Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T06:34:25.240Z Has data issue: false hasContentIssue false
  • English
  • Français

Seedbank Size and Emergence Pattern of Barnyardgrass (Echinochloa crus-galli) in Arkansas

Published online by Cambridge University Press:  20 January 2017

Muthukumar V. Bagavathiannan ,
Jason K. Norsworthy ,
Kenneth L. Smith  and
Nilda Burgos
Muthukumar V. Bagavathiannan*
Affiliation:
Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 West Altheimer Drive, Fayetteville, AR 72704
Jason K. Norsworthy
Affiliation:
Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 West Altheimer Drive, Fayetteville, AR 72704
Kenneth L. Smith
Affiliation:
Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 West Altheimer Drive, Fayetteville, AR 72704
Nilda Burgos
Affiliation:
Department of Crop, Soil, and Environmental Sciences, University of Arkansas, 1366 West Altheimer Drive, Fayetteville, AR 72704
*
Corresponding author's E-mail: muthu@uark.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Barnyardgrass is one of the most problematic weeds in Arkansas, and with the documentation of herbicide-resistant biotypes, there is a need to gain a detailed understanding of its ecology. In particular, knowledge on barnyardgrass seedbank size and emergence pattern is vital. An extensive seedbank survey was carried out in 2008 in 12 counties in eastern Arkansas to determine barnyardgrass seedbank size across the region. There was a great variability in seedbank size with a maximum of 215,000 seeds m−2. Among the fields surveyed, barnyardgrass seedbank was found only in 7% of the cotton fields, while it was 22 and 20%, respectively, for rice and soybean. To examine the emergence pattern of barnyardgrass, experiments were conducted in Rohwer (two sites), Stuttgart (one site), and Fayetteville (one site), Arkansas in 2008 and 2009. In each site, barnyardgrass emergence was quantified from naturally occurring seedbanks. Barnyardgrass exhibited an extended period of emergence with days to 100% emergence ranging from 99 to 165 across sites and years. Nevertheless, effective management may be achieved by targeting the peak emergence periods, which range from mid-April to mid-June in Arkansas. The four-parameter Weibull model provided a better fit to the cumulative emergence data. However, the thermal time (growing degree days, GDDs) or hydrothermal time (HTT) models did not predict barnyardgrass emergence any better than calendar days, perhaps because of the inherent variations associated with natural seedbanks. This study establishes seedbank size and general emergence pattern for barnyardgrass in Arkansas. Additionally, these results will be useful for parameterizing herbicide-resistance simulation models for barnyardgrass.

Keywords

Barnyardgrass, Echinochloa crus-galli (L.) Beauv. ECHCGrice, Oryza sativa Lcotton, Gossypium hirsutum Lsoybean, Glycine max (L.) MerrBarnyardgrass ecologyseedbanktemporal weed emergenceWeibull modelthermal timehydrothermal time
Type
Weed Biology and Ecology
Information
Weed Science , Volume 59 , Issue 3 , September 2011 , pp. 359 - 365
Copyright
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.)

