Methane Emissions Research Articles

Farmers’ perceptions and awareness of cattle feedlots as a climate-smart approach to enteric methane emissions

The perceptions of farmers regarding communal feedlots and their role in reducing enteric methane emissions have received limited attention in research. This study aims to examine farmers’ perceptions, assess awareness, and identify barriers to adopting feedlots as a climate-smart practice to enteric methane emissions in rural communities in the Eastern Cape Province, South Africa. 161 structured, paper-based questionnaires were distributed among smallholder cattle farmers around three feedlot locations. Most farmers had heard about feedlots (67.7 %); however, only a few (19.9 %) participated. The lack of knowledge about feedlot operations was the main factor influencing farmers’ participation in feedlots. Most farmers did not perceive feedlots as a climate-smart approach to reducing enteric methane output (53.8 %), with few sure (9.6 %) and others unsure (36.5 %). When asked about the reasons for their perceptions of feedlots as a climate-smart approach to mitigating methane, many farmers were unsure of the reasons (86.5 %), with the least believing it could be related to poor feed quality (1.9 %). The availability of financial resources and level of education were the key factors influencing farmers’ willingness to adopt feedlots as a climate-smart practice. These findings emphasise the importance of targeted educational workshops and support systems to enhance farmers’ awareness and promote the adoption of cattle feedlots as a climate-smart approach. The findings can also inform policy and practice to address perceived feedlot drawbacks, such as feed provision and technical assistance, and improve their successful implementation. However, further research is needed to explore farmers’ perceptions of cattle feedlots as a climate-smart practice.

Burp tax: Regulation and revenue potential in Indonesia

The increasingly urgent phenomenon of climate change requires concrete actions from various sectors, including the livestock sector which is a significant contributor to greenhouse gas emissions, particularly methane. This study explores the potential of implementing Burp Tax in Indonesia as a regulatory mechanism to reduce methane emissions from beef and dairy cattle farms. By referring to New Zealand's experience as a Burp Tax pioneer, this study analyzes the policy scheme that includes tax subjects, tax objects, tariffs, and fair and efficient collection mechanisms. In addition, this study identifies various obstacles that may be faced in the implementation of Burp Tax in Indonesia and provides recommendations to support the national commitment in achieving the Zero Emission target in 2045/2060. The research used qualitative methods with literature study data collection techniques. The research concludes that there is great potential for the imposition of Burp Tax, so that in addition to being a form of effort to reduce methane emissions from beef and dairy cattle farms, Burp Tax can also increase the portion of state revenue from the taxation sector. The specific tariff scheme is IDR30/KgCH4 with tax subjects in the beef and dairy cattle farming sector, including individuals with 30 cows and business entities. However, it is important to note that the imposition of Burp Tax may have an economic impact on livestock business actors and affect the price of meat and milk commodities. Therefore, further studies are needed regarding the potential and impact of Burp Tax implications in Indonesia. The results of this study are expected to make a significant contribution to the development of sustainable taxation policies that are responsive to the challenges of climate change.

Assessing the Impact of Climate Change on Methane Emissions from Rice Production Systems in Southern India

The impact of climate change on methane (CH4) emissions from rice production systems in the Coimbatore region (Tamil Nadu, India) was studied by leveraging field experiments across two main treatments and four sub-treatments in a split-plot design. Utilizing the closed-chamber method for gas collection and gas chromatography analysis, this study identified significant differences in CH4 emissions between conventional cultivation methods and the system of rice intensification (henceforth SRI). Over two growing seasons, conventional cultivation methods reported higher CH4 emissions (range: from 36.9 to 59.3 kg CH4 ha−1 season−1) compared with SRI (range: from 2.2 to 12.8 kg CH4 ha−1 season−1). Experimental data were subsequently used to guide parametrization and validation of the DeNitrification–DeComposition (DNDC) model. The validation of the model showed good agreement between the measured and modeled data, as denoted by the statistical tests performed, which included CRM (0.09), D-index (0.99), RMSE (7.16), EF (0.96), and R2 (0.92). The validated model was then used to develop future CH4 emissions projections under various shared socio-economic pathways (henceforth SSPs) for the mid- (2021–2050) and late (2051–2080) century. The analysis revealed a potential increase in CH4 emissions for the simulated scenarios, which was dependent on specific soil and irrigation management practices. Conventional cultivation produced the highest CH4 emissions, but it was shown that they could be reduced if the current practice was replaced by minimal flooding or through irrigation with alternating wetting and drying cycles. Emissions were predicted to rise until SSP 370, with a marginal increase in SSP 585 thereafter. The findings of this work underscored an urgency to develop climate-smart location-specific mitigation strategies focused on simultaneously improving current water and nutrient management practices. The use of methanotrophs to reduce CH4 production from rice systems should be considered in future work. This research also highlighted the critical interaction that exists between agricultural practices and climate change, and emphasized the need to implement adaptive crop management strategies that can sustain productivity and mitigate the environmental impacts of rice-based systems in southern India.

