Interpreting genotype-by-environment interaction for biomass production in hybrid poplars under short-rotation coppice in Mediterranean environments
Data de publicació2016-11-01
Gil, Paula M.
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Understanding genotype × environment interaction (GEI) is crucial to optimize the deployment of clonal material to field conditions in short‐rotation coppice poplar plantations. Hybrid poplars are grown for biomass production under a wide range of climatic and edaphic conditions, but their adaptive performance in Mediterranean areas remains poorly characterized. In this work, site regression (SREG) and factorial regression mixed models are combined to gain insight into the nature and causes underlying GEI for biomass production of hybrid poplar clones. SREG addresses the issue of clonal recommendation in multi‐environment trials through a biplot representation that visually identifies superior genotypes. Factorial regression, alternatively, involves a description of clonal reaction to the environment in terms of physical variables that directly affect productivity. Initially, SREG aided in identifying cross‐over interactions that often involved hybrids of different taxonomic background. Factorial regression then selected latitude, mean temperature of the vegetative period (MTVP) and soil sand content as main site factors responsible for differential clonal adaptation. Genotypic responses depended strongly on taxonomic background: P. deltoides Bartr. ex Marsh. × P. nigra L. clones showed an overall positive sensitivity to increased MTVP and negative sensitivity to increased sand content, whereas the opposite occurred for P. trichocarpa Torr. & Gray × P. deltoides clones; the three‐cross hybrid [(P. deltoides × P. trichocarpa) × P. nigra] often displayed an intermediate performance. This information can contribute toward the identification and biological understanding of adaptive characteristics relevant for poplar breeding in Mediterranean conditions and facilitate clonal recommendation at eco‐regional level.
És part deGlobal Change Biology Bioenergy, 2016, vol. 8, p. 1124-1135
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