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Background <p>Despite decades of study, current research on grazing management’s impacts on ecosystem health and its socioeconomic drivers remains too limited in scope and scale to enable adaptive, evidence-based decision making by producers. There is a pressing need for interdisciplinary research that collects ecosystem data at broader spatial and temporal scales while incorporating working farms and ranches. Such efforts are critical for informing grazing decisions and understanding grazinglands’ potential to deliver ecosystem services, including climate mitigation, water cycling, resilience, and rural livelihoods.</p> Results <p>The Metrics, Management, and Monitoring (3M) project addresses this need through a novel social-ecological framework that integrates biophysical, socioeconomic, and management data across U.S. grazinglands. The project combines controlled experiments at four intensively monitored “hubs” with data from 59 producer-managed farms and ranches. Its core objectives are to: (1) assess the social-ecological health of grazinglands across diverse ecoregions, (2) refine monitoring approaches to improve scalability and accuracy, and (3) integrate producer-led data to balance experimental rigor with real-world relevance. Over 50 scientists collaborate on 3M to evaluate how grazing strategies affect soil carbon, water dynamics, CO₂ fluxes, plant communities, productivity, social wellbeing, and producer economics.</p> Conclusions <p>These insights support the development of ecosystem models and decision-support tools to help producers make evidence-based choices. Beyond data generation, 3M offers a scalable research model that bridges ecological and social sciences to support adaptive, informed grazing management. This integrated framework provides a transferable template for studying any working landscape where human and ecological systems are deeply interconnected.</p>

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Merging rigor and relevance in grazingland research: a comprehensive social-ecological monitoring approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月3日

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  • Paige L. Stanley,
  • Alexandria S. Kuhl,
  • Isabella C.F. Maciel,
  • M. Francesca Cotrufo,
  • Douglas J. Goodwin,
  • Jennifer Hodbod,
  • Martha C. Anderson,
  • John D. Scasta,
  • Erika S. Peirce,
  • Micaela Branecky,
  • Sarah Brokus,
  • Robert J. Clement,
  • Nathan D. DeLay,
  • Justin D. Derner,
  • Morgan MathisonSlee,
  • Rebecca Mitchell,
  • Erica L. Patterson,
  • Yao Zhang,
  • Jeremiah Asher,
  • Feng Gao,
  • Dale T. Manning,
  • Glenn O’Neil,
  • Sangmi L. Pallickara,
  • Keith Paustian,
  • Matt R. Raven,
  • Joao P. Sacramento,
  • Yining Wu,
  • Jenna M. Likins,
  • Dabit Bista,
  • Florencia Colella,
  • Guilhermo F.S. Congio,
  • Zekuan Dong,
  • Ethan Gordon,
  • Hannah Gosnell,
  • Andrey K. Guber,
  • Sean P. Kearney,
  • Frank Lupi,
  • Abdul Matin,
  • Nicole M. Nimlos,
  • Jonathan Vivas,
  • Timm M. Gergeni,
  • Ada P. Smith,
  • Jason E. Rowntree

摘要

Background

Despite decades of study, current research on grazing management’s impacts on ecosystem health and its socioeconomic drivers remains too limited in scope and scale to enable adaptive, evidence-based decision making by producers. There is a pressing need for interdisciplinary research that collects ecosystem data at broader spatial and temporal scales while incorporating working farms and ranches. Such efforts are critical for informing grazing decisions and understanding grazinglands’ potential to deliver ecosystem services, including climate mitigation, water cycling, resilience, and rural livelihoods.

Results

The Metrics, Management, and Monitoring (3M) project addresses this need through a novel social-ecological framework that integrates biophysical, socioeconomic, and management data across U.S. grazinglands. The project combines controlled experiments at four intensively monitored “hubs” with data from 59 producer-managed farms and ranches. Its core objectives are to: (1) assess the social-ecological health of grazinglands across diverse ecoregions, (2) refine monitoring approaches to improve scalability and accuracy, and (3) integrate producer-led data to balance experimental rigor with real-world relevance. Over 50 scientists collaborate on 3M to evaluate how grazing strategies affect soil carbon, water dynamics, CO₂ fluxes, plant communities, productivity, social wellbeing, and producer economics.

Conclusions

These insights support the development of ecosystem models and decision-support tools to help producers make evidence-based choices. Beyond data generation, 3M offers a scalable research model that bridges ecological and social sciences to support adaptive, informed grazing management. This integrated framework provides a transferable template for studying any working landscape where human and ecological systems are deeply interconnected.