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This article in JEQ

  1. Vol. 25 No. 3, p. 425-432
     
    Received: Apr 14, 1995


    * Corresponding author(s): tyoung@cert.ucr.edu
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doi:10.2134/jeq1996.00472425002500030007x

Use of Logistic Regression and GIS Modeling to Predict Groundwater Vulnerability to Pesticides

  1. Roberto R. Teso,
  2. Minn P. Poe,
  3. Theodore Younglove * and
  4. Patrick M. McCool
  1. California Dep. of Pesticide Regulation, Trailer 14, SAPRC, Univ. of California, Riverside, CA 92521;
    Statewide Air Pollution Center, Trailer 14, SAPRC, Univ. of California, Riverside, CA 92521
    CE-CERT, Univ. of California, Riverside, CA 92521.

Abstract

Abstract

Soils are considered important components of many pesticide contamination models and are frequently the direct or indirect targets of pesticides applied during agricultural activities. Soil texture is commonly referenced on pesticide labels as an important factor in the selection and application of pesticides and in identifying target areas that are vulnerable to leaching. In general, no guidelines exist for the common interpretation of generic soil texture terms found on pesticide labels, for example, coarse or coarse-textured soils. In the present study, a significant logistic regression model (P = 0.017) was developed that is based on the soil particle-size class composition of sections containing wells sampled for DBCP (1,3-dibromochloropropane). Particle-size class is a concept used in Soil Taxonomy to describe soil family texture and is a component of soil family names. The model contains terms for the sandy and fine particle-size classes. The model was validated using data obtained from sources independent of those used to develop the model. Records in the California Soils Map Unit Inventory database that describes the soil map unit composition of >65 000 sections (1600 m2 or 1 mi2) were used to generate probability scores for >15 000 sections located in the San Joaquin Valley, CA. A geographic information system, an information management technique that is becoming an accepted tool for a wide range of regulatory agencies, was used to generate visual images of the probability scores. A map was developed that depicts four distinct probability classes of the DBCP-contamination status of section-sized areas and their distribution within the study area.

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