Surfing the Hyperbola Equations of the Steady-State Farquhar-von Caemmerer-Berry C3 Leaf Photosynthesis Model: What Can a Theoretical Analysis of Their Oblique Asymptotes and Transition Points Tell Us?

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

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.

A Machine Learning Model for Photorespiration Response to Multi-Factors

Photorespiration results in a large amount of leaf photosynthesis consumption. However, there are few studies on the response of photorespiration to multi-factors. In this study, a machine learning model for the photorespiration rate of cucumber leaves’ response to multi-factors was established. It provides a theoretical basis for studies related to photorespiration. Machine learning models of different methods were designed and compared. The photorespiration rate was expressed as the difference between the photosynthetic rate at 2% O2 and 21% O2 concentrations. The results show that the XGBoost models had the best fit performance with an explained variance score of 0.970 for both photosynthetic rate datasets measured using air and 2% O2, with mean absolute errors of 0.327 and 0.181, root mean square errors of 1.607 and 1.469, respectively, and coefficients of determination of 0.970 for both. In addition, this study indicates the importance of the features of temperature, humidity and the physiological status of the leaves for predicted results of photorespiration. The model established in this study performed well, with high accuracy and generalization ability. As a preferable exploration of the research on photorespiration rate simulation, it has theoretical significance and application prospects.