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Spray solution pH and soybean injury as influenced by synthetic auxin formulation and spray additives

Published online by Cambridge University Press:  18 August 2020

Sarah Striegel Open the ORCID record for Sarah Striegel [Opens in a new window] ,
Maxwel C. Oliveira Open the ORCID record for Maxwel C. Oliveira [Opens in a new window] ,
Nicholas Arneson Open the ORCID record for Nicholas Arneson [Opens in a new window] ,
Shawn P. Conley Open the ORCID record for Shawn P. Conley [Opens in a new window] ,
David E. Stoltenberg Open the ORCID record for David E. Stoltenberg [Opens in a new window]  and
Rodrigo Werle Open the ORCID record for Rodrigo Werle [Opens in a new window]
Sarah Striegel
Affiliation:
Graduate Student, Department of Agronomy, University of Wisconsin-Madison, Madison, WI, USA
Maxwel C. Oliveira
Affiliation:
Postdoctoral Researcher, Department of Agronomy, University of Wisconsin-Madison, Madison, WI, USA
Nicholas Arneson
Affiliation:
Research Associate, Department of Agronomy, University of Wisconsin-Madison, Madison, WI, USA
Shawn P. Conley
Affiliation:
Professor, Department of Agronomy, University of Wisconsin-Madison, Madison, WI, USA
David E. Stoltenberg
Affiliation:
Professor, Department of Agronomy, University of Wisconsin-Madison, Madison, WI, USA
Rodrigo Werle*
Affiliation:
Assistant Professor, Department of Agronomy, University of Wisconsin-Madison, Madison, WI, USA
*
Author for correspondence: Rodrigo Werle, Department of Agronomy, University of Wisconsin-Madison, 1575 Linden Drive, Madison, WI53705. (Email: [email protected])
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Abstract

Use of synthetic auxin herbicides has increased across the midwestern United States after adoption of synthetic auxin-resistant soybean traits, in addition to extensive use of these herbicides in corn. Off-target movement of synthetic auxin herbicides such as dicamba can lead to severe injury to sensitive plants nearby. Previous research has documented effects of glyphosate on spray-solution pH and volatility of several dicamba formulations, but our understanding of the relationships between glyphosate and dicamba formulations commonly used in corn and for 2,4-D remains limited. The objectives of this research were to (1) investigate the roles of synthetic auxin herbicide formulation, glyphosate, and spray additives on spray solution pH; (2) assess the impact of synthetic auxin herbicide rate on solution pH; and (3) assess the influence of glyphosate and application time of year on dicamba and 2,4-D volatility using soybean as bioindicators in low-tunnel field volatility experiments. Addition of glyphosate to a synthetic auxin herbicide decreased solution pH below 5.0 for four of the seven herbicides tested (range of initial pH of water source, 7.45–7.70). Solution pH of most treatments was lower at a higher application rate (4× the labeled POST rate) than the 1× rate. Among all treatment factors, inclusion of glyphosate was the most important affecting spray solution pH; however, the addition of glyphosate did not influence area under the injury over distance stairs (P = 0.366) in low-tunnel field volatility experiments. Greater soybean injury in field experiments was associated with high air temperatures (maximum, >29 C) and low wind speeds (mean, 0.3–1.5 m s−1) during the 48 h after treatment application. The two dicamba formulations (diglycolamine with VaporGrip® and sodium salts) resulted in similar levels of soybean injury for applications that occurred later in the growing season. Greater soybean injury was observed after dicamba than after 2,4-D treatments.

Keywords

2,4-DdicambaglyphosatecornZea mays L.soybeanGlycine max L. Merr.Off-target movementspray additivesspray solutionsynthetic auxinsvapor drift
Type
Research Article
Information
Weed Technology , Volume 35 , Issue 1 , February 2021 , pp. 113 - 127
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Weed Science Society of America

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Footnotes

Associate Editor: William Johnson, Purdue University

References

Abraham, W (2018) The Chemistry Behind Low-Volatility Dicamba . St. Louis, MO: Bayer Crop Science. 2 p Google 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 Sustain Dev 96:750–760 Google Scholar
Anonymous (2010) Clarity herbicide product label. BASF Corporation Publication No. NVA 2010-04-065-0154. Research Triangle Park, NC: BASF Corp. 22 pGoogle Scholar
Anonymous (2017a) Enlist One with Colex-D Technology herbicide product label. Dow AgroSciences Publication No. D02-427-002. Indianapolis, IN: Dow AgroScience. 7 pGoogle Scholar
Anonymous (2017b) Enlist Duo with Colex-D Technology herbicide product label. Dow AgroSciences Publication No. D02-407-003. Indianapolis, IN: Dow AgroScience. 7 pGoogle Scholar
Anonymous (2019a) XtendiMax with VaporGrip technology herbicide product label. EPA Registration No. 524-617. St. Louis, MO: Monsanto Company. 10 pGoogle Scholar
Anonymous (2019b) Engenia herbicide product label. BASF Corporation Publication No. NVA 2018-04-385-0080. Research Park, NC: BASF Corp. 29 pGoogle Scholar
Baraibar, B, Mortensen, DA, Hunter, MC, Barbercheck, ME, Kaye, JP, Finney, DM, Curran, WS, Bunchek, J, White, CM (2018) Growing degree days and cover crop type explain weed biomass in winter cover crops. Agron Sustain Dev 38:1–9 CrossRefGoogle Scholar
Behrens, M, Mutlu, N, Chakraborty, S, Dumitru, R, Jiang, WZ, LaVallee, BJ, Herman, PL, Clemente, TE, Weeks, DP (2007) Dicamba resistance: enlarging and preserving biotechnology-based weed management strategies. Science 316:1185–1188 CrossRefGoogle ScholarPubMed
Behrens, MR, Lueschen, WE (1979) Dicamba volatility. Weed Sci 27:466–493 CrossRefGoogle Scholar
Bernards, M, Culpepper, S, Hartzler, RG, Li, S, Nolte, S, Oakley, G, Reynolds, DB, Smeda, RJ, Sprague, CL, Werle, R (2020) Low tunnel evaluation of dicamba premixes. Page 80 in Proceedings of the 60th Annual Meeting of the Weed Science Society of America Meeting. Maui, HI: Weed Science Society of AmericaGoogle Scholar
Bish, MD, Farrell, ST, Lerch, RN, Bradley, KW (2019) Dicamba losses to air after applications to soybean under stable and nonstable atmospheric conditions. J Environ Qual 48:1675–1682 CrossRefGoogle Scholar
Bourgoin, C, Blanc, L, Bailly, J-S, Cornu, G, Berenguer, E, Oszwald, J, Tritsch, I, Laurent, F, Hasan, AF, Sist, P, Gond, V (2018) The potential of multisource remote sensing for mapping the biomass of a degraded Amazonian forest. Forests 9:1–21 CrossRefGoogle Scholar
Bradley, KW (2017) A final report on dicamba-injured soybean acres. https://ipm.missouri.edu/IPCM/2017/10/final_report_dicamba_injured_soybean/. Accessed: January 23, 2020Google Scholar
Bradley, KW (2019) Survey of Dicamba and Xtend Use and Satisfaction . Columbia, MO: University of Missouri Division of Plant Sciences. 31 p Google Scholar
Breiman, L (2001) Random forests. Mach Learn 25:5–32 CrossRefGoogle Scholar
