Farm-level emission intensities of smallholder cattle (Bos indicus; B. indicus–B. taurus crosses) production systems in highlands and semi-arid regions

Ruminants are central to the economic and nutritional life of much of sub-Saharan Africa, but cattle are now blamed for having a disproportionately large negative environmental impact through emissions of greenhouse gas (GHG). However, the mechanism underlying excessive emissions occurring only on some farms is imperfectly understood. Reliable estimates of emissions themselves are frequently lacking due to a paucity of reliable data. Employing individual animal records obtained at regular farm visits, this study quantified farm-level emission intensities (EIs) of greenhouse gases of smallholder farms in three counties in Western Kenya. CP was chosen as the functional unit to capture the outputs of both milk and meat. The results showed that milk is responsible for 80–85% of total CP output. Farm EI ranged widely from 20 to >1 000 kg CO₂-eq/kg CP. Median EIs were 60 (Nandi), 71 (Bomet), and 90 (Nyando) kg CO₂-eq/kg. Although median EIs referenced to milk alone (2.3 kg CO₂-eq/kg milk) were almost twice that reported for Europe, up to 50% of farms had EIs comparable to the mean Pan-European EIs. Enteric methane (CH₄) contributed >95% of emissions and manure ～4%, with negligible emissions attributed to inputs to the production system. Collecting data from individual animals on smallholder farms enabled the demonstration of extremely heterogeneous EI status among similar geographical spaces and provides clear indicators on how low EI status may be achieved in these environments. Contrary to common belief, our data show that industrial-style intensification is not required to achieve low EI. Enteric CH₄ production overwhelmingly drives farm emissions in these systems and as this is strongly collinear with nutrition and intake, an effort will be required to achieve an “efficient frontier” between feed intake, productivity, and GHG emissions.     

Estimating the impact of clinical mastitis in dairy cows on greenhouse gas emissions using a dynamic stochastic simulation model: a case study

The increasing attention for global warming is likely to contribute to the introduction of policies or other incentives to reduce greenhouse gas (GHG) emissions related to livestock production, including dairy. The dairy sector is an important contributor to GHG emissions. Clinical mastitis (CM), an intramammary infection, results in reduced milk production and fertility, increases culling and mortality of cows and, therefore, has a negative impact on the efficiency (output/input) of milk production. This may increase GHG emissions per unit of product. Our objective was to estimate the impact of CM in dairy cows on GHG emissions of milk production for the Dutch situation. A dynamic stochastic simulation model was developed to simulate the dynamics and losses of CM for individual lactations. Cows receive a parity (1 to 5+), a milk production and a calving interval (CI). Based on the parity, cows have a risk of CM, with a maximum of three cases in a lactation. Pathogens causing CM were classified as gram-positive bacteria, gram-negative bacteria, or other. Based on the parity and pathogen combinations, cows had a reduced milk production, discarded milk, prolonged CI and a risk of removal (culling and mortality) that reduce productivity of dairy cows and therefore increase GHG emissions per unit of product. Using life cycle assessment, emissions of GHGs were estimated from cradle to farm gate for processes along the milk production chain that are affected by CM. Processes included were feed production, enteric fermentation, and manure management. Emissions of GHGs were expressed as kg CO₂ equivalents per ton of fat-and-protein-corrected milk (kg CO₂e/t FPCM). Emissions of cows with CM increased on average by 57.5 (6.2%) kg CO₂e/t FPCM compared with cows without CM. This increase was caused by removal (39%), discarded milk (38%), reduced milk production (17%) and prolonged CI (6%). The GHG emissions increased by 48 kg CO₂e/t FPCM for cows with one case of CM, by 69 kg CO₂e/t FPCM for cows with two cases of CM and by 92 kg CO₂e/t FPCM for cows with three cases of CM compared with cows without CM. Preventing CM can be an effective strategy for farmers to reduce GHG emissions and can contribute to sustainable development of the dairy sector, because this also can improve the income of farmers and the welfare of cows. The impact of CM on GHG emissions, however, will vary between farms due to environmental conditions and management practices.     

Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle

In response to the increased concern over agriculture’s contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH₄) per cow per year after accounting for replacement costs, ranging 8.9–323 kg CH₄/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.     

Reducing methane on-farm by feeding diets high in fat may not always reduce life cycle greenhouse gas emissions

Purpose

To consider whether feed supplements that reduce methane emissions from dairy cows result in a net reduction in greenhouse gas (GHG) intensity when productivity changes and emissions associated with extra manufacturing and management are included.

