of 0.492 and 0.485 for 1YM and MED, respectively. The prediction of MED (397) was more accurate than 1YM (405) in comparison with the corresponding in vivo value of 396 g of CH4/d. The results of this study showed that the average values proposed by IPCC (2019) can adequately predict CH4 emissions from cows fed typical Mediterranean diets. However, the use of specific factors for the Mediterranean area, such as DE, improved the accuracy of the models.

Enteric Methane Emissions Prediction in Dairy Cattle and Effects of Monensin on Methane Emissions: A Meta-Analysis

Greenhouse gas emissions, such as enteric methane (CH₄) from ruminant livestock, have been linked to global warming. Thus, easily applicable CH₄ management strategies, including the inclusion of dietary additives, should be in place. The objectives of the current study were to: (i) compile a database of animal records that supplemented monensin and investigate the effect of monensin on CH₄ emissions; (ii) identify the principal dietary, animal, and lactation performance input variables that predict enteric CH₄ production (g/d) and yield (g/kg of dry matter intake DMI); (iii) develop empirical models that predict CH₄ production and yield in dairy cattle; and (iv) evaluate the newly developed models and published models in the literature. A significant reduction in CH₄ production and yield of 5.4% and 4.0%, respectively, was found with a monensin supplementation of ≤24 mg/kg DM. However, no robust models were developed from the monensin database because of inadequate observations under the current paper's inclusion/exclusion criteria. Thus, further long-term in vivo studies of monensin supplementation at ≤24 mg/kg DMI in dairy cattle on CH₄ emissions specifically beyond 21 days of feeding are reported to ensure the monensin effects on the enteric CH₄ are needed. In order to explore CH₄ predictions independent of monensin, additional studies were added to the database. Subsequently, dairy cattle CH₄ production prediction models were developed using a database generated from 18 in vivo studies, which included 61 treatment means from the combined data of lactating and non-lactating cows (COM) with a subset of 48 treatment means for lactating cows (LAC database). A leave-one-out cross-validation of the derived models showed that a DMI-only predictor model had a similar root mean square prediction error as a percentage of the mean observed value (RMSPE, %) on the COM and LAC database of 14.7 and 14.1%, respectively, and it was the key predictor of CH₄ production. All databases observed an improvement in prediction abilities in CH₄ production with DMI in the models along with dietary forage proportion inclusion and the quadratic term of dietary forage proportion. For the COM database, the CH₄ yield was best predicted by the dietary forage proportion only, while the LAC database was for dietary forage proportion, milk fat, and protein yields. The best newly developed models showed improved predictions of CH₄ emission compared to other published equations. Our results indicate that the inclusion of dietary composition along with DMI can provide an improved CH₄ production prediction in dairy cattle.

Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows

Dairy cows' urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine the adequacy of existing models to predict UN from total mixed ration (TMR)-fed and fresh forage (FF)-fed cows. Next, we aimed to develop equations to predict UN based on animal factors [milk urea nitrogen (MUN; mg/dL) and body weight (BW, kg)] and to explore how these equations are improved when dietary factors, such as diet type, dry matter intake (DMI), or dietary characteristics [neutral detergent fiber (NDF) and crude protein (CP) content], are considered. A dataset was obtained from 51 published experiments composed of 174 treatment means. The whole dataset was used to evaluate the mean and linear biases of three existing equations including diet type as an interaction term; all models had significant linear and mean biases and two of the three models had poor predictive capabilities as indicated by their large relative prediction error (RPE; root mean square error of prediction as a percent of the observed mean). Next, the complete data set was split into training and test sets, which were used to develop and to evaluate new models, respectively. The first model included MUN and BW, and there was a significant interaction between diet type and the coefficients. This model had the worst 1:1 agreement [Lin's concordance correlation coefficient (CCC) = 0.50] and largest RPE (24.7%). Models that included both animal and dietary factors performed the best, and when included in the model, the effect of diet type was no longer significant (p > 0.10). These models all had very good agreement (CCC ≥ 0.86) and relatively low RPE (≤13.1%). This meta-analysis developed precise and accurate equations to predict UN from dairy cows in both confined and pasture-based systems.

Prevotella: A Key Player in Ruminal Metabolism

Ruminants are foregut fermenters that have the remarkable ability of converting plant polymers that are indigestible to humans into assimilable comestibles like meat and milk, which are cornerstones of human nutrition. Ruminants establish a symbiotic relationship with their microbiome, and the latter is the workhorse of carbohydrate fermentation. On the other hand, during carbohydrate fermentation, synthesis of propionate sequesters H, thus reducing its availability for the ultimate production of methane (CH4) by methanogenic archaea. Biochemically, methane is the simplest alkane and represents a downturn in energetic efficiency in ruminants; environmentally, it constitutes a potent greenhouse gas that negatively affects climate change. Prevotella is a very versatile microbe capable of processing a wide range of proteins and polysaccharides, and one of its fermentation products is propionate, a trait that appears conspicuous in P. ruminicola strain 23. Since propionate, but not acetate or butyrate, constitutes an H sink, propionate-producing microbes have the potential to reduce methane production. Accordingly, numerous studies suggest that members of the genus Prevotella have the ability to divert the hydrogen flow in glycolysis away from methanogenesis and in favor of propionic acid production. Intended for a broad audience in microbiology, our review summarizes the biochemistry of carbohydrate fermentation and subsequently discusses the evidence supporting the essential role of Prevotella in lignocellulose processing and its association with reduced methane emissions. We hope this article will serve as an introduction to novice Prevotella researchers and as an update to others more conversant with the topic.

Effect of Sodium Nitrate and Cysteamine on In Vitro Ruminal Fermentation, Amino Acid Metabolism and Microbiota in Buffalo

Nitrate is used as a methane inhibitor while cysteamine is considered as a growth promoter in ruminants. The present study evaluated the effect of sodium nitrate and cysteamine on methane (CH4) production, rumen fermentation, amino acid (AA) metabolism, and rumen microbiota in a low protein diet. Four treatments containing a 0.5 g of substrate were supplemented with 1 mg/mL sodium nitrate (SN), 100 ppm cysteamine hydrochloride (CS), and a combination of SN 1 mg/mL and CS 100 ppm (CS+SN), and a control (no additive) were applied in a completely randomized design. Each treatment group had five replicates. Two experimental runs using in vitro batch culture technique were performed for two consecutive weeks. Total gas and CH4 production were measured in each fermentation bottle at 3, 6, 9, 12, 24, 48, and 72 h of incubation. The results showed that SN and CS+SN reduced the production of total gas and CH4, increased the rumen pH, acetate, acetate to propionate ratio (A/P), and microbial protein (MCP) contents (p < 0.05), but decreased other volatile fatty acids (VFA) and total VFA (p = 0.001). The CS had no effect on CH4 production and rumen fermentation parameters except for increasing A/P. The CSN increased the populations of total bacteria, fungi, and methanogens but decreased the diversity and richness of rumen microorganisms. In conclusion, CS+SN exhibited a positive effect on rumen fermentation by increasing the number of fiber degrading and hydrogen-utilizing bacteria, with a desirable impact on rumen fermentation while reducing total gas and CH4 production.

