Land-use changes are critical for climate policy because native vegetation and soils store abundant carbon and their losses from agricultural expansion, together with emissions from agricultural production, contribute about 20 to 25 per cent of greenhouse gas emissions1,2. Most climate strategies require maintaining or increasing land-based carbon3 while meeting food demands, which are expected to grow by more than 50 per cent by 20501,2,3,,2,4. A finite global land area implies that fulfilling these strategies requires increasing global land-use efficiency of both storing carbon and producing food. Yet measuring the efficiency of land-use changes from the perspective of greenhouse gas emissions is challenging, particularly when land outputs change, for example, from one food to another or from food to carbon storage in forests. Intuitively, if a hectare of land produces maize well and forest poorly, maize should be the more efficient use of land, and vice versa. However, quantifying this difference and the yields at which the balance changes requires a common metric that factors in different outputs, emissions from different agricultural inputs (such as fertilizer) and the different productive potentials of land due to physical factors such as rainfall or soils. Here we propose a carbon benefits index that measures how changes in the output types, output quantities and production processes of a hectare of land contribute to the global capacity to store carbon and to reduce total greenhouse gas emissions. This index does not evaluate biodiversity or other ecosystem values, which must be analysed separately. We apply the index to a range of land-use and consumption choices relevant to climate policy, such as reforesting pastures, biofuel production and diet changes. We find that these choices can have much greater implications for the climate than previously understood because standard methods for evaluating the effects of land use4,5,6,7,8,9,10,11 on greenhouse gas emissions systematically underestimate the opportunity of land to store carbon if it is not used for agriculture.
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LPJmL modelling results, in the form of global carbon and native net primary productivity maps, are available at https://doi.org/10.1594/PANGAEA.893761. The different datasets used to run LPJmL for this study are publicly available and described in Supplementary Information along with links. Any other materials generated for this study are available from the corresponding author on reasonable request.
Searchinger, T. et al. Creating a Sustainable Food Future. A Menu of Solutions to Sustainably Feed More Than 9 Billion People by 2050 (World Resources Institute, Washington, 2014).
Edenhofer, O. et al. in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Edenhofer, O. et al.) 1–30 (Cambridge Univ. Press, Cambridge, 2014).
Valin, H. et al. The future of food demand: understanding differences in global economic models. Agric. Econ. 45, 51–67 (2014).
Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).
Eshel, G., Shepon, A., Makov, T. & Milo, R. Land, irrigation water, greenhouse gas, and reactive nitrogen burdens of meat, eggs, and dairy production in the United States. Proc. Natl Acad. Sci. USA 111, 11996–12001 (2014).
Food and Agriculture Organization of the United Nations. EX-Ante Carbon-balance Tool (EX-ACT) http://www.fao.org/tc/exact/ex-act-home/en/ (2017).
Colomb, V. et al. Selection of appropriate calculators for landscape-scale greenhouse gas assessment for agriculture and forestry. Environ. Res. Lett. 8, 015029 (2013).
Gerber, P. et al. Tackling Climate Change Through Livestock: A Global Assessment of Emissions and Mitigation Opportunities (Food and Agriculture Organization of the United Nations, Rome, 2013).
Escobar, N., Ribal, J., Clemente, G. & Sanjuán, N. Consequential LCA of two alternative systems for biodiesel consumption in Spain, considering uncertainty. J. Clean. Prod. 79, 61–73 (2014).
Audsley, E. et al. How Low Can We Go? An Assessment of Greenhouse Gas Emissions from the UK Food System and the Scope Reduction by 2050 (WWF-UK, 2009).
Hertel, T. W. et al. Effects of US maize ethanol on global land use and greenhouse gas emissions: estimating market-mediated responses. Bioscience 60, 223–231 (2010).
Ranganathan, J. et al. Shifting Diets for a Sustainable Food Future (World Resources Institute, Washington, 2016).
Johnson, J. A., Runge, C. F., Senauer, B., Foley, J. & Polasky, S. Global agriculture and carbon trade-offs. Proc. Natl Acad. Sci. USA 111, 12342–12347 (2014).
Estes, L. D. et al. Reconciling agriculture, carbon and biodiversity in a savannah transformation frontier. Phil. Trans. R. Soc. Lond. B 371, 20150316 (2016).
Searchinger, T. D., Edwards, R., Mulligan, D., Heimlich, R. & Plevin, R. Do biofuel policies seek to cut emissions by cutting food? Science 347, 1420–1422 (2015).
Strassburg, B. B. N. et al. When enough should be enough: improving the use of current agricultural lands could meet production demands and spare natural habitats in Brazil. Glob. Environ. Change 28, 84–97 (2014).
Cardoso, A. S. et al. Impact of the intensification of beef production in Brazil on greenhouse gas emissions and land use. Agric. Syst. 143, 86–96 (2016).
Luyssaert, S. et al. CO2 balance of boreal, temperate, and tropical forests derived from a global database. Glob. Change Biol. 13, 2509–2537 (2007).
Xu, B., Yang, Y., Li, P., Shen, H. & Fang, J. Global patterns of ecosystem carbon flux in forests: a biometric data-based synthesis. Glob. Biogeochem. Cycles 28, 962–973 (2014).
Hudiburg, T. W., Davis, S. C., Parton, W. & Delucia, E. H. Bioenergy crop greenhouse gas mitigation potential under a range of management practices. Glob. Change Biol. Bioenergy 7, 366–374 (2015).
Harris, Z. M., Spake, R. & Taylor, G. Land use change to bioenergy: a meta-analysis of soil carbon and GHG emissions. Biomass Bioenergy 82, 27–39 (2015).
Bryngelsson, D., Wirsenius, S., Hedenus, F. & Sonesson, U. How can the EU climate targets be met? A combined analysis of technological and demand-side changes in food and agriculture. Food Policy 59, 152–164 (2016).
Moll, S. & Remond-Tiedrez, I. CO 2 Emissions Induced by EU’s Final Use of Products are Estimated to Be 9 Tonnes per Capita (Eurostat, Luxembourg, 2011).
Neumann, C. G., Demment, M. W., Maretzki, A., Drorbaugh, N. & Galvin, K. A. in Livestock in a Changing Landscape, Volume 1: Drivers, Consequences, and Responses (eds Steinfeld, H. et al.) 221–248 (Island Press, Washington, 2010).
Regmi, A., Deepak, M. S., Seale, J. L. Jr & Bernstein, J. in Changing Structure of Global Food Consumption and Trade (ed. Regmi, A.) 14–22 (United States Department of Agriculture Economic Research Service, Washington, 2001).
Prestele, R. et al. Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison. Glob. Change Biol. 22, 3967–3983 (2016).
Plevin, R., O’Hare, M., Jones, A., Torn, M. & Gibbs, H. Greenhouse gas emissions from biofuels’ indirect land use change are uncertain but may be much greater than previously estimated. Environ. Sci. Technol. 44, 8015–8021 (2010).
California Air Resources Board. Low Carbon Fuel Standard. Final Regulation Order Table 5, https://www.arb.ca.gov/regact/2015/lcfs2015/lcfsfinalregorder.pdf (2015).
Corley, R. H. V. How much palm oil do we need? Environ. Sci. Policy 12, 134–139 (2009).
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003).
Bondeau, A. et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679–706 (2007).
Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. (eds) 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IGES, Hayama, 2006).
Jobbágy, E. G. & Jackson, R. B. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol. Appl. 10, 423–436 (2000).
Malhi, Y., Baldocchi, D. D. & Jarvis, P. G. The carbon balance of tropical, temperate and boreal forests. Plant Cell Environ. 22, 715–740 (1999).
Roy, J., Saugier, B. & Mooney, H. A. (eds) Terrestrial Global Productivity (Academic Press, San Diego, 2001).
Trumper, K. et al. The Natural Fix? The role of Ecosystems in Climate Mitigation (UNEP-WCMC, Cambridge, 2009).
West, P. C. et al. Trading carbon for food: global comparison of carbon stocks vs. crop yields on agricultural land. Proc. Natl Acad. Sci. USA 107, 19645–19648 (2010).
You, L. et al. Spatial Production Allocation Model (SPAM) 2005 v3.2 http://MapSPAM.info (2017).
