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Engineered nanomaterials reduce metal(loid) accumulation and enhance staple food production for sustainable agriculture

Nature Food volume 5, pages 951–962 (2024)Cite this article

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Abstract

Metal(loid) contaminants in food pose a global health concern. This study offers a global analysis of the impact of nanomaterials (NMs) on crop responses to metal(loid) stresses. Our findings reveal that NMs have a positive effect on the biomass production of staple crops (22.8%), while showing inhibitory effects on metal(loid) accumulation in plants (−38.3%) and oxidative damage (−21.6%) under metal(loid) stress conditions. These effects are influenced by various factors such as NM dose, exposure duration, size and composition. Here we introduce a method using interval-valued intuitionistic fuzzy values by integrating the technique for order preference by similarity to an ideal solution and entropy weights to compare the effectiveness of different NM application patterns. These results offer practical insights for the application of NMs in similar multi-criteria decision-making scenarios, contributing to sustainable agriculture and global food safety.

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Fig. 1: Map of sampling location and effects of NMs on metal(loid) accumulation and physiological responses in crops.
Fig. 2: Responses of plants and soil to NM applications under metal(loid) stress.
Fig. 3: Mechanisms of NM-mediated effects on metal(loid) uptake and translocation in soil–crop systems and plant defensive actions against metal(loid) stresses.
Fig. 4: Performance of the RF model.
Fig. 5: Effects of NM size, dose and exposure time on different metrics after NM applications under metal(loid) stresses.
Fig. 6: The framework of the IVIF–TOPSIS–EW method for calculating the comprehensive importance weights for selecting optimal NMs.

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Data availability

The data used herein are publicly available in the Analysis and Supplementary Information. Source data are provided with this paper. All other data that support the findings of this study are available from the corresponding authors upon reasonable request.

Code availability

All codes used for this study are available in Methods, Supplementary Information and Source Data.

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Acknowledgements

This work was supported by the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (grant number 2019ZT08L213 (C.M.)); National Natural Science Foundation of China (grant numbers 52261135625 (C.M.) and 42107423 (Yini Cao)); Natural Science Foundation for Excellent Young Scientists of Hunan Province, China (grant number 2023JJ20099 (Yini Cao)); Creative Research Groups of Provincial Natural Science Foundation of Hunan, China (grant number 2024JJ1016 (W.Y.)); United States Department of Agriculture, National Institute of Food and Agriculture (grant number USDA NIFA AFRI 2023-67017-40721 (J.C.W.)); and the USDA Hatch Program (grant number MAS00616 (B.X.)).

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Authors and Affiliations

  1. National Engineering Laboratory for Applied Technology of Forestry & Ecology in South China, College of Life and Environmental Science, Central South University of Forestry and Technology, Changsha, China

    Yini Cao, Yuchi Cao & Wende Yan

  2. Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, China

    Chuanxin Ma & Yi Hao

  3. Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou, China

    Chuanxin Ma & Yi Hao

  4. The Connecticut Agricultural Experiment Station, New Haven, CT, USA

    Jason C. White

  5. School of Soil and Water Conservation, Beijing Forestry University, Beijing, China

    Fan Zhang

  6. Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, China

    Ran Tong

  7. Institute of Plant Sciences, University of Bern, Bern, Switzerland

    Hao Yu

  8. School of Environment, University of Auckland, Auckland, New Zealand

    Melanie Kah

  9. Stockbridge School of Agriculture, University of Massachusetts, Amherst, MA, USA

    Baoshan Xing

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Contributions

Yini Cao, C.M. and B.X. conceptualized the idea. Yini Cao, H.Y. and R.T. collected and analysed the data on meta-analysis. Yuchi Cao and F.Z. analysed the data on machine learning models and the IVIF–TOPSIS–EW method. Yini Cao, C.M., J.C.W., Y.H., M.K., W.Y. and B.X. reviewed and analysed the results and contributed to the writing and editing of the paper.

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Correspondence to Chuanxin Ma, Wende Yan or Baoshan Xing.

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Nature Food thanks Honghong Wu, Pei Zhou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–13, Methods 1–3, Results and Discussion; references for meta-analysis; machine learning Python code and R script; and PRISMA checklist.

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Cao, Y., Ma, C., White, J.C. et al. Engineered nanomaterials reduce metal(loid) accumulation and enhance staple food production for sustainable agriculture. Nat Food 5, 951–962 (2024). https://doi.org/10.1038/s43016-024-01063-1

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