References

Literature Cited

Alignier, A., Meiss, H., Petit, S., and Reboud, X. 2008. Variation of post-dispersal weed seed predation according to weed species, space and time. J. Plant Dis. Prot. 21:221–226.Google Scholar
Allison, P. D. 1999. Logistic regression using the SAS system: theory and application. Cary, NC SAS Institute. 304 p.Google Scholar
Baltazar, A. M. and Smith, R. J. Jr. 1994. Propanil-resistant barnyardgrass (Echinochloa crus-galli) control in rice (Oryza sativa). Weed Technol. 8:576–581.Google Scholar
Bradford, K. J. 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci. 50:248–260.Google Scholar
Brown, R. F. and Mayer, D. G. 1988. Representing cumulative germination. The use of the Weibull function and other empirically derived curves. Ann. Bot. 61:127–138.Google Scholar
Burgos, N. R., Norsworthy, J. K., Scott, R. C., and Smith, K. L. 2008. Red rice (Oryza sativa) status after 5 years of imidazolinone-resistant rice technology in Arkansas. Weed Technol. 22:200–208.Google Scholar
Chauvel, B., Gasquez, J., and Darmency, H. 1989. Changes of weed seed bank parameters according to species, time and environment. Weed Res. 29:213–219.Google Scholar
Dawson, J. H. and Bruns, V. F. 1962. Emergence of barnyardgrass, green foxtail, and yellow foxtail seedlings from various soil depths. Weeds. 10:136–139.Google Scholar
Egley, G. H. 1995. Seed germination in soil: dormancy cycles. Pages 529–543 in Kiegel, J., and Galili, G., eds. Seed Development and Germination. New York Marcel Dekker.Google Scholar
Egley, G. H. and Chandler, J. M. 1983. Weed seeds after 5.5 years in the Stoneville 50-year buried-seed study. Weed Sci. 31:264–270.Google Scholar
Forcella, F. 1993. Seedling emergence model for velvetleaf. Agron. J. 85:929–933.Google Scholar
Foresman, C. and Glasgow, L. 2008. US grower perceptions and experiences with glyphosate-resistant weeds. Pest Manag. Sci. 64:388–391.Google Scholar
Gallandt, E. R. 2006. How can we target the weed seedbank? Weed Sci. 54:588–596.Google Scholar
Givens, W. A., Shaw, D. R., Johnson, W. G., Weller, S. C., Young, B. G., Wilson, R. G., Owen, M. D. K., and Jordan, D. 2009. A grower survey of herbicide use patterns in glyphosate-resistant cropping systems. Weed Technol. 23:156–161.Google Scholar
Grundy, A. C. 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Res. 43:1–11.Google Scholar
Grundy, A. C. and Mead, A. 2000. Modeling weed emergence as a function of meteorological records. Weed Sci. 48:594–603.Google Scholar
Grundy, A. C., Peters, N. C. B., Ramussen, I. A., Hartmann, K. M., Sattin, M., Andersson, L., Mead, A., Murdoch, A. J., and Forcella, F. 2003. Emergence of Chenopodium album and Stellaria media of different origins under different climatic conditions. Weed Res. 43:163–176.Google Scholar
Grundy, A. C., Phelps, K., Reader, R. J., and Burston, S. 2000. Modelling the germination of Stellaria media using the concept of hydrothermal time. New Phytol. 148:433–444.Google Scholar
Heap, I. 2011. The International Survey of Herbicide Resistant Weeds. http://www.weedscience.com. Accessed: March 10, 2011.Google Scholar
Hilgenfeld, K. L., Martin, A. R., Mortensen, D. A., and Mason, S. C. 2004. Weed management in glyphosate resistant soybean: weed emergence patterns in relation to glyphosate treatment timing. Weed Technol. 18:277–283.Google Scholar
Holm, L. G., Pancho, J. V., Herberger, J. P., and Plucknett, D. L. 1977. The World's Worst Weeds. Honolulu, HI University Press of Hawaii. 609 p.Google Scholar
Jha, P. and Norsworthy, J. K. 2009. Soybean canopy and tillage effects on emergence of Palmer amaranth (Amaranthus palmeri) from a natural seed bank. Weed Sci. 57:644–651.Google Scholar
