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Abstract: The Pasture Simulation model (PaSim) was used to simulate agronomic and environmental services at 12 grassland sites in France, comparing near past climate conditions (1970–1999) and projections for near (2020–2049) and far future (2070–2099). Modelled climate change impacts were assessed using statistical and threshold-based analysis on managed (intensive, extensive) grasslands (permanent, sown), comparing shallow and deep soils, and for an ensemble of emission scenarios, climate models, and downscaling/initialization methods. Results show a significant drift of all sites towards more arid climates by the end of the century. Significant reductions in annual water drainage and herbage protein content are projected, together with new opportunities for annual and seasonal herbage production, in spring and winter especially. Simulated future conditions also show an increased interannual and seasonal variability of production. Contrary to expectations, current natural conditions reflecting large water availability are not expected to reduce future risks of negative climate change impacts on grassland systems. Notably, milk production from aftermath grazing of permanent grasslands established on deep soils at Mirecourt (humid site) is estimated to fall down below one-third of the current median value in four out of 30 years (∼13%) for 2070–2099, whereas similar shortfalls were not observed with the baseline climate. Balancing risks and opportunities, permanent extensive grasslands can be viewed as a trade-off between the continuity of grassland service provision and the mitigation of greenhouse gas emissions. [Copyright &y& Elsevier]

Abstract: Meteorological stations placed in remote areas (such as mountainous sites) often record only precipitation and air temperature data, summarised into monthly tables. There is therefore a need for appropriate methods to estimate solar radiation data from monthly inputs. It is important to know the quality and characteristics of the estimates made in order to understand what impacts the modelled data may have on the use to which they are meant. This paper evaluates the behaviour of two empirical models based on the difference between maximum and minimum air temperatures and compares results with a newly developed model, for 24 stations (11 for model calibration, the rest for model validation) in Italian sites. The new model (SRMCT, solar radiation model for complex terrains) replaces the minimum air temperature with a monthly function and uses rainy days number to correct the air temperature range. Comparisons were made using a fuzzy-logic-based multiple-statistics assessment approach (Irad). An appraisal of the temporal distribution of mean errors over a year was also performed. The new model produced the best overall estimates. The two air temperature-based models can be an alternative approach when only air temperature data are available, but at many sites they introduced large inaccuracies and seasonal patterns in the error distribution. The study demonstrates the appropriateness of both air temperature range and rain correction factors for monthly based solar radiation estimates. At the same time, it shows the value and importance of using a range of assessment statistics, even aggregated into an overall indicator, to evaluate model estimates. [Copyright &y& Elsevier]

• Grassland model PaSim used to simulate a mown grassland system in central France. • Climate-dependent timeseries of optimal forage yield generated. • Remarkable increase in simulated forage yield (>29%) observed in the long term. • Annual forage yield values robustly correlated with teleconnection patterns. • Skillful seasonal forecasts of forage yield attained from climatic predictors. Climate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) was used to simulate, for the period 1959–2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim was to generate purely climate-dependent timeseries of optimal forage production, a variable that was maximized by brighter and warmer weather conditions at the grassland. A long-term increase was observed in simulated forage yield, with the 1995–2015 average being 29% higher than the 1959–1979 average. Such increase seems consistent with observed rising trends in temperature and CO 2 , and multi-decadal changes in incident solar radiation. At interannual timescales, sea surface temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation) and the North Atlantic Oscillation (NAO) index were found robustly correlated with annual forage yield values. Relying only on climatic predictors, we developed a stepwise statistical multi-regression model with leave-one-out cross-validation. Under specific management conditions (e.g., three annual cuts) and from one to five months in advance, the generated model successfully provided a p -value<0.01 in correlation (t -test), a root mean square error percentage (%RMSE) of 14.6% and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. This is the first modeling study on the possible role of large-scale oceanic–atmospheric teleconnections in driving forage production in Europe. As such, it provides a useful springboard to implement a grassland seasonal forecasting system in this continent. [ABSTRACT FROM AUTHOR]