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Methane is a powerful greenhouse gas, more potent than carbon dioxide, and emitted from a variety of natural sources including wetlands, permafrost, mammalian guts and termites. As increases in global temperatures continue to break records, quantifying the magnitudes of key methane sources has never been more pertinent. Over the last 40 years, the contribution of termites to the global methane budget has been subject to much debate. The most recent estimates of termite emissions range between 9 and 15 Tg CH4 year-1, approximately 4% of emissions from natural sources (excluding wetlands). However, we argue that the current approach for estimating termite contributions to the global methane budget is flawed. Key parameters, namely termite methane emissions from soil, deadwood, living tree stems, epigeal mounds and arboreal nests, are largely ignored in global estimates. This omission occurs because data are lacking and research objectives, crucially, neglect variation in termite ecology. Furthermore, inconsistencies in data collection methods prohibit the pooling of data required to compute global estimates. Here, we summarise the advances made over the last 40 years and illustrate how different aspects of termite ecology can influence the termite contribution to global methane emissions. Additionally, we highlight technological advances that may help researchers investigate termite methane emissions on a larger scale. Finally, we consider dynamic feedback mechanisms of climate warming and land-use change on termite methane emissions. We conclude that ultimately the global contribution of termites to atmospheric methane remains unknown and thus present an alternative framework for estimating their emissions. To significantly improve estimates, we outline outstanding questions to guide future research efforts.

Methane, a type of greenhouse gas, poses considerable concern for humans. This study uses field experiments and satellite measurements to explore methane emission mechanisms during the freezing of seasonal permafrost and the contributing factors. In the transitional seasons of autumn and winter, as soil begins to freeze, methane emissions surge dramatically in a brief period. During this phase, the emissions peak, enabling the soil to accumulate over 9000 mg/m3 of methane rapidly. Snow cover also plays a crucial role in mitigating methane emissions. The porous nature of a sufficiently thick snow cover aids in temporarily trapping methane through a stratified blocking process, effectively matching the inhibitory capability of unfrozen soil. In comparison to unfrozen soil (54–237 mg/m3), snow cover can suppress methane emissions up to 20 times more, reducing emissions by as much as 3399 mg/m3. [ABSTRACT FROM AUTHOR]

Numbers do matter; the Intergovernmental Panel on Climate Change (IPCC)'s 2010 data that the waste sector is responsible for just 3% of global greenhouse gas (GHG) emissions has led to the misperception that solid waste management (SWM) has little to contribute to climate mitigation. Global efforts to control methane emissions and divert organic waste from landfills had already reduced direct emissions. But end-of-pipe SWM has also been evolving into more circular waste and resource management, with indirect GHG savings from the 3Rs (reduce, reuse, recycle) which IPCC accounts for elsewhere in the economy. The evidence compiled here on both direct emissions and indirect savings demonstrates with high confidence that better waste and resource management can make a significant contribution to climate mitigation, and must form a core part of every country's nationally determined contribution. Even the most advanced countries can still achieve much from the 3Rs. In the Global South, the challenge of extending waste collection to all and stopping open dumping and burning (sustainable development goal 11.6.1), essential to improve public health, can be turned into a huge opportunity. Moving early to divert waste from landfill by separation at source and collecting clean organic and dry recycling fractions, will mitigate global GHG emissions, slash ocean plastics and create decent livelihoods. But this can only happen with targeted climate, plastics and extended producer responsibility finance; and help to local communities to help themselves. [ABSTRACT FROM AUTHOR]

The present study highlights the dual benefits of landfill gas utilization:mitigating greenhouse gas emissions and generating renewable energy. Municipal waste, containing a large proportion of organic matter, is dumped into Solid Waste Disposal Sites (SWDS), which, after anaerobic decomposition, releases methane (CH4). This study aims to assess the methane emissions from the SWDS of Chandigarh, India, and evaluate the potential for electricity generation from captured landfill gas. Waste samples were obtained using the quartile method and examined for proximate and ultimate analysis. The inventory and estimation of CH4 generation were done using the International Panel on Climate Change (IPCC) methodology, including the potential of electricity generation from the CH4 generated. It has been observed that Chandigarh generates around 350 tons per day (TPD) of waste, with organic material constituting over 50% on average. The estimated amount of municipal waste with observed composition led to the generation of 0.34 Gg/yr of CH4. Depending on the efficiency of the gas energy system, 0.27 MW to 0.77 MW of electric power can be produced in Chandigarh. The study also provides a broader context for solid waste management in India. Analysis of national data revealed that India generates about 160,038.9 TPD of solid waste, with only about half (79,956.3 TPD) being treated. The country has 3,184 existing dumpsites, with significant variations across states. As per the present analysis, when extrapolated to a national scale, India could potentially generate 5,167 MWto 14,355 MWof power fromall its SWDS, resulting in annual financial gains of 4.7 to 13 billion USD. These findings underscore the importance of implementing integrated waste management strategies that prioritize waste reduction, efficient treatment, and energy recovery from landfill gas. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION The demand for animal products is increasing in developing countries due to population growth. However, livestock production contributes significantly to global warming, accounting for 25%. Probiotics can help improve livestock efficiency by enhancing gut microbes and fat metabolism. They can modify rumen populations, enhance fermentation, reduce methane emissions and improve feed digestion. In this study, the goal was to determine the most effective method of reducing methane emissions in the rumen of sheep in vitro by adding different concentrations of Saccharomyces cerevisiae and Bacillus subtilis.Adding 8 × 106 CFU g−1 S. cerevisiae during fermentation reduced pH levels after 48 h. This also increased the concentrations of NH3‐N, microbial protein and total gas production. At the same time, it decreased methane emissions. Furthermore, adding 20 × 106 CFU g−1 B. subtilis to the mixture increased total gas production (TGP) and methane production, with the highest production observed after 48 h. However, it did not affect pH levels after 48 h.It can be concluded that S. cerevisiae had significantly increased microbial protein and NH3‐N concentrations after fermentation without altering pH. Additionally, the addition of S. cerevisiae enhanced TGP and reduced methane emissions. It is worth noting that TGP increased because B. subtilis was added at a concentration of 20 × 106 CFU g−1, with no significant differences between concentrations. Therefore, we recommend adding S. cerevisiae and B. subtilis to the diet at doses of 8 and 20 × 106 CFU g−1, as it resulted in higher TGP and reduced methane emissions. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Simple Summary: The methane (CH4) production in ruminants not only decreases the efficiency of dietary energy utilization but also contributes to the greenhouse effect. The rumen epithelium serves as the primary site for the absorption of essential nutrients. This study systematically investigated the effects of tea polyphenols on methane production and the rumen epithelial cell transport capability in cattle using both in vitro and animal experiments, employing multi-omics techniques. In vitro results demonstrated that, compared to the control group, tea polyphenols significantly reduced CH4 production and the acetate/propionate ratio. Tea polyphenols reduce the generation of CH4 in the rumen by influencing the diversity and community structure of microbes such as bacteria, protozoa, and methanogens, thereby minimizing the loss of feed energy. Animal experiments showed that tea polyphenols significantly increased the concentrations of T-AOC and GSH-PX in bovine blood. In addition, by affecting the microbial community in the bovine rumen, tea polyphenols alter the fermentation patterns, thereby enhancing the host's rumen epithelial cells' capacity to transport VFAs. In production practice, the use of tea polyphenols can enhance the efficiency of nutrient utilization in dairy cows, thereby improving their productive performance. This study systematically investigated the effects of tea polyphenols on methane (CH4) production and the rumen epithelial cell transport capability in cattle using both in vitro and animal experiments, employing multi-omics techniques. The in vitro results demonstrated that, compared to the control group, tea polyphenols significantly reduced CH4 production and the acetate/propionate ratio (p < 0.05). Tea polyphenols reduced CH4 production by inhibiting the relative abundance of unclassified_d_Archaea methanogens and the protozoa Pseudoentodinium and g__Balantioides. The animal experiments showed that tea polyphenols significantly increased the concentrations of T-AOC and GSH-PX in bovine blood (p < 0.05). In addition, microbial groups such as Rikenellaceae_RC9_gut_group, Ruminococcaceae_NK4A214_group, and Butyrivibrio_2 were significantly enriched in the ruminal fluid of the tea polyphenol group (p < 0.05). The proteomic results indicated significant upregulation of proteins such as COIII, S100A8, FABP1, SLC2A8, and SLC29A1 (p < 0.05) and downregulation of proteins including HBB, RAB4A, RBP4, LOC107131172, HBA, and ZFYVE19 (p < 0.05), with FABP1 showing a positive correlation with propionate concentration, and RAB4A had a negative correlation (p < 0.05). Overall, tea polyphenols modulate the microbial composition within the rumen, inhibiting CH4 production and enhancing the host's rumen epithelial cell transport capacity for volatile fatty acids. [ABSTRACT FROM AUTHOR]

The USA has significant potential to produce energy from anaerobic digesters (AD) due to the size of its agricultural sector. The use of ADs reduces greenhouse gas (GHG) emissions from manure management. The financial benefits to farmers come from the on‐farm use, or off‐farm sales, of biogas and its end products, namely renewable natural gas (RNG) or electricity. Current energy prices and policies in the USA are insufficient to trigger large‐scale construction of ADs; however, payments to avoid GHG emissions and sequester carbon could become sufficiently high to prompt investment. This analysis quantifies the economic incentives necessary for the construction of ADs for swine producers and can easily be expanded to include other feedstocks. Various end‐use pathways to produce RNG and electricity are considered to account for location, herd size, and other parameters to deliver a comprehensive analysis for the USA. The analysis and results are composed of a generic part to illustrate the effects of carbon payment on profitability in general as well as a specific analysis for states representing 83.6% of the US hog inventory. Our results indicate that carbon payments would be a stronger determinant than energy prices in farm‐level decisions to install ADs, but that energy prices would be influential in determining the optimal biogas end use. The potential need for long‐term contracts – both for energy and carbon payments – to reduce investment uncertainty and increase investment in ADs is also discussed. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Methane is a potent greenhouse gas produced during the ruminal fermentation and is associated with a loss of feed energy. Therefore, efforts to reduce methane emissions have been ongoing in the last decades. Methane production is highly influenced by factors such as the ruminal microbiome and host genetics. Previous studies have proposed to use the ruminal microbiome to reduce long-term methane emissions, as ruminal microbiome composition is a moderately heritable trait and genetic improvement accumulates over time. Lactation stage is another important factor that might influence methane production, but potential associations with the ruminal microbiome have not been evaluated previously. This study sought to examine the changes in ruminal microbiome over the lactation period of primiparous Holstein cows differing in methane intensity (MI) and estimate the heritability of the abundance of relevant microorganisms. Ruminal content samples from 349 primiparous Holstein cows with 14 to 378 DIM were collected from May 2018 to June 2019. Methane intensity of each cow was calculated as methane concentration/milk yield. Up to 64 taxonomic features (TF) from 20 phyla had a significant differential abundance between cows with low and high MI early in lactation, 16 TF during mid lactation, and none late in lactation. Taxonomical features within the Firmicutes, Proteobacteria, Melainabacteria, Cyanobacteria, Bacteroidetes , and Actinobacteria phyla were associated with low MI, whereas eukaryotic TF and those within the Euryarchaeota, Verrucomicrobia, Kiritimatiellaeota , and Lentisphaerae phyla were associated with high MI. Out of the 60 TF that were found to be differentially abundant between early and late lactation in cows with low MI, 56 TF were also significant when cows with low and high MI were compared in the first third of the lactation. In general, microbes associated with low MI were more abundant early in lactation (e.g., Acidaminococcus, Aeromonas , and Weimeria genera) and showed low to moderate heritabilities (0.03 to 0.33). These results suggest some potential to modulate the rumen microbiome composition through selective breeding for lower MI. Differences in the ruminal microbiome of cows with extreme MI levels likely result from variations in the ruminal physiology of these cows and were more noticeable early in lactation, probably due to important interactions between the host phenotype and environmental factors associated with that period. Our results suggest that the ruminal microbiome evaluated early in lactation may be more precise for MI difference, and hence, this should be considered to optimize sampling periods to establish a reference population in genomic selection scenarios. [ABSTRACT FROM AUTHOR]

This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the dataset, incorporating statistical measures like mean, median, and standard deviation to understand the dataset. The CNN model was trained using the ReLU activation function and mean absolute error as the loss function. The model performance was evaluated through MAPE graphs, comparing the mean absolute percentage error (MAPE) between training and validation sets and between true and predicted emissions, and analyzing trends in yearly greenhouse gas emissions. The results demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions, with a low MAPE between the expected and actual values. This approach should enhance our understanding of methane emissions from Sargassum, contributing to more accurate environmental impact assessments and effective mitigation strategies. [ABSTRACT FROM AUTHOR]

Commitments to emissions reductions following the landmark Paris Climate Change Agreement have proliferated. Though it is promising that 145 countries have declared a net-zero emissions target, with 33 enshrining this goal into law, comparison of country-level emissions inventories can only be effectively carried out with uniform and consistent data. The extent to which greenhouse gas (GHG) inventory comparison is possible, and the ancillary climate governance implications, are the motivation for this article. Based on time-series correlation analyses over 32 years and 43 Annex-I countries, we uncover issues that are likely to inveigh against country-country comparison of GHGs—with the potential to weaken climate governance systems that are based mainly on emissions inventory tracking. First, the Global Warming Potentials (GWPs)—which convert each respective GHG into carbon equivalents (CO2-e), and are revised with each IPCC report—are not immediately or consistently integrated into GHG inventories. Second, GHGs apart from carbon dioxide, based on the data analysis, do not appear to be tracked uniformly. Should comparison of emissions remain a cornerstone of global climate governance, an overhaul of country-level GHG inventories is called for, specifically to enable effective reporting and tracking of GHGs apart from only carbon dioxide. [ABSTRACT FROM AUTHOR]

Background: Silage is widely used to formulate dairy cattle rations, and the utilization of antibiotics and methane emissions are 2 major problems for a sustainable and environmentally beneficial ruminant production systems. Bacteriocin has received considerable attention because of its potential as an alternative to antibiotics in animal husbandry. However, the impact of bacteriocin-producing lactic acid bacteria on the microbiological conversion process of whole-plant corn silage and rumen fermentation remains limited. The purpose of this study was to assess the effect of 2 class IIa bacteriocin-producing strains Lactiplantibacillus plantarum ATCC14917 and CICC24194 on bacterial community composition and ensiling profiles of whole-plant corn silage and its in vitro rumen fermentation, microbiota, and CH4 emissions. Results: Both bacteriocin-producing strains increased the lactic acid concentration in silage fermented for 7 d, whereas the lowest lactic acid was observed in the ATCC14917 inoculated silage fermented for 90 d (P < 0.05). The highest DM content was observed in the CICC24194 treatment (P < 0.05), and the silages treated with both strains had the lowest DM loss (P < 0.05). Bacteriocin-producing strains promoted the growth of Levilactobacillus brevis on d 60 of ensiling. In addition, treatment with bacteriocin-producing strains increased the in vitro DM digestibility (P < 0.05) and decreased the CH4 production (P < 0.05). The results of random forest and clustering analyses at the genus level showed that ATCC14917 increased the relative abundance of the influential variable Bacillus compared to that in the control group, whereas CICC24194 decreased the relative abundance of the influential variable Ruminococcaceae UCG-005. The CICC24194 treatment had the lowest total bacterial, fungal, protozoan, and methanogen populations (P < 0.05). Conclusions: Both class IIa bacteriocin-producing L. plantarum strains improved the fermentation quality of whole-plant corn silage by regulating the bacterial community composition during ensiling, with CICC24194 being the most effective. Both bacteriocin-producing strains mitigated CH4 production and improved digestibility by modulating the interactions among rumen bacteria, protozoa, methanogens, and the composition of fibrolytic bacteria. [ABSTRACT FROM AUTHOR]