Evaluation of Feed Near-Infrared Reflectance Spectra as Predictors of Methane Emissions from Ruminants

Feed chemical composition is associated with methane (CH4) formation in the rumen, and thus CH4 yields (Ym; CH4 emitted from per unit of dry matter intake) could be predicted using near-infrared reflectance spectroscopy (NIRS) of feeds fed to ruminants. Two databases of NIRS data were compiled from feeds used in experiments in which CH4 yields had been quantified in respiration chambers. Each record in the databases represented a batch of feed offered to a group of experimental animals and the mean CH4 yield for the group. A near-infrared reflectance spectrum was obtained from each feed, and these spectra were used to generate a predictive equation for Ym. The predictive model generated from brassica crops and pasture fed at a similar feeding level (n = 40 records) explained 53% of the variation in Ym and had a reasonably good agreement (concordance correlation coefficient of 0.77). The predictive ability of the NIRS calibration could be useful for screening purposes, particularly for predicting the potential Ym of multiple feeds or feed samples, rather than measuring Ym in animal experiments at high expenses. It is recommended that the databases for NIRS calibrations are expanded by collecting feed information from future experiments in which methane emissions are measured, using alternative algorithms and combining other techniques, such as terahertz time-domain spectroscopy.

Evaluation of gaseous and solid waste in fermentation bedding system and its impact on animal performance: A study of breeder ducks in winter

In order to further optimize the application of fermentation bedding system and evaluate its improvements on low temperature, low humidity and other adverse environments of animal breeding in winter, we tested the waste discharge and performance of 12,921 breeder ducks from November to January. The fresh bedding materials and fermentation microbes showed significant advantages. They increased the temperature and temperature humidity index (THI) by more than 3.48 °C and 5.54, reduced the emissions of H₂S, NH₃, CO₂, fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀) and total suspended particulate (TSP) by more than 29.92%, 47.21%, 13.69%, 25.90%, 23.43% and 25.94% respectively, compared to control. The stale bedding materials increased the mortality rate and breeding egg loss rate of breeder ducks by 0.17% and 4.22% significantly, reduced the healthy duckling rate and total effective hatching rate by 2.41% and 4.22%, compared to control, had limited effects on the improvement of house environments. PM_2.5, TSP, CO₂, H₂S and NH₃ were important environmental parameters affecting the productive and reproductive performance of ducks. These data can provide an insight that the fermentation bed system could reduce the waste emission of breeder ducks, improve their production and increase the health rate of their offspring. Its application in livestock farming would create a better breeding environment and economic benefits. In addition, the stale bedding materials were not recommended to recycle in fermentation bed system for cleaner production and sustainable development.

Space allowance: a tool for improving behavior, milk and meat production, and reproduction performance of buffalo in different housing systems-a review

Buffalo population has dramatically increased during the last two decades, especially in tropical and subtropical regions. Although buffalo are important milk and meat-producing animal, still practices of buffalo farming and welfare aspects are not well established. Housing system and stocking density are significant factors that affect the welfare and production of animals; however, no space allowance standards have been demonstrated for buffalo at different ages. This review article presents the following: (1) an overview of buffalo subtypes and the geographical distribution of buffalo populations and their production; (2) the effect of housing systems and space allowance on the social behavior and welfare indices; (3) the effects of space allowance on milk production and growth performance of buffalo; and (4) the relationship between space allowance and reproductive performance. Although the limited data in this area of research, it can be driven that a larger space allowance with access to a pool, especially during the hot season, maintains buffalo production at optimal levels. Moreover, optimal floor space improves the welfare and social indices of buffalo; however, there are discrepancies in aggressive and agonistic behavior results. Surprisingly, the reproductive performance of buffalo was not affected by space allowance. Therefore, further research is needed to identify the impact of the housing aspects, including space allowance and enrichment tools, on the productive performance, and welfare indices of buffalo. This would assist in implementing welfare-economic standards for buffalo production and reveal the potentiality of this eco-friendly animal.

Estimates of the genetic contribution to methane emission in dairy cows: a meta-analysis

The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits [methane yield (METY), methane intensity (METINT), and methane production (METP)] in dairy cows. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers published between 2015 and 2021 were used in this study. The heritability estimates for METY, METINT, and METP were 0.244, 0.180, and 0.211, respectively. The genetic correlation estimates between METY and METINT with corrected milk yield for fat, protein, and or energy (CMY) were negative (- 0.433 and - 0.262, respectively). Also, genetic correlation estimates between METINT with milk fat and protein percentages were 0.254 and 0.334, respectively. Although the genetic correlation estimate of METP with daily milk yield was 0.172, its genetic correlation with CMY was 0.446. All genetic correlation estimates between METP with milk fat and protein yield or percentage ranged from 0.005 (between METP-milk protein yield) to 0.185 (between METP-milk protein percentage). The current meta-analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows that could be exploited in genetic selection plans.

Quantification of methane emitted by ruminants: a review of methods

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH4), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH4 emissions. This review documents existing methods and methodologies to measure and estimate CH4 emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4 have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH4 for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH4 from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4 emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

A Review: Plant Carbohydrate Types—The Potential Impact on Ruminant Methane Emissions

Carbohydrates are the major component of most ruminant feeds. The digestion of carbohydrates in the rumen provides energy to the ruminants but also contributes to enteric methane (CH₄) emissions. Fresh forage is the main feed for grazing ruminants in temperate regions. Therefore, this review explored how dietary carbohydrate type and digestion affect ruminant CH₄ emissions, with a focus on fresh forage grown in temperate regions. Carbohydrates include monosaccharides, disaccharides, oligosaccharides, and polysaccharides. Rhamnose is the only monosaccharide that results in low CH₄ emissions. However, rhamnose is a minor component in most plants. Among polysaccharides, pectic polysaccharides lead to greater CH₄ production due to the conversion of methyl groups to methanol and finally to CH₄. Thus, the degree of methyl esterification of pectic polysaccharides is an important structural characteristic to better understand CH₄ emissions. Apart from pectic polysaccharides, the chemical structure of other polysaccharides per se does not seem to affect CH₄ formation. However, rumen physiological parameters and fermentation types resulting from digestion in the rumen of polysaccharides differing in the rate and extent of degradation do affect CH₄ emissions. For example, low rumen pH resulting from the rapid degradation of readily fermentable carbohydrates decreases and inhibits the activities of methanogens and further reduces CH₄ emissions. When a large quantity of starch is supplemented or the rate of starch degradation is low, some starch may escape from the rumen and the escaped starch will not yield CH₄. Similar bypass from rumen digestion applies to other polysaccharides and needs to be quantified to facilitate the interpretation of animal experiments in which CH₄ emissions are measured. Rumen bypass carbohydrates may occur in ruminants fed fresh forage, especially when the passage rate is high, which could be a result of high feed intake or high water intake. The type of carbohydrates affects the concentration of dissolved hydrogen, which consequently alters fermentation pathways and finally results in differences in CH₄ emissions. We recommend that the degree of methyl esterification of pectic polysaccharides is needed for pectin-rich forage. The fermentation type of carbohydrates and rumen bypass carbohydrates should be determined in the assessment of mitigation potential.

Metabolite Profile, Ruminal Methane Reduction, and Microbiome Modulating Potential of Seeds of Pharbitis nil

We identified metabolites in the seeds of Pharbitis nil (PA) and evaluated their effects on rumen methanogenesis, fiber digestibility, and the rumen microbiome in vitro and in sacco. Four rumen-cannulated Holstein steers (mean body weight 507 ± 32 kg) were used as inoculum donor for in vitro trial and live continuous culture system for in sacco trial. PA was tested in vitro at doses ranging from 4.5 to 45.2% dry matter (DM) substrate. The in sacco trial was divided into three phases: a control phase of 10 days without nylon bags containing PA in the rumen, a treatment phase of 11 days in which nylon bags containing PA (180 g) were placed in the rumen, and a recovery phase of 10 days after removing the PA-containing bags from the rumen. Rumen headspace gas and rumen fluid samples were collected directly from the rumen. PA is enriched in polyunsaturated fatty acids dominated by linoleic acid (C18:2) and flavonoids such as chlorogenate, quercetin, quercetin-3-O-glucoside, and quinic acid derivatives. PA decreased (p &lt; 0.001) methane (CH4) production linearly in vitro with a reduction of 24% at doses as low as 4.5% DM substrate. A quadratic increase (p = 0.078) in neutral detergent fiber digestibility was also noted, demonstrating that doses &lt; 9% DM were optimal for simultaneously enhancing digestibility and CH4 reduction. In sacco, a 50% decrease (p = 0.087) in CH4 coupled with an increase in propionate suggested increased biohydrogenation in the treatment phase. A decrease (p &lt; 0.005) in ruminal ammonia nitrogen (NH3-N) was also noted with PA in the rumen. Analysis of the rumen microbiome revealed a decrease (p &lt; 0.001) in the Bacteroidetes-to-Firmicutes ratio, suggesting PA to have antiprotozoal potential. At the genus level, a 78% decrease in Prevotella spp. and a moderate increase in fibrolytic Ruminococcus spp. were noted in the treatment phase. In silico binding of PA metabolites to cyclic GMP-dependent protein kinase of Entodinium caudatum supported the antiprotozoal effect of PA. Overall, based on its high nutrient value and antiprotozoal activity, PA could probably replace the ionophores used for CH4 abatement in the livestock industry.

Gentiana straminea supplementation improves feed intake, nitrogen and energy utilization, and methane emission among Simmental calves in northwest China

Objective

Native plants can be used as additives to replace antibiotics to improve ruminant feed utilization and animal health. An experiment was conducted to evaluate the effects of Gentiana straminea (GS) on nutrient digestibility, methane emissions, and energy metabolism of Simmental calves.

Methods

Thirty-two (5-week-old) male Simmental clves, with initial body weight (BW) of 155±12 kg were fed the same basal diet of concentrates (26%), alfalfa hay (37%), and oat hay (37%) and were randomly separated into four treatment groups according to the amount of GS that was added to their basal diet. The four different groups received different amounts of GS as a supplement to their basal diet during whole experiment: (0 GS) 0 mg/kg BW, the control; (100 GS) 100 mg/kg BW; (200 GS) 200 mg/kg BW; and (300 GS) 300 mg/kg BW.

Results

For calves in the 200 GS and 300 GS treatment groups, there was a significant increase in dry matter (DM) intake (p<0.01), average daily gain (ADG) (p<0.05), organic matter intake (p<0.05), DM digestibility (p<0.05), neutral detergent fibre (NDF) digestibility (p<0.05), and acid detergent fibre (ADF) digestibility (p<0.05). Dietary GS supplementation result in quadratic increases of DM intake (p<0.01), ADG (p<0.05), NDF intake (p<0.05), and ADF intake (p<0.05). Supplementing the basal diet with GS significantly increased nitrogen (N) retention (p<0.001) and the ratio of retention N to N intake (p<0.001). Supplementing the basal diet with GS significantly decreased methane (CH₄) emissions (p<0.01), CH₄/BW^0.75 (p<0.05) and CH₄ energy (CH₄-E) (p<0.05). Dietary GS supplementation result in quadratic increases of CH₄ (p<0.01) and CH₄/DM intake (p<0.01). Compared with 0 GS, GS-supplemented diets significantly improved their gross energy intake (p<0.05). The metabolizable energy and digestive energy intake were significantly greater for calves in the 100 GS and 200 GS calves than for 0 GS calves (p<0.05).

Conclusion

From this study, we conclude that supplementing calf diets with GS could improve utilization of feed, energy, and N, and may reduce CH₄ emissions without having any negative effects on animal health.

Effect of Slow-Release Urea Administration on Production Performance, Health Status, Diet Digestibility, and Environmental Sustainability in Lactating Dairy Cows

The effects of partially replacing soybean meal (SBM) with a slow-release urea source (SRU) on production performance, feed efficiency, digestibility, and environmental sustainability of dairy cows were evaluated. A total of 140 lactating Holstein Frisian cows were allocated into two study groups: (i) control (diet entirely based on SBM), and (ii) treatment (diet of 0.22% on dry matter basis (d.m.)) of SRU. Milk yield, dry matter intake (DMI), feed conversion rate (FCR), body condition score (BCS), reproductive parameters, and milk quality were evaluated. The chemical composition of the feeds and feces were analyzed to calculate the in vivo digestibility of the two diets. The carbon footprint (CFP) and predicted methane (CH₄) emissions were evaluated. The inclusion of SRU significantly increases milk yield, DMI, and FCR (p < 0.0001), whereas milk quality, BCS, and reproductive indicators were not affected (p > 0.05). In the treatment group, the digestibility of crude protein (CP) (p = 0.012), NDF (p = 0.039), and cellulose (p = 0.033) was significantly higher, while the other nutritional parameters weren't affected. All the environmental parameters were significantly improved in the treatment group (p < 0.0001). Replacing SBM with SRU can be a strategy to enhance dairy cows' sustainability due to improved production efficiency, reduced feed CFP, and predicted CH₄ production.

Enteric methane emissions by young Brahman bulls grazing tropical pastures at different rainfall seasons in the Peruvian jungle

The aim of this research was to measure enteric methane (CH₄) emissions by young Brahman bulls grazing tropical pastures at different rainfall seasons in the Peruvian jungle. Fourteen 1.5-year-old, young bulls (280 kg ± 18 kg BW) were grazed on tropical grasses and legumes dominated by German grass [Echinochloa polystachya (Kunth) Hitch.] and minor proportion of Torourco grass [Paspalum conjugatum (P.J. Bergius) Roxb] and Leguminous Calopo (Calopogonium mucunoides Desv.) and Kudzú [Pueraria phaseoloides (Roxb.) Benth]. Enteric CH₄ emission was measured by the sulfur hexafluoride (SF₆) tracer-gas technique. Organic matter intake (OMI) was determined from organic matter digestibility (OMD) using a fecal protein crude index and fecal output estimated by the dosage of external markers. There was a difference in OMD between seasons (68 and 66% for the dry and rainy seasons, respectively; P < 0.0001). The OMI (6.7 and 7.4 kg/day) and CH4 (178.7 and 298 g/day) were higher (P < 0.05) in the dry season than in the rainy season, respectively. The yield of CH₄ was lower (P < 0.0001) during rainy season (7.1%) than at the dry season (10.6%). The CH₄ emission (g/day) was correlated with OMD (%) (r = 0.74, P < 0.0001). Enteric CH₄ emissions of young bulls grazing mixtures of tropical pastures were significantly lower in animals grazing on the rainy-season, expressed either through unit of absolute emission, intake or as percentage of the GEI. Likewise, OMD of consumed pasture was the most important factor determining CH₄ emission.

Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program

The dairy industry has been scrutinized for the environmental impact associated with rearing and maintaining cattle for dairy production. There are 3 possible opportunities to reduce emissions through genetic selection: (1) a direct methane trait, (2) a reduction in replacements, and (3) an increase in productivity. Our aim was to estimate the independent effects of traits in the Australian National Breeding Objective on the gross methane production and methane intensity (EI) of the Australian dairy herd of average genetic potential. Based on similar published research, the traits determined to have an effect on emissions include production, fertility, survival, health, and feed efficiency. The independent effect of each trait on the gross emissions produced per animal due to genetic improvement and change in EI due to genetic improvement (intensity value, IV) were estimated and compared. Based on an average Australian dairy herd, the gross emissions emitted per cow per year were 4,297.86 kg of carbon dioxide equivalents (CO₂-eq). The annual product output, expressed in protein equivalents (protein-eq), and EI per cow were 339.39 kg of protein-eq and 12.67 kg of CO₂-eq/kg of protein-eq, respectively. Of the traits included in the National Breeding Objective, genetic progress in survival and feed saved were consistently shown to result in a favorable environmental impact. Conversely, production traits had an unfavorable environmental impact when considering gross emissions, and favorable when considering EI. Fertility had minimal impact as its effects were primarily accounted for through survival. Mastitis resistance only affected IV coefficients and to a very limited extent. These coefficients may be used in selection indexes to apply emphasis on traits based on their environmental impact, as well as applied by governments and stakeholders to track trends in industry emissions. Although initiatives are underway to develop breeding values to reduce methane by combining small methane data sets internationally, alternative options to reduce emissions by utilizing selection indexes should be further explored.

Challenges and opportunities to capture dietary effects in on-farm greenhouse gas emissions models of ruminant systems

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH₄) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N₂O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH₄ and/or N₂O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH₄ and N₂O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm.

Seasonal Influence on Rumen Microbiota, Rumen Fermentation, and Enteric Methane Emissions of Holstein and Jersey Steers under the Same Total Mixed Ration

Seasonal effects on rumen microbiome and enteric methane (CH4) emissions are poorly documented. In this study, 6 Holstein and 6 Jersey steers were fed the same total mixed ration diet during winter, spring, and summer seasons under a 2 × 3 factorial arrangement for 30 days per season. The dry matter intake (DMI), rumen fermentation characteristics, enteric CH4 emissions and rumen microbiota were analyzed. Holstein had higher total DMI than Jersey steers regardless of season. However, Holstein steers had the lowest metabolic DMI during summer, while Jersey steers had the lowest total DMI during winter. Jersey steers had higher CH4 yields and intensities than Holstein steers regardless of season. The pH was decreased, while ammonia nitrogen concentration was increased in summer regardless of breed. Total volatile fatty acids concentration and propionate proportions were the highest in winter, while acetate and butyrate proportion were the highest in spring and in summer, respectively, regardless of breed. Moreover, Holstein steers produced a higher proportion of propionate, while Jersey steers produced a higher proportion of butyrate regardless of season. Metataxonomic analysis of rumen microbiota showed that operational taxonomic units and Chao 1 estimates were lower and highly unstable during summer, while winter had the lowest Shannon diversity. Beta diversity analysis suggested that the overall rumen microbiota was shifted according to seasonal changes in both breeds. In winter, the rumen microbiota was dominated by Carnobacterium jeotgali and Ruminococcus bromii, while in summer, Paludibacter propionicigenes was predominant. In Jersey steers, Capnocytophaga cynodegmi, Barnesiella viscericola and Flintibacter butyricus were predominant, whereas in Holstein steers, Succinivibrio dextrinosolvens and Gilliamella bombicola were predominant. Overall results suggest that seasonal changes alter rumen microbiota and fermentation characteristics of both breeds; however, CH4 emissions from steers were significantly influenced by breeds, not by seasons.

In Vitro Screening of East Asian Plant Extracts for Potential Use in Reducing Ruminal Methane Production

Indiscriminate use of antibiotics can result in antibiotic residues in animal products; thus, plant compounds may be better alternative sources for mitigating methane (CH₄) production. An in vitro screening experiment was conducted to evaluate the potential application of 152 dry methanolic or ethanolic extracts from 137 plant species distributed in East Asian countries as anti-methanogenic additives in ruminant feed. The experimental material consisted of 200 mg total mixed ration, 20 mg plant extract, and 30 mL diluted ruminal fluid-buffer mixture in 60 mL serum bottles that were sealed with rubber stoppers and incubated at 39 °C for 24 h. Among the tested extracts, eight extracts decreased CH₄ production by >20%, compared to the corresponding controls: stems of Vitex negundo var. incisa, stems of Amelanchier asiatica, fruit of Reynoutria sachalinensis, seeds of Tribulus terrestris, seeds of Pharbitis nil, leaves of Alnus japonica, stem and bark of Carpinus tschonoskii, and stems of Acer truncatum. A confirmation assay of the eight plant extracts at a dosage of 10 mg with four replications repeated on 3 different days revealed that the extracts decreased CH₄ concentration in the total gas (7-15%) and total CH₄ production (17-37%), compared to the control. This is the first report to identify the anti-methanogenic activities of eight potential plant extracts. All extracts decreased ammonia (NH₃-N) concentrations. Negative effects on total gas and volatile fatty acid (VFA) production were also noted for all extracts that were rich in hydrolysable tannins and total saponins or fatty acids. The underlying modes of action differed among plants: extracts from P. nil, V. negundo var. incisa, A. asiatica, and R. sachalinensis resulted in a decrease in total methanogen or the protozoan population (p < 0.05) but extracts from other plants did not. Furthermore, extracts from P. nil decreased the population of total protozoa and increased the proportion of propionate among VFAs (p < 0.05). Identifying bioactive compounds in seeds of P. nil by gas chromatography-mass spectrometry analysis revealed enrichment of linoleic acid (18:2). Overall, seeds of P. nil could be a possible alternative to ionophores or oil seeds to mitigate ruminal CH₄ production.

