DOI: 10.5061/dryad.q2bvq83vj
title: Cold hardiness, deacclimation, and budbreak phenology in grapevine

description: To survive the harsh conditions of winter, woody perennial species such as
 grapevine have adapted to use environmental cues to trigger physiological
 changes to induce dormancy, acquire cold hardiness, and measure the length
 of winter to properly time spring budbreak. Human induced climate change
 disrupts these cues by prolonging warm temperatures in fall, reducing the
 depth and consistency of midwinter, and triggering early budbreak through
 false spring events. We evaluated variation in dormant bud cold hardiness
 and chilling hour requirements of 31 different grapevine varieties over 3
 years. Differential thermal analysis was used to track changes in cold
 hardiness and deacclimation resistance was assessed throughout the season
 to track dormancy progression. Results demonstrate wide variation in
 maximum deacclimation rate (1.03 – 2.87 °C/day) among varieties under
 forcing conditions. Absolute maximum rates of deacclimation show
 signatures of species-level responses to forcing temperatures. When
 integrated with variation in cold hardiness, these rates revealed a
 relationship between winter cold hardiness, changes in deacclimation rate
 and budbreak phenology. Standardizing rates among varieties as
 deacclimation potential demonstrated a conserved response to chilling
 exposure among varieties that alters our interpretation of the concept of
 high and low chill varieties and chilling requirement in grapevine.
description: <em>Plant Sampling</em> This study
 was conducted over three consecutive winter seasons: 20 September 2018- 16
 April 2019 (Year 1), 5 September 2019- 2 April 2020 (Year 2), and 2
 October 2020- 31 March 2021 (Year 3). Thirty-one cultivars of grapevine
 were sampled from local commercial and experimental vineyard locations
 (Table S1) at approximately weekly intervals in each of the three years to
 evaluate cold hardiness in response to natural field conditions,
 deacclimation resistance, and budbreak phenology under controlled
 temperature conditions. Cultivars included both red (R) and white (W)
 fruited European <em>Vitis vinifera</em> cultivars (hereafter,
 <em>vinifera</em>); Cabernet Franc (R), Cabernet Sauvignon
 (R), Chardonnay (W), Gewurztraminer (W), Gruner Veltliner (W), Lemberger
 (R), Merlot (R), Pinot gris (W), Pinot noir (R), Riesling (W), Sangiovese
 (R), Saperavi (R), Sauvignon blanc (W), Syrah (W), and Tocai Friulano (W),
 and hybrid cultivars (hereafter, <em>hybrid</em>) with various
 wild species genetic backgrounds; Aromella (W), Cayuga White (W),
 Chambourcin (R), Chancellor (R), Concord (R), Corot noir (R), La Crescent
 (W), Marechal Foch (R), Marquette (R), Niagara (W), Noiret (R), St. Croix
 (R), Traminette (W), Valvin Muscat (W), Vidal (W), and Vignoles (W). These
 cultivars represent a subsample of the diversity of cultivars grown in the
 Northeastern United States and were chosen in part to also represent
 diversity in optimum climate niche. For the <em>vinifera</em>
 cultivars, we included both “cool” and “warm” climate varieties (Jones
 <em>et
 al.</em>, 2012)
 and for <em>hybrid</em> cultivars, we used cultivars that
 represent both French American hybrids and New American hybrids, as well
 as hybrids derived from wild grapevine species that have Northern
 (<em>V. labrusca</em> or <em>V. riparia</em>), or
 Southern (<em>V. rupestris</em> or <em>V.
