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Agrárinformatika / Agricultural Informatics (2010) Vol. 1, No. 2:1-7 Ghaffari, A., Cook, H.F., Lee, H.C. 2002. Climate change and winter wheat management: A modeling scenario for South-Eastern England. Climate Change 55 :509–533. Hardaker, J. B., Huirne, R. B. M., Anderson, J. R., Lien, G. (2004) Coping with Risk in Agriculture. 2nd edn. CABI Publishing, Wallingford-Cambridge Harnos, N. (2003): Simulation studies on the effect of climate change on winter wheat production (In Hungarian) “AGRO-21” Füzetek, 31, 56–73. Hornok, M.: 2008. Effects of the most important agrotechnical elements on the yield of winter wheat. Cereal Research Communications. Volume 36, Number 3, pp. 1243–1246. Jolánkai, M. 2005. Effect of climate change on plant cultivation. (In Hungarian) In: “AGRO-21” Füzetek 41 :47–58. Ladányi M. 2007. Risk methods and their applications in agriculture, Applied Ecology and Environmental Research 6(1), 147-164. Ladányi, M. and Erdélyi, É. (2008) A review of risk methods in climate change impact researches in Hungary. In: Environmental, Health and Humanity Issues in the Down Danubian Region, Multidisciplinary Approaches, Eds: D. Mihailovic, M. Vojinovic Miloradov, World Scientific, 2008. 245-254. Láng, I. 2005. Weather and climate change: change-effect-response. (In Hungarian) In: “AGRO-21” Füzetek 43 :3– 10. Láng, I., Harnos, Zs., Jolánkai, M. 2004. Strategies of adaptation to climatic changes: international experiences and possibilities in Hungary. (In Hungarian) In: “AGRO-21” Füzetek 35 :70–77. Márton, L. 2008. Long-term study of precipitation and fertilization interactions on winter wheat (Triticum aestivum L.) yield in the Nyírlugos Field Trial in Hungary between 1973 and 1990, Cereal Research Communications Volume 36, Number 3, pp. 511-522. Olesen J. E, Bindi M. 2002. Consequences of climate change for European agricultural productivity, land use and policy. European Journal of Agronomy. 16. 4: 239–262. Pepó, P. 2009. Interactive effect of different crop years and agrotechnical factors on the yield of winter wheat (in Hungarian) Növénytermelés Volume 58, Number 2, pp.107-122. Szalay D. K, Farkas I, Szalay D. 2009. Evaluation of nutrient supply as abiotic stressor on winter wheat Triticum aestivum L. performance. Cereal Research Communication 37. (2), pp. 21- 24. Szalay D, Szalay D. K, Hárs T, Klupács H. 2006. Copper – An important element influencing wheat quality. Cereal Research Communication 34. (1): pp. 77-80. Szalay D., Szalay D. K., Farkas I., 2008: Grain quality- a soil plant indicator. Cereal Research Communication 36. (2): 1295-1298 pp. Szenteleki K, Révész A, Hufnagel L, Erdélyi É, Ladányi M, Solymosi N, Horváth L. 2007. Introducing the KKT Climate Research Database Management Software. EFITA Conference, Glasgow, CD-ROM Varga-Haszonits Z. 1987. Agrometeorológiai információk és hasznosításuk, Mezőgazdasági Kiadó, Budapest ISSN 2061-862X http://www.magisz.org/journal 7 Aliz Novák, Éva Erdélyi: How barley growing conditions and its output change in Hungary
Agrárinformatika / Agricultural Informatics (2010) Vol. 1, No. 2:8-16 Magyar Agrárinformatikai Szövetség Hungarian Association of Agricultural Informatics Agrárinformatika Folyóirat. 2010. 1. évfolyam 2. szám Journal of Agricultural Informatics. 2010 Vol. 1, No. 2 From phenology models to risk indicator analysis Márta Ladányi 1 , Szilvia Persely 2 , József Nyéki 3 , Zoltán Szabó 4 I N F O Received 19 Oct. 2010 Accepted 24 Nov. 2010 Available on-line 30 Nov. 2010 Responsible Editor: K. Rajkai Keywords: phenology model, weather indicator, climate change, climate model, sour cherry A B S T R A C T In this paper we outline a phenology model for estimating budbreak and full bloom starting dates of sour cherry on the effective heat sums with reasonable accuracy. With the help of RegCM3.1 model the possible trends of the phenology timing in the middle of the 21st century the shift of 12-13 days earlier budbreak and 6-7 days earlier of full bloom due to the warmer weather conditions can be clearly indicated. For the climatic characterization of sour cherry bloom period in between 1984-2010 and for the description of the expected changes in this very sensitive period of sour cherry with respect to the time slice 2021-2050, we introduce seven climatic indicators as artificial weather parameters such as the numbers of days when the temperature was under 0°C and above 10 °C, the numbers of days when there was no and more than 5 mm precipitation as well as the absolute minimum, the mean of minimum and the mean of maximum daily temperatures. We survey the changes of the indicators in the examined period (1984- 2010) and, regarding the full bloom start model results, we formulate the expectations for the future and make comparisons. 1. Introduction The closing decades of the 20 th century and the early years of the present century in Hungary were unusually warm with several extreme events (droughts, storms, hails, frosts, floods etc.). Living systems have their own capacity and speed of adaptation to the particular changes they face in their environment. In case the changes are small and occur slowly enough, the success of adaptation is almost sure. Estimations of warming by about 2-3°C or more in a century or more may seem to be not too much, however, a change of this speed can be too fast to keep up with (Houghton, 2009). Of course, estimations regarding the near or far future are companied with considerable uncertainties which are caused by imperfect knowledge of way and scale of climate change and its direct and indirect impacts. However, modeling and simulating climate change impacts has of great importance for having a sight of the possible future in order to be able to prepare. In this work we focus on a method with which we can characterize the expected changes of a very sensitive period of sour cherry, namely bloom and ten days before. First we introduce a phenology model to estimate the starting dates of budbreak and full bloom. To learn the characteristics and the way of change of the early vegetation period of sour cherry is very important because the success of production together with the one of plant protection and technology techniques scheduling is depending mainly on phenological information. Moreover, several risk factors such as frost, infection, insufficient pollination etc. can be traced back to the connection of weather and 1 Márta Ladányi Corvinus Unversity of Budapest, 1118 Budapest, Villányi út 29., Hungary marta.ladanyi@uni-corvinus.hu 2 , 3, 4 Szilvia Persely, József Nyéki, Zoltán Szabó Institute for Extension and Development, University of Debrecen, 4032 Debrecen, Böszörményi út 138., Hungary suto@agr.unideb.hu nyeki@agr.unideb.hu zszabo@agr.unideb.hu ISSN 2061-862X http://www.magisz.org/journal 8 Márta Ladányi, Szilvia Persely, József Nyéki, Zoltán Szabó: From phenology models to risk indicator analysis
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