Location: Range Management Research
Project Number: 3050-11210-009-067-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jul 15, 2019
End Date: Dec 31, 2022
1) Develop new information on the characteristics, economics, and environmental effects of Raramuri Criollo and cross-breeds that enable ranchers to evaluate the merits of adopting Criollo-based livestock production systems. 2) Expand data support for and the functionality of the Ecosystem Dynamics Interpretative Tool to link ecological site descriptions to conservation planning. 3) Develop new restoration approaches to accelerate perennial grass production in shrub-dominated areas and target restoration investments for optimal outcomes. 4) Develop approaches for managing and analyzing large environmental monitoring datasets and linking them to process models, especially wind erosion and production models.
Obj. 1 is shared with numerous livestock producers and involves experimental tests of the characteristics of recently-introduced Raramuri Criollo compared to traditional Angus cattle to model the potential benefits of the Criollo biotype and identify management strategies to improve ranch profitability and environmental conditions, and will take advantage of expertise in the Animal and Range Science Department. Obj. 2 is shared by the Natural Resources Conservation Service to develop science-based support for conservation planning and link resource data in Ecological Site Descriptions to conservation planning tools. This effort will take advantage of computational expertise in the Plant and Environmental Science Department. Obj. 3 is shared by several U.S. agencies interested in evaluating large landscape scale restoration treatments to optimize restoration investments and maximize outcomes in terms of rangeland health and forage benefits. This effort will take advantage of expertise in several academic departments. Obj. 4 is shared with the Bureau of Land Management and Natural Resources Conservation Service who wish to find ways to link monitoring datasets to resource decisions based on multiple ecosystem services. This effort will take advantage of modeling and database expertise in the Plant and Environmental Science Department.