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Nitrogen cycles in global croplands altered by elevated CO2

Nature Sustainability volume 6, pages 1166–1176 (2023)Cite this article

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Abstract

Croplands are the foundation of global food security and represent the largest nitrogen flows on Earth. Elevated atmospheric CO2 levels are a key driver of climate change with multiple impacts on food production and environmental sustainability. However, our understanding of how the cropland nitrogen cycle responds to elevated CO2 levels is not well developed. Here we demonstrate that elevated CO2 (eCO2) alone would induce a synergistic intensification of the nitrogen and carbon cycles, promoting nitrogen-use efficiency by 19% (95% confidence interval, 14–26%) and biological nitrogen fixation by 55% (95% confidence interval, 28–85%) in global croplands. This would lead to increased crop nitrogen harvest (+12 Tg yr−1), substantially lower fertilizer input requirements (−34 Tg yr−1) and an overall decline in reactive nitrogen loss (−46 Tg yr−1) under future eCO2 scenarios by 2050. The impact of eCO2 on the altered cropland nitrogen cycle would amount to US$668 bn of societal benefits by avoiding damages to human and ecosystem health. The largest benefits are expected to materialize in China, India, North America and Europe. It is paramount to incorporate the effect of rising CO2 on the nitrogen cycle into state-of-the-art Earth system models to provide robust scientific evidence for policymaking.

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Fig. 1: Effects of elevated CO2 levels on nitrogen and carbon cycles in global croplands.
Fig. 2: Nitrogen budgets of global cropland and their changes between baseline scenario (no climate change) and eCO2 scenario (SSP2-4.5) in 2050.
Fig. 3: Nitrogen flows in global croplands under eCO2 scenario (SSP2-4.5) by 2050.
Fig. 4: Time series of nitrogen budgets in global croplands over the period 2000–2050 under multiple scenarios.
Fig. 5: Impact assessment of elevated atmospheric CO2 levels as a single climate change factor under the eCO2 SSP2-4.5 scenario relative to the baseline scenario (no climate change) in 2050.

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Data availability

Data on the main findings can be found in the Supplementary Information. Further data supporting the findings can be found in refs. 61,62,63,64,65,66,67. Source data are provided with this paper.

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Acknowledgements

This study was supported by the National Key Research and Development Project of China (2022YFE0138200 and 2022YFD1700700) and the National Natural Science Foundation of China (42261144001 and 42061124001) received by B.G. We thank M. Zheng, Z. Qiu and X. Zhang for their hard work on metadata collection and validation.

Author information

Authors and Affiliations

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

    Jinglan Cui, Xiuming Zhang, Chen Wang, Sitong Wang, Peiying He, Hongyi Chen & Baojing Gu

  2. Policy Simulation Laboratory, Zhejiang University, Hangzhou, China

    Jinglan Cui

  3. UK Centre for Ecology & Hydrology, Penicuik, UK

    Stefan Reis

  4. The University of Edinburgh, School of Chemistry, Edinburgh, UK

    Stefan Reis

  5. PBL Netherlands Environmental Assessment Agency, The Hague, Netherlands

    Hans J. M. van Grinsven

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

    Baojing Gu

  7. Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou, China

    Baojing Gu

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Contributions

B.G. and J.C. designed the study. J.C. prepared and analysed the data. B.G. and J.C. interpreted the results and wrote the first draft of the paper. X.Z. provided support for modelling and impact assessment. S.R. revised the paper. C.W. and H.C. collected data from climate change experiments. S.W. and P.H. provided visualization support. H.J.M.v.G. provided modelling support.

Corresponding author

Correspondence to Baojing Gu.

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Ethics statement

We confirm that this study adheres to all applicable ethical regulations. As an independent study, there was no formal board, committee or institution that approved the study protocol.

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Nature Sustainability thanks Shiqiang Wan, Li An and the other, anonymous, reviewer for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Effects of elevated CO2 levels on crop yield and grain N content in global croplands.

Effects of elevated CO2 levels on (a) crop yield and (b) grain N content by crop groups; (c) crop yield by manipulation methods, including FACE (Free-air CO2 Enrichment Experiment), OTC (Open-top Chamber), and GC (Growth Chamber); (d) BNF by climate zones. The error bars indicate the 95% confidence interval of the mean, which is significant if the 95% confidence interval does not overlap zero. Numbers in the parentheses denote the number of observations in the meta-analysis.

Source data

Extended Data Fig. 2 Meta-regressions between response ratios (RR%) of variables and environmental factors.

(a) RR of crop yield versus ΔCO2 (elevated CO2 level relative to ambient CO2); (b) RR of NUE versus MAP (mean annual precipitation); (c) RR of NH3 versus ΔCO2; (d) RR of N2O versus ΔCO2. The bubbles represent the response ratios of individual observations, with bubble sizes indicating the weights of the response ratios. The P values were obtained from a two-sided F-test for the fitting lines with no adjustments for multiple comparisons. Unit ppm denotes parts per million.

Source data

Extended Data Fig. 3 Meta-regressions between logarithm-transformed response ratios (LnRR) of variables for specific crop type and environmental factors.

(a) LnRR of yield versus ΔCO2 for barley; (b) LnRR of yield versus MAT (mean annual temperature) for maize; (c) LnRR of yield versus MAP (mean annual precipitation) for rice. The bubbles represent the response ratios of individual observations, with bubble sizes indicating the weights of the response ratios. The P values were obtained from a two-sided F-test for the fitting lines with no adjustments for multiple comparisons.

Source data

Extended Data Fig. 4 Scenario design of the study.

(a) Simplified narratives of the scenarios. (b) Historical and future atmospheric CO2 levels in the baseline scenario and elevated CO2 scenario during 1950–2100.

Source data

Extended Data Fig. 5 N input of global cropland and their changes under elevated CO2 SSP2-4.5 scenario relative to baseline scenario in 2050.

Biological N fixation (BNF) in baseline scenario (a), eCO2 scenario (b), and ΔBNF (c); Fertilizer in baseline scenario (d), eCO2 scenario (e), and ΔFertilizer (f); Manure in baseline scenario (g), eCO2 scenario (h), and ΔManure (i); Deposition in baseline scenario (j), eCO2 scenario (k), and ΔDeposition (l). Values in the legend reflect the average annual N budget from croplands within a grid cell (0.5 by 0.5 degree). The base map is applied without endorsement from GADM data (https://gadm.org/).

Source data

Extended Data Fig. 6 Reactive N loss of global cropland and their changes under elevated CO2 SSP2-4.5 scenario relative to baseline scenario in 2050.

NH3 in baseline scenario (a), eCO2 scenario (b), and ΔNH3 (c); N2O in baseline scenario (d), eCO2 scenario (e), and ΔN2O (f); NOx in baseline scenario (g), eCO2 scenario (h), and ΔNOx (i); N leaching and runoff in baseline scenario (j), eCO2 scenario (k), and ΔN leaching and runoff (l). Values in the legend reflect the average annual N budget from croplands within a grid cell (0.5 by 0.5 degree). The base map is applied without endorsement from GADM data (https://gadm.org/).

Source data

Extended Data Fig. 7 CHANS (Coupled Human and Natural System) model with detailed representation of cropland sub-system.

(a) Overview of the CHANS model. The CHANS model contains 14 subsystems that are divided into four functional groups: processors (indicated by a green outline), consumers (red outline), removers (seagreen outline), and life-supporters (blue outline). The focus of this study is on the cropland sub-system. (b) Detailed representation of cropland sub-system. The model utilizes input data from multiple sources including social-economic data (indicated by orange) and N cycling data (indicated by blue). The N flows associated with N input, harvest, and surplus serve as CHANS model variables in the N-balance operation, which generates outputs of cropland N budgets for different geographical regions. Additionally, the CHANS model enables impact assessment to estimate the monetized impacts on health and the environment.

Extended Data Fig. 8 Methodology framework.

Meta-analysis is conducted to assess the impacts of elevated CO2 (eCO2) on N and C cycles. Crop N budgets are generated based on the IMAGE and CHANS models using multi-source data. In scenario analysis, we simulate rising CO2 levels by integrating the eCO2 impacts on N and C cycles in meta-analysis into the CHANS model for optimizing parameterization and projecting future cropland N budgets.

Supplementary information

Supplementary Information

Supplementary methods, Fig. 1, Tables 1 and 2 and references.

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Cui, J., Zhang, X., Reis, S. et al. Nitrogen cycles in global croplands altered by elevated CO2. Nat Sustain 6, 1166–1176 (2023). https://doi.org/10.1038/s41893-023-01154-0

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