Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T09:27:13.294Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigations of the sensitivity of ornamental, fruit, and nut plant species to driftable rates of 2,4-D and dicamba

Published online by Cambridge University Press:  15 November 2019

Brian R. Dintelmann Open the ORCID record for Brian R. Dintelmann [Opens in a new window] ,
Michele R. Warmund ,
Mandy D. Bish  and
Kevin W. Bradley
Brian R. Dintelmann*
Affiliation:
Graduate Research Assistant, Division of Plant Sciences, University of Missouri, Columbia, MO, USA
Michele R. Warmund
Affiliation:
Professor, Division of Plant Sciences, University of Missouri, Columbia, MO, USA
Mandy D. Bish
Affiliation:
Extension Weed Specialist, Division of Plant Sciences, University of Missouri, Columbia, MO, USA
Kevin W. Bradley
Affiliation:
Professor, Division of Plant Sciences, University of Missouri, Columbia, MO, USA
*
Author for Correspondence: Brian R. Dintelmann, Graduate Research Assistant, Division of Plant Sciences, University of Missouri, 5 Waters Hall, Columbia, MO65211. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

An experiment was conducted in 2017 and 2018 to determine the sensitivity of driftable rates of 2,4-D and dicamba with or without glyphosate on common ornamental, fruit, and nut species. Three driftable rates corresponding to ½, 1/20th, and 1/200th of the manufacturer’s labeled rate (1 × rate) of 2,4-D (1.09 kg ae ha−1), 2,4-D plus glyphosate (1.09 kg ae ha−1 plus 1.10 kg ae ha−1), dicamba (0.56 kg ae ha−1), and dicamba plus glyphosate (0.56 kg ae ha−1 plus 1.10 kg ae ha−1) were applied to apple, crabapple, dogwood, American elderberry, American elm, grapevine, hydrangea, red maple, pin oak, peach, pecan, eastern redbud, rose, red raspberry, strawberry, sweetgum, nannyberry viburnum, and black walnut plants. Visible estimates of injury were recorded 28 and 56 days after treatment (DAT). Plant measurements included leaf malformation, tree trunk growth, and shoot length. Across all species, the ½ × rate of 2,4-D plus glyphosate resulted in 61% injury 28 DAT, whereas the ½ × rate of dicamba plus glyphosate resulted in 51% injury. Across plant species and herbicides, ½ ×, 1/20 ×, and 1/200 × rates caused injury ranging from 3% to 100%, 0% to 66%, and 0% to 19%, respectively. Hydrangea was the least sensitive species; grapevine was most sensitive. Changes in plant measurements were dependent on the species and herbicide applied. Treatments at the ½ × or 1/20 × rate resulted in shoot length, leaf malformation, and trunk tree diameter differences for 11, 10, and 7 species, respectively, compared with nontreated plants. Collectively, the measurements and visual injury assessments indicated apple, red maple, peach, and pin oak were more sensitive to treatments containing dicamba, whereas black walnut, grapevine, and American elm were more sensitive to 2,4-D. Although the 1/200 × rates of 2,4-D and dicamba did not result in changes to plant measurements, obvious injury symptoms were observed, which could render these plants unsalable.

Keywords

2,4-Ddicambaglyphosateapple, Malus domesticacrabapple, Malus sargentiidogwood, Cornus floridaAmerican elderberry, Sambucus canadesisAmerican elm, Ulmus americanagrapevine, Vitis aestivalishydrangea, Hydrangea macrophyllared maple, Acer rubrumpin oak, Quercus palustrispeach, Prunus persicapecan, Carya illinoinensiseastern redbud, Cercis canadensisrose, Rosa sp.red raspberry, Rubus idaeusstrawberry, Fragaria × ananassasweetgum, Liquidambar styracifluanannyberry viburnum, Viburnum lentagoblack walnut, Juglans nigraHerbicide injurytreesdriftoff-target movement
Type
Research Article
Information
Weed Technology , Volume 34 , Issue 3 , June 2020 , pp. 331 - 341
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.)

