Estimates Of CH4 Emissions Research Articles

Continuous atmospheric in-situ measurements of the CH4/CO ratio at the Mt. Cimone station (Italy, 2165 m a.s.l.) and their possible use for estimating regional CH4 emissions

Methane (CH4) is an important climate forcer, contributing about 17% of the total radiative forcing by long living greenhouse gases. The Po basin is one of the most polluted and densely populated areas in Europe representing an important source region for CH4.The aim of this work was to test an inter-species correlation approach to derive estimates of anthropogenic CH4 emissions for the period 2015–2019 from the Po basin by combining CO bottom-up inventory data and continuous CH4 and CO observations from a mountain site in the northern Italy. The tested methodology suggested lower emissions in respect to EDGAR (−17%) and the Italian National Inventory (−40%) for the Po basin. However, despite the two bottom-up inventories, the emissions derived from the atmospheric observations reported an increasing tendency from 2015 to 2019 for the CH4 emissions. A sensitivity study revealed that using different subsets of the atmospheric observations implied a difference of 26% in the CH4 emission estimates. The highest agreement with two bottom-up CH4 inventories (EDGAR and the Italian national inventory) were obtained when atmospheric data were strictly selected for periods representative of air mass transport from the Po basin.Our study identified various challenges when using this methodology as a benchmark to verify bottom-up CH4 inventories. Issues could be attributed to the annual aggregation of the proxies used to derive the emission amounts, to the CO bottom-up inventory used as input information and to the relatively high sensitivity of the results to the different subsets of the atmospheric observations. However, the use of different bottom-up inventories as input data for CO emissions can potentially provide information that should be carefully considered for the purpose of integrating CH4 bottom-up inventories.

Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019

Abstract. The Arctic is a critical region in terms of global warming. Environmental changes are already progressing steadily in high northern latitudes, whereby, among other effects, a high potential for enhanced methane (CH4) emissions is induced. With CH4 being a potent greenhouse gas, additional emissions from Arctic regions may intensify global warming in the future through positive feedback. Various natural and anthropogenic sources are currently contributing to the Arctic's CH4 budget; however, the quantification of those emissions remains challenging. Assessing the amount of CH4 emissions in the Arctic and their contribution to the global budget still remains challenging. On the one hand, this is due to the difficulties in carrying out accurate measurements in such remote areas. Besides, large variations in the spatial distribution of methane sources and a poor understanding of the effects of ongoing changes in carbon decomposition, vegetation and hydrology also complicate the assessment. Therefore, the aim of this work is to reduce uncertainties in current bottom-up estimates of CH4 emissions as well as soil oxidation by implementing an inverse modelling approach in order to better quantify CH4 sources and sinks for the most recent years (2008 to 2019). More precisely, the objective is to detect occurring trends in the CH4 emissions and potential changes in seasonal emission patterns. The implementation of the inversion included footprint simulations obtained with the atmospheric transport model FLEXPART (FLEXible PARTicle dispersion model), various emission estimates from inventories and land surface models, and data on atmospheric CH4 concentrations from 41 surface observation sites in the Arctic nations. The results of the inversion showed that the majority of the CH4 sources currently present in high northern latitudes are poorly constrained by the existing observation network. Therefore, conclusions on trends and changes in the seasonal cycle could not be obtained for the corresponding CH4 sectors. Only CH4 fluxes from wetlands are adequately constrained, predominantly in North America. Within the period under study, wetland emissions show a slight negative trend in North America and a slight positive trend in East Eurasia. Overall, the estimated CH4 emissions are lower compared to the bottom-up estimates but higher than similar results from global inversions.

Significant monthly and diel variations of CH4 emission from a shallow eutrophic lake with submerged aquatic vegetation

Shallow eutrophic lakes with submerged aquatic vegetation (SAV) are a large source of atmospheric methane (CH4) emissions. However, current estimates of CH4 emissions from lakes are uncertain owing to the lack of data on the diel and monthly variability of CH4 emissions. In this study, we conducted monthly diel measurements in Ulansuhai Lake, China during the ice-free period (April to October, 2019) to determine the diel and monthly variability of CH4 emissions. The diffusive CH4 emission flux (FCH4d) from June to September was significantly higher (∼5- to 10-fold) than that from April, May, and October, accounting for > 92% of the total emissions. Daytime measurements would overestimate emissions if extrapolated to the whole day because FCH4d during daytime was higher than that during nighttime. Mean daily FCH4d emission estimates are overestimated by 25%, ranging from 11% in summer to 46% in spring based solely on daytime measurement. The sampling sites were net sources of CH4 emissions, with total FCH4 of 9.74 ± 9.06 mmol m−2 d−1. FCH4d increased sharply when the water temperature was above 20 °C. FCH4 (CO2 eq) accounted for over 90% of the total greenhouse gas emissions (CH4 plus CO2), with the majority occurring from June to September. Our findings indicate that diel and monthly variations should be considered for more accurate estimation of CH4 emissions from the lakes with SAV.

Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates.

The recent rise in atmospheric methane (CH4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking systemto examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year-1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Quantifying methane emissions under field conditions under 2 different dairy production scenarios: Low-input versus high-input milk production

Livestock production systems with ruminants play a relevant role in the emission of the greenhouse gas CH4, which is known to significantly contribute to global warming. Consequently, it is a major societal concern to develop strategies in mitigating such emissions. In addition to breeding toward low-emitting cows, management strategies could also help in reducing greenhouse gas emissions from dairy farms. However, information is required for appropriate decision making. To the best of our knowledge, this is the first study that considers different, already available equations to estimate CH4 emissions of small-scale dairy farms in the mountain region, which largely differ from large dairy farms in the lowlands concerning management and production. For this study, 2 different production systems, both typical for small-scale dairy farming in mountain regions, were simultaneously run over 3 yr at an experimental farm as follows: (1) a high-input production system, characterized by intensive feeding with high amounts of external concentrates and maize silage, year-round housing, and high yielding Simmental cattle breed, and (2) a low-input production system, characterized by prevailing hay and pasture feeding and silage ban, thus covering most of the energy requirements by forage harvested on-farm and the use of the local Tyrolean Grey cattle breed. Results reveal that feeding management has a significant effect on the amount of CH4 emissions. The low-input production system produced less CH4 per cow and per day compared with the high-input production system. However, if calculated per kilogram of milk, the high-input scenario produced proportionally less CH4 than the low-input one. Findings of this study highlight the potential to assess in a fast and cost-effective way the CH4 emission in different dairy production systems. This information contributes to the debate about the future of sustainable milk production in mountain regions, where the production of feed resources is climatically constrained, and could be useful for breeding purposes toward lower CH4-emissions.

Local-to-regional methane emissions from the Upper Silesian Coal Basin (USCB) quantified using UAV-based atmospheric measurements

Abstract. Coal mining accounts for ∼12 % of the total anthropogenic methane (CH4) emissions worldwide. The Upper Silesian Coal Basin (USCB), Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coal bed CH4 emissions into the atmosphere are poorly characterized. As part of the carbon dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May–June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speed, and surface wind direction. We used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2=0.7–0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified. As an alternative, we have developed three upscaling approaches, i.e., by scaling the European Pollutant Release and Transfer Register (E-PRTR) annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate, which are derived from the hourly emission inventory. These estimates are in the range of 256–383 kt CH4 yr−1 for the inverse Gaussian (IG) approach and 228–339 kt CH4 yr−1 for the mass balance (MB) approach. We have also estimated the total CO2 emissions from coal mining ventilation shafts based on the observed ratio of CH4/CO2 and found that the estimated regional CO2 emissions are not a major source of CO2 in the USCB. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Vegetation impacts ditch methane emissions from boreal forestry-drained peatlands—Moss-free ditches have an order-of-magnitude higher emissions than moss-covered ditches

