C-3 and C-4 photosynthesis models: An overview from the perspective of crop modelling

摘要

Nearly three decades ago Farquhai. von Caemmerer and Berry published a biochemical model for C-3 photosynthetic rates (the FvCB model) The model predicts net photosynthesis (A) as the minimum of the Rubisco-limited rate of CO2 assimilation (A(c)) and the electron transport-limited rate of CO2 assimilation (A(J)) Given us simplicity and the growing availability of the required enzyme kinetic constants, the FvCB model has been used for a wide range of studies, from analysing underlying C-3 leaf biochemistry to predicting photosynthetic fluxes of ecosystems in response to global warming However, surprisingly, this model has seen limited use ill existing crop growth models. Here we highlight the elegance, simplicity, and robustness of this model In the light of some uncertainties with photosynthetic electron transport pathways, a recently extended FvCB model to calculate A(J) is summarizedApplying the FvCB-type model in crop growth models for predicting leaf photosynthesis requires a stomatal conductance (g(s)) model to be incorporated, so that inter cellular CO2 concentration (C-1) can be estimated In recent years great emphasis has been put on the significant drawdown of Rubisco carboxylation-sue CO2 concentration (C-c) relative to C-1 To account for this drawdown, mesophyll conductance (g(m)) for CO2 transfer can be added We present an analytical algorithm that incorporates a g(s) model and uses g(m) as a temperature-dependent parameter for calculating A under vat ions environmental scenarios.Finally we discuss a C-4-equivalent version of the FvCB model In addition to the algorithms already elaborated for C-3 photosynthesis, most important algorithms for C-4 photosynthesis are those that capture the CO2 concentrating mechanism and the extra ATP requirement by the C-4 cycle Although the cui rent estimation of the C-4 enzyme kinetic constants is less certain, applying FvCB-type models to both C-3 and C-4 crops is recommended to accurately predict the response of crop photosynthesis to multiple, interactive environmental variables