Abstract: Different datasets have been completed and are now available for the analysis of interannual  and seasonal variations of productive, reproductive or health data relative to  intensively dairy cows and also to establish the relationships between temperature  humidity index (THI) and dairy cow performances. Datasets are referred to different  European countries (Italy, Belgium, Luxembourg and Slovenia) with different climatic  features. All these datasets have data relative to Animal Pedigree (Cow ID, Birth date,  Breed, Sire ID and Dam ID), Test-day records (Cow ID, Herd ID, Parity, Calving date, Test  date, Milk yield, Milk fat and protein (%), Milk somatic cell score), Reproductive events  (Cow ID, Herd ID, Parity, Calving date, AI date, Sire ID, Days Open, NRR-56 day), and Daily  meteorological records (Meteo station ID, Zip code of the meteo station, Observation date,  Max temperature, Min temperature, Mean temperature, Max relative humidity, Min  relative humidity, Mean relative humidity, Solar radiation, Wind speed). The dataset  relative to Italy includes also Mortality data (Animal ID, Herd ID, Death date) and Bulk milk  quality data (Herd ID, Test date, Fat & protein (%), Somatic cell score, Bacterial count,  Herd latitude, Herd longitude, Herd elevation). An additional database is still under  construction and will be based on Spanish data from organic dairy farms. No Label

Abstract: Datasets from Germany and the United Kingdom containing information on geographic  (European Union 27 countries), climatic, meteorological, host and infectious agents’  parameters (figure 2) have been completed and are now available for preliminary analysis  relating to data quality and consistency. Data set information will continue to be added over  the next 12 months. No Label

Abstract: 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 x 10 km; 20 km x 20 km; 50 km x 50 km and 100 km x 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. (C) 2013 Elsevier B.V. All rights reserved.