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Reducing global land-use pressures with seaweed farming

Nature Sustainability volume 6, pages 380–390 (2023)Cite this article

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Abstract

Agricultural expansion to meet humanity’s growing needs for food and materials is a leading driver of land-use change, exacerbating climate change and biodiversity loss. Seaweed biomass farmed in the ocean could help reduce demand for terrestrial crops and reduce agricultural greenhouse gas emissions by providing a substitute or supplement for food, animal feed and biofuels. Here we model the global expansion potential of seaweed farming and explore how increased seaweed utilization under five different scenarios that consider dietary, livestock feed and fuel production seaweed usage may affect the environmental footprint of agriculture. For each scenario, we estimate the change in environmental impacts on land from increased seaweed adoption and map plausible marine farming expansion on the basis of 34 commercially important seaweed species. We show that ~650 million hectares of global ocean could support seaweed farms. Cultivating Asparagopsis for ruminant feed provided the highest greenhouse gas mitigation of the scenarios considered (~2.6 Gt CO2e yr−1). Substituting human diets at a rate of 10% globally is predicted to spare up to 110 million hectares of land. We illustrate that global production of seaweed has the potential to reduce the environmental impacts of terrestrial agriculture, but caution is needed to ensure that these challenges are not displaced from the land to the ocean.

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Fig. 1: Methodological approach.
Fig. 2: Scenario implications for sea use.
Fig. 3: Total and marginal effects of substituting seaweed on land use, emissions, water use, nitrogenous fertilizer use and biodiversity intactness.

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Data availability

The data that support the findings of this study are from publicly available datasets, including the following:

1. Macroalgal Herbarium Portal Collections search results, September 2022 (https://macroalgae.org/portal/collections/list. php).

2. Ocean Biodiversity Information System (OBIS), September 2022 (http://www.obis.org).

3. GBIF.org occurrence download, September 2022 (https://doi.org/10.15468/dl.bd74c9).

4. GBIF.org occurrence download, September 2022 (https://doi.org/10.15468/dl.khbhnz).

5. GBIF.org occurrence download, September 2022 (https://doi.org/10.15468/dl.gwsja7).

6. Atlas of Living Australia occurrence download, 2022 (https://doi.org/10.26197/ala.9a9f5159-2c05-4949-80be-b14ecbc80061).

7. Atlas of Living Australia occurrence download, 2022 (https://doi.org/10.26197/ala.42c6ed7d-fb85-4b09-ac09-5b5c910babff).

8. Atlas of Living Australia occurrence download, 2022 (https://doi.org/10.26197/ala.5fb60371-cf43-43e0-812a-2a75f5c5c704).

Additional data are available at https://zenodo.org/deposit/7374943.

Code availability

The code that supports the findings of this study is available at https://zenodo.org/deposit/7374943.

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Acknowledgements

We thank P. Lauri (International Institute for Applied Systems Analysis) for help with modelling afforestation. Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the United States National Member Organization. S.S. was funded by a Research Training Program Scholarship from the Australian Government.

Author information

Authors and Affiliations

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

    Scott Spillias, Richard S. Cottrell & Eve McDonald-Madden

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

    Scott Spillias, Richard S. Cottrell & Eve McDonald-Madden

  3. Biodiversity and Natural Resources Program, International Institute for Applied Systems Analysis, Laxenburg, Austria

    Scott Spillias, Hugo Valin, Miroslav Batka, Frank Sperling, Petr Havlík & David Leclère

  4. CSIRO Agriculture and Food, St Lucia, Queensland, Australia

    Frank Sperling

  5. Centre for Marine Socioecology, University of Tasmania, Hobart, Tasmania, Australia

    Richard S. Cottrell

  6. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Australia

    Richard S. Cottrell

  7. School of Chemical Engineering, The University of Queensland, St Lucia, Queensland, Australia

    Katherine R. O’Brien

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  1. Scott Spillias
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Contributions

S.S. and H.V. initiated and designed the project; S.S. compiled all of the data sources and performed the species distribution modelling and GLOBIOM modelling and developed code for the spatial prioritization with key suggestions from H.V., M.B., F.S. and E.M.-M.; H.V. and M.B. provided data on the properties of commodities; H.V., P.H., F.S., R.S.C., K.R.O. and E.M.-M. provided supervision and key suggestions for the manuscript; D.L. provided code for the biodiversity metric; S.S. wrote the manuscript with input from all authors.

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Correspondence to Scott Spillias.

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Nature Sustainability thanks Alice Jones, Jordan Hollarsmith and Heidi Alleway for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Overall constraints.

In each cell, the constraint is a product of six constrtaints that have been normalized to be between 0 and 1. Cells with a constraint value of 1 indicate ideal conditions and no constraints. Values close to 0 indicate prohibitive conditions for seaweed farming in one or several constraint layers.

Extended Data Fig. 2 Global potential for seaweed farming.

A. The global potential for seaweed farming, shown here in dark green, represents an overlay of all suitable cells for seaweed farming, based on species distribution models from 34 commercially important species and constrained by depth, distance to the nearest port, shipping traffic, mean wave energy, the presence of marine protected areas and limiting potential to areas where each species is likely to natively occur. B. The amount of suitable space per GLOBIOM region. NB: regions that contribute less than 1% of the total global potential are omitted.

Extended Data Fig. 3 Food scenario extent and statistics.

A. Extent of ocean area required to meet the ‘Food’ scenario, B. Area in most utilized regions, C. Depth and distance to nearest port of utilized cells.

Extended Data Fig. 4 Feed scenario extent and statistics.

A. Extent of ocean area required to meet the ‘Feed’ scenario, B. Area in most utilized regions, C. Depth and distance to nearest port of utilized cells.

Extended Data Fig. 5 Fuel scenario extent and statistics.

A. Extent of ocean area required to meet the ‘Fuel’ scenario, B. Area in most utilized regions, C. Depth and distance to nearest port of utilized cells.

Extended Data Fig. 6 Aspa scenario extent and statistics.

A. Extent of ocean area required to meet the ‘Aspa’ scenario, B. Area in most utilized regions, C. Depth and distance to nearest port of utilized cells.

Extended Data Fig. 7 Regional potential to meet demand.

The potential for the world and each GLOBIOM region (See Table S1–5 for definitions) to produce enough seaweed in the waters of their EEZ to supply each of the scenarios analysed here: 10% of human diets (‘Food’), 10% of livestock diets (‘Feed’), 50% of biofuel energy (‘Fuel’), all three of the above (‘All’) and Asparagopsis for 0.5% of ruminant livestock diets (‘Aspa’).

Extended Data Fig. 8 Changes in consumption of selected products in GLOBIOM by scenario.

The changes in global consumption over the course of the GLOBIOM run (2000–2050).

Extended Data Fig. 9 Regional impacts on terrestrial biodiversity.

The regional impacts on terrestrial biodiversity (BII), compared to the baseline scenario in 2050.

Extended Data Fig. 10 Clustered nutritional profiles of terrestrial products and selected seaweeds.

A principle components analysis was used to cluster terrestrial commodities and seaweed species by energy (kcal/kg), protein (g/kg) and lipid density (g/kg). The diversity of seaweeds means that they are well distributed amongst the major terrestrial commodities and could therefore substitute for these terrestrial products on a nutritional basis. For terrestrial product definitions see Table S1–9.

Supplementary information

Supplementary Information

Supplementary Tables 1.1–1.10 and Figs. 1.1 and 1.2. Supplement 2. Supplementary Methods, Figs. 2.1–2.8 and References.

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Spillias, S., Valin, H., Batka, M. et al. Reducing global land-use pressures with seaweed farming. Nat Sustain 6, 380–390 (2023). https://doi.org/10.1038/s41893-022-01043-y

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