Greenhouse gas mitigation potentials in the livestock sector

Transcription

1 SUPPLEMENTARY INFORMATION DOI: /NCLIMATE2925 Greenhouse gas mitigation potentials in the livestock sector Mario Herrero 1*, Benjamin Henderson 1, Petr Havlík 2, Philip K. Thornton 1,3, Richard T. Conant 4, Pete Smith 5, Stefan Wirsenius 1,6, Alexander N. Hristov 7, Pierre Gerber 8,9, Margaret Gill 5, Klaus Butterbach-Bahl 10,11, Hugo Valin 2, Tara Garnett 12 and Elke Stehfest 13 1 Commonwealth Scientific and Industrial Research Organization (CSIRO), 306 Carmody Road, St Lucia, QLD 4067, Australia. 2 Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria. 3 CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS), ILRI, PO Box 30709, Nairobi 00100, Kenya. 4 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO , United States. 5 Scottish Food Security Alliance-Crops & Institute of Biological & Environmental Sciences, University of Aberdeen, 23 St Machar Drive, Aberdeen AB24 3UU, UK. 6 Chalmers University of Technology, Department of Energy and Environment, SE Gothenburg, Sweden 7 Department of Animal Science, Pennsylvania State University, 324 Henning Building, University Park, PA16802, United States. 8 Food and Agriculture Organization of the United Nations, Animal Production and Health Division, Viale delle Terme di Caracalla, Rome, Italy. 9 Animal 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, Garmisch- Partenkirchen, Germany. 11 International Livestock Research Institute, Old Naivasha Road, Nairobi 00100, Kenya 12 University of Oxford, Oxford OX13QY, United Kingdom 13 PBL Netherlands Environmental Assessment Agency, Bilthoven, 3720 AH, The Netherlands. *Requests for materials/information:: Mario.Herrero@csiro.au NATURE CLIMATE CHANGE 1

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 CO 2 -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 25 th percentile. 2

3 28, 34, Figure S1 Compilation of estimates 40 of supply-side mitigation potentials, expressedd as a percentage of baseline emissions. The estimates by Hedenus et al. (ref. 28) and Havlík et al. (ref. 40) refer to potentials in year 2030, whereas Gerber et al. (ref. 34) ) are potentialss based on the spread in current (circa 2005) emission intensities. S2.1.1 Soil carbon sequestration in grasslands Construction of grazing land area We used the Century and Daycent modelss 1,2 to estimate soil C stocks, s N 2 O emissions and forage production from grazing lands globally at 0.5 resolution. We separated grazingg lands into rangelands (where we only consider adjustmentss to grazingg management) and pasturelands (where in addition to grazingg management we considered legume planting). We defined rangelands as uncultivated land on which the native vegetation is predominantly grasses, grass-like plants, forbs or shrubs suitable for grazing orr browsing use, primarily managed through the manipulation of grazing 3. Pasturelands, on the other hand, were defined as those areas on which there is i the periodic cultivation of grasses and other agronomic inputs such as irrigation and fertilization 4,5. 3

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 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. 4

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 In most cases this optimum offtake rate was different than the baseline ( ) offtake rates, with baseline rates being greater than or less than our computed optima. On the assumption that climate changeinduced 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 N 2 O 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 N 2 O 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 5

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 N 2 O 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 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 N 2 O and CH 4 emission of livestock husbandry, covering all relevant emission sources, and historically consistent with the EDGAR database 17. CO 2 emissions from land use change as well as CO 2 uptake on abandoned land. CO 2 emissions from land use change as well as CO 2 uptake on abandoned land. Energy-CO 2 emissions e.g. linked to farm operations and processing not covered. Emission reduction 4.3 Gt CO 2 eq/yr in the Healthy Diet scenario, 5.8, 6.4, and 7.8 Gt CO 2 eq/yr for No Ruminant Meat, No Meat, and No Animal Product scenario (split into CO 2 and non- CO 2 ). Model/Method IMAGE 2.4 integrated assessment model 18. Over the historical period ( ), land use in IMAGE is consistent with FAO statistics, and in the model set-up for this study, future 6

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. CH 4 and N 2 O 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 N 2 O, CH 4 and CO 2 emissions. While CH 4 and N 2 O emissions are mostly coupled to the production process and the total amount of production, CO 2 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 CH 4 and N 2 O emission are rather stable in time, while changes in the CO 2 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 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 CO 2 emissions from land use change, and afforestation or bio-energy on spare land. N 2 O and CH 4 emission of livestock husbandry not covered, and also further LCA emissions not covered. Emission reduction Gt CO 2 eq/yr for low or high yielding bioenergy, 4.6 Gt CO 2 eq/yr if spare land is afforested. 7

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 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 kcal/cap/day, depending on the region 25. Emission sources covered CO 2 emissions from land use change, N 2 O and CH 4 emission of livestock husbandry, and also further LCA emissions. Emission reduction 5.8 and 6.4 Gt CO 2 eq/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 8

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 (N 2 O from fertilizer, CH 4 from rice, and enteric fermentation, and CH 4 and N 2 O 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 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 N 2 O from agricultural soils and manure management, and CH 4 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 CO 2 eq/yr in the Climate Carnivore scenario, and 5.2 Gt CO 2 eq/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 9

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 34, , 33, Tilman and Clark 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. CH 4 and N 2 O from livestock husbandry, and all emissions occurring during transport and processing. CO 2 emissions from land use change only a simple function of land use change. Emission reduction 1.2, 1.9 and 2.3 Gt CO 2 eq/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 CO 2 eq/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 CO 2 eq/yr for 540 Mha of cropland expansion. 10

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-co 2 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 tco 2 eq. Two policies are presented here: Livestock only targeting non- CO 2 emissions from livestock production only (enteric fermentation CH 4, manure management CH 4, manure management N 2 O, and manure crop- and grassland N 2 O), 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 N 2 O, rice CH 4, deforestation CO 2, and other land use change CO 2 ). 11

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 12

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-tolong 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. 13

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. 14

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. FAOSTAT 1 Ref 45 EDGAR 2 EPA 2012 Ref 32 FAO Herrero et al GLOBIOM 4 (year 2000) Hedenus et al (year 2000) Ref 28 MAgPIE 5 (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 15

16 FAOSTAT 1 Ref 45 EDGAR 2 EPA 2012 Ref 32 FAO Herrero et al GLOBIOM 4 (year 2000) Hedenus et al (year 2000) Ref 28 MAgPIE 5 (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 CH 4 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 N 2 O (from mineral soils) EF applied to soil-n fluxes EF applied to soil-n fluxes EF applied to soil-n fluxes EF applied to soil-n fluxes EF applied to soil-n fluxes EF applied to soil-n fluxes EF applied to soil-n fluxes Global EF data (IPCC Tier 1) N fluxes incl.: fertil., manure, arable-land crop residues N fluxes incl.: fertil., manure, arable-land crop residues Global EF data (IPCC Tier 1) N fluxes incl.: fertil., manure, arable-land crop residues Global EF data (IPCC Tier 1) N fluxes included: fertilizer, manure, arable-land crop residues Global EF data (IPCC Tier 1) N fluxes included: fertilizer, manure Global EF data (IPCC Tier 1) N fluxes included: fertilizer, manure, crop residues arable land and perm. pasture Global EF data (IPCC Tier 1) N fluxes included: fertilizer, manure, arable-land crop residues, SOM loss 16

17 FAOSTAT 1 Ref 45 EDGAR 2 EPA 2012 Ref 32 FAO Herrero et al GLOBIOM 4 (year 2000) Hedenus et al (year 2000) Ref 28 MAgPIE 5 (year 1995) Emissions of manure N 2 O and CH 4 (in stables/lots) Regional data on manure mgmt. system Methodology not described Regional data on manure mgmt system Manure mgmt system specific to livestock system and region Manure mgmt system specific to livestock system and region Manure mgmt system specific to livestock system and region Manure mgmt system specific to livestock system Global N 2 O EF applied to N in manure Global N 2 O EF applied to N in manure EFs applied to N and volatile-c in manure (IPCC Tier 1/2) EFs applied to N and volatile-c in manure (IPCC Tier 1/2) EFs applied to N and volatile-c in manure (IPCC Tier 1/2) EFs applied to N in manure (IPCC Tier 1/2) Regional CH 4 EF applied to number of animals (IPCC Tier 1) Regional CH 4 EF applied to number of animals (IPCC Tier 1) Global (N 2 O) and regional (CH 4 ) EF data Global (N 2 O) and regional (CH 4 ) EF data Global (N 2 O) and regional (CH 4 ) EF data Global (N 2 O) and regional (CH 4 ) 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 1 From reference 45 2 From reference 17 3 From references 34, 46, 47 4 From references 35, 40, , 49 Refer to characteristics in the most recent version of MAgPIE 6 Source 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 Source did not state feed intake data. Number shown is the (presumed) median in equation used for calculating CH 4 as a fraction of feed intake

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 CO 2 eq per year 1. FAOSTAT Ref 45 EDGAR Ref 17 EPA 2012 Ref 32 FAO Herrero et al GLOBIOM 3 (year 2000) Hedenus et al (year 2000) Ref 28 MAgPIE 4 (year 1995) Soil N 2 O (1.7) 6 (0.9) Enteric fermentation CH Manure management CH Manure management N 2 O Rice CH n.e Biomass burning (CH 4 & N 2 O) n.e. n.e. n.e. n.e. Organic soils (N 2 O, CO 2 ) 0.88 n.e. n.e. n.e. n.e. n.e. n.e. Land use change CO 2 n.e. n.e. n.e. (0.7) 6 (1.9) 7 n.e. (0.9) 8 n.e: not estimated 1 Calculated into CO 2 equivalents using GWP factors 34 for CH 4 and 298 for N 2 O as compiled in IPCC AR Numbers from references 34, 46, 47 3 Numbers from reference 35 (rice, land use change) and reference 52 (all others) 4 Numbers from reference 48 (soil N 2 O and manure N 2 O), reference 49 (land use change CO 2 ), and reference 33 (all others) 5 Including indirect N 2 O 6 Includes only emissions related to livestock production (i.e. feed production and pasture) 7 Refers to annual average for the period Refers to annual average for the period

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 CO 2 eq per year 1. Total CH 4 and N 2 O livestock Soil N 2 O Enteric fermentation CH 4 Manure management N 2 O and CH 4 FAOSTAT Ref 45 FAO GLOBIOM 3 (year 2000) FAOSTAT Ref 45 FAO GLOBIOM 3 (year 2000) FAOSTAT Ref 45 FAO GLOBIOM 3 (year 2000) FAOSTAT Ref 45 FAO GLOBIOM 3 (year 2000) All livestock n.e n.e Cattle & buffalo n.e n.e Cattle, non-dairy 4 n.e n.e Cattle, dairy 5 n.e n.e Buffalo n.e n.e. n.e n.e n.e n.e. Sheep & goats n.e n.e Pigs n.e n.e Poultry n.e n.e n.e: not estimated 1 Calculated into CO 2 equivalents using GWP factors 34 for CH 4 and 298 for N 2 O as compiled in IPCC AR Numbers from references 34, 46, 47 3 Numbers from reference 52 4 Includes single-purpose cattle and surplus dairy calves reared for meat production 5 Includes dairy cows and replacement dairy heifers 6 Includes non-dairy buffalo 7 Includes dairy buffalo 19

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 Taxes, subsidies, trading measures 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 5 Inform, educate, promote or empower through community initiatives, labelling and other means Changing the choice architecture, nudge, store layouts, catering provision etc.. Labelling, gardening or cooking projects, media or other campaigns, education programs 20

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, (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, (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, 2 nd 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, (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, (2000). 8 Melillo, J.M., McGuire, A.D., Kicklighter, D.W. et al. Global climate change and terrestrial net primary production. Nature. 363, (1993). 9 IIASA/FAO. Global Agro-ecological Zones (GAEZ v3.0). IIASA, Laxenburg and FAO, Rome (2012). 10 FAOSTAT. Resources (Land). Available from: (accessed 22 April 2013). 11 FAO. Gridded Livestock of the World 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, (2010). 21

22 15 Stehfest, E., Bouwman, L., Vuuren, D.P., et al. Climate benefits of changing diet. Clim Change 95: (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. (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: (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: (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: (2012). 25 Bajželj, B., Richards, K.S., Allwood, J.M., et al. Importance of food-demand management for climate mitigation. Nature Clim Chang 4: (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. 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). 22

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: (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: 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: (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: (2013). 36 Tilman, D., Clark, M. Global diets link environmental sustainability and human health. Nature 515: (2014). 37 Seventh-day Adventist Diet. Seventh-day Adventist Diet, (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: (2003). 39 Bach-Faig, A., Berry, E.M., Lairon, D., et al. Mediterranean diet pyramid today. Science and cultural updates. Public Health Nutr 14: (2011). 40 Havlík, P., Valin, H., Herrero, M., et al. Climate change mitigation through livestock system transitions. PNAS 111: (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). 23

24 45 FAOSTAT. FAO Statistics. (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: (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 (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: (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 Phys. Sci. Basis. Contrib. Work. Gr. I to Fifth Assess. Rep. Intergov. Panel Clim. Chang. Cambridge University Press, Cambridge, UK, pp (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: (2013). 24