Footnotes

Associate Editor: Robert Nurse, Agriculture and Agri-Food Canada

References

Al-Khatib, K, Parker, R, Fuerst, EP (1992) Sweet cherry (Prunus avium) response to simulated drift from selected herbicides. Weed Technol 6:975–979CrossRefGoogle Scholar
Al-Khatib, K, Parker, R, Fuerst, EP (1993) Wine grape (Vitis vinifera L.) response to simulated herbicide drift. Weed Technol 7:97–102CrossRefGoogle Scholar
Alves, GS, Kruger, GR, da Cunha, JP, de Santana, DG, Pinto, LA, Guimarães, F, Zaric, M (2017) Dicamba spray drift as influenced by wind speed and nozzle type. Weed Technol 31:724–731CrossRefGoogle Scholar
Bajwa, AA, Mahajan, G, Chauhan, BS (2015) Nonconventional weed management strategies for modern agriculture. Weed Sci 63:723–747CrossRefGoogle Scholar
Bauerle, M, Griffin, J, Alford, J, Curry, A, III, Kenty, M (2015) Field evaluation of auxin herbicide volatility using cotton and tomato as bioassay crops. Weed Technol 29:185–197CrossRefGoogle Scholar
Behrens, MR, 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 316:1185–1188CrossRefGoogle ScholarPubMed
Behrens, R, Lueschen, W (1979) Dicamba volatility. Weed Sci 27:486–493CrossRefGoogle Scholar
Bhatti, MA, Al-Khatib, K, Parker, R (1996) Wine grape (Vitis vinifera) response to repeated exposure of selected sulfonylurea herbicides and 2,4-D. Weed Technol 10:951–956CrossRefGoogle Scholar
Bhatti, MA, Al-Khatib, K, Parker, R (1997) Wine grape (Vitis vinifera) response to fall exposure of simulated drift from selected herbicides. Weed Technol 11:532–536CrossRefGoogle Scholar
Bish, M, Farrell, S, Lerch, R, Bradley, K (2019) Dicamba losses to air following applications to soybean under stable and nonstable atmospheric conditions. J Environ Qual 48:1675–1682, 10.2134/jeq2019.05.0197CrossRefGoogle Scholar
Blouin, DC, Webster, EP, Bond, JA (2011) On the analysis of combined experiments. Weed Technol 25:165–169CrossRefGoogle Scholar
Bondada, B (2011) Anomalies in structure, growth characteristics, and nutritional composition as induced by 2,4-dichlorophenoxy acetic acid drift phytotoxicity in grapevine leaves and clusters. J Am Soc Hort Sci 136:165–173CrossRefGoogle Scholar
Carmer, S, Nyquist, W, Walker, W (1989) Least significant differences for combined analyses of experiments with two-or three-factor treatment designs. Agron J 81:665–672CrossRefGoogle Scholar
Comes, RD, Marquis, LY, Kelley, AD (1984) Response of Concord grapes (Vitis labrusca) to 2,4-D in irrigation water. Weed Sci 32:455–459CrossRefGoogle Scholar
Craigmyle, BD, Ellis, JM, Bradley, KW (2013) Influence of weed height and glufosinate plus 2,4-D combinations on weed control in soybean with resistance to 2,4-D. Weed Technol 27:271–280CrossRefGoogle Scholar
Culpepper, AS, Sosnoskie, LM, Shugart, J, Leifheit, N, Curry, M, Gray, T (2018) Effects of low-dose applications of 2,4-D and dicamba on watermelon. Weed Technol 32:267–272CrossRefGoogle Scholar
Daniell, JW, Hardcastle, WS (1972) Response of peach trees to herbicide and mechanical weed control. Weed Sci 20:133–136CrossRefGoogle 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
Egan, JF, Mortensen, DA (2012) Quantifying vapor drift of dicamba herbicides applied to soybean. Environ Toxicol Chem 31:1023–1031CrossRefGoogle Scholar
Everitt, JD, Keeling, JW (2009) Cotton growth and yield response to simulated 2,4-D and dicamba drift. Weed Technol 23:503–506CrossRefGoogle Scholar
Feucht, JR (1988) Herbicide injuries to trees—symptoms and solutions. J Arboric 14:215–219Google Scholar
