4.7 Article

Model analysis of flowering phenology in recombinant inbred lines of barley

期刊

JOURNAL OF EXPERIMENTAL BOTANY
卷 56, 期 413, 页码 959-965

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OXFORD UNIV PRESS
DOI: 10.1093/jxb/eri089

关键词

daylength; ecophysiological modelling; flowering; genotype-by-environment interaction; Hordeum vulgare; phenology; photoperiod; temperature

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A generic model for flowering phenology as a function of daily temperature and photoperiod was applied to predict differences of flowering times among 96 individuals (including the two parents) of a recombinant inbred line population in barley (Hordeum vulgare L.). Because of the large number of individuals to study, there is a need for simple ways to derive model parameters for each genotype. Therefore the number of genotype-specific parameters was reduced to four, namely f(o) (the minimum number of days to flowering at the optimum temperature and photoperiod), theta(1) and theta(2) (the development stages for the start and the end of the photoperiod-sensitive phase, respectively), and delta (the photoperiod sensitivity). Values of these parameters were estimated using a newly described methodological framework based on data from a photoperiod-controlled experiment where plants were mutually transferred between long-day and short-day environments at regular intervals. This modelling approach was tested in eight independent field environments of different sowing dates in two growing seasons. The four-parameter model predicted 37-67% of observed phenotypic variation in an environment, 76% of variation in across-environment mean days to flowering among the genotypes, and 96% of variation in across-genotype mean among the eight environments. When all the observations of the 96 genotypes across the eight environments were pooled, the model explained 81% of the total variation. Sensitivity analysis showed that all four model parameters were important for predicting differences in flowering time among the genotypes; but their relative importance differed and the ranking was in the order of f(o), delta, theta(1), and theta(2). This study highlighted the potential of using ecophysiological models to assist the genetic analysis of quantitative crop traits whose phenotype is often environment-dependent.

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