Browne, FB, Li, S, Price, KJ, Langemeier, RD, Kruger, GR (2020) Soybean response to sublethal dosages of dicamba particle drift vs. vapor. Page 545 in Proceedings of the 60th Annual Meeting of the Weed Science Society of America Meeting. Maui, HI: Weed Science Society of AmericaGoogle Scholar
Busi, R, Goggin, DE, Heap, IM, Horak, MJ, Jugulam, M, Masters, RA, Napier, RM, Riar, DS, Satchivi, NM, Torra, J, Westra, P, Wright, TR (2018) Weed resistance to synthetic auxin herbicides. Pest Manag Sci 74:2265–2276 CrossRefGoogle ScholarPubMed
DeSimone, LA, McMahon, PB, Rosen, MR (2014) The Quality of Our Nation’s Waters: Water Quality in Principal Aquifers of the United States, 1991-2010 . Circular 1360. Reston, VA: U.S. Department of the Interior. 161 p Google 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 62:193–206 CrossRefGoogle Scholar
Egan, JF, Mortensen, DA (2012) Quantifying vapor drift of dicamba herbicides applied to soybean. Environ Toxicol Chem 31:1023–1031 CrossRefGoogle Scholar
[EPA] U.S. Environmental Protection Agency (2014) 2,4-D. https://www.epa.gov/ingredients-used-pesticide-products/24-d. Accessed: January 22, 2020Google Scholar
[EPA] U.S. Environmental Protection Agency (2019) Registration of dicamba for use on dicamba-tolerant crops. https://www.epa.gov/ingredients-used-pesticide-products/registration-dicamba-use-dicamba-tolerant-crops. Accessed: January 23, 2020Google Scholar
Hayden, NC, Young, JM, Ghaste, MS, Johnson, WG, Widhalm, JR, Young, BG (2019) The effects of adjuvants and carrier water characteristics on dicamba volatilization in a controlled environment. Page 176 in Proceedings of the 74th Annual Meeting of the North Central Weed Science Society. Columbus, OH: North Central Weed Science SocietyGoogle Scholar
Husted, S, Schjoerring, JK (1995) Apoplastic pH and ammonium concentration in leaves of Brassica napus L. Plant Physiol 109:1453–1460 CrossRefGoogle ScholarPubMed
[IDOA] Illinois Department of Agriculture (2019) Dicamba. https://www2.illinois.gov/sites/agr/Pesticides/Pages/Dicamba.aspx. Accessed: January 23, 2020Google 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 26:195–206 CrossRefGoogle Scholar
Jones, GT, Norsworthy, JK, Barber, T (2019a) Off-target movement of diglycolamine dicamba to non-dicamba soybean using practices to minimize primary drift. Weed Technol 33:24–40 CrossRefGoogle Scholar
Jones, GT, Norsworthy, JK, Barber, T, Gbur, E, Kruger, GR (2019b) Off-target movement of DGA and BAPMA dicamba to sensitive soybean. Weed Technol 33:51–65 CrossRefGoogle Scholar
Kniss, AR (2018) Soybean response to dicamba: a meta-analysis. Weed Technol 32:507–512 CrossRefGoogle Scholar
Kuhn, M, Wickham, H (2020) tidymodels: Easily install and load the “Tidymodels” packages. R package version 0.1.0. https://cran.r-project.org/package=tidymodels. Accessed: May 29, 2020Google Scholar
Langemeier, CB, Robertson, AE, Wang, D, Jackson-Ziems, TA, Kruger, GR (2017) Factors affecting the development and severity of Goss’s bacterial wilt and leaf blight of corn, caused by Clavibacter michiganensis subsp. nebraskensis . Plant Dis 101:54–61 CrossRefGoogle ScholarPubMed
Langemeier, RD, Li, S, Price, KJ, Browne, FB (2020) Influence of pH buffers on volatility of dicamba tank mixtures. Page 294 in Proceedings of the 60th Annual Meeting of the Weed Science Society of America Meeting. Maui, HI: Weed Science Society of AmericaGoogle Scholar
