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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2925
NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1 1
Greenhouse gas mitigation potentials in the livestock sector
SUPPLEMENTARY INFORMATION
Mario Herrero1*, Benjamin Henderson1, Petr Havlík2, Philip K. Thornton1,3, Richard T.
Conant4, Pete Smith5, Stefan Wirsenius1,6, Alexander N. Hristov7, Pierre Gerber8,9, Margaret
Gill5, Klaus Butterbach-Bahl10,11, Hugo Valin2, Tara Garnett12 and Elke Stehfest13
1Commonwealth Scientific and Industrial Research Organization (CSIRO), 306 Carmody
Road, St Lucia, QLD 4067, Australia. 2Ecosystems Services and Management Program, International Institute for Applied Systems
Analysis, Laxenburg, Austria. 3CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS),
ILRI, PO Box 30709, Nairobi 00100, Kenya. 4Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-
1499, United States. 5 Scottish Food Security Alliance-Crops & Institute of Biological & Environmental Sciences,
University of Aberdeen, 23 St Machar Drive, Aberdeen AB24 3UU, UK. 6Chalmers University of Technology, Department of Energy and Environment, SE-41296
Gothenburg, Sweden 7Department of Animal Science, Pennsylvania State University, 324 Henning Building,
University Park, PA16802, United States. 8Food and Agriculture Organization of the United Nations, Animal Production and Health
Division, Viale delle Terme di Caracalla, 00153 Rome, Italy. 9Animal Production Systems group, Wageningen University , P.O. Box 338, Wageningen,
The Netherlands. 10 Institute of Meteorology and Climate Research, Atmospheric Environmental Research
(IMK-IFU) Karlsruhe Institute of Technology (KIT), Kreuzeckbahnstr. 19, 82467 Garmisch-
Partenkirchen, Germany. 11 International Livestock Research Institute, Old Naivasha Road, Nairobi 00100, Kenya 12University of Oxford, Oxford OX13QY, United Kingdom 13PBL Netherlands Environmental Assessment Agency, Bilthoven, 3720 AH, The
Netherlands.
*Requests for materials/information:: [email protected]
Greenhouse gas mitigation potentials in the livestock sector
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2
S1. Current and baseline emission levels
Methods and data making up global estimates of greenhouse gas emissions from livestock
systems from seven studies are shown in Table S1. The table includes implicit emission
factors for the year 2005, unless otherwise stated, per unit of animal and of land are given for
comparison purposes. Table S2a shows a comparison of sector-wide, global data on
agricultural greenhouse gas emissions for the year 2005, unless otherwise stated. These are
disaggregated by livestock species and system in Table S2b, where this is possible.
List of the United National Framework Convention on Climate Change (UNFCCC)
Annex 1 countries: Australia, Austria, Belarus, Belgium, Bulgaria, Canada, Croatia, Cyprus,
Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland,
Ireland, Italy, Japan, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Monaco,
Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russian Federation,
Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, United Kingdom of Great
Britain and Northern Ireland, United States of America.
All other countries apart from these are denominated non-Annex-1 countries.
S2. Mitigation potentials
S2.1 Supply-side options and potentials
Figure S1 shows estimated potentials for supply-side mitigation, as fractions of baseline
emission levels. It should be noted that a great deal of the variance is due to methodological
differences between the studies. Hedenus et al. (ref. 28) used a technology-specific bottom-up
approach, but without any explicit economic analysis; numbers shown in Fig. S1 refer to their
“Technical mitigation” scenario. Havlík et al (ref. 40) used an economic equilibrium model
that capture structural and geographical changes in the supply system, but did not include
specific mitigation technology options; the potentials shown refer to their $100/t CO2-eq
mitigation scenario. The mitigation potentials in Gerber et al. (ref. 34) were not based on
forward-looking explicit modelling, but on analysis of the current (circa 2005) spread in
emission intensities within each livestock system and region; the potentials shown in Fig. S1
correspond to an alignment of average emission intensities to the 25th percentile.
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Figure S1baseline e2030, whintensitie
S2.1.1 S
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4
The Century model was initiated with 2000 year spin-ups using mean monthly climate from
the Climate Research Unit (CRU) of the University of East Anglia 6 with vegetation for each
grid cell, except cells dominated by rock, ice, water, forest, and croplands, which were
excluded. Soils data were derived from the FAO Soil Map of the World 7. For rangelands,
information about native vegetation was derived for the Potsdam model inter-comparison
study 8. Production in pasturelands was simulated using high productivity plant
parameterizations based on cool-season (high latitudes), warm-season (low latitudes), or
mixed (mid-latitudes) grasses. Pastures were assumed to be replanted in the late winter every
ten years, with grazing starting in the second year.
