Methane emissions from enteric fermentation in Alberta’s beef cattle population

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.

Development and validation of a small SNP panel for feed efficiency in beef cattle

The objective of this study was to develop and validate a customized cost-effective single nucleotide polymorphism (SNP) panel for genetic improvement of feed efficiency in beef cattle. The SNPs identified in previous association studies and through extensive analysis of candidate genomic regions and genes, were screened for their functional impact and allele frequency in Angus and Hereford breeds used as validation candidates for the panel. Association analyses were performed on genotypes of 159 SNPs from new samples of Angus (n = 160), Hereford (n = 329), and Angus-Hereford crossbred (n = 382) cattle using allele substitution and genotypic models in ASReml. Genomic heritabilities were estimated for feed efficiency traits using the full set of SNPs, SNPs associated with at least one of the traits (at P ≤ 0.05 and P < 0.10), as well as the Illumina bovine 50K representing a widely used commercial genotyping panel. A total of 63 SNPs within 43 genes showed association (P ≤ 0.05) with at least one trait. The minor alleles of SNPs located in the GHR and CAST genes were associated with decreasing effects on residual feed intake (RFI) and/or RFI adjusted for backfat (RFIf), whereas minor alleles of SNPs within MKI67 gene were associated with increasing effects on RFI and RFIf. Additionally, the minor allele of rs137400016 SNP within CNTFR was associated with increasing average daily gain (ADG). The SNPs genotypes within UMPS, SMARCAL, CCSER1, and LMCD1 genes showed significant over-dominance effects whereas other SNPs located in SMARCAL1, ANXA2, CACNA1G, and PHYHIPL genes showed additive effects on RFI and RFIf. Gene enrichment analysis indicated that gland development, as well as ion and cation transport are important physiological mechanisms contributing to variation in feed efficiency traits. The study revealed the effect of the Jak-STAT signaling pathway on feed efficiency through the CNTFR, OSMR, and GHR genes. Genomic heritability using the 63 significant (P ≤ 0.05) SNPs was 0.09, 0.09, 0.13, 0.05, 0.05, and 0.07 for ADG, dry matter intake, midpoint metabolic weight, RFI, RFIf, and backfat, respectively. These SNPs contributed to genetic variation in the studied traits and thus can potentially be used or tested to generate cost-effective molecular breeding values for feed efficiency in beef cattle.

Comparison of models for estimating methane emission factor for enteric fermentation of growing-finishing Hanwoo steers

The methodology provided by the Intergovernmental Panel on Climate Change (IPCC) guidelines is widely used for estimating enteric methane (CH4) production by cattle. No attempt other than the default values in the IPCC Tier 1 has been made for estimating CH4 emission from Hanwoo, a dominant beef species in Korea raised in a unique feeding system. The objective of this study was to compare models for estimating the CH4 emission factor (MEF; kg CH4/head/year) for enteric fermentation in Hanwoo steers. The MEF was estimated based on Korea- and Hanwoo-specific data obtained from the literature using several models. The models include the IPCC Tier 1 (T1), the IPCC Tier 2 method (T2), the IPCC Tier 2 methodology with actual dry matter intake (T2DMI), and the Japanese Tier 3 method (JT3). The JT3 was included due to the similarity in the beef cattle production system between the two countries. Estimated MEF using T2 were 43.4, 33.9, and 36.2 kg CH4/head/year for the growing, finishing, and overall period, respectively. The overall MEF estimated using T2 was 23 % lower than the estimate by T1 (47.0 kg CH4/head/year). There were significant differences in the estimated MEF for enteric fermentation of Hanwoo steers among the methods (P < 0.05). The overall MEF estimated by JT3 was 69.1 kg CH4/head/year, which was significantly higher than the estimates by T2 (36.2 kg) and T2DMI (33.5 kg). The JT3 estimated the highest values in all periods possibly due to overestimation of the conversion ratio of feed energy to CH4. No significant difference was found in the overall MEF of Hanwoo steers between T2 and T2DMI. However, T2DMI estimated 8 % higher and 14 % lower MEF than T2 for the growing and finishing period, respectively, mainly because the T2 significantly over-predicts the gross energy intake of Hanwoo steers at the high level of intake. The IPCC default methods have limitations in their use for a feeding systems in non-western countries, and thus development of a country-specific methodology and parameter estimates for enteric CH4 production is required for Hanwoo and other cattle production systems.

A typological characterization of Canadian beef cattle farms based on a producer survey

The diverse nature of beef production was captured by establishing a farm typology based on an extensive survey of 1005 Canadian farms in 2011. The survey provided information on the type of operation, cattle numbers, feed storage and management, manure management, land use, producer demographics and attitudes to risk, and technology adoption. Principal component analysis and cluster analysis were used to understand the relationships among variables and to statistically identify farm types. A total of 41 diagnostic variables from 133 survey questions were used to define 16 principal components explaining 68% of the variation. Cluster analysis yielded eight major clusters as distinct farm types. The largest number of farms (37%) was grouped as small-scale, part-time cow–calf operations. Mixed operations (crop–beef) were next most frequent (22%), followed by large cow–calf backgrounding (18%) and diversified cow–calf operations that included crop–beef mixed operations as well as off-farm activities (11%). Cow–calf operations that finished calves comprised 8% of the total farms surveyed. Extensive cow–calf backgrounding operations, large backgrounding/finishing operations, and large finishing operations represented the remaining 3% of the farms. The typology not only provides a strategy by which the Canadian beef cattle industry can be characterized, but also improves understanding of the diversity of farm management practices to help develop policies and beneficial management practices.

Life-cycle assessment of greenhouse gas emissions from dairy production in Eastern Canada: a case study

The objective of this study was to conduct a life-cycle assessment (LCA) of greenhouse gas (GHG) emissions from a typical nongrazing dairy production system in Eastern Canada. Additionally, as dairying generates both milk and meat, this study assessed several methods of allocating emissions between these coproducts. An LCA was carried out for a simulated farm based on a typical nongrazing dairy production system in Quebec. The LCA was conducted over 6 yr, the typical lifespan of dairy cows in this province. The assessment considered 65 female Holstein calves, of which 60 heifers survived to first calving at 27 mo of age. These animals were subsequently retained for an average of 2.75 lactations. Progeny were also included in the analysis, with bulls and heifers in excess of replacement requirements finished as grain-fed veal (270 kg) at 6.5 mo of age. All cattle were housed indoors and fed forages and grains produced on the same farm. Pre-farm gate GHG emissions and removals were quantified using Holos, a whole-farm software model developed by Agriculture and Agri-Food Canada and based on the Intergovernmental Panel for Climate Change Tier 2 and 3methodologies with modifications for Canadian conditions. The LCA yielded a GHG intensity of 0.92 kg of CO(2) Eq/kg of fat- and protein-corrected milk yield. Methane (CH(4)) accounted for 56% of total emissions, with 86% originating from enteric fermentation. Nitrous oxide accounted for 40% of total GHG emissions. Lactating cows contributed 64% of total GHG emissions, whereas calves under 12 mo contributed 10% and veal calves only 3%. Allocation of GHG emissions between meat and milk were assessed as (1) 100% allocation to milk, (2) economics, (3) dairy versus veal animals, and (4) International Dairy Federation equation using feed energy demand for meat and milk production. Comparing emissions from dairy versus veal calves resulted in 97% of the emissions allocated to milk. The lowest allocation of emissions to milk (78%) was associated with the International Dairy Federation equation. This LCA showed that greatest reductions in GHG emissions would be achieved by applying mitigation strategies to reduce enteric CH(4) from the lactating cow, with minimal reductions being achievable in young stock. Choice of coproduct allocation method can also significantly affect the relative allocation of GHG emissions to milk and meat.

Greenhouse Gas Emissions from Calf- and Yearling-Fed Beef Production Systems, With and Without the Use of Growth Promotants

A spring calving herd consisting of about 350 beef cows, 14-16 breeding bulls, 60 replacement heifers and 112 steers were used to compare the whole-farm GHG emissions among calf-fed vs. yearling-fed production systems with and without growth implants. Carbon footprint ranged from 11.63 to 13.22 kg CO₂e per kg live weight (19.87-22.52 kg CO₂e per kg carcass weight). Enteric CH₄ was the largest source of GHG emissions (53-54%), followed by manure N₂O (20-22%), cropping N₂O (11%), energy use CO₂ (9-9.5%), and manure CH₄ (4-6%). Beef cow accounted for 77% and 58% of the GHG emissions in the calf-fed and yearling-fed. Feeders accounted for the second highest GHG emissions (15% calf-fed; 35-36% yearling-fed). Implants reduced the carbon footprint by 4.9-5.1% compared with hormone-free. Calf-fed reduced the carbon footprint by 6.3-7.5% compared with yearling-fed. When expressed as kg CO₂e per kg carcass weight per year the carbon footprint of calf-fed production was 73.9-76.1% lower than yearling-fed production, and calf-fed implanted was 85% lower than hormone-free yearling-fed. Reducing GHG emissions from beef production may be accomplished by improving the feed efficiency of the cow herd, decreasing the days on low quality feeds, and reducing the age at harvest of youthful cattle.

