Greenhouse gas mitigation potentials in the …...Greenhouse gas mitigation potentials in the...

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

© 2016 Macmillan Publishers Limited. All rights reserved.

http://dx.doi.org/10.1038/nclimate2925

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.


Figure S1baseline e2030, whintensitie

S2.1.1 S

Constru

We used

forage p

into ran

pasturel

defined

grasses,

manage

as those

such as

1 Compilationemissions. Th

hereas Gerber s.

Soil carbon

uction of gra

d the Centu

production f

ngelands (wh

lands (wher

rangelands

, grass-like

ed through th

e areas on w

irrigation a

n of estimates he estimates byet al. (ref. 34)

n sequestrat

azing land a

ury and Day

from grazin

here we onl

re in additio

s as uncultiv

plants, forb

he manipul

which there i

and fertilizat

28, 34, 40 of suppy Hedenus et a) are potentials

tion in gras

area

cent models

ng lands glob

ly consider

on to grazing

vated land o

bs or shrubs

ation of gra

is the period

tion 4,5.

pply-side mitigal. (ref. 28) ans based on the

sslands

s 1,2 to estim

bally at 0.5

adjustments

g managem

on which the

suitable for

azing 3. Past

dic cultivati

gation potentiand Havlík et ae spread in cur

mate soil C s

° resolution

s to grazing

ment we cons

e native veg

r grazing or

turelands, o

ion of grass

als, expressedl. (ref. 40) refrrent (circa 20

stocks, N2O

n. We separa

g manageme

sidered legu

getation is p

r browsing u

n the other

ses and othe

d as a percentafer to potentia005) emission

O emissions

ated grazing

ent) and

ume plantin

predominant

use, primari

hand, were

er agronomi

3

age of ls in year

and

g lands

ng). We

tly

ily

defined

ic inputs


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.


5

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


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


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.


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.


8

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.


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


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)


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


10

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.


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.


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).


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


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.


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.


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





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)


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


16

FAOSTAT1

Ref 45

EDGAR2 EPA 2012

Ref 32

FAO 20133 Herrero et al 201352

GLOBIOM4

(year 2000)

Hedenus et al. 2014


(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:


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 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


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








N fluxes included: fertilizer, manure, arable-land crop residues



N fluxes included: fertilizer, manure



N fluxes included: fertilizer, manure, crop residues arable land and perm. pasture



N fluxes included: fertilizer, manure, arable-land crop residues, SOM loss


17

FAOSTAT1

Ref 45

EDGAR2 EPA 2012

Ref 32


GLOBIOM4

(year 2000)

Hedenus et al. 2014


(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

EFs applied to N in manure (IPCC Tier 1/2)


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.


18

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


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


19

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


20

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


21

References

1 Parton, W.J., Schimel, D.S., Cole, C.V., Ojima, D.S. Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Sci. Soc. Am. J. 51, 1173-1179 (1987). 2 Parton, W.J., Hartman, M.D., Ojima, D.S., Schimel, D.S. DAYCENT: its land surface submodel: description and testing. Glob. Planet. Chang. 19, 35-48 (1998). 3 NRCS [Natural Resources Conservation Service]. National Range and Pasture Handbook. US Department of Agriculture, Washington D.C. (1997). 4 NRCS [Natural Resources Conservation Service]. Instructions for Collecting 1992 National Resources Inventory Sample Data. US Department of Agriculture, Washington D.C. (1992). 5 Eagle, A., Henry, L., Olander, L., Haugen-Kozyra, K., Millar, N., Robertson, G. Greenhouse Gas Mitigation Potential of Agricultural Land Management in the United States A Synthesis of the Literature. 3rd edition. Report NI R 10-04, 2nd edition. Nicholas Institute for Environmental Policy Solutions, Duke University (2011). 6 Mitchell, T.D., Jones, P.D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25, 693-712 (2005). 7 Reynolds, C.A., Jackson, T.J., Rawls, W.J. Estimating soil water-holding capacities by linking the Food and Agriculture Organization soil map of the world with global pedon databases and continuous pedotransfer functions. Water Resour. Res. 36, 3653-3662 (2000). 8 Melillo, J.M., McGuire, A.D., Kicklighter, D.W. et al. Global climate change and terrestrial net primary production. Nature. 363, 234-240 (1993). 9 IIASA/FAO. Global Agro-ecological Zones (GAEZ v3.0). IIASA, Laxenburg and FAO, Rome (2012). 10 FAOSTAT. Resources (Land). Available from: http://faostat.fao.org/site/377/default.aspx#ancor (accessed 22 April 2013). 11 FAO. Gridded Livestock of the World 2007. Food and Agriculture Organization of the United Nations, Rome, Italy (2007). 12 FAO. Global Livestock Production Systems. Food and Agriculture Organization of the United Nations, Rome, Italy (2011). 13 Gerber, P., Steinfeld, H., Henderson, B. et al. Tackling climate change through livestock – a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations, Rome, Italy (2013). 14 Pineiro, G.J., Paruelo, M., Oesterheld, M., Jobbagy, E.G. Pathways of grazing effects on soil organic carbon and nitrogen. Rangel. Ecol. Manag. 63, 109-119 (2010).


