Climate change has been shown to impact aspects of agriculture and phenology. This study aims to quantify changes in the timing of garden strawberry blooms and harvests in the Baltic States using Regional Climate Models (RCMs). First, parameters for a strawberry phenology model based on the growing degree day (GDD) methodology were determined. Growing degree days were calculated using a modified sine wave method that estimates the diurnal temperature cycle from the daily maximum and minimum temperature. Model parameters include the base temperature and the required cumulative GDD sum, estimated from phenological and meteorological observations in Latvia for the years 2010–2013 via iterative calibration. Then an ensemble of bias-corrected RCM results (ENSEMBLES project) was used as input to the phenological model to estimate the timing of strawberry phenological processes for the years 1951–2099.
The results clearly show that strawberry phenological processes can be expected to occur earlier in the future, with a significant change in regional patterns. Differences between coastal and inland regions are expected to decrease over time. The uncertainty of the results was estimated using the RCM ensemble spread, with northern coastal locations showing the largest spread.
Ahas, R. 1999. Long-term phyto-, ornitho- and ichthyophenological time-series analysis in Estonia. International Journal of Biometeorology, 42, 119–123.
http://dx.doi.org/10.1007/s004840050094
Ahas, R., Jaagus, J. & Aasa, A. 2000. The phenological calendar of Estonia and its correlation with mean air temperature. International Journal of Biometeorology, 44, 159–166.
http://dx.doi.org/10.1007/s004840000069
Ahmed, K. F., Wang, G., Silander, J., Wilson, A. M., Allen, J. M., Horton, R. & Anyah, R. 2013. Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the US northeast. Global and Planetary Change, 100, 320–332.
http://dx.doi.org/10.1016/j.gloplacha.2012.11.003
Allen, J. C. 1976. A modified sine wave method for calculating degree days. Environmental Entomology, 5, 388–396.
http://dx.doi.org/10.1093/ee/5.3.388
Baskerville, G. L. & Emin, P. 1969. Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology, 50, 514–517.
http://dx.doi.org/10.2307/1933912
Bleiholder, H., Weber, E., Lancashire, P. D., Feller, C., Buhr, L., Hess, M., Wicke, H., Hack, U., Meier, U., Klose, F. R., van den Boom, T. & Stauss, R. 2001. Growth Stages of Mono and Dicotyledonous Plants. BBCH-Monograph. Federal Biological Research Centre for Agriculture and Forestry, 158 pp.
Bonhomme, R. 2000. Bases and limits to using ‘degree.day’ units. European Journal of Agronomy, 13, 1–10.
http://dx.doi.org/10.1016/S1161-0301(00)00058-7
Cesaraccio, C., Spano, D., Duce, P. & Snyder, R. L. 2001. An improved model for determining degree-day values from daily temperature data. International Journal of Biometeorology, 45, 161–169.
http://dx.doi.org/10.1007/s004840100104
Chmielewski, F.-M. & Rötzer, T. 2001. Response of tree phenology to climate change across Europe. Agricultural and Forest Meteorology, 108, 101–112.
http://dx.doi.org/10.1016/S0168-1923(01)00233-7
Chmielewski, F.-M., Müller, A. & Bruns, E. 2004. Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961–2000. Agricultural and Forest Meteorology, 121, 69–78.
http://dx.doi.org/10.1016/S0168-1923(03)00161-8
Christensen, H. J., Kjellström, E., Giorgi, F., Lenderink, G. & Rummukainen, M. 2010. Weight assignment in regional climate models. Climate Research, 44, 179–194.
http://dx.doi.org/10.3354/cr00916
Christensen, H. J., Kjellström, E. & Zorita, E. 2015. Projected change – atmosphere. In Second Assessment of Climate Change for the Baltic Sea Basin (Bolle, H.-J., Menenti, M. & Rasool, S. I., eds), pp. 217–233. Springer International Publishing.
http://dx.doi.org/10.1007/978-3-319-16006-1_11
Christidis, N., Stott, P. A., Brown, S., Karoly, D. J. & Caesar, J. 2007. Human contribution to the lengthening of the growing season during 1950–99. Journal of Climate, 20, 5441–5454.
http://dx.doi.org/10.1175/2007JCLI1568.1
Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. 2007. Shifting plant phenology in response to global change. TRENDS in Ecology and Evolution, 22, 357–365.
http://dx.doi.org/10.1016/j.tree.2007.04.003
[EC] European Commission. 2013. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. An EU Strategy on Adaptation to Climate Change. COM (2013), 216, 1–11.
Falloon, P. & Betts, R. 2010. Climate impacts on European agriculture and water management in the context of adaptation and mitigation – the importance of an integrated approach. Science of the Total Environment, 408, 5667–5687.
http://dx.doi.org/10.1016/j.scitotenv.2009.05.002
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Glecker, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C. & Rummukainen, M. 2013. Evaluation of climate models. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Stocker, T. F., et al., eds), pp. 741–866. Cambridge University Press.
Fu, Y. H., Campioli, M., Van Oijen, M., Deckmyn, G. & Janssens, I. A. 2012. Bayesian comparison of six different temperature-based budburst models for four temperate tree species. Ecological Modelling, 230, 92–100.
http://dx.doi.org/10.1016/j.ecolmodel.2012.01.010
Fu, Y. H., Campioli, M., Deckmyn, G. & Janssens, I. A. 2013. Sensitivity of leaf unfolding to experimental warming in three temperate tree species. Agricultural and Forest Meteorology, 181, 125–132.
http://dx.doi.org/10.1016/j.agrformet.2013.07.016
Inouye, D. W. 2008. Effects of climate change on phenology, frost damage, and floral abundance of montane wildflowers. Ecology, 89, 353–362.
http://dx.doi.org/10.1890/06-2128.1
Jaagus, J. & Ahas, R. 2000. Space-time variations of climatic seasons and their correlation with the phenological development of nature in Estonia. Climate Research, 15, 207–219.
http://dx.doi.org/10.3354/cr015207
Johnson, M. E. & Fitzpatrick, E. A. 1977. A comparison of methods of estimating a mean diurnal temperature curve during the daylight hours. Archiv für Meteorologie, Geophysik und Bioklimatologie, Serie B, 25, 251–263.
http://dx.doi.org/10.1007/BF02243056
Kalvāne, G., Romanovskaja, D., Briede, A. & Bakšiene, E. 2009. Influence of climate change on phenological phases in Latvia and Lithuania. Climate Research, 39, 209–219.
http://dx.doi.org/10.3354/cr00813
Kalvāns, A., Bitāne, M. & Kalvāne, G. 2014. Forecasting plant phenology: evaluating the phenological models for Betulapendula and Padusracemosa spring phases, Latvia. International Journal of Biometeorology, 59, 165–179.
http://dx.doi.org/10.1007/s00484-014-0833-5
Kristensen, K., Schelde, K. & Olesen, J. E. 2011. Winter wheat yield response to climate variability in Denmark. The Journal of Agricultural Science, 149, 33–47.
http://dx.doi.org/10.1017/S0021859610000675
Krug, J., Eriksson, H., Heidecke, C., Kellomäki, S., Köhl, M., Lindner, M. & Saikkonen, K. 2015. Socio-economic impacts – forestry and agriculture. In Second Assessment of Climate Change for the Baltic Sea Basin (Bolle, H.-J., Menenti, M. & Rasool, S. I., eds), pp. 399–409. Springer International Publishing.
http://dx.doi.org/10.1007/978-3-319-16006-1_21
Laugale, V. 2000. Strawberry production in Latvia. IOBC wprs Bulletin, 23(11), 11–16.
Laugale, V. & Lepse, L. 2007. Research trials on strawberry cultivars in Pūre Horticultural Research Station (Latvia) during the last 10 years. Sodininkystė ir Daržininkystė, 26, 81–92.
