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Agronomy Journal Abstract - SPATIAL VARIABILITY

The Spatial Distribution of Soybean Cyst Nematode in Relation to Soil Texture and Soil Map Unit


This article in AJ

  1. Vol. 96 No. 1, p. 181-194
    Received: Nov 21, 2002

    * Corresponding author(s): avendano@iastate.edu
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  1. Felicitas Avendaño *a,
  2. Francis J. Pierceb,
  3. Oliver Schabenbergerc and
  4. Haddish Melakeberhand
  1. a Dep. of Plant Pathol., 351 Bessey Hall, Iowa State Univ., Ames, IA 50011
    b Cent. for Precision Agric. Syst., Washington State Univ., 24106 North Bunn Rd., Prosser, WA 99350
    c SAS Inst., Cary, NC 27513
    d Dep. of Entomol., 243 Nat. Sci. Bldg., Michigan State Univ., East Lansing, MI 48824


Evidence suggests that variation in soil texture may be key to explain the variability of soybean cyst nematode (Heterodera glycines Ichinohe) population density within infested fields and may be important to the delineation of soybean cyst nematode (SCN) management zones. The purpose of this work was to assess the spatial structure of soil texture in two fields of known SCN population density and its relationship to published soil survey maps and to quantify the relationship between soil texture and SCN population density variability across fields and over time. Cysts were extracted by elutriation from single-core soil samples collected in a geostatistical sampling design. Soil texture analysis was performed using a modified hydrometer method. Classical and geostatistical tools were employed to characterize and map soil texture and correlate sand, silt, and clay with SCN population. Cyst population density was consistently higher in loamy sand than in sandy clay loam. Sand, clay, and silt in the soil were spatially structured and strongly correlated with SCN population density consistently over time. The number of eggs per cyst was not related to soil type or texture. This study demonstrates the value of soil survey maps as indicators of where SCN can be expected in an infested field and how the addition of site-specific texture data can improve the spatial prediction of SCN. This study provides the basis for future experimentation to define soil texture tolerance limits for SCN and lays out foundations for new and integrated approaches to site-specific management of SCN.

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