To confine our analysis to those areas that are subject to grazing, we area-corrected the results
by scaling them to match the area of grazing land within each half-degree pixel. First, the
maximum spatial extent of the world’s grazing lands was defined by selecting the grassland
and woodland land cover classes in the Global Agro-Ecological Zone (GAEZ) data layers
produced by the UN Food and Agricultural Organization and the International Institute for
Applies Systems Analysis Global 9. This area was then adjusted to match the national area of
permanent pastures and meadows reported in FAOSTAT in the year 2005 10. Next, areas
where animals were not present 11,12 were excluded. The resulting total grazing land area
following this procedure was approximately 2.6 billion hectares. Finally, to separate this total
grazing land area into rangelands and pasturelands, rangelands were identified as the portion
of the grazing lands that included native vegetation 8 with pasturelands residually identified as
the remainder of the total grazing land area.
Improved grazing management scenario
Forage offtake, defined as the proportion of aboveground live and dead material removed by
livestock, is a key management driver in the Century and Daycent models. Forage
consumption by ruminants was based on data from the Global Livestock Environmental
Assessment Model (GLEAM) 13, which is a process-based model of livestock production
systems that models the biophysical relationships between livestock populations 11,12 and feed
inputs (including the relative contribution of feed types including forages, crop residues and
concentrates to animal diets) for each livestock species, country, and production system. We
translated a map of forage consumption from GLEAM into an estimate of forage removal
rates by ruminants for each grid cell to represent offtake rates in the Century model.
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We ran the Century model for a set of grazing offtake scenarios to explore the soil C and
forage benefits that producers might realize by shifting to grazing management that optimizes
forage production. Since it is more feasible and beneficial for producers to attempt to
maximize forage production than soil C sequestration (because forage production is easier to
observe and it benefits farm income), we defined optimum as the offtake rate that led to
maximum forage production within each pixel. This optimum can differ from one based on
maximized soil C because of shifts from C inputs to soil to C offtake by livestock 14. All
grazing was restricted to the growing season excluding the month in which plant growth
initiated. We identified optimum offtake rates by conducting a set of global runs for a range of
offtake rates (ranging from 0-100% in 10% increments) and selecting the offtake rate that
maximized forage production averaged between 1987-2006. In most cases this optimum
offtake rate was different than the baseline (1901-1986) offtake rates, with baseline rates
being greater than or less than our computed optima. On the assumption that climate change-
induced changes in GHG fluxes over the next decades will be modest in comparison with the
simulated management effects, the findings from this assessment are considered to reflect the
future sequestration potential over the same 20-year time frame. We confined our estimation
of the mitigation potential to those grazing land areas where the changes in soil C stocks were
positive.
Legume planting scenario
Legume planting was only considered to be feasible in pasturelands which are more amenable
to agronomic inputs, because of their agroecological conditions (e.g. soil moisture
availability). The Daycent model 2 was used to simulate N2O emissions from pasturelands
under the baseline scenario and scenarios with legume sowing. The Daycent model runs
required daily climate data (also from CRU TS3.0 6, but otherwise relied on the same soil,
plant, and grazing management drivers as the Century soil C runs for pasturelands.
Legumes were represented within the same warm/cool season grass mixtures as described
above for grasses, and were assumed to be oversown on grass to achieve approximately 20%
cover, and to persist over the course of the simulation without re-sowing or additional inputs.
The impact of the legume sowing scenario on forage production, soil C stocks, and soil N2O
emissions were compared with the “no-legume” baseline, using the same driving data and
parameterizations as described above. This simplifying assumption was necessary due to a
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6
lack of spatial global databases with precise information about agronomic management
practices on grazed land. The net GHG impacts were estimated by subtracting increases in
soil N2O emissions from the amount of soil C sequestered, for the legume sowing practice. As
with improved grazing management, we confined our estimation of the mitigation potential to
the pastureland areas in which the changes in soil C stocks were positive.
S2.2 Demand-side options and potentials
S2.2.1 Mitigation potentials from dietary changes
This section briefly summarizes recent, global studies on greenhouse gas mitigation potentials
from dietary changes and other demand-side options.
Stehfest et al. 2009 15
Dietary scenarios
“No animal products”, “No meat”, “No ruminant meat”, and “Healthy diet” (the latter based
on ref 16), compared to a reference case based on FAO assumptions. Reduction in animal
protein intake was assumed to be fully compensated by higher intake of pulses.
Emission sources covered
N2O and CH4 emission of livestock husbandry, covering all relevant emission sources, and
historically consistent with the EDGAR database 17. CO2 emissions from land use change as
well as CO2 uptake on abandoned land. CO2 emissions from land use change as well as CO2
uptake on abandoned land. Energy-CO2 emissions e.g. linked to farm operations and
processing not covered.
Emission reduction
4.3 Gt CO2eq/yr in the Healthy Diet scenario, 5.8, 6.4, and 7.8 Gt CO2eq/yr for No Ruminant
Meat, No Meat, and No Animal Product scenario (split into CO2 and non- CO2).
Model/Method
IMAGE 2.4 integrated assessment model 18. Over the historical period (1970 – 2005), land
use in IMAGE is consistent with FAO statistics, and in the model set-up for this study, future
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7
land use is driven by projections of crop and livestock production, yields and livestock
efficiencies according to the FAO scenario 19. The IMAGE model allocates this production on
a spatial scale, and calculates the resulting environmental impacts, including land use,
greenhouse gas emissions, and climate change under the respective scenario. CH4 and N2O
emissions of the agricultural system are consistent to the EDGAR database 17, and thus cover
all relevant emission sources. More information on the most recent version of the IMAGE
model (IMAGE 3.0) is provided in reference 20.
Change in the agricultural and livestock sector, like the reduction of livestock production, lead
to changes in N2O, CH4 and CO2 emissions. While CH4 and N2O emissions are mostly
coupled to the production process and the total amount of production, CO2 emission/uptake
from land use change is mostly coupled to a change in activity, i.e. an increase or decrease in
agricultural area. As a consequence, reduction potentials in CH4 and N2O emission are rather
stable in time, while changes in the CO2 balance of land use is only temporary. When the
transition to a low-meat diet reduces the agricultural area required, land is abandoned and the
regrowing vegetation can take up carbon until a new equilibrium is reached.
Smith et al. 2013 21
Dietary scenarios
“Dietary change”, compared to a “trend scenario. Dietary change scenario assumes a
convergence towards a global daily per-capita calorie intake of 2800 kcal/cap/day (11.7
MJ/cap/day), paired with a relatively low level of animal product supply 22, while the
reference scenario largely follow the FAO projections 23.
Emission sources covered
CO2 emissions from land use change, and afforestation or bio-energy on spare land. N2O and
CH4 emission of livestock husbandry not covered, and also further LCA emissions not
covered.
Emission reduction
0.7-7.3 Gt CO2eq/yr for low or high yielding bioenergy, 4.6 Gt CO2eq/yr if spare land is
afforested.
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Model/Method
A biophysical biomass-balance model as described in references 22 and 24, based on material
flow accounting. Land use and net primary production are described at a global grid level of 5
min resolution, while data on primary and final biomass use are described at the country level.
From this database, factors and multipliers are derived to match the demand for final products
of biomass (food, fibres) with gross agricultural production and land use for eleven world
regions. Starting from demand for food, fibre and livestock, and factors for livestock
husbandry, required production for crops (fibre, food and feed) and livestock grass demand
are derived. These are then combined with crop yields and grass yields to derive the demand
for cropland and grazing land. The trend scenario is largely based on projections by FAO 19 .
In the dietary change scenario (“fair and frugal”), food energy demand is slightly reduced
compared to the trend scenario, and the contribution of animal products is reduced from about
16 to about 8 % 22. Both the trend and the dietary change scenario are evaluated with the
biophysical biomass-balance model. Under the dietary change scenario substantial areas of
“spare land” would allow afforestation or additional production of bio-energy.
Bajželj et al. 2014 25
Dietary scenarios
“Healthy diet”, implemented on top of two reference cases (one with low waste, one with low
waste and high yields). Healthy diet 16, 26, 27 with 2500 kcal/cap/day in 2050, while reference
cases have 2520-3027 kcal/cap/day, depending on the region 25.
Emission sources covered
CO2 emissions from land use change, N2O and CH4 emission of livestock husbandry, and also
further LCA emissions.
Emission reduction
5.8 and 6.4 Gt CO2eq/yr depending on the reference chosen.
Model/Method
Data driven method to estimate the future land use based on population, yields and diets.
Starting from population and diet projections, future consumption and biomass flows are
calculated. Depending on assumptions in waste, trade and livestock management, future
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9
required production of grass and crops is calculated. Together with information on future
yields, based on literature, future cropland and pasture areas are derived. These are then
allocated to land suitability classes and global biomes 25 . Greenhouse gas emissions are
calculated for land use change from pasture and cropland expansion and contraction, and for
the major agricultural sources (N2O from fertilizer, CH4 from rice, and enteric fermentation,
and CH4 and N2O from manure management, and emissions from energy use in agriculture).
Emissions are calculating by scaling todays emissions with changes in emission sources, thus
assuming no improvements in manure management or enteric fermentation 25.
Hedenus et al. 2014 28
Dietary scenarios
“Climate carnivore”, in which 75 percent of the baseline-consumption of ruminant meat (beef,
lamb) and dairy is replaced by pork and poultry meat (on kcal basis), and “Flexitarian”, in
which 75 percent of the baseline-consumption of meat and dairy is replaced by pulses and
cereal products (on kcal basis)
Emission sources covered
N2O from agricultural soils and manure management, and CH4 from feed digestion (enteric
fermentation), manure management, and paddy rice fields (further details are given in Table
S1 below).
Emission reduction
In the year 2050, 3.4 Gt CO2eq/yr in the Climate Carnivore scenario, and 5.2 Gt CO2eq/yr in
the Flexitarian scenario. These potentials are relative to a supply-side mitigation scenario,
which incorporates mitigation effects from increased livestock productivity and technical
interventions (e.g improved manure management technology).
Model/Method
A simplified representation of agricultural biomass and nitrogen flows 29,30 that calculates
required biomass production as a function of food consumption and productivity in crop and
livestock production. This model calculates application rates of nitrogen fertilizer in crop
production from the nitrogen content in harvested crops and assumptions of nitrogen use
efficiencies for different crops and regions. Feed intake in livestock production is estimated
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from feed conversion efficiencies and feed rations for different livestock systems and regions.
From the estimated flows of nitrogen and crop/pasture production, the model calculates
greenhouse gas emissions using methods similar to those detailed in reference 31; see Table
S1 for further details. The model’s emission data were validated against several sources 32, 33,
34, 35.
Tilman and Clark 2014 36
Dietary scenarios
“Pescetarian”, “Mediterranean”, “Vegetarian”, compared to a reference diet. Vegetarian diet
is based on reference 37, the pescetarian diet was modified from the vegetarian diet, including
one serving of fish per day, but reduced milk, egg and cereal demand; the Mediterranean diet
is derived from recommendations 38, 39. Demand for the reference diet in 2050 is calculated
based on a relationship between GDP and consumption.
Emission sources covered
Full LCA emissions covered, i.e. CH4 and N2O from livestock husbandry, and all emissions
occurring during transport and processing. CO2 emissions from land use change only a simple
function of land use change.
Emission reduction
1.2, 1.9 and 2.3 Gt CO2eq/yr based on LCA database, excluding land use change, for the
Mediterranean, Pescetarian and Vegetarian Diet, respectively. Reduction in global cropland
by about 450, 580 and 600 million ha, avoiding about 1.8 to 2.4 Gt CO2eq/yr.
Model/Method
Greenhouse gas emissions for food products excluding land use change are derived from an
extensive LCA database. Also cropland use for food products is derived from this database 36.
Differences in cropland use between the reference diet and the alternative diets were
converted to avoided deforestation emission, yielding 0.6 Gt CO2eq/yr for 540 Mha of
cropland expansion.
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11
S2.2.2 Effects on food demand from emissions pricing
“Total Abatement Calorie Cost” (TACC) curves 40 plot the level of abatement of GHG
emissions from agriculture and land use change as a result of a particular mitigation policy
and a range of carbon prices compared to the baseline, against the corresponding change in
calorie consumption (mostly a loss). In this way, TACC curves allow the level of trade-offs
between mitigation and food security targets to be assessed. This concept is proposed to
complement the well-established mitigation policy efficiency measure, Marginal Abatement
Cost (MAC) curves, which consider only the monetary cost of different mitigation strategies.
TACC curves here are obtained from GLOBIOM as a result of adjustment of the whole
agricultural system in response to climate change policies. GLOBIOM provides the
opportunity to study a rich set of adjustment possibilities, by which the agricultural sector can
respond to a carbon tax and reduce greenhouse gas emissions. The most relevant mechanisms
for the livestock sector are i) improving livestock diets, represented through switches from
grass-based diets to diets supplemented with concentrates, which allow reductions in both
non-CO2 emissions per unit of product as well as pressure on agricultural land expansion, ii)
optimized livestock production location within a given region exploiting more productive
grasslands and limiting forest conversion, and iii) reallocation of livestock production through
international markets towards the most GHG efficient production systems. However, a carbon
tax will always lead to an increase in the production cost of livestock products and hence to a
reduction in consumption, which indirectly will also contribute to climate change mitigation.
Climate change policies were implemented as a carbon price in the form of a tax levied
directly on emissions. Simulations were carried out for five possible price levels: USD 5, 10,
20, 50 and 100 per tCO2eq. Two policies are presented here: “Livestock only” targeting non-
CO2 emissions from livestock production only (enteric fermentation CH4, manure
management CH4, manure management N2O, and manure crop- and grassland N2O), and
“Agriculture and land use” targeting emissions from both agricultural and land-use change
sectors (all emission sources targeted under “Livestock only” plus crop fertilizer N2O, rice
CH4, deforestation CO2, and other land use change CO2).
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12
S2.2.3 Policies for managing the demand for livestock products
Despite a growing evidence basis quantifying the climate mitigation potential arising from
demand side changes (with a strong focus on reduced meat consumption), there has been far
less research investigating how the necessary shifts in consumption might be achieved.
Nevertheless one study 41 makes a start at filling this knowledge gap. Examining the evidence
on the effectiveness of interventions aimed at shifting diets in healthier and more sustainable
directions, it identified five target shifts in consumption practice, of which one was meat
reduction. A review of the health and environmental interventions literature focusing on these
consumption shifts was undertaken and a typology of interventions constructed (Table S3).
Three points can be highlighted. First, the evidence on effective interventions aimed at
shifting consumption patterns largely comes from the public health community and associated
disciplines. There is very little evidence that can be drawn from the environmental-food
literature. Among the health studies, most of the focus was on increasing fruit and vegetable
consumption and reducing intakes of sugary foods. There was little specific focus on meat –
and diets rich in fruit and vegetables may or may not include large quantities of meat as well.
Second, very few interventions aimed at reducing meat consumption were in evidence, and of
the handful of studies that did focus on meat, the vast majority were model-based rather than
experiment-based. Third, most of the interventions-oriented research focuses on developed
countries. There has been little research into the drivers underpinning consumption practices
in low and middle income countries nor of interventions that may be effective in moderating
current consumption trajectories. This is a serious omission given that this is where most of
the growth in anticipated meat consumption is expected to arise.
It is thus not possible to quantify the impact that any given intervention will have on meat
consumption within a given population. However, some conclusions can be drawn from the
review.
Restrict, eliminate or incentivise choices through fiscal measures: Model-based studies
dominate, a practical research necessity given the paucity of governments currently willing to
intervene in the market. Models may not be able to capture or describe all the multiple
influences on consumption. In particular, the substitution effects of an imposed tax are hard to
model. Most experimental and model-based studies focus on sugary drink reduction; the
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13
evidence suggest that they have some effect provided they are set sufficiently high. These
may have cross-tranferrable lessons for meat reduction. One concern is that taxes risk being
regressive and negatively affecting low income communities. Subsidies for healthy foods may
potentially help address this. Targeted incentives aimed at particularly vulnerable groups may
also be a way forward.
Change the governance of production or consumption: International macro-political and
economic measures including trade agreements, support for inward investment and
agricultural subsidies have had significant and (from a nutritional and environmental
perspective) negative effects on what and how people consume. The inference is that if
substantive and positive changes in dietary patterns are to be achieved then macro-economic
and political interventions of commensurate strength will be needed to reverse the negative
effects of the powerful measures that have been put in place to date. Moving from the
international to the national level, governments have a strong role to play in shaping the
regulatory and physical environment via the introduction of standards and planning policies.
Collaborations and shared agreements: The evidence reviewed indicates that certification
schemes and standards have helped shift the market – although evidence of their measurable
benefits for the environment is more mixed. However, as the certification sector grows, the
risk is that standards are diluted in order to expand their reach and involve more stakeholders.
