Model Enhanced Phenotyping: Understanding N Impacts on Photosynthetic Traits in Brassica Rapa.

Phenotyping is a varied group of methods to assess plant morphological and physiological traits. It has been argued that phenotyping of plant traits can be advanced through coupling observational data to mechanistic models. Here, an iterative model improvement sequence was developed for phenotyping photosynthetic traits under nitrogen (N) treatments. Two experimental cycles used genotypic information from leaf gas-exchange and chlorophyll a fluorescence combined with mechanistic models for describing photosynthetic variation in Brassica rapa genotypes. In the first experimental-modeling cycle, A/C_i curves of six rapa genotypes grown under well-fertilized and well-watered conditions were evaluated using eight photosynthesis models for revealing genotypic traits while comparing genotypic model complexity. This multi-model evaluation revealed genotypic differentiation in trait posterior distributions, demonstrated a hierarchy in trait variation and focused attention towards model mechanisms needing improvement. In the second cycle, model refinement addressed areas of uncertainty while trait priors were updated and a hierarchical trait structure was established. Experimentally, the number of genotypes evaluated was reduced to two while data acquisition added light response data and spectrophotometric analysis to inform trait modeling under different N treatment conditions. Genotypic differentiation was found in numerous traits under both iterations including maximum rate of carboxylation (V_cmax), maximum rate of electron transport (J_max), and quantum yield for CO₂ (ϕ_J). Potential genotypic differences revealed in first analysis were clarified under the second experimental-modeling cycle. Specifically, through model refinement we found differential leaf absorbance characteristics and trait differentiation in quantum yield of photosystem II (PSII) (ϕ_PSII) in the sub-population evaluated. Further, different nitrogen treatments showed genotypic responses including for V_cmax , J_max and relative chlorophyll content. This framework represents a statically rigorous method for phenotyping traits within crop breeding populations while providing the flexibility to integrate new data for testing updated hypothesis.