A model-guided holistic review of exploiting natural variation of photosynthesis traits in crop improvement

Abstract

Breeding for improved leaf photosynthesis is considered as a viable approach to increase crop yield. Whether it should be improved in combination with other traits has not been assessed critically. Based on the quantitative crop model GECROS that inter-connects various traits to crop productivity, we review natural variation in relevant traits, from biochemical aspects of leaf photosynthesis to morpho-physiological crop characteristics. While large phenotypic variations (sometimes >two-fold) for leaf photosynthesis and its underlying biochemical parameters were reported, few quantitative trait loci (QTL) were identified, accounting for a small percentage of phenotypic variation. More QTL were reported for sink size (that feeds back on photosynthesis) or morpho-physiological traits (that affect canopy productivity and duration), together explaining a much greater percentage of their phenotypic variation. Traits for both photosynthetic rate and sustaining it during grain filling were strongly related to nitrogen-related traits. Much of the molecular basis of known photosynthesis-QTL thus resides in genes controlling photosynthesis indirectly. Simulation using GECROS demonstrated the overwhelming importance of electron transport parameters, compared to the maximum Rubisco activity that largely determines the commonly-studied light-saturated photosynthetic rate. Exploiting photosynthetic natural variation might significantly improve crop yield if nitrogen uptake, sink capacity and other morpho-physiological traits are co-selected synergistically.

Neglecting acclimation of photosynthesis under drought can cause significant errors in predicting leaf photosynthesis in wheat

“…Moreover, simulation analysis showed that omitting drought acclimation of κ 2LL and J max , both for values estimated at a reference T leaf and the shape parameters, resulted in significant errors in predicting leaf photosynthetic rate; while the influence of omitting the acclimation of V cmax , T p or R d was limited (Figure 7). These results are in line with the reports of Gu et al (2014), Yin et al (2022) and Harbinson and Yin (2022), who showed electron transport parameters, rather than Rubisco-related parameters (i.e. V cmax ), contribute most to the variation of plant productivity.…”

Genetics as a key to improving crop photosynthesis

“…To assess the overall impact of the variation for a photosynthetic trait in a given environment, it is essential to use crop models to take into account the dynamic properties and broad ranges of environments ( Wu et al ., 2017 , 2019 ; Coast et al , 2021 ). Models will also be very useful to identify the potential contribution of photosynthesis improvements on yield, given the variation on other yield components, such as root nutrient uptake or sink capacity ( Yin et al , 2022 ). Using an approach where the impact of genetic variation on crop performance in a given agricultural context can be predicted will allow the identification of alleles that are deemed interesting to incorporate in breeding material.…”

Genetic variation in photosynthesis: many variants make light work

“…A striking example of such an approach is provided by seminal work from Messina et al (2020 , Preprint) on model-aided breeding of drought-tolerant maize, which approximately doubled the rate of crop improvement under drought. While the genetic mechanisms that control many parameters in the photosynthesis module of crop models are not yet sufficiently understood to mimic this approach, Yin et al (2022) take a first step in this direction by reviewing how photosynthetic model parameters have been observed to vary within germplasm of C 3 crop species. Using their GECROS model, they go on to show the relative importance of genetic variation in model parameters describing photosynthetic electron transport, relative to parameters controlling the light-saturated rate of CO 2 assimilation, such as maximum Rubisco activity.…”