Hi All,
Please feel free to circulate the following announcement.
Funded PhD Research Assistantship at Kansas State University in Human-Environment Geography
The Department of Geography and Geospatial Sciences at Kansas State University invites applications for a 3-year funded Doctoral Research Assistantship to support an interdisciplinary study on how wildfire risk interacts with conservation incentives in rural landscapes as part of the NSF-funded project, "Agri-environmental Conservation Incentives in the Extreme Wildfire Context of the U.S. Southern Plains." The PhD researcher will work primarily with Audrey Joslin and Marcellus Caldas (faculty in Geography), but will also engage with collaborators Jason Bergtold (Agricultural Economics) and Ignacio Ciampitti (Agronomy), all at Kansas State University.
In the U.S., many conservation programs provide financial incentives to private landowners of farms and ranches to reduce soil erosion, protect wildlife habitat, and improve watersheds. Droughts and hotter temperatures over recent years, however, have increased threats of wildfire in the grasslands of the southern Plains, and landowners may perceive conservation efforts as furthermore contributing to wildfire events. This project examines the effects of wildfire in rural landscapes and how perceived wildfire risks shape agricultural land management and landowner decisions to engage in conservation programs and practices. Studying the interactions between wildfires, rural conservation efforts, and responses of farmers and landowners provides a critical foundation for understanding how environmental changes and extreme events may influence conservation activities. Knowledge about the relationships between wildfire and conservation land management practices will help both policy-makers and landowners make informed decisions about conservation programs.
Key responsibilities & opportunities:
Required qualifications:
Desired qualifications:
Starting date: The starting date for the assistantship is August 15, 2022.
To apply: