Using Bayesian growth models to predict grape yield

Abstract

Background and aims: Seasonal differences in vine yield need to be managed to ensure appropriate fruit composition at harvest. Differences in yield are the result of changes in vine management (e.g., the number of nodes retained after harvest) and weather conditions (in particular, temperature) at key vine development stages. Early yield prediction enables growers to manage vines to achieve target yields and prepare the required infrastructure for the harvest.Methods and results: Bunch mass data was collected during the 2016/17, 2017/18 and 2018/19 seasons from a commercial vineyard on the Wairau Plains, Marlborough, New Zealand (41o2’23”S; 173o51’15”E). A Bayesian growth model, assuming a double sigmoidal curve, was used to predict the yield at the end of each season. The accuracy of the prediction was investigated using the Monte-Carlo simulation for yield prediction at various growth stages assuming different prior information.Conclusion: The results show that the model is sensitive to prior assumption and that having a non-informative prior may be more beneficial than having an informative prior based on one unusual year.