Relationship between urinary energy and urinary nitrogen or carbon excretion in lactating Jersey cows

Measurement of urinary energy (UE) excretion is essential to determine metabolizable energy (ME) supply. Our objectives were to evaluate the accuracy of using urinary N (UN) or C (UC) to estimate UE and ultimately improve the accuracy of estimating ME. Individual animal data (n = 433) were used from 11 studies with Jersey cows at the University of Nebraska-Lincoln, where samples were analyzed after drying (n = 299) or on an as-is basis (n = 134). Dried samples resulted in greater estimated error variance compared with as-is samples, and thus only as-is samples were used for final models. The as-is data set included a range (min to max) in dry matter intake (11.6-24.6 kg/d), N intake (282-642 g/d), UE excretion (1,390-3,160 kcal/d), UN excretion (85-220 g/d or 20.6-59.5% of N intake), and UC excretion (130-273 g/d). As indicated by a bias in residuals between observed and predicted ME as dietary crude protein (CP; range of 14.9-19.1%) increased, the National Research Council dairy model did not accurately predict ME of diets, as dietary CP varied. The relationship between UE (kcal/d) and UN (g/d) excretion was linear and had an intercept of 880 ± 140 kcal. Because an intercept of 880 is biologically unlikely, the intercept was forced through 0, resulting in linear and quadratic relationships. The regressions of UE (kcal/d) on UN (g/d) excretion were UE = 14.6 ± 0.32 × UN, and UE = 20.9 ± 1.0 × UN - 0.0357 ± 0.0056 × UN². In the quadratic regression, UE increased, but at a diminishing rate as UN excretion increased. As UC increased, UE linearly and quadratically increased. However, error variance was greater for regression with UC compared with UN as explanatory variables (8.42 vs. 7.42% of mean UE). The use of the quadratic regression between UN and UE excretion to predict ME resulted in a slope bias in ME predictions as dietary CP increased. The linear regression between UE and UN excretion removed slope bias between predicted ME and CP, and thus may be more appropriate for predicting UE across a wider range of dietary CP. Using equations to predict UE from UN should improve our ability to predict diet ME in Jersey cows compared with calculating ME directly from digestible energy.

Holstein and Jersey Steers Differ in Rumen Microbiota and Enteric Methane Emissions Even Fed the Same Total Mixed Ration

Previous studies have focused on the rumen microbiome and enteric methane (CH₄) emissions in dairy cows, yet little is known about steers, especially steers of dairy breeds. In the present study, we comparatively examined the rumen microbiota, fermentation characteristics, and CH₄ emissions from six non-cannulated Holstein (710.33 ± 43.02 kg) and six Jersey (559.67 ± 32.72 kg) steers. The steers were fed the same total mixed ration (TMR) for 30 days. After 25 days of adaptation to the diet, CH₄ emissions were measured using GreenFeed for three consecutive days, and rumen fluid samples were collected on last day using stomach tubing before feeding (0 h) and 6 h after feeding. CH₄ production (g/d/animal), CH₄ yield (g/kg DMI), and CH₄ intensity (g/kg BW^0.75) were higher in the Jersey steers than in the Holstein steers. The lowest pH value was recorded at 6 h after feeding. The Jersey steers had lower rumen pH and a higher concentration of ammonia-nitrogen (NH₃-N). The Jersey steers had a numerically higher molar proportion of acetate than the Holstein steers, but the opposite was true for that of propionate. Metataxonomic analysis of the rumen microbiota showed that the two breeds had similar species richness, Shannon, and inverse Simpson diversity indexes. Principal coordinates analysis showed that the overall rumen microbiota was different between the two breeds. Both breeds were dominated by Prevotella ruminicola, and its highest relative abundance was observed 6 h after feeding. The genera Ethanoligenens, Succinivibrio, and the species Ethanoligenens harbinense, Succinivibrio dextrinosolvens, Prevotella micans, Prevotella copri, Prevotella oris, Prevotella baroniae, and Treponema succinifaciens were more abundant in Holstein steers while the genera Capnocytophaga, Lachnoclostridium, Barnesiella, Oscillibacter, Galbibacter, and the species Capnocytophaga cynodegmi, Galbibacter mesophilus, Barnesiella intestinihominis, Prevotella shahii, and Oscillibacter ruminantium in the Jersey steers. The Jersey steers were dominated by Methanobrevibacter millerae while the Holstein steers by Methanobrevibacter olleyae. The overall results suggest that sampling hour has little influence on the rumen microbiota; however, breeds of steers can affect the assemblage of the rumen microbiota and different mitigation strategies may be needed to effectively manipulate the rumen microbiota and mitigate enteric CH₄ emissions from these steers.

In Vitro Assessment of Enteric Methane Emission Potential of Whole-Plant Barley, Oat, Triticale and Wheat

The study determined in vitro enteric methane (CH₄) emission potential of whole-plant cereal (WPC) forages in relationship to nutrient composition, degradability, and rumen fermentation. Two varieties of each WPC (barley, oat, triticale, and wheat) were harvested from two field replications in each of two locations in central Alberta, Canada, and an in vitro batch culture technique was used to characterize gas production (GP), fermentation, and degradability. Starch concentration (g/kg dry matter (DM)) was least (p < 0.001) for oat (147), greatest for wheat (274) and barley (229), and intermediate for triticale (194). The aNDF concentration was greater for oat versus the other cereals (531 vs. 421 g/kg DM, p < 0.01). The 48 h DM and aNDF degradabilities (DMD and aNDFD) differed (p < 0.001) among the WPCs. The DMD was greatest for barley, intermediate for wheat and triticale, and least for oat (719, 677, 663, and 566 g/kg DM, respectively). Cumulative CH₄ production (MP; mL) from 12 h to 48 h of incubation was less (p < 0.001) for oat than the other cereals, reflecting its lower DMD. However, CH₄ yield (MY; mg of CH₄/g DM degraded) of barley and oat grown at one location was less than that of wheat and triticale (28 vs. 31 mg CH₄/g DM degraded). Chemical composition failed to explain variation in MY (p = 0.35), but it explained 45% of the variation in MP (p = 0.02). Variation in the CH₄ emission potential of WPC was attributed to differences in DMD, aNDFD, and fermentation end-products (R² ≥ 0.88; p < 001). The results indicate that feeding whole-plant oat forage to ruminants may decrease CH₄ emissions, but animal performance may also be negatively affected due to lower degradability, whereas barley forage may ameliorate emissions without negative effects on animal performance.

Genetic parameters for methane emission traits in Australian dairy cows

Methane is a greenhouse gas of high interest to the dairy industry, with 57% of Australia's dairy emissions attributed to enteric methane. Enteric methane emissions also constitute a loss of approximately 6.5% of ingested energy. Genetic selection offers a unique mitigation strategy to decrease the methane emissions of dairy cattle, while simultaneously improving their energy efficiency. Breeding objectives should focus on improving the overall sustainability of dairy cattle by reducing methane emissions without negatively affecting important economic traits. Common definitions for methane production, methane yield, and methane intensity are widely accepted, but there is not yet consensus for the most appropriate method to calculate residual methane production, as the different methods have not been compared. In this study, we examined 9 definitions of residual methane production. Records of individual cow methane, dry matter intake (DMI), and energy corrected milk (ECM) were obtained from 379 animals and measured over a 5-d period from 12 batches across 5 yr using the SF₆ tracer method and an electronic feed recording system, respectively. The 9 methods of calculating residual methane involved genetic and phenotypic regression of methane production on a combination of DMI and ECM corrected for days in milk, parity, and experimental batch using phenotypes or direct genomic values. As direct genomic values (DGV) for DMI are not routinely evaluated in Australia at this time, DGV for FeedSaved, which is derived from DGV for residual feed intake and estimated breeding value for bodyweight, were used. Heritability estimates were calculated using univariate models, and correlations were estimated using bivariate models corrected for the fixed effects of year-batch, days in milk, and lactation number, and fitted using a genomic relationship matrix. Residual methane production candidate traits had low to moderate heritability (0.10 ± 0.09 to 0.21 ± 0.10), with residual methane production corrected for ECM being the highest. All definitions of residual methane were highly correlated phenotypically (>0.87) and genetically (>0.79) with one another and moderately to highly with other methane candidate traits (>0.59), with high standard errors. The results suggest that direct selection for a residual methane production trait would result in indirect, favorable improvement in all other methane traits. The high standard errors highlight the importance of expanding data sets by measuring more animals for their methane emissions and DMI, or through exploration of proxy traits and combining data via international collaboration.

