Agroecoregions resulting from novel clustering methods: Production variables

Authors: Holifield Collins, Chandra; Baffaut, Claire; BEAN, A. - Idaho Conservation League; Clark, Pat; Coffin, Alisa; Goslee, Sarah; Hendrickson, John; PONCE-CAMPOS, G. - University Of Arizona; SCLATER, V. - Archbold Biological Station; Strickland, Timothy - Tim

Submitted to: US-International Association for Landscape Ecology
Publication Type: Abstract Only
Publication Acceptance Date: 2/21/2022
Publication Date: 4/13/2022
Citation: Holifield Collins, C.D., Baffaut, C., Bean, A., Clark, P., Coffin, A.W., Goslee, S.C., Hendrickson, J.R., Ponce-Campos, G., Sclater, V., Strickland, T.C. 2022. Agroecoregions resulting from novel clustering methods: Production variables. US-International Association for Landscape Ecology.

Interpretive Summary: The USDA’s Long-Term Agroecosystem Research (LTAR) Network consists of eighteen sites across the contiguous United States (CONUS) that are oriented to researching and enhancing sustainable production in cropping and grazing systems. If LTAR is to benefit all US agriculture, agricultural production across the nation must be accurately represented. This includes the identification of the most important agricultural production variables and their associated regions. Crop and livestock production systems are often constrained by environmental resources, geography, adaptive capacity, and management outcomes. Conceptually, coupled human-natural system models rely upon interacting domains of biophysical and socioeconomic factors to characterize production. To assess patterns of agricultural production, we utilized national agricultural statistics of production, which are collected regularly by the USDA-National Agricultural Statistics Service (NASS) for the entire United States. A group of agricultural production experts conducted a multi-phase assessment to determine a list of key variables describing crop and livestock production systems from the NASS datasets. Following data selection, exploratory clustering approaches were assessed, considering both the optimal number of clusters and the conceptual and practical appropriateness of a suite of methods. Through an iterative process, the team identified the optimal regionalizations for crop and livestock production to both capture the diversity of production zones and encompass broad patterns of production. The differences between the crop and livestock regions, and anomalies within each regionalization, offer insights into the spatial variation in agricultural systems across the United States, and provide a spatial framework for long-term sustainability analysis.

Technical Abstract: The USDA’s Long-Term Agroecosystem Research (LTAR) Network consists of eighteen sites across the contiguous United States (CONUS) that are oriented to researching and enhancing sustainable production in cropping and grazing systems. If LTAR is to benefit all US agriculture, agricultural production across the nation must be accurately represented. This includes the identification of the most important agricultural production variables and their associated regions. Crop and livestock production systems are often constrained by environmental resources, geography, adaptive capacity, and management outcomes. Conceptually, coupled human-natural system models rely upon interacting domains of biophysical and socioeconomic factors to characterize production. To assess patterns of agricultural production, we utilized national agricultural statistics of production, which are collected regularly by the USDA-National Agricultural Statistics Service (NASS) for the entire United States. A group of agricultural production experts conducted a multi-phase assessment to determine a list of key variables describing crop and livestock production systems from the NASS datasets. Following data selection, exploratory clustering approaches were assessed, considering both the optimal number of clusters and the conceptual and practical appropriateness of a suite of methods. Through an iterative process, the team identified the optimal regionalizations for crop and livestock production to both capture the diversity of production zones and encompass broad patterns of production. The differences between the crop and livestock regions, and anomalies within each regionalization, offer insights into the spatial variation in agricultural systems across the United States, and provide a spatial framework for long-term sustainability analysis.