Ditches of forestry-drained peatlands are an important source of methane (CH4) to the atmosphere. These CH4 emissions are currently estimated using the IPCC Tier 1 emission factor (21.7 g CH4 m−2 y−1), which is based on a limited number of observations (11 study sites) and does not take into account that the emissions are affected by the condition and age of the ditches. Furthermore, the total area of different kinds of ditches remains insufficiently estimated. To construct more advanced ditch CH4 emission factors for Finland, we measured CH4 emissions in ditches of 3 forestry-drained peatland areas (manual chamber technique) and amended this dataset with previously measured unpublished and published data from 18 study areas. In a predetermined 2-type ditch classification scheme, the mean CH4 emissions (±standard error) were 2.6 ± 0.8 g CH4 m−2 y−1 and 20.6 ± 7.0 g CH4 m-2 y−1 in moss-covered and moss-free ditches, respectively. In a more detailed 4-type classification scheme, the yearly emissions were 0.6 ± 0.3, 3.8 ± 1.1, 8.8 ± 3.2, and 25.1 ± 9.7 g CH4 m−2 y−1 in Sphagnum-covered, Sphagnum- and vascular plant—covered, moss-free and vascular plant-covered, and plant - free ditches, respectively. Hence, we found that Tier 1 emission factor may overestimate ditch CH4 emissions through overestimation of the emissions of moss-covered ditches, irrespective of whether they harbor potentially CH4 conducing vascular plants. Based on the areal estimates and the CH4 emission factors for moss-covered and moss-free ditches, CH4 emissions of ditches of forestry-drained peatlands in Finland were 8,600 t a−1, which is 63% lower than the current greenhouse gas inventory estimates for ditch CH4 emissions (23,200 t a−1). We suggest that the Tier 1 emission factor should be replaced with more advanced emission factors in the estimation of ditch CH4 emissions of boreal forestry-drained peatlands also in other countries than in Finland. Furthermore, our results suggest that the current practice in Finland to minimize ditch-network maintenance by ditch cleaning will likely decrease CH4 emissions from ditches, since old moss-covered ditches have very low emissions.

Magnitude and seasonal variation of N2O and CH4 emissions over a mixed agriculture-urban region

Inventory estimates of N2O and CH4 emissions disregard temporal and spatial variabilities, which hinders the search for effective local strategies to lower greenhouse gas emissions. We have quantified the emissions of N2O and CH4 in a mixed agriculture-urban region using two independent approaches, i.e., the vertical gradient method (VGM) and the radon-tracer method (RTM), compared the estimated annual fluxes with the EDGARv6.0 emissions, revealed the seasonal variations of the VGM fluxes, and inferred the sources that most likely cause the seasonal variations based on the footprint analysis even though our methods cannot attribute different sources. We show that the annual RTM estimates represented by the mode of lognormal fit for N2O and CH4 are 0.4 g m−2 yr−1 and 12 g m−2 yr−1, and the VGM estimates are 0.6 ± 0.3 g m−2 yr−1 and 13 ± 4 g m−2 yr−1, respectively. Furthermore, the average EDGARv6.0 emissions constrained by the VGM and the RTM footprints are 1.3 g m−2 yr−1 and 0.9 g m−2 yr−1 for N2O, and 21 g m−2 yr−1 and 18 g m−2 yr−1 for CH4. Compared to our estimated fluxes, EDGARv6.0 N2O and CH4 emissions are both overestimated; for N2O, it is mainly caused by an overestimation of the chemical industry's emission. Moreover, in contrast to EDGARv6.0′s nearly constant monthly emissions throughout the year, the VGM estimates of N2O and CH4 show seasonal variations with relatively high values from March to September, which is most likely caused by agricultural activities. Our study demonstrates that large nighttime vertical gradients of atmospheric N2O and CH4 mole fractions at a tall tower can be used to derive surface fluxes by the VGM; taken together with the RTM fluxes, both the annual means and the temporal variations of N2O and CH4 emissions can be constrained on a regional scale.