Estimation of enteric methane emissions trends (1990–2008) from Manitoba beef cattle using empirical and mechanistic models

Alemu, A. W., Ominski, K. H. and Kebreab, E. 2011. Estimation of enteric methane emissions trends (1990–2008) from Manitoba beef cattle using empirical and mechanistic models. Can. J. Anim. Sci. 91: 305–321. The objective of this study was to estimate and assess trends in enteric methane (CH4) emissions from the Manitoba beef cattle population from the base year of 1990 to 2008 using mathematical models. Two empirical (statistical) models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis), and two dynamic mechanistic models: MOLLY (v3) and COWPOLL were used. Beef cattle in Manitoba were categorized in to 29 distinct subcategories based on management practice, physiological status, gender, age and production environment. Data on animal performance, feeding and management practices and feed composition were collected from the literature as well as from provincial and national sources. Estimates of total enteric CH4 production from the Manitoba beef cattle population varied between 0.9 and 2.4 Mt CO2 eq. from 1990 to 2008. Regardless of the type of models used, average CH4 emissions for 2008 were estimated to be 45.2% higher than 1990 levels. More specifically, CH4 emissions tended to increase between 1990 and 1996. Emissions were relatively stable between 1996 and 2002, increased between 2003 and 2005, but declined by 13.2% between 2005 and 2008, following the same trend as that observed in the beef cattle population. Models varied in their estimates of CH4 conversion rate (Ym, percent gross energy intake), emission factor (kg CH4 head−1 yr−1) and CH4 production. Total CH4 production estimates ranged from 1.2 to 2.0 Mt CO2 eq. for IPCC Tier 2, from 0.9 to 1.5 Mt CO2 eq. for Ellis, from 1.3 to 2.1 Mt CO2 eq. for COWPOLL and from 1.5 to 2.4 Mt CO2 eq. for MOLLY. The results indicate that enteric CH4 estimates and emission trends in Manitoba were influenced by the type of model and beef cattle population. As such, it is necessary to use appropriate models for reliable estimates for enteric CH4 inventory. A more robust approach may be to integrate different models by using mechanistic models to estimate regional Ym values, which may then be used as input for the IPCC Tier 2 model.

Greenhouse gas and alcohol emissions from feedlot steers and calves

Livestock's contributions to climate change and smog-forming emissions are a growing public policy concern. This study quantifies greenhouse gas (GHG) and alcohol emissions from calves and feedlot steers. Carbon dioxide (CO) methane (CH), nitrous oxide (NO), ethanol (EtOH), and methanol (MeOH) were measured from a total of 45 Holstein and Angus steers and 9 Holstein calves representative of four different growth stages commonly present on calf ranches and commercial feedlots. Individuals from each animal type were randomly assigned to three equal replicate groups of nine animals per group. Steers were fed a high concentrate diet and calves a milk replacer and grain supplement. Cattle and calves were housed in groups of three animals in an environmental chamber for 24 h. The CO, NO, EtOH, and MeOH concentrations from the air inlet and outlet of the chamber were measured using an INNOVA 1412 monitor and CH using a TEI 55C methane analyzer. Emission rates (g head h) were calculated. The GHGs were mainly produced by enteric fermentation and respiration and differed across life stages of cattle. Compared with dairy cows, feedlot steers produce relatively less GHG. In general, ethanol and methanol, the most important volatile organic compound (VOC) group in the dairy sector, were below the lower limit of detection of the gas analyzer. The present data will be useful to verify models and to enhance GHG emission inventories for enteric fermentation, respiration, and fresh excreta for numerous cattle life stages across the beef industry.

Prediction of methane production from dairy and beef cattle

Methane (CH4) is one of the major greenhouse gases being targeted for reduction by the Kyoto protocol. The focus of recent research in animal science has thus been to develop or improve existing CH4 prediction models to evaluate mitigation strategies to reduce overall CH4 emissions. Eighty-three beef and 89 dairy data sets were collected and used to develop statistical models of CH4 production using dietary variables. Dry matter intake (DMI), metabolizable energy intake, neutral detergent fiber, acid detergent fiber, ether extract, lignin, and forage proportion were considered in the development of models to predict CH4 emissions. Extant models relevant to the study were also evaluated. For the beef database, the equation CH4 (MJ/d) = 2.94 (+/- 1.16) + 0.059 (+/- 0.0201) x metabolizable energy intake (MJ/d) + 1.44 (+/- 0.331) x acid detergent fiber (kg/d) - 4.16 (+/- 1.93) x lignin (kg/d) resulted in the lowest root mean square prediction error (RMSPE) value (14.4%), 88% of which was random error. For the dairy database, the equation CH4 (MJ/d) = 8.56 (+/- 2.63) + 0.14 (+/- 0.056) x forage (%) resulted in the lowest RMSPE value (20.6%) and 57% of error from random sources. An equation based on DMI also performed well for the dairy database: CH4 (MJ/d) = 3.23 (+/- 1.12) + 0.81 (+/- 0.086) x DMI (kg/d), with a RMSPE of 25.6% and 91% of error from random sources. When the dairy and beef databases were combined, the equation CH4 (MJ/d) = 3.27 (+/- 0.79) + 0.74 (+/- 0.074) x DMI (kg/d) resulted in the lowest RMSPE value (28.2%) and 83% of error from random sources. Two of the 9 extant equations evaluated predicted CH4 production adequately. However, the new models based on more commonly determined values showed an improvement in predictions over extant equations.