Phosphorus is a crucial macronutrient for plants playing a critical role in many cellular signaling and energy cycling processes. In light of this, phosphorus acquisition efficiency is an important target trait for crop improvement, but it also provides an ecological adaptation for growth of plants in low nutrient environments. Increased root hair density has been shown to improve phosphorus uptake and plant health in a number of species. In several plant families, including Brassicaceae, root hair bearing cells are positioned on the epidermis according to their position in relation to cortex cells, with hair cells positioned in the cleft between two underlying cortex cells. Thus the number of cortex cells determines the number of epidermal cells in the root hair position. Previous research has associated phosphorus-limiting conditions with an increase in the number of cortex cell files in Arabidopsis thaliana roots, but they have not investigated the spatial or temporal domains in which these extra divisions occur or explored the consequences this has had on root hair formation. In this study, we use 3D reconstructions of root meristems to demonstrate that the radial anticlinal cell divisions seen under low phosphate are exclusive to the cortex. When grown on media containing replete levels of phosphorous, A. thaliana plants almost invariably show eight cortex cells; however when grown in phosphate limited conditions, seedlings develop up to 16 cortex cells (with 10–14 being the most typical). This results in a significant increase in the number of epidermal cells at hair forming positions. These radial anticlinal divisions occur within the initial cells and can be seen within 24 h of transfer of plants to low phosphorous conditions. We show that these changes in the underlying cortical cells feed into epidermal patterning by altering the regular spacing of root hairs.

African rice (Oryza glaberrima) has adapted to challenging environments and is a promising source of genetic variation. We analysed dynamics of photosynthesis and morphology in a reference set of 155 O. glaberrima accessions. Plants were grown in an agronomy glasshouse to late tillering stage. Photosynthesis induction from darkness and the decrease in low light was measured by gas exchange and chlorophyll fluorescence along with root and shoot biomass, stomatal density and leaf area. Steady state and kinetic responses were modelled. We describe extensive natural variation in O. glaberrima for steady state, induction and reduction responses of photosynthesis that has value for gene discovery and crop improvement. Principle component analyses indicated key clusters of plant biomass, kinetics of photosynthesis (CO2 assimilation, A) and photoprotection induction and reduction (measured by Non Photochemical Quenching, NPQ), consistent with diverse adaptation. Accessions also clustered according to countries with differing water availability, stomatal conductance (gs), A and NPQ and indicating that dynamic photosynthesis has adaptive value in O.glaberrima. Kinetics of NPQ, A and gs showed high correlation with biomass and leaf area. We conclude that dynamic photosynthetic traits and NPQ are important within O.glaberrima and we highlight NPQ kinetics and NPQ under low light.

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Stomata are dynamic structures that control the gaseous exchange of CO2 from the external to internal environment and water loss through transpiration. The density and morphology of stomata have important consequences in crop productivity and water use efficiency, both are integral considerations when breeding climate change resilient crops. The phenotyping of stomata is a slow manual process and provides a substantial bottleneck when characterising phenotypic and genetic variation for crop improvement. There are currently no open-source methods to automate stomatal counting. We used 380 human annotated micrographs of O. glaberrima and O. sativa at x20 and x40 objectives for testing and training. Training was completed using the transfer learning for deep neural networks method and R-CNN object detection model. At a x40 objective our method was able to accurately detect stomata (n = 540, r = 0.94, p<0.0001), with an overall similarity of 99% between human and automated counting methods. Our method can batch process large files of images. As proof of concept, characterised the stomatal density in a population of 155 O. glaberrima accessions, using 13,100 micrographs. Here, we present developed Stomata Detector; an open source, sophisticated piece of software for the plant science community that can accurately identify stomata in Oryza spp., and potentially other monocot species.