Regression analysis to evaluate herbicide drift and injury in Roundup Ready cotton in wind tunnel
ABSTRACT The aim was: (i) to propose a regression model for the association between injury in Roundup Ready (RR) cotton plants and drift of auxins, isolated or associated with glyphosate; (ii) to evaluate the effect of adding glyphosate to dicamba and 2,4-D auxinic herbicides on the physicochemical...
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Published in | Ciência agronômica Vol. 52; no. 2 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English Portuguese |
Published |
Universidade Federal do Ceará
01.01.2021
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Subjects | |
Online Access | Get full text |
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Summary: | ABSTRACT The aim was: (i) to propose a regression model for the association between injury in Roundup Ready (RR) cotton plants and drift of auxins, isolated or associated with glyphosate; (ii) to evaluate the effect of adding glyphosate to dicamba and 2,4-D auxinic herbicides on the physicochemical properties of the spray solutions; and (iii) to validate the wind tunnel as a tool to perform herbicide injury prediction. Three experiments were conducted using the following spray solutions: glyphosate (0.225 kg a.e. ha-1); dicamba (0.120 kg a.e. ha-1); 2,4-D (0.168 kg a.e. ha-1); glyphosate + dicamba (0.225 + 0.120 kg a.e. ha -1) and glyphosate + 2,4-D (0.225 + 0.168 kg a.e. ha-1). Experiment I assessed drift in wind tunnel at distances of 5, 10 and 15 m from the nozzle spray drift. Experiment II assessed injury on cotton plants arranged at the same distances as in experiment I. Experiment III studied the physicochemical characteristics of the spray solutions used in experiment I, assessing surface tension, pH and density. The collected drift and injury are directly proportional, following a simple linear regression model, with increased drift and injury potential at 5 m from the sprayer. The addition of glyphosate to 2,4-D and dicamba herbicides reduced surface tension and pH of the spray solutions. The wind tunnel, besides evaluating drift potential, was found to be an alternative for the prediction of injury in RR cotton plants. |
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ISSN: | 1806-6690 1806-6690 |
DOI: | 10.5935/1806-6690.20210025 |