Invited Review: Glucosinolates Might Result in Low Methane Emissions From Ruminants Fed Brassica Forages

Methane is formed from the microbial degradation of feeds in the digestive tract in ruminants. Methane emissions from ruminants not only result in a loss of feed energy but also contribute to global warming. Previous studies showed that brassica forages, such as forage rape, lead to less methane emitted per unit of dry matter intake than grass-based forages. Differences in rumen pH are proposed to partly explain these low emissions. Rumen microbial community differences are also observed, but the causes of these are unknown, although altered digesta flow has been proposed. This paper proposes a new mechanism underlying the lower methane emissions from sheep fed brassica forages. It is reported that feeding brassica forages to sheep can increase the concentration of free triiodothyronine (FT3) in serum, while the intramuscular injection of FT3 into sheep can reduce the mean retention time of digesta in the rumen. The short retention time of digesta is associated with low methane production. Glucosinolates (GSLs) are chemical components widely present in plants of the genus Brassica. After ruminants consume brassica forages, GSLs are broken down in the rumen. We hypothesize that GSLs or their breakdown products are absorbed into the blood and then may stimulate the secretion of thyroid hormone FT3 in ruminants, and the altered thyroid hormone concentration may change rumen physiology. As a consequence, the mean retention time of digesta in the rumen would be altered, resulting in a decrease in methane emissions. This hypothesis on mitigation mechanism is based on the manipulation of animal physiological parameters, which, if proven, will then support the expansion of this research area.

Animal, feed and rumen fermentation attributes associated with methane emissions from sheep fed brassica crops

Methane emissions from ruminants enhance global warming and lead to a loss of feed energy. The emissions are low when fed brassica crops, but the factors contributing to low emissions are unknown. A meta-analysis was conducted with individual animal data collected from seven experiments. In these experiments, methane emissions were measured using respiration chambers. Animal characteristics, feed chemical composition and rumen fermentation parameters were included for the analysis using multiple regression models. Feed intake level, animal live weight and age were animal factors that were weakly and negatively related to methane yield (g/dry matter intake). The duration in which sheep were fed brassica crops was a significant contributor in the model, suggesting that the effect on emissions diminishes with time. Among a range of feed chemical composition characters, acid detergent fibre and hot-water-soluble carbohydrate contributed significantly to the model, suggesting that both structural and soluble carbohydrates affect methane formation in the rumen. There was no significant correlation between the concentration of sulphate in brassicas and emissions, but nitrate was moderately and negatively correlated with methane yield (r = -.53). Short-chain fatty acid profiles in the rumen of animals fed brassicas were different from those fed pasture, but these parameters only moderately correlated to methane emissions (r = .42). Feeding forage rape resulted in low rumen pH. The pH before morning feeding was strongly correlated to methane yield (r = .90). Rumen pH, together with microbial communities mediated by pH, might lead to low emissions. Bacteria known to produce hydrogen were relatively less abundant in the rumen contents of brassica-fed animals than pasture-fed animals. In conclusion, animal and feed factors, rumen fermentation and microbial communities all affect methane emissions to some extent. The interactions of these factors with each other thus contribute to methane emissions from brassica-fed sheep.

Variability of Ammonia and Methane Emissions from Animal Feeding Operations in Northeastern Colorado

Concentrated animal feeding operations (CAFOs) are major emitters of both ammonia (NH₃) and methane (CH₄). However, current emission inventories have limited temporal resolution and use data derived from a small subset of farms. To this end, we deployed three mobile laboratories during the DISCOVER-AQ campaign in summer 2014 with a focus on northeastern Colorado. Observations of NH₃ and CH₄ plumes downwind of 43 CAFOs were used to investigate the diurnal and site-to-site variability of emissions with an inverse area source plume modeling approach. Ammonia emissions scaled to all permitted animals in Weld, Morgan, and Larimer counties were estimated at 1.9 Gg month^-1, 50% greater than the U.S. NEI 2014 and 360% greater than EDGAR for the month of August. Methane emissions were likewise estimated at 10.6 Gg month^-1, consistent with the U.S. GHGI but 99% greater than EDGAR. Significant differences between individual CAFOs with repeat observations were also observed for both CH₄ and NH₃ emissions. The large subfarm, site-to-site, and diurnal variabilities observed show the importance of measurements taken across these scales in order to derive representative emission factors.

Effects of rumen-protected lysine and histidine on milk production and energy and nitrogen utilization in diets containing hydrolyzed feather meal fed to lactating Jersey cows

Hydrolyzed feather meal (HFM) is high in crude protein, most of which bypasses rumen degradation when fed to lactating dairy cows, allowing direct supply of AA to the small intestine. Compared with other feeds that are high in bypass protein, such as blood meal or heat-treated soybean meal, HFM is low in His and Lys. The objectives of this study were to determine the effects of supplementing rumen-protected (RP) Lys and His individually or in combination in a diet containing 5% HFM on milk production and composition as well as energy and N partitioning. Twelve multiparous Jersey cows (mean ± SD: 91 ± 18 d in milk) were used in a triplicated 4 × 4 Latin square with 4 periods of 28 d (24-d adaptation and 4-d collection). Throughout the experiment, all cows were fed the same TMR, with HFM included at 5% of diet DM. Cows were grouped by dry matter intake and milk yield, and cows within a group were randomly assigned to 1 of 4 treatments: no RP Lys or RP His; RP Lys only [70 g/d of Ajipro-L (24 g/d of digestible Lys), Ajinomoto Co. Inc., Tokyo, Japan]; RP His only [32 g/d of experimental product (7 g/d of digestible His), Balchem Corp., New Hampton, NY]; or both RP Lys and His. Plasma Lys concentration increased when RP Lys was supplemented without RP His (77.7 vs. 66.0 ± 4.69 µM) but decreased when RP Lys was supplemented with RP His (71.4 vs. 75.0 ± 4.69 µM). Plasma concentration of 3-methylhistidine decreased with RP Lys (3.19 vs. 3.40 ± 0.31 µM). With RP His, plasma concentration of His increased (21.8 vs. 18.7 ± 2.95 µM). For milk production and milk composition, no effects of Lys were observed. Supplementing RP His increased milk yield (22.5 vs. 21.6 ± 2.04 kg/d) and tended to increase milk protein yield (0.801 vs. 0.772 ± 0.051 kg/d). Across treatments, dry matter intake (18.5 ± 0.83 kg/d) and energy supply (32.2 ± 2.24 Mcal of net energy for lactation) were not different. Supplementing RP His did not affect N utilization; however, supplementing RP Lys increased N balance (25 vs. 16 ± 9 g/d). The lack of production responses to RP Lys suggests that Lys was not limiting or that the increase in Lys supply was not large enough to cause an increase in milk protein yield. However, increased N balance and decreased 3-methylhistidine with RP Lys suggest that increased Lys supply increased protein accretion and decreased protein mobilization. Furthermore, His may be a limiting AA in diets containing HFM.

Effect of Feeding Improved Grass Hays and Eragrostis Tef Straw Silage on Milk Yield, Nitrogen Utilization, and Methane Emission of Lactating Fogera Dairy Cows in Ethiopia

The nutritionally imbalanced poor-quality diet feeding is the major constraint of dairy production in tropical regions. Hence, alternative high-quality roughage-based diets are required to improve milk yield and reduce methane emission (CH4). Thus, we tested the effects of feeding natural pasture hay, improved forage grass hays (Napier and Brachiaria Hybrid), and treated crop residues (Eragrostis tef straw) on nutrient digestibility, milk yield, nitrogen balance, and methane emission. The eight lactating Fogera cows selected for the experiment were assigned randomly to a 4 × 4 Latin square design. Cows were housed in well-ventilated individual pens and fed a total mixed ration (TMR) comprising 70% roughage and 30% concentrate. The four roughage-based basal dietary treatments supplemented with formulated concentrate were: Control (natural pasture hay (NPH)); treated teff straw silage (TTS); Napier grass hay (NGH); and Brachiaria hybrid grass hay (BhH). Compared with the control diet, the daily milk yield increased (p < 0.01) by 31.9%, 52.9%, and 71.6% with TTS, NGH, and BhH diets, respectively. Cows fed BhH had the highest dry matter intake (8.84 kg/d), followed by NGH (8.10 kg/d) and TTS (7.71 kg/d); all of these intakes were greater (p = 0.01) than that of NPH (6.21 kg/d). Nitrogen digestibility increased (p < 0.01) from the NPH diet to TTS (by 27.7%), NGH (21.7%), and BhH (39.5%). The concentration of ruminal ammonia nitrogen was higher for cows fed NGH than other diets (p = 0.01) and positively correlated with plasma urea nitrogen concentration (R² = 0.45). Feeding TTS, NGH, and BhH hay as a basal diet changed the nitrogen excretion pathway from urine to feces, which can help protect against environmental pollution. Estimated methane yields per dry matter intake and milk yield were decreased in dairy cows fed BhH, NGH, and TTS diets when compared to cows fed an NPH diet (p < 0.05). In conclusion, feeding of TTS, NGH, and BhH roughages as a basal diet to lactating dairy cows in tropical regions improved nutrient intake and digestibility, milk yield, nitrogen utilization efficiency, and reduced enteric methane emission.

Looking for high-production and sustainable diets for lactating cows: A survey in Italy

The aim of the present study was to evaluate, through a survey conducted on commercial farms, the global warming potential (GWP) of different lactating cow total mixed rations (TMR) and to identify the best dietary strategies to increase feed efficiency (FE) and reduce enteric CH4 emission. A total of 171 dairy herds were selected: data about dry matter intake (DMI), lactating cow TMR composition, and milk production and composition were provided by farmers. Diet GWP (kg of CO2 equivalents; CO2eq) was calculated as sum of GWP (kg of CO2eq) of each included ingredient, considering inputs needed at field level, feed processing, and transport. For soybean solvent meal, land use change was included in the assessment. Enteric methane production (g/d) was estimated [using the equation CH4 (g/d) = 2.54 + 19.14 × DMI] to calculate CH4 emission for kilograms of fat- and protein-corrected milk (FPCM). The data set was analyzed by generalized linear model and logistic analysis using SAS 9.4 (SAS Institute Inc., Cary, NC). The frequency distribution showed wide variation among farms for GWP (kg of CO2eq) of TMR: approximately 25% of the surveyed farms showed a diet GWP of 15 kg of CO2eq, 20% showed a GWP of 13 kg of CO2eq, and 16.7% showed a GWP of 17 kg of CO2eq. The variation among farms was due to the feedstuffs used. Among feedstuffs, soybean meal (SBM) had the highest correlation with the GWP of the TMR as shown by the following equation: TMR GWP (kg of CO2eq) = 2.49 × kg of SBM + 6.9 (R2 = 0.547). Moreover, diets with inclusion of SBM >15% of dry matter (DM) did not result in higher milk production than diets with a lower inclusion of SBM (≤15%). Average daily milk production of cows was 29.8 [standard deviation (SD) 4.83] kg with fat and protein contents of 3.86% (SD 0.22) and 3.40% (SD 0.14), respectively. The average DMI (kg/d) of lactating cows was 22.3 (SD 2.23). Logistic analysis demonstrated that corn silage ≤30% of diet DM was associated with higher FE. Almost 50% of farms had an average value of 15.0 g of CH4/kg of FPCM and about 30% of farms had an average of 12.5 g of CH4/kg of FPCM. The results demonstrated that lower enteric CH4 production was related to inclusion (% of diet DM) of ≤12% alfalfa hay and >30% corn silage. Diets with >34% neutral detergent fiber had higher CH4 production (>14.0 g/kg of FPCM) than those with lower neutral detergent fiber content. In contrast, lower enteric CH4 production (≤14.0 g/kg of FPCM) was related to diets characterized by net energy of lactation (NEL) >1.61 Mcal/kg and >4% ether extract. The variability in TMR GWP shows significant potential for reducing the GWP of a diet through choice and inclusion levels of ingredients (mainly SBM) and the possibility of decreasing methane enteric emission associated with milk production on a commercial scale.

Beginning to offer drinking water at birth increases the species richness and the abundance of Faecalibacterium and Bifidobacterium in the gut of preweaned dairy calves

We previously demonstrated that dairy calves having access to drinking water since birth (W0) achieved greater body weight, fiber digestibility, and feed efficiency than those that first received drinking water at 17 d of age (W17). Since gut microbiota composition could be linked to growth and development of animals, the objective of this study was to examine the effect of offering drinking water to newborn calves on composition of bacteria in the gut using a fecal microbiota analysis. Fresh feces were collected directly from the rectum of calves in W0 (n = 14) and W17 (n = 15) at 2, 6, and 10 wk of age. All of the calves were fed pasteurized waste milk, weaned at 7 wk of age, and offered tap water according to the treatment. The DNA was sequenced using 16S rRNA gene-amplicon sequencing on an Illumina MiSeq system (Illumina Inc., San Diego, CA). The sequences were clustered into operational taxonomic units (OTU) with a 99% similarity threshold. Treatment effects on α-diversity indices and relative abundance of the 10 most abundant genera were analyzed using GLIMMIX procedure of SAS (SAS Institute Inc., Cary, NC). Statistical significance (q-value) of treatment effects on the 50 most abundant OTU was determined with a false discovery rate analysis. At 2 wk of age, W0 had a greater number of observed OTU (5,908 vs. 4,698) and species richness (Chao 1 index) than W17. The number of OTU and richness indices increased from wk 2 to 6, but the increment of W17 was greater than that of W0. The Shannon and inverse-Simpson indices increased linearly with age, but no difference was observed between W0 and W17 at any time point. The Firmicutes to Bacteroidetes ratios were also similar at every time point but decreased markedly when calves were weaned. The relative abundance of genera Faecalibacterium and Bacteroides was greater in W0 than W17 at 2 wk of age. The genus Faecalibacterium continued to be more abundant in W0 than W17 at 6 wk of age but had similar abundance 3 wk after weaning (10 wk of age). The abundance of Faecalibacterium at wk 6 was positively correlated with apparent total-tract digestibility of acid detergent fiber at 10 wk of age. Calves receiving water since birth had greater abundance of OTU related to Faecalibacterium prausnitzii, and Bifidobacterium breve at 6 wk of age (q < 0.085). These species are known to improve growth in preweaned calves. The abundance of none of the genera and OTU was different between W0 at W17 at 10 wk of age (q > 0.100). Overall, beginning to offer drinking water at birth has a potential to modulate gut microbiota composition and thereby positively affect performance of young dairy heifer calves (≤10 wk of age).

Greenhouse gas, water, and land footprint per unit of production of the California dairy industry over 50 years

Food production including dairy has been associated with environmental impacts and resource use that has been steadily improving when adjusted per unit of product. The objective of this study was to conduct a cradle-to-farm gate environmental impact analysis and resource inventory of the California dairy production system to estimate the change in greenhouse gas emissions and water and land use over the 50-yr period between 1964 and 2014. Using a life cycle assessment according to international standards and the Food and Agriculture Organization of the United Nations guidelines, we analyzed contributions from dairy production in California to global environmental change. Production of 1 kg of energy- and protein-corrected milk (ECM) in California emitted 1.12 to 1.16 kg of CO₂ equivalents (CO₂e) in 2014 compared with 2.11 kg of CO₂e in 1964, a reduction of 45.0 to 46.9% over the last 50 yr, depending on the model used. Greater reductions in enteric methane intensity (i.e., methane production per kilogram of ECM) were observed (reduction of 54.1 to 55.7%) compared with manure GHG (reduction of 8.73 to 11.9%) in 2014 compared with 1964. This was mainly because manure management in the state relies on lagoons for storage, which has a greater methane conversion factor than solid manure storage. Water use intensity was reduced by 88.1 to 89.9%, with water reductions of 88.7 to 90.5% in crop production, 55.3 to 59.2% in housing and milking, and 52.4 to 54% in free water intake. Improved crop genetics and management have contributed to large efficiencies in water utilization. Land requirements for crop production were reduced by 89.4 to 89.7% in 2014 compared with 1964. This was mainly due to dramatic increases in crop yields in the last 50 yr. The increases in milk production per cow through genetic improvements and better nutrition and animal care have contributed to reductions in greenhouse gas emissions and land and water usage when calculated per unit of production (intensity) basis.

Construction and Operation of a Respiration Chamber of the Head-Box Type for Methane Measurement from Cattle

Simple Summary

The aim of the present work is to describe the construction and operation of a respiration chamber of the head-box type for measuring methane emissions from bovines. Methane is a greenhouse gas 28 times more potent than CO₂ in its capacity of producing the greenhouse effect and global warming. This gas is produced in considerable amounts by cattle as part of its normal digestion process; approximately 37% of the global anthropogenic methane emissions originate from the livestock industry. Measuring emissions of methane by cattle is necessary for inventory calculation and the evaluation of mitigation policies of this gas. The gold standard technique for measuring methane emissions from cattle is the respiration chamber; however, respiration chambers are expensive pieces of equipment that are not easily available for developing countries. Since a large proportion of the world’s cattle population is in the developing countries, a cheaper option is necessary. A respiration chamber of the head-box type is an option because of its low cost and high accuracy in estimating emissions. This chamber can be used to determine in vivo methane emission factors for those countries that do not have full respiration chambers. It can also be used to conduct experiments to evaluate the anti-methanogenic effects of different compounds.

Abstract

This paper aims to describe the construction and operation of a respiration chamber of the head-box type for methane (CH₄) measurements in bovines. The system consists of (1) a head box with a stainless steel frame and acrylic walls, floor, and ceiling; (2) a stainless steel feeder; (3) an automatic drinking water bowl; (4) a hood made from reinforced canvas; (5) an infrared (IR) CH₄ gas analyzer, a mass flow generator, a data-acquisition system; and (6) a steel metabolic box. Six assays were conducted to determine the pure CH₄ recovery rate of the whole system in order to validate it and comply with standards of chamber operation. The gravimetrical method was used for the recovery test and the recovery rate obtained was 1.04 ± 0.05. Once the system was calibrated, measurements of CH₄ were conducted using eight animals consisting of four Holstein cows with a live weight of 593.8 ± 51 kg and an average milk yield of 23.3 ± 1.8 kg d⁻¹ and four heifers with a live weight of 339 ± 28 kg. The CH₄ production values were 687 ± 123 and 248 ± 40 L CH₄ d⁻¹ for cows and heifers, respectively. The CH₄ yield was 19.7 ± 3.4 g and 17.1 ± 3.4 g CH₄ kg⁻¹ of dry matter consumed for cows and heifers, respectively. These results are consistent with those reported in the literature.

A meta-analysis comparing four measurement methods to determine the relationship between methane emissions and dry-matter intake in New Zealand dairy cattle

Enteric methane (CH4) emissions and dry-matter intake (DMI) can be accurately and precisely measured in respiration chambers (RC), whereas automated head chambers (GreenFeed; GF) and the SF6 tracer method can provide estimates of CH4 emissions from grazing cattle. In New Zealand, most dairy cattle graze pasture and, under these conditions, DMI also has to be estimated. The objective of the current study was to compare the relationship between CH4 production and DMI of New Zealand dairy cattle fed forages using the following four measurement methods: RC with measured DMI (RC); sulfur hexafluoride (SF6) with measured DMI (SF6-DMI); SF6 with DMI estimated from prediction equations or indigestible markers (SF6); GF with measured or estimated DMI (GF). Data were collected from published literature from New Zealand trials with growing and lactating dairy cattle fed forage-based diets and data were analysed using a mixed-effect model. The intercept of the linear regression between CH4 production and DMI was not significantly different from zero and was omitted from the model. However, residual variance (observed–predicted values) increased with an increasing DMI, which was addressed by log-transforming CH4 per unit of DMI and this model was used for final data analysis. The accuracy of the four methods for predicting log CH4 per unit of DMI was similar (P = 0.55), but the precision (indicated by residuals) differed (P &lt; 0.001) among methods. The residual standard deviations for SF6, GF and SF6-DMI were 4.6, 3.4 and 2.1 times greater than the residuals for RC. Hence, all methods enabled accurate prediction of CH4 per unit of DMI, but methodology for determining both CH4 and DMI affected their precision (residuals).