The unprecedented surge in global methane levels has raised global concerns in recent years, casting a spotlight on China as a pivotal emitter. China has taken several actions to curb the methane emissions, but their effects remain unclear. Here, we developed the Global ObservatioN-based system for monitoring Greenhouse GAses for methane (GONGGA-CH4) and assimilate GOSAT XCH4 observations to assess changes in China's methane emissions. We find the average rate of increase in China's methane emissions (0.1 ± 0.3 Tg CH4 yr−2) during 2016–2021 slowed down compared to the preceding years (2011–2015) (0.9 ± 0.5 Tg CH4 yr−2), in contrast to the concurrent acceleration of global methane emissions. As a result, the contribution of China to global methane emissions dropped significantly. Notably, the slowdown of China's methane emission is mainly attributable to a reduction in biogenic emissions from wetlands and agriculture, associated with the drying trend in South China and the transition from double-season to single-season rice cropping, while fossil fuel emissions are still increasing. Our results suggest that GONGGA-CH4 provides the opportunity for independent assessment of China's methane emissions from an atmospheric perspective, providing insights into the implementation of methane-related policies that align with its ambitious climate objectives. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Dairy producers are experiencing production and animal welfare pressures from the increasing frequency and severity of heat stress events due to global climate change. Offspring performance during the preweaning and lactating periods is compromised when exposed to heat stress during late gestation (in utero). However, knowledge of the lingering effects of in utero heat stress on yearling dairy heifers is limited. Herein, we investigated the long-term effects of in utero heat stress on heifer growth, feed efficiency, and enteric methane emissions in postpubertal heifers. During the last 56 d of gestation, 38 pregnant cows carrying heifer calves were exposed to either heat stress (IUHT; n = 17) or artificial cooling (IUCL; n = 21). At 18 ± 1 mo of age, the resulting IUCL and IUHT heifers were enrolled in the present 63-d study. Heifers were blocked by weight and randomly assigned to 3 pens with Calan gates. Body weights were recorded on 3 consecutive days at the start and end of the trial and used to calculate ADG. Body condition score, hip width, body length, and chest girth were measured at the start and end of the study. All heifers were fed a TMR comprised of 46.6% oatlage, 44.6% grass/alfalfa haylage, 7.7% male-sterile corn silage, 0.3% urea, and 0.8% mineral/vitamin supplement (on a DM basis). The TMR and refusal samples were obtained daily, composited weekly, and dried to calculate DMI. During the study, each pen had access to a GreenFeed unit for 8 ± 1d to measure CH 4 and CO 2 gas fluxes. During the last 3 d of measuring CH 4 and CO 2 fluxes, fecal samples were collected, composited by animal, dried, and analyzed to calculate NDF, OM, and DM digestibility. On the last day of fecal sampling, blood samples were also collected via coccygeal venipuncture, and GC time-of-flight MS analysis was performed. Residual feed intake (RFI; predicted DMI − observed DMI), and feed conversion efficiency (FCE; DMI/ADG) were calculated to estimate feed efficiency. No differences were found in initial or final BW, hip width, chest girth, or BCS; however, IUCL heifers were longer in body length compared with IUHT heifers. Dry matter intake, ADG, RFI, and FCE were similar between IUHT and IUCL heifers. In utero heat-stressed and IUCL heifers produced similar amounts of CH 4 and CO 2 , and no differences were found in the number of GreenFeed visits or latency to approach the GreenFeed. The concentrations of 6 blood metabolites involved in lipogenic pathways were different between in utero treatments. In conclusion, in utero heat stress does not seem to have long-term effects on feed efficiency or methane emissions during the postpubertal growing phase; however, IUCL heifers maintained a body-length advantage over their IUHT counterparts and differed in concentrations of several candidate metabolites that encourage further exploration of their potential function in key organs, such as the liver and mammary gland. [ABSTRACT FROM AUTHOR]

This research project aimed to address the growing concern about methane emissions from seaweed by developing a Convolutional Neural Network (CNN) model capable of accurately predicting these emissions. The study used PANDAS to read and analyze the dataset, incorporating statistical measures like mean, median, and standard deviation to understand the dataset. The CNN model was trained using the ReLU activation function and mean absolute error as the loss function. The model performance was evaluated through MAPE graphs, comparing the mean absolute percentage error (MAPE) between training and validation sets and between true and predicted emissions, and analyzing trends in yearly greenhouse gas emissions. The results demonstrated that the CNN model achieved a high level of accuracy in predicting methane emissions, with a low MAPE between the expected and actual values. This approach should enhance our understanding of methane emissions from Sargassum, contributing to more accurate environmental impact assessments and effective mitigation strategies.

ABSTRACT: Methane is a potent greenhouse gas produced during the ruminal fermentation and is associated with a loss of feed energy. Therefore, efforts to reduce methane emissions have been ongoing in the last decades. Methane production is highly influenced by factors such as the ruminal microbiome and host genetics. Previous studies have proposed to use the ruminal microbiome to reduce long-term methane emissions, as ruminal microbiome composition is a moderately heritable trait and genetic improvement accumulates over time. Lactation stage is another important factor that might influence methane production, but potential associations with the ruminal microbiome have not been evaluated previously. This study sought to examine the changes in ruminal microbiome over the lactation period of primiparous Holstein cows differing in methane intensity (MI) and estimate the heritability of the abundance of relevant microorganisms. Ruminal content samples from 349 primiparous Holstein cows with 14 to 378 DIM were collected from May 2018 to June 2019. Methane intensity of each cow was calculated as methane concentration/milk yield. Up to 64 taxonomic features (TF) from 20 phyla had a significant differential abundance between cows with low and high MI early in lactation, 16 TF during mid lactation, and none late in lactation. Taxonomical features within the Firmicutes, Proteobacteria, Melainabacteria, Cyanobacteria, Bacteroidetes, and Actinobacteria phyla were associated with low MI, whereas eukaryotic TF and those within the Euryarchaeota, Verrucomicrobia, Kiritimatiellaeota, and Lentisphaerae phyla were associated with high MI. Out of the 60 TF that were found to be differentially abundant between early and late lactation in cows with low MI, 56 TF were also significant when cows with low and high MI were compared in the first third of the lactation. In general, microbes associated with low MI were more abundant early in lactation (e.g., Acidaminococcus, Aeromonas, and Weimeria genera) and showed low to moderate heritabilities (0.03 to 0.33). These results suggest some potential to modulate the rumen microbiome composition through selective breeding for lower MI. Differences in the ruminal microbiome of cows with extreme MI levels likely result from variations in the ruminal physiology of these cows and were more noticeable early in lactation, probably due to important interactions between the host phenotype and environmental factors associated with that period. Our results suggest that the ruminal microbiome evaluated early in lactation may be more precise for MI difference, and hence, this should be considered to optimize sampling periods to establish a reference population in genomic selection scenarios.

Animal manure pollution constitutes a significant environmental issue, particularly in areas with intensive livestock activities. Manure, due to its various pollution loads such as nitrogen, phosphorus, heavy metals, and pathogens, can have adverse effects on soil, water resources, and air quality. Among manure management practices, sustainable methods such as composting, biogas production, and advanced treatment techniques stand out. These methods offer effective solutions for reducing the environmental impact of manure, while also holding the potential to be valued as valuable resources for agriculture and energy production. The growth in the global population along with urbanization has intensified the demand in commerce, industry, and agriculture sectors. This need to satisfy the supply-demand dynamics has led to increased production activities, subsequently giving rise to significant waste management challenges. This review examines the environmental impacts of pollution originating from animal manure, current management strategies, and research developments (R&D) in this field.

Abstract Background Silage is widely used to formulate dairy cattle rations, and the utilization of antibiotics and methane emissions are 2 major problems for a sustainable and environmentally beneficial ruminant production systems. Bacteriocin has received considerable attention because of its potential as an alternative to antibiotics in animal husbandry. However, the impact of bacteriocin-producing lactic acid bacteria on the microbiological conversion process of whole-plant corn silage and rumen fermentation remains limited. The purpose of this study was to assess the effect of 2 class IIa bacteriocin-producing strains Lactiplantibacillus plantarum ATCC14917 and CICC24194 on bacterial community composition and ensiling profiles of whole-plant corn silage and its in vitro rumen fermentation, microbiota, and CH4 emissions. Results Both bacteriocin-producing strains increased the lactic acid concentration in silage fermented for 7 d, whereas the lowest lactic acid was observed in the ATCC14917 inoculated silage fermented for 90 d (P

This study aimed to provide a comprehensive evaluation of the greenhouse gases (GHG) emission between 1991 and 2021 of both the entire Italian dairy cattle sector (ITA_POP) and the registered Holstein Friesian population only (HF). An attributional cradle-to-farm gate Life Cycle Assessment was performed, including emissions (methane–CH4, nitrous oxide–N2O, carbon dioxide–CO2) due to animal and manure management, feed production, and farm materials. The functional units were 1 year and 1 kg of fat- and protein-corrected milk. We applied 100-year global warming potential (GWP) metric. For 2011–2021 period, we also applied GWP star (GWP*). Data originated from Italian GHG accounting and official milk recording systems. From 1991 to 2021, annual GHG emission of ITA_POP decreased from 20.4 ± 2.7 to 15.5 ± 2.1 Mt CO2e and emission intensity (EI) from 1.90 ± 0.25 to 1.17 ± 0.16 kg CO2e/kg corrected milk. The share of officially registered HF cows in ITA_POP increased from 36 to 70%. The annual emission of HF increased by 32% to 12.2 ± 1.8 Mt CO2e, but its EI decreased by 34% to 1.09 ± 0.16 kg CO2e/kg corrected milk. The accumulation in GHGs in 2011–2021 period due to ITA_POP (100-year GWP) continuously increased. When considering the short lifetime of CH4 (GWP*), the rate of increase in the same years was quite lower, with a flat pattern in the last 5 years. The decrease in CH4 emission is compensating the accumulation of N2O and CO2 from fossil fuels, highlighting the need of considering GHGs lifetime in the estimation of dairy cow sector emissions.

Background and Aim: In the livestock sector, particularly ruminants, an approach to minimize methane emissions can be carried out through a feeding strategy involving herbal plants containing bioactive compounds that can reduce protozoa and decrease methane gas emissions. The aim of this in vitro study was to analyze the effects of herbal plant supplementation on rumen fermentation, total gas, and methane production, in vitro dry matter digestibility (IVDMD), in vitro organic matter digestibility (IVOMD), and protozoa populations within the rumen. Materials and Methods: Two experiments were conducted in this study. Experiment 1 was conducted to determine the most promising herbal plants capable of increasing total gas production and reducing protozoan populations. Three potential herbals selected in Experiment 1 were continued in Experiment 2 as supplements in the palm kernel meal (PKM)-based ration (70% PKM + 30% herbal plants). Results: Experiment 1 revealed that Eurycoma longifolia (EL), Cola acuminata (CLA), and Cassia alata (CSA) were potential herbal candidates for enhancing total gas production and the percentages of IVDMD and IVOMD. In Experiment 2, supplementation with EL, CLA, and CSA significantly increased IVDMD from 62.84% to 70.15%, IVOMD from 61.61% to 53.18%, and NH3 from 13 mM to 17 mM, as well as reduced partial volatile fatty acids and total gas production. In addition, the methane gas and protozoan populations were reduced. Conclusion: The utilization of EL, CLA, and CSA effectively increased the production of total gas, IVDMD, and IVOMD while reducing methane gas protozoa populations in rumen fermentation compared with the control.

Simple Summary: Methane (CH4) is a by-product of ruminant microbial fermentation, leading to a 2–12% loss of feed energy. The greenhouse gas effect of methane in the atmosphere is 28 times greater than that of carbon dioxide. Recent studies have shown that supplementing feed additives can effectively reduce ruminal methane emissions. This study aimed to evaluate the effectiveness of Sargassum mcclurei (S. mcclurei) in mitigating methane emissions using different treatment methods and supplementation levels through in vitro rumen fermentation. Three supplementation levels were tested for both dried and freeze-dried treatments over 48 h of in vitro rumen fermentation. Fermentation gas production was recorded, and after fermentation, methane production, dry matter degradation (DMD), and various fermentation parameters were measured. The addition of S. mcclurei affected crude protein degradation (CPD) and volatile fatty acid (VFA) production. The freeze-dried S. mcclurei at a 2% supplementation level reduced methane emissions by 18.85%. Methane emissions from ruminants significantly contribute to greenhouse gases. This study explores the methane mitigation effect and mechanism of S. mcclurei through in vitro rumen fermentation, aiming to establish its potential as a feed additive. We investigated the effects of freeze-dried and dried S. mcclurei at supplementation levels of 2%, 5%, and 10% of dry matter on nutrient degradation, ruminal fermentation, methane inhibition, and microbial community structure in in vitro rumen fermentation. The freeze-dried S. mcclurei at 2% supplementation significantly reduced CH4 emissions by 18.85% and enhanced crude protein degradability. However, total VFA and acetate concentrations were lower in both treatments compared to the control. The microbial shifts included a decrease in Lachnospiraceae_NK3A20_group and Ruminococcus and an increase in Selenomonas, Succinivibrio, and Saccharofermentans, promoting propionate production. Additionally, a significant reduction in Methanomicrobium was observed, indicating direct methane mitigation. Freeze-dried S. mcclurei at a 2% supplementation level shows potential as an effective methane mitigation strategy with minimal impact on rumen fermentation, supported by detailed insights into microbial community changes. [ABSTRACT FROM AUTHOR]

Short-lived climate pollutants (SLCPs) and municipal solid wastes (MSWs) have been found to be viable sources of clean energy. This study integrates the Intergovernmental Panel on Climate Change (IPCC) guidelines for methane flow rate estimation in the software Long-Range Energy Alternatives Planning-Integrated Benefit Calculator (LEAP-IBC) system to estimate and project the methane emissions coming from the waste generated by Metro Manila, disposed in sanitary landfills. It aims to analyze the environmental impacts of the emissions coming from the non-energy sector using the IBC feature of LEAP and by developing two scenarios with 2010 and 2050 as the base and end years: the baseline and methane recovery scenario, where the latter represents the solid waste management undertaken to counter the emissions. Under the baseline, 97.30 million metric tonnes of methane emissions are expected to be produced and are predicted to continuously increase. In the same scenario, the cities of Quezon, Manila, and Caloocan account for the biggest methane emissions. On the other hand, in the methane recovery scenario, the methane emissions are expected to have a decline of 36% from 127.036 to 81.303 million metric tonnes by 2025, 52% from 135.358 to 64.972 million metric tonnes by 2030, and 54% from 150.554 to 69.254 million metric tonnes by 2040. For the 40-year projection of the study under the 100-year global warming potential analysis, a total of 10,249 million metric tonnes of CO2 equivalent is avoided in the methane recovery compared to the BAU, and a maximum of 0.019 °C temperature increase can also be avoided. Moreover, electricity costs without LFG technology increase from 2.21 trillion to 8.75 trillion, while costs with LFG technology also rise but remain consistently lower, ranging from 2.20 trillion to 8.74 trillion. This consistent reduction in electricity costs underscores the long-term value and importance of adopting LFG technology, even as its relative savings impact diminishes over time. Finally, the fixed effects and random effects panel data regression analysis reinforces and asserts that the solid waste management is really improved by means of the methane recovery technology, leading the methane emissions to decrease. [ABSTRACT FROM AUTHOR]

Reservoirs are a significant source of carbon (C) to the atmosphere, but their emission rates vary in space and time. We compared C emissions via diffusive and ebullitive pathways at several stations in six large hydropower reservoirs in the southeastern US that were previously sampled in summer 2012. We found that carbon dioxide (CO2) diffusion was the dominant flux pathway during 2012 and 2022, with only three exceptions where methane (CH4) diffusion or CH4 ebullition dominated. CH4 diffusion rates were positively associated with water temperature. However, we found no clear predictors of CH4 ebullition, which had extremely high variability, with rates ranging from 0 to 739 mg C m−2 day−1. For CO2 diffusion, the direction of the flux shifted between 2012 and 2022, where all but three stations across all reservoirs emitted CO2 in summer 2012, but every station sequestered CO2 in summer 2022. Here, indicators of greater algal production were associated with CO2 sequestration, including surface chlorophyll‐a concentration, surface dissolved oxygen saturation, and pH. Additional sampling campaigns outside the summer season highlighted the importance of seasonal phenology in primary production on the direction of CO2 diffusive fluxes, which shifted to positive CO2 fluxes by the end of August as productivity decreased. Our results demonstrate the importance of capturing CO2 sequestration in field and modeling measurements and understanding the seasonal drivers of these estimates. Measuring C emissions from multiple pathways in reservoirs and understanding their spatiotemporal responses and variability are vital to reducing uncertainties in global upscaling efforts. Plain Language Summary: Inland waters, including reservoirs used for many different purposes, contribute greenhouse gases like carbon dioxide and methane to the atmosphere. With 34 times more global warming potential than carbon dioxide, methane is of particular concern since reservoirs emit disproportionally large amounts of it. However, our ability to quantify reservoir emissions is limited by high variability in these emissions over space and time. In this study, we measured emissions from several stations across six reservoirs and compared these emissions to a previous study from 2012 to understand potential longer‐term variation in emissions and assess driver variables. Overall, we found that algal productivity was an important driver of fluxes among the reservoirs and could in fact lead to carbon dioxide sequestration. However, the seasonal phenology in algal productivity and related variables were also important to understand when considering emissions variability within a reservoir and over time. As we continue to collect emissions measurements from reservoirs, studies focused on multiple spatial and temporal scales (i.e., day and night) can improve upscaled estimates of greenhouse gas emissions to better quantify the role of reservoirs in the global carbon cycle. Key Points: Reservoirs are a major source of carbon emissions, but spatial and temporal variability leads to uncertain modeled or upscaled estimatesAcross six reservoirs, summer CO2 diffusion was the dominant flux pathway and showed sequestration, linked to increased algal productivityChanges in algal productivity influence the direction of CO2 diffusion with key implications for assessing net reservoir emissions [ABSTRACT FROM AUTHOR]

In the European Union, greenhouse gas emissions statistics indicate only a slight decreasing trend over the last number of years in emissions from agricultural sources. Unless drastic action is taken in other sectors, the European Union's 2030 and subsequent climate targets are unlikely to be met without greater reductions made in agricultural emissions. The policy instruments aimed at reducing agricultural emissions that are currently in place have proven to be ineffective; therefore, there is a need to look for new approaches towards bringing agricultural emissions down faster and farther. One obvious new approach is to integrate agricultural emissions into the European Union Emissions Trading System, which, so far, has proven very successful in reducing greenhouse gas emissions in the energy and industrial sectors. Hardly any attention has been paid in the scholarly legal literature to the question of integrating agricultural GHG emissions into emission trading systems. This article seeks to fill this gap. This paper presents the concluding findings of a Dutch Research Council-funded research project that aimed to assess whether and under what conditions the European Union Emissions Trading System could play a role in compelling the agricultural sector to reduce its greenhouse gas emissions. We answered this question by looking at lessons learned from existing examples in the world of market-based approaches to integrating agriculture into emission reduction schemes. To do this, we performed an ex-post assessment of three of the very few examples that exist in the world of such schemes in Canada, California, and Australia, followed by an ex-ante assessment of the prospect of including agricultural emissions under the European Union Emissions Trading System based on the practical experiences of those examples. In the ex-ante study, we evaluated how such inclusion could work, either indirectly, through allowing on-farm offset programs to reward increased carbon sequestration, or directly, by requiring farmers and/or other actors in the agricultural sector to surrender allowances for their direct emissions. As lawyers, we focused mainly on the legal considerations of such a proposition. Having conducted both the ex-ante and ex-post assessments, we conclude that introducing stricter legal instruments of one form or another that will reduce agricultural greenhouse gas emissions and increase carbon removal on agricultural land seems necessary for the European Union if it is serious about achieving its commitments under the Paris Agreement and meeting its obligations under its own Climate Law. The project makes a novel contribution to the legal scholarship in concluding that the most viable starting point for such stricter legislation would be to include methane and nitrous oxide emissions from livestock keeping and synthetic fertilizer use, respectively, under the European Union Emissions Trading System. To start with, this could be conducted by obliging meat and dairy processors and synthetic fertilizer producers to surrender allowances for the on-farm emissions associated with their products. This could be complemented by introducing a voluntary, but still highly regulated, carbon credits scheme that could encourage and reward farmers for reducing their own emissions and for transitioning to net-zero, and overall, more climate-resilient and environmentally friendly farming practices. Such credits could be offered for sale on the private carbon market as well as to Member State governments and the European Commission (through, for example, the Common Agricultural Policy, State Aid schemes, or the Innovation Fund). [ABSTRACT FROM AUTHOR]

This article examines the conditions for the formation of the hydroecological regime of the Ivankov Reservoir according to data from complex hydrological, hydrochemical, and hydrobiological surveys carried out in August 2020−2022. The differences in hydroecological characteristics are associated with the peculiarities of weather conditions in the summer seasons. Despite the significant flow of the reservoir, in its bottom layer during prolonged hot weather, the formation of oxygen-free conditions is possible, affecting exchange processes with the bottom, hydrobionts, and methane emissions. The increased bioturbation of bottom sediments (BSs) by benthos under oxygen deficiency activates metabolic processes at the water–BSs boundary, including the release of methane. A strong "bloom" of the reservoir, characteristic of hot weather conditions in 2022, led to a reduction in the specific flux of methane from the surface due to its oxidation with an excess of oxygen in the surface layer. [ABSTRACT FROM AUTHOR]

The oil and gas industry heavily relies on lean burn spark ignited natural gas reciprocating engines. These engines are used in many applications, such as oil and gas exploration, production, processing, compression, transmission, and power generation. These engines produce criteria pollutants, such as nitrogen oxides (NOx) and carbon monoxide (CO), but due to their premixed nature, also produce relatively large amounts of unburned methane (CH4) emissions. These pollutant emissions not only impact air quality, but methane is a very powerful greenhouse gas, with a global warming potential ~25-84 times higher than that of CO2. Therefore, it is essential to decrease these emissions for the protection of our environment and public health. The primary source of methane emissions in lean burn engines is the crevices and near wall quench layers. Thus, one method to dramatically reduce methane emissions is to alter the combustion process to be nonpremixed, mixing-controlled combustion. High auto-ignition resistance fuels, like natural gas, are not conducive to mixing-controlled combustion due to very long ignition delays. This study investigates using an active prechamber ignition source and a direct injector to achieve mixing-controlled combustion of natural gas with very short ignition delays. In this concept, the active prechamber acts as a reliable ignition source for the direct injected natural gas, which is referred to as prechamber ignited mixing-controlled combustion (PC-MCC). The PC-MCC concept enables a ~10× reduction in methane emissions, making it a promising technology for reducing the environmental impact of reciprocating engines. In this study, computational fluid dynamics (CFD) simulations have been used to compare two modeling approaches for PC-MCC: a pure Eulerian gaseous injection approach and a gas-parcels injection method. Using the parcel method to model the gas injection enables an engineering approach to study and design the PC-MCC concept in a timely manner with coarser computational grids. This study also investigated the impact of several variables that may contribute to the performance and emissions of the PC-MCC strategy. The parameters that were examined include prechamber passageway characteristics like nozzle diameter, number of nozzles, and the orientation of nozzle orifices. Understanding the effects of each of these parameters will allow PC-MCC with natural gas to be optimized for low methane emissions. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies. [ABSTRACT FROM AUTHOR]

Over the past few decades, Alberta has witnessed a remarkable expansion in its oil and gas sector. Unfortunately, this growth has come at a cost, as Alberta has become the fastest-growing source of pollutant emissions in greenhouse gases (GHGs), sulphur emissions, and water pollution in Canada. Among these GHGs, methane stands out as the second most prevalent GHG, possessing a global warming potential ~ 28 times higher than carbon dioxide over a span of 100 years, and ~ 80 times higher over a period of 20 years. Since 1986, the Alberta Energy Regulator (AER) has been diligently gathering data on methane concentrations. Although this data is publicly available, its analysis has not been thoroughly explored. Our study aims to investigate the impact of temperature, wind speed, and wind direction on the predictions of methane concentration time series data, utilizing a long short-term memory (LSTM) neural network model. Our findings indicate that the inclusion of climate variables enhances the predictive capabilities of the LSTM model. However, the results show that it is not obvious which variable has the most impact on the improvement although temperature appears to have a better effect on improving predictive performance compared to wind speed and direction. The results also suggest that the variance of the input data does not affect forecasting performance. [ABSTRACT FROM AUTHOR]

