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Food and feed trade has greatly impacted global land and nitrogen use efficiencies over 1961–2017

Nature Food volume 2, pages 780–791 (2021)Cite this article

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Abstract

International trade of agricultural products has complicated and far-reaching impacts on land and nitrogen use efficiencies. We analysed the productivity of cropland and livestock and associated use of feed and fertilizer efficiency for over 240 countries, and estimated these countries’ cumulative contributions to imports and exports of 190 agricultural products for the period 1961–2017. Crop trade has increased global land and partial fertilizer nitrogen productivities in terms of protein production, which equalled savings of 2,270 Mha cropland and 480 Tg synthetic fertilizer nitrogen over the analysed period. However, crop trade decreased global cropland productivity when productivity is expressed on an energy (per calorie) basis. Agricultural trade has generally moved towards optimality, that is, has increased global land and nitrogen use efficiencies during 1961–2017, but remains at a relatively low level. Overall, mixed impacts of trade on resource use indicate the need to rethink trade patterns and improve their optimality.

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Fig. 1: Productivity distribution curves.
Fig. 2: Illustrations of the concept of trade functionality and optimality, as determined by the CPHE and CWPE of exporting and importing countries.
Fig. 3: Cumulative productivity-trade distribution curves.
Fig. 4: Cumulative potential saving.
Fig. 5: Changes per decade in the impacts of trade.
Fig. 6: Cumulative productivity–trade distribution curves of exporting and importing countries.
Fig. 7: Trade optimality and functionality levels.

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Data availability

All data needed to evaluate the conclusions of this study are available in the paper itself and/or the Supplementary Information file. Source data are provided with this paper.

Code availability

The custom code and algorithm used for this study are available in the Methods and the Supplementary Information.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (31572210, 31272247), Program of International S&T Cooperation (2015DFG91990), President’s International Fellowship Initiative (PIFI) of CAS (2016DE008, 2016VBA073 and 2019VCA0017), the Youth Innovation Promotion Association, CAS (2019101) and Distinguished Young Scientists Project of Natural Science Foundation of Hebei (D2017503023). The input of P.S. contributes to the N-Circle China–UK Virtual Joint Centre on Nitrogen, funded by the Newton Fund via UK BBSRC/NERC (grant BB/N013484/1). Z.B. also thanks Francesco N. Tubiello from FAOSTAT for help with interpreting the data and results, FAOSTAT for providing the functional data used in this study, and Y. Cui, J. Liu, S. Xu, Y. Wang, M. Guo, S. Zhao and Y. Cao for helping collect the data at early stage.

Author information

Author notes

  1. These authors contributed equally: Zhaohai Bai, Wenqi Ma.

Authors and Affiliations

  1. Key Laboratory of Agricultural Water Resources, Hebei Key Laboratory of Soil Ecology, Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, The Chinese Academy of Sciences, Shijiazhuang, China

    Zhaohai Bai, Hao Zhao, Chunsheng Hu, Nannan Zhang, Ling Liu, Sujuan Guo, Xiangwen Fan & Lin Ma

  2. Wageningen University, Department of Soil Quality, Wageningen, The Netherlands

    Zhaohai Bai & Oene Oenema

  3. College of Resources & Environmental Sciences, Hebei Agricultural University, Baoding, China

    Wenqi Ma

  4. College of Resources and Environmental Sciences, Centre for Resources, Environment and Food Security, Key Lab of Plant–Soil Interactions, MOE, China Agricultural University, Beijing, China

    Mengchu Guo

  5. Wageningen Environmental Research, Wageningen, The Netherlands

    Oene Oenema & Gerard Velthof

  6. Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK

    Pete Smith

  7. School of Mathematics and Science, Hebei GEO University, Shijiazhuang, China

    Xia Liu

  8. College of Mathematics and Information Science, Hebei University, Baoding, China

    Peiguang Wang

  9. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Wilfried Winiwarter

  10. The Institute of Environmental Engineering, University of Zielona Góra, Zielona Góra, Poland

    Wilfried Winiwarter

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Contributions

Z.B., W.M., L.M. and O.O. designed the research. Z.B., H.Z., X.L., P.W., N.Z., L.L., S.G., X.F. and W.W. performed the research and analysed data. Z.B., W.M., L.M., O.O., G.V., P.S., M.L. and C.H. wrote the paper. All authors contributed to analysis of the results. All authors read and commented on various drafts of the paper.