Latorre, DO, Reynolds, DB, Young, BG, Norsworthy, JK, Culpepper, S, Bradley, KW, Bish, MD, Kruger, GR, Stephenson, DO (2017) Evaluation of volatility of dicamba formulations in soybean crop. Page 94 in Proceedings of the 72nd Annual Meeting of the North Central Weed Science Society. St. Louis, MO: North Central Weed Science SocietyGoogle Scholar
Long, JL (2017) Influence of Application Factors on Dicamba Volatility. M.Sc thesis. West Lafayette, IN: Purdue University. 110 pGoogle Scholar
Louppe, G, Wehenkel, L, Sutera, A, Geurts, P (2013) Understanding variable importances in forests of randomized trees. Pages 431–439 in 2013 Proceedings of Neural Information Processing Systems 26. Lake Tahoe, NV: Neural Information Processing SystemsGoogle Scholar
MacInnes, A (2016) VaporGrip technology; how it works and its benefits. Page 85 in Proceedings of the 71st Annual Meeting of the North Central Weed Science Society. Des Moines, IA: North Central Weed Science SocietyGoogle Scholar
[MDA] Minnesota Department of Agriculture (2019) Dicamba - damage and complaints. https://www.mda.state.mn.us/dicamba-damage-complaints. Accessed: January 23, 2020Google Scholar
Miller, JJ, Schepers, JS, Shapiro, CA, Arneson, NJ, Eskridge, KM, Oliveira, MC, Giesler, LJ (2018) Characterizing soybean vigor and productivity using multiple crop canopy sensor readings. Field Crops Res 216:22–31 CrossRefGoogle Scholar
Mueller, TC, Steckel, LE (2019a) Dicamba volatility in humidomes as affected by temperature and herbicide treatment. Weed Technol 33:541–546 CrossRefGoogle Scholar
Mueller, TC, Steckel, LE (2019b) Spray mixture pH as affected by dicamba, glyphosate, and spray additives. Weed Technol 33:547–554 CrossRefGoogle Scholar
Mueller, TC, Wright, DR, Remund, KM (2013) Effect of formulation and application time of day on detecting dicamba in the air under field conditions. Weed Sci 61:586–593 CrossRefGoogle Scholar
Norsworthy, JK, Barber, T (2019) Dicamba findings in 2018. Page 334 in Proceedings of the 59th Annual Meeting of the Weed Science Society of America. New Orleans, LA: Weed Science Society of AmericaGoogle Scholar
Oakley, G, Culpepper, AS, Reynolds, DB, Smeda, R, Sprague, C, Werle, R (2020) Low tunnel evaluation of dicamba premixes. Page 80 in Proceedings of the 60th Annual Meeting of the Weed Science Society of America. Maui, HI: Weed Science Society of AmericaGoogle Scholar
Oseland, E, Bish, M, Steckel, L, Bradley, K (2020) Identification of environmental factors that influence the likelihood of off-target movement of dicamba [published online ahead of print May 9, 2020]. Pest Manag Sci DOI: 10.1002/ps.5887 CrossRefGoogle Scholar
Oseland, EG, Bish, M, Bradley, KW (2018) Investigations of the effects of soil pH on the volatility of dicamba formulations. Page 81 in Proceedings of the 73rd Annual Meeting of the North Central Weed Science Society. Milwaukee, WI: North Central Weed Science SocietyGoogle Scholar
Osterholt, MJ, Young, BG (2019) The influence of simulated dew on dicamba volatility and soybean sensitivity. Page 19 in Proceedings of the 74th Annual Meeting of the North Central Weed Science Society. Columbus, OH: North Central Weed Science SocietyGoogle Scholar
Ou, J, Thompson, CR, Stahlman, PW, Bloedow, N, Jugulam, M (2018) Reduced translocation of glyphosate and dicamba in combination contributes to poor control of Kochia scoparia: evidence of herbicide antagonism. Sci Rep 8:1–11 CrossRefGoogle ScholarPubMed
Rice, TC, Billman, SM (2019) Use of low tunnels to identify chemical factors influencing dicamba movement. Page 18 in Proceedings of the 74th Annual Meeting of the North Central Weed Science Society. Columbus, OH: North Central Weed Science SocietyGoogle Scholar
