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Modeling of Glyphosate Application Timing in Glyphosate-Resistant Soybean

Published online by Cambridge University Press:  20 January 2017

Ivan Sartorato ,
Antonio Berti ,
Giuseppe Zanin  and
Claudio M. Dunan
Ivan Sartorato*
Affiliation:
Istituto di Biologia Agroambientale e Forestale del CNR, Legnaro, viale dell'Università 16, 35020 Legnaro, Italy
Antonio Berti
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, viale dell'Università 16, 35020 Legnaro, Italy
Giuseppe Zanin
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, viale dell'Università 16, 35020 Legnaro, Italy
Claudio M. Dunan
Affiliation:
Sintesis Quimica, Av. Scalabrini Ortiz 3333 Buenos Aires, Argentina
*
Corresponding author's E-mail: [email protected]
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Abstract

The introduction of herbicide-resistant crops and postemergence herbicides with a wide action spectrum shifted the research focus from how to when crops should be treated. To maximize net return of herbicide applications, the evolution of weed–crop competition over time must be considered and its effects quantified. A model for predicting the yield trend in relation to weed removal time, considering emergence dynamics and density, was tested on data from glyphosate-resistant soybean grown in cropping systems in Italy and Argentina. Despite an ample variation of weed emergence dynamics and weed load in the four trials, the model satisfactorily predicted yield loss evolution. The estimated optimum time for weed control (OTWC) varied from about 18 d after soybean emergence in Argentina to 20 to 23 d in Italy, with time windows for spraying ranging from 14 to 28 d. Within these limits a single glyphosate application ensures good weed control at low cost and avoids side effects like the more probable unfavorable weed flora evolution with double applications and the presence of residues in grains. Despite the apparent simplicity of weed control based on nonselective herbicides, the study outlines that many variables have to be considered to optimize weed management, particularly for the time evolution of the infestation and, subsequently, a proper timing of herbicide application.

Keywords

Glyphosatesoybean, Glycine max (L.) Merr., ‘Asgrow ASRR19’, ‘Asgrow XP2101R’, ‘Asgrow 6401’Time density equivalentweed emergence dynamicsweed-crop competition
Type
Weed Management
Information
Weed Science , Volume 59 , Issue 3 , September 2011 , pp. 390 - 397
Copyright
Copyright © Weed Science Society of America 

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