Predictive Models in Horticulture: A Case Study with Royal Gala Apples

• Activity Number: 123
• Type: Contributed
• Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Tom M. Logan* and Stella McLeod and Seth Guikema
• Companies: University of Michigan and Mr. Apple New Zealand and University of Michigan
• Keywords: Predictive modelling ; Statistical learning ; Royal Gala apples ; Ensemble

Abstract

Decision makers in horticulture want to forecast their crop characteristics. Predictions of the crop inform decisions which influence pricing, marketing, logistics, and even consumer satisfaction. This article summarises predictive horticultural models in the literature, and finds confusion exists between predictive and explanatory models. It encourages the use of statistical learning and nonlinear methods for future predictive models. Then it demonstrates how predictive models can be constructed using data for Royal Gala apples from orchards within New Zealand. For the eight years of data available the model has been shown to have a mean predictive error of 6.7%. The best model was an ensemble of a linear model, a BART, and a Boosted CART. Statistical learning techniques present substantial opportunity to the horticultural industry and to future attempts to develop more accurate predictive models.