Hostname: page-component-76fb5796d-wq484 Total loading time: 0 Render date: 2024-04-26T03:23:42.314Z Has data issue: false hasContentIssue false
  • English
  • Français

Development of a model to predict soybean yield loss from dicamba exposure

Published online by Cambridge University Press:  25 March 2019

Matthew R. Foster ,
James L. Griffin Open the ORCID record for James L. Griffin [Opens in a new window] ,
Josh T. Copes  and
David C. Blouin
Matthew R. Foster
Affiliation:
Graduate Research Assistant, School of Plant, Environmental, and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA, USA
James L. Griffin*
Affiliation:
Professor Emeritus, School of Plant, Environmental, and Soil Sciences, Louisiana State University Agricultural Center, Baton Rouge, LA, USA
Josh T. Copes
Affiliation:
Assistant Professor, LSU Agricultural Center Northeast Research Station, St. Joseph, LA, USA
David C. Blouin
Affiliation:
Professor, Department of Experimental Statistics, Louisiana State University Agricultural Center, Baton Rouge, LA, USA
*
Author for correspondence: James L. Griffin, Email: jgriffin@agcenter.lsu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Although dicamba-resistant crops can provide an effective weed management option, risk of dicamba off-site movement to sensitive crops is a concern. Previous research with indeterminate soybean identified 14 injury criteria associated with dicamba applied at V3/V4 or R1/R2 at 0.6 to 280 g ae ha−1. Injury criteria rated on a 0 to 5 scale (none to severe), along with percent visible injury and plant height reduction, and canopy height collected 7 and 15 d after treatment (DAT) were analyzed using multiple regression with a forward-selection procedure to develop yield prediction models. Variables included in the 15 DAT models (in order of selection) for V3/V4 were lower stem base lesions/cracking, plant height reduction, terminal leaf epinasty, leaf petiole droop, leaf petiole base swelling, and stem epinasty, whereas for R1/R2 variables were lower stem base lesions/cracking, terminal leaf chlorosis, leaf petiole base swelling, stem epinasty, terminal leaf necrosis, and terminal leaf cupping. To validate the models, experiments including the same dicamba rates and application timings used in previous research were conducted at two locations. For the variables specific to each model, data collected for the dicamba rates were used to predict yield. For the V3/V4 15 DAT model, predicted yield reduction (compared with the nontreated control for dicamba at 0.6 to 4.4 g ha−1) underestimated or overestimated observed yield reduction by an average of 1 and 3 percentage points. For 8.8 g ha−1, predicted yield reduction overestimated observed yield reduction by 8 points and for 17.5 g ha−1 by 20 points. For the R1/R2 15 DAT model, predicted yield reduction for 0.6 to 4.4 g ha−1 overestimated observed yield reduction by an average of 3 to 5 percentage points. For dicamba at 8.8 g ha−1, predicted yield reduction underestimated observed yield reduction by 8 points and for 17.5 g ha−1 overestimated by 6 points.

