Luis A. Apiolaza edited Introduction .tex  about 8 years ago

Commit id: 269db2f53ed647cca1545f503ad7c62e32e5e5cc

deletions | additions      

       

\section{Introduction}  The main use of plantation \textit{Eucalyptus} species is the production of biomass for pulp and paper, and bioenergy. These trees are fast growing and can potentially produce high-quality timber for appearance, structural and engineered wood products. However, Unfortunately,  this potential is hindered by the frequent presence of large growth-strains, which are associated with log splitting, warp, collapse and brittleheart, which impose imposing  substantial costs on processing \cite{yamamoto2007slides}. Costly A few technological  mitigation strategies have been developed to reduce growth-strain induced the incidence of  wood defects that have been caused by growth-strain, but they are costly and  only partially effective \cite{yamamoto2007slides}. With growth-strain being highly heritable, as is shown here, an An  alternative approach to the problem  is to rely on the genetic control of growth-strain---shown in this article to be highly heritable---to  select and grow individuals which display with  low growth-strain. Until now it has been difficult, time consuming and expensive to measure growth-strain, preventing the assessment of the However, measuring growth strains in  large number numbers  of trees (as  needed in for  a successful breeding programme, previously programme) has been difficult, time consuming and expensive until now. As an example,  the largest reported studies \cite{Murphy_2005} \cite{naranjo2012early} conducted growth-strain testing on to date assessed only  164 \cite{Murphy_2005}  and 216 \cite{naranjo2012early}  trees respectively. Utilising the developments made by \cite{Chauhan_2010} \cite{Entwistle_2014}, a rapid growth strain testing procedure has been developed. In order to minimise the time taken to conduct the growth strain testing on each individual the rapid testing procedure can not account for negative values, where the wood in the centre of the stem is under tension rather than compression, resulting in a left censored dataset.