Mitigating Climate Impact: A Machine Learning Approach to Forecast Methane Emissions from Indian Livestock

Mitigating Climate Impact: A Machine Learning Approach to Forecast Methane Emissions from Indian Livestock

Modeling of methane emissions from waste disposal sites at selected Egyptian governorates and potential energy production from waste-to-energy projects

Waste and energy sectors have significant contributions to the greenhouse gas (GHG) emissions caused primarily by the population expansion. Waste-to-Energy (WtE) is introduced to address the issue raised by both sectors simultaneously through utilization of the potential energy stored in municipal solid waste (MSW) as well as offsetting GHG emissions. Limited research have been conducted in Egypt to assess the current situation of MSW management and associated methane emissions. The current study focused on estimating the baseline methane emissions for six Egyptian governorates and determining the energy production potential from WtE projects. To achieve this aim, three scenarios have been assessed: Baseline, Landfill Gas to Energy (LFGE), and Incineration scenarios. Key results revealed that a total of 3.7 million tonnes of methane would be emitted from all studied governorates generated over 50 years. Incineration also found to be more favorable in all governorates in terms of energy production, quantity of avoided GHG emissions, and in terms of economic viability over LFGE. Implementing incineration in all governorates would generate about 5.6 TWh energy annually and could avoid about 5 Mt CO2 eq annually in comparison to LFGE that would generate about 0.6 TWh annually and could avoid about 2.5 Mt CO2 eq annually. In terms of economic viability of WtE projects, while they were generally not economically viable under the assumptions made in the current study, incineration technology deemed promising, but policy adjustments, such as competitive Feed-in Tariff (FiT) rates and the inclusion of gate fees, are necessary. Specific minimum gate fees and FiT were identified for each governorate, providing essential guidance for decision makers to ensure the viability of WtE implementation. This study would support the decision makers in assessing technically and financially feasible options for WtE technologies in the selected governorates.

Editorial: Natural methane emissions in a changing arctic – implications for climate and environment

Editorial: Natural methane emissions in a changing arctic – implications for climate and environment

Polyphenol rewiring of the microbiome reduces methane emissions.

Methane mitigation is regarded as a critical strategy to combat the scale of global warming. Currently, about 40% of methane emissions originate from microbial sources, which is causing strategies to suppress methanogens, either through direct toxic effects or by diverting their substrates and energy, to gain traction. Problematically, current microbial methane mitigation knowledge derives from rumen studies and lacks detailed microbiome-centered insights, limiting translation across ecosystems. Here we utilize genome-resolved metatranscriptomes and metabolomes to assess the impact of a proposed methane inhibitor, catechin, on greenhouse gas emissions for high-methane-emitting peatlands. In microcosms, catechin drastically reduced methane emissions by 72-84% compared to controls. Longitudinal sampling allowed for reconstruction of a novel catechin degradation pathway involving Actinomycetota and Clostridium, which break down catechin into smaller phenolic compounds within the first 21 days, followed by degradation of phenolic compounds by Pseudomonas_E from days 21 to 35. These genomes also co-expressed hydrogen-uptake genes, suggesting that hydrogenases may act as a hydrogen sink during catechin degradation, depriving methanogens of substrates. This was supported by decreased gene expression in hydrogenotrophic and hydrogen-dependent methylotrophic methanogens under catechin treatment. We also saw reduced gene expression from genomes inferred to be functioning syntrophically with hydrogen-utilizing methanogens. We propose that catechin metabolic redirection effectively starves hydrogen-utilizing methanogens, offering a potent avenue for curbing methane emissions across diverse environments including ruminants, landfills, and constructed or managed wetlands.

