From Prediction to Prescription: Intelligent Decision Support for Variable Rate Fertilization
Raymond K. Fink, Reed L. Hoskinson, John Richard Hess · 2001 Sacramento, CA July 29-August 1,2001 · 2001
We describe the use of machine learning methods in the analysis of spatial soilfertility, soil physical characteristics, and yield data, with a particular objective of determininglocal (field- to farm-scale) crop response patterns. For effective prescriptive use, the output ofthese tools is augmented with economic data and operational constraints, and recast as a rule-baseddecision support tool to maximize economic return in variable rate fertilization systems.We describe some of the practical issues addressed in development of one such system,including data preparation, adaptation of regression tree output for use in a rule-based expertsystem, and incorporation of real-world limits on system recommendations. Results fromvarious field trials of this system are summarized.