Towards the Reliable Prediction of Time to Flowering in Six Annual Crops. VI. Applications in Crop ImprovementExport / Share PlumX View Altmetrics View AltmetricsLawn, R.J., Summerfield, R.J., Ellis, R.H., Qi, A., Roberts, E.H., Chay, P.M., Brouwer, J.B., Rose, J.L. and Yeates, S.J. (1995) Towards the Reliable Prediction of Time to Flowering in Six Annual Crops. VI. Applications in Crop Improvement. Experimental Agriculture, 31 (1). pp. 89-108. ISSN 0014-4797 Full text not currently attached. Access may be available via the Publisher's website or OpenAccess link. Article Link: https://doi.org/10.1017/S0014479700025047 AbstractVariation in time from sowing to flowering (f) was examined for 44 cultivars of soyabean, mungbean, black gram, ricebean, cowpea, chickpea, lentil and barley, when grown in up to 21 diverse environments obtained by making one or more sowings at each of six locations spanning tropical, sub-tropical and temperate climates in Australia. The utility of simple linear models, relating rate of development (l/f) towards flowering to mean photoperiod and temperature prevailing between sowing and flowering, was evaluated. The models were highly efficient, explaining most (86.7%) of the variation observed across species, cultivars and environments. They were particularly efficient in describing responses where cultivars were relatively well-adapted, in agronomic terms, and least efficient where cultivars were exposed to unfavourable temperature and, to a lesser extent, photoperiod. Opportunities for exploiting the models in applied crop improvement include their use in interpretation of G × E interaction, genotypic characterization and selection of parental genotypes, selection of test environments, designing screening procedures, and more efficiently matching genotypes to target environments. The main strengths of these linear, additive rate models in crop improvement are their wide applicability across species and genotypes, their relative simplicity, and the requirement for few genotype-specific response parameters. Their main weakness is their lack of precision in describing responses when plants are exposed to unfavourable photothermal extremes, albeit in circumstances that are sometimes unrealistic for cropping those particular genotypes.
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