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Changes in meat consumption can improve groundwater quality

Nature Food volume 6, pages 703–714 (2025)Cite this article

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Abstract

Large-scale, centralized livestock production is recognized as a significant contributor to environmental pollution, including groundwater contamination. Here we assess the impact of traditional meat production on nitrate contamination in groundwater across the contiguous USA from 1985 to 2020. In addition, we evaluate potential changes in groundwater quality resulting from the substitution of traditional meat sources with three alternative meat options. We find that substituting 10% of the protein intake from conventional meat sources with meat alternatives can lead to an average reduction of 3.4%, 10.7% and 4.5% in the required nitrogen fertilizer, manure and water footprint, respectively. This substitution could potentially decrease the risk of groundwater nitrate exceedance (concentration exceeding 10 mg l−1 as N) by up to ~20%. These results highlight the potential of long-term dietary shifts to achieve Sustainable Development Goal (SDG) 6 and support other SDG targets.

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Fig. 1: Sum and variability of feature impacts across time.
Fig. 2: Average resource requirements and environmental impacts for conventional and alternative meat production to provide 1 kg of protein.
Fig. 3: Influence of protein source substitution.
Fig. 4: Reduction in groundwater nitrate risk from 50% meat substitution, shaped by local agriculture and climate.

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Data availability

The data that support the findings of this study are available within the Article and its Supplementary Information. Groundwater quality data from 32,294 sites from 1985 to 2020 spread out over the CONUS were collected from the National Water-Quality Assessment (NAWQA) Project and the National Groundwater Monitoring Network (NGWMN) database and summarized every 5 years. A total of 38 potential influencing features were considered, including 10 environmental features representing meteorological, topographical and hydrogeological conditions as well as 28 anthropogenic features related to agricultural and livestock activities (Supplementary Tables 1 and 2). County-level data on the livestock population by livestock type from 1985 to 2010 were obtained for 3,066 counties from tabular data for selected items from the Census of Agriculture for the period 1950–2017 for counties in the conterminous USA (ref. 113). These data were reshaped into a 5-year temporal resolution for each HUC-10 watershed delineated by USGS based on surface hydrologic features and linked to the groundwater quality data based on the locations of sampling wells for statistical modelling. A network of 9,869,002 potential links of food transportation pathways within the USA was obtained from ref. 111. Source data are provided with this paper.

Code availability

The codes for the machine learning model and the Monte Carlo model are available via GitHub at https://github.com/Yzedd/Meat-alternatives-impact.git.

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Acknowledgements

This research was supported by the National Key R&D Program of China (grant number 2021YFC3200502 to Z.G.), National Natural Science Foundation of China (grant number 42377045 to Z.G.), the Guangdong Provincial Basic and Applied Basic Research Fund (grant number 2024B1515020038 to Z.G.), Guangdong-Hong Kong Joint Laboratory for Soil and Groundwater Pollution Control (grant number 2023B1212120001 to Z.G. and C.Z.), High-level University Special Funds (grant numbers G03050K001 and G030290001 to Z.G.), the Swiss Federal Office of Energy (SFOE) (AVARIs project, grant number SI-501914-01 to J.P.), the Swiss Agency for Development and Cooperation (SDC) (WABES project, grant number 7F-09963.02.01 to J.P.) and the Center for Computational Science and Engineering of Southern University of Science and Technology.

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Authors and Affiliations

  1. State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Contamination Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Yang Zhan, Zhilin Guo, Zhengzhong Zeng, Kewei Chen, Rixin Wu, Chen Ding, Charles Andrews & Chunmiao Zheng

  2. Guangdong-Hong Kong Joint Laboratory for Soil and Groundwater Pollution Control, Southern University of Science and Technology, Shenzhen, China

    Yang Zhan, Zhilin Guo, Zhengzhong Zeng & Chunmiao Zheng

  3. Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore

    Yang Zhan & Vladan Babovic

  4. Department Water Resources and Drinking Water, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

    Joel Podgorski

  5. Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China

    Peng Xu

  6. World Resources Institute, Washington, DC, USA

    Liqing Peng

  7. China-Canada Joint Center for Global Groundwater Sustainability, Eastern Institute of Technology, Ningbo, China

    Chunmiao Zheng

Authors
  1. Yang Zhan
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  2. Zhilin Guo
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  9. Chen Ding
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  10. Charles Andrews
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  11. Vladan Babovic
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  12. Chunmiao Zheng
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Contributions

Y.Z. and Z.G. conceptualized and designed the study. Y.Z. and Z.G. performed the analyses and created illustrations. Y.Z. and Z.G. wrote the initial draft of the paper. J.P., Z.Z., P.X., L.P., K.C., R.W., C.D., C.A., V.B. and C.Z. reviewed and revised the paper. All authors contributed to the discussion and interpretation of the results.

Corresponding author

Correspondence to Zhilin Guo.

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The authors declare no competing interests.

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Nature Food thanks Deyi Hou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–13, discussion and Tables 1–11.

Reporting Summary

Source data

Source Data Fig. 1

Feature importance.

Source Data Fig. 2

Resource requirements and environmental impacts.

Source Data Fig. 3

Total changes in total nitrogen fertilizer application, the production of manure, and the water footprint (columns 2–8) and percentage decrease of groundwater nitrate exceedance risk (columns 9–14).

Source Data Fig. 4

Reduction in groundwater nitrate risk from 50% meat substitution, shaped by local agriculture and climate.

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Zhan, Y., Guo, Z., Podgorski, J. et al. Changes in meat consumption can improve groundwater quality. Nat Food 6, 703–714 (2025). https://doi.org/10.1038/s43016-025-01188-x

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