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This study explored the ability of a simple phenology model to explain differences in flowering time among individuals in an RIL population of spring barley. The model was parameterized for each RIL from a well-established reciprocal transfer greenhouse experiment where plants were transferred between LD and SD photoperiods at regular intervals throughout development. The reciprocal transfer experiment has long been shown to be a powerful tool in quantifying the length of the photoperiodically sensitive phase and the photoperiod sensitivity of distinct cultivars of a crop species (Chandraratna, 1948; Kiniry et al., 1983; Ellis et al., 1992; Mozley and Thomas, 1995; Yin et al., 1997a; Adams et al., 1998). As far as is known, the present study is the first one where this type of experiment was applied to individual genotypes of an entire segregating genetic population.
The model with parameter values estimated from the reciprocal transfer experiment was then used for predicting flowering times of the same individual RIL in independent multiple field environments. Although the parameterization and validation experiments were completely independent and the model ignores any genetic difference in responsiveness to temperature, the model yielded a reasonable prediction of differences in flowering time across genotypes and across environments (Fig. 3). The prediction of genetic difference among genotypes within a single specific environment remains a challenge, despite the possibility that the model performance in this respect would be associated with random noise during experimentation. The resolution of the model would be improved by considering (i) genetic difference in temperature response, and (ii) low temperature requirement for vernalization during the early development phase (Loomis and Connor, 1992). Such a consideration would need more expensive, temperature-controlled experimentation for model parameterization than the one used in the current study.
Ecophysiological models separate different aspects of responses of the trait under study to environmental variables and dissect a phenotype into elementary traits known as model-input parameters (Yin et al., 2000). These parameters represent certain genetic characteristics, and are sometimes called ‘genetic coefficients’ (Boote et al., 2003). They are genetically determined and are not altered by environment, but predict the expression of a genotype under a wide range of environments. Ecophysiological models could thus be a powerful tool for predicting genotype–phenotype relationships (Hammer et al., 2002; Slafer, 2003; Yin et al., 2003). Based on similar lines of reasoning, Reymond et al. (2003) demonstrated a potential value of combining ecophysiological modelling and genetic mapping in predicting genotypexenvironment interaction, using maize (Zea mays L.) leaf elongation rate as the example. Demonstration of the role of ecophysiological modelling in assisting the genetic unravelling of genotypexenvironment interaction for time to flowering is the subject of our next paper.
Acknowledgements This research was funded by the National Natural Science Foundation of China (contract No. 30060036). We thank Hui Li and Peilian Zhang for their assistance in managing the experiments.
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