Abstract. Light inside a canopy constantly fluctuates. Under fluctuating light (FL) conditions, stomatal conductance and photosynthetic rate constantly change. In this study, we explored whether this dynamics of stomata movements upon FL influenced the water use efficiency of rice in the field. We used a USDA-curated rice mini-core diversity panel consisting of 204 worldwide distributed accessions. A priori model on dynamic stomatal response to FL was utilised to identify kinetic parameters describing the stomatal delays during the closing (t cl ) and the opening (t op ) phase. Result showed that t cl had a larger variation than t op across the mini-core panel. t cl was negatively correlated with water use efficiency (WUE) related traits, stem diameter, grain weight per tiller and heading time, but positively correlated with maximum annual temperature, carbon assimilation related traits and biomass (P < 0.05). We further showed a strong correlation of t cl with the relative decrease of biomass under drought in 14 accessions with different t cl . We discussed the adjustment of stomatal conductance under fluctuating light in light of the trade-off between optimising CO 2 uptake and optimising water saving. This study suggests that stomatal dynamics under fluctuating light is closely related to drought resistance and hence detailed study is needed to enable its application in breeding drought tolerance in rice.

Mining natural variations is a major approach to identify new options to improve crop light use efficiency. So far, successes in identifying photosynthetic parameters positively related to crop biomass accumulation through this approach are scarce, possibly due to the earlier emphasis on properties related to leaf instead of canopy photosynthetic efficiency. This study aims to uncover rice () natural variations to identify leaf physiological parameters that are highly correlated with biomass accumulation, a surrogate of canopy photosynthesis. To do this, we systematically investigated 14 photosynthetic parameters and four morphological traits in a rice population, which consists of 204 U.S. Department of Agriculture-curated minicore accessions collected globally and 11 elite Chinese rice cultivars in both Beijing and Shanghai. To identify key components responsible for the variance of biomass accumulation, we applied a stepwise feature-selection approach based on linear regression models. Although there are large variations in photosynthetic parameters measured in different environments, we observed that photosynthetic rate under low light (A) was highly related to biomass accumulation and also exhibited high genomic inheritability in both environments, suggesting its great potential to be used as a target for future rice breeding programs. Large variations in A among modern rice cultivars further suggest the great potential of using this parameter in contemporary rice breeding for the improvement of biomass and, hence, yield potential.

Canopy photosynthesis (Ac) describes photosynthesis of an entire crop field and the daily and seasonal integrals of Ac positively correlate with daily and seasonal biomass production. Much effort in crop breeding has focused on improving canopy architecture and hence light distribution inside the canopy. Here, we develop a new integrated canopy photosynthesis model including canopy architecture, a ray tracing algorithm, and C3 photosynthetic metabolism to explore the option of manipulating leaf chlorophyll concentration ([Chl]) for greater Ac and nitrogen use efficiency (NUE). Model simulation results show that (a) efficiency of photosystem II increased when [Chl] was decreased by decreasing antenna size and (b) the light received by leaves at the bottom layers increased when [Chl] throughout the canopy was decreased. Furthermore, the modelling revealed a modest ~3% increase in Ac and an ~14% in NUE was accompanied when [Chl] reduced by 60%. However, if the leaf nitrogen conserved by this decrease in leaf [Chl] were to be optimally allocated to other components of photosynthesis, both Ac and NUE can be increased by over 30%. Optimizing [Chl] coupled with strategic reinvestment of conserved nitrogen is shown to have the potential to support substantial increases in Ac, biomass production, and crop yields.