Roskamp, JM, Chahal, GS, Johnson, WG (2013) The effect of cations and ammonium sulfate on the efficacy of dicamba and 2,4-D. Weed Technol 27:72–77 CrossRefGoogle Scholar
Ross, MA, Lembi, CA (2008) Applied weed science: including the ecology and management of invasive plants. 3rd ed. Upper Saddle River, NJ: Pearson Education, Inc. 531 p Google Scholar
Sall, ED, Huang, K, Pai, N, Schapaugh, AW, Honegger, JL, Orr, TB, Riter, LS (2020) Quantifying dicamba volatility under field conditions: part II, comparative analysis of 23 dicamba volatility field trials. J Agric Food Chem 68:2286–2296 CrossRefGoogle ScholarPubMed
Schleier, JJ, Ouse, D, Gifford, J (2017) Relative volatility of auxin herbicide formulations. Page 69 in Proceedings of the 72nd Annual Meeting of the North Central Weed Science Society. St. Louis, MO: North Central Weed Science SocietyGoogle Scholar
Scholtes, AB, Sperry, BP, Reynolds, DB, Irby, JT, Eubank, TW, Barber, LT, Dodds, DM (2019) Effect of soybean growth stage on sensitivity to sublethal rates of dicamba and 2,4-D. Weed Technol 33:555–561 CrossRefGoogle Scholar
Schreiber, F, De Avila, LA, Scherner, A, Moura, DDS, Martini, AT (2016) Volatility of clomazone formulations under field conditions. Rev Bras 15:271–280 Google Scholar
Sciumbato, AS, Chandler, JM, Senseman, SA, Bovey, RW, Smith, KL (2004a) Determining exposure to auxin-like herbicides. II. Practical application to quantify volatility. Weed Technol 18:1135–1142 Google Scholar
Sciumbato, AS, Chandler, JM, Senseman, SA, Bovey, RW, Smith, KL (2004b) Determining exposure to auxin-like herbicides. I. Quantifying injury to cotton and soybean. Weed Technol 18:1125–1134 CrossRefGoogle Scholar
Shaner, DL, Jachetta, JJ, Senseman, S, Burke, I, Handson, B, Jugulam, M, Tan, S, Reynolds, J, Strek, H, McAllister, R, Green, J, Glenn, B, Turner, P, Pawlak, J (2014) Herbicide Handbook. 10th ed. Champaign, IL: Weed Science Society of America. 495 p Google Scholar
Shaner, G, Finney, RE (1977) The effect of nitrogen fertilization on the expression of slow-mildewing resistance in knox wheat. Phytopathology 67:1051–1056 CrossRefGoogle Scholar
Simko, I, Piepho, HP (2012) The area under the disease progress stairs: calculation, advantage, and application. Phytopathology 102:381–389 CrossRefGoogle ScholarPubMed
Simpson, D (2019) Enlist E3 soybean tolerance and weed control programs. Page 220 in Proceedings of the 74th Annual Meeting of the North Central Weed Science Society. Columbus, OH: North Central Weed Science SocietyGoogle Scholar
Smidt, ER, Conley, SP, Zhu, J, Arriaga, FJ (2016) Identifying field attributes that predict soybean yield using random forest analysis. Agron J 108:637–646 CrossRefGoogle Scholar
Soltani, N, Oliveira, MC, Alves, GS, Werle, R, Norsworthy, JK, Sprague, CL, Young, BG, Reynolds, DB, Brown, A, Sikkema, PH (2020) Off-target movement assessment of dicamba in North America. Weed Technol 34:318–330 CrossRefGoogle Scholar
Soltani, N, VanEerd, LL, Vyn, RJ, Shropshire, C, Sikkema, PH (2010) Weed control, environmental impact and profitability with glyphosate tank mixes in glyphosate-tolerant corn. Can J Plant Sci 90:125–132 CrossRefGoogle Scholar
Spaunhorst, DJ, Siefert-Higgins, S, Bradley, KW (2014) Glyphosate-resistant giant ragweed (Ambrosia trifida) and waterhemp (Amaranthus rudis) management in dicamba-resistant soybean (Glycine max). Weed Technol 28:131–141 CrossRefGoogle Scholar
