Agro-Landscapes and Communities in Transition

Defining Agroecological Classes

• PIs: David Huggins and Alycia Bean; co-PIs: Rick Rupp, Harsimran Kaur, J.D. Wulfhorst, and Bryan Carlson
• Land use is a dynamic, emergent property of multiple socioeconomic and biophysical determinants and for agroecosystems. Land use classifications have increasing applications for structuring and implementing agroecosystem management strategies. The agroecological classification (AEC) approach originates from land use classifications that incorporate biophysical and socioeconomic data. This research will spatially classify current AECs in the Northwest and therefore the capacity to detect spatial and systemic changes over time. Additionally, this project will help identify hypotheses to analyze relationships among biophysical (e.g. climate, soils, terrain) and socioeconomic (e.g. land prices, commodities grown) factors that are useful for understanding the distribution and predicting changes in AEC.

Perceptions of land cover change & community change in the Great Basin

• PI: Amanda Bentley Brymer; co-PIs: J.D. Wulfhorst, Fred Pierson, Pat Clark
• This study employs a qualitative approach to understand how northern Great Basin stakeholders perceive and adapt to change across their landscape and communities. Using semi-structured interviews and participatory mapping during ranch visits, we prompt participants to share their experiences and perception of the spatial extent of invading cheatgrass (Bromus tectorum) 20 years ago, currently, and what they expect in the year 2050. Participants also share their experiences and perceptions of community change over the same time frames, (e.g., de-population, consolidating land ownership). The sampling frame includes multi-generational ranching families (in ID, NV, OR), allowing for cross-state and cross-generational comparisons. Findings from this research will illuminate critical differences and commonalities among stakeholders within an LTAR site (Great Basin) as they adjust their grazing strategies to cope with land cover and community changes. Additionally, this research lays the groundwork for broadening the geographic scope of stakeholder engagement for the LTAR network.

Landscapes in Transitions: Food System Modeling

• PI: Claire Friedrichsen; co-PIs: Zachary Hurst, J.D. Wulfhorst; Collaborators: Washington State University, University of Idaho, Oregon State University
• This project examines how food system stakeholders perceive the food system, the challenges currently faced by the food system, and leverage points within forecasting to make positive transformational change in the food system. Action research with stakeholders through workshops and inductive research design will provide a holistic understanding of vulnerability and leverage points within the local food system in the face of climate change. This research advances our collaborators’ capacity to operationalize and analyze vulnerabilities and leverage points within the food system. An additional outcome will be innovation of a methodology to increase the human dimension capacity of researchers and producers to think about the externalities of ASP treatments.

Stakeholders’ Perception of Land-Use Change Trade-offs in Rangelands

• PI: Gwendŵr Meredith; co-PIs: Zach Hurst, J.D. Wulfhorst
• This research project will critically examine stakeholders’ perceptions of land use change in rangelands at 3 different LTAR sites (Great Basin, Northern Plains, and Central Florida) to represent a diversity of grazing lands. In the first stage of this research, we will conduct focus groups with a broad array of stakeholders in order to capture a diversity of ecosystem valuations and establish a baseline understanding of perceptions of ecosystem service tradeoffs resulting from both local (rangeland management choices) and global drivers (climate variability, patterns of urbanization). In the second stage, stakeholders will be interviewed about: a) most valued ecosystem services, b) scenario(s) to build collaboration between different stakeholders, and (c) possible solutions to achieve these targets. Q-sort methodology will elicit stakeholder preferences and identify potential commonalities across groups, which will assist in partnership development for future collaborations.