Prediction of methane production from dairy and beef cattle

Dietary concentrate supplementation increases milk production and reduces predicted greenhouse gas emission intensity in pasture-based commercial dairy farms

Controlled studies have extensively documented that concentrate supplements typically increase enteric CH4 emissions and milk yield and reduce emissions per unit of milk produced and dry matter intake. However, no studies have been conducted to determine the effect of concentrate on predicted greenhouse gas emissions from dairy farms representing the Australian pasture-based farming system. Thus, this study sought to determine how dietary concentrate supplementation affects enteric and manure CH4, and N2O of Australian pasture-based dairy farms. The Australian Dairy Carbon Calculator was used, which incorporates emission factors and methodologies used in the National Greenhouse Gas Inventory as reported to the Intergovernmental Panel on Climate Change. Primary data were collected and analyzed from 120 commercial farms in Australia's major dairy regions. Then the farms were divided into 4 groups based on their dietary concentrate supplementation: ≤1 (low; 15 farms), 1 to 2 (moderate; 35 farms), 2 to 3 (high; 35 farms), and ≥3 (very high; 35 farms) t of concentrate dry matter (tDM) per cow per year. Sources of greenhouse gas emissions were CO2 from concentrate production, enteric CH4, and manure CH4 and N2O. Total dry matter intake, milk yield, and daily enteric CH4 production (g/d) quadratically increased with concentrate level, whereas greenhouse gas emission intensity of milk production (kg of CO2 equivalent per kg of fat- and protein-corrected milk) decreased by 14% for farms supplementing with ≥3 tDM/cow per year compared with those supplementing with ≤1 tDM/cow per year of dietary concentrate. The N2O and CH4 emissions from manure increased quadratically and linearly, respectively, with the increasing supplementation of concentrate. Farms supplementing 2 to 3 tDM/cow per year showed substantial increases in gross income, gross margin, earnings before interest and tax, and net income ($/cow per year) compared with those supplementing of ≤1, 1 to 2, and ≥3 tDM/cow per year. Overall, increasing dietary concentrate supplementation for dairy cows resulted in increased milk production per cow, reduced greenhouse gas emissions per unit of milk produced, and increased income and profit. However, a comprehensive life cycle assessment study is needed to account for carbon sequestration by other farm components, such as pastures and trees, which were not considered in the present study. In addition, the present study was based on modeling and did not gather ground truth information for DMI, digestibility, crude protein, and urinary and fecal N excretion. Therefore, data should be interpreted with caution, and studies gathering such information are encouraged.

Enhanced CH4/N2 Separation Efficiency of UiO-66-Br2 through Hybridization with Mesoporous Silica

Efficient separation of CH4 from N2 is essential for the purification of methane from nitrogen. In order to address this problem, composite materials consisting of rod-shaped SBA-15-based UiO-66-Br2 were synthesized for the purpose of separating a CH4/N2 mixture. The materials were characterized via PXRD, N2 adsorption-desorption, SEM, TEM, FT-IR, and TGA. The adsorption isotherms of CH4 and N2 under standard pressure conditions for the composites were determined and subsequently compared. The study revealed that the composites were formed through the growth of MOF nanocrystals on the surfaces of the SBA-15 matrix. The enhancements in surface area and adsorption capacity of hybrid materials were attributed to the structural modifications resulting from the interactions between surface silanol groups and metal centers. The selectivity of the composites towards a gas mixture of CH4 and N2 was assessed utilizing the Langmuir adsorption equation. The results of the analysis revealed that the U6B2S5/SBA-15 sample exhibited the greatest selectivity for CH4/N2 adsorption compared to the other samples, with an adsorption selectivity parameter (S) of 20.06. Additional research is necessary to enhance the enrichment of methane from CH4/N2 mixtures using SBA-15-based metal-organic framework materials.

- Invited Review - Chemical signalling within the rumen microbiome

Ruminants possess a specialized four-compartment forestomach, consisting of the reticulum, rumen, omasum, and abomasum. The rumen, the primary fermentative chamber, harbours a dynamic ecosystem comprising bacteria, protozoa, fungi, archaea, and bacteriophages. These microorganisms engage in diverse ecological interactions within the rumen microbiome, primarily benefiting the host animal by deriving energy from plant material breakdown. These interactions encompass symbiosis, such as mutualism and commensalism, as well as parasitism, predation, and competition. These ecological interactions are dependent on many factors, including the production of diverse molecules, such as those involved in quorum sensing (QS). QS is a density-dependent signalling mechanism involving the release of autoinducer (AIs) compounds, when cell density increases AIs bind to receptors causing the altered expression of certain genes. These AIs are classified as mainly being N-acyl-homoserine lactones (AHL; commonly used by Gram-negative bacteria) or autoinducer-2 based systems (AI-2; used by Gram-positive and Gram-negative bacteria); although other less common AI systems exist. Most of our understanding of QS at a gene-level comes from pure culture in vitro studies using bacterial pathogens, with much being unknown on a commensal bacterial and ecosystem level, especially in the context of the rumen microbiome. A small number of studies have explored QS in the rumen using 'omic' technologies, revealing a prevalence of AI-2 QS systems among rumen bacteria. Nevertheless, the implications of these signalling systems on gene regulation, rumen ecology, and ruminant characteristics are largely uncharted territory. Metatranscriptome data tracking the colonization of perennial ryegrass by rumen microbes suggest that these chemicals may influence transitions in bacterial diversity during colonization. The likelihood of undiscovered chemicals within the rumen microbial arsenal is high, with the identified chemicals representing only the tip of the iceberg. A comprehensive grasp of rumen microbial chemical signalling is crucial for addressing the challenges of food security and climate targets.

Agroecological Zone-Specific Diet Optimization for Water Buffalo (Bubalus bubalis) through Nutritional and In Vitro Fermentation Studies

The water buffalo faces challenges in optimizing nutrition due to varying local feed resources. In response to this challenge, the current study introduces originality by addressing the lack of region-specific feeding strategies for water buffaloes. This is achieved through the formulation of 30 different diets based on locally available resources, offering a tailored approach to enhance nutritional optimization in diverse agroecological contexts. These diets were segmented into three groups of ten, each catering to the maintenance (MD1 to MD10), growth (GD1 to GD10), and lactation/production (PD1 to PD10) needs of buffaloes. Utilizing local feed ingredients, each diet was assessed for its chemical composition, in vitro gas and methane emissions, and dry matter (DM) disappearance using buffalo rumen liquor. The production diets (127 and 32.2 g/kg DM) had more protein and fats than the maintenance diets (82.0 and 21.0 g/kg DM). There was less (p < 0.05) fiber in the production diets compared to the maintenance ones. Different protein components (PB1, PB2) were lower (p < 0.05) in the maintenance diets compared to the growth and production ones, but other protein fractions (PB3, Pc) were higher (p < 0.05) in the maintenance diet. Furthermore, the growth diets had the highest amount of other protein components (PA), while the maintenance diets had the highest amount of soluble carbohydrates (586 g/kg DM), whereas the carbohydrate fraction (CB1) was highest (p < 0.05) in the production diets (187 g/kg DM), followed by the growth (129 g/kg DM) and maintenance diets (96.1 g/kg DM). On the contrary, the carbohydrate CA fraction was (p < 0.05) higher in the maintenance diets (107 g/kg DM) than in the growth (70.4 g/kg DM) and production diets (44.7 g/kg DM). The in vitro gas production over time (12, 24, and 48 h) was roughly the same for all the diets. Interestingly, certain components (ether extract, lignin, NDIN, ADIN, and PB3 and CC) of the diets seemed to reduce methane production, while others (OM, NPN, SP, PA and PB1, tCHO and CB2) increased it. In simple words, this study reveals that different diets affect gas production during digestion, signifying a significant step towards a promising future for buffalo farming through tailored, region-specific formulations.