Development of a Mobile Open-Circuit Respiration Head Hood System for Measuring Gas Exchange in Camelids in the Andean Plateau.

Peru has the largest inventory of alpacas worldwide. Despite their importance as a source of net income for rural communities living at the Andean Plateau, data on energy requirements and methane (CH4) emissions for alpacas are particularly lacking. In 2019, the International Panel on Climate Change (IPCC; 2006, and Refinement 2019) outlined methods for estimating CH4 emissions from enteric fermentation and no methane (CH4) conversion factors were reported for camelids. IPCC has since updated its guidelines for estimating CH4 emissions from the enteric fermentation of livestock at a national scale. For greenhouse gas (GHG) inventory purposes, conversion factors were developed for ruminants but not for domestic South American camelids (SAC), with this category including alpacas. A mobile open-circuit respirometry system (head hood) for the rapid determination of CH4 and CO2 production, O2 consumption, and thereafter, heat production (HP) for camelids was built and validated. In addition, an experimental test with eight alpacas was conducted for validation purposes. The average HP measured by indirect calorimetry (respiratory quotient (RQ) method) was close to the average HP determined from the carbon-nitrogen balance (CN method); 402 kJ/kg BW0.75 and 398 kJ/kg BW0.75, respectively. Fasting HP was determined by the RQ method and 250 kJ/kg BW0.75 was obtained. The metabolizable energy requirement for maintenance (MEm) was calculated to be 323 kJ/kg BW0.75 with an efficiency of energy utilization of 77%. When intake was adjusted to zero energy retention by linear regression, the MEm requirement increased to 369 kJ/kg BW0.75 and the efficiency decreased up to 68%. The CH4 conversion factor (Ym) was 5.5% on average. Further research is required to gain a better understanding of the energy requirements and CH4 emissions of alpacas in conditions of the Andean Plateau and to quantify them with greater accuracy.

Methane Emission and Metabolic Status in Peak Lactating Dairy Cows and Their Assessment Via Methane Concentration Profile

Abstract Ruminant husbandry contributes to global methane (CH4) emissions and beside its negative impact on the environment, enteric CH4 emissions cause a loss of gross energy intake in cows. The study is aimed to estimate CH4 emission and metabolic status in dairy cows via the methane concentration profile as a tool for analyzing the CH4 production pattern. The study included eighteen cows whose enteric CH4 emission was measured during three consecutive days in three periods: 2 hours before (P1), 2–4 hours (P2) and 6–8 hours (P3) after the morning feeding. Based on CH4 enteric emissions, cows were divided into two groups (n=6, respectively): HM (average CH4 concentration: 5430.08 ± 365.92 ppm) and LM (average CH4 concentration: 1351.85 ± 205.20 ppm). Following CH4 measurement, on day 3, venous blood was sampled to determine the indicators of the metabolic status. HM cows had significantly higher average CH4 concentrations, maximum and average CH4 peak amplitude than LM cows in all measuring periods (P1-P3), while the number of CH4 peaks tended to be higher in HM than in LM cows in P2. There were no differences in the maximum and average CH4 peak width and average distance among two CH4 peaks between examined groups of cows. HM cows had significantly higher total protein concentrations and significantly lower total bilirubin and NEFA concentrations than LM cows. In conclusion, HM cows have a greater number of eructations and release more CH4 per eructation than LM cows, hence the differences in metabolic status are most likely related to the differences in their liver function.

The role of rice cultivation in changes in atmospheric methane concentration and the Global Methane Pledge.

