Enhancing the productivity of grasses under high-density planting by engineering light responses : from model systems to feedstocks

© The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissionsoup.com.

Bibliographische Detailangaben
Veröffentlicht in:Journal of experimental botany. - 1985. - 65(2014), 11 vom: 01. Juni, Seite 2825-34
1. Verfasser: Warnasooriya, Sankalpi N (VerfasserIn)
Weitere Verfasser: Brutnell, Thomas P
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2014
Zugriff auf das übergeordnete Werk:Journal of experimental botany
Schlagworte:Journal Article Research Support, U.S. Gov't, Non-P.H.S. Review Biomass far-red high-density planting phytochrome red shade avoidance.
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520 |a The successful commercialization of bioenergy grasses as lignocellulosic feedstocks requires that they be produced, processed, and transported efficiently. Intensive breeding for higher yields in food crops has resulted in varieties that perform optimally under high-density planting but often with high input costs. This is particularly true of maize, where most yield gains in the past have come through increased planting densities and an abundance of fertilizer. For lignocellulosic feedstocks, biomass rather than grain yield and digestibility of cell walls are two of the major targets for improvement. Breeding for high-density performance of lignocellulosic crops has been much less intense and thus provides an opportunity for improving the feedstock potential of these grasses. In this review, we discuss the role of vegetative shade on growth and development and suggest targets for manipulating this response to increase harvestable biomass under high-density planting. To engineer grass architecture and modify biomass properties at increasing planting densities, we argue that new model systems are needed and recommend Setaria viridis, a panicoid grass, closely related to major fuel and bioenergy grasses as a model genetic system 
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