Methods

A life cycle assessment was undertaken using a model farm based on dairy farms in Victoria, Australia. The system boundary included the creation of farm inputs and on-farm activities up to the farm gate where the functional unit was 1 L of fat and protein corrected milk (FPCM). Electricity and diesel (scaled per cow), and fertiliser inputs (scaled on farm size) to the model farm were based on average data from a survey of farms. Fertiliser applied to crops was calculated per area of crop. Animal characteristics were based on available data from farms and literature. Three methane-reducing diets (containing brewers grain, hominy or whole cotton seed) and a control diet (cereal grain) were modelled as being fed during summer, with the control diet being fed for the remainder of the year in all cases.

Results and discussion

Greenhouse gas intensity (kg CO₂-eq/L FPCM) was lower than the control diet when the hominy (97 % compared with control) and brewers grain (98 %) diets were used but increased when the whole cottonseed diet was used (104 %). On-farm GHG emissions (kg CO₂-eq) were lower than the control diet when any of the methane-reducing diets were used (98 to 99.5 % of emissions when control diet fed). Diesel use in production and transport of feed supplements accounted for a large portion (63 to 93 %) of their GHG intensity (kg CO₂-eq/t dry matter). Adjusting fertiliser application, changing transport method, changing transport fuel, and using nitrification inhibitors all had little effect on GHG emissions or GHG intensity.

Conclusions

Although feeding strategies that reduce methane emissions from dairy cows can lower the GHG emissions up to the farm gate, they may not result in lower GHG intensities (g CO₂-eq/L FPCM) when pre-farm emissions are included. Both transport distance and the effect of the feed on milk production have important impacts on the outcomes.     

Effect of improved nitrogen management on greenhouse gas emissions from intensive dairy systems in the Netherlands

Dairy systems in Europe contribute to the emissions of the greenhouse gases (GHGs) nitrous oxide (N₂O), methane (CH₄) and carbon dioxide (CO₂). In this paper, the effects of improved nitrogen (N) management on GHG emissions from Dutch dairy farms are determined. The GHG emissions are calculated using the panel on climate change (IPCC) methodology for the Netherlands, an updated and refined IPCC methodology, and a full accounting approach. The changes in dairy farming over the last 20 years, and the consequences for N management are described using detailed farm‐level data, collected in 1985, 1997 and 2002. The selected years represent distinct stages in the implementation of N policies. The changes in N management have reduced the GHG emissions. A reduction of the N surplus per kilogram milk with 1 g N reduced the GHG emissions per kilogram milk with approximately 29 g CO₂‐equivalents. The reduction of the N surpluses was mainly brought about by reduced fertilizer use and reduced grazing time. The use of updated and refined emission factors resulted in higher CH₄ emissions and lower N₂O emissions. On average, the overall emission was 36% higher with the refined method. Full accounting, including all direct and indirect emissions of CH₄, N₂O and CO₂, increased the emission with 36% compared with the refined IPCC methodology. We conclude that the N surplus at farm level is a useful indicator of GHG emissions. A full accounting system as presented in this study may effectively enable farmers to address the issue of emissions of GHGs in their operational management decisions. Both approaches serve their own specific objectives: full accounting at the farm level to explore mitigation options, and the IPCC methods to report changes in GHG emissions at the national level.     

Cattle feed or bioenergy? Consequential life cycle assessment of biogas feedstock options on dairy farms

On‐farm anaerobic digestion (AD) of wastes and crops can potentially avoid greenhouse gas (GHG) emissions, but incurs extensive environmental effects via carbon and nitrogen cycles and substitution of multiple processes within and outside farm system boundaries. Farm models were combined with consequential life cycle assessment (CLCA) to assess plausible biogas and miscanthus heating pellet scenarios on dairy farms. On the large dairy farm, the introduction of slurry‐only AD led to reductions in global warming potential (GWP) and resource depletion burdens of 14% and 67%, respectively, but eutrophication and acidification burden increases of 9% and 10%, respectively, assuming open tank digestate storage. Marginal GWP burdens per Mg dry matter (DM) feedstock codigested with slurry ranged from –637 kg CO₂e for food waste to +509 kg CO₂e for maize. Codigestion of grass and maize led to increased imports of concentrate feed to the farm, negating the GWP benefits of grid electricity substitution. Attributing grass‐to‐arable land use change (LUC) to marginal wheat feed production led to net GWP burdens exceeding 900 kg CO₂e Mg^?1 maize DM codigested. Converting the medium‐sized dairy farm to a beef‐plus‐AD farm led to a minor reduction in GWP when grass‐to‐arable LUC was excluded, but a 38% GWP increase when such LUC was attributed to marginal maize and wheat feed required for intensive compensatory milk production. If marginal animal feed is derived from soybeans cultivated on recently converted cropland in South America, the net GWP burden increases to 4099 kg CO₂e Mg^?1 maize DM codigested – equivalent to 55 Mg CO₂e yr^?1 per hectare used for AD‐maize cultivation. We conclude that AD of slurry and food waste on dairy farms is an effective GHG mitigation option, but that the quantity of codigested crops should be strictly limited to avoid potentially large international carbon leakage via animal feed displacement.     

