Abstract
AbstractGiant reed (Arundo donax L.) is a perennial rhizomatous grass, which has attracted great attention as a potential lignocellulosic feedstock for bioethanol production due to high biomass yield in marginal land areas, high polysaccharide content and low inhibitor levels in microbial fermentations. However, little is known about the trait variation that is available across a broad ecotypic panel of A. donax nor the traits that contribute most significantly to yield and growth in drought prone environments. A collection of 82 ecotypes of A. donax sampled across the Mediterranean basin was planted in a common garden experimental field in Savigliano, Italy. We analysed the collection using 367 clumps representing replicate plantings of 82 ecotypes for variation in 21 traits important for biomass accumulation and to identify the particular set of ecotypes with the most promising potential for biomass production. We measured morpho‐physiological, phenological and biomass traits and analysed causal relationships between traits and productivity characteristics assessed at leaf and canopy levels. The results identified differences among the 82 ecotypes for all studied traits: those showing the highest level of variability included stomatal resistance, stem density (StN), stem dry mass (StDM) and total biomass production (TotDM). Multiple regression analysis revealed that leaf area index, StDM, StN, number of nodes per stem, stem height and diameter were the most significant predictors of TotDM and the most important early selection criteria for bioenergy production from A. donax. These traits were used in a hierarchical cluster analysis to identify groups of similar ecotypes, and a selection was made of promising ecotypes for multiyear and multisite testing for biomass production. Heritability estimates were significant for all traits. The potential of this ecotype collection as a resource for studies of germplasm diversity and for the analysis of traits underpinning high productivity of A. donax is highlighted.
Highlights
The results identified differences among the 82 ecotypes for all studied traits: those showing the highest level of variability included stomatal resistance, stem density (StN), stem dry mass (StDM) and total biomass production (TotDM)
Giant reed (Arundo donax L.) is a perennial rhizomatous grass, which has attracted great attention as a potential lignocellulosic biomass due to rapid growth, high apparent productivity and adaptability to marginal land conditions combined with low input requirements (Angelini, Ceccarini, & Bonari, 2005; Angelini, Ceccarini, Nassi o Di Nasso, & Bonari, 2009; Corno, Pilu, & Adani, 2014; Lewandowski, Scurlock, Lindvall, & Christou, 2003; Pilu, Bucci, Badone, & Landoni, 2012)
Based on a comprehensive utilization indexes obtained from an evaluation system that takes into consideration numerous factors deemed essential for the conversion of lignocellulosic biomass to bioethanol, a recent study revealed that A. donax is a highly promising lignocellulosic feedstock for producing bioethanol compared to corn stalks, switchgrass, pennisetum and silvergrass (Kou, Song, Zhang, & Tan, 2017)
Summary
Giant reed (Arundo donax L.) is a perennial rhizomatous grass, which has attracted great attention as a potential lignocellulosic biomass due to rapid growth, high apparent productivity and adaptability to marginal land conditions combined with low input requirements (Angelini, Ceccarini, & Bonari, 2005; Angelini, Ceccarini, Nassi o Di Nasso, & Bonari, 2009; Corno, Pilu, & Adani, 2014; Lewandowski, Scurlock, Lindvall, & Christou, 2003; Pilu, Bucci, Badone, & Landoni, 2012). This study aimed: (a) to characterize a broad Euro‐ Mediterranean panel of 82 A. donax ecotypes by assaying 21 biomass, morphology, physiology and phenology traits in a common garden experiment and to understand how these may correlate to each other; (b) to identify traits that may have contributed to differences in biomass production in field conditions; and (c) to select potential high yielding clusters through a multivariate approach. During the sampling period and from 19 July to 23 August, the weather was extremely dry and warm, with a daily average maximum temperature of 29.4°C and 12 mm of accumulated rainfall (Figure 2) as assessed by a weather station located 1 km from the common garden experimental field
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