Genomic prediction (GP) has revolutionized crop breeding despite remaining issues of transfer- ability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction (PP) models that can account, in part, for this challenge. Here, we compare and contrast GP and PP models for three growth-related traits, namely, leaf count, tree height, and trunk diameter, from two coffee three-way hybrid (H3W) populations exposed to a series of treatment-inducing environmental conditions. The models are based on seven different statistical methods built with genomic markers and chlorophyll a fluorescence (ChlF) data used as predictors. This comparative analysis demonstrates that the best performing PP models show higher predictability than the best GP models for the considered traits and environments in the vast majority of comparisons within H3W populations. In addition, we show that PP models are transferrable between conditions, but to a lower extent between populations and we conclude that ChlF data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.