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Assessing climate change effects on land use and ecosystems: from regional analysis to the european scale

Deliverables

The Almeria Case Study is a regional database of the most dry and arid region of Europe, located in SE Spain. It is configured as a Geographic Information System and covers an area of ca. 10000km², consisting of a variety of raster layers at resolutions ranging from 10m to 1000m, vector coverages at scales from 1:10000 to 1:1000000, and associated relational databases. Its thematic contents include: - Climate (both present-day and selected IPCC-SRES scenarios for the years 2020, 2050, and 2080); - Topography (DEMs and derived topographic variables); - Vegetation cover and classes (satellited derived); nature reserves (N-2000 reserves and habitats); - Species distributions (habitat builders from Annex I of the EC habitat Directive); - Ecological connectivity of those species within the area (outputs of the ALCOR connectivity model, both for present conditions and for scenarios); - Land cover (CORINE LC); and - Corrected satellite imagery (AVHRR, Landsat TM, and IKONOS). Most of the data have been error assessed and can be used for general purposes concerning their thematic content, or as a benchmark for related models and projects. This database is a cumulative effort together with other RTD projects, and targets to integrate in global or continental wide monitoring networks such as GTOS. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
This paper analyses farmers’ decision-making with respect to nature protection. The focus is on farmers’ motives and incentives. The paper is based on literature and empirical findings from a survey in Denmark. The overall purpose of the study is to provide guidelines for organising farmers’ provision of public goods. The public goods include e.g. to maintain extensively managed agro-ecosystems, planting of hedges and woodlands, maintaining or re-establishing ponds and wetlands, or reducing emissions of pollutants. The paper should support the design of policy strategies for the abatement of potential negative impacts of changes in climate and socio-economic drivers on agro-ecosystems which are analysed in ACCELERATES. First, a theoretical model of farmers’ supply of public goods is presented. Based on this model a survey of farmers in Haderslev County was performed. 38 farmers were interviewed about their engagement in voluntary adoption of environmentally-friendly farming practices and activities to enhance the provision of environmental goods. Furthermore, a questionnaire sent to 200 farmers supplemented the interviews. The analysis indicated that farmers take environmental effects into consideration in their decision-making motivated by the private utility of providing rural amenities or the benefits of improving the image of farming to the public. However, these voluntary measures only have a minor local effect and will be insufficient to protect Natura 2000 habitats. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
The ACCELERATES socio-economic scenarios for the simulation of the impact of climate change scenarios were developed using an integrated approach. Available information and research results from the climate change literature were combined with a socio-economic approach. This information was synthesised in three matrices reporting the impact of the main economic driving forces on model simulation parameters. Matrix 1 contains information about the weight and the role of Global Driving Forces and represents the reference background at the global level, for a panel of socio-economic and agricultural experts. Matrix 2 contains information about Agricultural Sector Driving Forces. Information derived from downscaled climate change models, together with expert knowledge and model results (e.g. statistics and outputs of general equilibrium models) were compiled by the panel. In the final step, the expert panel utilises the ASDF data stored in Matrix 2, to identify their effects on the Agricultural Model Parameters at the local scale (Matrix 3), with the help of data about local agricultural systems and climate change patterns. Matrix 3 contains the values required to run the simulation models. This methodology is coherent with the approach developed by the IPCC for the Third Assessment Report, in which it is recognised that, owing to the present degree of understanding of global change phenomena formal modelling techniques should be coupled with expert judgement to obtain future projections. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