Hemphill, DD, Montgomery, ML (1981) Response of vegetable crops to sublethal application of 2,4-D. Weed Sci 29:632–635CrossRefGoogle Scholar
Holterman, H, Van De Zande, J, Porskamp, H, Huijsmans, J (1997) Modelling spray drift from boom sprayers. Comput Electron Agric 19:1–22CrossRefGoogle Scholar
Johnson, B, Young, B, Matthews, J, Marquardt, P, Slack, C, Bradley, K, York, A, Culpepper, S, Hager, A, Al-Khatib, K (2010) Weed control in dicamba-resistant soybeans. Crop Manag 9, 10.1094/CM-2010-0920-01-RSCrossRefGoogle Scholar
Jones, G, Norsworthy, K, Barber, T, Gbur, E, Krueger, G (2019) Off-target movement of DGA and BAPMA dicamba to sensitive soybean. Weed Technol 33:51–65CrossRefGoogle Scholar
Kniss, AR (2018) Soybean response to dicamba: A meta-analysis. Weed Technol 32:507–512CrossRefGoogle Scholar
Kruger, GR, Davis, VM, Weller, SC, Johnson, WG (2010) Growth and seed production of horseweed (Conyza canadensis) populations after exposure to postemergence 2,4-D. Weed Sci 58:413–419CrossRefGoogle Scholar
Kruger, GR, Johnson, WG, Doohan, DJ, Weller, SC (2012) Dose response of glyphosate and dicamba on tomato (Lycopersicon esculentum) injury. Weed Technol 26:256–260CrossRefGoogle Scholar
Maas, JL (1998) Compendium of strawberry diseases (2nd ed.). St. Paul, MN: American Phytopathological Society. 14 pCrossRefGoogle Scholar
Marple, ME, Al-Khatib, K, Shoup, D, Peterson, DE, Claassen, M (2007) Cotton response to simulated drift of seven hormonal-type herbicides. Weed Technol 21:987–992CrossRefGoogle Scholar
Mattson, MP (2008) Hormesis defined. Ageing Res Rev 7:1–7CrossRefGoogle ScholarPubMed
McCown, S, Barber, T, Norsworthy, J (2018) Response of non-dicamba-resistant soybean to dicamba as influenced by growth stage and herbicide rate. Weed Technol 32:513–519CrossRefGoogle Scholar
McMurray, G, Monks, D, Leidy, R (1996) Clopyralid use in strawberries (Fragaria × ananassa Duch.) grown on plastic mulch. Weed Sci 44:350–354CrossRefGoogle Scholar
Mohseni-Moghadam, M, Doohan, D (2015) Response of bell pepper and broccoli to simulated drift rates of 2,4-D and dicamba. Weed Technol 29:226–232CrossRefGoogle Scholar
Mohseni-Moghadam, M, Wolfe, S, Dami, I, Doohan, D (2015) Response of wine grape cultivars to simulated drift rates of 2,4-D, dicamba, and glyphosate, and 2,4-D or dicamba plus glyphosate. Weed Technol 30:807–814CrossRefGoogle Scholar
Mueller, T, Steckel, L (2019) Dicamba volatility in humidomes as affected by temperature and herbicide treatment. Weed Technol 33:541–546CrossRefGoogle Scholar
Nath, U, Crawford, BC, Carpenter, R, Coen, E (2003) Genetic control of surface curvature. Science 299:1404–1407CrossRefGoogle ScholarPubMed
Nordby, A, Skuterud, R (1974) The effects of boom height, working pressure and wind speed on spray drift. Weed Res 14:385–395CrossRefGoogle Scholar
Norsworthy, JK, Ward, SM, Shaw, DR, Llewellyn, RS, Nichols, RL, Webster, TM, Bradley, KW, Frisvold, G, Powles, SB, Burgos, NR (2012) Reducing the risks of herbicide resistance: best management practices and recommendations. Weed Sci (Special Issue I) 60:31–62CrossRefGoogle Scholar
Norton, JA, Storey, JB (1970) Effect of herbicides on weed control and growth of pecan trees. Weed Sci 18:522–524CrossRefGoogle Scholar
Ogg, AG, Ahmedullah, MA, Wright, GM (1991) Influence of repeated applications of 2,4-D on yield and juice quality of concord grapes (Vitis labruscana). Weed Sci 39:284–295CrossRefGoogle Scholar