This study aims to analyze the relationship between environmental degradation, namely carbon dioxide (CO2) and methane (CH4) emissions, and the per capita income in ASEAN countries. The data used is secondary data in the form of panel data for 1993-2020 originating from the World Bank and Our World in Data. The method used is quantitative descriptive analysis with the Granger Causality Test. The results of the study indicate that there is a one-way relationship that flows from environmental degradation in the form of CO2 and CH4 emissions to per capita income in ASEAN countries, but the reverse does not apply. [ABSTRACT FROM AUTHOR]

Abstract: Hundreds of thousands of documented and undocumented orphaned oil and gas wells exist in the United States (U.S.). These wells have the potential to contaminate water supplies, degrade ecosystems, and emit methane and other air pollutants. Thus, orphaned wells present risks to climate stability and to environmental and human health, which can be reduced by plugging. To quantify environmental risks and opportunities of well plugging at the national level, we analyze data on 81 857 documented orphaned wells across the U.S. We find that > 4.6 million people live within 1 km of a documented orphaned well. 35% of the documented orphaned wells are located within 1 km of a domestic groundwater well, yet only 8% of the wells have groundwater quality data within a 1 km radius. Methane emissions from the documented orphaned wells represent approximately 3%–6% of total U.S. methane emissions from abandoned oil and gas wells, but this estimate is based on measurements at < 0.03% of U.S. abandoned wells. 91% of the documented orphaned wells overlie formations favorable for geologic storage of carbon dioxide and hydrogen, meaning that orphaned well plugging can reduce leakage risks from future storage projects. Finally, we estimate plugging costs for documented orphaned wells to exceed the $4.7 billion federal funding by 30%–80%, emphasizing the importance of prioritizing federal spending on wells with large remediation benefits. Overall, environmental monitoring data are not extensive enough to quantify risks, especially those related to air and water quality and human health. Plugging orphaned wells can provide opportunities for geologic storage of carbon dioxide and hydrogen and geothermal energy development, thereby facilitating efforts to transition to net-zero energy systems. Our analysis on environmental risks and opportunities of orphaned wells provides a framework that can be used to manage the millions of documented and undocumented orphaned wells in the U.S. and abroad.

The present study highlights the dual benefits of landfill gas utilization: mitigating greenhouse gas emissions and generating renewable energy. Municipal waste, containing a large proportion of organic matter, is dumped into Solid Waste Disposal Sites (SWDS), which, after anaerobic decomposition, releases methane (CH4). This study aims to assess the methane emissions from the SWDS of Chandigarh, India, and evaluate the potential for electricity generation from captured landfill gas. Waste samples were obtained using the quartile method and examined for proximate and ultimate analysis. The inventory and estimation of CH4 generation were done using the International Panel on Climate Change (IPCC) methodology, including the potential of electricity generation from the CH4 generated. It has been observed that Chandigarh generates around 350 tons per day (TPD) of waste, with organic material constituting over 50% on average. The estimated amount of municipal waste with observed composition led to the generation of 0.34 Gg/yr of CH4. Depending on the efficiency of the gas energy system, 0.27 MW to 0.77 MW of electric power can be produced in Chandigarh. The study also provides a broader context for solid waste management in India. Analysis of national data revealed that India generates about 160,038.9 TPD of solid waste, with only about half (79,956.3 TPD) being treated. The country has 3,184 existing dumpsites, with significant variations across states. As per the present analysis, when extrapolated to a national scale, India could potentially generate 5,167 MW to 14,355 MW of power from all its SWDS, resulting in annual financial gains of 4.7 to 13 billion USD. These findings underscore the importance of implementing integrated waste management strategies that prioritize waste reduction, efficient treatment, and energy recovery from landfill gas.

The emergence of waterlogged Oryza species ∼15Mya (million years ago) supplied an anoxic warm bed for methane-producing microorganisms, and methane emissions have hence accompanied the entire evolutionary history of the genus Oryza. However, to date no study has addressed how methane emission has been altered during Oryza evolution. In this paper we used a diverse collection of wild and cultivated Oryza species to study the relation between Oryza evolution and methane emissions. Phylogenetic analyses and methane detection identified a co-evolutionary pattern between Oryza and methane emissions, mediated by the diversity of the rhizospheric ecosystems arising from different oxygen levels. Fumarate was identified as an oxygen substitute used to retain the electron transport/energy production in the anoxic rice root, and the contribution of fumarate reductase to Oryza evolution and methane emissions has also been assessed. We confirmed the between-species patterns using genetic dissection of the traits in a cross between a low and high methane-emitting species. Our findings provide novel insights on the evolutionary processes of rice paddy methane emissions: the evolution of wild rice produces different Oryza species with divergent rhizospheric ecosystem attributing to the different oxygen levels and fumarate reductase activities, methane emissions are comprehensively assessed by the rhizospheric environment of diversity Oryza species and result in a co-evolution pattern.

Climate change is a pressing global challenge, and animal production is a major contributor to methane emissions. This study examines the academic landscape of research on CH4 emissions and animal productivity, with a focus on cattle, sheep, and goats. Using a bibliometric analysis of 2500 documents published between 1987 and 2023, the study finds that research on this topic has increased significantly over time, with a record high in 2022. The leading countries in terms of research output are the United States, China, Brazil, Canada, and Italy. The study identifies several key research themes, including the impact of CH4 emissions on animal productivity parameters, the development of mitigation strategies, and the assessment of trade-offs and synergies between CH4 emissions reduction and other sustainability goals. The study concludes by highlighting the importance of continued research on CH4 emissions and animal productivity to develop and implement effective mitigation strategies. This study has important implications for policymakers, researchers, and the livestock industry. Policymakers can use the findings to inform the development of policies and regulations that support the reduction of CH4 emissions from animal production. Researchers can use the findings to identify gaps in the existing knowledge base and to develop new research directions. The livestock industry can use the findings to develop more sustainable production practices. By working together, policymakers, researchers, and the livestock industry can develop and implement effective mitigation strategies that reduce greenhouse gas emissions, protect the environment, and support sustainable food production.

Methane (CH4), an often-overlooked greenhouse gas (GHG), has a significant impact on the environment. Although it receives less attention than carbon dioxide (CO2), it is the second most important GHG in terms of its ability to trap heat in the atmosphere. Few studies have analyzed the determinants of CH4 emissions, especially those from the energy sector. Therefore, this study provides relevant information on the impact of GDP, primary and renewable energy consumption, human development index and trade openness on methane emissions in OECD countries. Using advanced cointegration approaches, we find that GDP and primary energy consumption increase CH4 emissions, while renewable energy consumption and human development mitigate their growth. However, the impact of these variables varied over time. No significant effect of trade openness on methane emissions was found. We recommend specific policies for OECD countries to reduce methane emissions, especially for the most polluting countries. Governments should promote renewable energy sources (solar, wind, hydro) to reduce reliance on fossil fuels, thereby minimizing methane leakage during extraction and transport. In addition, investing in human development can promote sustainable behaviors and further reduce emissions, addressing both environmental and social concerns.

