Producing high-quality fiber and maintaining high quality during harvesting and ginning comprise one of the most important technical issues in American cotton production. Fiber quality has been proven to be significantly variable within agricultural fields, and so it is important to develop management practices that can optimize fiber quality on individual management zones within individual fields. Yield maps have allowed producers to understand the relationships between field-management practices and yield, and now quality maps are needed to afford producers the ability to understand relationships between field-management practices and economically important fiber-quality characteristics. There are no fiber-quality sensors available for field use on harvesters, but numerous quality factors are measured either during or as an associated activity with the ginning process. So how can the cotton quality data measured during and after ginning be linked back with field positions in a way that would allow them to be readily used in precision-agriculture management decisions? This paper begins to develop the concepts required to bring about information-integration mechanisms that would allow producers to site-specifically optimize field-input decisions within economic and environmental constraints, all the while taking cotton fiber quality into account. Specific objectives of the project are as follows: (1) to develop a practical and reliable methodology for using GPS to define the area from which a module (or whatever smallest field-harvest unit is being considered) was harvested, and (2) to develop a conceptual mechanism for integrating field-level GIS databases containing yield data, remote-sensing data, soil-property data, etc., with bale-sample cotton-quality data, by using module location and bale-to-module relationships as the data bridge. Accomplishment of these objectives will lead to, ultimately, (1) a practical system of software and hardware for use with GPS-equipped cotton harvesters that will allow data on module-harvest location to be incorporated into maps and precision-agriculture management decisions, and (2) a system linking farm geospatial databases with gin fiber-quality data that will allow transparently adding fiber-quality data to producers' geospatial databases, which would give them the opportunity to make fiber quality maps and consider their management practices with respect to fiber quality.