Maine Potato Farms: Remotely-Sensed Cropping System Dynamics and Applied Econometrics.

Geospatial interdependencies of cropping systems as well as intensities of key enterprises linked to farm sizes are often overlooked components of agricultural sustainability. We have developed a remotely-sensed framework for bridging the gap between agronomic and econometric modeling families to evaluate fine-scale systems-level dynamics and farming community-based economic geography. The objectives of this investigation were to: (1) profile potato cropping systems for three sentinel counties in Maine (Aroostook, Penobscot and Oxford) using Cropland Data Layer (CDL, 2008-2012), National Agricultural Image Program (NAIP, 2009 and 2011), Common Land Unit (CLU), soils (SSURGO) and PRISM climate datasets; (2) assess dominant crop sequences and geospatial relationships of remotely-sensed enterprises; and (3) evaluate potential economic impacts of select alternate crops across 5 years using 3 potato farm model sizes (calculating representative enterprise as well as whole-farm budget scenarios). Geospatial integration of classified imagery, soils and CLUs revealed a 5-year potato footprint estimated at 63,000 ha; 87% of production soils were considered potentially highly erodible or highly erodible land. Close to 95% of Maine potato production occurs in 3 counties; potato-small grain sequences dominate Aroostook, whereas tightly-coupled potato-corn rotations characterize Penobscot and Oxford counties. Potato varied from 23,000-24,000 ha detected per annum; other crops generating positive net farm income (NFI) included broccoli (15% of potato land base), soybean, alfalfa, corn silage and canola. These agricultural systems assessments help rural communities, policy makers, and other stakeholders deepen their understanding of land base requirements (to accommodate for rotational complexities) as well as refine tracking spatiotemporal stability of productive capacity pools and potential food system security risks (linked to farmland quality, crop adjacencies and natural resource use). Development of an interactive, web-based version of these farm budget models is underway to facilitate improvements in adaptive management strategies that increase whole-farm profitability and foster sustainable land use.