Increasing farm size is an effective way to decrease the carbon footprint in dairy cattle production

The purpose of this study was to investigate the effects of the farm size on the carbon footprint of dairy cattle farms in Isparta province in Türkiye. For this purpose, face-to-face interviews were conducted with 159 farms which represent 1866 dairy cattle farms in Isparta province. The number of animals on the farm was converted into animal unit (AU) and the farms were divided into three groups. Accordingly, farms were classified as small, medium, and large farms. The carbon footprint produced per AU in the farm was the sum of feed, enteric fermentation, CH4 from manure, CO2 from manure, N2O from manure, and anthropogenic emissions. The milk produced in the farms was standardized according to 4% fat and 3.3% protein (FPCM) and the ratio of meat to milk was found by dividing the total live weight gain produced except for cows by FPCM. Accordingly, 65% of the greenhouse gas emissions of dairy farms were allocated to milk and 35% to meat. Of the total emissions, enteric fermentation and emission on feed contributed the highest proportion. Results showed that when using the IPCC (2021) global warming potential (GWP) values, the carbon footprint for 1 kg of FPCM milk was 1.26 kg CO2-eq on average, whereas the carbon footprint for 1 kg of meat was 11.78 kg CO2-eq on average. Results showed that as farm size increased carbon footprint for a kilogram of FPCM and meat decreased and this showed the effectiveness of farm size on decreasing carbon footprint per unit of product.

Taking the diet of cows into consideration in designing payments to reduce enteric methane emissions on dairy farms

Enteric fermentation from dairy cows is a major source of methane. Significantly and rapidly reducing those emissions would be a powerful lever to mitigate climate change. For a given productivity level, introducing fodder with high sources of n-3 content, such as grass or linseed, in the feed ration of dairy cows both improves the milk nutritional profile and reduces enteric methane emissions per liter. Changing cows' diet may represent additional costs for dairy farmers and calls for the implementation of payments for environmental services to support the transition. This paper analyzes 2 design elements influencing the effectiveness of a payment conditioned toward the reduction of enteric methane emissions: (1) the choice of emission indicator capturing the effect of farmers' practices, and (2) the payment amount relative to the additional milk production costs incurred. Using representative farm-level economic data from the French farm accountancy data network, we compare enteric methane emissions per liter of milk calculated with an Intergovernmental Panel on Climate Change Tier 2 method, to baseline emissions from a Tier 3 method accounting for diet effects. We also quantify the additional milk production costs of integrating more grass in the fodder systems by estimating variable cost functions for different dairy systems in France. Our results show the relevance of using an emission indicator sensitive to diet effects, and that the significance and direction of the additional costs for producing milk with a diet containing more grass differ according to the production basin and the current share of grasslands in the fodder crop rotation. We emphasize the importance of developing payments for environmental services with well-defined environmental indicators accounting for the technical problems addressed, and the need to better characterize heterogeneous funding requirements for supporting a large-scale adoption of more environment-friendly practices by farmers.

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.

Impact of essential oils on methane emissions, milk yield, and feed efficiency and resulting influence on the carbon footprint of dairy production systems

Reducing CO₂ emissions is one of the highest priorities in animal production. Regarding methane reduction, feed additives are of growing importance. As shown in a meta-analysis, the use of the essential oil (EO) blend Agolin Ruminant affects methane production per day (- 8.8%), milk yield (+ 4.1%), and feed efficiency (+ 4.4%). Building on these results, the present study investigated the effect of varying individual parameters on the carbon footprint of milk. The environmental and operational management system REPRO was applied to calculate the CO₂ emissions. Calculation of CO₂ emissions include enteric and storage-related CH₄, storage-, and pasture-related N₂O as well as direct and indirect energy expenditures. Three feed rations were created, differing in their basic feed components such as grass silage, corn silage, and pasture. Each feed ration was differentiated into three variants: variant 1 CON (no additive), variant 2 EO, and variant 3 (15% reduction of enteric methane compared to CON). Due to the reducing effect of EO on enteric methane production, a reduction potential of up to 6% could be calculated for all rations. Considering other variable parameters, such as the positive effects on ECM yield and feed efficiency, a GHG reduction potential of up to 10% can be achieved for the silage rations and almost 9% for the pasture ration. Modeling showed that indirect methane reduction strategies are important contributors to environmental impacts. Reduction of enteric methane emissions is fundamental, as they account for the largest share of GHG emissions from dairy production.

Beef Steers and Enteric Methane: Reducing Emissions by Managing Forage Diet Fiber Content

Understanding the methane (CH4) emissions that are produced by enteric fermentation is one of the main problems to be solved for livestock, due to their GHG effects. These emissions are affected by the quantity and quality of their diets, thus, it is key to accurately define the intake and fiber content (NDF) of these forage diets. On the other hand, different emission prediction equations have been developed; however, there are scarce and uncertain results regarding their evaluation of the emissions that have been observed in forage diets. Therefore, the objectives of this study were to evaluate the effect of the NDF content of a forage diet on CH4 enteric emissions, and to evaluate the ability of models to predict the emissions from the animals that are consuming these forage diets. In total, thirty-six Angus steers (x¯ = 437 kg live weight) aged 18 months, blocked by live weight and placed in three automated feeding pens, were used to measure the enteric CH4. The animals were randomly assigned to two forage diets (n = 18), with moderate (<50%, MF) and high (>50%, HF) NDF contents. Their dry matter intake was recorded individually, and the CH4 emissions were measured using the SF6 tracer gas technique. For the model evaluation, six prediction equations were compared with 29 studies (n = 97 observations), analyzing the accuracy and precision of their estimates. The emission intensities per unit of DMI, per ADG, and per gross energy intake were significantly lower (p < 0.05) in the animals consuming the MF diet than in the animals consuming the HF diet (21.7 vs. 23.7 g CH4/kg DMI, 342 vs. 660 g CH4/kg ADG, and 6.7% vs. 7.5%, respectively), but there were no differences in the absolute emissions (p > 0.05). The best performing model was the IPCC 2006 model (r2 = 0.7, RMSE = 74.04). These results show that reducing the NDF content of a forage diet by at least 10% (52 g/kg DM) reduces the intensity of the g CH4/kg DMI by up to 8%, and that of the g CH4/kg ADG by almost half. The use of the IPCC 2006 model is suitable for estimating the CH4 emissions from animals consuming forage-based diets.

