Plant acclimation to temperature: Developments in the Pasture Simulation model
Résumé
A modelling approach for the thermal acclimation of plant photosynthesis and respiration is presented that
accounts for changes in the maximum carbon (C) assimilation with changing growth temperature. It is motivated
by one key observation, i.e. the optimum temperature for plant processes increases with increasing growth
temperature, and two corollary expectations: (i) this determines a modification of the response curve of C assimilation,and (ii) plant C release (respiration) is also affected by changing growth temperature. Simple relations are proposed to model these phenomena, consistent with the Farquhar model of photosynthesis. The incorporation of temperature acclimation of plant photosynthesis and respiration into the Farquhar-based scheme
of the Pasture Simulation model (PaSim; EMS: existing modelling solution, MMS: modified modelling solution) is
proposed as a way to reduce the uncertainty in estimations of harvested or standing above ground biomass and C fluxes from grassland systems in Central France. Here we show that, across a flux tower grassland site spanning two alternative grazing regimes (Laqueuille, 45° 38′ N, 02° 44′ E, 1040 m a.s.l.), acclimation parameterizations improve model ability to reproduce observed ecosystem respiration (especially with extensive grazing, where root mean square error [RMSE] lowered from 15.20 to 11.59 g C m−2 week−1). An assessment at two grassland systems (Saint-Genès-Champanelle and Theix, 45° 43′ N, 03° 01′ E, 880 m a.s.l.) with alternative cutting regimes and climate conditions also showed some improvements in biomass estimates (e.g. with frequent cutting and experimental extreme summer event RMSE changed from 0.86 to 0.40 t DM ha−1). The consequences of acclimation for simulated grassland outputs depend on the conditions evaluated which requires further studies. However, our results suggest that grassland modelling omitting plant temperature acclimation is likely to overestimate C emissions, thus biasing projections of future C storage and estimates of policy-making indicators.