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In-Field and Soil-Related Factors that Affect the Presence and Prediction of Glyphosate-Resistant Horseweed (Conyza canadensis) Populations Collected from Indiana Soybean Fields

Published online by Cambridge University Press:  20 January 2017

Vince M. Davis ,
Kevin D. Gibson ,
Valerie A. Mock  and
William G. Johnson
Vince M. Davis
Affiliation:
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1155
Kevin D. Gibson
Affiliation:
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1155
Valerie A. Mock
Affiliation:
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1155
William G. Johnson*
Affiliation:
Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907-1155
*
Corresponding authors's E-mail: wgj@purdue@edu
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Abstract

Glyphosate-resistant (GR) crops have been rapidly adopted in the United States and the evolution of GR weeds throughout the world has also been on the rise. With experience, weed scientists and crop advisers develop “intuition” on the basis of field history and current in-field conditions for predicting whether escaped weed biotypes may be herbicide resistant. However, there are no previous reports on the association of in-field crop management factors with the prediction of herbicide resistance. By using in-field survey data, we tested the accuracy of predicting glyphosate resistance in late-season horseweed escapes. We hypothesized that glyphosate resistance in late-season horseweed populations found in soybean fields could be predicted using in-field knowledge of crop residues and the appearance and distribution of weeds in the field. Field survey data were collected to determine the distribution and frequency of GR horseweed populations in Indiana soybean fields during September and October of 2003, 2004, and 2005. After the in-field survey, soil properties for sampled field locations were also collected from the U.S. Department of Agriculture Natural Resources Conservation Service Web Soil Survey. GR horseweed predictions used in-field presence of crop residues and the appearance, abundance, and distribution of weeds in the field. The significance of independent data factors were determined by chi-square statistics. The interactions and relative significance of multiple factors were modeled using classification and regression tree analysis. Our results indicated that the most important factor for predicting GR populations was the identification of an altered plant phenotype after injury from POST glyphosate. This was followed by crop rotation, field distribution, and the presence of other escaped weed species in the field in a model with a classification rate of 0.68.

Keywords

Glyphosatehorseweed, Conyza canadensis (L.) Cronq. ERICAsoybean, Glycine max (L.) Merr.Weed surveyherbicide resistancedecision treesCART
Type
Weed Management
Information
Weed Science , Volume 57 , Issue 3 , June 2009 , pp. 281 - 289
Copyright
Copyright © Weed Science Society of America 

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