Abstract: Climate change scenarios of seasonal minimum and maximum temperature over different Italian agricultural areas, during the periods 2021–2050 and 2071–2100 against 1961–1990, are assessed. The areas are those selected in the framework of the Agroscenari project and are represented by: Padano–Veneta plain, Marche, Beneventano, Destra Sele, Oristano, Puglia and Sicilia, all areas of prominent agricultural vocation with excellence productions. A statistical downscaling technique applied to ENSEMBLES global climate simulations, emission scenario A1B, is used to achieve this objective. The statistical scheme consists of a multivariate regression based on Canonical Correlation Analysis. The scheme is constructed using large-scale fields derived from ECMWF reanalysis and seasonal mean minimum, maximum temperature derived from national observed daily gridded data that cover 1959–2008 period. Once the most skillful model has been selected for each season and variable, this is then applied to GCMs of ENSEMBLES runs. The statistical downscaling method developed reveals good skill over the case studies of the present work, underlying the possibility to apply the scheme over whole Italian peninsula. In addition, the results emphasize that the temperature at 850 hPa is the best predictor for surface air temperature. The future projections show that an increase could be expected to occur under A1B scenario conditions in all seasons, both in minimum and maximum temperatures. The projected increases are about 2 °C during 2021–2050 and between 2.5 and 4.5 °C during 2071–2100, respect to 1961–1990. The spatial distribution of warming is projected to be quite uniform over the territory to the end of the century, while some spatial differences are noted over 2021–2050 period. For example, the increase in minimum temperature is projected to be slightly higher in areas from northern and central part than those situated in the southern part of Italian peninsula, during 2021–2050 period. The peak of changes is projected to appear during summer season, for both minimum and maximum temperature. The probability density function tends to shift to warmer values during both periods, with increases more intense during summer and to the end of the century, when the lower tail is projected to shift up to 3 °C and the upper tail up to 6 °C. All these projected changes have important impacts on viticulture, intensive fruit and tomatoes, some of the main agricultural systems analyzed in the Agroscenari project.

Abstract: This report describes grassland-livestock production systems, as selected for model-basedstudies. A list of grassland models was identified for evaluation against such datasets(WP2) and application at reference farm (WP3) and regions (WP4) across Europe and peri-European countries. No Label

Abstract: • We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems.