The objective of this research was to determine if unsupervised classification of topographic attributes and soil electrical conductivity could identify management zones for use in precision agriculture. Data collected in two fields located in central Missouri were used to test the proposed methodology. Principal component analysis was used to determine which layers of data were most important for representing within-field variability. Unsupervised clustering algorithms implemented in geographic information system (GIS) software were then used to divide the fields into potential management zones. Grain yield data obtained using a full-size combine equipped with a commercial yield sensing system and global positioning system (GPS) receiver were used to analyze the "goodness" of the potential management zones defined for each field. Principal component analysis of input variables for Field 1 indicated that elevation and bulk soil electrical conductivity (EC) were more important attributes than slope and Compound Topographic Index (CTI) for defining claypan soil management zones. The optimum number of zones to use when dividing a field may vary from year to year and was mainly a function of weather and the crop planted. The number of zones decreased if adequate moisture conditions were present throughout the cropping season (unpredictable) or if crops tolerant to water stress were planted (predictable). This classification procedure is fast, can be easily automated in commercially available GIS software, and has considerable advantages when compared to other methods for delineating within-field management zones.

Producers using site‐specific crop management (SSCM) have a need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management zones Quick and automated procedures are desirable for creating management zones and for testing the question of the number of zones to create. A software program called Management Zone Analyst (MZA) was developed using a fuzzy c‐means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones. An advantage of MZA over many other software programs is that it provides concurrent output for a range of cluster numbers so that the user can evaluate how many management zones should be used. Management Zone Analyst was developed using Microsoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are presented as are the sequential steps of the program. Management Zone Analyst calculates descriptive statistics, performs the unsupervised fuzzy classification procedure for a range of cluster numbers, and provides the user with two performance indices [fuzziness performance index (FPI) and normalized classification entropy (NCE)] to aid in deciding how many clusters are most appropriate for creating management zones. Example MZA output is provided for two Missouri claypan soil fields using soil electrical conductivity, slope, and elevation as clustering variables. Management Zone Analyst performance indices indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be divided into four (using NCE or FPI) management zones.

Crop simulation models have historically been used to predict field average crop development and yield under alternative management and weather scenarios. The objective of this research was to calibrate and test a new version of the CERES-Maize model, modified to improve the simulation of site-specific crop development and yield. Seven sites within a field located in central Missouri were selected based on landscape position, elevation, depth to a claypan soil horizon, and past yield history. Detailed monitoring of crop development and soil moisture during the 1997 season provided data for calibration and evaluation of model performance at each site. Mid-season water stress caused a large variation in measured yield with values ranging from 2.6 Mg ha-1 in the eroded side-slope areas to 10.1 Mg ha-1 in the deeper soils located in the low areas of the field. The model was calibrated against measured data for root zone soil moisture content, leaf area index, and grain yield. The results demonstrated that modifications included in the model to simulate root growth and development are important in soils with a high-clay restrictive layer such as the claypan soils. Although the model performed well in simulating yield variability, simulated leaf area indices were below measured values at five out of seven monitoring sites, suggesting a need for model improvements. Results showed that accurate simulation of crop growth and development for areas of the study field that receive run-on or subsurface flow contributions from upland areas will require enhancement of the model to account for the effects of these processes.

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