Global agricultural production information is the key to many economic decisions. National level planners use it to plan imports or to assess balance of payments, farmers use it to make planting decisions, lending and aid institutions use it to plan loans and aid needs, commodity buyers use it to plan purchases. Traditional information systems are slow, offer little confidence and may be inaccurate; systems based on the use of space remote sensor systems are, on the other hand, fast, provide good confidence and are demonstrating improving accuracies. The system structure for remote sensor assisted agricultural information systems is centered on a geobased structure, mapped outputs pinpoint locations where plant stress is impacting yields. Meteorological satellite assessments pinpoint where rainfall and significant solar radiation is impacting the plant environment. The CROPCAST Agricultural Information System offers an opportunity to examine an operating system which contains characteristics essential to all future systems. CROPCAST's use of a grid/cell geobased structure provides a mechanism to effectively use remote-sensor derived data of all types, i.e., Landsats, metsats, aircraft and human eyeball derived data. Predictive models operating in CROPCAST provide updated agricultural assessments in the time intervals when no Landsat or other field observation data are available. Economic models provide the opportunity to merge CROPCAST diagnostic and predictive output with the market place at both the cash and futures level. This presentation will examine the CROPCAST structure as a model for future uses of remote sensing data from civil remote sensing systems in assessing global agricultural production. A review of the future direction to be taken by the CROPCAST System will be included to identify new avenues for remote sensor-based agricultural information system growth over the coming decade of change in remote sensor systems.