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Impacts of global trade on cropland soil-phosphorus depletion and food security

Nature Sustainability (2024)Cite this article

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Abstract

Globalization intensifies the demand for agricultural products from specific regions, resulting in intensive farming practices that can exacerbate local cropland soil phosphorus (P) depletion, thereby undermining long-term food security. By integrating global data on international trade and soil-P reserves and deficits from 1970 to 2017, we demonstrate that the contribution of trade to global soil-P deficits increased from 7% in 1970 to 18% in 2017, with 54% of this impact driven by non-food consumption. Over these 48 years, developing regions exported a net of 5.8 Mt P through agricultural trade, resulting in a net increase of 13 Mt soil-P deficits. These deficits are primarily concentrated in regions with low soil-P reserves, such as sub-Saharan Africa, Latin America and Southeast Asia, thereby heightening the risks of soil-P depletion in these areas and amplifying long-term concerns about food security. This insight underscores the imperative for a broader perspective on food security—prioritizing national soil productivity rather than merely boosting the availability of food in the global market when shaping global trade policies.

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Fig. 1: Spatially explicit SPB on cropland.
Fig. 2: Soil-P deficits driven by the consumption of different regions or sectors.
Fig. 3: Top ten flows of soil-P deficits embodied in traded commodities between regions in selected years.
Fig. 4: Trade balance of soil-P deficits by region.
Fig. 5: Sectoral breakdown of the trade balance of soil-P deficits.

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Data availability

The data supporting the findings of this study are available within the article and its Supplementary Information files. The data used in this study are mainly collected from the online statistical databases of the FAO (FAOSTAT; http://www.fao.org/statistics/databases/en/) and the International Fertilizer Association (IFASTAT; https://www.ifastat.org/nutrient-use-efficiency). The basemaps shown in Figs. 1 and 3 and Extended Data Figs. 2, 7 and 8 are sourced from Natural Earth (http://www.naturalearthdata.com), a public domain map dataset. The global soil-P deficit database that we established is available via Figshare at https://doi.org/10.6084/m9.figshare.25782579 (ref. 66). Source data are provided with this paper.

Code availability

The environmentally extended MRIO model analysis was conducted using the Australian IELab (https://ielab.info/) infrastructure and is accessible from the authors upon request.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (grant nos. 72104239 (K.N.), 42325707 (B.G.) and 42261144001 (B.G.)); the Agricultural Science and Technology Innovation Program (grant nos. 10-IAED-ZK-04-2024 (K.N.), 10-IAED-06-2024 (K.N.) and 10-IAED-SZD-01-2024 (K.N.)); the Australian Research Council through its Discovery Project grants (nos. DP0985522 (M. Lenzen), DP130101293 (M. Lenzen), DP190102277 (M. Lenzen), DP200103005 (A.M.) and DP200102585 (M. Lenzen and M. Li)), its Linkage Project (grant no. LP200100311 (A.M.)) and its Large Infrastructure, Equipment and Facilities grant (no. LE160100066 (T.W. and M. Lenzen)); the Australian Research Council Research Hub (grant no. IH190100009 (A.M.)); the Discovery Early Career Researcher Award (A.M.); and the National eResearch Collaboration Tools and Resources project (NeCTAR, T.W. and M. Lenzen) through its Industrial Ecology Virtual Laboratory VR201. NeCTAR projects are Australian government projects conducted as part of the Super Science initiative and financed by the Education Investment Fund. M. Lenzen acknowledges financial support from the Hanse-Wissenschaftskolleg in Delmenhorst, Germany, through its HWK Fellowships. M. Li acknowledges support from the University of Sydney Horizon Fellowship. A.M. acknowledges support from the University of Sydney SOAR Prize.

Author information

Author notes

  1. These authors contributed equally: Kunyu Niu, Mengyu Li.

Authors and Affiliations

  1. Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, China

    Kunyu Niu

  2. ISA, School of Physics A28, University of Sydney, Sydney, New South Wales, Australia

    Mengyu Li, Manfred Lenzen & Arunima Malik

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

    Mengyu Li

  4. Hanse-Wissenschaftskolleg, Delmenhorst, Germany

    Manfred Lenzen

  5. Sustainability Assessment Program, School of Civil and Environmental Engineering, UNSW, Sydney, New South Wales, Australia

    Thomas Wiedmann

  6. Water Research Center, School of Civil and Environmental Engineering, UNSW, Sydney, New South Wales, Australia

    Xudong Han

  7. Research Center for Rural Economy, Ministry of Agriculture and Rural Affairs of China, Beijing, China

    Shuqin Jin

  8. Discipline of Accounting, Sydney Business School, University of Sydney, Sydney, New South Wales, Australia

    Arunima Malik

  9. Sydney Nano Institute, University of Sydney, Sydney, New South Wales, Australia

    Arunima Malik

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

    Baojing Gu

  11. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou, China

    Baojing Gu

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Contributions

K.N. and M. Li conceptualized and planned the paper. M. Li, M. Lenzen and A.M. constructed the MRIO matrix. K.N., M. Li and X.H. conducted the data collection, processing and chart creation. K.N., B.G., A.M., X.H., M. Li, M. Lenzen, T.W. and S.J. wrote the paper. All authors contributed to revising the manuscript.