Otta, J (1974) Effects of 2,4-D herbicide on Siberian elm. Forest Sci 20:287–290Google Scholar
Palhano, MG, Norsworthy, JK, Barber, T (2018) Cover crops suppression of Palmer amaranth (Amaranthus palmeri) in cotton. Weed Technol 32:60–65CrossRefGoogle Scholar
Patterson, MG, Goff, WD (1994) Effects of weed control and irrigation on pecan (Carya illinoinensis) growth and yield. Weed Technol 8:717–719CrossRefGoogle Scholar
Perry, PW, Upchurch, RP (1968) Growth analysis of red maple and white ash seedlings treated with eight herbicides. Weed Sci 16:32–37CrossRefGoogle Scholar
Putnam, A (1976) Fate of glyphosate in deciduous fruit trees. Weed Sci 24:425–430CrossRefGoogle Scholar
Robinson, AP, Simpson, DM, Johnson, WG (2012) Summer annual weed control with 2,4-D and glyphosate. Weed Technol 26:657–660CrossRefGoogle Scholar
Samtani, JB, Masiunas, JB, Appleby, JE (2008) Injury on white oak seedlings from herbicide exposure simulating drift. HortSci 43:2076–2080CrossRefGoogle Scholar
Shergill, LS, Bish, MD, Biggs, ME, Bradley, KW (2017) Monitoring the changes in weed populations in a continuous glyphosate-and dicamba-resistant soybean system: A five-year field-scale investigation. Weed Technol 32:166–173CrossRefGoogle Scholar
Shaner, D (2014) Herbicide handbook. Champaign, IL: Weed Science Society of America. 315 pGoogle Scholar
Solomon, CB, Bradley, KW (2014) Influence of application timings and sublethal rates of synthetic auxin herbicides on soybean. Weed Technol 28:454–464CrossRefGoogle Scholar
Sosnoskie, LM, Culpepper, AS, Braxton, LB, Richburg, JS (2015) Evaluating the volatility of three formulations of 2,4-D when applied in the field. Weed Technol 29:177–184CrossRefGoogle 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 28:131–141CrossRefGoogle Scholar
Sterrett, JP (1968) Response of oak and red maple to herbicides applied with an injector. Weed Sci 16:159–160CrossRefGoogle Scholar
Sterrett, JP (1969) Injection of hardwoods with dicamba, picloram, and 2,4-D. J Forest 67:820–821Google Scholar
[USDA-APHIS] US Department of Agriculture–Animal and Plant Health Inspection Service. (2014) Determination of nonregulated status for Dow AgroSciences DAS-68416-4 soybean. Washington, DC: US Department of Agriculture. https://www.aphis.usda.gov/brs/aphisdocs/11_23401p_det.pdf Accessed February 7, 2018Google Scholar
[USDA-APHIS] US Department of Agriculture–Animal and Plant Health Inspection Service. (2015) Determination of nonregulated status for Monsanto Company MON 88708 soybean. Washington, DC: US Department of Agriculture. https://www.aphis.usda.gov/brs/aphisdocs/10_18801p_det.pdf. Accessed February 7, 2018Google Scholar
Van Wychen, L (2017) WSSA survey ranks most common and most troublesome weeds in broadleaf crops, fruits and vegetables. http://wssa.net/2017/05/wssa-survey-ranks-most-common-and-most-troublesome-weeds-in-broadleaf-crops-fruits-and-vegetables/ Accessed February 5, 2019Google Scholar
Wang, M, Rautmann, 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
Wolf, TM, Grover, R, Wallace, K, Shewchuk, SR, Maybank, J (1993) Effect of protective shields on drift and deposition characteristics of field sprayers. Can J Plant Sci 73:1261–1273CrossRefGoogle Scholar
Wright, TR, Shan, G, Walsh, TA, Lira, JM, Cui, C, Song, P, Zhuang, M, Arnold, NL, Lin, G, Yau, K (2010) Robust crop resistance to broadleaf and grass herbicides provided by aryloxyalkanoate dioxygenase transgenes. Proc Natl Acad Sci 107:20240–20245CrossRefGoogle ScholarPubMed