Horticulturists are often interested in evaluating the effect of several treatment factors on plant growth in order to determine optimal growing conditions. Factors could include three or more nutrient elements, or types and rates of irrigation, pesticides or growth regulators, possibly in combination with one another. Two problems with such experiments are how to characterize plant response to treatment combinations and how to design such experiments so that they are manageable. The standard statistical approach is to use linear and quadratic (a.k.a. response surface) regression to characterize treatment effects and to use response surface designs, e.g., central-composite designs. However, these often do a poor job characterizing plant response to treatments. Hence the need for more generally applicable methods. While our goal is to be able to analyze three and higher factor experiments, we started by tweaking two-factor nutrient analysis data. The result was a hybrid model which allows for a given factor to respond linearly or non-linearly. We will show how this was done and our current “in progress” model and analysis for analyzing three quantitative factors.