An appraisal of carbon footprint of milk from commercial grass-based dairy farms in Ireland according to a certified life cycle assessment methodology

Purpose

Life cycle assessment (LCA) studies of carbon footprint (CF) of milk from grass-based farms are usually limited to small numbers of farms (<30) and rarely certified to international standards, e.g. British Standards Institute publicly available specification 2050 (PAS 2050). The goals of this study were to quantify CF of milk from a large sample of grass-based farms using an accredited PAS 2050 method and to assess the relationships between farm characteristics and CF of milk.

Materials and methods

Data was collected annually using on-farm surveys, milk processor records and national livestock databases for 171 grass-based Irish dairy farms with information successfully obtained electronically from 124 farms and fed into a cradle to farm-gate LCA model. Greenhouse gas (GHG) emissions were estimated with the LCA model in CO₂ equivalents (CO₂-eq) and allocated economically between dairy farm products, except exported crops. Carbon footprint of milk was estimated by expressing GHG emissions attributed to milk per kilogram of fat and protein-corrected milk (FPCM). The Carbon Trust tested the LCA model for non-conformities with PAS 2050. PAS 2050 certification was achieved when non-conformities were fixed or where the effect of all unresolved non-conformities on CF of milk was?<?±5 %.

Results and discussion

The combined effect of LCA model non-conformities with PAS 2050 on CF of milk was <1 %. Consequently, PAS 2050 accreditation was granted. The mean certified CF of milk from grass-based farms was 1.11 kg of CO₂-eq/kg of FPCM, but varied from 0.87 to 1.72 kg of CO₂-eq/kg of FPCM. Although some farm attributes had stronger relationships with CF of milk than the others, no attribute accounted for the majority of variation between farms. However, CF of milk could be reasonably predicted using N efficiency, the length of the grazing season, milk yield/cow and annual replacement rate (R ²?=?0.75). Management changes can be applied simultaneously to improve each of these traits. Thus, grass-based farmers can potentially significantly reduce CF of milk.

Conclusions

The certification of an LCA model to PAS 2050 standards for grass-based dairy farms provides a verifiable approach to quantify CF of milk at a farm or national level. The application of the certified model highlighted a wide range between the CF of milk of commercial farms. However, differences between farms’ CF of milk were explained by variation in various aspects of farm performance. This implies that improving farm efficiency can mitigate CF of milk.     

Variability in greenhouse gas footprints of the global wind farm fleet

While technological characteristics largely determine the greenhouse gas (GHG) emissions during the construction of a wind farm and meteorological circumstances the actual electricity production, a thorough analysis to quantify the GHG footprint variability (in g CO₂eq/kWh electricity produced) between wind farms is still lacking at the global scale. Here, we quantified the GHG footprint of 26,821 wind farms located across the globe, combining turbine-specific technological parameters, life-cycle inventory data, and location- and temporal-specific meteorological information. These wind farms represent 79% of the 651 global wind (GW) capacity installed in 2019. Our results indicate a median GHG footprint for global wind electricity of 10 g CO₂eq/kWh, ranging from 4 to 56 g CO₂eq/kWh (2.5th and 97.5th percentiles). Differences in the GHG footprint of wind farms are mainly explained by spatial variability in wind speed, followed by whether the wind farm is located onshore or offshore, the turbine diameter, and the number of turbines in a wind farm. We also provided a metamodel based on these four predictors for users to be able to easily obtain a first indication of GHG footprints of new wind farms considered. Our results can be used to compare the GHG footprint of wind farms to one another and to other sources of electricity in a location-specific manner.     