Resumption of the increase in atmospheric methane (CH4 ) concentrations since 2007 is of global concern and may partly have resulted from emissions from rice cultivation. Estimates of CH4 emissions from rice fields and abatement potential are essential to assess the contribution of improved rice management in achieving the targets of the Global Methane Pledge agreed upon by over 100 countries at COP26. However, the contribution of CH4 emissions from rice fields to the resumed CH4 growth and the global abatement potential remains unclear. In this study, we estimated the global CH4 emissions from rice fields to be 27 ± 6 Tg CH4 year-1 in the recent decade (2008-2017) based on the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The trend of CH4 emissions from rice cultivation showed an increase followed by no significant change and then, a stabilization over 1990-2020. Consequently, the contribution of CH4 emissions from rice fields to the renewed increase in atmospheric CH4 concentrations since 2007 was minor. We summarized the existing low-cost measures and showed that improved water and straw management could reduce one-third of global CH4 emissions from rice fields. Straw returned as biochar could reduce CH4 emissions by 12 Tg CH4 year-1 , equivalent to 10% of the total reduction of all anthropogenic emissions. We conclude that other sectors than rice cultivation must have contributed to the renewed increase in atmospheric CH4 concentrations, and that optimizing multiple mitigation measures in rice fields could contribute significantly to the abatement goal outlined in the Global Methane Pledge.

High-Spatial-Resolution Methane Emissions Calculation Using TROPOMI Data by a Divergence Method

Methane (CH4) is the second-largest greenhouse gas emitted by human activity and natural sources after carbon dioxide (CO2). Its relatively short lifetime in the atmosphere (about 12 years) means that we can mitigate the human impacts of climate change in a relatively short period of time by reducing CH4 emissions. The creation of CH4 emissions management policies can be based on the distribution maps of surface CH4 concentration that are in large-scale and at high-resolution. The estimate of CH4 emissions with broad coverage are provided by currently extensively used satellite data supplemented with data from model simulations. However, it is at low spatial resolution. In this paper, through the combination of atmospheric CH4 observations from the TROPOMI sensor and wind data from the ECMWF global reanalysis, a straightforward divergence method is proposed to estimate the surface CH4 emissions in China from March 2019 to September 2022 at a resolution of 7 km × 7 km. This method was compared with the average annual CH4 emissions of Emissions Database for Global Atmospheric Research (EDGARv7.0), and the Root Mean Square Error (RMSE) is 2.53 kg/km2/h and within error envelop (EE) is 72.93%, which represents the proportion of reliable values under certain uncertain conditions. We estimated that the average annual CH4 emissions in China from 2019 to 2022 is 81 Tg, with the lowest emissions in 2021 (75 Tg) due to the impact of COVID-19. In 2021, the largest anthropogenic emissions in China are from agriculture, energy activities and livestock, accounting for 28% (20.8 Tg), 25% (18.9 Tg) and 19% (13.9 Tg) of total emissions, respectively, while wetlands, as the largest natural source, produce 14% (10.5 Tg) of CH4 emissions.

Modelling methane emissions and grain yields for a double-rice system in Southern China with DAYCENT and DNDC models

Methane (CH4) is an important greenhouse gas that contributes to climate change and one of its major sources is rice cultivation. The main aim of this paper was to compare two well-established biogeochemical models, namely Daily Century (DAYCENT) and DeNitrification-DeComposition (DNDC) for estimating CH4 emissions and grain yields for a double-rice cropping system with tillage practice and/or stubble incorporation in the winter fallow season in Southern China. Both models were calibrated and validated using field measured data from November 2008 to November 2014. The calibrated models performed effectively in estimating the daily CH4 emission pattern (correlation coefficient, r = 0.58–0.63, p < 0.001), but model efficiency (EF) values were higher in stubble incorporation treatments, with and without winter tillage (treatments S and WS) (EF = 0.22–0.28) than that in winter tillage without stubble incorporation treatment (W) (EF = −0.06–0.08). We recommend that algorithms for the impacts of tillage practice on CH4 emission should be improved for both models. DAYCENT and DNDC also estimated rice yields for all treatments without a significant bias. Our results showed that tillage practice in the winter fallow season (treatments WS and W) significantly decreased annual CH4 emissions, by 13–37 % (p < 0.05) for measured values, 15–20 % (p < 0.05) for DAYCENT-simulated values, and 12–32 % (p < 0.05) for DNDC-simulated values, respectively, compared to no-till practice (treatments S), but had no significant impact on grain yields.

