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Crop Modeling, QTL Mapping, and Their Complementary Role in Plant Breeding
Author(s) -
Yin Xinyou,
Stam Piet,
Kropff Martin J.,
Schapendonk Ad H. C. M.
Publication year - 2003
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2003.9000a
Subject(s) - ideotype , quantitative trait locus , trait , crop simulation model , plant breeding , family based qtl mapping , crop , biology , population , simulation modeling , microbiology and biotechnology , computer science , agronomy , mathematics , gene mapping , genetics , demography , mathematical economics , sociology , chromosome , gene , programming language
Crop modelers and geneticists have developed a vision of their roles in plant breeding from their own perspective. However, to improve breeding efficiency, interdisciplinary collaboration becomes increasingly important. The objective of this paper is to explore opportunities for collaboration between modelers and geneticists in ideotype breeding for high crop yield. The advent of molecular markers enables variation of a complex trait to be dissected into the effects of quantitative trait loci (QTL) and assists the transfer of these QTL into desired cultivars or lines. A recent study in which QTL information was linked to crop modeling has shown that QTL analysis removes part of random errors of measured model input parameters and that QTL information can successfully be coupled with crop models to replace measured parameters. The QTL‐based modeling overcomes the limitations in designing ideotypes by using models that ignore the inheritance of model input traits. On the other hand, crop modeling can potentially be a powerful tool to resolve genotype × environment interactions and to dissect yield into characters that might be under simpler genetic control. Based on the complementary aspects of crop modeling and QTL mapping, we propose an approach that integrates marker‐assisted selection into model‐based ideotype framework to support breeding for high crop yield. For this approach to be effective, there is a need to develop crop models that are capable of predicting yield differences among genotypes in a population under various environmental conditions.