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This article in AJ

  1. Vol. 99 No. 3, p. 650-656
    Received: May 18, 2006

    * Corresponding author(s): jboard@agctr.lsu.edu
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Development of Vegetation Indices for Identifying Insect Infestations in Soybean

  1. James E. Board *a,
  2. Vijay Makaa,
  3. Randy Priceb,
  4. Dina Knightc and
  5. Matthew E. Baurd
  1. a Dep. of Agronomy and Environmental Management, Rm. 104 Sturgis Hall, Louisiana State Univ. Agricultural Center, Baton Rouge, LA 70803
    b Dep. of Biological and Agricultural Engineering, 148 Seaton Hall, Kansas State Univ., Manhattan, KS 66506
    c Center for Geoinformatics, Louisiana State Univ., Baton Rouge, LA 70803
    d Dep. of Entomology, Rm. 402 Life Sciences Bldg., Louisiana State Univ. Agricultural Center, Baton Rouge, LA 70803


Because of greater efficiency relative to conventional methods, interest has developed for using vegetation indices in soybean [Glycine max (L.) Merr.] for identifying areas in a field experiencing injury by defoliating insects. Vegetation indices can indicate leaf area index (LAI) and light interception levels, canopy parameters affected by defoliating insects. Our objectives were to determine the relative accuracy of three vegetation indices for predicting LAI and light interception, and to outline a method for using vegetation indices for identifying areas in a field experiencing insect injury. Several commercial soybean cultivars were planted on a Commerce silt loam soil (fine-silty, mixed, nonacid, thermic Aeric Fluvaquent) near Baton Rouge, LA (USA) (30° N lat) in May 2004 and June 2005. In 2004, differences in LAI and light interception were created by manual defoliation, whereas in 2005, LAI/light interception differences occurred because of cultivars and planting dates. Results indicated that across canopies ranging from very low LAI to canopy closure (95% light interception), the normalized difference vegetation index (NDVI) most accurately predicted LAI and light interception (r 2 = 0.93–0.97). Light interception and LAI were linked to NDVI by strong linear regression models, and did not show the quadratic response reported by others. A proposed method for adopting NDVI to identify insect-infested areas is presented.

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