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This article in CS

  1. Vol. 42 No. 5, p. 1488-1492
     
    Received: July 30, 2001
    Published: Sept, 2002


    * Corresponding author(s): tjh@wheat.ksu.edu
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doi:10.2135/cropsci2002.1488

Single Kernel Protein Variance Structure in Commercial Wheat Fields in Western Kansas

  1. Tod Bramblea,
  2. Timothy J. Herrman *b,
  3. Thomas Loughinc and
  4. Floyd Dowelld
  1. a King Arthur Flour Company, 135 Route 5 South, Norwich, VT 05055
    b Dep. of Grain Science and Industry, Kansas State Univ., 201 Shellenberger Hall, Manhattan, KS 66506-2201
    c Dep. of Statistics, Kansas State Univ., 101 Dickens Hall, Manhattan KS 66506
    d Grain Marketing and Production Research Center, USDA-ARS, 1515 College Avenue, Manhattan, KS 66502-2796

Abstract

This research was undertaken to quantify the structure of protein variation in a commercial hard red winter (HRW) wheat (Triticum aestivum L.) production system. This information will augment our knowledge and practices of sampling, segregating, marketing, and varietal development to improve uniformity and end-use quality of HRW wheat. The allocation of kernel protein variance to specific components in southwestern Kansas was performed by a hierarchical sampling design. Sources of variability included Field, Plot (plots within a field), Row (rows within a plot), Plant (plants within a row), Head (heads within a plant), Position (spikelets at a specific position on a head), Spikelet (spikelets within a position), and Kernel (kernels within a spikelet). Individual kernels (10 152) were collected from 46 fields planted to one of four cultivars: Jagger, 2137, Ike, or TAM 107. Kernels were evaluated for protein concentration by a single kernel characterization system equipped with a diode array near-infrared (NIR) spectrometer. For the cultivars 2137 and Ike, all sources of variability except Spikelet were statistically significant (P < 0.05). For Jagger, all sources except Row were significant and for TAM 107, variation attributed to Field and Plant were not significant. Field and Plot sources of variability contributed the greatest amount of variance within the hierarchy for Jagger, 2137, and Ike. For TAM 107, Plot was the greatest source of variability. The least squares means were calculated for the fixed effect Position. Jagger, Ike, and 2137 showed a significant protein gradient in which the highest protein concentration occurred at the base of the head and the lowest protein content at the top. For TAM 107, the greatest protein content was found at the base. Results of this study provide a benchmark for future efforts to improve wheat consistency through breeding and crop management. The protein variance structure described during this study also defines practical limits for managing and marketing protein content in HRW.

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Copyright © 2002. Crop Science Society of AmericaPublished in Crop Sci.42:1488–1492.