Skip to main content
ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #390647
Research Project: Sustainable Intensification of Integrated Crop-Pasture-Livestock Systems in Northeastern Landscapes

Location: Pasture Systems & Watershed Management Research

Title: Agroecoregions resulting from novel clustering methods: biophysical variables

Author
item Goslee, Sarah
item Baffaut, Claire
item Clark, Pat
item Coffin, Alisa
item Pisarello, Kathryn
item PONCE-CAMPOS, GUILLERMO - UNIVERSITY OF ARIZONA
item SCLATER, VIVIENNE - ARCHBOLD BIOLOGICAL STATION
item SWAIN, HILARY - ARCHBOLD BIOLOGICAL STATION

Submitted to: US-International Association for Landscape Ecology
Publication Type: Abstract Only
Publication Acceptance Date: 2/21/2022
Publication Date: 4/13/2022
Citation: Goslee, S.C., Baffaut, C., Clark, P., Coffin, A.W., Pisarello, K., Ponce-Campos, G., Sclater, V., Swain, H. 2022. Agroecoregions resulting from novel clustering methods: biophysical variables [abstract]. US-International Association for Landscape Ecology. P.1.

Interpretive Summary:

Technical Abstract: The USDA’s Long-Term Agroecosystem Research (LTAR) Network currently comprises eighteen research sites across the contiguous United States (CONUS). Given the variability in climate and soils across this extent, how well does the current set of sites represent the biophysical domain, and over what area could site-specific research results feasibly be extrapolated. Conversely, what regions are not currently represented? If LTAR is to benefit all US agriculture, thorough representation of regional variation is crucial. An agroecological regionalization using biophysical variables was developed as part of the LTAR Regionalization Project, an organized effort to provide consistent and meaningful agriculturally-relevant spatial clusterings for the CONUS across multiple conceptual domains. The team selected climate, soil, and topographic variables related to temperature, light, and water availability. Because of the computational challenges and the inherently different spatial and temporal scales of these datasets, the regionalization effort proceeded in two stages: a coarse-scale climate-only clustering step, followed by a second clustering on finer-scale soils and topographic variables within the defined climate regions. This approach not only reduces the size of the analysis dataset at each step, it also facilitates the identification of regional variability in biophysical drivers. The semi-hierarchical agroecoregionalization delineates biophysical zones relevant for US agriculture, and will be updated regularly in response to changing climate.