22

15 Stehfest, E., Bouwman, L., Vuuren, D.P., et al. Climate benefits of changing diet. Clim Change 95:83–102 (2009). 16 Willett, W.C. Eat, Drink, and Be Healthy: The Harvard Medical School Guide to Healthy Eating. Free Press, New York, N.Y. (2001). 17 JRC-IES. EDGAR - Emission database for global atmospheric research. http://edgar.jrc.ec.europa.eu/ (2015). 18 MNP. Integrated modelling of global environmental change: An overview of IMAGE 2.4. Bilthoven, The Netherlands (2006). 19 Bruinsma, J. World agriculture : towards 2015/2030. FAO, Rome, Italy (2003). 20 PBL. Integrated Assessment of Global Environmental Change with IMAGE 3.0. Model description and policy applications. The Hague, The Netherlands (2014). 21 Smith, P., Haberl, H., Popp, A., et al. How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Glob Chang Biol 19:2285–2302 (2013). 22 Erb, K.H., Haberl, H., Plutzar, C. Dependency of global primary bioenergy crop potentials in 2050 on food systems, yields, biodiversity conservation and political stability. Energy Policy 47:260–269 (2012). 23 FAO. World agriculture: towards 2030/2050. The 2012 Revision. FAO, Rome, Italy (2012). 24 Haberl, H., Steinberger, J.K., Plutzar, C., et al. Natural and socioeconomic determinants of the embodied human appropriation of net primary production and its relation to other resource use indicators. Ecol Indic 23:222–231 (2012). 25 Bajželj, B., Richards, K.S., Allwood, J.M., et al. Importance of food-demand management for climate mitigation. Nature Clim Chang 4:924–929 (2014). 26 WHO. Diet, nutrition and the prevention of chronic diseases. Report of a Joint WHO/FAO Expert Consultation. World Health Organ Tech Rep Ser (2003). 27 American Heart Association. The American Heart Association’s Diet and Lifestyle Recommendations. http://www.heart.org/HEARTORG/GettingHealthy/ NutritionCenter/HealthyEating/The-American-Heart-Associations-Diet-and-Lifestyle-Recommendations_UCM_305855_Article.jsp (2015). 28 Hedenus, F., Wirsenius, S., Johansson, D.J.A. The importance of reduced meat and dairy consumption for meeting stringent climate change targets. Clim Change 124:79–91 (2014). 29 Wirsenius, S. Human use of land and organic materials: Modeling the turnover of biomass in the global food system. Chalmers University of Technology, Gothenburg, Sweden (2000).


