Data-Driven Decision Support to Guide Sustainable Grazing Management

Matthew C. Reeves (), Joseph Swisher, Michael Krebs, Kelly Warnke, Brice B. Hanberry, Tip Hudson and Sonia A. Hall
Additional contact information
Matthew C. Reeves: USDA Forest Service, Rocky Mountain Research Station, Missoula, MT 59801, USA
Joseph Swisher: USDA Forest Service, Inyo National Forest, Mammoth Lakes, CA 93546, USA
Michael Krebs: USDA Forest Service, Rocky Mountain Research Station, Missoula, MT 59801, USA
Kelly Warnke: USDA Forest Service, Enterprise Program, Rapid City, SD 57702, USA
Brice B. Hanberry: USDA Forest Service, Rocky Mountain Research Station, Rapid City, SD 57702, USA
Tip Hudson: Rangeland & Livestock Management Extension, Washington State University, Ellensburg, WA 98926, USA
Sonia A. Hall: Center for Sustaining Agriculture & Natural Resources, Washington State University, Wenatchee, WA 98801, USA

Land, 2025, vol. 14, issue 1, 1-21

Abstract: Data-driven decision support can help guide sustainable grazing management by providing an accurate estimate of grazing capacity, in coproduction with managers. Here, we described the development of a decision support model to estimate grazing capacity and illustrated its application on two sites in the western United States. For the Montgomery Pass Wild Horse Territory in California and Nevada, the upper limit estimated in the capacity assessment was 398 horses and the current population was 654 horses. For the Eagle Creek watershed of the Apache–Sitgreaves National Forest of eastern Arizona, the lower end of capacity was estimated at 1560 cattle annually, compared to the current average of 1090 cattle annually. In addition to being spatio-temporally comprehensive, the model provides a repeatable, cost-effective, and transparent process for establishing and adjusting capacity estimates and associated grazing plans that are supported by scientific information, in order to support livestock numbers at levels that are sustainable over time, including levels that are below average forage production during drought conditions. This modeling process acts as a decision support tool because it enables different assumptions to be used and explored to accommodate multiple viewpoints during the planning process.

Keywords: capacity; forage; livestock; management; modeling; stocking rate (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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