Data-Driven Decision Support to Guide Sustainable Grazing Management
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/14/1/140 |
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| author | Matthew C. Reeves Joseph Swisher Michael Krebs Kelly Warnke Brice B. Hanberry Tip Hudson Sonia A. Hall |
| author_facet | Matthew C. Reeves Joseph Swisher Michael Krebs Kelly Warnke Brice B. Hanberry Tip Hudson Sonia A. Hall |
| author_sort | Matthew C. Reeves |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d36d4e6703a34858b0046bbc1b940aa3 |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-d36d4e6703a34858b0046bbc1b940aa32025-01-24T13:38:04ZengMDPI AGLand2073-445X2025-01-0114114010.3390/land14010140Data-Driven Decision Support to Guide Sustainable Grazing ManagementMatthew C. Reeves0Joseph Swisher1Michael Krebs2Kelly Warnke3Brice B. Hanberry4Tip Hudson5Sonia A. Hall6USDA Forest Service, Rocky Mountain Research Station, Missoula, MT 59801, USAUSDA Forest Service, Inyo National Forest, Mammoth Lakes, CA 93546, USAUSDA Forest Service, Rocky Mountain Research Station, Missoula, MT 59801, USAUSDA Forest Service, Enterprise Program, Rapid City, SD 57702, USAUSDA Forest Service, Rocky Mountain Research Station, Rapid City, SD 57702, USARangeland & Livestock Management Extension, Washington State University, Ellensburg, WA 98926, USACenter for Sustaining Agriculture & Natural Resources, Washington State University, Wenatchee, WA 98801, USAData-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.https://www.mdpi.com/2073-445X/14/1/140capacityforagelivestockmanagementmodelingstocking rate |
| spellingShingle | Matthew C. Reeves Joseph Swisher Michael Krebs Kelly Warnke Brice B. Hanberry Tip Hudson Sonia A. Hall Data-Driven Decision Support to Guide Sustainable Grazing Management Land capacity forage livestock management modeling stocking rate |
| title | Data-Driven Decision Support to Guide Sustainable Grazing Management |
| title_full | Data-Driven Decision Support to Guide Sustainable Grazing Management |
| title_fullStr | Data-Driven Decision Support to Guide Sustainable Grazing Management |
| title_full_unstemmed | Data-Driven Decision Support to Guide Sustainable Grazing Management |
| title_short | Data-Driven Decision Support to Guide Sustainable Grazing Management |
| title_sort | data driven decision support to guide sustainable grazing management |
| topic | capacity forage livestock management modeling stocking rate |
| url | https://www.mdpi.com/2073-445X/14/1/140 |
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