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

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

Nitrogen utilisation, energy utilisation and methane emissions of sheep grazing in two types of pasture

Livestock grazing plays a significant role in maintaining grasslands and promoting animal production globally. To understand the livestock performance in sown pasture (SP) vs native pasture (NP) is important to ensure more effective grassland-livestock interactions with minimal environmental impact. A 2 (treatment) * 2 (period) Latin Square design experiment was conducted with 10 growing Hu sheep ♂ × thin-tailed Han sheep ♀ rams grazed perennially SP vs NP in an inland arid region of China. The objectives were to evaluate the effects of grazing management on nutrient digestibility, nitrogen (N) and energy utilisation and methane (CH₄) emission. The N intake, N retained and energy intake (gross energy (GE), and digestible and metabolisable energy) of sheep grazing in SP were significantly increased compared with those grazing in NP. There were significant linear relationships between DM intake (DMI) (g/kg BW or g/kg BW^0.75) or CH₄ (g/kg BW or g/kg BW^0.75) emissions and forage nutrient and GE concentrations within each grassland type. The linear regression analysis indicated that forage CP or ether extract concentration was a good predictor for DMI (g/kg BW or g/kg BW^0.75) (R² = 0.756 or 0.752), and CH₄ emission could be predicted using forage nutrient and GE concentrations (R² = 0.381-0.503). These results suggest that DMI and CH₄ emissions per unit metabolic BW were accurately predicted by multiple-factor combinations of forage nutrients, including ether extract and CP paired with GE. The present output could provide useful information for the development of sustainable sheep grazing systems in the inland arid regions of the world.

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

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

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

Simple Summary

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

Abstract

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

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

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

Enteric Methane Emissions and Animal Performance in Dairy and Beef Cattle Production: Strategies, Opportunities, and Impact of Reducing Emissions

Enteric methane (CH4) emissions produced by microbial fermentation in the rumen resulting in the emission of greenhouse gases (GHG) into the atmosphere. The GHG emissions reduction from the livestock industry can be attained by increasing production efficiency and improving feed efficiency, by lowering the emission intensity of production, or by combining the two. In this work, information was compiled from peer-reviewed studies to analyze CH4 emissions calculated per unit of milk production, energy-corrected milk (ECM), average daily gain (ADG), dry matter intake (DMI), and gross energy intake (GEI), and related emissions to rumen fermentation profiles (volatile fatty acids [VFA], hydrogen [H2]) and microflora activities in the rumen of beef and dairy cattle. For dairy cattle, there was a positive correlation (p < 0.001) between CH4 emissions and DMI (R2 = 0.44), milk production (R2 = 0.37; p < 0.001), ECM (R2 = 0.46), GEI (R2 = 0.50), and acetate/propionate (A/P) ratio (R2 = 0.45). For beef cattle, CH4 emissions were positively correlated (p < 0.05−0.001) with DMI (R2 = 0.37) and GEI (R2 = 0.74). Additionally, the ADG (R2 = 0.19; p < 0.01) and A/P ratio (R2 = 0.15; p < 0.05) were significantly associated with CH4 emission in beef steers. This information may lead to cost-effective methods to reduce enteric CH4 production from cattle. We conclude that enteric CH4 emissions per unit of ECM, GEI, and ADG, as well as rumen fermentation profiles, show great potential for estimating enteric CH4 emissions.

Metagenomics reveals differences in microbial composition and metabolic functions in the rumen of dairy cows with different residual feed intake

Background

Rumen microbial composition and functions have vital roles in feed digestion and fermentation and are linked to feed efficiency in cattle. This study selected Holstein cows, which are high in both milk protein content and milk yield, to analyse the relationship between the rumen microbiota and residual feed intake (RFI). Eighteen multiparous lactating cows were divided into low RFI (LRFI, high efficiency, n = 9) and high RFI (HRFI, low efficiency, n = 9) groups to investigate the differences in microbial composition and functions.

Results

The relative abundances of butyrate producers, including the Clostridium, Butyrivibrio, Eubacterium and Blautia genera, were higher in HRFI cows than in LRFI cows (P < 0.05). Four carbohydrate metabolic pathways (glycolysis/gluconeogenesis, pentose phosphate pathway, fructose and mannose metabolism, and butanoate metabolism) and one energy metabolism pathway (methane metabolism), were more abundant in HRFI animals (P < 0.05). Quorum sensing and DNA replication pathways were more abundant in HRFI cows. For CAZyme profiles, 14 out of 19 genes encoding carbohydrates-deconstructing enzymes were more abundant in HRFI cows (P < 0.05). Seven Lachnospiraceae species associated with carbohydrate metabolism and quorum sensing may contribute to the difference in feed efficiency. Moreover, the LRFI cows had lower abundances of Methanosphaera (P < 0.01), Methanobrevibacter ruminantium (P = 0.09) and methanogenesis functions (P = 0.04).

Conclusions

The rumen microbiota of low-efficiency cows has stronger abilities to degrade carbohydrates and produce methane, and quorum sensing pathways could also be associated with differences in feed efficiency. This study provides a deeper understanding of the microbial ecology of dairy cows with different feed efficiencies and highlights the possibility of modulating the rumen microbiome or microbial functions to improve the feed efficiency of dairy cows.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-022-00170-3.

Predicting Metabolizable Energy from Digestible Energy for Growing and Finishing Beef Cattle and Relationships to Prediction of Methane

Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE – 0.3926; r² = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = −0.567 to −0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE – 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH₄, Mcal/kg = 0.3344 – 0.05639 × multiple of maintenance; r² = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.

Fibrolytic rumen bacteria of camel and sheep and their applications in the bioconversion of barley straw to soluble sugars for biofuel production

Lignocellulosic biomass such as barley straw is a renewable and sustainable alternative to traditional feeds and could be used as bioenergy sources; however, low hydrolysis rate reduces the fermentation efficiency. Understanding the degradation and colonization of barley straw by rumen bacteria is the key step to improve the utilization of barley straw in animal feeding or biofuel production. This study evaluated the hydrolysis of barley straw as a result of the inoculation by rumen fluid of camel and sheep. Ground barley straw was incubated anaerobically with rumen inocula from three fistulated camels (FC) and three fistulated sheep (FR) for a period of 72 h. The source of rumen inoculum did not affect the disappearance of dry matter (DMD), neutral detergent fiber (NDFD). Group FR showed higher production of glucose, xylose, and gas; while higher ethanol production was associated with cellulosic hydrolysates obtained from FC group. The diversity and structure of bacterial communities attached to barley straw was investigated by Illumina Mi-Seq sequencing of V4-V5 region of 16S rRNA genes. The bacterial community was dominated by phylum Firmicutes and Bacteroidetes. The dominant genera were RC9_gut_group, Ruminococcus, Saccharofermentans, Butyrivibrio, Succiniclasticum, Selenomonas, and Streptococcus, indicating the important role of these genera in lignocellulose fermentation in the rumen. Group FR showed higher RC9_gut_group and group FC revealed higher Ruminococcus, Saccharofermentans, and Butyrivibrio. Higher enzymes activities (cellulase and xylanase) were associated with group FC. Thus, bacterial communities in camel and sheep have a great potential to improve the utilization lignocellulosic material in animal feeding and the production of biofuel and enzymes.