23

30 Wirsenius, S., Azar, C., Berndes, G. How much land is needed for global food production under scenarios of dietary changes and livestock productivity increases in 2030? Agric Syst 103:621–638 (2010). 31 IPCC. IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. IGES, Japan (2006). 32 EPA. Global Anthropogenic Non-CO2 Greenhouse Gas Emissions: 1990-2020. U.S. Environmental Protection Agency, Washington, D.C (2006). 33 Popp, A., Lotze-Campen, H., Bodirsky, B. Food consumption, diet shifts and associated non-CO2 greenhouse gases from agricultural production. Glob Environ Chang 20:451–462 (2010). 34 Gerber, P.J., Steinfeld, H., Henderson, B., et al. Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. FAO, Rome, Italy (2013). 35 Valin, H., Havlík, P., Mosnier, A., et al. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? Environ Res Lett 8:035019 (2013). 36 Tilman, D., Clark, M. Global diets link environmental sustainability and human health. Nature 515:518–522 (2014). 37 Seventh-day Adventist Diet. Seventh-day Adventist Diet, http://www.seventhdayadventistdiet.com/ (2013). 38 Trichopoulou, A., Costacou, T., Bamia, C., Trichopoulos, D. Adherence to a Mediterranean Diet and Survival in a Greek Population. N Engl J Med 348:2599–2608 (2003). 39 Bach-Faig, A., Berry, E.M., Lairon, D., et al. Mediterranean diet pyramid today. Science and cultural updates. Public Health Nutr 14:2274–2284 (2011). 40 Havlík, P., Valin, H., Herrero, M., et al. Climate change mitigation through livestock system transitions. PNAS 111:3709–14 (2014). 41 Garnett, T., Mathewson, S., Angelides, P., Borthwick, F. Policies and actions to shift eating patterns: What works? A review of the evidence of the effectiveness of interventions aimed at shifting diets in more sustainable and healthy directions. Food Climate Research Network and Chatham House, University of Oxford (2015). 42 NRC. Nutrient Requirements of Beef Cattle, 7th ed. National Academy Press, Washington, D.C. (2000). 43 NRC. Nutrient Requirements of Dairy Cattle, 7th ed. National Academy Press, Washington, D.C. (2001). 44 Alderman, G., Cottrill, B.R. Energy and protein requirements of ruminants: an advisory manual prepared by the AFRC Technical Committee on Responses to Nutrients. CAB International (1993).


24

45 FAOSTAT. FAO Statistics. http://faostat3.fao.org/ (2015). 46 MacLeod, M., Gerber, P., Mottet, A., et al. Greenhouse gas emissions from pig and chicken supply chains. FAO, Rome, Italy (2013). 47 Opio, C., Gerber, P., Mottet, A., et al. Greenhouse gas emissions from ruminant supply chains. FAO, Rome, Italy (2013). 48 Bodirsky, B.L., Popp, A., Weindl, I., et al. N2O emissions from the global agricultural nitrogen cycle – current state and future scenarios. Biogeosciences 9:4169–4197 (2012). 49 Humpenöder, F., Popp, A., Stevanovic, M., et al. Land-Use and Carbon Cycle Responses to Moderate Climate Change: Implications for Land-Based Mitigation? Environ Sci Technol 150519131022001 (2015). 50 Herrero, M., Wirsenius, S., Henderson, B., et al. Livestock and the Environment: What Have We Learned in the Last Decade? Annu. Rev. Environ. Resour. 40:177-202 (2015). 51 Myhre, G., Shindell, D., Bréon, F.-M., et al. Anthropogenic and Natural Radiative Forcing. In: Stocker TF, Qin D, Plattner G-K, et al. (eds) Clim. Chang. 2013 Phys. Sci. Basis. Contrib. Work. Gr. I to Fifth Assess. Rep. Intergov. Panel Clim. Chang. Cambridge University Press, Cambridge, UK, pp 659–740 (2013). 52 Herrero, M., Havlík, P., Valin, H., et al. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. PNAS 110:20888–20893 (2013).


Date post:	30-May-2020
Category:	Documents
Upload:	others
View:	5 times
Download:	0 times

Greenhouse gas mitigation potentials in the …...Greenhouse gas mitigation potentials in the...

Documents