Submitted to: Transactions of the ASAE
Publication Type: Peer reviewed journal
Publication Acceptance Date: 8/1/2000
Publication Date: 1/31/2001
Citation: Fraisse, C.W., Sudduth, K.A., Kitchen, N.R. 2001. Delineation of site-specific management zones by unsupervised classification of topographic attributes and soil electrical conductivity. Transactions of the American Society of Agricultural Engineers. 44(1):155-166. Interpretive Summary: Precision agriculture or site-specific crop management is a management strategy in which cropping inputs such as fertilizers are applied at varying rates across a field in accordance with local crop needs. Currently, decision rules for varying inputs are most often based on recommendation algorithms obtained from multiple locations over large geographic areas. The delineation of sub-field areas that are subject to unique combination of yield-limiting factors would allow a better management of crop inputs and determination of recommendations that fit better the local conditions. This study evaluated the use of automated classification procedures based on Geographic Information Systems (GIS) to classify sub-areas in a field with similar topography and soil characteristics. GIS are computer-based tools to capture, manipulate, process and display spatial information, such as soil types and topography. .The results of this work will benefit producers and crop consultants by allowing the development of crop input recommendations for sub-areas in a field with similar topography and soil characteristics. Scientists investigating and developing site-specific management practices will also benefit from this new method that automates the definition of with-field management zones.
Technical Abstract: Site-specific management addresses the issue of managing large fields more efficiently by applying crop inputs in accordance with the specific requirements of a location. Development of specific agronomic strategies specific for areas of the field that are subject to a unique combination of potential yield-limiting factors would allow more accurate management of inputs. However, determining these sub-field areas is difficult due to th complex combination of factors that may affect crop yield. This research evaluated use of unsupervised classification algorithms to delineate potential within-field management zones based on topographic attributes and soil electrical conductivity. Data collected in two central Missouri fields were used to test the proposed methodology. Principal component analysis was used to determine which data layers were most important for representing within-field variability. Second, GIS-based unsupervised clustering algorithms were used to divide the fields into management zones Yield data were used to analyze the "goodness" of the management zones defined for each field. Elevation and soil EC were the most important attributes for defining management zones in claypan soils. The ideal number of within-field zones decreased if adequate moisture conditions were present throughout the cropping season. Additional layers of information such as remote sensing images, crop scouting maps for diseases or insect damage, and soil fertility maps, could easily be added to the classification process. This classification procedure is fast, can be automated in commercially available GIS software, and has considerable advantages over other methods for delineating within-field management zones.