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Searching for consistent postemergence weed control in progressively inconsistent weather

Published online by Cambridge University Press:  18 November 2024

Christopher Landau*
Affiliation:
Postdoctoral Research Agronomist, Global Change and Photosynthesis Unit, USDA-ARS, Urbana, IL, USA
Kevin Bradley
Affiliation:
Professor, Division of Plant Sciences, University of Missouri, Columbia, MO, USA
Erin Burns
Affiliation:
Assistant Professor, Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA
Anthony Dobbels
Affiliation:
Research Specialist, Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA
Alyssa Essman
Affiliation:
Assistant Professor, Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA
Michael Flessner
Affiliation:
Associate Professor, School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA
Karla Gage
Affiliation:
Assistant Professor, School of Agricultural Sciences/School of Biological Sciences, Southern Illinois University Carbondale, Carbondale, IL, USA
Aaron Hager
Affiliation:
Professor, Department of Crop Sciences, University of Illinois, Urbana, IL, USA
Amit Jhala
Affiliation:
Associate Department Head/Professor, Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, USA
Paul O Johnson
Affiliation:
Extension Weed Science Coordinator, Agronomy, Horticulture, & Plant Science, South Dakota State University, Brookings, SD, USA
William Johnson
Affiliation:
Professor, Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, USA
Sarah Lancaster
Affiliation:
Assistant Professor, Department of Agronomy, Kansas State University, Manhattan, KS, USA
Dwight Lingenfelter
Affiliation:
Extension Weed Scientist, Department of Plant Science, Penn State University, University Park, PA, USA
Mark Loux
Affiliation:
Professor Emeritus, Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA
Eric Miller
Affiliation:
Assistant Scientist, School of Agricultural Sciences, Southern Illinois University Carbondale, Carbondale, IL, USA
Micheal Owen
Affiliation:
University Professor Emeritus, Department of Agronomy, Iowa State University, Ames, IA, USA
Debalin Sarangi
Affiliation:
Assistant Professor, Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, USA
Peter Sikkema
Affiliation:
Professor, Department of Plant Agriculture, University of Guelph Ridgetown Campus, Ridgetown, ON, Canada
Christy Sprague
Affiliation:
Professor, Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA
Mark VanGessel
Affiliation:
Professor, Department of Plant and Soil Sciences, University of Delaware, Georgetown, DE, USA
Rodrigo Werle
Affiliation:
Associate Professor, Department of Plant and Agroecosytem Science, University of Wisconsin–Madison, Madison WI, USA
Bryan Young
Affiliation:
Professor, Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, USA
Martin Williams II
Affiliation:
Research Ecologist, Global Change and Photosynthesis Unit, USDA-ARS, Urbana, IL, USA
*
Corresponding author: Christopher Landau; Email: Christopher.landau@usda.gov
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Abstract

Foliar-applied postemergence herbicides are a critical component of corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] weed management programs in North America. Rainfall and air temperature around the time of application may affect the efficacy of herbicides applied postemergence in corn or soybean production fields. However, previous research utilized a limited number of site-years and may not capture the range of rainfall and air temperatures that these herbicides are exposed to throughout North America. The objective of this research was to model the probability of achieving successful weed control (≥85%) with commonly applied postemergence herbicides across a broad range of environments. A large database of more than 10,000 individual herbicide evaluation field trials conducted throughout North America was used in this study. The database was filtered to include only trials with a single postemergence application of fomesafen, glyphosate, mesotrione, or fomesafen + glyphosate. Waterhemp [Amaranthus tuberculatus (Moq.) Sauer], morningglory species (Ipomoea spp.), and giant foxtail (Setaria faberi Herrm.) were the weeds of focus. Separate random forest models were created for each weed species by herbicide combination. The probability of successful weed control deteriorated when the average air temperature within the first 10 d after application was <19 or >25 C for most of the herbicide by weed species models. Additionally, drier conditions before postemergence herbicide application reduced the probability of successful control for several of the herbicide by weed species models. As air temperatures increase and rainfall becomes more variable, weed control with many of the commonly used postemergence herbicides is likely to become less reliable.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
To the extent this is a work of the US Government, it is not subject to copyright protection within the United States. Published by Cambridge University Press on behalf of Weed Science Society of America.
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article.
Copyright
© United States Department of Agriculture - Agricultural Research Service and University of Illinois Urbana-Champaign, 2024.
Figure 0

Figure 1. Postemergence herbicide data were compiled from 14 U.S. states and 1 Canadian province (1992–2021). Data from two universities (University of Illinois and Southern Illinois University) were collected for Illinois.

Figure 1

Table 1. Random forest model variable importance and performance.

Figure 2

Figure 2. Partial dependency plots of the effects of total precipitation and average air temperature 10 d before postemergence herbicide application on the probability of successful control (≥85% weed control).

Figure 3

Figure 3. Partial dependency plots of the effects of total precipitation and average air temperature 10 d after postemergence herbicide application on the probability of successful control (≥85% weed control).