Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes

Abstract

An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible.