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The Thaya is a trans-national river basin that is situated in the Czech Republic and Austria. Different human activities in the basin and multiple water uses increase the water demand. This increase, combined with the recent droughts events in 2017 and 2018, has recently resulted in reconsidering the water management strategies for future climates. This contribution aims to evaluate the effect of climate change on the water balance of the Thaya. The aim is to apply two different hydrological models in an identical setting (the same model inputs, scenarios, and regional and water use data) and to identify water availability and its change under various climate and water use scenarios. The assessments compare BILAN and TUWmodel hydrological models coupled with the WATERRES water use module and a large sample of climate projections (the CMIP5 and CMIP6 models), which represent various socioeconomic pathways combined with projections of possible changes in water use. The results will demonstrate an insight into how the water balance in different parts of the Thaya basin has changed in the past and what are the possible effects of climate change on these water resources in the future.

Linked to major volcanic eruptions around 536 and 540 CE, the onset of the Late Antique Little Ice Age has been described as the coldest period of the past two millennia. The exact timing and spatial extent of this exceptional cold phase are, however, still under debate because of the limited resolution and geographical distribution of the available proxy archives. Here, we use 106 wood anatomical thin sections from 23 forest sites and 20 tree species in both hemispheres to search for cell-level fingerprints of ephemeral summer cooling between 530 and 550 CE. After cross-dating and double-staining, we identified 89 Blue Rings (lack of cell wall lignification), nine Frost Rings (cell deformation and collapse), and 93 Light Rings (reduced cell wall thickening) in the Northern Hemisphere. Our network reveals evidence for the strongest temperature depression between mid-July and early-August 536 CE across North America and Eurasia, whereas more localised cold spells occurred in the summers of 532, 540-43, and 548 CE. The lack of anatomical signatures in the austral trees suggests limited incursion of stratospheric volcanic aerosol into the Southern Hemisphere extra-tropics, that any forcing was mitigated by atmosphere-ocean dynamical responses and/or concentrated outside the growing season, or a combination of factors. Our findings demonstrate the advantage of wood anatomical investigations over traditional dendrochronological measurements, provide a benchmark for Earth system models, support cross-disciplinary studies into the entanglements of climate and history, and question the relevance of global climate averages.

The main aim of the current study was to present the abilities of widely used crop models to simulate four different field crops (winter wheat, spring barley, silage maize and winter oilseed rape). The 13 models were tested under Central European conditions represented by three locations in the Czech Republic, selected using temperature and precipitation gradients for the target crops in this region. Based on observed crop phenology and yield from 1991 to 2010, performances of individual models and their ensemble were analyzed. Modelling of anthesis and maturity was generally best simulated by the ensemble median (EnsMED) compared to the ensemble mean and individual models. The yield was better simulated by the best models than estimated by an ensemble. Higher accuracy was achieved for spring crops, with the best results for silage maize, while the lowest accuracy was for winter oilseed rape according to the index of agreement (IA). Based on EnsMED, the root mean square errors (RMSEs) for yield was 1365 kg/ha for winter wheat, 1105 kg/ha for spring barley, 1861 kg/ha for silage maize and 969 kg/ha for winter oilseed rape. The AQUACROP and EPIC models performed best in terms of spread around the line of best fit (RMSE, IA). In some cases, the individual models failed. For crop rotation simulations, only models with reasonable accuracy (i.e. without failures) across all included crops within the target environment should be selected. Application crop models ensemble is one way to increase the accuracy of predictions, but lower variability of ensemble outputs was confirmed.