Knowledge of the vulnerability of species and habitats to climate change is critical to conservation management and policy. The climate space outputs of the SPECIES model are used to derive vulnerability indices for each modelled species for Europe and for each country represented by a case study area. Vulnerability is defined in terms of the sensitivity and adaptive capacity of species to climate change. Sensitivity is calculated using the relationship between the current and future distribution and is a function of the amount of gained and lost climate space, the degree of overlap and the size of the future distribution (as an indicator of the future rarity of the species). Two vulnerability indices are calculated, one which includes adaptation and is assumed to occur under the B1 and B2 SRES scenarios and one which assumes no planned adaptation consistent with the A1 and A2 SRES storylines. For the index, which includes adaptation, it is assumed that a species can make full use of its new climate space either by autonomous adaptation (i.e. dispersal) or planned adaptation (e.g. translocations, creation of new habitats). The degree to which planned adaptation can be utilised is assumed to be a function of the extent of new climate space. This is the first time a quantitative index of species' vulnerability has been derived. This will inform European and national conservation planning by highlighting vulnerable species, thus enabling prioritisation of species actions and identifying where conservation policy needs adjustment, in order to protect a species for the future. The indices are likely to be adopted by other research projects e.g. MONARCH. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
The Danish case study incorporates results from the SPECIES model and downscaled SPECIES on the potential distribution of species on a 5km x 5km grid throughout Denmark (except the island Bornholm) for current conditions (1961-90) and for selected climate change and land use change scenarios for the years 2020, 2050 and 2080. Results are available for 8 species (six species applying the downscaled SPECIES); 4 associated with lowland heaths and 4 associated with active raised bogs, including their vulnerability to climate change. These results would be useful to those making decisions about the management of habitats and species in Denmark and they could provide local guidance on setting nature conservation objectives, taking account of climate change impacts. The ACCELERATES land use model was validated against current land use in Denmark. The modelled climate change impacts on land use indicate a continued demand for land for intensive agricultural production, however, crop rotations may change. Based on a questionnaire survey, farmers' decision-making in relation to land use and nature conservation was investigated in a Danish region. This study provides guidance for designing agri-environmental policies to protect biodiversity in agro-ecosystems. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
A spatially explicit cellular automata model has been developed which simulates the stochastic dispersal of species through fragmented landscapes. Species dispersal over a specified time step is simulated within the boundaries of the climate and land-cover suitability surfaces generated by the downscaled SPECIES model. This approach enables new climate and land-cover surfaces to be introduced at future time steps, representing a range of climate and/or land use change scenarios. The model is based on a cellular automata which simulates the stochastic dispersal of species in terms of two main processes: the release of a number of propagules (seeds, spore, insects, etc.) by an existing population and the redistribution of the propagules according to a dispersal function. The number of propagules released by a population is related to its population growth curve. Thus, once populations have become established, they will release a number of propagules proportional to the population density within a cell. The dispersal function used to redistribute the propagules includes a fat-tailed dispersal kernel. This enables rare, long-distance dispersal events to be simulated, which are thought to be the primary driver of rapid migration events, such as those evident in the palaeo record following the last glacial maximum. The function is controlled by two parameters: a 'distance' parameter, which controls the mean distance a propagule can travel, and a 'shape' parameter which controls the fatness of the tail. The stochasticity of the model reflects the random nature of many dispersal events and it is therefore necessary to run the simulations many times using a Monte Carlo approach to build up a probability surface showing likely patterns of spread. The model requires parameterisation for six species-dependent variables. These are maximum and mean dispersal distance, the shape parameter for the dispersal kernel, net reproductive rate, years to reach reproductive maturity and fecundity. A parameter database has been created containing this information for the 11 species modelled in the project based on an extensive search of the ecological literature, supplemented by expert opinion (Campanula glomerata, Helictotrichon pratense, Silene gallica, Papaver dubium, Legousia hybrida, Lepus europaeus, Chorthippus dorsatus dorsatus, Ostrya carpinofolia, Quercus ilex, Pinus pinaster and Pinus halepensis). Results from the dispersal model indicate the ability of a species to track changing climate and land use space and thus provides a means of assessing the effectiveness of current conservation policy if continued into the future. It will also facilitate the identification of bottlenecks or gaps in the landscape for dispersal and show where further Natura 2000 sites or similar reserves should be located to enable species to adapt to climate change. More information on the ACCELERATES project can be found at: http://www.geo.ucl.ac.be/accelerates/.