Investigating the inhibitory mechanism of methanogenesis during composting under the combined influence of amoxicillin and copper pollution

This study investigated the effects of different levels of combined amoxicillin and copper (Cu) pollution on the methanogenesis of microbial communities during aerobic composting of dairy manure. Three groups were established: the control group (CK), a low-level combined pollution group (L), and a high-level combined pollution group (H). As the level of pollution increased, carbohydrate metabolism decreased during the thermophilic phase of composting, while signal translation increased. Compared with the initial phase, functional genes related to the acetoclastic pathway decreased significantly in abundance during the thermophilic phase, and cdh had the lowest relative abundance among acetoclastic pathway with a decrease of 81.52%, 81.88%, and 84.73% in groups CK, L, and H, respectively. The cumulative methane emissions in group H decreased by 31.56% and 9.23%, respectively, compared with those from groups CK and L. These results contribute to understanding the effects of combined amoxicillin and Cu pollution on methane emissions during composting.

Emission Factors for Biochar Production from Various Biomass Types in Flame Curtain Kilns

Simple and low-cost flame curtain (“Kon-Tiki”) kilns are currently the preferred biochar technology for smallholder farmers in the tropics. While gas and aerosol emissions have been documented for woody feedstocks (twigs and leaves) with varying moisture contents, there is a lack of data on emissions from other types of feedstocks. This study aims to document the gas and aerosol emissions for common non-woody feedstocks and to compare emissions from finely grained, high-lignin feedstock (coffee husk) with those from coarser, low-lignin feedstocks (maize cobs, grass, sesame stems). Throughout each pyrolysis cycle, all carbon-containing gases and NOx were monitored using hand-held sensitive instruments equipped with internal pumps. Carbon balances were used to establish emission factors in grams per kilogram of biochar. The resulting methane emissions were nearly zero (<5.5 g/kg biochar) for the pyrolysis of three dry (~10% moisture) maize cobs, grass, and a 1:1 mixture of grass and woody twigs. For sesame stems, methane was detected in only two distinct spikes during the pyrolysis cycle. Carbon monoxide (CO) and aerosol (Total Suspended Particles, TSP) emissions were recorded at levels similar to earlier data for dry twigs, while nitrogen oxide (NOx) emissions were negligible. In contrast, the pyrolysis of finely grained coffee husks generated significant methane and aerosol emissions, indicating that technologies other than flame curtain kilns are more suitable for finely grained feedstocks. The emission results from this study suggest that certification of biochar made from dry maize, sesame, and grass biomass using low-tech pyrolysis should be encouraged. Meanwhile, more advanced systems with syngas combustion are needed to sufficiently reduce CO, CH4, and aerosol emissions for the pyrolysis of finely grained biomasses such as rice, coffee, and nut husks. The reported data should aid overarching life-cycle analyses of the integration of biochar practice in climate-smart agriculture and facilitate carbon credit certification for tropical smallholders.

Near-Infrared Dual-Range Methane Sensor Using the OAIC-HC Mode Based on VMD-SG-Assisted Optical Noise Suppression.

Based on tunable diode laser absorption spectroscopy and off-axis integrated cavity output spectroscopy, a dual-range methane hybrid sensor was constructed utilizing the overtone absorption band of CH4 gas molecules at 1653.7 nm. By simultaneously utilizing an off-axis integrated cavity and Herriott cell with an effective absorption path of 11 and 405 m, respectively, the two received photoelectric signals are decomposed into different frequency components by VMD and then reconstructed after SG filtering. Applying the global optimization algorithm DA-ELM to CH4 concentration inversion, the correlation coefficient R2 is as high as 0.9995. Through long-term stability verification, the instrument's standard deviation at 1 ppm is 27 ppb. After Allan-Werle deviation analysis, the sensor's limit of detection is 2.298 ppb at an integration time of 138 s. Using the self-developed sensor, the detection of dual-range trace CH4 gas is achieved, enabling a dynamic detection range of trace CH4 gas ranging from 400 ppb to 1000 ppm. The sensor realizes dual-range methane trace detection and actively controls methane emissions to improve environmental quality while taking into account the safety benefits of reducing production accidents.

