Abstract
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 Brassica rapa. Infrared leaf gas exchange and chlorophyll fluorescence observations are analyzed using Bayesian methods. Three advantages of Bayesian approaches are employed: a hierarchical model structure, the testing of parameter estimates with posterior predictive checks and a multimodel complexity analysis. In all, eight models of photosynthesis are compared for fit to data and penalized for complexity using deviance information criteria (DIC) at the genotype scale. The multimodel evaluation improves the credibility of trait estimates using posterior distributions. Traits with important implications for yield in crops, including maximum rate of carboxylation (Vcmax) and maximum rate of electron transport (Jmax) show genotypic differentiation. B. rapa shows phenotypic diversity in causal traits with the potential for genetic enhancement of photosynthesis. This multimodel screening represents a statistically rigorous method for characterizing genotypic differences in traits with clear biophysical consequences to growth and productivity within large crop breeding populations with application across plant processes.
Highlights
Maintaining food security for the world’s rapidly growing population is a paramount challenge for science
Deviance Information Criterion (DIC) strongly favored derivation of electron transport rate (ETR), and AJ, from Equation (3.6) (Table 5), as Jm based models were in the top tier in seven cases, while Jf models were rated as top-tier only two times and only for oil
Fluorometry estimates all photosystem II (PSII) e− excitation at the beginning of the e− transport chain; using this to estimate the assimilatory outcome of e− transport does not distinguish e−’s used for photosynthetic linear electron flow and the alternative pathways of e− transport (Miyake, 2010)
Summary
Maintaining food security for the world’s rapidly growing population is a paramount challenge for science. Mechanistic modeling using known or theorized ecological, biochemical and biophysical principles further advance understanding through connecting yield to causal traits (Laisk and Nedbal, 2009; Tardieu, 2010). These mechanistic models of plant physiology use statistical tools to estimate trait variation by organizing phenomenological data into meaningful mathematical representations of enzymatic and protein activity responsible for plant processes (DeWitt, 1965; von Caemmerer, 2000; Patrick et al, 2009; McDowell et al, 2013). In this way models can estimate valuable phenotypic information through specifying physiologically meaningful trait values from data
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