Dataset Information

Reduction in Methane Emissions From Acidified Dairy Slurry Is Related to Inhibition of Methanosarcina Species.

ABSTRACT: Liquid dairy manure treated with sulfuric acid was stored in duplicate pilot-scale storage tanks for 120 days with continuous monitoring of CH4 emissions and concurrent examination of changes in the structure of bacterial and methanogenic communities. Methane emissions were monitored at the site using laser-based Trace Gas Analyzer whereas quantitative real-time polymerase chain reaction and massively parallel sequencing were employed to study bacterial and methanogenic communities using 16S rRNA and methyl-coenzyme M Reductase A (mcrA) genes/transcripts, respectively. When compared with untreated slurries, acidification resulted in 69-84% reductions of cumulative CH4 emissions. The abundance, activity, and proportion of bacterial communities did not vary with manure acidification. However, the abundance and activity of methanogens (as estimated from mcrA gene and transcript copies, respectively) in acidified slurries were reduced by 6 and 20%, respectively. Up to 21% reduction in mcrA transcript/gene ratios were also detected in acidified slurries. Regardless of treatment, Methanocorpusculum predominated archaeal 16S rRNA and mcrA gene and transcript libraries. The proportion of Methanosarcina, which is the most metabolically-diverse methanogen, was the significant discriminant feature between acidified and untreated slurries. In acidified slurries, the relative proportions of Methanosarcina were ? 10%, whereas in untreated slurries, it represented up to 24 and 53% of the mcrA gene and transcript libraries, respectively. The low proportions of Methanosarcina in acidified slurries coincided with the reductions in CH4 emissions. The results suggest that reduction of CH4 missions achieved by acidification was due to an inhibition of the growth and activity of Methanosarcina species.

SUBMITTER: Habtewold J

PROVIDER: S-EPMC6255968 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Reduction in Methane Emissions From Acidified Dairy Slurry Is Related to Inhibition of <i>Methanosarcina</i> Species.

Habtewold Jemaneh J Gordon Robert R Sokolov Vera V VanderZaag Andrew A Wagner-Riddle Claudia C Dunfield Kari K

Frontiers in microbiology 20181120

Liquid dairy manure treated with sulfuric acid was stored in duplicate pilot-scale storage tanks for 120 days with continuous monitoring of CH<sub>4</sub> emissions and concurrent examination of changes in the structure of bacterial and methanogenic communities. Methane emissions were monitored at the site using laser-based Trace Gas Analyzer whereas quantitative real-time polymerase chain reaction and massively parallel sequencing were employed to study bacterial and methanogenic communities us ...[more]

PMID: 30515146

Similar Datasets

Project description:Simple Summary Within anthropogenic sources, agriculture contributes the most to greenhouse gas (GHG) emissions. The on-field assessment of methane emissions from livestock is crucial for testing and implementing mitigation strategies to reduce the deleterious effects of GHG on climate. Therefore, this article presents a longitudinal study in which measurements of enteric methane emissions from dairy cows (Jersey and Holstein-Freisian raised under a semi-extensive management system) were performed with a tool previously described as reliable, portable, and cost-effective: the laser methane detector (LMD). The results showed breed-related differences in methane emissions when milk yield was not considered, challenging us to rethink milk traits and breeding choices. Moreover, methane emissions were lower in cows in early lactation as well as in primiparous, likely reflecting concentrate supplementation and energy metabolism. The study pointed out the opportunity to design and test regional tailored mitigation strategies, including nutritional management, for higher methane emission periods within cows’ lactation cycle, while ensuring the enhancement of productivity. Abstract Reversing climate change requires broad, cohesive, and strategic plans for the mitigation of greenhouse gas emissions from animal farming. The implementation and evaluation of such plans demand accurate and accessible methods for monitoring on-field CH4 concentration in eructating breath. Therefore, this paper describes a longitudinal study over six months, aiming to test a protocol using a laser methane detector (LMD) to monitor CH4 emissions in semi-extensive dairy farm systems. Over 10 time points, CH4 measurements were performed in dry (late gestation) and lactating cows at an Azorean dairy farm. Methane traits including CH4 concentration related to eructation (E_CH4) and respiration (R_CH4), and eructation events, were automatically computed from CH4 measured values using algorithms created for peak detection and analysis. Daily CH4 emission was estimated from each profile’s mean CH4 concentration (MEAN_CH4). Data were analyzed using a linear mixed model, including breed, lactation stage, and parity as fixed effects, and cow (subject) and time point as random effects. The results showed that Holsteins had higher E_CH4 than Jersey cows (p < 0.001). Although a breed-related trend was found in daily CH4 emission (p = 0.060), it was not significant when normalized to daily milk yield (p > 0.05). Methane emissions were lower in dry than in lactation cows (p < 0.05) and increased with the advancement of the lactation, even when normalizing it to daily milk yield (p < 0.05). Primiparous cows had lower daily CH4 emissions related to R_ CH4 compared to multiparous (p < 0.001). This allowed the identification of periods of higher CH4 emissions within the milk production cycle of dairy cows, and thus, the opportunity to tailor mitigation strategies accordingly.

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

Project description:Direct-fed microbials (DFM) are considered as a promising technique to improve animal productivity without affecting animal health or harming the environment. The potential of three bacterial DFM to reduce methane (CH4) emissions, modulate ruminal fermentation, milk production and composition of primiparous dairy cows was examined in this study. As previous reports have shown that DFM respond differently to different diets, two contrasting diets were used in this study. Eight lactating primiparous cows were randomly divided into two groups that were fed a corn silage-based, high-starch diet (HSD) or a grass silage-based, high-fiber diet (HFD). Cows in each dietary group were randomly assigned to four treatments in a 4 × 4 Latin square design. The bacterial DFM used were selected for their proven CH4-reducing effect in vitro. Treatments included control (without DFM) and 3 DFM treatments: Propionibacterium freudenreichii 53-W (2.9 × 1010 colony forming units (CFU)/cow per day), Lactobacillus pentosus D31 (3.6 × 1011 CFU/cow per day) and Lactobacillus bulgaricus D1 (4.6 × 1010 CFU/cow per day). Each experimental period included 4 weeks of treatment and 1 week of wash-out, with measures performed in the fourth week of the treatment period. Enteric CH4 emissions were measured during 3 consecutive days using respiration chambers. Rumen samples were collected for ruminal fermentation parameters and quantitative microbial analyses. Milk samples were collected for composition analysis. Body weight of cows were recorded at the end of each treatment period. Irrespective of diet, no mitigating effect of DFM was observed on CH4 emissions in dairy cows. In contrast, Propionibacterium increased CH4 intensity by 27% (g CH4/kg milk) in cows fed HSD. There was no effect of DFM on other fermentation parameters and on bacterial, archaeal and protozoal numbers. Similarly, the effect of DFM on milk fatty acid composition was negligible. Propionibacterium and L. pentosus DFM tended to increase body weight gain with HSD. We conclude that, contrary to the effect previously observed in vitro, bacterial DFM Propionibacterium freudenreichii 53-W, Lactobacillus pentosus D31 and Lactobacillus bulgaricus D1 did not alter ruminal fermentation and failed to reduce CH4 emissions in lactating primiparous cows fed high-starch or high-fiber diets.

Dataset Information

Reduction in Methane Emissions From Acidified Dairy Slurry Is Related to Inhibition of Methanosarcina Species.

Publications

Reduction in Methane Emissions From Acidified Dairy Slurry Is Related to Inhibition of <i>Methanosarcina</i> Species.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets