From Cells to Sustainability: Bridging Scales in Livestock Production Research

As animal production researchers, we are often struck by the different scales at which livestock production challenges are discussed and investigated. At the farm level, we focus on animal health, productivity, feed efficiency, and environmental performance. At the global level, discussions revolve around food security, climate change, and sustainability. Yet many of the biological processes underlying these outcomes occur at scales that are rarely visible to animal scientists—within microbial communities, individual cells, and molecular pathways.

This disconnect between the scales at which livestock production challenges are measured and the scales at which they originate has become increasingly apparent in recent years. Advances in microbiology, genomics, and cell biology are revealing mechanisms that were previously invisible, creating new opportunities to connect fundamental biological discoveries with practical challenges in animal production and sustainability. This challenge is increasingly recognized within animal production science, where systems-based approaches have been proposed to integrate biological, environmental, economic, and societal dimensions of livestock production¹.

Historically, research has often progressed within disciplinary boundaries. Microbiologists investigate cellular mechanisms, animal scientists focus on production systems, ecologists examine ecosystem processes, and climate scientists develop models of environmental change. While these approaches have generated substantial advances, many contemporary challenges require stronger integration across disciplines and scales.

Many of the environmental impacts associated with livestock production emerge from biological processes occurring far below what we can observe with the naked eye. Microbial communities in livestock digestive systems contribute to feed utilization, animal health, and methane production^{2, 3}. These processes occur at microscopic scales, yet collectively shape outcomes with global significance.

The importance of this integration is becoming increasingly apparent as new technologies reveal previously hidden aspects of biological systems. Advances in metagenomics have transformed our understanding of microbial diversity⁴, while high-resolution imaging techniques in cell biology have further revealed complex subcellular organization that was previously inaccessible to observation⁵. Single-cell approaches are now extending this resolution further, revealing functional heterogeneity within microbial populations^{6, 7}. Together, these tools are reshaping our understanding of how biological systems function across scales.

One recent discovery that caught our attention was the identification of hydrogen-producing organelles in rumen ciliates⁸. As animal production researchers with primary expertise in animal science rather than cellular biology, we found the discovery noteworthy not only because of the organelle itself, but because of what it represents. It serves as a reminder that important biological processes affecting livestock systems may still be hidden within cellular structures and microbial interactions that remain poorly understood. Whether such discoveries ultimately reshape our understanding of rumen ecology, animal performance, or environmental sustainability, they underscore the value of connecting research across biological scales.

At the same time, computational advances are enabling researchers to connect information across scales. Machine learning, systems biology, and ecological modelling now allow integration of molecular, cellular, organismal, and environmental data. Such approaches are increasingly used to link microbiome composition with ecosystem function and host physiology^9-11.

This perspective is particularly relevant in the context of climate change. Agricultural greenhouse gas emissions are commonly quantified at farm, regional, or national levels. However, the mechanisms underlying these emissions originate in microbial metabolisms, biochemical pathways, and ecological interactions. Improved mechanistic understanding is essential for developing predictive models and mitigation strategies that are grounded in biology rather than statistics alone.

The need for cross-scale thinking extends beyond greenhouse gases. Antimicrobial resistance, animal health, soil degradation, feed efficiency, and nutrient management all involve interactions spanning multiple levels of biological organization. Addressing these issues requires collaboration among researchers working from the molecular to the ecosystem scale.

The One Health framework provides a useful lens through which to view these connections. Human health, animal health, and environmental health are deeply interconnected, and understanding these relationships often requires linking processes occurring at very different scales. Microbial processes within animal and environmental systems can ultimately influence food production, environmental sustainability, and public health outcomes¹².

Looking ahead, some of the most transformative advances in livestock production research may emerge from efforts to bridge traditional disciplinary and spatial boundaries. Rather than viewing cellular biology, microbiology, ecology, animal science, and climate science as separate domains, researchers increasingly have opportunities to integrate these perspectives into a more unified understanding of livestock systems.

From an animal production perspective, one of the most exciting opportunities for future research lies in strengthening these connections. How can discoveries made at the cellular and microbial levels be translated into improvements in livestock management, sustainability, and food production? Conversely, how can challenges observed at the farm level guide fundamental biological research?

We would be interested to hear how researchers from different disciplines approach this question. Where do you see the greatest opportunities for bridging the gap between microscopic mechanisms and real-world livestock production outcomes?

References

1. Martin, G.B. (2024). Perspective: science and the future of livestock industries. Frontiers in Veterinary Science, 11:1359247. https://doi.org/10.3389/fvets.2024.1359247
2. Malmuthuge, N., & Guan, L. L. (2017). Understanding host-microbial interactions in rumen: searching the best opportunity for microbiota manipulation. Journal of animal science and biotechnology, 8(1), 8. https://link.springer.com/article/10.1186/s40104-016-0135-3
3. Janssen, P. H. (2010). Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Animal feed science and technology, 160(1-2), 1-22. 1016/j.anifeedsci.2010.07.002
4. Wooley, J. C., Godzik, A., & Friedberg, I. (2010). A primer on metagenomics. PLoS computational biology, 6(2), e1000667. https://doi.org/10.1371/journal.pcbi.1000667
5. Schermelleh, L., Heintzmann, R., & Leonhardt, H. (2010). A guide to super-resolution fluorescence microscopy. The Journal of cell biology, 190(2), 165. https://doi.org/10.1083/jcb.201002018
6. Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023). Methods and applications for single-cell and spatial multi-omics. Nature Reviews Genetics, 24(8), 494-515. https://www.nature.com/articles/s41576-023-00580-2
7. Svensson, V., Vento-Tormo, R., & Teichmann, S.A. (2018). Exponential scaling of single-cell RNA-seq in the past decade. Nature Protocols, 13, 599–604. https://doi.org/10.1038/nprot.2017.149
8. Xie, Y. et al.  (2026).Rumen ciliates modulate methane emissions in ruminants. Science, 392(6797), eadv4244.10.1126/science.adv424
9. Knights, D., Ward, T. L., McKinlay, C. E., Miller, H., Gonzalez, A., McDonald, D., & Knight, R. (2014). Rethinking “enterotypes”. Cell host & microbe, 16(4), 433-437. https://www.cell.com/cell-host-microbe/fulltext/S1931-3128(14)00346-1
10. Faust, K. & Raes, J. Microbial interactions: from networks to models. Nature Reviews Microbiology 10, 538–550 (2012). https://www.nature.com/articles/nrmicro2832
11. Chetty, A., & Blekhman, R. (2024). Multi-omic approaches for host-microbiome data integration. Gut microbes, 16(1), 2297860. https://doi.org/10.1080/19490976.2023.2297860
12. Destoumieux-Garzón, D., Mavingui, P., Boetsch, G., Boissier, J., Darriet, F., Duboz, P., ... & Voituron, Y. (2018). The one health concept: 10 years old and a long road ahead. Front Vet Sci 5: 14. https://doi.org/10.3389/fvets.2018.00014

Declaration: The authors used Wordtune to assist with language editing. All scientific content, interpretations, and conclusions are the authors’ own. The poster image was generated using an AI-based image tool and was reviewed and edited by the authors for accuracy and presentation.