Linderhorm, H. W. 2006. Growing season changes in the last century. Agricultural and Forest Meteorology, 137, 1–14.
http://dx.doi.org/10.1016/j.agrformet.2006.03.006
Linkosalo, T., Lappalainen, H. K. & Hari, P. 2008. A comparison of phenological models of leaf bud burst and flowering of boreal trees using independent observations. Tree Physiology, 28, 1873–1882.
http://dx.doi.org/10.1093/treephys/28.12.1873
Meier, H. M., Höglund, A., Döscher, R., Andersson, H., Löptien, U. & Kjellström, E. 2011. Quality assessment of atmospheric surface fields over the Baltic Sea from an ensemble of regional climate model simulations with respect to ocean dynamics. Oceanologia, 53, 193–227.
http://dx.doi.org/10.5697/oc.53-1-TI.193
Olesen, J. E., Carter, T. R., Diaz-Ambrona, C. H., Fronzek, S., Heidmann, T., Hickler, T., Holt, T., Minguez, M. I., Morales, P., Palutikof, J. P., Quemada, M., Ruiz-Ramos, M., Rubaek, G. H., Sau, F., Smith, B. & Sykes, M. T. 2007. Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models. Climatic Change, 81, 123–143.
http://dx.doi.org/10.1007/s10584-006-9216-1
Parton, W. J. & Logan, J. A. 1981. A model for diurnal variation in soil and air temperature. Agricultural and Forest Meteorology, 23, 205–216.
http://dx.doi.org/10.1016/0002-1571(81)90105-9
Reicosky, D. C., Winkelman, L. J., Baker, J. M. & Baker, D. G. 1989. Accuracy of hourly air temperature calculated from daily minima and maxima. Agricultural and Forest Meteorology, 46, 193–209.
http://dx.doi.org/10.1016/0168-1923(89)90064-6
Richardson, A. D. & O’Keefe, J. 2009. Phenological differences between understory and overstory: a case study using the long-term Harvard forest records. In Phenology of Ecosystem Processes (Noormets, A., ed.), pp. 87–117. Springer, New York.
http://dx.doi.org/10.1007/978-1-4419-0026-5_4
Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O. & Toomey, M. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agriculture and Forest Meteorology, 169, 156–173.
http://dx.doi.org/10.1016/j.agrformet.2012.09.012
Roltsch, W. J., Zalom, F. G., Strawn, A. J., Strand, J. F. & Pitcairn, M. J. 1999. Evaluation of several degree-day estimation methods in California climates. International Journal of Biometeorology, 42, 169–176.
http://dx.doi.org/10.1007/s004840050101
Sennikovs, J. & Bethers, U. 2009. Statistical downscaling method of regional climate model results for hydrological modeling. In Proceedings of the 18th World IMACS / MODSIM Congress, Cairns, Australia 13–17 July 2009 (Anderssen, R. S., Braddock, R. D. & Newham, L. T. H., eds), pp. 3962–3968.
Snyder, R. L., Spano, D., Cesaraccio, C. & Duce, P. 1999. Determining degree-day thresholds from field observations. International Journal of Biometeorology, 42, 177–182.
http://dx.doi.org/10.1007/s004840050102
Teets, D. A. 2003. Predicting sunrise and sunset times. The College Mathematics Journal, 34, 317–321.
http://dx.doi.org/10.2307/3595771
Teutschbein, C. & Seibert, J. 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. Journal of Hydrology, 456, 12–29.
http://dx.doi.org/10.1016/j.jhydrol.2012.05.052
Tõnutare, T., Keert, K., Szajdak, L. & Moor, U. 2014. Composition of commercially produced organic and conventional strawberries. Nutritional and Food Science, 44, 562–575.
http://dx.doi.org/10.1108/NFS-12-2013-0151
Van der Linden, P. & Mitchell, J. E. 2009. ENSEMBLES: Climate Change and Its Impacts: Summary of Research and Results from the ENSEMBLES Project. Met Office Hadley Centre, Exeter, 160 pp.
Wang, J. Y. 1960. A critique of the heat unit approach to plant response studies. Ecology, 41, 785–790.
http://dx.doi.org/10.2307/1931815
Wibig, J., Maraun, D., Benestad, R., Kjellström, E., Lorenz, P. & Christensen, O. B. 2015. Projected change – models and methodology. In Second Assessment of Climate Change for the Baltic Sea Basin (Bolle, H.-J., Menenti, M. & Rasool, S. I., eds), pp. 189–215. Springer International Publishing.
http://dx.doi.org/10.1007/978-3-319-16006-1_10
Wilson, L. T. & Barnett, W. W. 1983. Degree-days: an aid in crop and pest management. California Agriculture, 37, 4–7.
© 2016 Authors. This is an Open Access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License.