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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: ivan.sartorato@ibaf.cnr.it
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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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References

Literature Cited

Arregui, M. C., Lenardon, A., Sanchez, D., Maitre, M. I., Scotta, R., and Enrique, S. 2004. Monitoring glyphosate residues in transgenic glyphosate-resistant soybean. Pest Manag. Sci. 60:163–166.Google Scholar
Ateh, C. M. and Harvey, R. G. 1999. Annual weed control by glyphosate in glyphosate-resistant soybean (Glycine max). Weed Technol. 13:394–398.Google Scholar
Berti, A., Dunan, C., Sattin, M., Zanin, G., and Westra, P. 1996. A new approach to determine when to control weeds. Weed Sci. 44:496–503.Google Scholar
Berti, A., Sattin, M., Baldoni, G., Del Pino, A. M., Ferrero, A., Montemurro, P., Tei, F., Viggiani, P., and Zanin, G. 2008. Relationships between crop yield and weed time of emergence/removal: modelling and parameter stability across environments. Weed Res. 48:378–388.Google Scholar
Berti, A. and Zanin, G. 1994. Density equivalent: a method for forecasting yield loss caused by mixed weed populations. Weed Res. 34:327–332.Google Scholar
Bonny, S. 2008. Genetically modified glyphosate-tolerant soybean in the USA: adoption factors, impacts and prospects–a review. Agron. Sustain. Dev. 28:21–32.Google Scholar
Dalley, C. D., Kells, J. J., and Renner, K. A. 2004. Effect of glyphosate application timing and row spacing on corn (Zea mays) and soybean (Glycine max) yields. Weed Technol. 18:165–176.Google Scholar
Duke, S. O. 2005. Taking stock of herbicide-resistant crops ten years after introduction. Pest Manag. Sci. 61:211–218.Google Scholar
Freyssinet, G. 2003. Herbicide-resistant transgenic crop: a benefit for agriculture. Phytoparasitica. 31:105–107.Google Scholar
Gianessi, L. P. 2005. Economic and herbicide use impacts of glyphosate-resistant crops. Pest Manag. Sci. 61:241–245.Google Scholar
Gower, S. A., Loux, M. M., Cardina, J., Harrison, S. K., Sprankle, P. L., Probst, N. J., Bauman, T. T., Bugg, W., Curran, W. S., Currie, R. S., Harvey, R. G., Johnson, W. G., Kells, J. J., Owen, M. D. K., Regehr, D. L., Slack, C. H., Spaur, M., Sprague, C. L., Vangessel, M., and Young, B. G. 2003. Effect of postemergence glyphosate application timing on weed control and grain yield in glyphosate-resistant corn: results of a 2-yr multistate study. Weed Technol. 17:821–828.Google Scholar
Halford, C., Hamill, A. S., Zhang, J., and Doucet, C. 2001. Critical period of weed control in no-till soybean (Glycine max) and corn (Zea mays). Weed Technol. 15:737–744.Google Scholar
Hamill, A. S., Knezevic, S. Z., Chandler, K., Sikkema, P. H., Tardif, F. J., Shrestha, A., and Swanton, C. J. 2000. Weed control in glufosinate-resistant corn (Zea mays). Weed Technol. 14:578–585.Google Scholar
Hilgenfeld, K. L., Martin, A. R., Mortensen, D. A., and Mason, S. C. 2004. Weed management in glyphosate resistant soybean: weed emergence patterns in relation to glyphosate treatment timing. Weed Technol. 18:277–283.Google Scholar
Jha, P., Norsworthy, J. K., Bridges, W., and Riley, M. B. 2008. Influence of glyphosate timing and row width on palmer Amaranth (Amaranthus palmeri) and Pusley (Richardia spp.) demographics in glyphosate-resistant soybean. Weed Sci. 56:408–415.Google Scholar
Knezevic, S. Z., Evans, S. P., Blankenship, E. E., Van Acker, R. C., and Lindquist, J. L. 2002. Critical period for weed control: the concept and data analysis. Weed Sci. 50:773–786.Google Scholar
Knezevic, S. Z., Evans, S. P., and Mainz, M. 2003. Row spacing influences the critical timing for weed removal in soybean (Glycine max). Weed Technol. 17:666–673.Google Scholar
Krausz, R. F., Kapusta, G., and Matthews, J. L. 1996. Control of annual weeds with glyphosate. Weed Technol. 10:957–962.Google Scholar
Luttwak, E. N. 2001. Strategy: The Logic of War and Peace. Cambridge, MA Harvard University Press. 320 p.Google Scholar
Martinez-Ghersa, M. A., Worster, C. A., and Radosevich, S. R. 2003. Concerns a weed scientist might have about herbicide-tolerant crops: a revisitation. Weed Technol. 17:202–210.Google Scholar
Mulugeta, D. and Boerboom, C. M. 2000. Critical time of weed removal in glyphosate-resistant Glycine max . Weed Sci. 48:35–42.Google Scholar
Neve, P. 2008. Simulation modelling to understand the evolution and management of glyphosate resistance in weeds. Pest Manag. Sci. 64:392–401.Google Scholar
Reddy, K. N. and Norsworthy, J. K. 2010. Glyphosate-resistant crop production systems: impact on weed shifts. Pages 165–184 in Nandula, V. K., ed. Glyphosate Resistance in Crops and Weeds. Hoboken, NJ John Wiley & Sons.Google Scholar
Reddy, K. N. and Whiting, K. 2000. Weed control and economic comparisons of glyphosate-resistant, sulfonylurea-tolerant, and conventional soybean (Glycine max) systems. Weed Technol. 14:204–211.Google Scholar
Reddy, K. N. and Zablotowicz, R. M. 2003. Glyphosate-resistant soybean response to various salts of glyphosate and glyphosate accumulation in soybean nodules. Weed Sci. 51:496–502.Google Scholar
Sartorato, I., Berti, A., and Zanin, G. 2001. Modelling glyphosate use in roundup ready soybean [abstract]. Pages 93 in Abstracts of the Third International Weed Science Congress; 2000 June 6–11; Foz do Iguassu, Brazil. CD-ROM. Oxford, MS International Weed Science Society.Google Scholar
Sartorato, I. and Zanin, G. 1999. Il diserbo della soia transgenica: rivoluzione o evoluzione? L'Informatore Fitopatologico. 7–8:40–49.Google Scholar
Sattin, M., Berti, A., and Zanin, G. 1996. Crop yield loss in relation to weed time of emergence and removal: analysis of the variability with mixed weed infestations. Pages 67–72 in Proceedings of the Second International Weed Control Congress; 25–28 June 1996; Copenhagen, Denmark. Slagelse, Denmark Department of Weed Control and Pesticide Ecology.Google Scholar
Sattin, M., Zanin, G., and Berti, A. 1992. Case history for weed competition/population ecology: velvetleaf (Abutilon theophrasti) in corn (Zea mays). Weed Technol. 6:213–219.Google Scholar
Scursoni, J. A. and Satorre, E. H. 2010. Glyphosate management strategies, weed diversity and soybean yield in Argentina. Crop Protection. 29:957–962.Google Scholar
Swanton, C. J., Shrestha, A., Chandler, K., and Deen, W. 2000. An economic assessment of weed control strategies in no-till glyphosate-resistant soybean (Glycine max). Weed Technol. 14:755–763.Google Scholar
Tharp, B. E., Schabenberger, O., and Kells, J. J. 1999. Response of annual weed species to glufosinate and glyphosate. Weed Technol. 13:542–547.Google Scholar
Van Acker, R. C., Swanton, C. J., and Wiese, S. F. 1993. The critical period of weed control in soybean (Glycine max). Weed Sci. 41:194–200.Google Scholar
Wilkerson, G. G., Wiles, L. J., and Bennett, A. C. 2002. Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach. Weed Sci. 50:411–424.Google Scholar
Zimdahl, R. L. 1988. The concept and application of the critical weed-free period. Pages 145–155 in Altieri, M. A. and Liebmann, M., eds. Weed Management in Agroecosystems: Ecological Approaches. Boca Raton, FL CRC Press.Google Scholar