Rapid Chlorophyll Fluorescence Light Response Curves Mechanistically Inform Photosynthesis Modeling.

Crop improvement is crucial to ensuring global food security under climate change, and hence there is a pressing need for phenotypic observations that are both high throughput and improve mechanistic understanding of plant responses to environmental cues and limitations. In this study, chlorophyll fluorescence light response curves and gas-exchange observations are combined to test the photosynthetic response to moderate drought in four genotypes of The quantum yield of PSII ( ) is here analyzed as an exponential decline under changing light intensity and soil moisture. Both the maximum and the rate of decline across a large range of light intensities (0-1,000 μmol photons m s; ) are negatively affected by drought.

Conifers depend on established roots during drought: results from a coupled model of carbon allocation and hydraulics.

Trees may survive prolonged droughts by shifting water uptake to reliable water sources, but it is unknown if the dominant mechanism involves activating existing roots or growing new roots during drought, or some combination of the two. To gain mechanistic insights on this unknown, a dynamic root-hydraulic modeling framework was developed that set up a feedback between hydraulic controls over carbon allocation and the role of root growth on soil-plant hydraulics. The new model was tested using a 5 yr drought/heat field experiment on an established piñon-juniper stand with root access to bedrock groundwater.

A framework for genomics-informed ecophysiological modeling in plants.

Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first principles-based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L.

Phenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Genotypes.

Agronomists have used statistical crop models to predict yield on a genotype-by-genotype basis. Mechanistic models, based on fundamental physiological processes common across plant taxa, will ultimately enable yield prediction applicable to diverse genotypes and crops. Here, genotypic information is combined with multiple mechanistically based models to characterize photosynthetic trait differentiation among genotypes of .