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Correspondence to Arunima Malik or Baojing Gu.

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Extended data

Extended Data Fig. 1 Flow chart for this study.

We illustrate the process of calculating the soil-P budget at the production site (a) and constructing the soil-P deficits (SPD) database (b). The SPD is then integrated with the MRIO trade network (c) to assess the impact of food and non-food product consumption at the consumption site on the SPD of the production site (d). Subsequently, we combine this analysis with information on accumulated soil-P, natural soil-P level, soil properties and affected crops at the production site to determine the risk of soil-P depletion (e). The global agricultural trade network (ATN) centers around the agricultural supply chain indicated by the green arrows in the diagram. In contrast, the multi-regional input-output (MRIO) trade network employed in our analysis covers not only the impact of the agricultural supply chain (green arrows) but also that of the non-food supply chain, as illustrated by the red arrows. The cropland in the figure was drawn using Adobe illustrator.

Extended Data Fig. 2 Spatially explicit maps illustrating the soil-P budgets for various crop types in 1970 and 2017 across 44 nations exporting soil-P deficits (kg P/ha/yr).

a1-9 and b1-9 refer to the year 1970 and 2017, respectively. c1-c9 are the changes between 1970 and 2017, where blue signals rising soil-P deficits, and red signals declining soil-P deficits or increasing soil-P surplus. Basemaps from Natural Earth (https://www.naturalearthdata.com/).

Source data

Extended Data Fig. 3 Comparison of per capita consumption-driven soil-P deficits for 10 aggregated regions.

The size of the circle represents a region’s population. See Supplementary Table 7 for full names and abbreviations of aggregated regions.

Source data

Extended Data Fig. 4 Trade balance of soil-P deficits by region and sector over 48 years for top 20 net-importing and net-exporting countries/regions.

The green bars to the left of X = 0 signify the rise in soil-P deficits within a particular region’s agricultural system due to trade, whereas the varied bars on the right side of X = 0 indicate an escalation in soil-P deficits in foreign regions due to the consumption of products from a specific sector within that region. See Supplementary Table 6 for full names and abbreviations of 90 countries and regions. ‘Agri products’ refer to Agricultural products. ‘Manuf products’ refer to manufacturing products. ‘Acc & FS’ refer to Accommodation and food service. ‘Fin, Ret, & Whol’ refer to Finance, retail, and wholesale.

Source data

Extended Data Fig. 5 Modeling diagram of driving factors of net export of cropland soil-P deficits in each region.

The dashed downward curve in the figure represents the negative effect of population on the net export of soil-P deficits from a region, but it does not pass the statistical significance test.

Extended Data Fig. 6 Trade balance of soil-P deficits by region over 48 years.

A value less than 0 signals a rise in local soil-P deficits through trade, whereas a value greater than 0 indicates that imports of this region exacerbate the soil-P deficits in other foreign regions. The black bars represent low-income food-deficit nations recognized by the FAO, and it can be seen that most of them are net exporters of soil-P deficits. See Supplementary Table 6 for full names and abbreviations of 90 countries and regions. Unit, Mt.

Source data

Extended Data Fig. 7 Net-export density of soil-P deficits per unit area over 48 years.

a, countries with low level of food security; b, developing countries with large agricultural exports. c, sub-Saharan Africa’s net imports of P through food trade and soil-P deficits occurred due to trade from 1970 to 2017. Basemaps in a and b from Natural Earth (https://www.naturalearthdata.com/).

Source data

Extended Data Fig. 8 Aggregating 90 regions into 10 regions.

By considering information on economic development and geographic location, we aggregated 90 regions into 10 broad regions. See Supplementary Table 7 for full names and abbreviations of aggregated regions. Basemaps from Natural Earth (https://www.naturalearthdata.com/).

Supplementary information

Supplementary Information

Supplementary Tables 1–15 and text.

Reporting Summary

Supplementary Video 1

Top ten flows of soil-P deficits (in kt yr−1) embodied in traded commodities.

Source data

Source Data Figs. 1–5

Statistical source data.

Source Data Extended Data Figs. 2–4, 6 and 7

Statistical source data.

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Niu, K., Li, M., Lenzen, M. et al. Impacts of global trade on cropland soil-phosphorus depletion and food security. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01385-9

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