Climate mitigation by dairy intensification depends on intensive use of spared grassland

Milk and beef production cause 9% of global greenhouse gas (GHG) emissions. Previous life cycle assessment (LCA) studies have shown that dairy intensification reduces the carbon footprint of milk by increasing animal productivity and feed conversion efficiency. None of these studies simultaneously evaluated indirect GHG effects incurred via teleconnections with expansion of feed crop production and replacement suckler‐beef production. We applied consequential LCA to incorporate these effects into GHG mitigation calculations for intensification scenarios among grazing‐based dairy farms in an industrialized country (UK), in which milk production shifts from average to intensive farm typologies, involving higher milk yields per cow and more maize and concentrate feed in cattle diets. Attributional LCA indicated a reduction of up to 0.10 kg CO₂e kg^?1 milk following intensification, reflecting improved feed conversion efficiency. However, consequential LCA indicated that land use change associated with increased demand for maize and concentrate feed, plus additional suckler‐beef production to replace reduced dairy‐beef output, significantly increased GHG emissions following intensification. International displacement of replacement suckler‐beef production to the “global beef frontier” in Brazil resulted in small GHG savings for the UK GHG inventory, but contributed to a net increase in international GHG emissions equivalent to 0.63 kg CO₂e kg^?1 milk. Use of spared dairy grassland for intensive beef production can lead to net GHG mitigation by replacing extensive beef production, enabling afforestation on larger areas of lower quality grassland, or by avoiding expansion of international (Brazilian) beef production. We recommend that LCA boundaries are expanded when evaluating livestock intensification pathways, to avoid potentially misleading conclusions being drawn from “snapshot” carbon footprints. We conclude that dairy intensification in industrialized countries can lead to significant international carbon leakage, and only achieves GHG mitigation when spared dairy grassland is used to intensify beef production, freeing up larger areas for afforestation.     

Including GHG emissions from mangrove forests LULUC in LCA: a case study on shrimp farming in the Mekong Delta,Vietnam

Purpose

Mangrove forests have been recognized as important regulators of greenhouse gases (GHGs), yet the resulting land use and land-use change (LULUC) emissions have rarely been accounted for in life cycle assessment (LCA) studies. The present study therefore presents up-to-date estimates for GHG emissions from mangrove LULUC and applies them to a case study of shrimp farming in Vietnam.

Methods

To estimate the global warming impacts of mangrove LULUC, a combination of the International Panel for Climate Change (IPCC) guidelines, the Net Committed Emissions, and the Missed Potential Carbon Sink method were used. A literature review was then conducted to characterize the most critical parameters for calculating carbon losses, missed sequestration, methane fluxes, and dinitrogen monoxide emissions.

Results and discussion

Our estimated LUC emissions from mangrove deforestation resulted in 124 t CO₂ ha^?1 year^?1, assuming IPCC’s recommendations of 1 m of soil loss, and 96% carbon oxidation. In addition to this, 1.25 t of carbon would no longer be sequestered annually. Discounted over 20 years, this resulted in total LULUC emissions of 129 t CO₂ ha^?1 year^?1 (CV = 0.441, lognormal distribution (ln)). Shrimp farms in the Mekong Delta, however, can today operate for 50 years or more, but are 1.5 m deep (50% oxidation). In addition to this, Asian tiger shrimp farming in mixed mangrove concurrent farms (the only type of shrimp farm that resulted in mangrove deforestation since 2000 in our case study) resulted in 533 kg methane and 1.67 kg dinitrogen monoxide per hectare annually. Consequently, the LULUC GHG emissions resulted in 184 and 282 t CO₂-eq t^?1 live shrimp at farm gate, using mass and economic allocation, respectively. These GHG emissions are about an order of magnitude higher than from semi-intensive or intensive shrimp farming systems. Limitations in data quality and quantity also led us to quantify the uncertainties around our emission estimates, resulting in a CV of between 0.4 and 0.5.

Conclusions

Our results reinforce the urgency of conserving mangrove forests and the need to quantify uncertainties around LULUC emissions. It also questions mixed mangrove concurrent shrimp farming, where partial removal of mangrove forests is endorsed based upon the benefits of partial mangrove conservation and maintenance of certain ecosystem services. While we recognize that these activities limit the chances of complete removal, our estimates show that large GHG emissions from mangrove LULUC question the sustainability of this type of shrimp farming, especially since mixed mangrove farming only provide 5% of all farmed shrimp produced in Vietnam.

