4.6 Article

Impact of nitrogen allocation on growth and photosynthesis of Miscanthus (Miscanthus x giganteus)

Journal

GLOBAL CHANGE BIOLOGY BIOENERGY
Volume 4, Issue 6, Pages 688-697

Publisher

WILEY
DOI: 10.1111/j.1757-1707.2012.01167.x

Keywords

biofuel crop; canopy; GPP; nitrogen allocation

Funding

  1. Energy Bioscience Institute

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Nitrogen (N) addition typically increases overall plant growth, but the nature of this response depends upon patterns of plant nitrogen allocation that vary throughout the growing season and depend upon canopy position. In this study seasonal variations in leaf traits were investigated across a canopy profile in Miscanthus (Miscanthus x giganteus) under two N treatments (0 and 224 kg ha-1) to determine whether the growth response of Miscanthus to N fertilization was related to the response of photosynthetic capacity and nitrogen allocation. Miscanthus yielded 24.1 Mg ha-1 in fertilized plots, a 40% increase compared to control plots. Photosynthetic properties, such as net photosynthesis (A), maximum rate of rubisco carboxylation (Vcmax), stomatal conductance (gs) and PSII efficiency (Fv'/Fm'), all decreased significantly from the top of the canopy to the bottom, but were not affected by N fertilization. N fertilization increased specific leaf area (SLA) and leaf area index (LAI). Leaf N concentration in different canopy layers was increased by N fertilization and the distribution of N concentration within canopy followed irradiance gradients. These results show that the positive effect of N fertilization on the yield of Miscanthus was unrelated to changes in photosynthetic rates but was achieved mainly by increased canopy leaf area. Vertical measurements through the canopy demonstrated that Miscanthus adapted to the light environment by adjusting leaf morphological and biochemical properties independent of nitrogen treatments. GPP estimated using big leaf and multilayer models varied considerably, suggesting a multilayer model in which Vcmax changes both through time and canopy layer could be adopted into agricultural models to more accurately predict biomass production in biomass crop ecosystems.

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