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Crop Modeling and the Identification of Stable Coefficients that May Reflect Significant Groups of Genes

^a Dep. of Plant Agriculture, Univ. of Guelph, Guelph, ON, Canada N1G 2W1
^b International Maize and Wheat Improvement Centre (CIMMYT), Apt. Postal 6-641, 06600 Mexico, D.F., Mexico

* Corresponding author (j.white{at}cgiar.org)

Received for publication May 1, 2001. Knowledge about the functioning of a crop system can be embedded in simulation models of crop growth and development. Such quantitative models have until now made extensive use of physiological knowledge, but modeling could benefit greatly by incorporating genetic information. Equally, because models can help resolve environmentally varying characteristics into stable characteristics that reflect groups of genes, genomics research could benefit from modeling efforts. The use of the model Cropsim to analyze wheat (Triticum aestivum L.) growth and development is demonstrated. Data on reproductive development were well fitted when the life cycle was divided into phases, although photoperiodic sensitivity varied between phases. Describing leaf appearance satisfactorily required introducing an effect of photoperiod. Understanding the need for a breakdown into phases of development, for varying photoperiodic sensitivities, and for photoperiodic control of leaf appearance, could be enhanced by genomic studies. Simulating growth of wheat over successive seasons required changing supposedly stable genotypic characteristics. Furthermore, growth of near inbred lines incorporating the Lr19 chromosome translocation varied with genotypic background. These results also indicate that, given the variation in modeling outputs from year to year, care should be taken in the application of models to long-term problems, and that efforts should be devoted to model improvement. Further development of crop models will benefit from associating genomic analysis with field experiments and model analyses. Much could be gained from increased interaction among model developers, field experimenters, and genomics researchers.

Abbreviations: GCTE, global change and terrestrial ecosystems • PAR, photosynthetically active radiation • QTLs, quantitative trait loci

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