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This article in CS

  1. Vol. 45 No. 3, p. 981-987
    Received: Oct 14, 2003

    * Corresponding author(s): lizlee@uoguelph.ca
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Quantitative Genetic Analysis of the Physiological Processes underlying Maize Grain Yield

  1. E. A. Lee *,
  2. A. Ahmadzadeh and
  3. M. Tollenaar
  1. Dep. of Plant Agriculture, Crop Science Bldg., Univ. of Guelph, Guelph, ON, Canada, N1G 2W1


Few studies have examined the inheritance and interrelationships of both grain yield and the underlying physiological processes in maize (Zea mays L.). The objective of this study was to establish genetic relationships between the physiological components of grain yield and to examine the inheritance of grain yield and its component processes (i.e., additive and the nonadditive genetic effects). Twelve F1 hybrids, obtained by mating three male and four female inbred lines using a North Carolina Design II, were evaluated in trials conducted in Ontario from 2000 to 2002. Dry matter accumulation (DMA) at four stages of development, harvest index, leaf area index (LAI), stay green, and grain yield were measured. Variation among the 12 hybrids was significant for all traits evaluated, and the range in mean grain yield was 28% of the mean. Using the genetic effects partitioned by a Design II analysis, we dissected the physiological mechanisms that influenced favorable or unfavorable contributions to grain yield. Using the highest- and lowest-yielding hybrids in the study (i.e., maximum genetic variation), we attempted to dissect the physiological reasons for the difference in grain yield. This analysis, however, was unsuccessful in dissecting grain yield in terms of physiological mechanisms using a quantitative genetic model. Reasons for this failure may be, in part, (i) the relatively low contribution of statistically significant genetic effects to the differences between the hybrids; and (ii) partitioning of the difference between hybrids in four general combining ability (GCA) estimates and two specific combining ability (SCA) estimates results in small estimates relative to the grand mean.

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