|English, P.J. -|
Submitted to: Northeast Potato Technology Forum Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: March 4, 2012
Publication Date: March 7, 2012
Citation: Defauw, S.L., Larkin, R.P., English, P., Hoshide, A.K., Halloran, J.M. 2012. Mapping and monitoring potato cropping systems in Maine: geospatial methods and land use assessments. Northeast Potato Technology Forum Abstracts. pp. 24-25. Technical Abstract: Geospatial frameworks and GIS-based approaches were used to assess current cropping practices in potato production systems in Maine. Results from the geospatial integration of remotely-sensed cropland layers (2008-2011) and soil datasets for Maine revealed a four-year potato systems footprint estimated at 68,100 ha with 61% and 27% residing on ‘prime farmland’ (PF) and ‘farmland of statewide importance’ (FSI), respectively. Zonal geoprocessing of classified imagery indicated that over 85% of potato production soils were either ‘potentially highly erodible’ (PHEL) or ‘highly erodible’ (HEL); therefore, at least 58,000 ha require the highest standards in soil conservation practices. Compilation of three-year potato cropping systems footprints resulted in the identification of parcels of ‘continuous potato’ engaging over 1,800 ha. Examination of the geospatial relationships of four-year production footprints for potato, small grains, corn and broccoli revealed a strong interdependency between potato and small grains (barley, rye, oat, spring and winter wheat) with an overlap of approximately 70%, whereas 6% and 11% of the potato production land base is shared with corn and broccoli, respectively. These relationships help food system researchers assess land use interdependencies and refine their understanding of resource requirements in order to improve potential production capacity models for select crops across the region. Time-series GIS data assemblages also allow us to readily track farmscape-scale crop sequences as well as shifts in crop adjacencies from year-to-year. Additional outcomes extracted from these remotely-sensed datasets suggest farmers have diversified their operations and appear to be shifting to rotations of 3 or more years.