Inundation depth stimulates plant‐mediated CH4 emissions by increasing ecosystem carbon uptake and plant height in an estuarine wetland

Abstract Plant‐mediated CH4 emission is an important part of the ecosystem CH4 emission from vegetated wetlands. Inundation depth may alter the potential magnitude of CH4 releases by changing CH4 production and plant transport, but the relationships between plant‐mediated CH4 emissions and inundation depth are still uncertain, especially for estuarine wetlands with changeable hydrological processes. Besides, there are conflicting results regarding the role of inundation depth in plant‐mediated CH4 emissions. Here we conducted a novel inundation depth experiment (0, 5, 10, 20, 30 and 40 cm inundation depth) dominated by Phragmites australis in the Yellow River estuary, China. Soil CH4 emissions, ecosystem CH4 emissions, net ecosystem CO2 exchange (NEE), soil organic carbon (SOC) and plant traits were measured during the growing seasons of 2018, 2019 and 2020. Plant‐mediated CH4 emissions were the difference between ecosystem CH4 emissions and soil CH4 emissions. The results showed that inundation depth decreased soil CH4 emissions but increased ecosystem CH4 emissions. Plant‐mediated CH4 transport from Phragmites australis accounted for 99% of total ecosystem CH4 emissions under different inundation depths. Inundation depth strongly stimulated plant‐mediated CH4 emission from 0 to 20 cm during the growing seasons. The increased NEE enhanced plant‐mediated CH4 emissions by altering production, suggesting that carbon components derived from photosynthetic carbon input may benefit CH4 production. Additionally, the increased plant height promoted CH4 emission by regulating plant transport, indicating that plant traits may play an important role in transport of CH4. Our findings indicated that NEE and plant height play an important role in plant‐mediated CH4 emissions under different inundation depths in estuarine wetland. This study also highlights that hydrological regimes and plant traits are essential for the estimation of CH4 emissions in future projections of global wetland changes. Read the free Plain Language Summary for this article on the Journal blog.

Modelling CH4 emission from rice ecosystem: A comparison between existing empirical models

Rice is a staple food for more than three billion people and accounts for up to 11% of the global methane (CH4) emissions from anthropogenic sources. With increasing populations, particularly in less developed countries where rice is a major cereal crop, production continues to increase to meet demand. Implementing site-specific mitigation measures to reduce greenhouse gas emissions from rice is important to minimise climate change. Measuring greenhouse gases is costly and time-consuming; therefore, many farmers, supply chains, and scientists rely on greenhouse gas accounting tools or internationally acceptable methodologies (e.g., Intergovernmental Panel on Climate Change) to estimate emissions and explore mitigation options. In this paper, existing empirical models that are widely used have been evaluated against measured CH4 emission data. CH4 emission data and management information were collected from 70 peer-reviewed scientific papers. Model input variables such as soil organic carbon (SOC), pH, water management during crop season and pre-season, and organic amendment application were collected and used for estimation of CH4 emission. The performance of the models was evaluated by comparing the predicted emission values against measured emissions with the result showing that the models capture the impact of different management on emissions, but either under- or overestimate the emission value, and therefore are unable to capture the magnitude of emissions. Estimated emission values are much lower than observed for most of the rice-producing countries, with R correlation coefficient values varying from −0.49 to 0.87 across the models. In conclusion, current models are adequate for predicting emission trends and the directional effects of management, but are not adequate for estimating the magnitude of emissions. The existing models do not consider key site-specific variables such as soil texture, planting method, cultivar type, or growing season, which all influence emissions, and thus, the models lack sensitivity to key site variables to reliably predict emissions.

Comparative enteric-methane emissions of dairy farms in northern Victoria, Australia

Context Enteric methane (CH4) is a source of greenhouse gas (GHG) in agriculture, which needs to be reduced. A variety of feeding systems for dairy production is being used in south-eastern Australia, but there are few studies that compare CH4 emissions and emission intensity (EI) of milk production across these systems. Aims The objective was to estimate the lactating cows’ enteric-CH4 emissions, EI and their seasonal changes, across different feeding systems in northern Victoria, Australia. Methods A Tier 2 inventory methodology was used to estimate the enteric-CH4 emissions and EI. Four case-study farms were selected to represent a range of feeding systems, Farms A, B, C and D were categorised as System 4–5 (hybrid–total mixed ration system), System 4 (hybrid system), System 2 (moderate–high bail system) and System 2 respectively. Monthly feed, animal and production data were sourced from June 2019 to May 2020. Key results Average enteric-CH4 emissions of Farms A and B (13.1 and 12.9 kg CO2e/head.day respectively) were greater than those of Farms C and D (11.7 and 11.6 kg CO2e/head.day respectively). Furthermore, CH4 EI was greater in Farms C and D (0.49 and 0.48 CO2-e kg/kg fat- and protein-corrected milk (FPCM) respectively) and it was lower in both Farms A and B (0.46 CO2-e kg/kg FPCM). Overall, Farms A and B using Feeding-system 4–5 with greater-producing cows produced more CH4 but with less CH4 EI than did the Farms C and D, which are mainly pasture-based. Conclusions These findings suggest that to reduce CH4 EI requires a move towards Feeding-system 4–5. However, on the basis of the results of the current study, pasture-based systems have an advantage over hybrid/total mixed ration feeding systems, as these farms have lower absolute CH4 emissions, which helps address climate change. Implications Estimation of CH4 emissions, EI and seasonal changes in them gives farmers the opportunity to identify the mitigation strategies and plan specific strategies that fit the particular feeding system and season. However, more research needs to be conducted to check the feasibility of doing this.

Optimizing the sowing window for direct-seeded rice (Oryza sativa L.) considering high yield and methane emissions in Central China

CONTEXTElevated temperatures have had many adverse effects on rice (Oryza sativa L.) production. Methane (CH4) emissions from rice paddy fields can further increase global temperatures and in turn affect rice production. Many adaptations, such as shifting the sowing date, can help rice plants avoid the influences of high temperatures and reduce CH4 emissions by shortening the rice-growth duration. OBJECTIVEThis study explored the optimal sowing window for direct-seeded rice with the highest yield and lowest CH4 emissions. METHODSHere, we used the calibrated Crop Environment Resource Synthesis (CERES)-Rice model and a widely used CH4 estimation equation to determine the optimal sowing window under historical and future scenarios in Hubei Province, China. This province was divided into four zones, and three rice cultivars were selected. Numerical simulations were conducted for sowing dates at three-day intervals across the entire potential sowing window for each cultivar and zone. The simulated rice yields and estimated CH4 emissions for all sowing dates were compared to determine the optimal sowing window. RESULTS AND CONCLUSIONSOur results indicated that due to the lower emergence rate caused by low temperatures during the early stage of the potential sowing window, rice yield gradually increased with sowing date delay and tended to be stable, while CH4 emissions exhibited the opposite pattern. In this study, the optimal sowing window was determined to be when the lowest CH4 emissions and highest rice yields occurred simultaneously. Under heat stress, these two kinds of events often did not overlap, and the optimal sowing window was defined by the highest yields only. In addition, the current sowing date was mostly within the optimal sowing window during the historical period. The optimal sowing window appeared later under future scenarios than in the historical period, suggesting that the sowing date should be delayed in the future. SIGNIFICANCERice plants cultivated in optimal sowing windows can produce high yields and emit the least CH4. This study provides a better understanding of the effects of shifting the sowing date on food security and greenhouse gas emissions, ensuring sustainable rice production.