Certification should not be seen as a substitute for regulation although certification schemes
can be synergistic with regulatory approaches, as, for example, when public procurement
standards specify the provision of certified food. Regarding voluntary industry agreements,
the evidence is mixed and limited. Voluntary initiatives tend to be successful largely where
there is a business case for them. At present, the business case for companies to engage in
fostering sustainable healthy diets can certainly be made at least when thinking about mid-to-
long term risks and opportunities but may not be immediately obvious or credible in the
immediate term.
Changing the context, defaults and norms of production or consumption: The
interventions in this category included both the role of advertising and marketing – as
examples of large scale influences on the context of consumption – and more context specific
interventions in work- or school-based settings. Most advertising and marketing is aimed at
high sugar and high fat foods and drinks, including those that are meat based such as burgers.
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14
Evidence that advertising and marketing foster unhealthy consumption preferences and
consumption patterns, and contribute to negative health outcomes among children is robust.
There is also evidence that government regulation as opposed to industry self-regulation can
be effective. As regards other context-based interventions, most of these were undertaken in
schools, workplaces and other settings. The research finds that multiple-component
interventions tend to be effective especially when some price incentive (in the form of
coupons, differential pricing and so forth) is included in the mix and combined with some
educational and awareness raising approaches.
Information and awareness: Public awareness raising and labelling have formed the
backbone of health promotion policy in recent years and the growth in environmental
labelling suggests a similar approach. These approaches are seen as more politically
acceptable than regulatory or fiscal approaches. However the evidence reviewed here suggests
an almost inverse correlation between policy enthusiasm for such approaches, and their
effectiveness. While such activities have a role to play they cannot be seen as a substitute for
more robust measures.
In sum, there is no one single approach (such as taxation) that alone will be effective. An
integrated approach is needed, comprising a strong regulatory and fiscal framework and
enabling environment for voluntary industry activities and collaborations, in combination
with awareness raising and education.
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15
Tables Table S1 Methodology and data in global estimates of greenhouse gas emissions from livestock systems. Implicit emission factors (for year 2005 estimates, unless otherwise stated) per unit of animals and land given for comparison.
FAOSTAT1
Ref 45
EDGAR2 EPA 2012
Ref 32
FAO 20133 Herrero et al. 201352
GLOBIOM4
(year 2000)
Hedenus et al. 2014
(year 2000) Ref 28 MAgPIE5
(year 1995)
Physical representation
Main spatial scales Country Country Country 3-5 arc minutes
Ten world regions
28 world regions Nine world regions 30 arc minutes
Ten world regions
Animal numbers and herd/flock structures
Numbers from statistics
Herd structure and dynamics not estimated
Numbers from statistics
Herd structure and dynamics not estimated
Numbers from statistics
Herd structure and dynamics not estimated
Total numbers from statistics
Representation of herds in separate cohorts with constant, exogenous attributes
Total numbers from statistics
Representation of herds in separate cohorts with constant attributes
Rum. herd productivity from ruminant digestion model
Not explicitly represented
Total numbers from herd productivity and supply of livestock products
Representation of herds in separate cohorts with constant, exogenous attributes
Energy requirements Not estimated Not estimated Not estimated Herd and animal attributes (for ruminants, NRC system)
Herd and animal attributes (for ruminants, CNCPS and AFRC systems)
Not explicitly represented
Herd and animal attributes (for ruminants, NRC system)
Feed dry matter intake Not estimated Not estimated Not estimated From energy requirements and feed quality
Potential feed intake from ruminant digestion model
From feed-to-product ratios and supply of livestock products
From energy requirements and feed quality
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16
FAOSTAT1
Ref 45
EDGAR2 EPA 2012
Ref 32
FAO 20133 Herrero et al 201352
GLOBIOM4
(year 2000)
Hedenus et al. 2014
(year 2000) Ref 28 MAgPIE5
(year 1995)
Feed rations Not estimated Not estimated Not estimated Energy requirements in combination with feed availability
Potential feed intake in combination with feed availability
Feed requirements in combination with feed availability
Energy requirements in combination with feed availability
Land area (for livestock) Not estimated Not estimated Not estimated Feed intake and crop/ pasture yields
Arable land: Feed intake and yield; Perm. grassland:
Not explicitly represented
Arable land: Feed intake and yield; Perm. grassland: Fixed to 1995 levels
Excretion of faeces & urine (incl. content of N and volatile C)
Regional N excretion rates applied to number of animals (IPCC Tier 1)
Methodology not described
Regional N excretion rates applied to number of animals (IPCC Tier 1)
Feed quality and N retention in animal mass, specific to animal cohort (IPCC Tier 2)
Ruminants: Ruminant digestion model; Other: Feed quality and N retention; Both specific to animal cohort (IPCC Tier 2)
Feed quality and N retention in animal mass, specific to livestock system (IPCC Tier 2)
Feed quality and N retention in animal mass, specific to livestock system (IPCC Tier 2)
Emissions of enteric CH4 EF applied to number of animals (IPCC Tier 1)
EF applied to number of animals (IPCC Tier 1)
EF applied to number of animals (IPCC Tier 1)
EF applied to GE in feed int. (IPCC Tier 2)
Global EF data (as a function of DE of ration)
Endogenous from ruminant digestion model: based on stoichiometry.
