Rapid Chlorophyll a Fluorescence Light Response Curves Mechanistically Inform Photosynthesis Modeling.

Jonathan R Pleban,Carmela R Guadagno,David S Mackay,Brent E Ewers,Cynthia Weinig

doi:10.1104/pp.19.00375

Jonathan R Pleban, Carmela R Guadagno + Show 3 more

Open Access

https://doi.org/10.1104/pp.19.00375

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Abstract

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 a fluorescence light response curves and gas-exchange observations are combined to test the photosynthetic response to moderate drought in four genotypes of Brassica rapa The quantum yield of PSII (ϕ PSII ) is here analyzed as an exponential decline under changing light intensity and soil moisture. Both the maximum ϕ PSII and the rate of ϕ PSII decline across a large range of light intensities (0-1,000 μmol photons m-2 s-1; β PSII ) are negatively affected by drought. We introduce an alternative photosynthesis model (β PSII model) incorporating parameters from rapid fluorescence response curves. Specifically, the model uses β PSII as an input for estimating the photosynthetic electron transport rate, which agrees well with two existing photosynthesis models (Farquhar-von Caemmerer-Berry and Yin). The β PSII model represents a major improvement in photosynthesis modeling through the integration of high-throughput fluorescence phenotyping data, resulting in gained parameters of high mechanistic value.

Highlights

Increasing global populations and environmental change require greater mechanistic understanding of plant responses to fluctuating environmental factors along with meaningful phenotyping for tolerance to stress such as drought (Sheffield and Wood, 2008; Jin et al, 2018).Improved phenotyping technologies can advance our ability to link physiological mechanisms to rapidly improving genetic information
Droughted plants were assigned to three different groups and replicate plants observed on experimental days 4–7, 9–12, and 15
On experimental day 9, water was re-applied to a subset of droughted plants (R1) and they were observed on experimental day 9–12

Summary

Introduction

Increasing global populations and environmental change require greater mechanistic understanding of plant responses to fluctuating environmental factors along with meaningful phenotyping for tolerance to stress such as drought (Sheffield and Wood, 2008; Jin et al, 2018).Improved phenotyping technologies can advance our ability to link physiological mechanisms to rapidly improving genetic information. Improving predictive understanding of crop responses to changing environments will require that mechanistic models directly use phenotypic and environmental data to simulate outcomes sensitive enough to capture possible variation in the expressed traits among unknown genotypes. When these requirements are met, mechanistic models can assist in unraveling the genetic architecture underlying the complex quantitative traits of drought physiology (Reymond et al, 2003; Hammer et al, 2006; Chenu et al, 2009)

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