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Simulating the dynamics of plant species or types in grassland communities remains an open area of research for which the Community Simulation Model (CoSMo) offers novel approaches. The grassland model ModVege was first parameterised based on a functional vegetation typology, in which types "A" and "B" include fast-growing grass species with a phenology-dependent nutrient-capture strategy inherent to fertile grasslands, while the nutrient conservation strategy and late flowering characterise the other types as "b". ModVege was then coupled to the CoSMo rule set to dynamically simulate the relative abundance of plant functional types or individual species, assessed across fertilised and unfertilised, abandoned and mown conditions in a grassland site of the Massif Central of France. While for the simulation of aboveground biomass, model performance is not unambiguously linked to explicit consideration of plant diversity, the simulation of relative abundance for the whole community is satisfactory (relative root mean square error of ~13–25% when simulating functional types and ~28–52% when simulating species). This study extends previous studies by coupling CoSMo, for the first time, to a grassland-specific model and applying it to conditions (long-term observations, extended number of plant species, absence of fertilisation, frequent mowing and abandonment) never investigated before. [ABSTRACT FROM AUTHOR]

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) is 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 is to generate purely climate-dependent time series of optimal forage production, a variable that is found to maximize the years of brighter and warmer weather conditions at the grassland.In the long term, a remarkable increase in simulated forage yield (>29%) is obtained in all simulations between 1959-1979 and 1995-2015. Such increase seems consistent with observed rising trends in temperature and CO2 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, appear robustly correlated with annual forage yield values. Relying on climatic predictors uniquely, a stepwise statistical multi-regression model with leave-one-out cross-validation is developed in this work. Under specific management conditions (e.g., 3 annual cuts) and from 1 to 5 months in advance, the generated model successfully provides a p-value<0.01 in correlation (t test), a root mean square error (RMSE) percentage of 14.6% (1.24 t DM ha-1) and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. These results may help in stimulating the debate about understanding the climate mechanisms at play in European grasslands and provide a useful springboard to implement a grassland seasonal forecasting system in France. [ABSTRACT FROM AUTHOR]