To date, assessing the adaptive measures to climate change effects on cropping systems have generally been based on data from field trials and crop models. This strategy can only explore a restricted number of options with a limited spatial extent. Therefore, we designed a questionnaire that incorporated both qualitative and quantitative aspects of climate change adaptation in the agricultural sector. The questionnaire was distributed to experts from 15 European countries to map both the observed and planned climate adaptive measures in general and for five major crops (wheat, oilseed rape, maize, potato, and grapevine) in six environmental zones (EnZs) across Europe. In northern Europe, changed timing of field operations and introduction of new crops and cultivars were the already observed as the main adaptations to a longer growing season and reduced low-temperature stress under climate change. Farmers in central and southern Europe were mainly changing water and soil management as well as adopting drought-tolerant cultivars to cope with increasing evapotranspiration and higher variability and lower predictability of rainfall. Crop protection, crop insurance, and early warning/forecast systems were considered effective ways to reduce the economic losses from increased climate-related risks and extremes. The risks and associated adaptation measures vary for different crops in different EnZs. Across Europe, changes in field operation practices, fertilisation regime, crop protection, and cultivar selection are expected to be the most prominent adaptive measures under future projected climate change. In southern and central Europe, improved irrigation systems, changing cropping systems, and revised environmental regulations and subsidy schemes are being introduced as part of adaptation planning due to the projected warmer and drier climate. In northern Europe, there are also considerations of changing landscape and environmental regulations to cope with increasing rainfall variability and changing cropping practices due to longer growing seasons. The thorough understanding of the observed and foreseen adaptations in the different zones will be helpful for supporting decision making at both farm and policy levels across Europe.

The largest explosive volcanic eruption of the Common Era in terms of estimated sulphur yield to the stratosphere was identified in glaciochemical records 40 years ago, and dates to the mid-thirteenth century. Despite eventual attribution to the Samalas (Rinjani) volcano in Indonesia, the eruption date remains uncertain, and the climate response only partially understood. Seeking a more global perspective on summer surface temperature and hydroclimate change following the eruption, we present an analysis of 249 tree-ring chronologies spanning the thirteenth century and representing all continents except Antarctica. Of the 170 predominantly temperature sensitive high-frequency chronologies, the earliest hints of boreal summer cooling are the growth depressions found at sites in the western US and Canada in 1257 CE. If this response is a result of Samalas, it would be consistent with an eruption window of circa May–July 1257 CE. More widespread summer cooling across the mid-latitudes of North America and Eurasia is pronounced in 1258, while records from Scandinavia and Siberia reveal peak cooling in 1259. In contrast to the marked post-Samalas temperature response at high-elevation sites in the Northern Hemisphere, no strong hydroclimatic anomalies emerge from the 79 precipitation-sensitive chronologies. Although our findings remain spatially biased towards the western US and central Europe, and growth-climate response patterns are not always dominated by a single meteorological factor, this study offers a global proxy framework for the evaluation of paleoclimate model simulations.

Process-oriented agro-ecosystem models are increasingly applied to assess crop management options or impacts of climate change on agricultural production, food security and ecosystem services. Thereby, the aggregation of initial soil and climate information is a widely used approach for performing simulations at larger scales such as regions, nations or even globally. In this context, the ability of models to respond to different site conditions is essential for high quality impact assessment through the use of modelling tools. As part of a model inter-comparison the present study investigated models' yield response on variable site conditions using data sets from two well-documented fields, one located in Germany and one in Italy. The fields were sampled at 60 and 100 grid points, respectively, and soil and crop variables were recorded at varying intensity for the entire simulation period covering three growing seasons. The data was provided successively to the participating modelling groups in three calibration steps (a, b, and c) and the first growing season was considered for calibration. Model validation was based on these steps and each growing season as well as on the entire simulation period considering the soil state variables mineral nitrogen and water content (N, WC) as well as crop yield, biomass, and leaf area index (LAI). The WC was best depicted by the models, resulting in high correlation coefficients (r) up to 0.81 between simulated and observed values. The root mean square error (RMSE) of simulated N ranged from 20 kg ha(-1) to 1072 kg ha(-1) regarding all steps and growing seasons. The annual within-field variability of yields was better simulated by the models when observed subsoil information was provided. However, the RMSE ranged from 0.5 t ha(-1) to 3.5 t ha(-1) at the German field, and from 0.6 t ha(-1) to 5.9 t ha(-1) at the Italian field, respectively. It was found that intensified calibration did not necessarily lead to improved model output. Furthermore, single models showed specific inconsistencies in their algorithms when, for example, underestimated WC was associated with overestimated yields. In total, the sensitivity of models to spatially variable site conditions differed considerably. The importance of quality-assured soil and yield information for model improvement was highlighted.

