Case Study of an Integrated Framework for Quantifying Agroecosystem Health |
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| Authors: | Krishna Prasad Vadrevu John Cardina Fred Hitzhusen Isaac Bayoh Richard Moore Jason Parker Ben Stinner Deb Stinner Casey Hoy |
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| Institution: | (1) Department of Entomology, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, Ohio 44691, USA;(2) Department of Horticulture and Crop Sciences, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, Ohio 44691, USA;(3) Department of Agricultural, Environmental, and Development Economics, Ohio Agricultural Research and Development Center, The Ohio State University, Columbus, Ohio 43210, USA;(4) Department of Human and Community Resource Development, Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, Ohio 44691, USA;(5) Department of Anthropology, The Ohio State University, Columbus, Ohio 43210, USA;(6) Agroecosystems Management Program, Ohio Agricultural Research and Development Center, The Ohio State University, 1680 Madison Avenue, Wooster, Ohio 44691-4096, USA |
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| Abstract: | Agroecosystem health derives from a combination of biophysical and socioeconomic conditions that jointly influence such properties
as productivity, sustainability, stability, and equitability. In this case study, we describe and analyze a method to quantify
agroecosystem health through a combination of geographically referenced data at various spatial scales. Six key variables
were hypothesized to provide a minimum set of conditions required to quantify agroecosystem health: soil health, biodiversity,
topography, farm economics, land economics, and social organization. Each of these key variables was quantified by one or
more attributes of a study area near Wooster, Ohio. Data sources included remote sensing, digital elevation models, soil maps,
county auditor records, and a structured questionnaire of landowners in the study area. These data were combined by an analytical
hierarchy process to yield an agroecosystem health index. The two steps in the process were first to combine the data at the
pixel scale (30 m2) into key variables with normalized values, and then to combine the key variables into the final index. The analytical hierarchy
process model was developed by panels of experts for each key variable and by participants in the Ohio Agricultural Research
and Development Center’s Agroecosystems Management Program for the final agroecosystem health index value. Observed spatial
patterns of the agroecosystem health index were then analyzed with respect to the underlying data. Consistent with our hypothesis
and the definition of agroecosystems, spatial patterns in the agroecosystem health index were an emergent property of combined
socioeconomic and biophysical conditions not apparent in any of the underlying data or key variables. The method proposed
in this study permits estimation of agroecosystem health as a function of specific underlying conditions, which combine in
complex ways. Because values of the agroecosystem health index and the data underlying them can be analyzed for a particular
landscape, the method proposed could be useful to policy makers, educators, service agencies, organizations, and the people
who live in the area for finding opportunities to improve the health of their agroecosystem.
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.
†Dr. Ben Stinner is deceased |
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| Keywords: | ecosystem health index analytical hierarchy process agroecology sustainability remote sensing geographic information systems |
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