     

LIFE BEEF CARBON: a common framework for quantifying grass and corn based beef farms’ carbon footprints

Europe’s roadmap to a low-carbon economy aims to cut greenhouse gas (GHG) emissions 80% below 1990 levels by 2050. Beef production is an important source of GHG emissions and is expected to increase as the world population grows. LIFE BEEF CARBON is a voluntary European initiative that aims to reduce GHG emissions per unit of beef (carbon footprint) by 15% over a 10-year period on 2172 farms in four large beef-producing countries. Changes in farms beef carbon footprint are normally estimated via simulation modelling, but the methods current models apply differ. Thus, our initial goal was to develop a common modelling framework to estimate beef farms carbon footprint. The framework was developed for a diverse set of Western Europe farms located in Ireland, Spain, Italy and France. Whole farm and life cycle assessment (LCA) models were selected to quantify emissions for the different production contexts and harmonized. Carbon Audit was chosen for Ireland, Bovid-CO₂ for Spain and CAP’2ER for France and Italy. All models were tested using 20 case study farms, that is, 5 per country and quantified GHG emissions associated with on-farm live weight gain. The comparison showed the ranking of beef systems gross carbon footprint was consistent across the three models. Suckler to weaning or store systems generally had the highest carbon footprint followed by suckler to beef systems and fattening beef systems. When applied to the same farm, Carbon Audit’s footprint estimates were slightly lower than CAP’2ER, but marginally higher than Bovid-CO₂. These differences occurred because the models were adapted to a specific region’s production circumstances, which meant their emission factors for key sources; that is, methane from enteric fermentation and GHG emissions from concentrates were less accurate when used outside their target region. Thus, for the common modelling framework, region-specific LCA models were chosen to estimate beef carbon footprints instead of a single generic model. Additionally, the Carbon Audit and Bovid-CO₂ models were updated to include carbon removal by soil and other environmental metrics included in CAP’2ER, for example, acidification. This allows all models to assess the effect carbon mitigation strategies have on other potential pollutants. Several options were identified to reduce beef farms carbon footprint, for example, improving genetic merit. These options were assessed for beef systems, and a mitigation plan was created by each nation. The cumulative mitigation effect of the LIFE BEEF CARBON plan was estimated to exceed the projects reduction target (−15%).     

The Greenhouse Gas Balance of a Dairy Farm as Influenced by the Uptake of Biogas Production

The production of energy crops for farm-scale anaerobic digestion (AD) can affect emissions of greenhouse gases (GHG) in a number of ways. Some examples are: fugitive CH₄ emissions from the digester and the storage of the digestate, emissions of N₂O from soil and emissions of CO₂ from farm machinery. Moreover, uptake of AD may be accompanied by changes in the way the farm is operated, which may affect GHG emissions. The scale of these emissions was assessed from published data for the biogas feedstocks cattle slurry and grass silage. Emissions were compared to references representing current farm operation and energy generation by fossil fuels. Feeding the digester with cattle slurry for the entire year did not result in reduced emissions due to relatively high emissions from stored raw slurry in summer. If grass was used for digester feedstock, the level of N₂O emissions from the crop was the most important factor for the GHG balance of farm-scale AD. If N₂O emissions were low, biogas realised substantial savings of GHG in the order of 1 t CO₂ equivalents per hectare per year. At a high level of N₂O emissions, energy cropping might even result in increased GHG emissions compared to fossil fuels.     

Implementing an appropriate metric for the assessment of greenhouse gas emissions from livestock production: A national case study

Livestock farming is of major economic relevance but also severely contributes to environmental impacts, especially greenhouse gas (GHG) emissions such as methane (CH₄; particularly from ruminant production) and nitrous oxide (N₂O; mainly from manure management and soil cultivated for feed production). In this study, we analyse the impact of GHG emissions from Austrian livestock production, using two metrics: a) the commonly used global warming potential (GWP) over 100 years (GWP₁₀₀ in CO₂-equivalents, CO₂-e), and b) the recently introduced metric GWP*, which describes additional warming as a function of the timeline of short-lived GHG emissions (unit CO₂ warming equivalents, CO₂-we). We first compiled the sectoral (i.e. only direct emissions without upstream processes) GWP₁₀₀ for different livestock categories with a focus on dairy cattle, beef cattle and pigs in Austria between 1990 and 2019. We also estimated product-related (i.e. per kg carcass weight or per litre of milk) GWP₁₀₀ values, including upstream processes. We then calculated the corresponding GWP* metrics, both sectoral and product-related, and compared them with the GWP₁₀₀ values. Decreasing livestock numbers and improved production efficiency were found to result in strong sectoral emission reductions from dairy production (–32 % of GWP₁₀₀ from 1990 to 2019) and from pigs (–32 % CO₂-e). This contrasts with low reductions from other livestock categories and even increases for cattle other than dairy cows (+3 % CO₂-e), mainly due to rising suckler cow numbers. Allocated results per kg milk and kg body mass show quite similar results. Using the GWP* metric, the climate impacts of Austrian livestock production are less severe. When assuming constant management and emission intensity over a period of at least 20 years, the CO₂-we (GWP*) is almost 50 % less than CO₂-e (GWP₁₀₀) per kg Austrian raw milk due to the different impacts of the short-lived CH₄. A similar trend applies to an average cattle carcass (-40 % warming impact). The emission reductions of the shrinking Austrian livestock population represent an important contribution to a climate-neutral agriculture: The CH₄ reductions of livestock production during the past 20 years reduce the current total Austrian CO₂-we by 16 %. Continuous CH₄ reduction, as we show it here for Austrian livestock, is an effective option to tackle the climate crisis in the short term. It shall be stressed that a relatively low GWP* should not be interpreted as a concession for further CH₄ emissions but as an actual reduction of (additional) warming.     

Estimate of life-cycle greenhouse gas emissions from a vertical subsurface flow constructed wetland and conventional wastewater treatment plants: A case study in China

This study aims to estimate the three greenhouse gas (GHG) emissions (i.e. CO₂, CH₄, N₂O) from a vertical subsurface flow constructed wetland (VSSF CW, 1000 m²) and a cluster of conventional wastewater treatment plants (WWTPs) in the city of Changzhou, China. The two estimated emissions are set up for comparison. The results show that the WWTP system emits 7.3 kg CO₂-eq to remove 1 kg BOD in the studied life cycle, while the VSSF system only emits 3.18 kg CO₂-eq, which is only half of the amount given off by the WWTP system. Especially at the treatment stage, the WWTP system's GHG emissions are almost 7 times higher than the VSSF system's. N₂O emissions in both systems are only a minor fraction of the total emissions. Therefore, this study has concluded that the VSSF system is an effective option for GHG emissions mitigation in the wastewater sector. The study further suggests that developing countries like China should extensively build up VSSF systems for decentralized wastewater treatment, which could also potentially reduce GHG emissions by 8-17 million ton CO₂-eq per year compared with the centralized scenario.     

Progress in the studies on the greenhouse gas emissions from reservoirs

The green credentials of hydroelectricity in terms of greenhouse-gas (GHG) emissions have been tarnished with the finding of the researches on GHG emissions from hydroelectric reservoirs in the last two decades. Substantial amounts of GHGs release from the tropical reservoirs, especially methane (CH₄) from Brazil’s Amazonian areas. CH₄ contributes strongly to climate change because it has a global warming potential (GWP) 24 times higher than carbon dioxide (CO₂) on a per molecule basis over a 100-year time horizon. GHGs may emit from reservoirs through four different pathways to the atmosphere: (1) diffusive flux at the reservoir surface, (2) gas bubble flux in the shallow zones of a reservoir, (3) water degassing flux at the outlet of the powerhouse downstream of turbines and spillways, and (4) flux across the air–water interface in the rivers downstream of the dams. This paper reviewed the productions and emissions of CH₄, CO₂, and N₂O in reservoirs, and the environmental variables influencing CH₄ and CO₂ emissions were also summarized. Moreover, the paper combined with the progress of GHG emissions from Three Gorges Reservoir and proposed three crucial problems to be resolved on GHG emissions from reservoirs at present, which would be benefit to estimate the total GHG emissions from Three Gorges Reservoir accurately.     