This study quantified the crop water consumption, crop-specific irrigation requirements, and availability of water resources to catchments under climate change in the Czech Republic (CZ). Within the SoilClim model and BILAN-WATERES hydrological water balance modeling process, we tried to answer the question of whether there are at least theoretical water resources in the individual catchments of the CZ that could cover possible higher demands for irrigation. An ensemble of five global climate models under the moderate representative concentration pathway (RCP4.5) from the EURO-CORDEX initiative was chosen to project the future water use indicators. The irrigation water requirement indicators for the growing season (GS) of vineyards, hop gardens, orchards, vegetables, and fodder crops were calculated in 1143 catchments for two periods, 2031–2050 (Sc1) and 2061–2080 (Sc2), compared to the observed period 1961–2020 (Obs). To project irrigation scenarios in agricultural water management, the following water use indicators were quantified: relative soil moisture at 0–40 cm (AWR1) and 0–100 cm (AWR), crop water balance (Rain-ETa), irrigation water requirement (Irrig), and the ratio of actual and reference evapotranspiration (ETratio). To assess areas with a critically low water supply and quantify the frequency of water deficit during the GS of each crop, we calculated the number of days with extreme values of water use indicators. Quantification of the extreme irrigation characteristics reflected the highest depletion of soil moisture and the highest water demands, i.e., when the assessed indicators reached the 25th percentiles. For highly marketable vegetables, the largest deficit in Rain-ETa during the GS for Sc1 was projected. If current vegetable growing areas and cropping systems remain unchanged, Irrig will increase by 10.2% by the end of the 21st century under RCP4.5. Although current potato planting areas have soils with a high available water capacity, they will become controlled by the water deficit over the next few decades. The accumulated vineyard water required suggests that 15% and 25% of irrigation water will be lost by evaporation from the soil surface during the 2030s and 2080s, respectively. However, changes in future hopyard irrigation extent and amounts may have important implications in largely cropped irrigation hotspots. In the main traditional hop region for the 2030s, we project a 25% depletion of soil moisture and an increase of ETratio

Climate resilience refers to the capacity of a system to buffer core functions against climate-related uncertainty and variability (1). The occurrence of diversity in responses to weather variability within a functional group or species (2, 3), such as European wheat that supplies bread and pasta, can ensure a reasonable yield regardless of weather conditions and provides genetic material for selection under changing climate (4). Genetic diversity is not directly related to response diversity, as shown for forage crops (5). Since most of the forage crop species were distributed among several weather response clusters and most of the clusters contained several species, the genetic closeness did not fully explain responses to critical weather conditions. This phenomenon may represent a keystone for breeding and thus deserves to be explored further. Indeed, genetic response diversity deserves more attention with respect to yield and quality (6). The suitability of response diversity to describe agronomic fitness in wheat monocultures is questioned by Snowdon et al. (7), but no arguments are presented. We (8) consider the approach suitable to assess and enhance the resilience of monocultures and thereby increase the stability of total yield under weather variability regardless of whether the complementary cultivars are cultivated on one farm or within a region, national borders, or Europe. Cultivar (or crop) mixtures common in forage cultivation and sometimes used with cereals … [↵][1]1To whom correspondence should be addressed. Email: helena.kahiluoto{at}lut.fi. [1]: #xref-corresp-1-1

A long-term context is important for understanding past climatic variability. Although tree-ring widths (TRWs) are widely used as a proxy for reconstructing past climate, the use of annually-resolved values of δ13C and δ18O tree-ring stable isotopes (TRSIs) is increasing and may provide further valuable information. Here, we present a 487-year-long TRW chronology and 240-year-long TRSI chronology for Bosnian pine (Pinus heldreichii H. Christ) and compare them to each other. We demonstrate that both δ13C and δ18O values are better proxies for temperature, precipitation, and drought than TRW. The correlations between these climate parameters and TRSIs are strongest for the combined summer (JJA) period. The results of temporal and spatial field correlation indicate that TRSI chronologies are stable, reliable proxies for JJA precipitation reconstruction over the whole Balkan Peninsula and surrounding eastern Mediterranean region. However, the stability of the temperature signal of the both δ13C and δ18O chronologies declines after the 1950s. Our work supports the emerging evidence that TRSI data track climate variability more accurately than a conventional TRW approach and can be subsequently used for the reconstruction of past climate.

Tree-ring stable oxygen and carbon isotope ratios (δ18O and δ13C) are an important archive for climate reconstructions. However, it remains unclear whether the polyvinyl acetate emulsion, often used for the preservation and fixation of wood samples, influences δ18O and δ13C signals. Further uncertainties are associated with the possible effects of geographical origin and cambial age of historical samples. Here, we present annually-resolved and absolutely-dated δ18O and δ13C measurements of 21 living oaks (Quercus robur and Q. petraea) from the Czech Republic. We find that the δ18O and δ13C signals in the extracted alpha-cellulose are not affected by polyvinyl acetate treatment. Covering the entire 20th century and reaching until 2018 CE, our dataset reveals spatial and temporal coherency within and between the individual δ18O and δ13C chronologies of different oak species, sample locations, and tree ages. Highly significant (p

Leaf area index (LAI) is an important determinant of biomass production and yield of short rotation bio-energy plantation. An accurate measurement of LAI is critical for quantifying light interception and penetration within the canopy, and subsequently understanding its influence on the stand carbon and energy balance. The aim of the current study is validation of the Sunscan Plant Canopy Analyzer which serves as an indirect method for the evaluation of the seasonal patterns of LAI, relation between LAI and above ground woody dry biomass and to determine the specific leaf area in short-rotation poplar hybrid clone J-105 (Populus nigra × P. maximowiczii) in uncoppiced (1st rotation) and coppiced (2nd rotation), respectively. LAI was measured in uncoppiced and coppiced by two different methods using indirect (SunScan Plant Canopy Analyzer) and direct (litterfall collection). Sunscan Plant Canopy Analyzer was compared against litterfall collection (only way to retrieve the actual LAI). Simple regression (R2 = 0.82) model was fitted to validate indirect measurement method and a very good agreement (82%) was observed in LAI values estimated from SunScan Plant Canopy Analyzer and from litterfall collection. Seasonal variability of LAI in a short rotation coppice (SRC) culture of poplar clone J-105 was evaluated over six years period (2008-2013), for uncoppiced (2008 and 2009) and coppiced (2010, 2011, 2012 and 2013) culture. The maximum canopy LAI (LAImax) reached 7.3 (uncoppiced) and 9.5 (coppiced). The linear regression (R2 = 0.93) for average LAI and above ground woody dry biomass was determined, and it was found that LAI acts an indicator of biomass productivity. Specific leaf area (SLA) was estimated in both uncoppiced and coppiced culture of poplar. The maximum SLA was found to be 138.9 cm2g-1 in uncoppiced and 126.9 cm2g-1 in coppiced. To conclude, the evaluated indirect LAI measurement method is portable, reliable and faster than direct LAI measurement in high density poplar short-rotation coppice culture.

peer-reviewed Europe accounts for around 20% of the global cereal production and is a net exporter of ca. 15% of that production. Increasing global demand for cereals justifies questions as to where and by how much Europe’s production can be increased to meet future global market demands, and how much additional nitrogen (N) crops would require. The latter is important as environmental concern and legislation are equally important as production aims in Europe. Here, we used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions. In this way, we identified the yield gaps and the opportunities for increased cereal production for wheat, barley and maize, which represent 90% of the cereals grown in Europe. The combined mean annual yield gap of wheat, barley, maize was 239 Mt, or 42% of the yield potential. The national yield gaps ranged between 10 and 70%, with small gaps in many north-western European countries, and large gaps in eastern and south-western Europe. Yield gaps for rainfed and irrigated maize were consistently lower than those of wheat and barley. If the yield gaps of maize, wheat and barley would be reduced from 42% to 20% of potential yields, this would increase annual cereal production by 128 Mt (39%). Potential for higher cereal production exists predominantly in Eastern Europe, and half of Europe’s potential increase is located in Ukraine, Romania and Poland. Unlocking the identified potential for production growth requires a substantial increase of the crop N uptake of 4.8 Mt. Across Europe, the average N uptake gaps, to achieve 80% of the yield potential, were 87, 77 and 43 kg N ha−1 for wheat, barley and maize, respectively. Emphasis on increasing the N use efficiency is necessary to minimize the need for additional N inputs. Whether yield gap reduction is desirable and feasible is a matter of balancing Europe’s role in global food security, farm economic objectives and environmental targets. We received financial contributions from the strategic investment funds (IPOP) of Wageningen University & Research, Bill & Melinda Gates Foundation, MACSUR under EU FACCE-JPI which was funded through several national contributions, and TempAg (http://tempag.net/).

The spatial variability of soil properties is an important driver of yield variability at both field and regional scale. Thus, when using crop growth simulation models, the choice of spatial resolution of soil input data might be key in order to accurately reproduce observed yield variability. In this study we used four crop models (SIMPLACE, DSSAT-CSM, EPIC and DAISY) differing in the detail of modeling above-ground biomass and yield as well as of modeling soil water dynamics, water uptake and drought effects on plants to simulate winter wheat in two (agro-climatologically and geo-morphologically) contrasting regions of the federal state of North-Rhine-Westphalia (Germany) for the period from 1995 to 2008. Three spatial resolutions of soil input data were taken into consideration, corresponding to the following map scales: 1:50 000, 1:300 000 and 1:1 000 000. The four crop models were run for water-limited production conditions and model results were evaluated in the form of frequency distributions, depicted by bean-plots. In both regions, soil data aggregation had very small influence on the shape and range of frequency distributions of simulated yield and simulated total growing season evapotranspiration for all models. Further analysis revealed that the small influence of spatial resolution of soil input data might be related to: (a) the high precipitation amount in the region which partly masked differences in soil characteristics for water holding capacity, (b) the loss of variability in hydraulic soil properties due to the methods applied to calculate water retention properties of the used soil profiles, and (c) the method of soil data aggregation. No characteristic “fingerprint” between sites, years and resolutions could be found for any of the models. Our results support earlier recommendation to evaluate model results on the basis of frequency distributions since these offer quick and better insight into the distribution of simulation results as compared to summary statistics only. Finally, our results support conclusions from other studies about the usefulness of considering a multi-model approach to quantify the uncertainty in simulated yields introduced by the crop growth simulation approach when exploring the effects of scaling for regional yield impact assessments.

The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, iii) under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena sativa L.), winter rye (Secale cereale L.), pea (Pisum sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism.

Intensified climate and market turbulence requires resilience to a multitude of changes. Diversity reduces the sensitivity to disturbance and fosters the capacity to adapt to various future scenarios. What really matters is diversity of responses. Despite appeals to manage resilience, conceptual developments have not yet yielded a break-through in empirical applications. Here, we present an approach to empirically reveal the ‘response diversity’: the factors of change that are critical to a system are identified, and the response diversity is determined based on the documented component responses to these factors. We illustrate this approach and its added value using an example of securing food supply in the face of climate variability and change. This example demonstrates that quantifying response diversity allows for a new perspective: despite continued increase in cultivar diversity of barley, the diversity in responses to weather declined during the last decade in the regions where most of the barley is grown in Finland. This was due to greater homogeneity in responses among new cultivars than among older ones. Such a decline in the response diversity indicates increased vulnerability and reduced resilience. The assessment serves adaptive management in the face of both ecological and socio-economic drivers. Supplier diversity in the food retail industry in order to secure affordable food in spite of global price volatility could represent another application. The approach is, indeed, applicable to any system for which it is possible to adopt empirical information regarding the response by its components to the critical factors of variability and change. Targeting diversification in response to critical change brings efficiency into diversity. We propose the generic procedure that is demonstrated in this study as a means to efficiently enhance resilience at multiple levels of agrifood systems and beyond.

