BACKGROUND AND AIMS: Grapevine (Vitis spp.) cold hardiness varies dynamically throughout the dormant season, primarily in response to changes in temperature. The development and possible uses of a discrete-dynamic model of bud cold hardiness for three Vitis genotypes are described. METHODS: Iterative methods were used to optimize and evaluate model parameters by minimizing the root mean square error between observed and predicted bud hardiness, using up to 22 years of low-temperature exotherm data. Three grape cultivars were studied: Cabernet Sauvignon, Chardonnay (both V. vinifera) and Concord (V. labruscana). The model uses time steps of 1 d along with the measured daily mean air temperature to calculate the change in bud hardiness, which is then added to the hardiness from the previous day. Cultivar-dependent thermal time thresholds determine whether buds acclimate (gain hardiness) or deacclimate (lose hardiness). KEY RESULTS: The parameterized model predicted bud hardiness for Cabernet Sauvignon and Chardonnay with an r(2) = 0·89 and for Concord with an r(2) = 0·82. Thermal time thresholds and (de-)acclimation rates changed between the early and late dormant season and were cultivar dependent but independent of each other. The timing of these changes was also unique for each cultivar. Concord achieved the greatest mid-winter hardiness but had the highest deacclimation rate, which resulted in rapid loss of hardiness in spring. Cabernet Sauvignon was least hardy, yet maintained its hardiness latest as a result of late transition to eco-dormancy, a high threshold temperature required to induce deacclimation and a low deacclimation rate. CONCLUSIONS: A robust model of grapevine bud cold hardiness was developed that will aid in the anticipation of and response to potential injury from fluctuations in winter temperature and from extreme cold events. The model parameters that produce the best fit also permit insight into dynamic differences in hardiness among genotypes.
BACKGROUND AND AIMS: Grapevine (Vitis spp.) cold hardiness varies dynamically throughout the dormant season, primarily in response to changes in temperature. The development and possible uses of a discrete-dynamic model of bud cold hardiness for three Vitis genotypes are described. METHODS: Iterative methods were used to optimize and evaluate model parameters by minimizing the root mean square error between observed and predicted bud hardiness, using up to 22 years of low-temperature exotherm data. Three grape cultivars were studied: Cabernet Sauvignon, Chardonnay (both V. vinifera) and Concord (V. labruscana). The model uses time steps of 1 d along with the measured daily mean air temperature to calculate the change in bud hardiness, which is then added to the hardiness from the previous day. Cultivar-dependent thermal time thresholds determine whether buds acclimate (gain hardiness) or deacclimate (lose hardiness). KEY RESULTS: The parameterized model predicted bud hardiness for Cabernet Sauvignon and Chardonnay with an r(2) = 0·89 and for Concord with an r(2) = 0·82. Thermal time thresholds and (de-)acclimation rates changed between the early and late dormant season and were cultivar dependent but independent of each other. The timing of these changes was also unique for each cultivar. Concord achieved the greatest mid-winter hardiness but had the highest deacclimation rate, which resulted in rapid loss of hardiness in spring. Cabernet Sauvignon was least hardy, yet maintained its hardiness latest as a result of late transition to eco-dormancy, a high threshold temperature required to induce deacclimation and a low deacclimation rate. CONCLUSIONS: A robust model of grapevine bud cold hardiness was developed that will aid in the anticipation of and response to potential injury from fluctuations in winter temperature and from extreme cold events. The model parameters that produce the best fit also permit insight into dynamic differences in hardiness among genotypes.
Authors: Johann Martínez-Lüscher; Tomas Teitelbaum; Anthony Mele; Oliver Ma; Andrew Jordan Frewin; Jordan Hazell Journal: PLoS One Date: 2022-05-19 Impact factor: 3.752
Authors: Yazhini Velappan; Tinashe G Chabikwa; John A Considine; Patricia Agudelo-Romero; Christine H Foyer; Santiago Signorelli; Michael J Considine Journal: J Exp Bot Date: 2022-04-05 Impact factor: 6.992