Quantifying Methane Emissions from Aquaculture Ponds in China.

Small ponds are important methane (CH4) sources. However, current estimates of CH4 emissions from aquaculture ponds are largely uncertain due to data paucity, especially in China─the largest aquaculture producer in the world. Here, we present a nationwide metadata analysis with a database of 55 field observations to examine total CH4 emissions from aquaculture ponds in China. We found that the annual CH4 fluxes from aquaculture ponds are much larger than those from reservoirs and lakes. The total CH4 emission from aquaculture ponds is 1.60 ± 0.62 Tg CH4 yr-1, with an average growth rate of ∼0.03 Tg CH4 yr-2 during the period 2008-2019. Compared with global major protein-producing livestocks, aquaculture species have a lower (63%) emission intensity, defined by the amount of CH4 emitted per unit of animal proteins. Our study highlights the essential contribution of China's aquaculture ponds to national CH4 emissions and the lower environmental cost of the aquaculture sector for future animal protein production. More field measurements with multi-scale observations are urgently needed to reduce the uncertainty of CH4 emissions from aquaculture ponds.

Is hydrogen production through natural gas pyrolysis compatible with ambitious climate targets in the United States? A location-specific, time-resolved analysis

Pyrolysis of natural gas to produce H2 and solid carbon through methane cracking can be characterized as a high-CH4, low-CO2 process. It results in low CO2 emissions because no direct CO2 is generated at the point of H2 generation if solid carbon is not combusted further. However, it results in high CH4 emissions because of its higher natural gas consumption compared to the direct use of natural gas and, thus, higher CH4 losses along the natural gas supply chain. Here, I analyzed whether this process can provide climate benefit in comparison to the direct, unabated utilization of natural gas and also in comparison with H2 produced from water electrolysis with grid electricity. To this end, Monte Carlo simulations of time-resolved and US state-specific emission profiles and their impact on mid-century global warming under different CH4 mitigation scenarios were conducted. It was found that the climate benefit of natural gas pyrolysis is highly dependent on plant location and the speed at which CH4 emissions can be abated. New York, Pennsylvania, and Ohio emerged as the most promising locations. This is because of their projected long reliance on natural gas for power generation, which renders electrolysis using grid electricity less attractive, as well as the relatively low estimate of current CH4 emissions from the natural gas supply chain. However, without fast action on CH4 emission mitigation, the climate benefit of natural gas pyrolysis is small or non-existent, irrespective of the plant location. Overall, the uncertainty in the relative climate benefit of natural gas pyrolysis was found to be large; however, this study developed an easy-to-adapt MS Excel/visual basic for applications (VBA) tool that can be updated as soon as more accurate data on CH4 emissions becomes available. Policymakers, businesspeople, and scholars can use this tool to estimate the climate impact within their own scenarios and locations.

Technological avenues and market mechanisms to accelerate methane and nitrous oxide emissions reductions.

Strategies targeting methane (CH4) and nitrous oxide (N2O) emissions are critical to meeting global climate targets. Existing literature estimates the emissions of these gases from specific sectors, but this knowledge must be synthesized to prioritize and incentivize CH4 and N2O mitigation. Accordingly, we review emissions sources and mitigation strategies in all key sectors (fuel extraction and combustion, landfilling, agriculture, wastewater treatment, and chemical industry) and the role of carbon markets in reducing emissions. The most accessible reduction opportunities are in the hydrocarbon extraction and waste sectors, where half (>3 Gt-CO2e/year) of the emissions in these sectors could be mitigated at no net cost. In total, 60% of CH4 emissions can be mitigated at less than $50/t-CO2. Expanding the scope of carbon markets to include these emissions could provide cost-effective decarbonization through 2050. We provide recommendations for carbon markets to improve emissions reductions and set prices to appropriately incentivize mitigation.