Testing simulations of intra- and inter-annual variation in the plant production response to elevated CO(2) against measurements from an 11-year FACE experiment on grazed pasture.

Ecosystem models play a crucial role in understanding and evaluating the combined impacts of rising atmospheric CO2 concentration and changing climate on terrestrial ecosystems. However, we are not aware of any studies where the capacity of models to simulate intra- and inter-annual variation in responses to elevated CO2 has been tested against long-term experimental data. Here we tested how well the ecosystem model APSIM/AgPasture was able to simulate the results from a free air carbon dioxide enrichment (FACE) experiment on grazed pasture. At this FACE site, during 11 years of CO2 enrichment, a wide range in annual plant production response to CO2 (-6 to +28%) was observed. As well as running the full model, which includes three plant CO2 response functions (plant photosynthesis, nitrogen (N) demand and stomatal conductance), we also tested the influence of these three functions on model predictions. Model/data comparisons showed that: (i) overall the model over-predicted the mean annual plant production response to CO2 (18.5% cf 13.1%) largely because years with small or negative responses to CO2 were not well simulated; (ii) in general seasonal and inter-annual variation in plant production responses to elevated CO2 were well represented by the model; (iii) the observed CO2 enhancement in overall mean legume content was well simulated but year-to-year variation in legume content was poorly captured by the model; (iv) the best fit of the model to the data required all three CO2 response functions to be invoked; (v) using actual legume content and reduced N fixation rate under elevated CO2 in the model provided the best fit to the experimental data. We conclude that in temperate grasslands the N dynamics (particularly the legume content and N fixation activity) play a critical role in pasture production responses to elevated CO2 , and are processes for model improvement.