 lincecumii</em>) distributions. Distributions of wild species were
 obtained from county level data found at the Biota of North America
 Program (https://bonap.net/napa). Pedigree data for hybrid cultivars was
 retrieved from the Vitis International Variety Catelog (VIVC,
 https://www.vivc.de/). All cultivar information is included in Table
 S1. Dormant cane cuttings with nodes 3-20 (depending on
 cultivar and pruning method) were collected from the field and transported
 to the bud physiology lab at Cornell AgriTech for processing. During each
 collection time point in 2018-2019, 40 nodes of each cultivar were
 collected from the field by randomly pruning from at least 4 different
 individual vines at each collection. In 2019-2020 and 2020-2021, sampling
 was increased to 50 nodes of each cultivar at each collection. Upon
 returning from field collections, dormant cane/node material were promptly
 pruned into single node cuttings, randomized, and distributed into
 subsamples for experiments to determine an initial cold hardiness
 assessment (n = 5; 2018-2019, n = 10; 2019-2020 and 2020-2021),
 deacclimation resistance (n =25), and budbreak phenology (n
 =10). <em>Assessment of cold hardiness using
 differential thermal analysis</em> Cold hardiness
 response curves for each year and each cultivar were produced using the
 mean LTE value for the weekly collection (T0) timepoints. For determining
 dormant bud cold hardiness levels, grapevine supercooling ability was
 assessed following standard practices for differential thermal analysis
 (DTA) (Mills <em>et al.</em>, 2006; Ferguson <em>et al.</em>, 2011; Londo and Kovaleski, 2017; Londo <em>et al.</em>, 2023). This method uses thermoelectric modules to record voltage changes that are associated with the release of heat that occurs due to the phase change of water into ice (Mills <em>et al.</em>, 2006). The LTE is associated with the lethal temperature for each sampled primary bud and is correlated with manual assessments of bud death (Wolf and Cook, 1994). Dormant buds were excised from cane tissue leaving an intact bud cushion layer of approximately 1 mm in depth. Buds were placed with the cut-side down on a single ply layer of moistened kimwipe (Kimberly-Clark Corp., USA) to induce ice nucleation during freeze tests (Londo <em>et al.</em>, 2023). Buds were covered with a square of thermal open-cell foam and sample plates were placed in a programmable freezer (Tenney T2C, Tenney Environmental, New Columbia, PA, USA) connected to a Keithley 2700 or 2701 multimeter datalogger (Keithley Instruments). Voltage and temperature measurements were collected at 15-second intervals using the BudFreezer program (Brock University, Guelph ON, Canada). Freezing runs were conducted as follows: 1°C/minute cooling ramp to 0°C, hold for 1 hour, 4°C/hour cooling ramp to -40°C, hold one hour at -40°C, 4°C/hour warming ramp to room temperature. Low temperature exotherm peaks were manually annotated using BudProcessor/BudLTE software (Brock University, Guelph ON, Canada). In the first year, 2018-2019, 5 replicate buds were assessed for initial LTE measurements. In the second and third years of the study, 10 replicate buds were used for initial LTE measurements. This adjustment was made to increase the confidence in the mean LTE value as representative of field cold hardiness level. Here we report the mean LTE value for any given time point as the LT50. <em>Assessment of deacclimation resistance and deacclimation potential</em> To determine deacclimation resistance of the buds collected during weekly collections, twenty-five single node cuttings were placed in plastic cups with basal cut ends in water. Cups were placed in a Conviron growth chamber (Controlled Environments Inc., North Dakota, USA) and held at constant 20°C without light exposure. Cold hardiness was assessed as described above to measure the loss of cold hardiness under forcing conditions at repeated intervals (e.g. T0-field collected, T1 = T0+6 days, T2 = T0+12 days, ect). Resampling intervals varied throughout the experiments with longer intervals in early winter and shorter intervals in late winter, as deacclimation resistance declined. At each deacclimation time point, five replicate buds for each cultivar were assessed to determine the change in LT50 from the previous time points. Deacclimation rates for each cultivar at each collection time point were analyzed independently using linear regression and taking the slope of the regression as the deacclimation rate (e.g., lm(LTE~SampleDay). To compare deacclimation rates between cultivars as a function of chilling hour estimates, raw rate values for each year were normalized within cultivars using the mean of the three highest rate values observed across the study. The normalized percentage of total deacclimation is referred to as deacclimation potential (Ψ<sub>deacc</sub>). (Kovaleski <em>et al.</em>, 2018; Kovaleski, 2022; North <em>et al.</em>, 2022). The nls() function of the drc package (Ritz <em>et al.</em>, 2015) was used with default parameters (fct=LL.2(), type="continuous") to model Ψ<sub>deacc </sub>between cultivars, years, and in comparison with the various chill model estimates as described in North (2022). <em>Assessment of budbreak phenology</em> In tandem with field LTE assessments, an additional 10 single node cuttings were placed in plastic cups with basal ends trimmed and kept in water. Cups were left at 18-20°C temperatures and without specific light exposure (laboratory lighting). Budbreak phenology was manually assessed on alternating days during the week. Days to bud break (DTB) was recorded as the number of days between field collection and first evidence of green tissue, EL stage 3 (Coombe, 1995). When a cutting was marked as EL 3, they were removed from the experiment to prevent double counting. Budbreak data was manually curated to remove sample collection points where less than 5 of the 10 replicates achieved budbreak in under 150 days. For the remaining collections, budbreak was phenotyped as the average (meanDTB) time to budbreak.