This contribution employs documentary-based precipitation indices and long homogenized series of precipita- tion totals for quantitative reconstruction of seasonal and annual precipitation in the Czech Lands (now the Czech Republic) from AD 1501. Final calibration is based on linear regression using fully independent indices and data measured during the 1804-1854 overlap period, with subsequent variance scaling. Correlation analysis demonstrates that Czech documen- tary indices explain a significant amount of precipitation variability in all months and seasons of the entire overlapping period. Reconstruction results are best for annual values and for autumn (SON), for which proxy and target data share 36% of common variability. The coefficient of determination for summer (June-July-August - JJA) is 35%, for spring (March-April-May - MAM) 33%, while for winter (December-January-February - DJF) it is only 26%. Verification statistics (reduction of error (RE), coefficient of efficiency (CE)) computed for early (1804-1829) and late (1830-1854) overlapping periods indicate acceptable reconstruction skill for precipitation indices in JJA and annual values. However, for the other sea- sons they failed in the early or late calibration period, indicating possible chronological instability of reconstruction results in MAM, SON (September-October-November), and DJF seasons. The final reconstructions are complemented with uncertainty estimates. Reconstructed Czech precipitation series do not indicate long-term trends but reveal quite high inter-annual and inter-decadal variability. Smoothed reconstructed DJF and JJA precipitation totals show the highest values in the second part of the 16th century, while the driest 30-year period occurred during the 18th century in DJF, MAM, JJA, and in annual series. Direct comparisons with two other reconstructions (tree-ring-based for southern Moravia and gridded multi-proxy for Central Europe) not only show significant correlations for a substantial part of the common period, but also disclose several periods with loss of coherence. Finally, uncertainties in reconstructions are discussed.

Crop growth simulation models are increasingly used for regionally assessing the effects of climate change and variability on crop yields. These models require spatially and temporally detailed, location-specific, environmental (weather and soil) and management data as inputs, which are often difficult to obtain consistently for larger regions. Aggregating the resolution of input data for crop model applications may increase the uncertainty of simulations to an extent that is not well understood. The present study aims to systematically analyse the effect of changes in the spatial resolution of weather input data on yields simulated by four crop models (LINTUL-SLIM, DSSAT-CSM, EPIC and WOFOST) which were utilized to test possible interactions between weather input data resolution and specific modelling approaches representing different degrees of complexity. The models were applied to simulate grain yield of spring barley in Finland for 12 years between 1994 and 2005 considering five spatial resolutions of daily weather data: weather station (point) and grid-based interpolated data at resolutions of 10 km × 10 km; 20 km × 20 km; 50 km × 50 km and 100 km × 100 km. Our results show that the differences between models were larger than the effect of the chosen spatial resolution of weather data for the considered years and region. When displaying model results graphically, each model exhibits a characteristic ‘fingerprint’ of simulated yield frequency distributions. These characteristic distributions in response to the inter-annual weather variability were independent of the spatial resolution of weather input data. Using one model (LINTUL-SLIM), we analysed how the aggregation strategy, i.e. aggregating model input versus model output data, influences the simulated yield frequency distribution. Results show that aggregating weather data has a smaller effect on the yield distribution than aggregating simulated yields which causes a deformation of the model fingerprint. We conclude that changes in the spatial resolution of weather input data introduce less uncertainty to the simulations than the use of different crop models but that more evaluation is required for other regions with a higher spatial heterogeneity in weather conditions, and for other input data related to soil and crop management to substantiate our findings. Our results provide further evidence to support other studies stressing the importance of using not just one, but different crop models in climate assessment studies.

Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach. iCROPM 2016 International Crop Modelling Symposium "Crop Modelling for Agriculture and Food Security under Global Change"

Cereal crop harvests reflect the weather patterns of the period immediately preceding them, and thus the dates at which they begin may be used as a source of proxy data on regional climate. Using systematic phenological observations in the Czech Lands (now known as the Czech Republic) after 1845, together with exploration of further surviving documentary evidence (chronicles, diaries, financial accounts etc.), it has proved possible to create series of winter wheat harvest dates for the period 1501–2008. Employing linear regression, the harvesting dates of the main cereal species (wheat, rye, barley, oats) were first converted to winter wheat harvest days and then normalised to the same altitude above sea level. The next step consisted of using series of winter wheat harvest dates to reconstruct mean March–June temperatures in the Czech Republic, applying standard palaeoclimatological methods. Series reconstructed by linear regression explain 70% of temperature variability. A profound cold period corresponding with late winter wheat harvests was noted between 1659 and 1705. In contrast, warm periods (i.e. early winter wheat harvests) were found for the periods of 1517–1542, 1788–1834 and 1946–2008. The period after 1951 is the warmest of all throughout the entire 1501–2008 period. Comparisons with other European temperature reconstructions derived from documentary sources (including grape harvest dates), tree-rings and instrumental data reveal generally close agreement, with significant correlations. Lower correlations around A.D. 1650 and 1750 may be partly related to deterioration of socio-economic conditions in the Czech Lands resulting from prolonged wars. The results obtained demonstrate that it is possible to use widely-available cereal harvest data for climate analysis and also that such data constitute an independent proxy data series for the region of Central Europe crucial to further studies of the potential impact of climatic variability and climate change on agriculture.

Past, present, and projected fluctuations of the hydrological cycle, associated to anthropogenic climate change, describe a pending challenge for natural ecosystems and human civilizations. Here, we compile and analyze long meteorological records from Brno, Czech Republic and nearby tree-ring measurements of living and historic firs from Southern Moravia. This unique paleoclimatic compilation together with innovative reconstruction methods and error estimates allows regional-scale May–June drought variability to be estimated back to ad 1500. Driest and wettest conditions occurred in 1653 and 1713, respectively. The ten wettest decades are evenly distributed throughout time, whereas the driest episodes occurred in the seventeenth century and from the 1840s onward. Discussion emphasizes agreement between the new reconstruction and documentary evidence, and stresses possible sources of reconstruction uncertainty including station inhomogeneity, limited frequency preservation, reduced climate sensitivity, and large-scale constraints.

The impact of climate change on the production and quality of hops Humulus lupulus will depend on future weather conditions in the growing season. Our simulations suggest that hops will be particularly vulnerable to a change in climate. Even with the modest warming so far experienced yields have stagnated and quality declined. Recorded observations show an increase in air temperature which is associated with an earlier onset of hop phenological phases and a shortening of the vegetation period. Simulations using future climate predict a decline in both yields, of up to 7-10%, and α-acid content, of up to 13-32%, the latter a major determinant of quality. The concentration of hop cultivation in a comparatively small region in the Czech Republic makes it more vulnerable than if the crop were grown in more areas with different climates. Thus climate change may gradually lead to changes in the regionalization of hop production. Policy assistance may be necessary for the adaptation of the Czech hop growing industry to changed climatic conditions.

ŽALUD, Z., TRNKA, M., DUBROVSKÝ, M., KOCMANKOVA, E.: Impacts of climate change on the fi rst occurrence of the Light blight (Phytophthora infestans (Mont.) de Bary 1876). Acta univ. agric. et silvic. Mendel. Brun., 2008, LVI, No. 2, pp. 267–276 The increase in the infestation pressure of various pathogens will be one the most important factors limiting the crop production under the future climate conditions. Weather driven NegFry model has been used for estimating future Phytophthora infestans occurrence at four experimental potato stations of the State Institute for Agriculture Supervision and Testing. Both the infestation dates of Phytophthora infestans occurrence and the shape of the critical number curve were analyzed using observed weather data as well as datasets constructed according to four climate change scenarios that were based on two global circulation models. The results show the shi of the infestation pressure to the beginning of the year and describe increasing trend of critical number reaching to detecting of the fi rst Phytophtho ra infestans occurrence for 2025 and 2050. Scenarios created according to HadCM and SRES – A2 seem to be more suitable for disease development. NegFry, Global circulation model, emission scenario, canopy emergence, potato