Gibbs, H. K. et al. Carbon payback times for crop-based biofuel expansion in the tropics: the effects of changing yield and technology. Environ. Res. Lett. 3, 034001 (2008).
Searchinger, T. D. et al. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319, 1238–1240 (2008).
Guo, L. B. & Gifford, R. M. Soil carbon stocks and land use change: a meta analysis. Glob. Change Biol. 8, 345–360 (2002).
Don, A., Schumacher, J. & Freibauer, A. Impact of tropical land-use change on soil organic carbon stocks – a meta-analysis. Glob. Change Biol. 17, 1658–1670 (2011).
Wei, X., Shao, M., Gale, W. & Li, L. Global pattern of soil carbon losses due to the conversion of forests to agricultural land. Sci. Rep. 4, 4062 (2014).
Sanderman, J., Hengl, T. & Fiske, G. J. Soil carbon debt of 12,000 years of human land use. Proc. Natl Acad. Sci. USA 114, 9575–9580 (2017); correction 115, E1700 (2018).
Nyawira, S. S., Nabel, J. E. M. S., Don, A., Brovkin, V. & Pongratz, J. Soil carbon response to land-use change: evaluation of a global vegetation model using observational meta-analyses. Biogeosciences 13, 5661–5675 (2016).
Food and Agriculture Organization of the United Nations. FAOSTAT Database http://www.fao.org/faostat/en/#home (2016).
Anderson-Teixeira, K. J. & DeLucia, E. H. The greenhouse gas value of ecosystems. Glob. Change Biol. 17, 425–438 (2011).
Poeplau, C. et al. Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach. Glob. Change Biol. 17, 2415–2427 (2011).
Nordhaus, W. D. Revisiting the social cost of carbon. Proc. Natl Acad. Sci. USA 114, 1518–1523 (2017).
Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, L13402 (2010).
Hiraishi, T. et al. (eds) 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (IPCC, Geneva, 2014).
Biancalani, R. & Avagyan, A. Towards Climate-Responsible Peatlands Management (Food and Agriculture Organization of the United Nations, Rome, 2014).
Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).
Anderson-Teixeira, K. J., Wang, M. M. H., McGarvey, J. C. & LeBauer, D. S. Carbon dynamics of mature and regrowth tropical forests derived from a pantropical database (TropForC-db). Glob. Change Biol. 22, 1690–1709 (2016).
Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Glob. Biogeochem. Cycles 22, GB1003 (2008).
Wirsenius, S. Human Use of Land and Organic Materials. Modeling the Turnover of Biomass in the Global Food System. PhD Thesis, Chalmers Univ. of Technology and Göteborg Univ. (2000).
Bouwman, A. F., Van der Hoek, K. W., Eickhout, B. & Soenario, I. Exploring changes in world ruminant production systems. Agric. Syst. 84, 121–153 (2005).
Herrero, M. et al. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).
Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).
Klein Goldewijk, K.K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).
Stahl, C. et al. Soil carbon stocks after conversion of Amazonian tropical forest to grazed pasture: importance of deep soil layers. Reg. Environ. Change 16, 2059–2069 (2016).
Fujisaki, K. et al. From forest to cropland and pasture systems: a critical review of soil organic carbon stocks changes in Amazonia. Glob. Change Biol. 21, 2773–2786 (2015).
Zhou, G. et al. Grazing intensity significantly affects belowground carbon and nitrogen cycling in grassland ecosystems: a meta-analysis. Glob. Change Biol. 23, 1167–1179 (2017).
Le Mouël, C. Agrimonde-Terra Foresight: Land Use and Food Security in 2050 (CIRAD/INRA, Paris, 2016).
Le Mouël, C. et al. Le Système Agricole et Alimentaire de la Région Afrique du Nord–Moyen-Orient à L’Horizon 2050 : Projections de Tendance et Analyse de Sensibilité (INRA, Paris/Rennes, 2015).
EPA. Life-Cycle Analysis of Greenhouse Gas Emissions from Renewable Fuels (2010).
Yan, X., Akiyama, H., Yagi, K. & Akimoto, H. Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change Guidelines. Glob. Biogeochem. Cycles 23, GB2002 (2009).
Adhya, T. K., Linquist, B., Searchinger, T. D., Wassmann, R. & Yan, X. Wetting and Drying: Reducing Greenhouse Gas Emissions and Saving Water from Rice Production (World Resources Institute, Washington DC, 2014).
Food and Agriculture Organization of the United Nations. FertiStat http://www.fao.org/tempref/agl/agll/docs/fertusebycrop.xls (2006; accessed 2016).
Myhre, G. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) 659–740 (Cambridge Univ. Press, Cambridge, 2013).
Opio, C. et al. Greenhouse Gas Emission from Ruminant Supply Chains: A Global Life Cycle Assessment (Food and Agriculture Organization of the United Nations, Rome, 2013).
MacLeod, M. et al. Greenhouse Gas Emissions from Pig and Chicken Supply Chains: A Global Life Cycle Assessment (Food and Agriculture Organization of the United Nations, Rome, 2013).
Edwards, R., Larivé, J. F., Rickeard, D. & Weindorf, W. Well-to-Tank Report Version 4.a: JEC Well-to-Wheels Analysis (European Commission, Joint Research Centre, Ispra, 2014).
Evans, S. G., Ramage, B. S., DiRocco, T. L. & Potts, M. D. Greenhouse gas mitigation on marginal land: a quantitative review of the relative benefits of forest recovery versus biofuel production. Environ. Sci. Technol. 49, 2503–2511 (2015).
Klasing, K. C. Displacement ratios for US corn DDGS (International Council on Clean Transportation, Washington, 2012).
Scharlemann, J. P., Tanner, E. V., Hiederer, R. & Kapos, V. Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag. 5, 81–91 (2014).
Estes, L. et al. A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses. Glob. Change Biol. 24, 322–337 (2017).
Fritz, S. et al. Highlighting continued uncertainty in global land cover maps for the user community. Environ. Res. Lett. 6, 044005 (2011).
We thank the David and Lucille Packard Foundation and the Norwegian Agency for Development Cooperation for financial support. We thank L. Germer for work on programming the Carbon Benefits Calculator, and J. Moretti and C. Klirs for help with graphics. We thank R. Boddey and A. Cardoso for additional data, advice and citations necessary for the Brazil examples. We thank R. Conant, K. Erb and H. Haberl for contributions to early thinking for this project, and C. Malins, S. Yeh and M. O’Hare for comments on early drafts.
Nature thanks L. Firbank and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Carbon benefits of different crop production systems based on the carbon benefits index.
Error bars reflect the range of literature estimates of vegetation and soil carbon stocks. Source data
Extended Data Fig. 2 Carbon benefits of different potential Iowa cropland uses based on the carbon benefits index.
Error bars reflect the range of literature estimates of vegetation and soil carbon stocks. Source data
Extended Data Fig. 3 Above- and below-ground carbon stocks of potential natural vegetation under current climate, used to derive COCs with the carbon loss method.
Data simulated with the LPJmL model and adjusted at the biome level according to reference values from the literature (see Supplementary Information).
Extended Data Fig. 4 Soil carbon stocks of potential natural vegetation under current climate used to derive COCs with carbon loss method.
Data simulated with LPJmL and adjusted at the biome level according to reference values from the literature (see Supplementary Information).
Extended Data Fig. 5 Annual net primary productivity of potential native vegetation under current climate used to derive COCs with carbon gain method.
Data simulated with LPJmL.
This document explains the basic concepts behind the carbon benefits index and its components, and includes Supplementary Fig. 1 and Supplementary Tables 1–9. It provides sensitivity calculations and describes the sources for information presented in the examples in the main manuscript.
This file contains the Carbon Benefits Calculator, which allows users to calculate the carbon benefits of a farm or hectare of land under existing and proposed new uses or management. Users may use various default values or provide site-specific information.
Zipped file containing ESRI ASCII files in geographic coordinates (WGS-84) for the maps shown in Extended Data Fig. 3, which shows estimated carbon stocks of native vegetation under current climate simulated with LPJmL and adjusted at the biome level according to literature reference values.
Zipped file containing ESRI ASCII files in geographic coordinates (WGS-84) for the map shown in Extended Data Fig. 4, which shows soil carbon stocks of native vegetation under current climate simulated with LPJmL.
Source Data for Supplementary Fig. 1.
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