Effect of Suckling Management and Ewe Concentrate Level on Methane-Related Carbon Footprint of Lamb Meat in Sardinian Dairy Sheep Farming

Simple Summary

Suckling lamb meat is the secondary product of the Mediterranean traditional dairy sheep industry. Similar to the main production, i.e., milk, lamb meat contributes to the emission of greenhouse gases (GHG), whose main portion is represented by enteric methane produced by the lamb dams. Such an emission, although limited in quantitative terms, should be mitigated by appropriate feeding or compensation techniques. Among all the sources of variation of meat lamb emissions, sex of the lamb and type of lambing (single or twins) showed the largest effect.

Abstract

The aim of this study was to estimate the methane-linked carbon footprint (CF) of the suckling lamb meat of Mediterranean dairy sheep. Ninety-six Sarda dairy ewes, divided into four groups of 24 animals each, were assigned to 2 × 2 factorial design. The experiment included the suckling lamb feeding system: traditional (TS), in which lambs followed their mothers on pasture during grazing time, vs. separated (SS), in which lambs remained indoors, separated from their mothers during the grazing time. Each group was divided into high (HS) and low (LS) supplemented ewes (600 g/d vs. 200 g/d of concentrate). The estimated CH₄ emission of the ewes, calculated per kg of body weight (BW) gain of the lamb during the suckling period, was then converted to CO₂eq with multiplying factor of 25. The TS lambs showed lower methane-linked emissions than SS ones (p < 0.05). The sex of lambs affected their methane-linked CF, with males having lower (p < 0.05) values than females. Twins displayed much lower methane-linked CF than singles (4.56 vs. 7.30 kg of CO₂eq per kg of BW gained), whereas the level of supplementation did not affect greenhouse gases (GHG) emission. Interaction displayed lower and not-different GHG emissions for both indoor- and outdoor-reared twins. In conclusion, the methane-linked CF of the suckling lamb meat can be reduced by maintaining the traditional lamb rearing system and by improving flock prolificacy.

Effect of Methane Inhibitors on Ruminal Microbiota During Early Life and Its Relationship With Ruminal Metabolism and Growth in Calves

The present study aimed to determine whether dietary supplementation with methanogen inhibitors during early life may lead to an imprint on the rumen microbial community and change the rumen function and performance of calves to 49-weeks of rearing. Twenty-four 4-day-old Friesian x Jersey cross calves were randomly assigned into a control and a treatment group. Treated calves were fed a combination of chloroform (CF) and 9,10-anthraquinone (AQ) in the solid diets during the first 12 weeks of rearing. Afterward, calves were grouped by treatments until week 14, and then managed as a single group on pasture. Solid diets and water were offered ad libitum. Methane measurements, and sample collections for rumen metabolite and microbial community composition were carried out at the end of weeks 2, 4, 6, 8, 10, 14, 24 and 49. Animal growth and dry matter intake (DMI) were regularly monitored over the duration of the experiment. Methane emissions decreased up to 90% whilst hydrogen emissions increased in treated compared to control calves, but only for up to 2 weeks after treatment cessation. The near complete methane inhibition did not affect calves’ DMI and growth. The acetate:propionate ratio decreased in treated compared to control calves during the first 14 weeks but was similar at weeks 24 and 49. The proportions of Methanobrevibacter and Methanosphaera decreased in treated compared to control calves during the first 14 weeks; however, at week 24 and 49 the archaea community was similar between groups. Bacterial proportions at the phylum level and the abundant bacterial genera were similar between treatment groups. In summary, methane inhibition increased hydrogen emissions, altered the methanogen community and changed the rumen metabolite profile without major effects on the bacterial community composition. This indicated that the main response of the bacterial community was not a change in composition but rather a change in metabolic pathways. Furthermore, once methane inhibition ceased the methanogen community, rumen metabolites and hydrogen emissions became similar between treatment groups, indicating that perhaps using the treatments tested in this study, it is not possible to imprint a low methane microbiota into the rumen in the solid feed of pre-weaned calves.

Effect of a Low-Methane Diet on Performance and Microbiome in Lactating Dairy Cows Accounting for Individual Pre-Trial Methane Emissions

This study examined the effects of partly replacing grass silage (GS) with maize silage (MS), with or without rapeseed oil (RSO) supplementation, on methane (CH₄) emissions, production performance, and rumen microbiome in the diets of lactating dairy cows. The effect of individual pre-trial CH₄-emitting characteristics on dietary emissions mitigation was also examined. Twenty Nordic Red cows at 71 ± 37.2 (mean ± SD) days in milk were assigned to a replicated 4 × 4 Latin square design with four dietary treatments (GS, GS supplemented with RSO, GS plus MS, GS plus MS supplemented with RSO) applied in a 2 × 2 factorial arrangement. Partial replacement of GS with MS decreased the intake of dry matter (DM) and nutrients, milk production, yield of milk components, and general nutrient digestibility. Supplementation with RSO decreased the intake of DM and nutrients, energy-corrected milk yield, composition and yield of milk fat and protein, and general digestibility of nutrients, except for crude protein. Individual cow pre-trial measurements of CH₄-emitting characteristics had a significant influence on gas emissions but did not alter the magnitude of CH₄ emissions. Dietary RSO decreased daily CH₄, yield, and intensity. It also increased the relative abundance of rumen Methanosphaera and Succinivibrionaceae and decreased that of Bifidobacteriaceae. There were no effects of dietary MS on CH₄ emissions in this study, but supplementation with 41 g RSO/kg of DM reduced daily CH₄ emissions from lactating dairy cows by 22.5%.

The effect of Macleaya cordata extract on in vitro ruminal fermentation and methanogenesis

Methane production is the main gas energy loss in ruminants and generates a powerful greenhouse gas that contributes to climate changes. Macleaya cordata is a plant commonly utilized additive in livestock diet as it contains various chemical compounds with beneficial health effects. This study aimed to explore the influence of M. cordata extract on in vitro methanogenesis and rumen fermentation. Three cannulated Jingjiang cattle were used as rumen fluid donors. The mixture of rumen fluid and a buffer (60 ml, 1:2 volume ratio) was treated with 6 different concentrations of M. cordata extract (0.01%, 0.11%, 0.21%, 0.31%, 0.41%, and 0.51%) and incubated for 12 and 24 hr. The control sample, with no addition of plant extract, was also analyzed. At each time point, pH, total gas, methane, dry matter (DM) digestibility, neutral detergent fiber (NDF), acid detergent fiber (ADF), ammonia nitrogen (NH3-N), microbial protein (MCP), and volatile fatty acids (VFA) concentrations were determined. Total gas production decreased with increasing the amount of M. cordata extract at all time points. Methane production also decreased dose-dependently with M. cordata extract after 3, 6, 9, and 12 hr of incubation, but increased after 24 hr. M. cordata extract decreased the concentration of NH3-N and VFA, and the amount of acetic, propionic, and butyric acid. M. cordata extract decreased the MCP concentration after 12 hr, but its level returned to the initial value after 24 hr. Supplementation with 0.01, 0.11, and 0.21% of M. cordata extract did not affect the DM digestibility. However, supplementation with 0.31, 0.41, and 0.51% of M. cordata extract significantly decreased the DM digestibility. Supplementation with 0.11% of M. cordata extract effectively reduced methane production without affecting the DM digestion in vitro. However, its effect on in vivo methane production, rumen fermentation, and ruminant production requires further investigation.

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.

Development of a Mathematical Model and Validation for Methane Production Using Cow Dung as Substrate in the Underground Biogas Digester

South Africa is highly dependent on conventional fuels from non-renewable energy sources such as coal and fossil fuels. The methane from biogas is a renewable energy source for generating electricity using a combined heat and power plant. Therefore, the aim of the study is to develop and validate a mathematical model for predicting methane production in an underground biogas digester. The developed model was able to predict the production of methane gas as separate entity differing from other models. A total of 286 datasets were used as a trained dataset for the model development, and 144 datasets served as test data for the validation of the model, making a total of 430 measured datasets of all the predictors. The determination coefficient (R2) and the p-value of the predicted and calculated methane yield were 0.962 and 0.920, respectively. The high R2 in the present study confirms a good correlation between the model and experimental value. Hence, the model is of significance because it is applicable in predicting the performance of methane production of systems of the same design used in different locations, thereby arriving at the same constant values. From the study, the ambient weather factors (ambient temperature, relative humidity, and global horizontal irradiance) affected the methane production. Additionally, the indoor parameters (pH, gas temperature, slurry bottom and slurry top temperature) impacted on the yield of the methane production because the scaling factors associated with these quantities are non-zero real numbers. Hence, the scope of the study did not consider the volume of the biogas digester as an input parameter to the response.

Comparative analyses of enteric methane emissions, dry matter intake, and milk somatic cell count in different residual feed intake categories of dairy cows

This study compared the different residual feed intake (RFI) categories of lactating Holsteins with respect to methane (CH4) emissions, dry matter intake (DMI, kg), milk somatic cell count (SCC, 103∙mL−1), and β-hydroxybutyrate (BHB, mmol∙L−1). The RFI was calculated in 131 lactating Holstein cows that were then categorized into −RFI (RFI < 0) vs. +RFI (RFI > 0) and low- [RFI < −0.5 standard deviation (SD)] vs. high-RFI (RFI > 0.5 SD) groups. Milk traits were recorded in 131 cows, whereas CH4 and carbon dioxide were measured in 83. Comparisons of −RFI vs. +RFI and low- vs. high-RFI showed 7.9% (22.3 ± 0.40 vs. 24.2 ± 0.39) and 12.8% (21.1 ± 0.40 vs. 24.2 ± 0.45) decrease (P < 0.05) in DMI of −RFI and low-RFI groups, respectively. Similarly, −RFI and low-RFI cows had lower (P < 0.05) CH4 (g∙d−1) by 9.7% (343.5 ± 11.1 vs. 380.4 ± 10.9) and 15.5% (332.5 ± 12.9 vs. 393.5 ± 12.6), respectively. Milk yield was not different (P > 0.05) in −RFI vs. +RFI and low vs. high comparisons. The −RFI and low-RFI cows had lower (P < 0.05) SCC in −RFI vs. +RFI and low-RFI vs. high-RFI comparisons. The BHB was lower (P < 0.05) in low-RFI compared with the high-RFI group. Low-RFI dairy cows consumed less feed, emitted less CH4 (g∙d−1), and had lower milk SCC and BHB without differing in milk yield.

Contributions of dairy products to environmental impacts and nutritional supplies from United States agriculture

Questions regarding the balance between the contribution to human nutrition and the environmental impact of livestock food products rarely evaluate specific species or how to accomplish the recommended depopulation. The objective of this study was to assess current contributions of the US dairy industry to the supply of nutrients and environmental impact, characterize potential impacts of alternative land use for land previously used for crops for dairy cattle, and evaluate the impacts of these approaches on US dairy herd depopulation. We modeled 3 scenarios to reflect different sets of assumptions for how and why to remove dairy cattle from the US food production system coupled with 4 land-use strategies for the potential newly available land previously cropped for dairy feed. Scenarios also differed in assumptions of how to repurpose land previously used to grow grain for dairy cows. The current system provides sufficient fluid milk to meet the annual energy, protein, and calcium requirements of 71.2, 169, and 254 million people, respectively. Vitamins supplied by dairy products also make up a high proportion of total domestic supplies from foods, with dairy providing 39% of the vitamin A, 54% of the vitamin D, 47% of the riboflavin, 57% of the vitamin B₁₂, and 29% of the choline available for human consumption in the United States. Retiring (maintaining animals without milk harvesting) dairy cattle under their current management resulted in no change in absolute greenhouse gas emissions (GHGE) relative to the current production system. Both depopulation and retirement to pasture resulted in modest reductions (6.8-12.0%) in GHGE relative to the current agricultural system. Most dairy cow removal scenarios reduced availability of essential micronutrients such as α-linolenic acid, Ca, and vitamins A, D, B₁₂, and choline. Those removal scenarios that did not reduce micronutrient availability also did not improve GHGE relative to the current production system. These results suggest that removal of dairy cattle to reduce GHGE without reducing the supply of the most limiting nutrients to the population would be difficult.

Study of Water Productivity of Industrial Hemp under Hot and Dry Conditions in Brandenburg (Germany) in the Year 2018

Hemp (Cannabis sativa L.) is a high-yielding multi-purpose crop, but its hydrological functioning is poorly understood. Studies on the interception processes in hemp have been lacking so far. This study contributes to the understanding of the influences of evaporation of intercepted water and other hydrological fluxes within plants of two cultivars, “Santhica 27” and “Ivory”, on the water productivity. To determine water productivity and evaporation from interception, field measurements were conducted on plants of both cultivars at different stages of development. Precipitation (P), throughfall (TF), transpiration (T), and volumetric water content (VWC) were measured along with leaf area index (LAI) and yield of selected plant components. For the entire vegetation period, the cumulative P of 44 mm was converted into 13 mm TF (30%). The inferred evaporation of intercepted water (I) was high at 31 mm (71%). For the assessment water fluxes, the evaporation of intercepted water must be considered in the decision-making process. Besides the LAI, the plant architecture and the meteorological conditions during the cropping cycle seem to be the main factors determining I in the case of plants of both cultivars. Water productivity (WPDM) of the whole plant varied between 3.07 kg m−3 for Ivory and 3.49 for Santhica 27. In the case of bast yield, WPDM was 0.39 kg m-3 for Santhica 27 and 0.45 kg m−3 for Ivory. After the propagation of the uncertainties, the bandwidth of the WPDM of the whole plant was between 0.42 kg m−3 and 2.57 kg m−3. For bast fiber a bandwidth of the WP between 0.06 kg m−3 and 0.33 kg m−3 was calculated. The results show furthermore that even with a precise examination of water productivity, a high bandwidth of local values is revealed on different cultivars. However, generic WP values for fiber crops are not attainable.

Development of prediction equation for methane-related traits in beef cattle under high concentrate diets

The objective of this study was to develop a prediction equation for methane-related traits in beef cattle and evaluate this equation using datasets with different cattle breeds and roughage rates. Enteric methane emission (CH₄ , l/day) was measured using open-circuit respiration chambers. Dry matter intake (DMI, kg/day), body weight (BW, kg), daily gain (DG, kg), total digestible nutrients (TDN, %DMI), and roughage rate (Rrate, %) were used as independent variables, and methane-related traits-CH₄ , CH₄ per DMI (CH₄ /DMI, l/kg), and methane conversion factor (MCF, %)-were used as dependent variables. The best-fit equations to predict methane-related traits using a total of 76 records were CH₄  = -676.7 + 0.04194 × BW + 29.88 × DMI + 7.883 × TDN + 4.367 × Rrate, CH₄ /DMI = -52.24 - 1.193 × 10^-3  × BW - 5.905 × DG + 1.077 × TDN + 0.5008 × Rrate, and MCF = -11.43 - 5.308 × 10^-4  × BW - 1.223 × DG + 0.2336 × TDN + 0.1157 × Rrate. The predictive ability of the developed equations differed between roughage rates but not between breeds. For CH₄ , the predictive ability of the developed equations was better compared with previously reported equations in the low roughage rate dataset, but not in the high roughage rate dataset. Our results suggest that the developed equations of methane-related traits can be applied in beef cattle fed with low roughage diets.

Between-cow variation in milk fatty acids associated with methane production

We evaluated the between-cow (b-cow) variation and repeatability in omasal and milk fatty acids (FA) related to methane (CH4) emission. The dataset was originated from 9 studies with rumen-cannulated dairy cows conducted using either a switch-back or a Latin square design. Production of CH4 per mole of VFA (Y_CH4VFA) was calculated based on VFA stoichiometry. Experiment, diet within experiment, period within experiment, and cow within experiment were considered as random factors. Empirical models were developed between the variables of interest by univariate and bivariate mixed model regression analysis. The variation associated with diet was higher than the b-cow variation with low repeatability (< 0.25) for milk odd- and branch-chain FA (OBCFA). Similarly, for de novo synthesized milk FA, diet variation was ~ 3-fold greater than the b-cow variation; repeatability for these FA was moderate to high (0.34-0.58). Also, for both cis-9 C18:1 and cis-9 cis-12 cis-15 C18:3 diet variation was more than double the b-cow variation, but repeatability was moderate. Among the de novo milk FA, C4:0 was positively related with stoichiometric Y_CH4VFA, while for OBCFA, anteiso C15:0 and C15:0 were negatively related with it. Notably, when analyzing the relationship between omasal FA and milk FA we observed positive intercept estimates for all the OBCFA, which may indicate endogenous post-ruminal synthesis of these FA, most likely in the mammary gland. For milk iso C13:0, iso C15:0, anteiso C15:0, and C15:0 were positively influenced by omasal proportion of their respective FA and by energy balance. In contrast, the concentration of milk C17:0, iso C18:0, C18:0, cis-11 C18:1, and cis-9 cis-12 cis-15 C18:3 were positively influenced by omasal proportion of their respective FA but negatively related to calculated energy balance. Our findings demonstrate that for most milk FA examined, a larger variation is attributed to diet than b-cow differences with low to moderate repeatability. While some milk FA were positively or negatively related with Y_CH4VFA, there was a pronounced effect of calculated energy balance on these estimates. Additionally, even though OBCFA have been indicated as markers of rumen function, our results suggest that endogenous synthesis of these FA may occur, which therefore, may limit the utilization of milk FA as a proxy for CH4 predictions for cows fed the same diet.

Development of a dynamic energy-partitioning model for enteric methane emissions and milk production in goats using energy balance data from indirect calorimetry studies

The main objective of this study was to develop a dynamic energy balance model for dairy goats to describe and quantify energy partitioning between energy used for work (milk) and that lost to the environment. Increasing worldwide concerns regarding livestock contribution to global warming underscore the importance of improving energy efficiency utilization in dairy goats by reducing energy losses in feces, urine and methane (CH4). A dynamic model of CH4 emissions from experimental energy balance data in goats is proposed and parameterized (n = 48 individual animal observations). The model includes DM intake, NDF and lipid content of the diet as explanatory variables for CH4 emissions. An additional data set (n = 122 individual animals) from eight energy balance experiments was used to evaluate the model. The model adequately (root MS prediction error, RMSPE) represented energy in milk (E-milk; RMSPE = 5.6%), heat production (HP; RMSPE = 4.3%) and CH4 emissions (E-CH4; RMSPE = 11.9%). Residual analysis indicated that most of the prediction errors were due to unexplained variations with small mean and slope bias. Some mean bias was detected for HP (1.12%) and E-CH4 (1.27%) but was around zero for E-milk (0.14%). The slope bias was zero for HP (0.01%) and close to zero for E-milk (0.10%) and E-CH4 (0.22%). Random bias was >98% for E-CH4, HP and E-milk, indicating non-systematic errors and that mechanisms in the model are properly represented. As predicted energy increased, the model tended to underpredict E-CH4 and E-milk. The model is a first step toward a mechanistic description of nutrient use by goats and is useful as a research tool for investigating energy partitioning during lactation. The model described in this study could be used as a tool for making enteric CH4 emission inventories for goats.