Plasma-based nitrogen fixation has recently been shown to be applicable in the domain of manure management, as it has the ability to reduce ammoniacal nitrogen losses and increase the nitrogen content of organic wastes, with air and electricity as the only input. In addition, the plasma treatment confers antimicrobial properties, which we hypothesize to be transferable to methanogenic archaea and hence prevent methane formation during manure storage – a major contributor to global anthropogenic greenhouse gas emissions. In this work we compared the methane formation from cow manure to the methane formation in nitrogen enriched cow manure, kept in two outdoor storage tanks for 70–80 summer days over three consecutive years. In all instances, the methane formation was eliminated completely. To investigate the cause of inhibition, a controlled incubation experiment was conducted to show that neither the acidification nor the addition of nitrate or nitrite, alone or in combination, could explain the inhibition of methanogenesis and denitrification that occurred in plasma treated cow manure at moderate pH. [ABSTRACT FROM AUTHOR]

To align with climate initiatives, multiple reporting programs are transitioning from generic activity-based emission factors to site-specific measured emissions data to estimate greenhouse gas emissions at oil and gas facilities. This study contemporaneously deployed two top-down (TD) aerial methods across 14 midstream facilities, building upon previous research in the field. The methods produced multiple whole-facility estimates at each facility, resulting in 773 individual paired estimates (same facility, same day), and robust mean estimates for each facility. Mean estimates for each facility, aggregated across all facilities, differed by nearly 2:1 (49% [32% to 69%]). At 6 of 14 facilities, the methods produced mean estimates that differed by more than a factor of two. These data suggest that one or both methods did not produce accurate facility-level estimates at a majority of facilities and in aggregate across all facilities. The overall results are augmented with two case studies where TD estimates at two pre-selected facilities were coupled with comprehensive onsite measurements to understand the factors driving the divergence between TD and bottom-up (BU) emissions estimates. In 3 of 4 paired comparisons between the intensive onsite estimates and one of the TD methods, the intensive onsite surveys did not conclusively diagnose the difference in estimates. In these cases, our work suggests that the TD methods mis-estimate emissions an unknown fraction of the time, for unknown reasons. While two methods were selected for this study, it is unlikely that the issues identified here are confined to these two methods; similar issues may exist for other similar whole-facility methods on midstream and/or other facility types. These findings have important implications for the construction of voluntary and regulatory reporting programs that rely on emission estimates for reporting fees or penalties, or for studies using whole-facility estimates to aggregate TD emissions to basin or regional estimates. [ABSTRACT FROM AUTHOR]

In this paper, the method for unsupervised learning of finite mixture regression (FMR) models is presented for evaluation using agricultural and emissions data sets. The FMR models can be written as problems with incomplete data, and the expectation-maximization (EM) algorithm can be used to estimate unknown variables. The goals of this research are to find the best clustering model with different sets of training and test data and examine the relationship between crop production index and methane emissions in 22 countries from 1990 to 2019 using FMR. In this study also use machine learning process for a FMR model from real world data. According to the findings, the performance of the random training data (RDM) in time series is preferable to that of the fixed training data (FXM). In addition, both RDM and FXM are capable of classifying the 22 countries into two distinct groups and constructing the parameters for the regression model. However, selecting training and test data will result in a good prediction; it is dependent on the data collected. Picking the right training and test data is crucial for accurate predictions-it all comes down to having good data in the first place. [ABSTRACT FROM AUTHOR]

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Simple Summary: There is increasing pressure on dairy systems worldwide to reduce greenhouse gas (GHG) emissions. Anticipated improvements in animal welfare and reduced mortality and disease rates are expected to result in increased animal productivity, hence reducing GHG emissions per unit of output. There has been a lack of focus on quantifying the intensity of GHG emissions and their impact on the health of dairy cows, encompassing both subclinical and clinical problems. The initial indications of illnesses in dairy cows typically manifest as a decline in both feed consumption and milk production. The implementation of emission-reducing strategies and the maintenance of optimal animal welfare are suggested as effective approaches to improve the sustainability of modern dairy production. More information regarding animal health prior to animal mortality is required to enhance the precision of the estimate for GHG emission intensity. Through the analysis of animal health data prior to mortality, a more accurate estimation of GHG emission intensity can be attained. This will allow dairy producers to have a better understanding of the environmental consequences of their operations and make well-informed choices regarding ways to reduce emissions. In addition, placing a high priority on achieving the best possible conditions for animal welfare not only supports the long-term viability of dairy production, but also guarantees the health and happiness of the cows. This, in turn, can result in enhanced overall productivity and a decrease in the occurrence of diseases. Therefore, it is essential to adopt a comprehensive approach that considers both environmental and animal health aspects to advance a sustainable and efficient dairy sector. Furthermore, the integration of technology such as precision farming and data analytics can significantly augment the efficiency and sustainability of dairy operations. The dairy industry is facing criticism for its role in exacerbating global GHG emissions, as climate change becomes an increasingly pressing issue. These emissions mostly originate from methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2). An optimal strategy involves the creation of an economical monitoring device to evaluate methane emissions from dairy animals. Livestock production systems encounter difficulties because of escalating food demand and environmental concerns. Enhancing animal productivity via nutrition, feeding management, reproduction, or genetics can result in a decrease in CH4 emissions per unit of meat or milk. This CH4 unit approach allows for a more accurate comparison of emissions across different animal production systems, considering variations in productivity. Expressing methane emissions per unit allows for easier comparison between different sources of emissions. Expressing emissions per unit (e.g., per cow) highlights the relative impact of these sources on the environment. By quantifying emissions on a per unit basis, it becomes easier to identify high-emission sources and target mitigation efforts accordingly. Many environmental policies and regulations focus on reducing emissions per unit of activity or output. By focusing on emissions per unit, policymakers and producers can work together to implement practices that lower emissions without sacrificing productivity. Expressing methane emissions in this way aligns with policy goals aimed at curbing overall greenhouse gas emissions. While it is true that total emissions affect the atmosphere globally, breaking down emissions per unit helps to understand the specific contributions of different activities and sectors to overall greenhouse gas emissions. Tackling cattle health issues can increase productivity, reduce GHG emissions, and improve animal welfare. Addressing livestock health issues can also provide favourable impacts on human health by reducing the prevalence of infectious illnesses in livestock, thereby mitigating the likelihood of zoonotic infections transmitting to humans. The progress in animal health offers the potential for a future in which the likelihood of animal diseases is reduced because of improved immunity, more effective preventative techniques, earlier identification, and innovative treatments. The primary objective of veterinary medicine is to eradicate clinical infectious diseases in small groups of animals. However, as the animal population grows, the emphasis shifts towards proactive treatment to tackle subclinical diseases and enhance production. Proactive treatment encompasses the consistent monitoring and implementation of preventive measures, such as vaccination and adherence to appropriate nutrition. Through the implementation of these measures, the livestock industry may enhance both animal well-being and mitigate the release of methane and nitrous oxide, thereby fostering environmental sustainability. In addition, advocating for sustainable farming methods and providing farmers with education on the significance of mitigating GHG emissions can bolster the industry's endeavours to tackle climate change and infectious illnesses. This will result in a more robust and environmentally sustainable agriculture industry. This review seeks to conduct a thorough examination of the correlation between the health condition of cattle, the composition of milk produced, and the emissions of methane gas. It aims to identify areas where research is lacking and to provide guidance for future scientific investigations, policy making, and industry practices. The goal is to address the difficulties associated with methane emissions in the cattle industry. The primary global health challenge is to identify the causative relationship between climate change and infectious illnesses. Reducing CH4 and N2O emissions from digestive fermentation and animal manure can be achieved by improving animal well-being and limiting disease and mortality. [ABSTRACT FROM AUTHOR]

• Collection efficiency estimates are necessary for accurate emissions modeling. • Measured collection efficiency data categorized and compared with current defaults. • Cover type was statistically significant indicator of collection efficiency. • Results: 41% efficiency for daily cover, 69 % for intermediate, and 71 % for final. • New defaults greatly reduce discrepancy between modeled and measured estimates. Landfill methane emissions are commonly estimated using cover-type dependent default collection efficiency values, with a first-order decay model or measured gas collection. Current default collection efficiencies used in the United States were predominately derived from 4 studies conducted during or prior to 2007 that relied on flux chambers. Flux chambers are limited by small sample sizes, placement restrictions, and the inability to measure emissions from gas or leachate collection systems. Since 2007, over 14 new studies have been completed using more advanced technologies that allow for direct measurement of methane plumes from most or all of a landfill's surface. On average, these measurements are 2–3 times greater than emissions predicted by current models and collection efficiency defaults. In lieu of measuring emissions from all landfills, updating collection efficiency defaults can bring modeled emissions into better alignment with measurements. To this end, collection efficiency estimates derived from measured data were categorized into cover types and then adjusted to account for cases where whole plume measurement was an amalgamation of multiple cover types. The resultant adjusted default values were 41% for daily cover, 69% for intermediate cover, and 71% for final cover. Direct measurement of landfill methane emissions is preferrable to account for the full range of variables driving landfill emissions, including collection system design and operation. However, applying these updated defaults back into the landfill emission models eliminates underprediction of landfill emissions for the dataset reviewed, and would provide a more accurate estimate of landfill gas emissions where measurements are unavailable. [ABSTRACT FROM AUTHOR]

