Advances in phenotyping photosynthesis: An experimental modeling approach using crop species Brassica rapa

Abstract

Plant populations vary widely in their response to environmental factors. Phenotyping, a group of tools for measuring differences in plant structure and function, can identify physiological traits expressed by plant populations in response to environmental stimuli. Obtaining phenotypic information from observations of plant behavior can be enhanced through mechanistic modeling. Mechanistic models use biophysical and biochemical principles to explain plant behavior where observational data of complex phenotypic traits such as CO2 assimilation can be broken down into component processes. Mechanistic models therefore can be used to characterize phenotypes across diverse populations and conditions. Genomic differences play a key role in the expression of phenotypic behaviors. Models can be developed to mimic genomic variation through the identification of mechanisms conserved across populations and mechanisms varying within a population. This dissertation quantified phenotypic trait variation in photosynthetic processes of Brassica rapa in response to environmental variation. B. rapa is a widely distributed and diverse species with natural and agricultural genotypes. Populations of genotypes were used to evaluate critical photosynthetic processes under multiple experimental manipulations. First, photosynthetic variability was investigated by comparing multiple models of varying assumptions to identify genotypic photosynthetic traits in unstressed conditions. Second, a photosynthesis model with expanded treatment of electron transport processes tested genotypic trait response to nitrogen availability. Third, the knowledge from the first two tests was incorporated into a whole plant model for improved understanding of the mechanisms conferring drought tolerance. Finally, a modeling investigation made predictions of B. rapa genotypes fitness under climate change. Overall, this dissertation provided advances in understanding phenotypic variation by leveraging physiological data into models. These can be applied broadly for informing both crop breeding decisions and global level biogeochemical modeling for partitioning populations based on the varying mechanisms they employ in response to the environment.