SOFTWARE Management Zone Analyst (MZA): Software for Subfield Management Zone Delineation

different management zones within a field? Two, how can information be processed into unique management Producers using site-specific crop management (SSCM) have a units (i.e., procedures for classification)? And three, how need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management many unique zones should a field be divided into? Quick, zones. Quick and automated procedures are desirable for creating efficient, and automated procedures are needed that admanagement zones and for testing the question of the number of zones dress these questions. to create. A software program called Management Zone Analyst A number of information sources have been used to (MZA) was developed using a fuzzy c-means unsupervised clustering delineate subfield management zones for SSCM. Tradialgorithm that assigns field information into like classes, or potential tional soil surveys often provide estimates of crop promanagement zones. An advantage of MZA over many other software ductivity for each soil map unit. In the USA, county programs is that it provides concurrent output for a range of cluster soil surveys report the average yield of major crops and numbers so that the user can evaluate how many management zones various soil properties by soil map unit; but the spatial should be used. Management Zone Analyst was developed using Microscale of county soil surveys has often been found inadesoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are prequate for use in SSCM (Mausbach et al., 1993). Digital sented as are the sequential steps of the program. Management Zone elevation data collected using global positioning systems Analyst calculates descriptive statistics, performs the unsupervised (GPS) or total station surveys have been used for classifuzzy classification procedure for a range of cluster numbers, and profying a field into management zones (McCann et al., vides the user with two performance indices [fuzziness performance 1996; Lark, 1998; MacMillan et al., 1998; van Alphen and index (FPI) and normalized classification entropy (NCE)] to aid in Stoorvogel, 1998). Fleming et al. (2000) used aerial phodeciding how many clusters are most appropriate for creating managetographs of bare soil along with landscape position and ment zones. Example MZA output is provided for two Missouri claythe management experience of the producer to delinpan soil fields using soil electrical conductivity, slope, and elevation as eate within-field management zones. Because of the clustering variables. Management Zone Analyst performance indices relationship of bulk soil apparent electrical conductivity indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be (ECa) to productivity on some soils (Kitchen et al., 1999, divided into four (using NCE or FPI) management zones. 2003), it has been used in the delineation of management units. Sudduth et al. (1996) and Fraisse et al. (2001a) used a combination of topographic attributes and soil ECa to delineate management zones. Long et al. (1994) S crop management promotes the concept investigated the accuracy and precision of field manageof identification and management of regions within ment maps created from several sources [e.g., soil survey the geographic area defined by field boundaries. Often map, aerial photograph, overlaying class values of kriged referred to as management zones, these subfield regions point data in a geographic information system (GIS)]. typically represent areas of the field that are similar based They concluded that aerial photographs of growing crops on some quantitative measure(s) (e.g., topography, yield, were the most accurate and precise for classifying a field and soil-test nutrients). Determination of subfield areas into management units to predict grain yield. Imagery is difficult due to the complex combination of soil, biotic, of a growing crop and yield data collected in the same and climate factors that may affect crop yield. These year would be highly correlated and thus an accurate factors dynamically interact, further complicating the representation of crop production potential for that spedecisions of how to manage by zones. Three questions typically arise when considering managing by zones. One, cific year (Boydell and McBratney, 1999). what information should be used as a basis for creating Delineating zones based on topographic attributes and/or soil physical properties most often captures yield J.J. Fridgen, ITD/Spectral Visions, 20407 South Neil Street Suite 2, variability due to differences in plant available water and Champaign, IL 61820; N.R. Kitchen, K.A. Sudduth, and S.T. Drumthus, crop production potential (McCann et al., 1996; mond, USDA–ARS, Cropping Syst. and Water Quality Res. Unit, van Alphen and Stoorvogel, 1998; Fraisse et al., 2001a). Columbia, MO 65211; W.J. Wiebold, Dep. of Agron., University of Missouri, Columbia, MO 65211; and C.W. Fraisse, Agric. and Biol. Eng. Dep., Univ. of Florida, Gainesville, FL 32611. Received 22 Aug. Abbreviations: ECa, apparent soil electrical conductivity; FPI, fuzzi2002. *Corresponding author ([email protected]). ness performance index; GIS, geographic information systems; ISODATA, Iterative Self-Organizing Data Analysis Technique; MZA, Published in Agron. J. 96:100–108 (2004).  American Society of Agronomy Management Zone Analyst; NCE, normalized classification entropy; SSCM, site-specific crop management. 677 S. Segoe Rd., Madison, WI 53711 USA