Simple Summary: As the dairy industry strives to improve the sustainability and efficiency of heifer rearing, it is important to understand the potential long-term impacts of early life events, such as social isolation stress, on the efficiency and performance of growing heifers. Pre-weaning pair housing has many immediate cognitive, growth, and feed intake benefits; however, little is known about the duration of these advantages over individually housed heifers. Pre-weaning isolation stress may have lasting adverse effects on heifer growth and feed efficiency, potentially inflating heifer rearing costs and decreasing farm profitability. To investigate possible long-term effects of pre-weaning housing, we measured the growth, feed efficiency, and methane emissions of 18-month-old heifers which had previously been paired or housed individually pre-weaning. Overall, pair-housed heifers maintained their initial body weight advantage over individually housed heifers with no adverse impacts on feed efficiency or methane emissions. Our objective in this exploratory study was to evaluate the long-term impacts of pre-weaning social isolation vs. contact on subsequent growth and feed efficiency of Holstein heifers. As pre-weaned calves, 41 heifers were housed individually (n = 15 heifers) or in pairs (n = 13 pairs; 26 heifers). At 18 months of age, heifers were blocked by body weight and randomly assigned to one of three pens within a block (six to eight heifers per pen; six pens total), with original pairs maintained. Body weight (BW), hip height and width, and chest girth were measured at the start and end of the study. Each pen was given 3 days of access to a GreenFeed greenhouse gas emissions monitor to assess potential physiological differences between treatments in enteric methane emissions or behavioral differences in propensity to approach a novel object. During the 9-week study, heifers were fed a common diet containing 62.3% male-sterile corn silage, 36.0% haylage, 0.7% urea, and 1.0% mineral (DM basis). To calculate daily feed intake, as-fed weights and refusals were recorded for individual heifers using Calan gates. Feed samples were collected daily, composited by week, and dried to calculate dry matter intake (DMI). Feed refusal and fecal samples were collected on 3 consecutive days at 3 timepoints, composited by heifer, dried, and analyzed to calculate neutral detergent fiber (NDF), organic matter (OM), and DM digestibility. Feed efficiency was calculated as feed conversion efficiency (FCE; DMI/average daily gain [ADG]) and residual feed intake (RFI; observed DMI-predicted DMI). Paired and individually housed heifers did not differ in DMI, ADG, FCE, or RFI. Although no differences were found in initial or final hip height, hip width, or chest girth, heifers which had been pair-housed maintained a greater BW than individually housed heifers during the trial. Methane production, intensity, and yield were similar between treatments. Pre-weaning paired or individual housing did not impact the number of visits or latency to approach the GreenFeed; approximately 50% of heifers in each treatment visited the GreenFeed within 8 h of exposure. Digestibility of OM, DM, and NDF were also similar between housing treatments. In conclusion, pre-weaning pair housing had no adverse effects on growth, feed efficiency, or methane emissions at 18 to 20 months of age. [ABSTRACT FROM AUTHOR]

Climate change is a pressing global challenge, and animal production is a major contributor to methane emissions. This study examines the academic landscape of research on CH4 emissions and animal productivity, with a focus on cattle, sheep, and goats. Using a bibliometric analysis of 2500 documents published between 1987 and 2023, the study finds that research on this topic has increased significantly over time, with a record high in 2022. The leading countries in terms of research output are the United States, China, Brazil, Canada, and Italy. The study identifies several key research themes, including the impact of CH4 emissions on animal productivity parameters, the development of mitigation strategies, and the assessment of trade-offs and synergies between CH4 emissions reduction and other sustainability goals. The study concludes by highlighting the importance of continued research on CH4 emissions and animal productivity to develop and implement effective mitigation strategies. This study has important implications for policymakers, researchers, and the livestock industry. Policymakers can use the findings to inform the development of policies and regulations that support the reduction of CH4 emissions from animal production. Researchers can use the findings to identify gaps in the existing knowledge base and to develop new research directions. The livestock industry can use the findings to develop more sustainable production practices. By working together, policymakers, researchers, and the livestock industry can develop and implement effective mitigation strategies that reduce greenhouse gas emissions, protect the environment, and support sustainable food production. [ABSTRACT FROM AUTHOR]

Composting food and garden waste generates less methane emissions than landfills, yet most organic waste is landfilled. This paper examines how local government provision of composting services affects the amount of household waste going to landfills. Using quasi-random adoption of curbside organics collection by local councils in Australia, we find that curbside organics collection diverted one-fourth of household waste from landfill to composting. We find no evidence that organics collection altered total household waste and weak evidence of a small negative spillover effect on dry-recycling waste. Our results suggest curbside organics collection could reduce emissions from landfills by 6–26%. [ABSTRACT FROM AUTHOR]

This article is a Commentary on the Virtual Issue 'Methane emissions from tree stems – current knowledge and challenges' that includes the following papers: Barba et al. (2019), Bréchet et al. (2021), Covey & Megonigal (2019), Feng et al. (2022), Flanagan et al. (2021), Jeffrey et al. (2019, 2021, 2023), Kohl et al. (2019), Machacova et al. (2021a,b, 2023), Megonigal et al. (2020), Pangala et al. (2013, 2014), Pitz & Megonigal (2017), Plain et al. (2019), Putkinen et al. (2021), Sjögersten et al. (2020), Takahashi et al. (2022), Tenhovirta et al. (2022), Wang et al. (2016), and Yip et al. (2018). Access the Virtual Issue at www.newphytologist.com/virtualissues. [ABSTRACT FROM AUTHOR]

The unprecedented population and anthropogenic activity rise have challenged the future look up for shifts in global temperature and climate patterns. Anthropogenic activities such as land fillings, building dams, wetlands converting to lands, combustion of biomass, deforestation, mining, and the gas and coal industries have directly or indirectly increased catastrophic methane (CH4) emissions at an alarming rate. Methane is 25 times more potent trapping heat when compared to carbon dioxide (CO2) in the atmosphere. A rise in atmospheric methane, on a 20-year time scale, has an impact of 80 times greater than that of CO2. With increased population growth, waste generation is rising and is predicted to reach 6 Mt by 2025. CH4 emitted from landfills is a significant source that accounts for 40% of overall global methane emissions. Various mitigation and emissions reduction strategies could significantly reduce the global CH4 burden at a cost comparable to the parallel and necessary CO2 reduction measures, reversing the CH4 burden to pathways that achieve the goals of the Paris Agreement. CH4 mitigation directly benefits climate change, has collateral impacts on the economy, human health, and agriculture, and considerably supports CO2 mitigation. Utilizing the CO2 from the environment, methanogens produce methane and lower their carbon footprint. NGOs and the general public should act on time to overcome atmospheric methane emissions by utilizing the raw source for producing carbon–neutral fuel. However, more research potential is required for green energy production and to consider investigating the untapped potential of methanogens for dependable energy generation. [ABSTRACT FROM AUTHOR]

Arctic-boreal lakes emit methane (CH4), a powerful greenhouse gas. Recent studies suggest ebullition might be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial data sets of lake area, lake perimeter, permafrost, land cover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R2 = 0.211), followed by lake surface area (adj. R2 = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes might explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R2 = 0.176) and temperature (adj. R2 = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R2 values are typical and informative for methane attribution studies. Our leading model, which uses lake area, temperature, and LWF (adj. R2 = 0.325, n = 5,130) performs slightly better than leading multivariate models from similar studies. Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-boreal lake methane emissions to readily available environmental variables. [ABSTRACT FROM AUTHOR]