EF applied to GE in feed intake (IPCC Tier 2)
Global EF data by feed category
EF applied to number of animals (IPCC Tier 1)
Emissions of soil N2O (from mineral soils)
EF applied to soil-N fluxes
Global EF data (IPCC Tier 1)
N fluxes incl.: fertil., manure, arable-land crop residues
EF applied to soil-N fluxes
N fluxes incl.: fertil., manure, arable-land crop residues
EF applied to soil-N fluxes
Global EF data (IPCC Tier 1)
N fluxes incl.: fertil., manure, arable-land crop residues
EF applied to soil-N fluxes
Global EF data (IPCC Tier 1)
N fluxes included: fertilizer, manure, arable-land crop residues
EF applied to soil-N fluxes
Global EF data (IPCC Tier 1)
N fluxes included: fertilizer, manure
EF applied to soil-N fluxes
Global EF data (IPCC Tier 1)
N fluxes included: fertilizer, manure, crop residues arable land and perm. pasture
EF applied to soil-N fluxes
Global EF data (IPCC Tier 1)
N fluxes included: fertilizer, manure, arable-land crop residues, SOM loss
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FAOSTAT1
Ref 45
EDGAR2 EPA 2012
Ref 32
FAO 20133 Herrero et al. 201352
GLOBIOM4
(year 2000)
Hedenus et al. 2014
(year 2000) Ref 28 MAgPIE5
(year 1995)
Emissions of manure N2O and CH4 (in stables/lots)
Regional data on manure mgmt. system
Global N2O EF applied to N in manure
Regional CH4 EF applied to number of animals (IPCC Tier 1)
Methodology not described
Regional data on manure mgmt system
Global N2O EF applied to N in manure
Regional CH4 EF applied to number of animals (IPCC Tier 1)
Manure mgmt system specific to livestock system and region
EFs applied to N and volatile-C in manure (IPCC Tier 1/2)
Global (N2O) and regional (CH4) EF data
Manure mgmt system specific to livestock system and region
EFs applied to N and volatile-C in manure (IPCC Tier 1/2)
Global (N2O) and regional (CH4) EF data
Manure mgmt system specific to livestock system and region
EFs applied to N and volatile-C in manure (IPCC Tier 1/2)
Global (N2O) and regional (CH4) EF data
Manure mgmt system specific to livestock system
EFs applied to N in manure (IPCC Tier 1/2)
Global (N2O) and regional (CH4) EF data
GE: gross energy; DE: digestible energy; EF: emission factor; SOM: soil organic matter; n.a.: not available; NRC: National Research Council 42,43; AFRC: Agricultural and Food Research Council 44. IPCC Tier levels are described in reference 31 1From reference 45 2From reference 17 3From references 34, 46, 47 4From references 35, 40, 52 5Refer to characteristics in the most recent version of MAgPIE 48, 49 6Source did not calculate and/or state data on arable land area used for livestock production. Number shown was calculated assuming arable land used for livestock 500 million ha 50 and permanent pastures 3.4 billion ha 45. 7Source did not state feed intake data. Number shown is the (presumed) median in equation used for calculating CH4 as a fraction of feed intake 47.
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Table S2a Compilation of sources providing sector-wide, global data on agricultural greenhouse gas emissions. Year 2005 (unless otherwise stated) emissions for all agriculture. All numbers in Pg CO2eq per year1.
