The importance of species-specific and temperature-sensitive parameterisation of A/Ci models: A case study using cotton (Gossypium hirsutum L.) and the automated 'OptiFitACi' R-package.

Abstract

Leaf gas exchange measurements are an important tool for inferring a plant's photosynthetic biochemistry. In most cases, the responses of photosynthetic CO2 assimilation to variable intercellular CO2 concentrations (A/Ci response curves) are used to model the maximum (potential) rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, Vcmax) and the rate of photosynthetic electron transport at a given incident photosynthetically active radiation flux density (PAR; JPAR). The standard Farquhar-von Caemmerer-Berry model is often used with default parameters of Rubisco kinetic values and mesophyll conductance to CO2 (gm) derived from tobacco that may be inapplicable across species. To study the significance of using such parameters for other species, here we measured the temperature responses of key in vitro Rubisco catalytic properties and gm in cotton (Gossypium hirsutum cv. Sicot 71) and derived Vcmax and J2000 (JPAR at 2000 µmol m-2 s-1 PAR) from cotton A/Ci curves incrementally measured at 15°C-40°C using cotton and other species-specific sets of input parameters with our new automated fitting R package 'OptiFitACi'. Notably, parameterisation by a set of tobacco parameters produced unrealistic J2000:Vcmax ratio of <1 at 25°C, two- to three-fold higher estimates of Vcmax above 15°C, up to 2.3-fold higher estimates of J2000 and more variable estimates of Vcmax and J2000, for our cotton data compared to model parameterisation with cotton-derived values. We determined that errors arise when using a gm,25 of 2.3 mol m-2 s-1 MPa-1 or less and Rubisco CO2-affinities in 21% O2 (KC 21%O2) at 25°C outside the range of 46-63 Pa to model A/Ci responses in cotton. We show how the A/Ci modelling capabilities of 'OptiFitACi' serves as a robust, user-friendly, and flexible extension of 'plantecophys' by providing simplified temperature-sensitivity and species-specificity parameterisation capabilities to reduce variability when modelling Vcmax and J2000.