Authors: Katherine Nelson*, Kansas State University, Emily Burchfield, Utah State University, Kaitlyn Spangler, Utah State University
Topics: Agricultural Geography, Land Use, Spatial Analysis & Modeling
Keywords: landscape diversity, agricultural production, Bayesian, spatiotemporal
Session Type: Paper
Start / End Time: 9:55 AM / 11:35 AM
Room: Buchanan, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Over the last century human activities, particularly the expansion and intensification of agriculture, have dramatically simplified global landscapes. While there is a growing body of evidence supporting the positive role of increasing diversity on ecosystem services and agricultural function at the field scale, the impact of diversity on agricultural function at larger scales has received little attention. This study examines the role of landscape diversity on yields of corn, soy, and winter wheat across the conterminous U.S. using a panel dataset of county-level climate, yield, and land-use data. Bayesian spatiotemporal modelling was employed to estimate the functional relationship between landscape diversity and annual county yields while simultaneously accounting for non-linear climate effects and spatial effects. Models were evaluated using three diversity metrics (Shannon Diversity Index, Simpson Diversity Index, and Richness) and across two landscape extents (entire county landscape and county agricultural landscape only). Model results suggest that highly diverse landscapes are consistently associated with increased agricultural productivity. This functional relationship between diversity and yield is highly variable across crop type, diversity metric, and landscape extent. These results generally agree with a large body of experimental evidence on diversity and ecosystem function, and furthermore illustrate the difficulties associated with interpretation of results of studies on the effects of diversity on agricultural system function.