Hostname: page-component-848d4c4894-v5vhk Total loading time: 0 Render date: 2024-06-24T20:28:50.116Z Has data issue: false hasContentIssue false
  • English
  • Français

POST control of Italian ryegrass in hazelnut orchards

Published online by Cambridge University Press:  08 June 2021

Marcelo L. Moretti Open the ORCID record for Marcelo L. Moretti [Opens in a new window]
Marcelo L. Moretti*
Affiliation:
Assistant Professor, Oregon State University, Department of Horticulture, Corvallis, OR, USA
*
Author for correspondence: Marcelo L Moretti, Assistant Professor, Oregon State University, 4017 Agriculture and Life Sciences, 2750 SW Campus Way, Corvallis, OR 97331. Email: marcelo.moretti@oregonstate.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Italian ryegrass has become a problematic weed in hazelnut orchards of Oregon because of the presence of herbicide-resistant populations. Resistant and multiple-resistant Italian ryegrass populations are now the predominant biotypes in Oregon; there is no information on which herbicides effectively control Italian ryegrass in hazelnut orchards. Six field studies were conducted in commercial orchards to evaluate Italian ryegrass control with POST herbicides. Treatments included flazasulfuron, glufosinate, glyphosate, paraquat, rimsulfuron, and sethoxydim applied alone or in selected mixtures during early spring when plants were in the vegetative stage. Treatment efficacy was dependent on the experimental site. The observed range of weed control 28 d after treatment was 13% to 76% for glyphosate, 1% to 72% for paraquat, 58% to 88% for glufosinate, 16% to 97% for flazasulfuron, 8% to 94% for rimsulfuron, and 25% to 91% for sethoxydim. Herbicides in mixtures improved control of Italian ryegrass compared to single active ingredients based on contrast analysis. Herbicides in mixture increased control by 27% compared to glyphosate, 18% to rimsulfuron, 15% to flazasulfuron, 19% to sethoxydim, and 12% compared to glufosinate when averaged across all sites, but mixture did not always improve reduction of ground coverage or of biomass. This complex site-specific response highlights the importance of record-keeping for efficient herbicide use. Glufosinate is an effective option to manage Italian ryegrass. However, the glufosinate-resistant biotypes documented in Oregon may jeopardize this practice. Nonchemical weed control options are needed for sustainable weed management in hazelnuts.

Keywords

FlazasulfuronglufosinateglyphosateparaquatrimsulfuronsethoxydimItalian ryegrass, Lolium perenne subsp. multiflorum (Lam.) Husn.hazelnut, Corylus avellana LResistance managementherbicide efficacy
Type
Research Article
Information
Weed Technology , Volume 35 , Issue 4 , August 2021 , pp. 638 - 643
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Weed Science Society of America

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: Darren Robinson, University of Guelph