Keywords

Kevin Bradley, University of MissouriDicambasoybean, Glycine max (L.) MerrCrop injury assessmentoff-target movementspray driftyield loss assessment
Type
Research Article
Information
Weed Technology , Volume 33 , Issue 2 , April 2019 , pp. 287 - 295
Copyright
© Weed Science Society of America, 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al-Khatib, K, Peterson, D (1999) Soybean (Glycine max) response to simulated drift from selected sulfonylurea herbicides, dicamba, glyphosate, and glufosinate. Weed Technol 13:264–270CrossRefGoogle Scholar
Andersen, SM, Clay, SA, Wrage, LJ, Matthees, D (2004) Soybean foliage residues of dicamba and 2, 4-D and correlation to application rates and yield. Agron J 96:750–760CrossRefGoogle Scholar
Anonymous (2018a) LSU AgCenter Pest Management Guides. http://www.lsuagcenter.com/portals/communications/publications/management_guides. Accessed: August 31, 2018Google Scholar
Anonymous (2018b) Stepwise regression. Chapter 311, p 1–9. NCSS Statistical Software, NCSS.com. https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Stepwise_Regression.pdf. Accessed: November 30, 2018Google Scholar
Anonymous (2018c) Selecting the Best Model for Multiple Linear Regression, North Dakota State University. https://www.ndsu.edu/faculty/horsley/Stepwise_regression_(HZAU).pdf. Accessed: November 30, 2018Google Scholar
Behrens, R, Lueschen, WE (1979) Dicamba volatility. Weed Sci 27:486–493CrossRefGoogle Scholar
Bradley, K (2017) A final report on dicamba-injured soybean acres. Integrated Pest and Crop Manage. Newsletter https://ipm.missouri.edu/IPCM/2017/10/final_report_dicamba_injured_soybean/.Google Scholar
Bradley, K (2018) Dicamba injury update. Different year same questions. Integrated Pest and Crop Manage. Newsletter. https://ipm.missouri.edu/IPCM/2018/7/July-15-Dicamba-injury-update-different-year-same-questions/. Accessed: August 9, 2018Google Scholar
Brown, RB, Carter, MH, Stephenson, GR (2004) Buffer zone and windbreak effects on spray drift deposition in a simulated wetland. Pest Manag Sci 60:1085–1090CrossRefGoogle Scholar
Carlsen, SCK, Spliid, NH, Svensmark, B (2006) Drift of 10 herbicides after tractor spray application. 2. Primary drift (droplet drift). Chemosphere 64:778–786CrossRefGoogle Scholar
de Jong, FMW, de Snoo, GR, van de Zande, JC (2008) Estimated nationwide effects of pesticide spray drift on terrestrial habitats in the Netherlands. J Environ Manage 86:721–730CrossRefGoogle ScholarPubMed
Egan, JF, Mortensen, DA (2012) Quantifying vapor drift of dicamba herbicides applied to soybean. Environ Toxicol Chem 31:1023–1031CrossRefGoogle Scholar
Egan, JF, Barlow, KM, Mortensen, DA (2014) A meta-analysis on the effects of 2, 4-D and dicamba drift on soybean and cotton. Weed Sci 62:193–206CrossRefGoogle Scholar
Fehr, WR, Caviness, CE (1977) Stages of soybean development. Special Report 80, Iowa Agriculture and Home Economics Experiment Station, Iowa State University. 11 pGoogle Scholar
Flessner, ML, McElroy, JS, McCurdy, JD, Toombs, JM, Wehtje, GR, Burmester, CH, Price, AJ, Ducar, JT (2015). Glyphosate-resistant horseweed (Conyza canadensis) control with dicamba in Alabama. Weed Technol 29:633–640CrossRefGoogle Scholar
Foster, MR, Griffin, JL (2018) Injury criteria associated with soybean exposure to dicamba. Weed Technol 32:608–617CrossRefGoogle Scholar
Griffin, JL, Bauerle, MJ, Stephenson, DO IV, Miller, DK, Boudreaux, JM (2013) Soybean response to dicamba applied at vegetative and reproductive growth stages. Weed Technol 27:696–703CrossRefGoogle Scholar
Grover, R, Yoshida, K, Maybank, J (1972) Droplet and vapor drift from butyl ester and dimethylamine salt of 2, 4-D. Weed Sci 20:320–324CrossRefGoogle Scholar
Hock, SM, Knezevic, SZ, Martin, AR, Lindquist, JL (2006) Performance of WeedSOFT for predicting soybean yield loss. Weed Technol 20:478–484CrossRefGoogle Scholar
Johnson, VA, Fisher, LR, Jordan, DL, Edmisten, KE, Stewart, AM, York, AC (2012) Cotton, peanut, and soybean response to sublethal rates of dicamba, glufosinate, and 2, 4-D. Weed Technol 26:195–206CrossRefGoogle Scholar
Robinson, AP, Simpson, DM, Johnson, WG (2013) Response of glyphosate-tolerant soybean yield components to dicamba exposure. Weed Sci 61:526–536CrossRefGoogle Scholar
Spaunhorst, DJ, Bradley, KW (2013). Influence of dicamba and dicamba plus glyphosate combinations on the control of glyphosate-resistant waterhemp (Amaranthus rudis). Weed Technol 27:675-681CrossRefGoogle Scholar
[US EPA] United States Environmental Protection Agency (2006) Reregistration Eligibility Decision for Dicamba and Associated Salts. Washington, DC: US EPA. 165 pGoogle Scholar
Wang, M, Rautman, D (2008) A simple probabilistic estimation of spray drift-factors determining spray drift and development of a model. Environ Toxicol Chem 27:2617–2626CrossRefGoogle ScholarPubMed
Weidenhamer, JD, Triplett, GB, Sobotka, FE (1989) Dicamba injury to soybean. Agron J 81:637–643CrossRefGoogle Scholar