Climate change effects on the maize growing season Márta Gaál1 and Levente Horváth2 1 Corvinus University of Budapest, Hungary, [email protected] 2 Adaptation to Climate Change Research Group, Hungarian Academy of Sciences, Hungary Abstract The potential future climatic conditions of maize growing in Hungary were examined based on the Hadley Centre’s HadCM3 model with A2 and B2 emission scenarios in different time periods. The scenario data set contains monthly data, which was a limiting factor of this work. There was no possibility to analyze for example the risk of spring frost, summer hot days or extreme precipitation events. Results show that the potential vegetative period – temperature higher than 10°C – could be longer with approximately 30 days in the middle of the century and about 50 days at the end. A drastic increase of the heat units can be observed with a decrease of precipitation. The unfavorable effects of the increasing temperature and decreasing precipitation could be characterized well by aridity indices. Based on the longer potential vegetative period and increasing effective heat units, in the near future would be possible to grow maize varieties with 2–3 higher FAO groups, which have a higher potential yield, but the precipitation would be a serious limiting factor. But later we have problems with the interpretation, as the heat unit values are going out of the present scope. Attempts were done also to cluster the climatic year types. These results show also a continuous shifting, and it can be observed that from about 2030 the minimum heat unit values become equal to the formerly high values. Keywords: maize growing season, climate scenarios, climatic year types Materials and methods Climate scenario data were taken from the Tyndall scenario data-sets TYN SC 1.0 (Mitchell et al, 2004, 2005). Data is supplied from 2001 to 2100 at a monthly time-step, on a 10’ grid covering European land only. Due to limitations, this paper focuses on the Hadley Centre’s HadCM3 model with the A2 emission scenario (IPCC, 2001). The baseline (1961-90) data were obtained from the CRU CL 2.0 database (New et al., 2002), which has – according to the scenarios – 10’ grid spatial resolution. As the baseline period was 30 years, the scenario data were analyzed in moving 30-years time periods. The aim of our work was to study the climatic conditions of maize growing in Hungary, therefore the area examined was a box around the country, located between 15.58°-23.42°E and 45.25°-49.42° N. To interpret the results GIS methods were used, too, using the ArcGIS program. Grid data were represented as ESRI grid raster with bilinear interpolation. For some calculations overlay functions were applied. Climatic year types were defined by cluster analysis, using the precipitation and effective heat units (>10°C) of the summer half year (April-September). Ward method with Euclidean

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distances was used, for standardized data. For this analysis not only the baseline data, but other historic data since 1951 were used from the database of the Hungarian Meteorological Office. Results Potential vegetative period Beginning and end of the vegetative period were specified with the day number, when mean temperature across the 10°C limit. The length of the potential vegetative period is the number of days between the beginning and the end day. As the database contains only monthly data, calculation was made by linear interpolation between the monthly averages. The disadvantage of this method is that can not take into consideration the late spring or early autumn frosts. However, many researches proved that extreme events, also late spring frost, have increased in the Carpathian basin and are expected to increase more in frequency (Bartholy – Pongrácz, 2006). Results show that the potential vegetative period could be longer with approximately 25-30 days in the middle of the century and about 50 days at the end. Expected changes in the 10°C crossing days are summarized in Table 1. Not only the length of the vegetative period would increase, but also the ranges of the changes have a slight increase with the time. Table 1 Expected changes in the length of the vegetative period (days) Periods Changes in Spring Changes in Autumn 2001-2030 -7 – -4 +4 – 6 2016-2045 -8 – -4 +8 – 10 2031-2060 -11 – -7 +10 – 13 2046-2075 -17 – -13 +15 – 20 2061-2090 -24 – -19 +19 – 23 Aridity index All of the scenarios indicate the increase of temperature, only the extent of it is different. In case of precipitation the changes do not have so monotone system, in some periods (or regions) a slightly increase could expected. Unfortunately even in these cases we must count with relative decrease, as the temperature will increase in greater degree. The combined effects of warmer temperatures and reduced mean summer precipitation would enhance the occurrence of heat waves and the risk of drought. The unfavorable effects of the increasing temperature and decreasing precipitation could be characterized well by aridity indices. In this work Ángyán’s aridity index was used, which was developed especially for maize growing. The index can be calculated from the effective (>10°C) heat units in April-September divided by the annual precipitation. The index was elaborated for the effective heat units of 1250-1750°C, while for the precipitation between 500-720 mm. As the scenarios are not correct forecasts for each year, results are presented in moving 30-years time periods (Fig. 1). If the value of the aridity index is below 1.00 maize can not grown due to the cold conditions, the optimal values are around 2.5 and above 3.11 growing is advised only with irrigation. Looking at the figures it can be seen that in the baseline period the whole country was climatically suitable for maize growing (some parts are not suitable due to the relief, but it is not indicated in the maps), and the great plain areas were near the optimum value. Going ahead in time areas with the need of irrigation become more and more great. At about 2030 the IAALD AFITA WCCA2008

