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Predicting weed emergence for eight annual species in the northeastern United States

  • Matthew W. Myers, William S. Curran (a1), Mark J. VanGessel (a2), Dennis D. Calvin (a3), David A. Mortensen (a1), Bradley A. Majek (a4), Heather D. Karsten (a1) and Gregory W. Roth (a1)...
Abstract

A 2-yr experiment assessed the potential for using soil degree days (DD) to predict cumulative weed emergence. Emerged weeds, by species, were monitored every 2 wk in undisturbed plots. Soil DD were calculated at each location using a base temperature of 9 C. Weed emergence was fit with logistic regression for common ragweed, common lambsquarters, velvetleaf, giant foxtail, yellow foxtail, large crabgrass, smooth pigweed, and eastern black nightshade. Coefficients of determination for the logistic models fit to the field data ranged between 0.90 and 0.95 for the eight weed species. Common ragweed and common lambsquarters were among the earliest species to emerge, reaching 10% emergence before 150 DD. Velvetleaf, giant foxtail, and yellow foxtail were next, completing 10% emergence by 180 DD. The last weeds to emerge were large crabgrass, smooth pigweed, and eastern black nightshade, which emerged after 280 DD. The developed models were verified by predicting cumulative weed emergence in adjacent plots. The coefficients of determination for the model verification plots ranged from 0.66 to 0.99 and averaged 0.90 across all eight weed species. These results suggest that soil DD are good predictors for weed emergence. Forecasting weed emergence will help growers make better crop and weed management decisions.

Copyright
Corresponding author
Corresponding author. Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA 16802; mwm133@psu.edu
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Weed Science
  • ISSN: 0043-1745
  • EISSN: 1550-2759
  • URL: /core/journals/weed-science
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