ABSTRACT: Landscape patterns across the vast centre of North America are intensively modified for agriculture. Being able to measure changing agricultural landscape patterns is important to understanding the consequences of this dominant land use. Land cover changes in agriculture can be rapid and are often predictable when associated with agronomics of crop rotations. Rotation diversity can be higher in moderately-complex landscapes and those with integrated crop-livestock enterprises. Measurement and estimation of changes in agricultural landscape patterns is challenging, then, because observations might be skewed by rotation sequencing or because of limited observations. Farming practices like double-cropping or cover crops increase management intensity and change landscape patterns within a single growing season, making landscape pattern classification and measurement more difficult. Temporal autocorrelation from crop rotations can occur in time series analysis but is not always considered in agricultural landscape pattern measurement. This presentation explains how short-term dynamism in agriculture can lead to skewed landscape pattern measurement for an intermediately-complex Great Lakes farm landscape. Focusing on typical crop rotation sequences and common agronomic practices, the presentation describes how fundamental landscape pattern attribute measures can vary in short terms. Typical and normative crop rotations are used to project field and farm conditions at different times (using LandSFACTS) with landscape pattern metrics measuring the outcomes (FRAGSTATS). Findings can be used to inform the application and interpretation of landscape pattern measures at short time steps in highly-dynamic landscapes.
Monday April 9, 2018 10:45am - 11:00am CDT
Spire Parlor
