PS2-13. Developing the RANGE Model to Predict Enteric Methane Emissions in Michigan Beef Grazing Systems.

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ID: 330638
2026
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Abstract
Abstract Grazing cow–calf systems are central to beef supply chains, yet ranch-level greenhouse gas (GHG) reporting still relies largely on generalized life cycle assessment (LCA) emission factors that poorly capture how grazing management influences animal performance, land use, and emissions intensity (Stanley et al., 2018). Our objective was to develop the first version of the Ranch Assessed Net Greenhouse Gas Emissions (RANGE) model for Michigan grazing cow–calf systems and test whether it better captures management-driven variation in enteric methane (CH4) than commonly used IPCC-based approaches. RANGE is being developed using >15 years of whole-farm data from the Michigan State University (MSU) Lake City Research Center (Michigan), which manages a commercial-scale herd of approximately 130 cow–calf pairs under adaptive multi-paddock grazing rather than fixed, prescriptive research protocols. The first version of RANGE focuses on enteric CH4, the largest GHG source in beef systems and the component most directly influenced by grazing management. The model structure integrates cow characteristics (e.g., body weight, age, body condition, physiological requirements), calf output (weaning performance), and pasture conditions (forage availability and quality) to estimate ranch-level enteric CH4 in a form directly interpretable for management decisions. To improve both scalability and input accessibility, RANGE will also use remotely sensed data to characterize pasture conditions, reducing reliance on inputs difficult to measure while retaining sensitivity to management. This design positions RANGE between empirical and mechanistic models, providing a practical yet biologically grounded approach for industry application. Model performance will be evaluated by comparing RANGE estimates with IPCC Tier 2 approaches (2006 and 2019 guidelines), focusing on whether each method captures observed variation in cow size, forage conditions, and animal productivity. Previous work from the MSU Lake City herd suggests that IPCC Tier 2 (2019) approach can differ from measured enteric CH4 by roughly 17-24% under grazing conditions (Garcia et al., in press), highlighting the limitations of generalized emissions factors and the need for models that better represent ranch-scale emissions. RANGE is designed to address this gap by explicitly linking emissions to cow biological efficiency, forage quality and land use, allowing users to identify how changes in cow size and grazing management influence CH4 production and emissions intensity. By pairing CH4 estimates with productivity metrics, RANGE enables evaluation of tradeoffs between emissions and performance at the ranch scale. Together, these features position RANGE as a data-grounded, management-sensitive framework for credible ranch-scale CH4 reporting and decision support in grazing beef systems, with a clear pathway for future integration of additional GHG sources and soil carbon as additional data and model components become available.
Reference Key
openalex_W7214786494 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lautaro Garcia, João Sacramento, Tonya Price, Jason E. Rowntree
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Journal italian journal of animal science
Year 2026
DOI
10.1093/jas/skag272.556
URL
https://doi.org/10.1093/jas/skag272.556
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