Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-29T02:07:28.859Z Has data issue: false hasContentIssue false
  • English
  • Français

Pinto bean response to seeding rate and herbicides

Published online by Cambridge University Press:  17 December 2020

Kathrin D. LeQuia ,
Don W. Morishita Open the ORCID record for Don W. Morishita [Opens in a new window] ,
Olga S. Walsh Open the ORCID record for Olga S. Walsh [Opens in a new window]  and
Albert T. Adjesiwor Open the ORCID record for Albert T. Adjesiwor [Opens in a new window]
Kathrin D. LeQuia
Affiliation:
Former Graduate Research Assistant, University of Idaho, Kimberly Research & Extension Center, Kimberly, ID, USA
Don W. Morishita*
Affiliation:
Professor Emeritus, University of Idaho, Kimberly Research & Extension Center, Kimberly, ID, USA
Olga S. Walsh
Affiliation:
Associate Professor, University of Idaho, Parma Research & Extension Center
Albert T. Adjesiwor
Affiliation:
Assistant Professor, University of Idaho, Kimberly Research & Extension Center, Kimberly, ID, USA
*
Author for correspondence: Don Morishita, University of Idaho, Kimberly Research & Extension Center, 3806 N 3600 E, Kimberly, ID83341. Email: don@uidaho.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Field experiments were conducted in 2016 and 2017 to evaluate the effects of seeding rate and herbicide programs on weed control and pinto bean yield under irrigation. The experiments comprised a 5 × 5 factorial randomized complete block design with five replications. The weed control treatments comprised a nontreated control, hand-weeded control, EPTC + ethalfluralin PRE, EPTC + ethalfluralin PRE followed by (fb) dimethenamid-P POST at V1, and EPTC + ethalfluralin PRE fb bentazon/imazamox POST. There were five seeding rates ranging from 247,000 to 494,000 seeds ha–1 planted in 19-cm rows. Weed biomass was reduced by 6 kg ha–1 with every additional 1,000 seeds ha–1. EPTC plus ethalfluralin fb either dimethenamid-P or bentazon plus imazamox reduced weed biomass by at least 29% compared to the nontreated control. There was a significant effect of weed control treatment on pinto bean yield (P = 0.0004). However, there was no significant seeding rate (P = 0.42) or seeding rate–by–weed control interaction effect on pinto bean yield (P = 0.38). Pinto bean yield ranged from 3,080 kg ha–1 in the nontreated control to 4,740 kg ha–1 hand-weeded treatment. Increased seeding rate in narrow rows is a cultural practice that can improve weed control in pinto bean but may not necessarily increase yield.

Keywords

Crop densitycultural weed controlnarrow row spacingintegrated weed managementweed suppressionEPTCethalfluralindimethenamid-Pbentazonimazamoxpinto bean, Phaseolus vulgaris L.
Type
Research Article
Information
Weed Technology , Volume 35 , Issue 4 , August 2021 , pp. 628 - 631
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: Robert Nurse, Agriculture and Agri-Food Canada

