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Managing for climate and production goals on crop-lands

Author

Listed:
  • Shelby C. McClelland

    (Cornell University
    New York University)

  • Deborah Bossio

    (The Nature Conservancy)

  • Doria R. Gordon

    (Environmental Defense Fund
    University of Florida)

  • Johannes Lehmann

    (Cornell University
    Technical University Munich
    Cornell University)

  • Matthew N. Hayek

    (New York University)

  • Stephen M. Ogle

    (Colorado State University
    Colorado State University)

  • Jonathan Sanderman

    (Woodwell Climate Research Center)

  • Stephen A. Wood

    (The Nature Conservancy
    Yale School of the Environment)

  • Yi Yang

    (Colorado State University)

  • Dominic Woolf

    (Cornell University
    Cornell University
    Cornell University)

Abstract

The assumption that crop-land natural climate solutions (NCS) have benefits for both climate change mitigation and crop production remains largely untested. Here we model GHG emissions and crop yields from crop-land NCS through the end of the century. We find that favourable (win–win) outcomes were the exception not the norm; grass cover crops with no tillage lead to cumulative global GHG mitigation of 32.6 Pg CO2 equivalent, 95% confidence interval (29.5, 35.7), by 2050 but reduce cumulative crop yields by 4.8 Pg, 95% confidence interval (4.0, 5.7). Legume cover crops with no tillage result in favourable outcomes through 2050 but increase GHG emissions for some regions by 2100. Crop-lands with low soil nitrogen and high clay are more likely to have favourable outcomes. Avoiding crop losses, we find modest GHG mitigation benefits from crop-land NCS, 4.4 Pg CO2 equivalent, 95% confidence interval (4.2, 4.6) by 2050, indicating crop-land soil will constitute a fraction of food system decarbonization.

Suggested Citation

  • Shelby C. McClelland & Deborah Bossio & Doria R. Gordon & Johannes Lehmann & Matthew N. Hayek & Stephen M. Ogle & Jonathan Sanderman & Stephen A. Wood & Yi Yang & Dominic Woolf, 2025. "Managing for climate and production goals on crop-lands," Nature Climate Change, Nature, vol. 15(6), pages 642-649, June.
    • Handle: RePEc:nat:natcli:v:15:y:2025:i:6:d:10.1038_s41558-025-02337-7
    DOI: 10.1038/s41558-025-02337-7
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    References listed on IDEAS

    as
    1. Avery W. Driscoll & Richard T. Conant & Landon T. Marston & Eunkyoung Choi & Nathaniel D. Mueller, 2024. "Greenhouse gas emissions from US irrigation pumping and implications for climate-smart irrigation policy," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. McClelland, Shelby C. & Paustian, Keith & Williams, Stephen & Schipanski, Meagan E., 2021. "Modeling cover crop biomass production and related emissions to improve farm-scale decision-support tools," Agricultural Systems, Elsevier, vol. 191(C).
    3. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    4. Stefan Wirsenius, 2003. "The Biomass Metabolism of the Food System: A Model‐Based Survey of the Global and Regional Turnover of Food Biomass," Journal of Industrial Ecology, Yale University, vol. 7(1), pages 47-80, January.
    5. Ogle, Stephen M. & Breidt, F. Jay & Easter, Mark & Williams, Steve & Paustian, Keith, 2007. "An empirically based approach for estimating uncertainty associated with modelling carbon sequestration in soils," Ecological Modelling, Elsevier, vol. 205(3), pages 453-463.
    6. Jingxiu Qin & Weili Duan & Shan Zou & Yaning Chen & Wenjing Huang & Lorenzo Rosa, 2024. "Global energy use and carbon emissions from irrigated agriculture," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
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