Diversification of crop rotations is considered an option to increase the resilience of European crop production under climate change. So far, however, many crop simulation studies have focused on predicting single crops in separate one-year simulations. Here, we compared the capability of fifteen crop growth simulation models to predict yields in crop rotations at five sites across Europe under minimal calibration. Crop rotations encompassed 301 seasons of ten crop types common to European agriculture and a diverse set of treatments (irrigation, fertilisation, CO2 concentration, soil types, tillage, residues, intermediate or catch crops).We found that the continuous simulation of multi-year crop rotations yielded results of slightly higher quality compared to the simulation of single years and single crops. Intermediate crops (oilseed radish and grass vegetation) were simulated less accurately than main crops (cereals). The majority of models performed better for the treatments of increased CO2 and nitrogen fertilisation than for irrigation and soil-related treatments. The yield simulation of the multi-model ensemble reduced the error compared to single-model simulations.The low degree of superiority of continuous simulations over single year simulation was caused by (a) insufficiently parameterised crops, which affect the performance of the following crop, and (b) the lack of growth-limiting water and/or nitrogen in the crop rotations under investigation. In order to achieve a sound representation of crop rotations, further research is required to synthesise existing knowledge of the physiology of intermediate crops and of carry-over effects from the preceding to the following crop, and to implement/improve the modelling of processes that condition these effects.

This contribution, which presents results of the work being currently in progress, is an update of our presentation in the previous ICFBR conference held in 2011 in Alghero. The updates consist in (i) using new GCM data (CMIP5 are used instead of the old CMIP3), (b) inclusion of the drought indices into the climate change projection (motivated by the fact that the wildfire risk is closely correlated with drought occurrence), (iii) updated stochastic weather generator used to simulate Fire Weather Index (FWI), and (iv) enhanced weather-friendly climate change scenarios, which include changes in climate characteristics not accounted for in our earlier experiments. The contribution will consist of two parts. In the first part, the CMIP5 data are used to create the multi-GCM climate change scenarios for the whole of Mediterranean in terms of temperature, precipitation and selected drought indices (including PDSI and SPEI). In the second part, the climate change impacts in terms of FWI characteristics and set of drought-related indicators will be determined for stations in four south European regions (Sardinia, Apulia, Croatia and Spain). The input weather series will be produced by the updated stochastic weather generator M&Rfi. To represent the future climate, the WG parameters will be modified by GCM-based climate change scenarios. We will focus on two aspects of the methodology: (a) Validity of WG, which will be examined by comparing the indicators obtained with the synthetic weather series vs observed weather series. Various settings of the WG will be considered to optimize its model. (b) Climate change scenarios will include changes in low frequency (monthly) and high-frequency (diurnal and interdiurnal) weather variability, which is mostly not taken into account in the climate change impact studies. We will assess an effect of these changes on results of our impact experiment.

International audience; It is hoped that lignocellulosic sources will provide energy security, offset carbon dioxide enrichment of the atmosphere, and stimulate the development of new economic sectors. However, little is known about the productivity and sustainability of plant cell-wall energy industries. In this study, we used 16 global circulation models to project the global distribution of relative water availability in the coming decades and summarized the available data on the water-use efficiency of tree- and grass-based bioenergy systems. The data on bioenergy water use were extremely limited. Productivity was strongly correlated with water-use efficiency, with C4 grasses having a distinct advantage in this regard. Our analysis of agroclimatic drivers of bioenergy productivity suggests that relative water availability will be one of the most important climatic changes to consider in the design of bioenergy systems.