References

Adjesiwor, AT, Claypool, DA, Kniss, AR (2020) Dry bean response to preemergence flumioxazin. Weed Technol 34:197–201 10.1017/wet.2019.87CrossRefGoogle Scholar
Blackshaw, RE, Molnar, LJ, Muendel, HH, Saindon, G, Li, XJ (2000) Integration of cropping practices and herbicides improves weed management in dry bean (Phaseolus vulgaris). Weed Technol 14:327–336 10.1614/0890-037X(2000)014[0327:IOCPAH]2.0.CO;2CrossRefGoogle Scholar
Blackshaw, R, Muendel, H, Saindon, G (1999) Canopy architecture, row spacing and plant density effects on yield of dry bean (Phaseolus vulgaris) in the absence and presence of hairy nightshade (Solanum sarrachoides). Can J Plant Sci 79:663–669 10.4141/P99-042CrossRefGoogle Scholar
Brouwer, B, Atterbury, KA, Miles, CA (2015) Commercial dry bean production in western Washington state. Washington State University Extension (EM092E). 20 p. https://research.libraries.wsu.edu:8443/xmlui/handle/2376/5279. Accessed: July 22, 2020Google Scholar
Hekmat, S, Soltani, N, Shropshire, C, Sikkema, PH (2008) Effect of imazamox plus bentazon on dry bean (Phaseolus vulgaris L.). Crop Prot 27:1491–1494 10.1016/j.cropro.2008.07.008CrossRefGoogle Scholar
Hesterman, OB, Kells, JJ, Vitosh, ML (1987) Producing soybeans in narrow rows. Cooperative Extension Service, Michigan State University. Extension Bulletin E-2080. https://archive.lib.msu.edu/DMC/extension_publications/e2080/e2080.pdf. 6 p. Accessed: July 22, 2020Google Scholar
Hothorn, T, Bretz, F, Westfall, P (2008) Simultaneous inference in general parametric models. Biom J 50:346–363 10.1002/bimj.200810425CrossRefGoogle ScholarPubMed
Kuznetsova, A, Brockhoff, PB, Christensen, RH (2017) lmerTest package: tests in linear mixed effects models. J Statistical Software 82:1–26 10.18637/jss.v082.i13CrossRefGoogle Scholar
Lenth, R (2020) emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.4.8. https://CRAN.R-project.org/package=emmeans.Google Scholar
Malik, VS, Swanton, CJ, Michaels, TE (1993) Interaction of white bean (Phaseolus vulgaris L.) cultivars, row spacing, and seeding density with annual weeds. Weed Sci:62–68Google Scholar
Norris, JL, Shaw, DR, Snipes, CE (2002) Influence of row wpacing and residual herbicides on weed control in glufosinate-resistant soybean (Glycine max) 1. Weed Technol 16:319–325 10.1614/0890-037X(2002)016[0319:IORSAR]2.0.CO;2CrossRefGoogle Scholar
Pynenburg, GM, Sikkema, PH, Gillard, CL (2011) Agronomic and economic assessment of intensive pest management of dry bean (Phaseolus vulgaris). Crop Prot 30:340–348 10.1016/j.cropro.2010.12.006CrossRefGoogle Scholar
R Core Team (2020) R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. https://www.R-project.org/ Google Scholar
Soltani, N, Bowley, S, Sikkema, PH (2005) Responses of dry beans to flumioxazin. Weed Technol 19:351–358 10.1614/WT-04-146R1CrossRefGoogle Scholar
Soltani, N, Dille, JA, Gulden, RH, Sprague, CL, Zollinger, RK, Morishita, DW, Lawrence, NC, Sbatella, GM, Kniss, AR, Jha, P (2018a) Potential yield loss in dry bean crops due to weeds in the United States and Canada. Weed Technol 32:342–346 10.1017/wet.2017.116CrossRefGoogle Scholar
Soltani, N, Shropshire, C, Sildema, PH (2018b) Response of dry bean to Group 15 herbicides applied preplant incorporated. Can J Plant Sci 98:1168–1175 10.1139/cjps-2018-0020CrossRefGoogle Scholar
Taziar, AN, Soltani, N, Shropshire, C, Robinson, DE, Long, M, Gillard, CL, Sikkema, PH (2017) Sulfentrazone plus a low rate of halosulfuron for weed control in white bean (Phaseolus vulgaris L.). Agric Sci 8:227 Google Scholar
Vangessel, MJ, Schweizer, EE, Wilson, RG, Wiles, LJ, Westra, P (1998) Impact of timing and frequency of in-row cultivation for weed control in dry bean (Phaseolus vulgaris). Weed Technol: 548–55310.1017/S0890037X00044298CrossRefGoogle Scholar
Waters, BM, Morishita, D (2001) Integrated weed management in dry edible beans. A Pacific Northwest Extension Publication (PNW 545). 8 p. https://www.extension.uidaho.edu/publishing/pdf/PNW/PNW0545.pdf Google Scholar
Wickham, H, Averick, M, Bryan, J, Chang, W, McGowan, LDA, François, R, Grolemund, G, Hayes, A, Henry, L, Hester, J (2019) Welcome to the Tidyverse. Journal of Open Source Software 4:1686 10.21105/joss.01686CrossRefGoogle Scholar
Wilson, RG (2005) Response of dry bean and weeds to fomesafen and fomesafen tank mixtures. Weed Technol 19:201–206 10.1614/WT-04-166RCrossRefGoogle Scholar
Wilson, RG, Sbatella, GM (2014) Integrating irrigation, tillage, and herbicides for weed control in dry bean. Weed Technol 28:479–485 10.1614/WT-D-13-00173.1CrossRefGoogle Scholar