Exploring the optimum nitrogen partitioning to predict the acclimation of C3 leaf photosynthesis to varying growth conditions.

Xinyou Yin,Ad H C M Schapendonk,Paul C Struik

doi:10.1093/jxb/ery277

Xinyou Yin, Ad H C M Schapendonk + Show 1 more

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https://doi.org/10.1093/jxb/ery277

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Abstract

The distribution of leaf nitrogen among photosynthetic proteins (i.e. chlorophyll, the electron transport system, Rubisco, and other soluble proteins) responds to environmental changes. We hypothesize that this response may underlie the biochemical aspect of leaf acclimation to the growth environment, and describe an analytical method to solve optimum nitrogen partitioning for maximized photosynthesis in C3 leaves. The method predicts a high investment of nitrogen in Rubisco under conditions leading to excessive energy supply relative to metabolic demand (e.g. low temperature, high light, low nitrogen, or low CO2). Conversely, more nitrogen is invested in chlorophyll when the energy supply is limiting. Overall, our optimization results are qualitatively consistent with literature reports. Commonly reported changes in photosynthetic parameters with growth temperature were emergent properties of the optimum nitrogen partitioning. The method was used to simulate dynamic acclimation under varying environmental conditions, using first-order kinetics. Simulated diurnal patterns of leaf photosynthetic rates as a result of acclimation differed greatly from those without acclimation (Awithout). However, differences in predicted photosynthesis integrated over a day or over the growing season from Awithout depended on the value of the kinetic time constant (τ), suggesting that τ is a critical parameter determining the overall impact of nitrogen distribution on acclimated photosynthesis.

Highlights

One of the major underlying components in predicting ecosystem productivity and crop yield is to model photosynthesis of individual leaves in a canopy under fluctuating environmental conditions
A prevailing approach is to use the steadystate photosynthesis model of Farquhar, von Caemmerer, and Berry (Farquhar et al, 1980; ‘the FvCB model’ hereafter). This model predicts photosynthesis as the minimum of the ribulose-1,5-bisphosphate (RuBP)-saturated rate of CO2 assimilation, which is a function of the maximum carboxylation capacity of Rubisco (Vc,max), and the RuBP regenerationlimited rate, which is a function of the maximum electron transport (Jmax)
We present an analytical procedure to determine the optimum distribution of Nphoto among photosynthetic protein complexes under a specific environmental condition, based on the FvCB model for C3 species

Summary

Introduction

One of the major underlying components in predicting ecosystem productivity and crop yield is to model photosynthesis of individual leaves in a canopy under fluctuating environmental conditions. A prevailing approach is to use the steadystate photosynthesis model of Farquhar, von Caemmerer, and Berry (Farquhar et al, 1980; ‘the FvCB model’ hereafter). This model predicts photosynthesis as the minimum of the ribulose-1,5-bisphosphate (RuBP)-saturated rate of CO2 assimilation, which is a function of the maximum carboxylation capacity of Rubisco (Vc,max), and the RuBP regenerationlimited rate, which is a function of the maximum electron transport (Jmax) (see Supplementary Appendix A at JXB online). A substantial body of experimental work has shown a strong empirical correlation between Vc,max or Jmax and leaf nitrogen An investment of nitrogen in a protein compound within a leaf ‘appropriate’ to its environment must be of adaptive significance (Walters, 2005)

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