Dataset Information

Estimation of Methane Emissions from Slurry Pits below Pig and Cattle Confinements.

ABSTRACT: Quantifying in-house emissions of methane (CH4) from liquid manure (slurry) is difficult due to high background emissions from enteric processes, yet of great importance for correct estimation of CH4 emissions from manure management and effects of treatment technologies such as anaerobic digestion. In this study CH4 production rates were determined in 20 pig slurry and 11 cattle slurry samples collected beneath slatted floors on six representative farms; rates were determined within 24 h at temperatures close to the temperature in slurry pits at the time of collection. Methane production rates in pig and cattle slurry differed significantly at 0.030 and 0.011 kg CH4 kg-1 VS (volatile solids). Current estimates of CH4 emissions from pig and cattle manure management correspond to 0.032 and 0.015 kg CH4 kg-1, respectively, indicating that slurry pits under animal confinements are a significant source. Fractions of degradable volatile solids (VSd, kg kg-1 VS) were estimated using an aerobic biodegradability assay and total organic C analyses. The VSd in pig and cattle slurry averaged 0.51 and 0.33 kg kg-1 VS, and it was estimated that on average 43 and 28% of VSd in fresh excreta from pigs and cattle, respectively, had been lost at the time of sampling. An empirical model of CH4 emissions from slurry was reparameterised based on experimental results. A sensitivity analysis indicated that predicted CH4 emissions were highly sensitive to uncertainties in the value of lnA of the Arrhenius equation, but much less sensitive to uncertainties in VSd or slurry temperature. A model application indicated that losses of carbon in VS as CO2 may be much greater than losses as CH4. Implications of these results for the correct estimation of CH4 emissions from manure management, and for the mitigation potential of treatments such as anaerobic digestion, are discussed.

SUBMITTER: Petersen SO

PROVIDER: S-EPMC4986936 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Methane (CH4) emission from pig slurry is a large contributor to the climate footprint of livestock production. Acidification of excreta from livestock animals with sulfuric acid, reduce CH4 emission and is practiced at many Danish farms. Possible interaction effects with other acidic agents or management practices (e.g. frequent slurry removal and residual slurry acidification) have not been fully investigated. Here we assessed the effect of pig slurry acidification with a range of organic and inorganic acids with respect to their CH4 inhibitor potential in several batch experiments (BS). After careful selection of promising CH4 inhibitors, three continuous headspace experiments (CHS) were carried out to simulate management of manure in pig houses. In BS experiments, more than <99% CH4 reduction was observed with HNO3 treatment to pH 5.5. Treatments with HNO3, H2SO4, and H3PO4 reduced CH4 production more than acetic acid and other organic acids when acidified to the same initial pH of 5.5. Synergistic effects were not observed when mixing inorganic and organic acids as otherwise proposed in the literature, which was attributed to the high amount of acetic acid in the slurry to start with. In the CHS experiments, HNO3 treatment reduced CH4 more than H2SO4, but increased nitrous oxide (N2O) emission, particularly when the acidification target pH was above 6, suggesting considerable denitrification activity. Due to increased N2O emission from HNO3 treatments, HNO3 reduced total CO2-eq by 67%, whereas H2SO4 reduced CO2-eq by 91.5% compared to untreated slurry. In experiments with daily slurry addition, weekly slurry removal, and residual acidification, HNO3 and H2SO4 treatments reduced CO2-eq by 27% and 48%, respectively (not significant). More cycles of residual acidification are recommended in future research. The study provides solid evidence that HNO3 treatment is not suitable for reducing CO2-eq and H2SO4 should be the preferred acidic agent for slurry acidification.

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.

Dataset Information

Estimation of Methane Emissions from Slurry Pits below Pig and Cattle Confinements.

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