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Network resilience of phosphorus cycling in China has shifted by natural flows, fertilizer use and dietary transitions between 1600 and 2012

Nature Food volume 1, pages 365–375 (2020)Cite this article

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Abstract

The resilience of the phosphorus (P) cycling network is critical to ecosystem functioning and human activities. Although P cycling pathways have been previously mapped, a knowledge gap remains in evaluating the P network’s ability to withstand shocks or disturbances. Applying principles of mass balance and ecological network analysis, we examine the network resilience of P cycling in China from 1600 to 2012. The results show that changes in network resilience have shifted from being driven by natural P flows for food production to being driven by industrial P flows for chemical fertilizer production. Urbanization has intensified the one-way journey of P, further deteriorating network resilience. Over 2000–2012, the network resilience of P cycling has decreased by 11% owing to dietary changes towards more animal-based foods. A trade-off between network resilience improvement and increasing food trade is also observed. These findings can support policy decisions for enhanced P cycling network resilience in China.

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Fig. 1: The evolution of resilience, α, and efficiency and redundancy of the P cycling network in China during 1600–2012.
Fig. 2: Relative contributions of changes in efficiency and redundancy to changes in the resilience of the P cycling network in China.
Fig. 3: Relative contributions of changes in the concentration degree of P flows, node inter-dependency and node inter-independency to changes in efficiency, redundancy and resilience of the P cycling network in China during 1600–2012.
Fig. 4: Critical links and nodes influencing the resilience of the P cycling network in China during 2000–2012.
Fig. 5: The P cycling network of China in 2012.

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Data availability

Calculations to generate 149-node P flow networks used data from Liu et al.12. All data used in this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

All computer codes generated in MATLAB 2019a and R version 3.6.1 for this study are available from the corresponding authors upon reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (71874014, 51721093, 71704055, 71704015 and 41661144023), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement CIFTRESS no. 840205, Natural Science Funds for Distinguished Young Scholar of Guangdong Province, China (2018B030306032) and the Fundamental Research Funds for the Central Universities.

Author information

Author notes

  1. These authors contributed equally: Sai Liang, Yadong Yu.

Authors and Affiliations

  1. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, P. R. China

    Sai Liang, Cuiyang Feng & Zhifeng Yang

  2. School of Business, East China University of Science and Technology, Shanghai, P. R. China

    Yadong Yu & Tieju Ma

  3. International Institute for Applied System Analysis, Laxenburg, Austria

    Ali Kharrazi, Brian D. Fath, Tieju Ma & Bing Zhu

  4. CMCC Foundation – Euro-Mediterranean Center on Climate Change and Ca’ Foscari University of Venice, Venice, Italy

    Ali Kharrazi

  5. Biology Department, Towson University, Towson, MD, USA

    Brian D. Fath

  6. Environmental Studies, Masaryk University, Brno, Czech Republic

    Brian D. Fath

  7. Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA

    Glen T. Daigger

  8. School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, P. R. China

    Shaoqing Chen

  9. Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology (Sun Yat-sen University), Guangzhou, P. R. China

    Shaoqing Chen

  10. Department of Chemical Engineering, Tsinghua University, Beijing, P. R. China

    Bing Zhu

  11. The Bartlett School of Construction and Project Management, University College London, London, UK

    Zhifu Mi

  12. Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou, P. R. China

    Zhifeng Yang

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Contributions

S.L., Y.Y., A.K. and Z.M. designed the study. S.L. and Y.Y. collected data and conducted calculations. S.L., Y.Y., A.K., B.D.F., G.T.D. and S.C. led the analysis. S.L., Y.Y., A.K., B.D.F., C.F., G.T.D., S.C., T.M., B.Z., Z.M. and Z.Y. contributed to the writing.

Corresponding authors

Correspondence to Sai Liang, Yadong Yu or Zhifu Mi.

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Extended data

Extended Data Fig. 1 Distribution of values for the resilience of the P cycling network in China during 1600–2012.

The resilience values of the P cycling network during 1600–2012 are distributed in the curve of the theoretical relationship between resilience and α.

Source data

Extended Data Fig. 2 Changes of human P demand in China during 1950–2012.

The contribution of each category of P demand to the total P demand is shown in a different colour.

Source data

Extended Data Fig. 3 Changes of food structure in China during 1950–2012.

Food structure is calculated as the sum of P content in all foods except for grain divided by P content in grain.

Source data

Extended Data Fig. 4 Uncertainties in the efficiency, redundancy, α, and resilience of the P cycling network in China during 1600–2012.

Uncertainties are calculated by Monte Carlo simulation sampling 10,000 times. The upper boundary of the range indicates maximum values; the lower boundary of the range represents minimum values; and the solid line stands for average values.

Source data

Extended Data Fig. 5 Comparisons of α values between the 149-node and 16-node networks.

The overly efficient area is separated from the overly redundant area by the line where α = 0.3679 in the figure.

Source data

Extended Data Fig. 6 Comparisons of network resilience between the 149-node and 16-node networks.

The maximum value of resilience is 0.3679 in the figure.

Source data

Supplementary information

Supplementary Information

Supplementary Table 1 title and caption, Tables 2–11, notes, methods, discussion and references.

Reporting Summary

Supplementary Table 1

The list of nodes in the Chinese P cycling network.

Source data

Source Data Fig. 1

Resilience, α, and efficiency and redundancy of the P cycling network in China during 1600–2012.

Source Data Fig. 2

Relative contributions of changes in efficiency and redundancy to changes in the resilience of the P cycling network in China during 1600–2012.

Source Data Fig. 3

Relative contributions of changes in concentration degree of P flows, node inter-dependency and node inter-independency to changes in efficiency, redundancy, and resilience of the P cycling network in China during 1600–2012.

Source Data Fig. 4

Contributions of changes in links and nodes to changes in the resilience of the P cycling network in China during 2000–2012.

Source Data Fig. 5

The P flow network of China in 2012.

Source Data Extended Data Fig. 1

α and resilience of the P cycling network in China during 1600–2012.

Source Data Extended Data Fig. 2

Human P demand in China during 1950–2012.

Source Data Extended Data Fig. 3

Food structure in China during 1950–2012.

Source Data Extended Data Fig. 4

Uncertainties in the efficiency, redundancy, α, and resilience of the P cycling network in China during 1600–2012.

Source Data Extended Data Fig. 5

α values for the 149-node and 16-node P cycling networks in China during 1600–2012.

Source Data Extended Data Fig. 6

Network resilience for the 149-node and 16-node P cycling networks in China during 1600–2012.

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Liang, S., Yu, Y., Kharrazi, A. et al. Network resilience of phosphorus cycling in China has shifted by natural flows, fertilizer use and dietary transitions between 1600 and 2012. Nat Food 1, 365–375 (2020). https://doi.org/10.1038/s43016-020-0098-6

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