An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production.
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ABSTRACT:
SUBMITTER: Hristov AN
PROVIDER: S-EPMC4553761 | biostudies-other | 2015 Aug
REPOSITORIES: biostudies-other
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Project description:Enteric methane (CH4) emission is one of the major greenhouse gasses originating from cattle. Iodoform has in studies been found to be a potent mitigator of rumen CH4 formation in vitro. This study aimed to quantify potential of iodoform as an anti-methanogenic feed additive for dairy cows and investigate effects on feed intake, milk production, feed digestibility, rumen microbiome, and animal health indicators. The experiment was conducted as a 4 × 4 Latin square design using four lactating rumen, duodenal, and ileal cannulated Danish Holstein dairy cows. The treatments consisted of four different doses of iodoform (1) 0 mg/day, (2) 320 mg/day, (3) 640 mg/day, and (4) 800 mg/day. Iodoform was supplemented intra-ruminally twice daily. Each period consisted of 7-days of adaptation, 3-days of digesta and blood sampling, and 4-days of gas exchange measurements using respiration chambers. Milk yield and dry matter intake (DMI) were recorded daily. Rumen samples were collected for microbial analyses and investigated for fermentation parameters. Blood was sampled and analyzed for metabolic and health status indicators. Dry matter intake and milk production decreased linearly by maximum of 48% and 33%, respectively, with increasing dose. Methane yield (g CH4/kg DMI) decreased by maximum of 66%, while up to 125-fold increases were observed in hydrogen yield (g H2/kg DMI) with increasing dose of iodoform. Total tract digestibility of DM, OM, CP, C, NDF, and starch were unaffected by treatments, but large shifts, except for NDF, were observed for ruminal to small intestinal digestion of the nutrients. Some indicators of disturbed rumen microbial activity and fermentation dynamics were observed with increasing dose, but total number of ruminal bacteria was unaffected by treatment. Serum and plasma biomarkers did not indicate negative effects of iodoform on cow health. In conclusion, iodoform was a potent mitigator of CH4 emission. However, DMI and milk production were negatively affected and associated with indications of depressed ruminal fermentation. Future studies might reveal if depression of milk yield and feed intake can be avoided if iodoform is continuously administered by mixing it into a total mixed ration.
| S-EPMC10406889 | biostudies-literature
Project description:BackgroundRuminant livestock production is a considerable source of enteric methane (CH4) emissions. In a previous study, we found that dietary inclusions of Bacillus subtilis (BS) and Macleaya cordata extract (MCE) increased dry matter intake and milk production, while reduced enteric CH4 emission in dairy cows. The objective of this study was to further elucidate the impact of feeding BS and MCE on rumen methanogenesis in dairy cows using rumen metagenomics techniques.ResultsSixty dairy cows were blocked in 20 groups of 3 cows accordingly to their live weight, milk yield, and days in milk, and within each group, the 3 cows were randomly allocated to 1 of 3 treatments: control diet (CON), control diet plus BS (BS), and control diet plus MCE (MCE). After 75 days of feeding experimental diets, 12 cows were selected from each treatment for collection of rumen samples for the metagenomic sequencing. Results showed that BS decreased ruminal acetate and butyrate, while increased propionate concentrations, resulting in decreased acetate:propionate ratio. The metagenomics analysis revealed that MCE reduced relative abundances of Methanobrevibacter wolinii, Methanobrevibacter sp. AbM4, Candidatus Methanomassiliicoccus intestinalis, Methanobrevibacter cuticularis, Methanomicrobium mobile, Methanobacterium formicicum, and Methanobacterium congolense. Both BS and MCE reduced relative abundances of Methanosphaera sp. WGK6 and Methanosphaera stadtmanae. The co-occurrence network analysis of rumen bacteria and archaea revealed that dietary treatments influenced microbial interaction patterns, with BS and MCE cows having more and stronger associations than CON cows. The random forest and heatmaps analysis demonstrated that the Halopenitus persicus was positively correlated with fat- and protein-corrected milk yield; Clostridium sp. CAG 269, Clostridium sp. 27 14, Haloarcula rubripromontorii, and Methanobrevibacter curvatus were negatively correlated with rumen acetate and butyrate concentrations, and acetate:propionate ratio, whereas Selenomonas rumiantium was positively correlated with those variables.ConclusionsThe present results provided new information for mitigation of enteric methane emissions of dairy cows by feeding BS and MCE to influence rumen microbial activities. This fundamental knowledge is essential for developing enteric CH4 reduction strategies to mitigate climate change and reduce dietary energy waste. Video Abstract.
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Project description:Mitigation of greenhouse gas emissions is relevant for reducing the environmental impact of ruminant production. In this study, the rumen microbiome from Holstein cows was characterized through a combination of 16S rRNA gene and shotgun metagenomic sequencing. Methane production (CH4 ) and dry matter intake (DMI) were individually measured over 4-6 weeks to calculate the CH4 yield (CH4 y = CH4 /DMI) per cow. We implemented a combination of clustering, multivariate and mixed model analyses to identify a set of operational taxonomic unit (OTU) jointly associated with CH4 y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH4 y. The taxonomic composition on R2 had lower abundance of Succinivibrionaceae and Methanosphaera, and higher abundance of Ruminococcaceae, Christensenellaceae and Lachnospiraceae. Metagenomic data confirmed the lower abundance of Succinivibrionaceae and Methanosphaera in R2 and identified genera (Fibrobacter and unclassified Bacteroidales) not highlighted by metataxonomic analysis. In addition, the functional metagenomic analysis revealed that samples classified in cluster R2 were overrepresented by genes coding for KEGG modules associated with methanogenesis, including a significant relative abundance of the methyl-coenzyme M reductase enzyme. Based on the cluster assignment, we applied a sparse partial least-squares discriminant analysis at the taxonomic and functional levels. In addition, we implemented a sPLS regression model using the phenotypic variation of CH4 y. By combining these two approaches, we identified 86 discriminant bacterial OTUs, notably including families linked to CH4 emission such as Succinivibrionaceae, Ruminococcaceae, Christensenellaceae, Lachnospiraceae and Rikenellaceae. These selected OTUs explained 24% of the CH4 y phenotypic variance, whereas the host genome contribution was ~14%. In summary, we identified rumen microbial biomarkers associated with the methane production of dairy cows; these biomarkers could be used for targeted methane-reduction selection programmes in the dairy cattle industry provided they are heritable.
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Project description:The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits [methane yield (METY), methane intensity (METINT), and methane production (METP)] in dairy cows. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers published between 2015 and 2021 were used in this study. The heritability estimates for METY, METINT, and METP were 0.244, 0.180, and 0.211, respectively. The genetic correlation estimates between METY and METINT with corrected milk yield for fat, protein, and or energy (CMY) were negative (- 0.433 and - 0.262, respectively). Also, genetic correlation estimates between METINT with milk fat and protein percentages were 0.254 and 0.334, respectively. Although the genetic correlation estimate of METP with daily milk yield was 0.172, its genetic correlation with CMY was 0.446. All genetic correlation estimates between METP with milk fat and protein yield or percentage ranged from 0.005 (between METP-milk protein yield) to 0.185 (between METP-milk protein percentage). The current meta-analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows that could be exploited in genetic selection plans.
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Project description:It has been suggested that the rumen microbiome and rumen function might be disrupted if methane production in the rumen is decreased. Furthermore concerns have been voiced that geography and management might influence the underlying microbial population and hence the response of the rumen to mitigation strategies. Here we report the effect of the dietary additives: linseed oil and nitrate on methane emissions, rumen fermentation, and the rumen microbiome in two experiments from New Zealand (Dairy 1) and the UK (Dairy 2). Dairy 1 was a randomized block design with 18 multiparous lactating cows. Dairy 2 was a complete replicated 3 x 3 Latin Square using 6 rumen cannulated, lactating dairy cows. Treatments consisted of a control total mixed ration (TMR), supplementation with linseed oil (4% of feed DM) and supplementation with nitrate (2% of feed DM) in both experiments. Methane emissions were measured in open circuit respiration chambers and rumen samples were analyzed for rumen fermentation parameters and microbial population structure using qPCR and next generation sequencing (NGS). Supplementation with nitrate, but not linseed oil, decreased methane yield (g/kg DMI; P<0.02) and increased hydrogen (P<0.03) emissions in both experiments. Furthermore, the effect of nitrate on gaseous emissions was accompanied by an increased rumen acetate to propionate ratio and consistent changes in the rumen microbial populations including a decreased abundance of the main genus Prevotella and a decrease in archaeal mcrA (log10 copies/g rumen DM content). These results demonstrate that methane emissions can be significantly decreased with nitrate supplementation with only minor, but consistent, effects on the rumen microbial population and its function, with no evidence that the response to dietary additives differed due to geography and different underlying microbial populations.
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Project description:Reducing methane emissions from livestock production is of great importance for the sustainable management of the Earth's environment. Rumen microbiota play an important role in producing biogenic methane. However, knowledge of how host genetics influences variation in ruminal microbiota and their joint effects on methane emission is limited. We analyzed data from 750 dairy cows, using a Bayesian model to simultaneously assess the impact of host genetics and microbiota on host methane emission. We estimated that host genetics and microbiota explained 24% and 7%, respectively, of variation in host methane levels. In this Bayesian model, one bacterial genus explained up to 1.6% of the total microbiota variance. Further analysis was performed by a mixed linear model to estimate variance explained by host genomics in abundances of microbial genera and operational taxonomic units (OTU). Highest estimates were observed for a bacterial OTU with 33%, for an archaeal OTU with 26%, and for a microbial genus with 41% heritability. However, after multiple testing correction for the number of genera and OTUs modeled, none of the effects remained significant. We also used a mixed linear model to test effects of individual host genetic markers on microbial genera and OTUs. In this analysis, genetic markers inside host genes ABS4 and DNAJC10 were found associated with microbiota composition. We show that a Bayesian model can be utilized to model complex structure and relationship between microbiota simultaneously and their interaction with host genetics on methane emission. The host genome explains a significant fraction of between-individual variation in microbial abundance. Individual microbial taxonomic groups each only explain a small amount of variation in methane emissions. The identification of genes and genetic markers suggests that it is possible to design strategies for breeding cows with desired microbiota composition associated with phenotypes.
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Project description:This study evaluated potential trade-offs between enteric methane (CH4) emissions and CH4 emissions from feces of dairy cows fed grass silage or partial replacement of grass silage with corn silage, both with and without supplementation of rapeseed oil. Measured data for eight dairy cows (two blocks) included in a production trial were analyzed. Dietary treatments were grass silage (GS), GS supplemented with rapeseed oil (GS-RSO), GS plus corn silage (GSCS), and GSCS supplemented with rapeseed oil (GSCS-RSO). Feces samples were collected after each period and incubated for nine weeks to estimate fecal CH4 emissions. Including RSO (0.5 kg/d) in the diet decreased dry matter intake (DMI) by 1.75 kg/d. Enteric CH4 emissions were reduced by inclusion of RSO in the diet (on average 473 vs. 607 L/d). In 9-week incubations, there was a trend for lower CH4 emissions from feces of cows fed diets supplemented with RSO (on average 3.45 L/kg DM) than cows with diets not supplemented with RSO (3.84 L/kg DM). Total CH4 emissions (enteric + feces, L/d) were significantly lower for the cows fed diets supplemented with RSO. Total fecal CH4 emissions were similar between treatments, indicating no trade-offs between enteric and fecal CH4 emissions.
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Project description:Does dietary mitigation of enteric methane production affect rumen function and animal productivity in dairy cows?
| PRJEB10579 | ENA
Project description:BackgroundFollowing global pledges to reduce greenhouse gas (GHG) emissions by 30% by 2030 compared to the baseline level of 2020, improved quantification of GHG emissions from developing countries has become crucial. However, national GHG inventories in most Sub-Saharan African countries use default (Tier I) emission factors (EFS) generated by the Intergovernmental Panel on Climate Change (IPCC) to estimate enteric CH4 emissions from animal agriculture. The present study provides an improved enteric CH4 emission estimate (Tier II) based on animal energy requirements derived from animal characteristics and performance data collected from about 2500 cattle in 480 households from three smallholder farming systems to represent the common dairy farming in the central highlands of Ethiopia. Using average seasonal feed digestibility data, we estimated daily methane production by class of animal and farming system and subsequently generated improved EF.ResultsOur findings revealed that the estimated average EF and emission intensities (EI) vary significantly across farming systems. The estimated value of EF for adult dairy cows was 73, 69, and 34 kg CH4/cow/year for urban, peri-urban, and rural farming systems, respectively. Rural dairy farming had significantly higher emission intensity (EI) estimated at 1.78 CO2-eq per kg of fat protein-corrected milk (FPCM) than peri-urban and urban 0.71 and 0.64 CO2-eq kg-1 FPCM dairy farming systems, respectively. The EF estimates in this study are lower than the IPCC's (2019) default value for both stall-fed high-productive and dual-purpose low-productive cows.ConclusionsThe current findings can be used as a baseline for the national emission inventory, which can be used to quantify the effects of future interventions, potentially improving the country's commitment to reducing GHG emissions. Similarly, this study suggests that increased animal productivity through improved feed has a considerable mitigation potential for reducing enteric methane emissions in smallholder dairy farming systems in the study area.
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Project description:Our objective was to characterize the protein coding portion of the bovine mammary gland transcriptome by magnitude of RNA-seq read counts. EBseq analysis determined no differentially expressed genes due to postruminal lysine infusion, therefore, read counts were averaged across treatment and block. DAVID functional annotation analysis was utilized to determine specific gene ontologies and KEGG pathways to describe the data by magnitude of reads.
2021-03-19 | GSE148587 | GEO