Effects of Oregano on Lactation Performance, Nutrient Digestibility, Ruminal Fermentation Parameters, and Methane Emissions in Dairy Cows: A Meta-Analysis

Growing concerns regarding antibiotic use in livestock, due to antibiotic resistance and potential human transmission, have led to increased interest in herbs and their derivatives, including essential oils, which possess antimicrobial properties that may enhance overall productivity and serve as a strategy for methane mitigation. The objective of this meta-analysis was to investigate the effects of adding oregano to the diet in different forms (essential oils, plant materials, or leaves) on the dry matter intake (DMI), milk yield (MY), milk components, nutrient digestibility, ruminal fermentation parameters, and methane (CH4) emissions of dairy cows. A literature search was conducted to identify papers published from 2000 to 2023. Effect size for all outcomes was reported as a standardized means difference (SMD) and raw means difference with 95% confidence intervals. Heterogeneity was determined using the Q test and I2 statistic. The results of the meta-analysis indicated that adding oregano had no effect on DMI (SMD = 0.081; p = 0.507) and MY (SMD = 0.060; p = 0.665). Milk fat percentage, milk protein percentage, and milk lactose percentage were not affected by oregano. The addition of oregano to the diet significantly decreased dry matter digestibility (SMD = −0.502; p = 0.013), crude protein digestibility (SMD = −0.374; p = 0.040), and neutral detergent fiber digestibility (SMD = −0.505; p = 0.014). Ruminal pH (SMD = −0.122; p = 0.411), total volatile fatty acids concentration (SMD = −0.038; p = 0.798), acetate (SMD = −0.046; p = 0.757), propionate (SMD = 0.007; p = 0.960), and butyrate (SMD = 0.037; p = 0.801) proportion were not affected by oregano. The addition of oregano to the diet tended to decrease CH4/DMI (SMD = −0.275; p = 0.095) but did not affect CH4 production (SMD = −0.156; p = 0.282). Heterogeneity (Q and I2) was non-significant for all parameters. We conclude that the inclusion of oregano in various forms (essential oils, plant materials, or leaves) in the diet of dairy cows reduces nutrient digestibility but does not significantly affect DMI, MY, milk components, ruminal fermentation parameters, or CH4 production. Future research should focus on optimizing the dosage of oregano (both EOs and plant materials) and exploring the impact of its form on lactation, nutrient digestibility, ruminal fermentation, and CH4 emissions in dairy cows.

Dynamic process simulation for life cycle inventory data acquisition – Environmental assessment of biological and chemical phosphorus removal

In Sweden, phosphorus is commonly removed from municipal wastewater treatment by chemical precipitation (CP). Recently, such alternatives as enhanced biological phosphorus removal (EBPR) have garnered interest due to the increased risk of chemical shortage. In this study, a life cycle assessment (LCA) was performed to compare EBPR and CP in three scenarios: 1) baseline – precipitation chemicals available, 2) stricter effluent requirements – precipitation chemicals available, and 3) chemical shortage – no precipitation chemicals available. Data acquisition that was based on dynamic process simulation was useful, yielding more site-specific results, in contrast to standard literature values. The results indicated substantial differences in greenhouse gas emissions between configurations (around three times higher methane emissions for EBPR compared to CP configurations – although this finding requires further validation). These differences suggest that different emission factors for EBPR and CP should be considered. Furthermore, it is suggested to include waterline methane emissions, at least when the configuration incorporates anaerobic reactors in the water line. Further validation of emissions is necessary, especially for EBPR plants with side-stream hydrolysis and digester reject water treatment. The LCA results showed a similar overall environmental impact for both configurations, but the results of individual impact categories differed. EBPR caused greater climate impact due to the larger direct emissions of methane. Toxicity was more important for CP, based on the inherent heavy metal content in precipitation chemicals. Freshwater eutrophication was similar for both configurations, assuming that precipitation chemicals were available. However, if the recipient is sensitive, implementing EBPR reduces the freshwater eutrophication potential by 75% during a chemical shortage, and should be considered.

Addition of Tannin-Containing Legumes to Native Grasslands: Effects on Enteric Methane Emissions, Nitrogen Losses and Animal Performance of Beef Cattle

Extensive cattle production on native grasslands is vital to the sustainability of the South American Pampas, and the inclusion of tannin-containing legumes can increase farm profitability, improve nitrogen (N) use and reduce greenhouse gas (GHG) emissions. This study quantified the effects of adding tannin-containing legumes to native grasslands on enteric methane (CH4) emissions, animal performance and N balance in cattle. A crossover design trial was conducted with 22 beef heifers under two treatments: native grassland (NG) and native grassland with the addition of Lotus uliginosus and L. angustissimus (NG+L). The results showed that forage mass disappearance was similar between treatments; however, 54% of the forage disappearance in the NG+L corresponded with legumes, indicating that the heifers in this treatment consumed a better-quality diet. While individual CH4 emissions were similar between treatments, heifers grazing the NG+L showed a higher average daily gain (ADG) and lower emissions intensity than heifers grazing NGs (0.25 vs. 0.58 g CH4/g ADG, p < 0.05). Additionally, they also ingested 20% more N and were more efficient in its utilization. Incorporating tannin-containing legumes into native grasslands can improve animal productivity and N use efficiency while reducing the intensity of enteric CH4 emissions.