FAOSTAT
Ref 45
EDGAR Ref 17
EPA 2012
Ref 32
FAO 20132 Herrero et al. 201352
GLOBIOM3
(year 2000)
Hedenus et al. 2014 (year 2000)
Ref 28
MAgPIE4 (year 1995)
Soil N2O 1.65 1.6 1.8 (1.7)6 (0.9)6 2.15 1.65 Enteric fermentation CH4 3.2 3.3 3.1 3.8 2.0 3.3 3.0 Manure management CH4 0.35 0.38 0.36 0.41 0.33 0.35 0.3 Manure management N2O 0.125 0.10 0.17 0.37 0.19 0.245 0.245 Rice CH4 0.80 1.2 0.81 n.e. 0.66 0.88 0.8 Biomass burning (CH4 & N2O) 0.71 0.45 1.4 n.e. n.e. n.e. n.e. Organic soils (N2O, CO2) 0.88 n.e. n.e. n.e. n.e. n.e. n.e. Land use change CO2 n.e. n.e. n.e. (0.7)6 (1.9)7 n.e. (0.9)8
n.e: not estimated 1Calculated into CO2 equivalents using GWP factors 34 for CH4 and 298 for N2O as compiled in IPCC AR5 51. 2Numbers from references 34, 46, 47 3Numbers from reference 35 (rice, land use change) and reference 52 (all others) 4Numbers from reference 48 (soil N2O and manure N2O), reference 49 (land use change CO2), and reference 33 (all others) 5Including indirect N2O 6Includes only emissions related to livestock production (i.e. feed production and pasture) 7Refers to annual average for the period 2000-2030 8Refers to annual average for the period 2000-2100
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Table S2b Compilation of sources providing global greenhouse gas emission data disaggregated by livestock systems/species. Year 2005 (unless otherwise stated). All numbers in Pg CO2eq per year1.
Total CH4 and N2O livestock Soil N2O Enteric fermentation CH4 Manure management N2O and CH4
FAOSTAT
Ref 45
FAO 20132 GLOBIOM3
(year 2000) FAOSTAT
Ref 45
FAO 20132 GLOBIOM3
(year 2000) FAOSTAT
Ref 45
FAO 20132 GLOBIOM3
(year 2000) FAOSTAT
Ref 45
FAO 20132 GLOBIOM3 (year 2000)
All livestock n.e. 6.3 3.5 n.e. 1.7 0.91 3.2 3.8 2.0 0.47 0.78 0.52
Cattle & buffalo n.e. 4.4 2.7 n.e. 1.1 0.73 2.7 2.9 1.7 0.23 0.34 0.28 Cattle, non-dairy4 n.e. 2.5 2.16 n.e. 0.72 0.616 1.8 1.6 1.36 0.11 0.16 0.206 Cattle, dairy5 n.e. 1.3 0.627 n.e. 0.32 0.127 0.58 0.84 0.427 0.083 0.14 0.0817 Buffalo n.e. 0.61 n.e. n.e. 0.10 n.e. 0.33 0.47 n.e. 0.034 0.038 n.e.
Sheep & goats n.e. 0.45 0.44 n.e. 0.11 0.091 0.37 0.32 0.32 0.016 0.022 0.026 Pigs n.e. 0.39 0.29 n.e. 0.11 0.071 0.035 0.052 0 0.14 0.23 0.22 Poultry n.e. 0.27 0.057 n.e. 0.19 0.032 0 0 0 0.079 0.078 0.025
n.e: not estimated 1Calculated into CO2 equivalents using GWP factors 34 for CH4 and 298 for N2O as compiled in IPCC AR5 51 2Numbers from references 34, 46, 47 3Numbers from reference 52 4Includes single-purpose cattle and surplus dairy calves reared for meat production 5Includes dairy cows and replacement dairy heifers 6Includes non-dairy buffalo 7Includes dairy buffalo
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Table S3
A typology of the health and environmental interventions aimed at fostering consumption
shifts 41
Approach Examples 1 Restrict, eliminate or incentivise choices through fiscal
measures Taxes, subsidies, trading
2 Change the governance of production or consumption Macro-economic policies and agreements, national public procurement and planning policies, other regulations
3 Encourage collaboration and shared agreements Voluntary industry agreements, certification schemes
4 Changing the context, defaults and norms of production or consumption
Changing the choice architecture, nudge, store layouts, catering provision etc..
5 Inform, educate, promote or empower through community initiatives, labelling and other means
Labelling, gardening or cooking projects, media or other campaigns, education programs
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