Regional Influences of Mean Temperature and Variance Changes on Freeze Risk in Apples

in HortScience

Both mean temperature and daily temperature variance affect freeze risk in apples. Freeze damage to blossoms was assessed using a sequential model. In the model, once the chilling requirement was reached, growing degree days were accumulated and phenological stages determined based on growing degree thresholds derived from historical phenological observations. Critical temperatures for each stage were obtained from the literature and used to identify the occurrence freeze injury based on minimum temperature occurrence. In New York, temperature variance was the dominant climatological factor controlling freeze risk. A small <5% increase in variance counteracted mean temperature increases of up to 5.5 °C leading to increased freeze risk despite warming temperatures. In other apple-growing regions in the northwestern and southeastern United States, changes in freeze risk were dominated by changes in mean temperature. This demonstrates that in some regions the risk of freeze injury under future climate conditions may be more sensitive to changes in temperature variance. Variance is currently not well simulated by climate models. Because freeze risk also increases when the chill requirement is reduced, adaptation decisions to transition to lower chill requirement cultivars may be ill-advised in northern climates similar to New York as even the highest chill requirements were satisfied under the conditions with the greatest warming. This was not the case in other regions where the adoption of lower chill requirement cultivars may be warranted.

Contributor Notes

This work was supported by NOAA Contract EA133E07CN0090, the New York State Agricultural Experiment Station and the New York State Energy Research and Development Authority.

We appreciate the guidance and motivation we received from our colleagues Greg Peck, Alan Lakso, David Wolfe, and Jonathan Comstock. Phenological data were supplied by David Kain and Peter Jentsch.

Corresponding author. E-mail: atd2@cornell.edu.

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Article Figures

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    Comparison of observed (white boxplot) and simulated (gray shaded and hatched rectangles) dates of (A) GDD-based development accumulations and (B) last occurrences of critical temperature thresholds for Ithaca, NY. Boxplot whiskers show the fifth and 95th percentile values. The rectangles show the 95% confidence intervals (CIs) of the 1000 simulated percentiles. Hatched rectangles show cases where the observed percentile falls outside of the simulated percentile CI.

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    Boxplots of date on which 1200 chill units (CU) accumulate for different changes in mean temperature and 80% (white) and 120% (gray) of current variance (A and C). Daily chill unit accumulation corresponding to a 5 °C increase (black) and 0.5 °C decrease (gray) in mean temperature for historical variance (B and D). Black dotted lines show the accumulation associated with a 5 °C increase in mean temperature and a 75% and 130% change in variance. The gray dotted lines show the same range of variance change for the 0.5 °C decrease in mean temperature. For reference, the horizontal line indicates 1200 accumulated CU. A and B use data for Ithaca. C and D use data for Asheville, NC.

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    Change in date of the first-pink stage for different levels of mean temperature change and 80% (white) and 120% (dark gray) of current variance at (A) Ithaca, (B) Yakima, and (C) Asheville. The superimposed light gray bars show the date of the last spring occurrence of −2.8 °C for each mean temperature variance change combination.

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    Contour plots of annual freeze risk for different combinations of change in mean temperature and variance for (A) first-pink stage T10, (B) green-tip stage T10, (C) full-bloom T50, and (D) full-bloom T90. The dotted contour highlights the freeze risk corresponding to observed historical data. The gray areas signify annual freeze risks outside the 95% confidence interval for current (mean change = 0, variance factor = 1.0) climate conditions.

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    Contour plots of annual freeze risk for different combinations of change in mean temperature and variance during full-bloom at (A) Yakima and (B) Asheville. The black dotted contours and shaded regions are as in Fig. 4. Lower panels show the seasonal cycle of (C) daily maximum and minimum temperature and (D) daily sd of maximum (translated upward by 4 °C for clarity) and minimum temperature at Ithaca (black solid), Asheville (black dotted), and Yakima (gray dashed). Circles show the juxtaposition of the average date of full-bloom and daily minimum temperature average and sd at each station.

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    Annual freeze risk for (A) LT10 (left panels) and LT90 (right panels) freeze events based on projected changes in monthly average temperature at Ithaca (A, B), Yakima (C, D), and Asheville (E, F). The gray bands represent the 95% confidence interval of freeze risk based on the stochastic simulation using the current temperature climatology with the dotted black line showing the median present-day risk. The black dots show the projected freeze risk given the temperature changes prescribed by individual Coupled Model Intercomparison Project Phase 5 projections, with the future all-model mean given by the thick solid black line.

Article References

  • AshcroftG.L.RichardsonE.A.SchuylerD.S.1977A statistical method of determining chill unit and growing degree hour requirements for deciduous fruit treesHortScience12347348

    • Search Google Scholar
    • Export Citation
  • CannellR.SmithR.I.1986Climatic warming, spring budburst and forest damage on treesJ. Appl. Ecol.231177191

  • CesaraccioC.SpanoD.SnyderR.L.PierpaoloD.2004Chilling and forcing model to predict bud-burst of crop and forest speciesAgr. For. Meteorol.126113

    • Search Google Scholar
    • Export Citation
  • DarbyshireR.PopeK.GoodwinI.2016An evaluation of the chill overlap model to predict flowering time in apple treeSci. Hort.198142149

  • DarbyshireR.WebbL.GoodwinI.BarlowE.W.2013Impact of future warming on winter chilling in AustraliaIntl. J. Biometeorol.57355366

  • EccelE.ReaR.CaffarraA.CrisciA.2009Risk of spring frost to apple production under future climate scenarios: The role of phenological acclimationIntl. J. Biometeorol.533273286

