Weed Research (2020) 60, 297-302
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Validation of predictive empirical weed emergence models of Abutilon theophrasti Medik based on intercontinental data
Weed Research 60 (4), 297-302
Abstract: Good weed management relies on the proper timing of weed control practices in relation to weed emergence dynamics. Therefore, the development of models that predict the timing of emergence may help provide growers with tools to make better weed management decisions. The aim of this study was to validate and compare two previously published predictive empirical thermal time models of the emergence of Abutilon theophrasti growing in maize with data sets from the USA and Europe, and test the hypothesis that a robust and general weed emergence model can be developed for this species. Previously developed Weibull and Logistic models were validated against new data sets collected from 11 site-years, using four measures of validation. Our results indicated that predictions made with the Weibull model were more reliable than those made with the Logistic model. However, Weibull model results still contained appreciable biases that prevent its use as a general model of A. theophrasti emergence. Our findings highlight the need to develop more accurate models if the ultimate goal is to make more precise predictions of weed seedling emergence globally to provide growers with universally consistent tools to make better weed management decisions.
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Link to article at publishers website
Database assignments for author(s): Kevin W. Bradley, Isabel M. Calha, Adam S. Davis, José Dorado, Frank Forcella, John L. Lindquist, Christy L. Sprague, Jose L. Gonzalez-Andujar
Research topic(s) for pests/diseases/weeds:
population dynamics/ epidemiology
Pest and/or beneficial records:
|Abutilon theophrasti (weed)||Maize/corn (Zea mays)|