Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) in the Inland Northwest region of the USA are nitrogen (N) deficient; however stem growth responses to N fertilizers are unpredictable, which may be due to poor accounting of other limiting nutrients. Screening trial experiments, including potassium (K), sulfur (S), and boron (B) multiple nutrient treatments, have been conducted to learn about Douglas-fir nutritional status and fertilizer growth response. The data from the screening trial experiments were compiled to test whether the soil parent materials of the region could be used to predict nutritional status. Estimating effects of fertilizers and soil parent materials on Douglas-fir growth from compilations of such experiments, however, poses challenges and opportunity; experiments clustered in time and space introduce latent variables that drive between-site variation. We used a two-stage modeling approach to efficiently take advantage of the information in these data. First, we employed a mixed model approach to test the primary hypothesis of soil parent material influence upon stem growth response to fertilizer. As the second- stage to the analysis, the predicted random effects estimated from the mixed model were used as a response variable to test how strongly precipitation drives between-site variation.