Study on Spatiotemporal Characteristics and Influencing Factors of High-Resolution Single-Season Rice

Single-season rice describes the area under rice cultivation from May–October of the year. Many scholars have used lower-resolution data to study single-season rice in different regions, but using high-precision and high-resolution single-season rice data can reveal new phenomena. This paper uses a long-time-series, high-precision, and high-resolution single-season rice cultivation dataset to conduct an in-depth analysis of the spatial–temporal variability characteristics of single-season rice in Jiangsu Province, China, from 2017 to 2021. It explores the correlation between meteorological factors and greenhouse gasses for single-season rice. It analyzes the driving role of social factors on single-season rice. The results showed that single-season rice was mainly grown in the central and northeastern regions of the study area. The single-season rice cultivation was significantly reduced in 2020 due to the impact of COVID-19. Single-season rice strongly correlates with meteorological factors in time but shows a weak spatial correlation. This is because human factors largely dominate the area under single-season rice cultivation. Methane emissions in the study area are mainly influenced by anthropogenic activities rather than single-season rice. Social factors are essential in controlling single-season rice cultivation in the study area. This study was conducted in Jiangsu Province, China. Still, the methodology and results have important implications for agricultural production and environmental management studies in other regions, and some findings have general applicability.

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom-up (i.e., activity level) or top-down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom-up and top-down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom-up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom-up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

Effects of GroMore® Program on Rice Yield and GHG Emissions in a Korean Paddy Rice

The agronomic benefits of pesticides combined with amino acid application to increase rice production have been recognized, but they are still not well-known for greenhouse gas (GHG) emissions and mitigation in irrigated paddy fields. Thus, this study was conducted to investigate the combined effects of pesticide and amino acid application on rice yield and methane (CH4) emissions in a Korean rice paddy. A field experiment was conducted with five levels: none (no pesticide application, T1), different conventional practices (combined application of insecticides and fungicide, T2 and T3), and GroMore® programs (combined application of insecticides, fungicides, and amino acids, T4 and T5). Rice grain yield and yield components were obtained using agronomic measurements. To determine the greenhouse gas intensity (GHGI) of each treatment, CH4 emissions were measured throughout the rice growing period. Results showed that the chemical applications in combination with amino acids in T4 obtained a higher grain yield and number of panicles per plant compared to T1, T2, and T3, while T4 and T5 showed no difference on filled spikelets except for T2. T3 and T5 showed lower respective cumulative CH4 emissions by 30% and 32% during the entire rice growing season, compared to no chemical application (T1). Meanwhile, N2O emissions were negligible in all treatments because the paddy field was flooded most of the growing season. The results of the impact of GroMore® programs on relatively higher grain yield and lower GHG emissions are presented. In conclusion, the application of pesticides combined with amino acids obtained lower GHGI values.

Warming increases CH4 emissions from rice paddies through shifts in methanogenic and methanotrophic communities

Warming often stimulates methane (CH4) emissions from rice paddies, one of the largest anthropogenic sources of CH4 emissions. However, the responses of methanogenic and methanotrophic communities to warming, particularly within active communities, remain unclear. Therefore, based on a field warming experiment in a rice-wheat system, we investigated the effects of warming on methanogenic and methanotrophic communities, using the DNA stable-isotope probing technology. Our results indicated that warming increased CH4 emissions by 27–49% over two rice growing seasons compared to ambient conditions. Warming significantly increased the abundance of methanogens by 53%, whereas did not affect activities of methanogens and active methanogenic community. Conversely, warming did not influence the abundance of methanotrophs, but reduced activities of methanotrophs by 44%. Notably, warming led to a significant rise in the relative abundance of the active type II methanotrophic community, which exhibits lower CH4 oxidation efficiency. These findings suggest that the observed increase in CH4 emissions under warming conditions is primarily driven by the enhanced abundance of methanogens and the increased presence of less efficient active type II methanotrophs. This study underscores the critical role of active microbial communities in understanding and managing CH4 emissions from rice paddies in a warming world.