The objective of this working paper is to perform a survey of the climate change literature to establish a policy tool box for regulation of agricultural land use in a changing environment. The policy tool box should serve as a source of inspiration for identifying adaptation options to climatic changes. Adaptation refers to changes in processes, practices or structures to moderate or offset potential damages or to take advantage of opportunities associated with changes in the climate. The working paper addresses the agronomic, socio-economic and environmental impacts of climate change on agro-ecosystems. The focus is on the adaptation options relevant for policy makers, i.e. adaptation measures representing conscious policy options or response strategies aimed at altering the adaptive capacity of the agricultural system or facilitating particular adaptations to climate change. The Working Paper does not make any recommendations between different adaptation options or policy measures but provides a gross list of the suggested options and measures reported in the literature. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
Farm typology and land use were characterized on the basis of official statistical data. These cover NUTS 1-2, and partly NUTS3. Farms were characterized by size, type of ownership and type of production. Present farm typology in the CEECs depends both on historical drivers and on political decisions taken during the transition period. In some CEECs, farms fill not only economic, but also social functions, such as securing employment (Poland, Romania). In Poland from 1996 to 2002 there was a general tendency to decrease farm numbers, and only farms in two size groups increased (1-2ha - social, and >20ha - market oriented farms). Especially large increases were observed in the size group >50ha (by 56% in comparison to 1996). Regional analysis undertaken for Poland showed a linear relationship between farm size and cattle numbers per 100ha in the range of farm size from 1 to 20ha. For larger farms, catlle numbers per 100ha decreased and was very small for farms >50ha. A similar trend was found for pigs with threshold farm sizes of 50ha. This suggests that farms >50ha could be diversified into 2 groups: those oriented to cash crops or to animal production. The latter group was smaller than the former. Land use in particular regions of the CEECs was characterized by a percentage of utilised agriculutral area (UAA) (arable land, meadows and pastures, and forest), crop structure and livestock number per 100ha. Generally, land use was dependent mainly on topographic influences. The exception is the large forested area (50%) of the lowland region, Lubuskie (Poland). The share of arable land in the UAA for Bulgaria ranged from 28.7 to 78.9% (average 61.3%), for the Czech Republic 45.8-84.2% (71.8%), Hungary 66.9-82.4% (76.9%) and Poland 64.2-88.2% (77.3%). The smallest values reflected regions that are located in mountain areas, for others - the share of arable land is >60%. Whilst land use is quite a stable parameter, the structure of arable land is more dynamic. The majority of arable land is under cereals (mainly wheat). Wheat areas are very large in regions located in the southern and central parts of the CEECs (>50%). For Poland, wheat areas are much lower (29.1%), being mainly the result of unsuitable soil conditions. For these conditions rye, triticale - hybrid of wheat and rye, and mixtures of barley and oats are more suitable for cultivation. Whilst, the range of both cattle and pig numbers per 100 ha of UAA are similar for the Czech Rep. and Poland, they are much lower for Bulgaria. The opposite situation is observed for sheep. Cattle numbers per 100 ha of UAA were weakly correlated with the percentage of meadow in the UAA for Poland and Bulgaria, and not correlated for the Czech Rep. Instead, cattle numbers were strongly correlated with the percentage of fodder crops within arable land (Poland and Czech Rep.) More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
PROBLEM: Nature reserves are usually designated through a process where conservation objectives are in conflict with the economic uses of land. Solutions are often designed for individual reserves rather than for a whole set. As a result, protected spaces trend to have a minimum surface and to distribute arbitrarily across the territory. As a result, individual spaces must be self-contained, but usually they are not. TARGET: The conservation objectives should be met through reserve networks that distribute a set of optimally conserved spaces in a matrix of land uses in variable states of conservation. Stability of the conservation system should be achieved by ensuring ecological connectivity between populations located in affined reserves. The non protected landscape in between presents variable degrees of resistance to such a flow. All these parameters should be quantifiable to assess 'what if' scenarios. THE ALCOR (algorithm for the regional connectivity) SOLUTION: It estimates the regional connectivity given: the geographical distribution of a species; its ecological or environmental niche; and a geographic framework. The model is based on the geometric complexity of cost surfaces representing the cumulative effort to reach every cell of the territory from its nearest population. The results are: a connectivity map; a map of ecological corridors; and a parameter assessing the geographical scale at which the extinction of every population is relevant at the regional scale. The model is sensitive to the statistical and spatial distributions of both the species populations and the external factors controlling its transit. A demonstration data set is available. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
The Belluno Case Study is a regional database of an alpine area located in NE Italy. It is represented by a Geographical Information System that covers an area of 3678km² included between 45° 50 and 46° 40 N latitude. The GIS database is composed of vector distribution maps of a large number of animal and plant species and presence/absence maps derived from a specific survey executed between 2001 and 2003 (i.e. Crex crex, Alopecurus pratensis, Gimnadenia gonopsea, Arrenatherum elatius, Chorthippus dorsatus, and Orthoptera in gender etc.) and raster layers (25 m ¿ 1000 m of resolution) about: - Topography (DTM and maps derived from this, i.e. aspect, slope, hillshade); - Land use (Corine L3 and 2020, 2050, 2080 years future ATEAM scenarios); - Climate (present-day and IPCC-SRES scenarios for years 2020, 2050, 2080); - Suitability maps of C. crex, O. carpinifolia, C. dorsatus; - Connectivity map for C. dorsatus. This database is an important instrument to drive the local, and not only, policy to protect and manage the environment and, more in a generalized manner, the biodiversity. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
The scenario adopted for the EU simulation are the “SRES scenarios” recently developed by the IPCC with the aim of representing the range of possible driving forces and emissions in different world development paths. In ACCELERATES research project the four marker scenarios of the IPCC were selected: - A1: World Market (WM), - A2: Regional Enterprise (RE), - B1: Global Sustainability (GS) and - B2: Local Stewardship (LS). Each scenario is identified and described by narrative storylines, representing the framework within which modelling teams, applying different models have developed the scenarios. Each storyline represents different plausible demographic, social, economic, technological, and environmental developments, within which the various scenarios represent specific quantitative interpretations. EU Global Driving Forces: The documentation produced by the Third Assessment Report of the IPCC is the main reference for building a matrix, with variables describing the GDF’s, which are determinants of socio-economic developments in the various sectors, including agriculture. This matrix summarises the state of those drivers in the four selected scenarios, as estimated comparing the present situation with the one referred to year 2020. The evaluations assumed that the policies, interventions and events, which characterise the four SRES scenarios, were effectively implemented. Having the general framework of Global Driving Forces at the global level, it is possible to focus on those drivers that are more significant for the agricultural sectors. This process consists in zooming those forces, which are considered to be the main drivers for the future developments of agricultural productions both in economic and technical terms, at a regional scale. The following driving forces were selected as the main drivers of future developments of the EU agriculture: - The evolution of EU Agricultural Policy (market and rural development); - The impact of environmental policy measures un agricultural technologies; - The enlargement of the EU to Central-Eastern European Countries (CEECs); - The competition with other sectors over the use of production resources; - The perspectives of the world market (supply/demand); - The World Trade Organisation Agreements (WTO GATT). The last step of the application of the methodology to EU consists in the compilation of the third level matrices aimed at estimating the variation in agricultural model parameters, which will drive land use change modelling at a local scale. Initially, a matrix reporting the 2020 situation in the four basic scenarios has been compiled. This matrix contains 38 variables representing the situation of the following parameters: - Prices of the most important production factors utilised by farmers; - Prices of the agricultural commodities; - Subsidies; - Yields; - Natural resources available for agricultural production; - Efficiency in natural resources agricultural use; - Chemicals input restrictions. Then, the 2020 parameters have been extrapolated to 2050 and 2080. So, a thorough and internally consistent set of parameters has been designed for simulating the land use pattern over time in the four different scenarios. This data may be used in other research or analysis was a general framework on future socio-economic situation is needed. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
The UK case study incorporates results from the SPECIES model and downscaled SPECIES model on the potential distribution of species on a 5km x 5km grid throughout the UK for current conditions (1961-90) and for selected climate change and land use change scenarios for the years 2020, 2050 and 2080. Results are available for 8 species; 4 associated with lowland calcareous grassland and 4 associated with cereal field margins, including their vulnerability to climate change. The climate and land use change scenarios have been downscaled to a 1km x 1km grid for East Anglia, a relatively homogeneous low-lying region with a dry temperate climate. Results from the downscaled SPECIES model and the species' dispersal model are available for the East Anglian region, showing the likely ability of species to track the predicted changes in their distributions. These results would be useful to those making decisions about the management of habitats and species in East Anglia and they could provide local guidance on setting nature conservation objectives, taking account of climate change impacts. More information on the ACCELERATES project can be found at: http://www.geo.ucl.ac.be/accelerates/.
The Belgium case study is a nested database of information at 2 spatial levels: Belgium (as a country) and the Dyle Catchement (catchment of 670km² in Central Belgium). The GIS database consists of vector shapefiles as well as raster layers. At the geographic extent of Belgium, agricultural census data are available at NUTS5, yield data for "circonscription agricoles", DTM at 1000m resolution, species distributions at 8x10km² grid, "Aardewerk" point profiles and soil association map, road and river networks, land use from CORINE and Pelcom. Data collected for the Dyle Catchement are: IACS parcel data (shapefile of declared agricultural parcels) for 1999, farm locations (918 farms), land use derived from LandsatTM 1999 image, aerial photography from 2000, scanned topographic maps 1:10000 from 1980, DTM at 30m resolution, detailed precipitation data. The Accelerates model, based on a Ricardian approach, produces acceptable results at country scale. Statistical analysis of the Dyle data showed that at the local level, different factors (topography, distance to farm, farm location, farm typology, neighbourhood interactions) determine agricultural practices, land use and landscape structure. A simple agent-based model showed that competition between farmers is partly responsible for the relative parcel distribution in space (relative to the farm). Aggregation experiments in the Dyle unravelled the very low accuracy of Corine and Pelcom land use datasets, compared to the IACS data. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
The methodology and scenarios developed by the research team of ACCELERATES were extended to Central and Eastern Europe, considering their different historical background and non homogeneity of transition process and EU integration. A hierarchical approach with 3 levels (EU Global, EU Agricultural Sector, and regional (NUTS, NUTS2 and/or NUTS3 level) was used in the development of socio-economic scenarios for the years 2020, 2050 and 2080. The global driving forces were based on the scenario storylines described in Special Report on Emission Scenarios. The SRES storylines do not describe explicitly the decision-making structure, institutions and type of government, but the project took these into consideration for the CEECs due to their historical importance. Key drivers (28 variables) that shape future agricultural land use were identified, including groups of drivers as follows: geographical situation, economy and policy, agricultural policy, resource competition, EU accession, role of international institutions, technological innovation and deployment, development of infrastructure, market demand and supply. On the third level the following indicators were examined for the present current situation: - Structure of economy, local economic importance of agriculture; - Share of utilised agricultural are and its structure (share of arable, permanent pasture, semi-natural grassland, mountain grassland area, area of organic production); - The farm structure, the production mix; - Yields and the prices of major products, availability, prices and quality of the most important production factors utilised by farmers; - Productivity and efficiency of resources use, trend in input-output ratio and farm income, trend in use of chemicals, in nitrogen balances of soil and so on. All available indicators were compared with their own before transition, with the same indicators of other CEECs countries and with those of the EU-15. Analysis included a systematic investigation of strengths, weaknesses, opportunities and threats of regions under the current situation and for different time horizons in different future scenario assumptions. The results also demonstrated weaknesses in the comparability of agricultural statistics in the CEECs. Considering that agriculture in the CEECs is not homogenous, the main drivers in a region were selected by the local, sector experts or stakeholders. The similarities, but also differences between the old members of the EU and the CEECs were identified. The results make it evident that general economic development is more important for structural change in land use than the economic situation in the agricultural sector itself. This is also likely to hold for the future. Countries with low GDP per capita tend to have a higher role of agriculture in the GDP and employment. A decline in economic indicators can be found as the distance increases to the EU border area (the western part of the CEECs) or to capital, and large cities. Due to less developed industry and underdeveloped infrastructure and, a larger share of rural areas, with expected net migration into the urban area, more land is required for road and building comparing to the EU-15 in all scenarios. During the transition period agriculture in the CEECs has suffered more than other sectors. Lack of capital, outdated machinery, low productivity and subsistence and semi-subsistence farms (especially in Romania and Poland), inadequate farm size, uncertainties in landownership, and under developed land markets create a barrier to the adaptation to change over the longer term. Because of this, the year 2020 was considered a transition year, and the scenarios included mechanisms for how to get to the future. An interesting result was the impact of the food chain as an important driver in all CEECs. Whilst in the 2020 scenarios the policy and market drivers were dominant, in the scenarios of 2050 and 2100, technical development, growth and breakdown of population, and environmental issues were considered to be more important driving forces. It was shown that some factors, which now seem to be barriers to development, would become stimulators later. Because of the low levels or lack of basic infrastructure, these are the main issues for the future viability and competitiveness of the CEECs. The role of WTO depends on international trade. Most of the CEECs face the problem of ill-defined property rights and its influence on agricultural practice. The direction of change in some model parameters is diverse between the old members of the EU and the CEECs due to differences in the baseline, for example, the trend in fertilizer and pesticide use and the level of subsidy measured by PSE. The results provide a qualitative description of the sensitivity and adaptive capacity of CEEC regions. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.
Data sets of crop yields from the Central and East European (CEE) project countries (Czech Republic, Hungary, Poland, Romania and Bulgaria) were established. These data sets were based on modelling approaches for a range of crop types and for the climate change scenarios specified in the ACCELERATES project. Three crops were approved to be used within the project: - Barley, - Maize and - Wheat. One of the main simulation tools with respect to the above data sets was the ROIMPEL model. This is a modular simulation model of crop yields limited by soil -water and -nitrogen availability, using limited easy-to-map soil and weather data. The ROIMPEL crop yield outputs were compared to the real observed crop yield data as well as to the simulated crop yield output from the CERES crop simulation model at selected specific sites and regions in the CEE project countries. Representative sites and regions from major agricultural regions in each project CEE partner country were selected. In relation to the (cross)validation of the ROIMPEL and CERES crop models within selected CEEC sites and regions, the following was developed and disseminated among the project partners: - A soil converter for linking the CERES model and the ROIMPEL European soil map; - ATEAM weather data for the period 1961-2000; - A software extracting weather data for a given ATEAM weather data set; - Climate change scenario data for four SRES storylines and three future time slices (2011-2020, 2041-2050, 2071-2080) based on the HADCM3 model; - Updated ROIMPEL versions creating weather files in CERES format; allowing to change the crop parameters related to water stress, water logging and nitrogen stress; tuning the stress parameters and sowing date as well as the sum of active air temperatures between emergence and maturity. After a calibration procedure a validation of the ROIMPEL and CERES ability to simulate the phenological development of the selected crops was undertaken. The simulated ROIMPEL and CERES grain yields were in most cases in agreement with the measured data, with predicted yield results mainly within acceptable limits of measured yields. Different statistical criteria for determining goodness of the simulation models performance were also applied. The obtained climate change scenario data for the four SRES storylines and the three future time slices based on the HADCM3 model were used in order to conduct climate change impact assessments in the project CEEC countries including: - Projections of the expected magnitude of the impacts, expressed qualitatively; - Description of the vulnerable/beneficial crops, as well as the reasons for their vulnerability/benefits; - Comparison between the ROIMPEL and CERES simulated crop yield responses to the climate change scenarios. The simulation model performance was compared with to the following important points: - Simulation of phenological development; - Simulation of final crop yield; - Yield sensitivity to changes in the CO2 concentration. Seven other GCM climate change scenarios for three time periods (2025, 2050 and 2100) and two SRES emission scenarios were also used in the Czech Republic. All applied climate change scenarios projected a shorter growing season for the selected crops in the respective CEE countries. The HaDCM3 scenarios, including the climate change effect only, projected for example in Bulgaria, reductions in grain yield of winter barley wheat and maize, caused by a shorter crop-growing period. When the direct effect of an increased CO2 level was assumed, most GCM climate change scenarios projected an increase in winter wheat and barley. An increased level of CO2 alone had no significant impact on the simulated maize yield reductions under climate change. The simulated climate change results for the Czech Republic showed that: - Wheat yields tend to increase in most locations in the range of 8-25% in all three time periods; - The site effect was caused by the site-specific soil and climatic conditions; - Temperature variability proved to be an important factor and influenced both mean and standard deviation of the yields. The obtained data sets include measured and ROIMPEL and CERES simulated yield (e.g. for the period 1981-2000 as well as for time slices during the 21st century) for chosen crops (wheat, barley and maize) for the selected CEEC sites and regions. The above results have potential consequences for farmers in CEE countries such as, for example, growing mainly C3 crops, which are more sensitive to CO2 ambient enrichment than C4 crops and growing new crop cultivars and hybrids which are more adapted to projected climate change. Agricultural policy in these countries needs to deal with such changes by developing and regularly updating action plans for adapting agriculture to climate change impacts. More information on the QUILT project can be found at: http://www.geo.ucl.ac.be/accelerates/.

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