Petr Hlavinka

Regional climate change impacts on agricultural crop production in Central and Eastern Europe – hotspots, regional differences and common trends

SUMMARY The present study investigates regional climate change impacts on agricultural crop production in Central and Eastern Europe, including local case studies with different focuses in Austria, the Czech Republic and Slovakia. The area studied experiences a continental European climate and is characterized by strong climatic gradients, which may foster regional differences or trends in the impacts of climate change on agriculture. To study the regional aspects and variabilities of climate change impacts on agriculture, the effect of climate change on selected future agroclimatic conditions, crop yield and variability (including the effect of higher ambient CO2 concentrations) and the most important yield limiting factors, such as water availability, nitrogen balance and the infestation risks posed by selected pests were studied. In general, the results predicted significant agroclimatic changes over the entire area during the 21st century, affecting agricultural crop production through various pathways. Simulated crop yield trends confirmed past regional studies but also revealed that yield-limiting factors maychange fromregionto region. Forexample, pestpressures,as demonstratedbyexamining two pests,arelikely to increase due to warmer conditions. In general, higher potentials for cereal yield increase are seen for wetter and cooler regions (i.e. uplands) than for the drier and warmer lowlands, where yield potentials will be increasingly limited by decreasing crop water availability and heat under most scenarios. In addition, yield variability will increase during the coming decades, but this may decrease towards the end of the 21st century. The present study contributes to the interpretation of previously conducted climate change impact and adaptation studies for agriculture and may prove useful in proposing future research in this field.

Consequences of climate change for the soil climate in Central Europe and the central plains of the United States

This study aims to evaluate soil climate quantitatively under present and projected climatic conditions across Central Europe (12.1°–18.9° E and 46.8°–51.1° N) and the U.S. Central Plains (90°–104° W and 37°–49° N), with a special focus on soil temperature, hydric regime, drought risk and potential productivity (assessed as a period suitable for crop growth). The analysis was completed for the baselines (1961–1990 for Europe and 1985–2005 for the U.S.) and time horizons of 2025, 2050 and 2100 based on the outputs of three global circulation models using two levels of climate sensitivity. The results indicate that the soil climate (soil temperature and hydric soil regimes) will change dramatically in both regions, with significant consequences for soil genesis. However, the predicted changes of the pathways are very uncertain because of the range of future climate systems predicted by climate models. Nevertheless, our findings suggest that the risk of unfavourable dry years will increase, resulting in greater risk of soil erosion and lower productivity. The projected increase in the variability of dry and wet events combined with the uncertainty (particularly in the U.S.) poses a challenge for selecting the most appropriate adaptation strategies and for setting adequate policies. The results also suggest that the soil resources are likely be under increased pressure from changes in climate.

Adaptation options for wheat in Europe will be limited by increased adverse weather events under climate change

Ways of increasing the production of wheat, the most widely grown cereal crop, will need to be found to meet the increasing demand caused by human population growth in the coming decades. This increase must occur despite the decrease in yield gains now being reported in some regions, increased price volatility and the expected increase in the frequency of adverse weather events that can reduce yields. However, if and how the frequency of adverse weather events will change over Europe, the most important wheat-growing area, has not yet been analysed. Here, we show that the accumulated probability of 11 adverse weather events with the potential to significantly reduce yield will increase markedly across all of Europe. We found that by the end of the century, the exposure of the key European wheat-growing areas, where most wheat production is currently concentrated, may increase more than twofold. However, if we consider the entire arable land area of Europe, a greater than threefold increase in risk was predicted. Therefore, shifting wheat production to new producing regions to reduce the risk might not be possible as the risk of adverse events beyond the key wheat-growing areas increases even more. Furthermore, we found a marked increase in wheat exposure to high temperatures, severe droughts and field inaccessibility compared with other types of adverse events. Our results also showed the limitations of some of the presently debated adaptation options and demonstrated the need for development of region-specific strategies. Other regions of the world could be affected by adverse weather events in the future in a way different from that considered here for Europe. This observation emphasizes the importance of conducting similar analyses for other major wheat regions.

Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization

Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley ( Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.

Influence of climatic factors on the low yields of spring barley and winter wheat in Southern Moravia (Czech Republic) during the 1961–2007 period

The paper aims to study the variability of spring barley and winter wheat yields, the most important crops in the Czech Republic, with respect to the variability of weather and climatic factors. Yields of both crops have been studied for 13 districts in Southern Moravia for the 1961–2007 period. From detrended series of spring barley and winter wheat yields, years with very low (lower than the mean minus a 2.5-multiple of the standard deviation) and extremely low (interval given by the mean minus a 1.5- and 2.5-multiple of the standard deviation) yields were selected. Years in which at least one of the districts had extremely low/very low yields were further analyzed. From 10 such years selected separately for spring barley and winter wheat, six of them agreed for both crops. Extreme years were studied using NUTS4-level yield data with respect to temperature, precipitation, the self-calibrated Palmer Drought Severity Index (scPDSI), snow cover, frost patterns, and the onset and duration of select phenophases. Extremely/very low barley yields in 1993, 2000, and 2007 were related to high April–June (AMJ) temperatures, low AMJ precipitation totals, and negative AMJ scPDSI (indicating drought) with an earlier onset of flowering and full ripeness and shorter intervals from tillering to flowering and from flowering to full ripeness compared to the entire 1961–2007 mean. As for extremely/very low winter wheat yields, in addition to the previously mentioned factors, winter patterns also played an important role, particularly the occurrence of severe frosts with a coinciding lack of snow cover and a long-lasting snow cover (in highlands), indicating that low yields are the result of not only one unfavorable factor but a combination of several of them.

Empirical model for estimating daily erythemal UV radiation in the Central European region

Because of its biological effects, erythemal ultraviolet (UV-ERY) radiation (280-400 nm) is a significant part of solar radiation spectrum. In this study a statistical model for estimating daily UV-ERY radiation values was developed. It is based on ground measurements conducted at eight Austrian stations (from 2000 to 2002). As inputs the model requires daily global radiation, daily extraterrestrial radiation, information about the column of total ozone in the atmosphere and the altitude of a selected station. Subsequently the performance of the model was verified by an independent data set originating from measurements in Austria and Czech Republic stations. The verification showed satisfactory performance of the model: the coefficient of determination (R 2 ) varied from 0.97 to 0.99, the root mean square error (RMSE) varied from 9.3 % to 17.7 % and the mean bias error (MBE) varied from -2.5 % to 2.0 %. In addition, the results of the model at Hradec Kralove station were compared with equivalent UV-ERY data from the Solar Radiation Database (SoDa), which are available on the Internet. After successful verification, the model was implemented within the ArcInfo GIS framework in order to carry out a spatial assessment of a stratospheric ozone reduction episode. The event of July 2005 in the Czech Republic was used as a case study. On 30 July 2005 the total ozone amount dropped 12.5 % below the long-term mean, which led to UV-ERY radiation increment ranging from 214 to 391 J·m -2 ·day -1 .

Climate Change Impacts on Czech Agriculture

Chapter summarizes the major impacts of changing climatic conditions in the Czech agriculture. Specific case studies are performed for the whole country (arable land) and are processed through GIS in the spatial grid 500 x 500 m respectively 1 x 1 km if middle Europe is considered. Contribution presents the impacts of climate change on the production of two major field crops (winter wheat and spring barley) in the Czech Republic for different future time horizons (2030, 2050 and 2100). The yield study includes not only the effect of climatic conditions but also the fertilization effect of carbon dioxide. Study is completed by effects of rising temperatures on the spread of temperature-depending biotic factors (selected pests) and changes in agroclimatic conditions for field crops. The basic data which are needed and used are long-term database of the national meteorological service and agricultural organizations which was used for evaluation of growth models (e.g. CERES). Other used tools are models which allow describe the evolution of pests in new climate conditions (e.g. CLIMEX or ECAMON) and various meteorological indieces. Description of expected weather conditions are based on two emission scenarios, according to the IPCC (mostly SRES-A2 and -B1) and three GCM models (NCAR-PCM, ECHAM5 and HadCM3). Their open access monthly outputs are published for the individual time horizons (e.g. 2030, 2050 and 2100) and are prepared in the daily time step by stochastic weather generator. The impacts of climate change are determined by comparing the current and expected state observed phenomena.