References

Ali, A, Streibig, JC, Duus, J, Andreasen, C (2013) Use of image analysis to assess color response on plants caused by herbicide application. Weed Technol 27:604–611 10.1614/WT-D-12-00136.1CrossRefGoogle Scholar
Aulakh, JS, Jhala, AJ (2015) Comparison of glufosinate-based herbicide programs for broad-spectrum weed control in glufosinate-resistant soybean. Weed Technol 29:419–430 10.1614/WT-D-15-00014.1CrossRefGoogle Scholar
Avila-Garcia, WV, Mallory-Smith, C (2011) Glyphosate-resistant Italian ryegrass (Lolium perenne) populations also exhibit resistance to glufosinate. Weed Sci 59:305–309 10.1614/WS-D-11-00012.1CrossRefGoogle Scholar
Beckie, HJ, Reboud, X (2009) Selecting for weed resistance: herbicide rotation and mixture. Weed Technol 23:363–370 10.1614/WT-09-008.1CrossRefGoogle Scholar
Bobadilla, LK, Hulting, AG, Berry, PA, Moretti, ML, Mallory-Smith, C (2021) Frequency, distribution, and ploidy diversity of herbicide-resistant Italian ryegrass (Lolium perenne spp. multiflorum) populations of western Oregon. Weed Sci, 69:177–185, 10.1017/wsc.2021.2:1–3310.1017/wsc.2021.2CrossRefGoogle Scholar
Brooks, ME, Kristensen, K, van Benthem, KJ, Magnusson, A, Berg, CW, Nielsen, A, Skaug, HJ, Machler, M, Bolker, BM (2017) glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal 9:378–400 10.32614/RJ-2017-066CrossRefGoogle Scholar
Cornelius, CD, Bradley, KW (2017) Herbicide programs for the termination of various cover crop species. Weed Technol 31:514–522 10.1017/wet.2017.20CrossRefGoogle Scholar
Costa, F, Kfouri, M, Fávaro, C, Jordao, A, da Silva, VG, Rodrigues, G, Ferguson, J (2019) Alternative tank mix adjuvant for glufosinate. Pages 116–124 in Linscott DL, ed. Pesticide Formulation and Delivery Systems: Vol. 39, Innovative Formulation, Application, and Adjuvant Technologies for Agriculture. West Conshohoken, PA: ASTM InternationalGoogle Scholar
Dekker, J (1997) Weed diversity and weed management. Weed Sci:357–36310.1017/S0043174500092985CrossRefGoogle Scholar
Donald, WW (2006) Mowing for weed management. Pages 329–372 in Singh H, Batish D, Kohli R, eds. Handbook of Sustainable Weed Management. New York: Food Products PressGoogle Scholar
Fearon, C, Hayward, M, Lawrence, M (1983) Self-incompatibility in ryegrass. Heredity 50:35–45 10.1038/hdy.1983.5CrossRefGoogle Scholar
Ferreira, T, Rasband, W (2012) ImageJ user guide: IJ 1.46r, Pp 1–198. https://imagej.nih.gov/ij/docs/guide/user-guide.pdf. Accessed: July 9, 2021Google Scholar
Gundel, P, Martínez-Ghersa, M, Ghersa, C (2008) Dormancy, germination and ageing of Lolium multiflorum seeds following contrasting herbicide selection regimes. Eur J Agron 28:606–613 10.1016/j.eja.2008.01.004CrossRefGoogle Scholar
Heap, IM (2021) International herbicide-resistant weed database. http://www.weedscience.com. Accessed: January 20, 2021Google Scholar
Karn, E, Jasieniuk, M (2017) Genetic diversity and structure of Lolium perenne ssp. multiflorum in California vineyards and orchards indicate potential for spread of herbicide resistance via gene flow. Evol Appl 10:616–629 10.1111/eva.12478CrossRefGoogle ScholarPubMed
Kaya-Altop, E, Haghnama, K, Sarıaslan, D, Phillippo, CJ, Mennan, H, Zandstra, BH (2016) Long-term perennial weed control strategies: economic analyses and yield effect in hazelnut (Corylus avellana). Crop Prot 80:7–14 10.1016/j.cropro.2015.10.022CrossRefGoogle Scholar
Kudsk, P (2002) Optimising herbicide performance. Pages 323–344 in Nayloer REL, ed. Weed Management Handbook. Oxford, UK: Blackwell Publishing10.1002/9780470751039.ch16CrossRefGoogle Scholar
Lenth, R (2019) Emmeans package: Estimated Marginal Means, aka Least-Squares Means. R package version 1.3. 5.1. https://cran.r-project.org/web/packages/emmeans/index.html. Accessed: July 9, 2021Google Scholar
Maity, A, Singh, V, Jessup, R, Bagavathiannan, M (2021) Seed traits correlate with herbicide resistance in Italian ryegrass (Lolium perenne ssp. multiflorum). Pest Manag Sci 77:2756–2765. https://doi.org/10.1002/ps.6304 CrossRefGoogle ScholarPubMed
Meier, U, Bleiholder, H, Brumme, H, Bruns, E, Mehring, B, Proll, T, Wiegand, J (2009) Phenological growth stages of roses (Rosa sp.): codification and description according to the BBCH scale. Ann Appl Biol 154:231–238 10.1111/j.1744-7348.2008.00287.xCrossRefGoogle Scholar
Moore, KJ, Dixon, PM (2015) Analysis of combined experiments revisited. Agron J 107:763–771 10.2134/agronj13.0485CrossRefGoogle Scholar
Ollerenshaw, J, Hodgson, D (1977) The effects of constant and varying heights of cut on the yield of Italian ryegrass (Lolium multiflorum Lam.) and perennial ryegrass (Lolium perenne L.). J Agri Sci 89:425–435 Google Scholar
Olsen, JL, Peachey, RE (2013) Growing hazelnuts in the Pacific Northwest: orchard floor management. Oregon State Univ Extension Service. https://catalog.extension.oregonstate.edu/em9079/html. Accessed: July 9, 2021Google Scholar
Perez-Jones, A, Park, KW, Colquhoun, J, Mallory-Smith, C, Shaner, D (2005) Identification of glyphosate-resistant Italian ryegrass (Lolium multiflorum) in Oregon. Weed Sci 53:775–779 10.1614/WS-04-200R.1CrossRefGoogle Scholar
RStudio Team (2021) RStudio: Integrated Development R. RStudio, Inc. http://www.rstudio.com. Accessed: January 20, 2021Google Scholar
Šidák, Z (1967) Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc 62:626–633 Google Scholar
Stroup, WW (2015) Rethinking the analysis of non-normal data in plant and soil science. Agron J 107:811–827 10.2134/agronj2013.0342CrossRefGoogle Scholar
United States Department of Agriculture [USDA] (2020) National Agricultural Statistics Service (NASS). https://www.nass.usda.gov/. Accessed: November 6, 2020Google Scholar
Whalen, DM, Bish, MD, Young, BG, Conley, SP, Reynolds, DB, Norsworthy, JK, Bradley, KW (2019) Herbicide programs for the termination of grass and broadleaf cover crop species. Weed Technol 34:1–10 10.1017/wet.2019.73CrossRefGoogle Scholar