How the USA can feasibly cut methane emissions 30% by 2030: anaerobic digestion of organic waste and various measures in oil and gas production

AbstractThe USA has committed to cutting its emissions of methane, an extremely potent greenhouse gas, by at least 30% by 2030 (‘30 × 30’). This is part of the Global Methane Pledge, signed by 155 countries, to keep global warming within 1.5 °C and thereby to forestall the disastrous effects of ‘runaway climate change’. It will be a major challenge for the USA to reach 30 × 30 and there is no consensus on how to do so. This paper provides a concrete, data‐driven roadmap to indicate how to reach 30 × 30 feasibly, focusing on two enormous sources of methane emissions: organic waste and oil and gas production. It calculates that building approximately 4700 anaerobic digesters (ADs) to process food waste and animal manure would cost about $74.2 billion in capital expenditure (capex) and cut 13.6% of total US methane emissions per year. On average, each project would take roughly 2–6 years to build. Of the various methane mitigation measures analyzed in the oil and gas sectors, the most impactful would be full compliance with the US Environmental Protection Agency's (EPA's) revised New Source Performance Standards (NSPS) for oil and gas production by 2029. This would cut 17.5% of total US methane emissions annually, at a cumulative capex of $20.7 billion from 2024 to 2029. Together, the 13.6% cut from food waste and manure ADs and the 17.5% cut from full NSPS compliance would amount to a 31.5% reduction, exceeding the 30 × 30 goal. Greater clarity from the federal government on funding eligibility and additional incentives would accelerate the buildout of ADs.

Using open-path dual-comb spectroscopy to monitor methane emissions from simulated grazing cattle

Abstract. Accurate whole-farm or herd-level measurements of livestock methane emissions are necessary for anthropogenic greenhouse gas inventories and to evaluate mitigation strategies. A controlled methane (CH4) release experiment was performed to determine if dual-comb spectroscopy (DCS) can detect CH4 concentration enhancements produced by a typical herd of beef cattle in an extensive grazing system. Open-path DCS was used to measure downwind and upwind CH4 concentrations from 10 point sources of methane simulating cattle emissions. The CH4 mole fractions and wind velocity data were used to calculate CH4 flux using an inverse dispersion model, and the simulated fluxes were then compared to the actual CH4 release rate. For a source located 60 m from the downwind path, the DCS system detected 10 nmol mol−1 CH4 horizontal concentration gradient above the atmospheric background concentration with a precision of 6 nmol mol−1 in 15 min interval. A CH4 release of 3970 g d−1 was performed, resulting in an average concentration enhancement of 24 nmol mol−1 of CH4. The calculated CH4 flux was 4002 g d−1, showing good agreement with the actual CH4 release rate. Periodically altering the downwind path, which may be needed to track moving cattle, did not adversely affect the ability of the instruments to determine the CH4 flux. These results give us confidence that CH4 flux can be determined by grazing cattle with low disturbance and direct field-scale measurements.

Analysis of Determinants of Envitonmrntal Awareness Among Beef Consumers

Objective: The objective of this study is to analyze the determinants of environmental awareness among beef consumers in Brazil, focusing on the acceptance of the Carbon Neutral Beef label. Theoretical Framework: The literature review covers the growing consumer concern for sustainability and animal welfare. The concept of Carbon Neutral Beef, developed by EMBRAPA, is discussed as a strategy to mitigate methane emissions in livestock production. Previous studies indicate that factors such as education, income, and environmental awareness influence consumers' willingness to pay more for sustainable beef. Method: The research employed a quantitative approach, applying a survey to beef consumers in Brazil. A total of 256 completed questionnaires were analyzed, and the data were processed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The Tobit econometric model was employed to identify the determinants of consumers' environmental awareness. Results and Discussion: The results show that most consumers prefer beef with sustainability attributes, influenced by income, education, and concern for the environment. The Tobit model indicated that variables such as educational level and household income are significant determinants of environmental awareness. These consumers are willing to pay more for beef that respects the environment and animal welfare. Research Implications: The study provides insights for the beef industry on the importance of adopting sustainable production practices. Originality/Value: The study provides insights for the beef industry on the importance of adopting sustainable production practices.