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appearance of the values above 3.51 can be observed and it indicates a new problem, as it means that the values of the effective heat units are going out of the normal range. Further research needed to analyze, whether it means that these areas become unsuitable for maize growing at all (turn into desert), or with new varieties and irrigation methods the cultivation could go on. 1961–90

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Fig.3. Ángyán’s aridity index bases on the A2 scenario

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Climatic year types Climatic year types were analyzed only for the region of Debrecen, which has a great importance in Hungarian agricultural production. Clustering the years from 1951 to 2100 (Fig.2.) we can divide four groups, and we can call them as wet-cold (1), dry-cold (2), wet-warm (3) and dry-warm (4).

Fig.2. Cluster analysis result of the years 1951-2100 Analyzing in 30 years periods, results show a continuous shifting (Fig. 3.). The long term tendencies indicate increase in the heat units and decrease in precipitation. In these parts the meaning of the four sub-groups can be the same (wet-cold, dry-cold, wet-warm and dry-warm), but the values are different. The separation of the groups is usually clear. To be easier to compare, the scaling of the figures are always the same. It can be observed that from about 2030 the minimum heat unit values become equal to the formerly high values.

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Discussion It is well-known that climate basically determines agriculture therefore it is important to analyze the possible changes in it. The aim of this work was to analyze the predicted spatial and temporal changes in the maize growing season in Hungary. Based on the longer potential vegetative period and increasing effective heat units, in the near future would be possible to grow maize varieties with higher FAO groups, which have a higher potential yield, but the lack of precipitation would be a limiting factor. Results indicate that short-term adaptation of agriculture may include changes in crop varieties or sowing dates. If sowing dates would not change then simulation modeling (Erdélyi et al., 2008) show that the phenological phases might be shorter and occur earlier as a result of increasing temperature. But later – in the second half of the century – we have problems with the interpretation, as the heat unit values are going out of the normal scope, will be almost double. It is problematic also in simulation modeling, as we can get easily false results. We hope that the presented results – together with the simulations – can help developing adaptation and mitigation strategies. Acknowledgements This work was supported by the NKFP 6-00079/2005 project. References Bartholy, J., Pongrácz, R. (2006) Szélsőséges éghajlati tendenciák alakulása a XX. században a Kárpát-medencében, in: Láng I. et al. (szerk): A globális klímaváltozás: hazai hatások és válaszok. Akaprint Kft., Budapest Erdélyi, É., Ferenczy, A. and Boksai, D. (2008) A klímaváltozás várható hatása a kukorica és búza fenofázisainak alakulására [The possible effects of climate change on the phenologycal phases of corn and wheat]. “Klíma-21” Füzetek 53: 115-130. IPCC (2001) Climate change 2001: Synthesis Report. Cambridge University Press, Cambridge, UK, Available at http://www.ipcc.ch Mitchell, T.D, Carter, T.R, Jones, P.D., Hulme, M. and New, M. (2004) A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). Tyndall Centre Working Paper 55, University of East Anglia, Norwich, UK. Available at http://www.tyndall.ac.uk/publications/working_papers/wp55.pdf Mitchell, T.D. and Jones, P.D. (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. Journal of Climatology 25: 693–712 New, M., Lister, D., Hulme M. and Makin I. (2002) A high-resolution data set of surface climate over global land areas. Climate Research 21: 1-25.

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