Corresponding author

Correspondence to Lin Ma.

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The authors declare no competing interests.

Additional information

Peer review information Nature Food thanks Baojing Gu, Robert Sabo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Illustration of the eight trade optimality and functionality levels.

Based on different combinations of the concentration of high-productivity countries (CPHE) and the concentration weighted production efficiency (CWPE), as defined in Fig. 2 (main text) and Supplementary Table 1.

Extended Data Fig. 2

Fate of nitrogen (N) embedded in traded food and feed in 2017.

Extended Data Fig. 3 Nitrogen (N) flows in traded food (a) and feed (b) in 2013 between nine selected regions (in Gg N).

Bars show the fate of sewage (a) and manure (b) in nine world regions (in Gg N).

Extended Data Fig. 4 Comparison of the concentration of production in high efficiency countries (CPHE) countries.

Based on crop productivity expressed as energy (upper panel) and protein (bottom panel) in 1960s, 1970s, 1980s, 1990s, 2000s and 2010s.

Extended Data Fig. 5 Trade optimality and functionality level of livestock products.

In terms of energy (upper panel) and protein (bottom panel) based crop productivity (left panel) and partial feed nitrogen (N) productivity (right panel) in 1960s, 1970s, 1980s, 1990s, 2000s and 2010s. The size of circle represents the differences of CWPE between exporting and importing countries. The open circles represent the positive trade optimality level (I-IV) as CWPEex / CWPEim > 1.0, and the solid circles represent the negative trade optimality level (V-VIII) as CWPEex / CWPEim < 1.0.

Extended Data Fig. 6 Comparison of the CPHE of exporting and importing countries.

For partial feed nitrogen (N) productivity (PFP) of livestock production expressed in energy (upper panel) and protein (bottom panel) for the 1960s, 1970s, 1980s, 1990s, 2000s and 2010s. 2010s including data of 2010–2017. CPHEim and CPHEex are the mean CPHE of importing and exporting countries, respectively (dimensionless). CWPEim and CWPEex were the weighted production efficiency for importing and exporting countries, respectively.

Extended Data Fig. 7 Trade optimality and functionality level of different crop products.

In terms of energy based (a) and protein based (b) productivity, and of different livestock products in terms of energy (c) and protein (d) based productivity from 1961 to 2017. The size of circle represents the differences of CWPE between exporting and importing countries. The red solid dots represent the positive trade optimality level (I-IV) as CWPEex / CWPEim ≥ 1.0, and the blue solid circles represent the negative trade optimality level (V-VIII) as CWPEex / CWPEim < 1.0. CPHEim and CPHEex are the mean CPHE of importing and exporting countries, respectively (dimensionless). CWPEim and CWPEex were the weighted production efficiency for importing and exporting countries, respectively.

Extended Data Fig. 8 Changes of trade and harvest area in different countries.

High crop energy efficiency importing countries (a-c), and medium crop-energy efficiency exporting countries (d-f) from 1961 to 2017.

Extended Data Fig. 9 Illustration of the sensitive of CPHE and CWPE to the selection different max productivity (MP).

Select of MP under 98.5%, 99.0% and 99.5% contributions to the total production.

Extended Data Fig. 10

Comparison of the results between using data from all the countries and from the top 100 populous countries (T100).

Supplementary information

Supplementary Information

Supplementary methods and discussion, Figs. 1–10 and Tables 1–6.

Source data

Source Data Fig. 3

Raw data and processed data.

Source Data Fig. 6

Raw data and processed data.

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Bai, Z., Ma, W., Zhao, H. et al. Food and feed trade has greatly impacted global land and nitrogen use efficiencies over 1961–2017. Nat Food 2, 780–791 (2021). https://doi.org/10.1038/s43016-021-00351-4

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