Hostname: page-component-77c89778f8-rkxrd Total loading time: 0 Render date: 2024-07-22T15:33:22.415Z Has data issue: false hasContentIssue false

Interference of large crabgrass (Digitaria sanguinalis) with snap beans

Published online by Cambridge University Press:  20 January 2017

Joseph N. Aguyoh
University of Illinois, Urbana, IL 61801


Field experiments were conducted to determine the effect of large crabgrass densities of 0.5 to 8 plants m−1 of row and emergence time on snap bean yield. Large crabgrass was planted either along with snap beans (early) or when the first trifoliate leaf of snap beans was opening (late). Observed yield loss ranged from 46 to 50%, and predicted yield loss ranged from 53 ± 29.3% to 63 ± 18.3%. Relative leaf area was correlated to snap bean yield (r2 = 0.88 to 0.92). The relative damage coefficient (q), an indication of the competitiveness of large crabgrass with snap bean, was 1.65 ± 1.03 and 1.26 ± 0.72 for early- and late-emerging large crabgrass, respectively. Early-emerging large crabgrass reduced snap bean biomass 10 to 28% and snap bean pod numbers 44 to 60%, depending on the density. Because of intraspecies competition, leaf area index and number of seed for large crabgrass were reduced with increasing density. Emergence of > 2 plants m−1 of large crabgrass with snap beans should be controlled to avoid significant yield loss.

Research Article
Copyright © 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.)


Literature Cited

Bananno, R. A. and Mack, H. J. 1983. Yield quality and pod quality of snap beans grown under differential irrigation. J. Am. Soc. Hortic. Sci. 108:832836.Google Scholar
Berti, A. and Sattin, M. 1996. Effect of weed position on yield loss in soybean and a comparison between relative weed cover and other regression models. Weed Res. 36:249258.CrossRefGoogle Scholar
Bridges, D. C. and Baumann, P. A. 1992. Weeds causing losses in the United States. Pages 75147 In Bridges, D. C., ed. Crop Losses Due to Weeds in Canada and the United States. Champaign, IL: Weed Science Society of America.Google Scholar
Buchanan, G. A. and Burns, E. R. 1971. Weed competition in cotton. II. Cocklebur and large crabgrass. Weed Sci. 19:580582.Google Scholar
Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci. 43:375380.Google Scholar
Cousens, R. 1985a. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.CrossRefGoogle Scholar
Cousens, R. 1985b. An empirical model relating crop yield to weed and crop density and statistical comparisons with other models. J. Agric. Sci. 105:513521.Google Scholar
Cousens, R. 1988. Misinterpretation of results in weed research through inappropriate use of statistics. Weed Res. 28:281289.CrossRefGoogle Scholar
Evanylo, G. K. and Zehnder, G. W. 1989. Common ragweed interference in snap bean at various soil potassium levels. Appl. Agric. Res. 4:101105.Google Scholar
Fu, R. and Ashley, R. A. 1999. Modeling interference of redroot pigweed, large crabgrass, and smallflower galinsoga in pepper. Proc. Northeast. Weed Sci. Soc. 53:7478.Google Scholar
Hartzler, G. H. and Foy, C. L. 1983. Efficacy of three postemergence grass herbicides for soybeans. Weed Sci. 31:557561.Google Scholar
Holm, L., Pancho, J. V., Herberger, J. P., and Plucknett, D. L. 1979. Geographical Atlas of World Weeds. New York: J. Wiley. pp. 9297.Google Scholar
Holm, L., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1991. The World's Worst Weeds: Distribution and Biology. Malabar, FL: Krieger. pp. 8125.Google Scholar
Johnson, W. C. III and Coble, H. D. 1986. Crop rotation and herbicide effects on the population dynamics of two annual grasses. Weed Sci. 34:452456.Google Scholar
King, C. A. and Oliver, L. R. 1994. A model for predicting large crabgrass (Digitaria sanguinalis) emergence as influenced by temperature and water potential. Weed Sci. 42:561567.CrossRefGoogle Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568573.Google Scholar
Kropff, M. J. and Spitters, C.J.T. 1991. A simple model of crop yield loss from early observations on relative leaf area of the weeds. Weed Res. 31:97105.Google Scholar
Lugo, M. and Talbert, R. E. 1989. Large crabgrass and smooth pigweed interference in snap bean. Proc. Annu. Meet. Ark. Hortic. Soc. 110:132.Google Scholar
Monks, D. W. and Schultheis, J. R. 1998. Critical weed-free period for large crabgrass (Digitaria sanguinalis) in transplanted watermelon (Citrullus lanatus). Weed Sci. 46:530532.CrossRefGoogle Scholar
Ngouajio, M., Lemieux, C., and Leroux, G. D. 1999. Prediction of corn (Zea mays) yield loss from early observations of the relative leaf area and the relative leaf cover of weeds. Weed Sci. 47:297304.Google Scholar
[SAS] Statistical Analysis Systems. 1995. SAS User's Guide. Cary, NC: Statistical Analysis System Institute.Google Scholar
Simpson, G. M. 1990. Seed Dormancy in Grasses. Cambridge: Cambridge University Press. p. 34.Google Scholar
Smith, B. S., Murray, D. S., Green, J. D., Wanyahaya, W. M., and Weeks, D. L. 1990. Interference of three annual grasses with grain sorghum (Sorghum bicolor). Weed Technol. 4:245249.Google Scholar
Wielderholt, R. J. and Stollenberg, D. E. 1995. Cross-resistance of similar large crabgrass (Digitaria sanguinalis) accessions to aryloxyphenoxypropionate and cyclohexanedione herbicides. Weed Technol. 9:518524.Google Scholar
Wielderholt, R. J. and Stollenberg, D. E. 1996. Similar fitness between large crabgrass (Digitaria sanguinalis) accessions resistant or susceptible to acetyl-coenzyme A carboxylase inhibitors. Weed Technol. 10:4149.Google Scholar