Underwood, MG, Soltani, N, Hooker, DC, Robinson, DE, Vink, JP, Swanton, CJ, Sikkema, PH (2017) Benefit of tank mixing dicamba with glyphosate applied after emergence for weed control in dicamba- and glyphosate-resistant soybean. Can J Plant Sci 97:891–901 Google Scholar
[USDA ERC] U.S. Department of Agriculture, Economic Research Center (2019) Recent trends in GE adoption. https://www.ers.usda.gov/data-products/adoption-of-genetically-engineered-crops-in-the-us/recent-trends-in-ge-adoption.aspx. Accessed: January 23, 2020Google Scholar
[USDA-NASS] U.S. Department of Agriculture, National Agricultural Statistics Service (2019) Quick stats. https://quickstats.nass.usda.gov/. Accessed: January 23, 2020Google Scholar
Vieira, BC, Samuelson, SL, Alves, GS, Gaines, TA, Werle, R, Kruger, GR (2018) Distribution of glyphosate-resistant Amaranthus spp. in Nebraska. Pest Manag Sci 74:2316–2324 CrossRefGoogle ScholarPubMed
Werle, R, Oliveira, MC, Jhala, AJ, Proctor, CA, Rees, J, Klein, R (2018) Survey of Nebraska farmers’ adoption of dicamba-resistant soybean technology and dicamba off-target movement. Weed Technol 32:754–761 CrossRefGoogle Scholar
Werle, R, Oliveira, MC, Rector, R (2019) Lessons from two years of dicamba off-target movement research in Wisconsin. Page 200 in Proceedings of the 74th Annual Meeting of the North Central Weed Science Society. Columbus, OH: North Central Weed Science SocietyGoogle Scholar
Westberg, D, Adams, A (2017) Application stewardship of Engenia herbicide in dicamba tolerant crops. Page 155 in Proceedings of the 70th Annual Meeting of the Southern Weed Science Society. Birmingham, AL: Southern Weed Science SocietyGoogle Scholar
Wright, M (2020) ranger: A fast implementation of random forests. https://cran.r-project.org/web/packages/ranger/ranger.pdf. Accessed: May 29, 2020Google Scholar
Yang, J, Carena, MJ, Uphaus, J (2010) Area under the dry down curve (AUDDC): a method to evaluate rate of dry down in maize. Crop Sci 50:2347–2354 CrossRefGoogle Scholar
Young, BG, Farrell, ST, Bradley, KW, Latorre, DO, Kruger, GR, Barber, TL, Norsworthy, JK, Scott, R, Reynolds, DB, Steckel, LE (2017) University research on dicamba volatility. Page in Proceedings of the 72nd Annual Meeting of the North Central Weed Science Society. St. Louis, MO: North Central Weed Science SocietyGoogle Scholar
Zaccaro, M., Norsworthy, JK, Houston, MM, Brabham, CB (2019) Use of low tunnel field trials to understand dicamba volatility. Page 103 in Proceedings of the 72nd Annual Meeting of the Southern Weed Science Society. Oklahoma City, OK: Southern Weed Science SocietyGoogle Scholar
Zhang, J, Huang, Y, Reddy, KN, Wang, B (2019) Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning. Pest Manag Sci 75:3260–3272 CrossRefGoogle ScholarPubMed
Zhang, T, Johnson, EN, Willenborg, CJ (2016) Evaluation of harvest-aid herbicides as desiccants in lentil production. Weed Technol 30:629–638 CrossRefGoogle Scholar
Zhou, J, Li, E, Wei, H, Li, C, Qiao, Q, Armaghani, DJ (2019) Random forests and cubist algorithms for predicting shear strengths of rockfill materials. Appl Sci 9:1–16 Google Scholar
Zollinger, RK (2018) Mid-west extension weed specialist concerns. Page 325 in Proceedings of the 58th Annual Meeting of the Weed Science Society of America. Arlington, VA: Weed Science Society of AmericaGoogle Scholar
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