Annual emissions of greenhouse gases from sheepfolds in Inner Mongolia

Sheepfolds represent significant hot spot sources of greenhouse gases (GHG) in semi-arid grassland regions, such as Inner Mongolia in China. However, the annual contribution of sheepfolds to regional GHG emissions is still unknown. In order to quantify its annual contribution, we conducted measurements of carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) fluxes at two sheepfold sites in the Baiyinxile administrative region of Inner Mongolia for 1 year, using static opaque chamber and gas chromatography methods. Our data show that, at an annual scale, both sheepfolds functioned as net sources of CO₂, CH₄ and N₂O. Temperatures primarily determined the seasonal pattern of CO₂ emission; 60–84% of the CO₂ flux variation could be explained by temperature changes. High rates of net CH₄ emissions from sheepfold soils were only observed when animals (sheep and goats) were present. While nitrous oxide emissions were also stimulated by the presence of animals, pulses of N₂O emissions were also be related to rainfall and spring-thaw events. The total annual cumulative GHG emissions in CO₂ equivalents (CO₂: 1; CH₄: 25; and N₂O: 298) were quantified as 87.4?±?18.4 t ha^?1 for the sheepfold that was used during the non-grazing period (i.e., winter sheepfold) and 136.7?±?15.9 t ha^?1 used during the grazing period (i.e., summer sheepfold). Of the annual total GHG emissions, CH₄ release accounted for approximately 1% of emissions, while CO₂ and N₂O emissions contributed to approximately 59% and 40%, respectively. The total GHG emission factor (CO₂?+?CH₄?+?N₂O) per animal for the sheepfolds investigated in this study was 30.3 kg CO₂ eq yr^?1 head^?1, which translates to 0.3, 18.8 and 11.2 kg CO₂ eq yr^?1 head^?1 for CH₄, CO₂ and N₂O, respectively. Sheepfolds accounted for approximately 34% of overall N₂O emissions in the Baiyinxile administrative region, a typical steppe region within Inner Mongolia. The contribution of sheepfolds to the regional CO₂ or CH₄ exchange is marginal.     

Variation in carbon footprint of milk due to management differences between Swedish dairy farms

To identify mitigation options to reduce greenhouse gas (GHG) emissions from milk production (i.e. the carbon footprint (CF) of milk), this study examined the variation in GHG emissions among dairy farms using data from previous CF studies on Swedish milk. Variations between farms in these production data, which were found to have a strong influence on milk CF, were obtained from existing databases of 1051 dairy farms in Sweden in 2005. Monte Carlo (MC) analysis was used to analyse the impact of variations in seven important parameters on milk CF concerning milk yield (energy-corrected milk (ECM) produced and delivered), feed dry matter intake (DMI), enteric CH4 emissions, N content in feed DMI, N-fertiliser rate and diesel used on farm. The largest between-farm variations among the analysed production data were N-fertiliser rate (kg/ha) and diesel used (l/ha) on farm (CV = 31% to 38%). For the parameters concerning milk yield and feed DMI, the CV was approximately 11% and 8%, respectively. The smallest variation in production data was found for N content in feed DMI. According to the MC analysis, these variations in production data led to a variation in milk CF of between 0.94 and 1.33 kg CO2 equivalents (CO2e)/kg ECM, with an average value of 1.13 kg CO2e/kg ECM. We consider that this variation of ±17%, which was found to be based on the used farm data, would be even greater if all Swedish dairy farms were included, as the sample of farms in this study was not totally unbiased. The variation identified in milk CF indicates that a potential exists to reduce GHG emissions from milk production on both the national and farm levels through changes in management. As milk yield and feed DMI are two of the most influential parameters for milk CF, feed conversion efficiency (i.e. units ECM produced/unit DMI) can be used as a rough key performance indicator for predicting CF reductions. However, it must be borne in mind that feeds have different CF due to where and how they are produced.     

Measurement and prediction of enteric methane emission

The greenhouse gas (GHG) emissions from the agricultural sector account for about 25.5% of total global anthropogenic emission. While CO₂ receives the most attention as a factor relative to global warming, CH₄, N₂O and chlorofluorocarbons (CFCs) also cause significant radiative forcing. With the relative global warming potential of 25 compared with CO₂, CH₄ is one of the most important GHGs. This article reviews the prediction models, estimation methodology and strategies for reducing enteric CH₄ emissions. Emission of CH₄ in ruminants differs among developed and developing countries, depending on factors like animal species, breed, pH of rumen fluid, ratio of acetate:propionate, methanogen population, composition of diet and amount of concentrate fed. Among the ruminant animals, cattle contribute the most towards the greenhouse effect through methane emission followed by sheep, goats and buffalos, respectively. The estimated CH₄ emission rate per cattle, buffaloe, sheep and goat in developed countries are 150.7, 137, 21.9 and 13.7 (g/animal/day) respectively. However, the estimated rates in developing countries are significantly lower at 95.9 and 13.7 (g/animal/day) per cattle and sheep, respectively. There exists a strong interest in developing new and improving the existing CH₄ prediction models to identify mitigation strategies for reducing the overall CH₄ emissions. A synthesis of the available literature suggests that the mechanistic models are superior to empirical models in accurately predicting the CH₄ emission from dairy farms. The latest development in prediction model is the integrated farm system model which is a process-based whole-farm simulation technique. Several techniques are used to quantify enteric CH₄ emissions starting from whole animal chambers to sulfur hexafluoride (SF6) tracer techniques. The latest technology developed to estimate CH₄ more accurately is the micrometeorological mass difference technique. Because the conditions under which animals are managed vary greatly by country, CH₄ emissions reduction strategies must be tailored to country-specific circumstances. Strategies that are cost effective, improve productivity, and have limited potential negative effects on livestock production hold a greater chance of being adopted by producers. It is also important to evaluate CH₄ mitigation strategies in terms of the total GHG budget and to consider the economics of various strategies. Although reductions in GHG emissions from livestock industries are seen as high priorities, strategies for reducing emissions should not reduce the economic viability of enterprises.     

Does increasing milk yield per cow reduce greenhouse gas emissions? A system approach

Milk yield per cow has continuously increased in many countries over the last few decades. In addition to potential economic advantages, this is often considered an important strategy to decrease greenhouse gas (GHG) emissions per kg of milk produced. However, it should be considered that milk and beef production systems are closely interlinked, as fattening of surplus calves from dairy farming and culled dairy cows play an important role in beef production in many countries. The main objective of this study was to quantify the effect of increasing milk yield per cow on GHG emissions and on other side effects. Two scenarios were modelled: constant milk production at the farm level and decreasing beef production (as co-product; Scenario 1); and both milk and beef production kept constant by compensating the decline in beef production with beef from suckler cow production (Scenario 2). Model calculations considered two types of production unit (PU): dairy cow PU and suckler cow PU. A dairy cow PU comprises not only milk output from the dairy cow, but also beef output from culled cows and the fattening system for surplus calves. The modelled dairy cow PU differed in milk yield per cow per year (6000, 8000 and 10 000 kg) and breed. Scenario 1 resulted in lower GHG emissions with increasing milk yield per cow. However, when milk and beef outputs were kept constant (Scenario 2), GHG emissions remained approximately constant with increasing milk yield from 6000 to 8000 kg/cow per year, whereas further increases in milk yield (10 000 kg milk/cow per year) resulted in slightly higher (8%) total GHG emissions. Within Scenario 2, two different allocation methods to handle co-products (surplus calves and beef from culled cows) from dairy cow production were evaluated. Results showed that using the 'economic allocation method', GHG emissions per kg milk decreased with increasing milk yield per cow per year, from 1.06 kg CO2 equivalents (CO2eq) to 0.89 kg CO2eq for the 6000 and 10 000 kg yielding dairy cow, respectively. However, emissions per kg of beef increased from 10.75 kg CO2eq to 16.24 kg CO2eq due to the inclusion of suckler cows. This study shows that the environmental impact (GHG emissions) of increasing milk yield per cow in dairy farming differs, depending upon the considered system boundaries, handling and value of co-products and the assumed ratio of milk to beef demand to be satisfied.     

Contribution of Urban Water Supply to Greenhouse Gas Emissions in China

Greenhouse gas (GHG) emissions from energy use in the water sector in China have not received the same attention as emissions from other sectors, but interest in this area is growing. This study uses 2011 data to investigate GHG emissions from electricity use for urban water supply in China. The objective is to measure the climate cobenefit of water conservation, compare China with other areas on a number of emissions indicators, and assist in development of policy that promotes low‐emission water supply. Per capita and per unit GHG emissions for water supplied to urban areas in China in 2011 were 24.5 kilograms carbon dioxide equivalent (kg CO₂‐eq) per capita per year and 0.213 kg CO₂‐eq per cubic meter, respectively. Comparison of provinces within China revealed that GHG emissions for urban water supply as a percentage of total province‐wide emissions from electricity use correlate directly with the rate of leakage and water loss within the water distribution system. This highlights controlling leakage as a possible means of reducing the contribution of urban water supply to GHG emissions. An inverse correlation was established between GHG emissions per unit water and average per capita daily water use, which implies that water demand tends to be higher when per unit emissions are lower. China's high emission factor for electricity generation inflates emissions for urban water supply. Shifting from emissions‐intensive electricity sources is crucial to reducing emissions in the water supply sector.