RT Journal Article SR Electronic T1 Predicting Key Phenological Stages for 17 Grapevine Cultivars (Vitis vinifera L.) JF American Journal of Enology and Viticulture JO Am J Enol Vitic. FD American Society for Enology and Viticulture SP ajev.2016.15077 DO 10.5344/ajev.2016.15077 A1 Diana Zapata A1 Melba Salazar-Gutierrez A1 Bernardo Chaves A1 Markus Keller A1 Gerrit Hoogenboom YR 2016 UL http://www.ajevonline.org/content/early/2016/09/20/ajev.2016.15077.abstract AB Weather conditions have a significant impact on crops and temperature is one of the main factors that controls plant development. Thermal time models that are based on temperature have been applied to predict the development of many species. To implement these models the determination of a proper base temperature (Tb) is required to characterize the differences among developmental stages and cultivars. The goal of this study was to determine the unique Tb and degree-days (DD) to predict budbreak, bloom and veraison for 17 cultivars. Tb’s were estimated through the minimum variance method using phenological data collected during 23 years in Prosser, Washington. Tb increased throughout grapevine development, and ranged from 6.1 to 8.4°C for budbreak, from 7.2 to 10.5°C for bloom, and from 9.4 to 12.8°C for veraison. Starting DD accumulation on January 1 and using the Tb’s estimated for each individual cultivar, duration to budbreak ranged from 78 to 180 DD, from budbreak to bloom ranged from 240 to 372 DD and from bloom to veraison ranged from 556 to 800 DD. Errors in prediction varied between 4.8 to 7.8 days to budbreak, 1.9 to 5.5 days to bloom and 7.1 to 12.4 days to veraison. Based on the errors in prediction, the models that used an estimated Tb specific for a phenological stage showed a better performance compared to the models that had a fixed of Tb of 0°C and 10°C. The estimated thermal time parameters provide a simple approach to characterize differences among cultivars and assist growers and industry in implementing management practices through simple decision support tools based on thermal time models.