The soil-borne disease Verticillium wilt causes significant yield losses in cotton. Developing resistant cotton varieties is a long-term solution to manage the disease. The phytopathological parameter used to assess variety resistance is the presence or absence of infection, where transverse sectioning of infected stems is discoloured/brown. The traditional method of selecting resistant varieties involves manual scoring of visual stem discolouration after cutting. However, this process is associated with high labour costs, delayed processing time and cognitive biases. Therefore, automatic detection of resistant varieties with scalable, cost-effective, and rapid phenotyping tools is needed in cotton breeding.

Leaf gas exchange measurements provide an important tool for inferring a plant’s photosynthetic biochemistry. In most cases, the responses of photosynthetic CO 2 assimilation to variable intercellular CO 2 concentrations ( A/ Ci response curves) are used to model the maximum rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, V cmax) and the rate of electron transport at a given photosynthetically active radiation (PAR; J PAR). The standard Farquhar-Von Caemmerer-Berry model is typically used with default parameters of Rubisco kinetic values and mesophyll conductance to CO 2 ( g m) derived from tobacco that impairs analytical reliability across species. To study this, here we measured the temperature responses of key in vitro Rubisco catalytic properties and g m in cotton ( Gossypium hirsutum cv. Sicot 71) and derived V cmax and J 2000 ( J at 2000 µmol m -2 s -1 PAR) from cotton A/ Ci curves incrementally measured at 15°C to 40°C using cotton and tobacco parameters with our new automated fitting R package ‘OptiFitACi’. When applied to cotton, the tobacco parameters produced unrealistic J 2000: V cmax ratio of <1 at 25°C, two- to three-fold higher estimates of V cmax, approximately 50% higher estimates of J 2000 and more variable estimates of V cmax and J 2000, compared to model parameterisation with cotton-derived values. We determined that errors arise when using a g m of 0.23 mol m -2 s -1 bar -1 or below and Rubisco CO 2-affinities under ambient O 2 ( K C 21%O2) outside 461 µbar to 627 µbar to model A/ C i responses in cotton. We show how the multi- A/ C i modelling capabilities of ‘OptiFitACi’ serves as a robust, user-friendly extension of ‘plantecophys’ by providing simplified temperature-sensitivity and species-specificity parameterisation capabilities to enable higher accuracy estimates of V cmax and J 2000.