    • Search Google Scholar
    • Export Citation
  • ErezA.FishmanS.Linsley-NoakesG.C.AllanP.1990The dynamic model for rest completion in peach bidsActa Hort.276165174

  • FrumhoffP.C.McCarthyJ.J.MellilloJ.M.MoserS.C.WuebblesD.J.2007Confronting climate change in the U.S. northeast: Science impacts and solutions. Synthesis report of the northeast climate impacts assessment (NECIA). Union of Concerned Scientists Cambridge MA

  • GuL.HansonP.J.MacPostW.KaiserD.P.YangB.NemaniR.PallardyS.G.MeyersT.2008The 2007 eastern US spring freeze: Increased cold damage in a warming world?Bioscience583253262

    • Search Google Scholar
    • Export Citation
  • HauaggeR.CumminsJ.N.1991Phenotype variation of length of bud dormancy in apple cultivars and related Malus speciesJ. Amer. Soc. Hort. Sci.116100106

    • Search Google Scholar
    • Export Citation
  • HoffmannH.LangnerF.RathT.2012Simulating the influence of climatic warming on future spring frost risk in northern german fruit productionActa Hort.957289296

    • Search Google Scholar
    • Export Citation
  • HoffmannH.RathT.2013Future bloom and blossom frost risk for Malus domestica considering climate model and impact model uncertaintiesPLoS One810E75033

    • Search Google Scholar
    • Export Citation
  • KaukorantaT.TahvonenR.YlämäkiA.2010Climatic potential and risks for apple growing by 2040Agr. Food Sci.19144159

  • KunzA.BlankeM.2011Effects of global climate change on apple ‘golden delicious’ phenology – Based on 50 years of meteorological and phenological data in Klein-AltendorfActa Hort.90311211126

    • Search Google Scholar
    • Export Citation
  • LegaveJ.M.BlankeM.ChristenD.GiovanniniD.MathieuV.OgerR.2013A comprehensive overview of the spatial and temporal variability of apple bud dormancy release and blooming phenology in western EuropeIntl. J. Biometeorol.57317331

    • Search Google Scholar
    • Export Citation
  • LinvillD.E.1990Calculating chilling hours and chill units from daily maximum and minimum temperature observationsHortScience251416

  • MaurerE.P.2010The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in CaliforniaHydrol. Earth Syst. Sci.1411251138

    • Search Google Scholar
    • Export Citation
  • Olcott-ReidB.ReidW.2007Fruit and nut production. Stipes Publishing Champaign IL

  • ProebstingE.L.JrMillsH.H.1978Low temperature resistance [frost hardiness] of developing flower buds of six deciduous fruit speciesJ. Amer. Soc. Hort. Sci.103192198

    • Search Google Scholar
    • Export Citation
  • ReaR.EccelE.2006Phenological models for blooming of apple in a mountainous regionIntl. J. Biometeorol.511116

  • RichardsonC.W.1981Stochastic simulation of daily precipitation, temperature, and solar radiationWater Resour. Res.17182190

  • RichardsonE.A.SeeleyS.D.WalkerD.R.1974A model for estimating the completion of rest for Redhaven and Elberta peach treesHortScience9331332

    • Search Google Scholar
    • Export Citation
  • RigbyJ.R.PorporatoA.2008Spring frost risk in a changing climateGeophys. Res. Lett.351215

  • RobinsonT.MirandaM.2013Predicting green tip in 2013Scaffolds Fruit J.22212

  • RosenzweigC.SoleckiW.DeGaetanoA.T.O’GradyM.HassolS.GrabhornP.2011Responding to climate change in New York State: The ClimAID integrated assessment for effective climate change adaptation. Synthesis report. New York State Energy Research and Development Authority (NYSERDA) Albany NY

  • ScheifingerH.MenzelA.KochE.PeterC.2003Trends in spring time frost events and phenological dates in central EuropeTheor. Appl. Climatol.744151

    • Search Google Scholar
    • Export Citation
  • SchwartzM.D.AhasR.AasaA.2006Onset of spring starting earlier across the Northern HemisphereGlob. Change Biol.12343351

  • SemenovM.A.BarrowE.M.1997Use of a stochastic weather generator in the development of climate change scenariosClim. Change35397414

  • SemenovM.A.BrooksR.J.BarrowE.M.RichardsonC.W.1998Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climatesClim. Res.1095107

    • Search Google Scholar
    • Export Citation
  • ShaultoutA.D.UnrathC.R.1983Rest completion prediction model for Starkrimson Delicious applesJ. Amer. Soc. Hort. Sci.108957961

  • SugiuraT.2010Characteristics of responses of fruit trees to climate changes in JapanActa Hort.8728588

  • TaylorK.E.StoufferR.J.MeehlG.A.2012An overview of CMIP5 and the experiment designBul. Amer. Meteorol. Soc.93485498

  • WilksD.S.2006Statistical methods in the atmospheric sciences. 2nd ed. Elsevier Amsterdam The Netherlands

  • WolfeD.W.SchwartzM.D.LaksoA.N.OtsukiY.PoolR.M.ShaulisN.J.2005Climate change and shifts in spring phenology of three horticultural woody perennials in northeastern USAIntl. J. Biometeorol.495303309

    • Search Google Scholar
    • Export Citation
  • WolfeD.W.ZiskaL.PetzoldtC.SeamanA.ChaseL.HayhoeK.2008Projected change in climate thresholds in the northeastern U.S.: Implications for crops, pests, livestock, and farmersMitig. Adapt. Strategies Glob. Change135555575

    • Search Google Scholar
    • Export Citation

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