Effects of Feeding either Red or White Grape Marc on Milk Production and Methane Emissions from Early-Lactation Dairy Cows

Simple Summary

Grape marc comprises the skins, seeds and stems of grapes remaining after grapes are pressed to make wine. Globally, about nine million tonnes of grape marc are produced annually. However, little is known about the comparative nutritional value of grape marc from red and white grapes and their effects on milk production and methane emissions when fed to dairy cows. Our experiment assessed the potential role of grape marc as a feed source for the grazing based, Australian dairy industry. We fed diets based on harvested perennial ryegrass to lactating dairy cows and compared milk production and methane emissions when grape marc from either red or white grapes was substituted for some of the perennial ryegrass. Diets containing grape marc from either red or white grapes equally decreased milk yields by approximately 10% and methane emissions by 15%. When fed to dairy cows, grape marc reduces methane emissions but at the cost of decreased milk production. The effects on methane emissions were mainly mediated by the high concentrations of lignin and fat in grape marc while decreased milk production was due to decreased intake of metabolizable energy.

Abstract

Globally, annual production of grape marc (GM), the residue of skins, seeds and stems remaining after making wine, has been estimated to be approximately nine million tonnes. No previous studies have compared effects on milk production and methane emissions when GM from either red or white grapes was fed to dairy cows. This experiment examines the effects of partial replacement of a perennial ryegrass (Lolium perenne L.) based diet with GM from either red or white grapes on yield and composition of milk and methane emissions. Thirty-two Holstein dairy cows in early lactation were offered either a control diet containing 15.0 kg dry matter (DM) of freshly harvested perennial ryegrass and 5.2 kg of a concentrate mix, or a diet similar to the control diet but with 5 kg DM of ryegrass replaced with 5 kg DM of GM from red grapes (RGM), or a diet similar to the RGM diet except the GM was from white grapes (WGM). Individual cow feed intakes, milk yields, and methane emissions were measured. Both diets containing GM decreased milk yields by approximately 10% and methane emissions by 15%. When fed to dairy cows, GM reduces methane emissions but at the cost of decreased milk production.

The Potential Effect of Dietary Tannins on Enteric Methane Emission and Ruminant Production, as an Alternative to Antibiotic Feed Additives – A Review

Antibiotic growth promoters in livestock nutrition cause microbial resistance which produces threats to human health. Therefore, tannins have been considered as natural alternative antibiotic feed additives which possess various biological properties including antimicrobial, anti-inflammatory, antioxidant and immunomodulatory. Additionally, these plants also have antiparasitic and anti-bloat characteristics which contribute to inhibit the enteric methane emission in order to improve nutrient digestibility, milk and meat quality, fatty acids composition and ruminant production. Antibiotic growth promoters have been practiced in animals feeding to increase feed intake, growth rate, weight gain as well as reduce metabolic disorders and energy losses in the rumen. In 2006, the European Union banned the usage of antibiotic growth promoters in the feeding of livestock. This antibiotic resistance issue has increased demand to explore the natural feed additives that might be useful for animal production system. Consequently, natural forages have been categorized as potential feed additives in animal production since it improves nutritive value, protein digestibility, increase amino acid absorption and growth rate. But, some plant materials are usually rich in tannins known as anti-nutritional factors. Therefore, the application of tannin-rich plants in ruminant nutrition needs great precaution due to its possible injurious effects (dose dependent) on animal health such as metabolic disorders. Hence, there is need to give attention to the usage of tannins in ruminant nutrition as an alternative to antibiotics feed additives to investigate its effects on enteric methane emissions and ruminants production. In addition, safety and risk associated with tannins feeding have also been briefly discussed.

Methane production and estimation from livestock husbandry: A mechanistic understanding and emerging mitigation options

Globally, livestock is an important contributor to methane (CH₄) emissions. This paper reviewed the various CH₄ measurement and estimation techniques and mitigation approaches for the livestock sector. Two approaches for enteric livestock CH₄ emission estimation are the top-down and bottom-up. The combination of both could further improve our understanding of enteric CH₄ emission and possible mitigation measures. We discuss three mitigation approaches: reducing emissions, avoiding emissions, and enhancing the removal of emissions from livestock. Dietary management, livestock management, and breeding management are viable reducing emissions pathways. Dietary manipulation is easily applicable and can bring an immediate response. Economic incentive policies can help the livestock farmers to opt for diet, breeding, and livestock management mitigation approaches. Carbon pricing creates a better option to achieve reduction targets in a given period. A combination of carbon pricing, feeding management, breeding management, and livestock management is more feasible and sustainable CH₄ emissions mitigation strategy rather than a single approach.

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.

The effects of improved performance in the U.S. dairy cattle industry on environmental impacts between 2007 and 2017

The U.S. dairy industry considerably reduced environmental impacts between 1944 and 2007, primarily through improved dairy cow productivity. However, although milk yield per cow has increased over the past decade, whole-system environmental impact analyses have not been conducted over this time period, during which environmental modeling science has improved considerably. The objective of this study was to compare the environmental impact of U.S. dairy cattle production in 2007-2017. A deterministic model based on population demographics, metabolism, and nutrient requirements of dairy cattle was used to estimate resource inputs, nutrient excretion, and greenhouse gas (GHG) emissions per 1.0 × 106 t (one million metric t or MMT) of energy-corrected milk (ECM) produced in 2007 and 2017. System boundaries extended from the manufacture and transport of cropping inputs to milk at the farm gate. Milk transport, processing, and retail were not included. Dairy systems were modeled using typical management practices, herd population dynamics, and production data from U.S. dairy farms. Cropping data were sourced from national databases. The resources required to produce 1.0 MMT ECM in 2017 were considerably reduced relative to those required in 2007, with 2017 production systems using 74.8% of the cattle, 82.7% of the feedstuffs, 79.2% of the land, and 69.5% of the water as compared to 2007. Waste outputs were similarly reduced, with the 2017 U.S. dairy industry producing 79.4%, 82.5%, and 85.7% of the manure, N, and P excretion, respectively. Dairy production in 2017 emitted 80.9% of the CH4 and 81.5% of the N2O per 1.0 MMT ECM compared to 2007. Enteric and manure emissions contributed the major proportion (80%) of GHG emissions per unit of milk, with lesser contributions from cropping (7.6%) and fertilizer application (5.3%). The GHG emissions per 1.0 MMT ECM produced in 2017 were 80.8% of equivalent milk production in 2007. Consequently, although total U.S. ECM production increased by 24.9% between 2007 and 2017, total GHG emissions from this milk production increased by only 1.0%. In line with previous historical analyses, the U.S. dairy industry has made remarkable productivity gains and environmental progress over time. To maintain this culture of continuous improvement, the dairy industry must build on gains made to date and demonstrate its commitment to reducing environmental impacts while improving both economic viability and social acceptability.

Increasing roughage quality by using alfalfa hay as a substitute for concentrate mitigates CH4 emissions and urinary N and ammonia excretion from dry ewes

Twelve Hu sheep × thin-tail Han crossbred dry ewes with an average body weight of 32.6 ± 0.68 kg and an age of 3 years were arranged in a 3 × 3 Latin square design, with each experimental period of 24 d to evaluate the effect of substituting alfalfa hay in a portion of concentrate on nutrient intake, digestibility, N utilisation efficiency and methane emissions. The ratios of corn straw to alfalfa to concentrate for 3 diet treatments were 60:0:40, 60:15:25 and 60:30:10, respectively. Intake and digestibility were measured for each of the ewes, which were housed in individual metabolism crates for 6 d after an adaptation period of 14 d, and the feed was offered at 1.2 MEm to ensure approximately 10% orts. Methane emissions were determined in a respiration chamber for 2 consecutive d. An increase in the levels of alfalfa as a substitute for concentrate significantly increased the roughage, NSC and ADF intake and faecal N output as a proportion of N intake and manure N output. Furthermore, this increase in alfalfa input levels decreased DE, ME and N intake; nutrient digestibility; DE/GE, ME/GE and CH4 emissions per day; CH4 output expressed as a portion of the DM, OM and GE intake; and urinary N and ammonia N output, especially between extreme treatments. Alfalfa input levels had no effect on the BW, DM and GE intake; the EB or EB/GE intake; and the retained N. This study indicated that increasing alfalfa input as a substitute for concentrate could significantly decrease the digestibility, CH4 emissions and urinary N and NH4 + -N outputs; and shift the N excretion from urine to faeces; and could sustain a similar DM intake.

Development of an equation to estimate the enteric methane emissions from Holstein dairy cows in Canada

The aim of this study was to use dietary factors, including the type of fats, and animal characteristics, to predict enteric methane (CH4) emissions from dairy cows under Canadian conditions. For this purpose, 193 individual observations from six different trials assessing the impact of dietary modification on enteric CH4 production were analyzed. Animal [milk yield (MY), milk fat content, milk protein content, days in milk, body weight (BW), and dry matter intake (DMI)] and dietary variables [organic matter, crude protein, neutral detergent fiber (NDF), acid detergent fiber (ADF), starch, ether extract (EE), rumen-inert fat, and unprotected fat (EE – rumen-inert fat)] were tested. A 5-fold cross validation was used to obtain the following equation: CH4 (g d−1) = −1260.4 + 1.9 × MY (kg d−1) + 62.8 × milk fat (%) –18.4 × milk protein (%) + 11.0 × DMI (kg d−1) + 0.3 × BW (kg) + 58.3 × NDF (% of DM) − 0.8 × NDF2 (% of DM) + 1.9 × starch (% of DM) − 2.5 × EE – rumen-inert fat (% of DM). The mean estimate from the proposed equation (474 g CH4 cow−1 d−1; r = 0.83, RMSE = 40.0) was close to the observed mean emission (476 g CH4 cow−1 d−1). The proposed model has a higher precision to predict CH4 emission from cows fed typical Canadian diets than other models, and it can be used to evaluate CH4 mitigation strategies.

Effect of different forage-to-concentrate ratios on ruminal bacterial structure and real-time methane production in sheep

Emission from ruminants has become one of the largest sources of anthropogenic methane emission in China. The structure of the rumen flora has a significant effect on methane production. To establish a more accurate prediction model for methane production, the rumen flora should be one of the most important parameters. The objective of the present study was to investigate the relationship among changes in rumen flora, nutrient levels, and methane production in sheep fed with the diets of different forage-to-concentration ratios, as well as to screen for significantly different dominant genera. Nine rumen-cannulated hybrid sheep were separated into three groups and fed three diets with forage-to-concentration ratios of 50:50, 70:30, and 90:10. Three proportions of the diets were fed according to a 3 × 3 incomplete Latin square, design during three periods of 15d each. The ruminal fluid was collected for real-time polymerase chain reaction (real-time PCR), high-throughput sequencing and in vitro rumen fermentation in a new real-time fermentation system wit. Twenty-two genera were screened, the abundance of which varied linearly with forage-to-concentration ratios and methane production. In addition, during the 12-hour in vitro fermentation, the appearance of peak concentration was delayed by 26-27min with the different structure of rumen bacteria. The fiber-degrading bacteria were positively correlated with this phenomenon, but starch-degrading and protein-degrading bacteria were negative correlated. These results would facilitate macro-control of rumen microorganisms and better management of diets for improved nutrition in ruminants. In addition, our findings would help in screening bacterial genera that are highly correlated with methane production.

Prediction of methane emission from sheep based on data measured in vivo from open-circuit respiratory studies

Objective

The current study analysed the relationships between methane (CH₄) output from animal and dietary factors.

Methods

The dataset was obtained from 159 Dorper×thin-tailed Han lambs from our seven studies, and CH₄ production and energy metabolism data were measured in vivo by an open-circuit respiratory method. All lambs were confined indoors and fed pelleted diet during the whole experimental period in all studies. Data from two-thirds of lambs were used to develop linear and multiple regressions to describe the relationship between CH₄ emission and dietary variables, and data from the remaining one third of lambs were used to validate the established models.

Results

CH₄ emission (g/d) was positively related to dry matter intake (DMI) and gross energy intake (GEI) (p<0.001). CH₄ energy/GEI was negatively related to metabolizable energy/gross energy and metabolizable energy/digestible energy (p<0.001). Using DMI to predict CH₄ emission (g/d) resulted in a coefficient of determination (R²) of 0.80. Using GEI, digestible energy intake, and metabolizable energy intake predict CH₄ energy/GEI resulted in a R² of 0.92.

Conclusion

the prediction equations established in the current study are useful to develop appropriate feeding and management strategies to mitigate CH₄ emissions from sheep.

A new Tier 3 method to calculate methane emission inventory for ruminants

Livestock is the main source of methane (CH₄) emissions. It is important to accurately determine emissions from ruminants that meet standardized international guidelines for national greenhouse gas inventories. A new method to improve the accuracy of CH₄ emissions that complies with IPCC rules for a Tier 3 method is described and evaluated. This method, developed by INRA (French Institute for Agricultural Research), was applied to the French inventory of CH₄ emissions by ruminants and compared with the IPCC Tier 2 method. For enteric CH₄, depending on the animal category, the INRA CH₄ emission estimates lay between 88% and 114% of IPCC's. The INRA/IPCC ratio for enteric emission was close to unity and did not differ between methods (P = 0.43) for adult cows (i.e. most cattle). In France, feedlot manure is stored in aerobic conditions, and so the INRA/IPCC fit for manure emission was poorer (P < 0.05). The INRA/IPCC fit for enteric CH₄ was very close between methods to that for total CH₄ (P = 0.39), enteric CH₄ representing 93% of total emissions. The main improvement is the use of a robust equation (from numerous data and diets), based on digestible organic matter intake (DOMI) corrected for the digestive interactions, to predict CH₄ consistently from enteric and manure sources. It was developed for the French livestock inventory but is customizable for other countries. This new improved CH₄ estimation method, based on equations from a large literature database, complies with IPCC rules for a Tier 3 method.