description: # Cold hardiness, deacclimation, and budbreak phenology in grapevine
 [https://doi.org/10.5061/dryad.q2bvq83vj](https://doi.org/10.5061/dryad.q2bvq83vj) ## Description of the data and file structure Grapevine cold hardiness, deacclimation response, and budbreak phenology was measured from 31 different grape cultivars across three sample years.  Samples were collected from dormant canes in New York State, United States.  Cold hardiness was assessed using differential thermal analysis.  Deacclimation response was computed as linear regressions of cold hardiness for dormant buds collected from the field, and then resampled from growth chambers over several days.  Budbreak phenology was assessed using forcing assays. ### Files and variables #### File: Budbreak.csv **Description:** Dataset of budbreak phenology data from forcing assays ##### Variables * Cultivar: Name of grapevine cultivar * Collection: Denotes sampling timepoint as collected in order from the beginning of each year to the end of each year using a 2 digit code. AA-AZ indicates timepoints 1-26.  BA-BE indicates timepoints 27-31.  Timepoints varied by year. * Date: Date of sampling point * Break: Days to budbreak. Days * Dataset: Year of data collection.  * DTB: Days to budbreak computed as mean. Unit is days. * Portions: Chilling hours computed with Dynamic Portions. Unit is dynamic portions. #### File: Rates.csv **Description:** Dataset of computed deacclimation rate data ##### Variables * Cultivar: Name of grapevine cultivar * Collection: Denotes sampling timepoint as collected in order from the beginning of each year to the end of each year using a 2 digit code. AA-AZ indicates timepoints 1-26.  BA-BE indicates timepoints 27-31.  Timepoints varied by year. * Dataset: Denotes year of sampling * Date: Denotes date of initial field collected sample * Portions:Denotes chilling hours computed with Dyanmic portions model. Unit is chilling portions. * DeacRate: Deacclimation rate: Slope of the linear regression of cold hardiness and days in forcing conditions. Units are loss of cold hardiness as degrees Celcius per Day. * Normalize: Deacclimation rate normalized to the maximum deacclimation rate observed in the study. Units are percent of total deacclimation. #### File: LTE\_All\_Years\_Marked\_For\_Outliers\_Final.csv **Description:** Dataset of cold hardiness as determined by low temperature exotherms ##### Variables * Cultivar: Name of grapevine cultivar sampled * Collection: Denotes sampling timepoint as collected in order from the beginning of each year to the end of each year using a 2 digit code. AA-AZ indicates timepoints 1-26.  BA-BE indicates timepoints 27-31.  Timepoints varied by year. * LTE:Low temperature exotherm data representing temperature which results in lethal freeze event in the primary bud. Degrees Celcius * Date:Date of cold hardiness assessment * Dataset:Sampled year * Days: Days in forcing conditions. Days * DateTime:Redundant DateTime object for Date * .std.resid: studentized residual  * outlier:Binary decision on outlier  * Manual curation: Manual curation choice for keeping or dropping datapoints * Justification: Method of outlier exclusion used ## Code/software All data processing and analysis in this study was conducted using R Statistical Software (v4.2.1; R Core Team 2022) with the following packages: tidyverse, ggplot2, chillR, lsmeans, dormancyR, drc, lubridate, broom, and patchwork.