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Navigating sustainability trade-offs in global beef production

Nature Sustainability volume 6, pages 284–294 (2023)Cite this article

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Abstract

Beef production represents a complex global sustainability challenge including reducing poverty and hunger and the need for climate action. Understanding the trade-offs between these goals at a global scale and at resolutions to inform land use is critical for a global transition towards sustainable beef. Here we optimize global beef production at fine spatial resolution and identify trade-offs between economic and environmental objectives interpretable to global sustainability ambitions. We reveal that shifting production areas, compositions of current feeds and informed land restoration enable large emissions reductions of 34–85% annually (612–1,506 MtCO2e yr−1) without increasing costs. Even further reductions are possible but come at a trade-off with costs of production. Critically our approach can help to identify such trade-offs among multiple sustainability goals, produces fine-resolution mapping to inform required land-use change and does so at the scale necessary to shift towards a globally sustainable industry for beef and to sectors beyond.

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Fig. 1: Efficient global beef production for the National Beef scenario.
Fig. 2: Land use strategies to achieve efficient beef production.
Fig. 3: Pareto frontiers of beef without borders (blue) compared to national beef scenario (grey) and simulated current production.
Fig. 4: Uncertainty range of optimization results for national beef scenario.

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Data availability

The datasets used to obtain results in this study are publicly available as referenced within the article and Supplementary Information and from the corresponding author upon request. The land-cover layer was obtained from https://www.esa-landcover-cci.org/. Current crop areas and yields were obtained from http://www.earthstat.org/. Annual net primary productivity was obtained from https://lpdaac.usgs.gov/products/mod17a3hv006/. Fraction of grain in cattle diet, fraction of feed exported, producer prices and beef demand were obtained from https://www.fao.org/faostat/en/. Quantity and value of fertilizer traded were retrieved from https://comtrade.un.org/data. Beef production, liveweight gain, emissions from enteric fermentation, emissions from manure management and feed consumption from beef cattle at year 2000 were obtained from https://data.csiro.au/collection/csiro:29893. Metabolizable energy in feed was obtained from https://www.feedipedia.org/ and residue-to-product ratio of grains were retrieved from https://doi.org/10.1016/j.wasman.2010.04.016 and https://doi.org/10.1111/gcbb.12305. Monthly air temperature and wind velocity were obtained from http://www.worldclim.com/version2. Average liveweight of calves, dressing percentage, energy use for meat processing and packaging and emission intensity of energy use were retrieved from https://www.oneplanetnetwork.org/sites/default/files/from-crm/gleam_2.0_model_description.pdf. Global field sizes were obtained from https://geo-wiki.org/Application/. Travel time to cities was retrieved from https://figshare.com/articles/dataset/Travel_time_to_cities_and_ports_in_the_year_2015/7638134. The location of major ports was obtained from https://geonode.wfp.org/layers/esri_gn:geonode:wld_trs_ports_wfp. Fuel prices were obtained from https://sutp.org/download/10008/. Nominal hourly wage was retrieved from https://www.ilo.org/ilostat-files/Documents/Excel/INDICATOR/EAR_4MMN_CUR_NB_A_EN.xlsx. Road transport payload capacity, average vehicle speed and average fuel efficiency were obtained from https://www.globalfueleconomy.org/media/404893/gfei-wp14.pdf. The country-specific discount rate was obtained from https://data.worldbank.org/indicator/FR.INR.LEND. Costs of land restoration were retrieved from https://www.jstor.org/stable/23297079. Trade margins for exported beef in each country were retrieved from https://www.gtap.agecon.purdue.edu/databases/archives.asp. Sea distance between countries were obtained from https://zenodo.org/record/240493. Aboveground and belowground carbon densities were retrieved from https://doi.org/10.3334/ORNLDAAC/1763. The carbon in potential vegetation layer was obtained from https://doi.org/10.1594/PANGAEA.893761. The preprocessed datasets used to conduct the analysis and the output datasets generated and used to generate figures in this article are available at https://doi.org/10.5281/zenodo.7085816.

Code availability

The software used to find solutions to this multi-objective optimization problem was Python v.3.7.3, with the following libraries: Numpy v.1.20.2, Pandas v.1.2.5, Rasterio v.1.0.21, as well as Anaconda v.4.8.3. The code used in this analysis is available from the following link: https://github.com/accastonguay/beef_simulation. The code used to analyse outputs of the optimization and generate figures is based on Python v.3.7.3 and Jupyter Notebook v.6.4.8 with the following libraries: Matplotlib v.3.5.1, Numpy v.1.20.2, Pandas v.1.2.5, Rasterio v.1.0.21, Cartopy v.0.20.2, Geopandas v.0.10.2 and Sklearn v.1.0.2. Results of the optimization can also be visualized online at https://accastonguay.shinyapps.io/beef_app/.

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Acknowledgements

This work was funded by the Australian Research Council—Future Fellowship Project ‘Where’s the beef? A systems model for taming a wicked environmental problem’ (ARC- FT170100140).

Author information

Authors and Affiliations

  1. School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Queensland, Australia

    Adam C. Castonguay, G. Bradd Witt, Katie Lee & Eve McDonald-Madden

  2. Centre for Biodiversity and Conservation Science, The University of Queensland, Brisbane, Queensland, Australia

    Adam C. Castonguay, Matthew H. Holden, Katie Lee & Eve McDonald-Madden

  3. Queensland Alliance for One Health Sciences, School of Veterinary Science, The University of Queensland, Gatton, Queensland, Australia

    Adam C. Castonguay

  4. Department of Applied Economics, University of Minnesota, St. Paul, MN, USA

    Stephen Polasky

  5. Natural Capital Project, University of Minnesota, St. Paul, MN, USA

    Stephen Polasky

  6. Centre for Applications in Natural Resource Mathematics, School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, Australia

    Matthew H. Holden

  7. Department of Global Development, College of Agriculture and Life Science, and Cornell Atkinson Center for Sustainability, Cornell University, Ithaca, NY, USA

    Mario Herrero & Daniel Mason-D’Croz

  8. Agricultural Economics and Rural Policy Group, Wageningen University and Research, Wageningen, the Netherlands

    Daniel Mason-D’Croz

  9. Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, Queensland, Australia

    Cecile Godde

  10. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Jinfeng Chang

  11. Institute on the Environment, University of Minnesota, St. Paul, MN, USA

    James Gerber

  12. The Nature Conservancy, Asia Pacific Resource Centre, South Brisbane, Queensland, Australia

    Edward T. Game

  13. School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia

    Brett A. Bryan

  14. School of Ecosystem and Forest Science, University of Melbourne, Melbourne, Victoria, Australia

    Brendan Wintle & Payal Bal

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  1. Adam C. Castonguay
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Contributions

E.M.M., E.G., S.P., M.H.H. and A.C.C. developed the concept and designed scenarios. A.C.C. ran the analysis with feedback from E.M.M., S.P., M.H.H., M.H., D.M.D. and K.L. M.H., J.C., J.G. and D.M.D. contributed data required for the analysis. A.C.C. drafted the manuscript and E.M.M., S.P., M.H.H., M.H., D.M.D, B.A.B., C.G., G.B.W, J.C., J.G., E.G., B.W., K.L. and P.B. contributed to the interpretation of findings and provided revisions to the manuscript.

Corresponding author

Correspondence to Adam C. Castonguay.

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Extended data

Extended Data Fig. 1 Total feed consumption for five solutions along the Pareto frontier for the two spatial scenarios (National Beef and Beef without Borders).

Total feed consumption (grass, grain and crop residues) for five solutions along the Pareto frontier compared to simulated current biomass consumption for the two spatial scenarios (National Beef and Beef without Borders).

Extended Data Fig. 2 Distribution of feed for two solutions along the Pareto frontier for the National Beef scenario.

Distribution of feed for two solutions along the Pareto frontier for the National Beef scenario. Feed options were grouped into five categories: 1) diet is composed of grass from unimproved grassland without N application, 2) diet composed of grass from improved grasslands with N application, 3. Diet composed of a mix of grass and crop residues, 4) diet with grain contributing to less than 50% of total biomass consumed, 5) diet with grain contributing to more than 50% of total biomass consumed.

Extended Data Fig. 3 Fraction of new area used for feed production, and type of land use lost from expansion for the two spatial scenarios.

Fraction of new area used for feed production, and type of land use lost from expansion for the two spatial scenarios. The land use classification is based on the land use types from Hoskins et al.67: Primary (undisturbed natural habitat), Secondary (recovering, previously disturbed natural habitat), Cropland (land used for crop production), Pasture (land used for the grazing), Other (dense urban settlement).

Extended Data Fig. 4 Pareto frontier and production where changes that occur in both minimizing costs and minimizing emissions solutions were implemented with the associated GHG reduction and costs saving.

Pareto frontier of the National Beef scenario in comparison with current production and production where changes that occur in both minimizing costs and minimizing emissions solutions were implemented, that is, strategies shown in Fig. 2c, with the associated GHG reduction and costs saving. The map shows the spatial distribution of net production changes if these strategies were implemented. This scenario ensures low- and middle-income nations see no reduction in current beef production levels in the interest of avoiding perverse impacts on national food security.

Extended Data Fig. 5 Range and mean beef production at regional level for the Beef Without Border scenario.

Range (grey bar) and mean beef production (black point) compared to current production (red cross) at regional level for all solutions across the Pareto frontiers for the Beef Without Border scenario.

Extended Data Fig. 6 Sources of emissions.

Sources of emissions for the simulated current production and two spatial scenarios. AGB and BGB stand for aboveground biomass and belowground biomass, respectively and include biomass change from land clearing in new area and reforestation.

Extended Data Fig. 7 Sources of economic costs.

Sources of economic costs for the simulated current production and two spatial scenarios.

Extended Data Fig. 8 Uncertainty from using different time horizons.

Uncertainty resulting from different time horizons used for annualizing change in biomass (from soil carbon, land clearing and reforestation), transition costs and land preparation costs for active reforestation.

Extended Data Fig. 9 Difference in Pareto frontiers if forest restoration options are considered and optimized, or if restoration is not considered.

Difference in Pareto frontiers if forest restoration options are considered and optimized, or if restoration is not considered, in comparison with simulated costs and emissions of current beef production.

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–13, Tables 1–7 and references.

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Castonguay, A.C., Polasky, S., H. Holden, M. et al. Navigating sustainability trade-offs in global beef production. Nat Sustain 6, 284–294 (2023). https://doi.org/10.1038/s41893-022-01017-0

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