Spatial and temporal methane emissions from urbanized subtropical estuaries in the northwest Gulf of Mexico.

Dairy products are important food sources which contain nutrients that are essential for human development and healthy ageing. Greenhouse gasses are formed during the production of dairy of which methane (CH4) emission by dairy cows is the single largest source. A reduction in CH4 emission could be achieved via selective breeding, though this requires genetic variation in CH4 emission. In order to quantify the genetic variation in CH4 emission, 3 different indicators were used. The first indicator was CH4 emission predicted based on milk fatty acids (FA) which were measured using gas chromatography. Different FA based CH4 prediction equations were used and 12 to 44% of the variation was due to genetic differences between cows. The second indicator was CH4 emission measured with breath sensors. The breath of cows was analysed during milking in automatic milking systems. Genetics explained 3 to 12% of the total variation in this CH4 indicator. The third indicator was CH4 emission predicted based on milk mid-infrared (MIR) spectra. Of this indicator, between 17 and 21% of the total variation could be attributed to genetic factors. These results suggest that there is genetic variation in CH4 emission and selective breeding for lower CH4 emission is possible. The correlations between sensor measured CH4 emission and milk MIR predicted CH4 emission were low, indicating that both indicators explain a different part of the variation in true CH4 emission. The accuracy of the estimated breeding values (EBV) of these CH4 indicators confirms this suggestion. Combining information from sensor measured CH­4 emission with milk MIR predicted CH4 emission increases the accuracy of the EBV compared to using them separately. Correlations of sensor measured CH4 emission and milk MIR predicted CH4 emission with breeding goal traits (production and fertility traits) were low to medium. Genetic correlations between CH4 emission and production traits ranged between -0.61 and 0.65, and genetic correlations between CH4 emission and fertility traits ranged between -0.32 and 0.38. These results suggest that inclusion of CH4 emission in the breeding goal has a minor impact on the breeding goal traits studied. These correlations, however, are estimated on relatively small datasets. Increasing the amount of data by using EBV, correlations between the EBV of the CH4 indicators and the EBV of six breeding goal traits were also low to medium. In conclusion, there is a possibility to use selective breeding to reduce CH4 emission by dairy cows with an anticipated minor impact on other breeding goal traits.

Methane emissions among individual dairy cows during milking quantified by eructation peaks or ratio with carbon dioxide

Mangrove ecosystems with high sediment deposition and active carbon cycling are a source of methane (CH4) to the coastal atmosphere. We investigated diurnal and seasonal variations in CH4 emissions from a subtropical mangrove estuary in southern Texas, northwest Gulf of Mexico. Tidal processes, amplitude (spring vs. neap tides) and topographic characteristics are crucial factors controlling CH4 cycling in mangrove creeks. Higher CH4 concentrations were observed during the ebb in spring tides due to the combination of processive export of CH4 along the creeks during ebb tides and the addition of porewater CH4 in upper intertidal sediment under water inundation in spring tides. The annual CH4 emissions offset approximately 0.15% of the carbon stock in normal years, indicating that these mangrove creeks are a weak CH4 source. However, significantly elevated CH4 emissions were observed from mangrove dieback after the extreme cold‐freezing event in February 2021. The average CH4 flux from the mangrove creeks (126.1 ± 128.3 μmol [m2·d]−1) increased 45% in 3 months after mangrove die‐off in comparison with the overall average in normal years (87.0 ± 64.4 μmol [m2·d]−1). It is obvious that the previous small CH4 offset of the healthy mangrove forest was enlarged by the dieback event. Because the mangrove forests in this study live close to the limit of their survival range, our study highlights the important management considerations for blue carbon projects in vulnerable areas.

Although invasions by non-native species represent a major threat to biodiversity and ecosystem functioning, little attention has been paid to the potential impacts of these invasions on methane (CH4) emission and its 13C-CH4-isotope signature in salt marshes. An invasive perennial C4 grass Spartina alterniflora has spread rapidly along the east coast of China since its introduction from North America in 1979. Since its intentional introduction to the Jiuduansha Island in the Yangtze River estuary in 1997, S. alterniflora monocultures have become the dominant component of the Jiuduansha’s vegetation, where monocultures of the native plant Scirpus mariqueter (a C3 grass) used to dominate the vegetation for more than 30 years. We investigated seasonal variation in soil CH4 emission and its 13C-CH4-isotope signature from S. alterniflora and S. mariqueter marshes. The results obtained here show that S. alterniflora invasion increased soil CH4 emissions compared to native S. mariqueter, possibly resulting from great belowground biomass of S. alterniflora, which might have affected soil microenvironments and /or CH4 production pathways. CH4 emissions from soils in both marshes followed similar seasonal patterns in CH4 emissions that increased significantly from April to August and then decreased from August to October. CH4 emissions were positively correlated with soil temperature, but negatively correlated with soil moisture for both S. alterniflora and S. mariqueter soils (p 0.05). Generally, the CH4 emissions from both invasive S. alterniflora and native S. mariqueter soils in the salt marshes of Jiuduansha Island were very low (0.01–0.26 mg m-2 h-1), suggesting that S. alterniflora invasion along the east coast of China may not be a significant potential source of atmospheric CH4.

Several studies on methane (CH4) emissions have focussed on selecting high and low CH4-emitting animals. One challenge faced by this work is the lack of consistency, or repeatability, in animal rankings over time. Repeatability for individual animals over time needs to be high to reliably detect high and low CH4-emitting animals. A possible explanation for the lack of repeatability is a relatively high within-animal variation in daily CH4 emissions, meaning that animals could then change their ranking when compared at different points in time. An experiment was undertaken with four non-lactating dairy cattle to assess the within- and between-animal variation in CH4 emissions over time when measured using the sulfur hexafluoride (SF6) tracer technique. Two contrasting diets were fed to the cattle at maintenance energy levels: lucerne silage (diet 1) and a cereal + lucerne + straw mixed ration diet (diet 2). Daily CH4 measurements were undertaken for 23 days on diet 1 and 30 days on diet 2. There was a significant (P < 0.001) difference between diet 1 and diet 2 in daily CH4 production, with mean (±s.e.) production of 124.3 (11.1) g CH4/day from diet 1 and 169.8 (±11.0) g CH4/day from diet 2. Lower CH4 yield (g CH4/kg dry matter intake) was recorded on diet 1 (22.8 ± 2.0) than diet 2 (32.0 ± 2.0). Cows differed significantly (P < 0.05) from one another in daily CH4 yield (diet 1: cow 1 = 19.4 ± 0.6, cow 2 = 22.2 ± 0.8, cow 3 = 23.2 ± 0.7, cow 4 = 25.4 ± 0.6; diet 2: cow 1 = 26.0 ± 0.7, cow 2 = 36.4 ± 0.7, cow 3 = 29.3 ± 0.7, cow 4 = 36.6 ± 0.7). Variances for daily CH4 yield were smaller for diet 1 (within animal = 6.91, between animals = 6.23) than for diet 2 (within animal = 10.09, between animals = 27.79). Estimates of repeatability (variation between animals/total variation) for daily CH4 yield were 47 and 73% in diet 1 and 2, respectively. Coefficients of variation in average daily CH4 emissions in this experiment ranged from 8 to 18% despite the fact that each animal received the same quantity and quality of feed each day. While further research is required, the high within-animal variability in CH4 emissions measured using the SF6 tracer technique may explain why there has been difficulty in obtaining consistent rankings in CH4 yields when animals are measured on multiple occasions. The results also suggest that the SF6 tracer technique may exaggerate apparent between animal differences in CH4 emissions.

Dynamic chamber as a more reliable technique for measuring methane emissions from aquatic ecosystems

To investigate temporal and spatial variations in diffusive CH4 emission from the surface of the Three Gorges Reservoir, CH4 emissions were measured using the static chamber technique along the mainstream of the reservoir from January to December 2010. The overall average CH4 flux is 7.93 mg CH4 m−2 d−1, which is comparable to those from other temperate reservoirs but significantly lower than those from tropical reservoirs. Seasonal variations showed that CH4 emission reached the maximum in the summer and turned to the low levels in the other seasons; such variations reflected the seasonal dynamics of temperature, dissolved oxygen, and water velocity. Moreover, the yearly average CH4 flux decreased from upstream to downstream before the Three Gorges Dam, but CH4 emission from the surface of the downstream river was higher than that from the surface at Zigui, the upstream water before the Three Gorges Dam. The differences in water velocity and allochthonous input of organic matter probably caused the spatial variations in CH4 emission. These results indicate that systematic sampling is needed to better estimate CH4 emission through coverage of the temporal and spatial scales and to better assess the influence of CH4 emission from the Three Gorges Reservoir on climate change in China, as well as the rest of the world.

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion together with their different transport rates and vulnerability to oxidation determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways have not been well characterized by experiments or modeling approaches. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: 1) the Ebullition Bubble Growth volume threshold approach (EBG) and 2) the modified Ebullition Concentration Threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model-data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion together with their different transport rates and vulnerability to oxidation determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways have not been well characterized by experiments or modeling approaches. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: 1) the Ebullition Bubble Growth volume threshold approach (EBG) and 2) the modified Ebullition Concentration Threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model-data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion together with their different transport rates and vulnerability to oxidation determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways have not been well characterized by experiments or modeling approaches. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: 1) the Ebullition Bubble Growth volume threshold approach (EBG) and 2) the modified Ebullition Concentration Threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model-data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion together with their different transport rates and vulnerability to oxidation determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways have not been well characterized by experiments or modeling approaches. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: 1) the Ebullition Bubble Growth volume threshold approach (EBG) and 2) the modified Ebullition Concentration Threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model-data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.

Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion together with their different transport rates and vulnerability to oxidation determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways have not been well characterized by experiments or modeling approaches. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: 1) the Ebullition Bubble Growth volume threshold approach (EBG) and 2) the modified Ebullition Concentration Threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model-data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.

Ruminants play an important role in global food security and nutrition. The rumen microbial community provides ruminants with a unique ability to convert human indigestible plant matter into high quality edible protein. However, enteric CH4 produced in the rumen is both a potent GHG and a ME loss for ruminants. As the rumen microbiome constitutes 15% to 40% of the interanimal variation in enteric CH4 emissions, understanding the microbiological mechanisms underpinning ruminal methanogenesis and its interaction with the host animal is crucial for developing CH4 mitigation strategies. Variation in the relative abundance of different microbial species has been observed in cattle with contrasting residual CH4 emission and CH4 yield, with up to 20% of the variation in interanimal CH4 emissions attributable to the presence of a small number of microbial species. The demonstration of ruminotypes associated with high or low CH4 emissions suggests that interactions within complex microbial consortia and with their host are a major source of variation in CH4 emissions. Consequently, microbiome-assisted genomic approaches are being developed to select low CH4-emitting cattle, with breeding values for enteric CH4 being included as part of national breeding programs. Generating rumen microbiome data for use in selection programs is expensive, therefore, identifying microbial biomarkers in milk or plasma to develop predictive models which include microbial predictors in equations based on animal-related data is required. A better understanding of the rumen microbiome has also aided the development and refinements of antimethanogenic feed additives. However, these strategies, which increase the amount of reducing equivalents in the rumen ecosystem, do not generally result in an enrichment of propionate or an improvement in animal performance. Current research aims to provide alternative sinks to reducing equivalents and to stimulate activity of commensal microbes or the supplementation of direct fed microbials to capture lost energy. Furthering our knowledge of the rumen microbiome and its interaction with the host will aid in the development of CH4 mitigation strategies for ruminant livestock.

Climatic zone effects of non-native plant invasion on CH4 and N2O emissions from natural wetland ecosystems

Abstract. Understanding the dynamics of peatland methane (CH4) emissions and quantifying sources of uncertainty in estimating peatland CH4 emissions are critical for mitigating climate change. The relative contributions of CH4 emission pathways through ebullition, plant-mediated transport, and diffusion, together with their different transport rates and vulnerability to oxidation, determine the quantity of CH4 to be oxidized before leaving the soil. Notwithstanding their importance, the relative contributions of the emission pathways are highly uncertain. In particular, the ebullition process is more uncertain and can lead to large uncertainties in modeled CH4 emissions. To improve model simulations of CH4 emission and its pathways, we evaluated two model structures: (1) the ebullition bubble growth volume threshold approach (EBG) and (2) the modified ebullition concentration threshold approach (ECT) using CH4 flux and concentration data collected in a peatland in northern Minnesota, USA. When model parameters were constrained using observed CH4 fluxes, the CH4 emissions simulated by the EBG approach (RMSE = 0.53) had a better agreement with observations than the ECT approach (RMSE = 0.61). Further, the EBG approach simulated a smaller contribution from ebullition but more frequent ebullition events than the ECT approach. The EBG approach yielded greatly improved simulations of pore water CH4 concentrations, especially in the deep soil layers, compared to the ECT approach. When constraining the EBG model with both CH4 flux and concentration data in model–data fusion, uncertainty of the modeled CH4 concentration profiles was reduced by 78 % to 86 % in comparison to constraints based on CH4 flux data alone. The improved model capability was attributed to the well-constrained parameters regulating the CH4 production and emission pathways. Our results suggest that the EBG modeling approach better characterizes CH4 emission and underlying mechanisms. Moreover, to achieve the best model results both CH4 flux and concentration data are required to constrain model parameterization.