Johnson, D. E., Dingkuhn, M., Jones, M. P., and Mahamane, M. C. 1998. The influence of rice plant type on the effect of weed competition on O. sativa and O. glaberrima . Weed Res. 38:207–216.Google Scholar
Keeley, P. E. and Thullen, R. J. 1991. Growth and interaction of barnyardgrass (Echinochloa crus-galli) with cotton (Gossypium hirsutum). Weed Sci. 39:369–375.Google Scholar
Leblanc, M. 2001. Modeling weed emergence as influenced by environmental conditions in corn in southwestern Quebec. Ph.D Dissertation. Montreal, Canada McGill University. 196 p.Google Scholar
Lovelace, M. L., Talbert, R. E., Skulman, B. W., and Scherder, E. F. 2002. Evaluation of physiological responses in quinclorac-resistant and susceptible barnyardgrass. Proc. South. Weed Sci. Soc. 55:114.Google Scholar
Masin, R., Loddo, D., Benvenuti, S., Zuin, M. C., Macchia, M., and Zanin, G. 2010. Temperature and water potential as parameters for modeling weed emergence in central northern Italy. Weed Sci. 58:216–222.Google Scholar
Maun, M. A. and Barrett, S.C.H. 1986. The biology of Canadian weeds. 77. Echinochloa crus-galli (L.) Beauv. Can. J. Plant Sci. 66:739–759.Google Scholar
McGiffen, M., Spokas, K., Forcella, F., Archer, D., Poppe, S., and Figueroa, R. 2008. Emergence prediction of common groundsel (Senecio vulgaris). Weed Sci. 56:58–65.Google Scholar
Mitch, L. W. 1990. Barnyardgrass. Weed Technol. 4:918–920.Google Scholar
Montgomery, D. C., Runger, G. C., and Hubele, N. F. 2001. Engineering Statistic. 2nd ed. New York J. Wiley. Pp. 448–480.Google Scholar
[NCDC] National Climatic Data Center. 2010. Archive of climate data. http://www.ncdc.noaa.gov/oa/climate/stationlocator.html. Accessed: June 16, 2010.Google Scholar
Neve, P., Diggle, A. J., Smith, F. P., and Powles, S. B. 2003. Simulating evolution of glyphosate resistance in Lolium rigidum II: past, present and future glyphosate use in Australian cropping. Weed Res. 43:418–427.Google Scholar
Neve, P., Norsworthy, J. K., Smith, K. L., and Zelaya, I. A. 2011. Modelling evolution and management of glyphosate resistance in Amaranthus palmeri . Weed Res. In press.Google Scholar
Norsworthy, J. K., Burgos, N. R., Scott, R. C., and Smith, K. L. 2007a. Consultant perspectives on weed management needs in Arkansas rice. Weed Technol. 21:832–839.Google Scholar
Norsworthy, J. K. and Oliveira, M. J. 2007. A model for predicting common cocklebur (Xanthium strumarium) emergence in soybean. Weed Sci. 55:341–345.Google Scholar
Norsworthy, J. K., Smith, K. L., Scott, R. C., and Gbur, E. E. 2007b. Current perspectives on weed management needs in Arkansas cotton. Weed Technol. 21:825–831.Google Scholar
Page, E. R., Gallagher, R. S., Kemanian, A. R., Zhang, H., and Fuerst, E. P. 2006. Modeling site-specific wild oat (Avena fatua) emergence across a variable landscape. Weed Sci. 54:838–846.Google Scholar
Roman, E. S., Murphy, S. D., and Swanton, C. J. 2000. Simulation of Chenopodium album seedling emergence. Weed Sci. 48:217–224.Google Scholar
Schutte, B. J., Regnier, E. E., Harrison, S. K., Schmoll, J. T., Spokas, K., and Forcella, F. 2008. A hydrothermal seedling emergence model for giant ragweed (Ambrosia trifida). Weed Sci. 56:555–560.Google Scholar
Sikkema, P. H., Shropshire, C., Hamill, A. S., Weaver, S., and Cavers, P. B. 2005. Response of barnyardgrass (Echinochloa crus-galli) to glyphosate application timing and rate in glyphosate-resistant corn (Zea mays). Weed Technol. 19:830–837.Google Scholar
Spokas, K. and Forcella, F. 2009. Software tools for weed seed germination modeling. Weed Sci. 57:216–227.Google Scholar
Sprague, C. L., Wax, L. M., Hartzler, R. G., and Harrison, S. K. 2004. Variations in emergence patterns of giant ragweed biotypes from Ohio, Illinois, and Iowa. Abstr. Weed Sci. Soc. Am. 44:60.Google Scholar
Vail, G. D. and Oliver, L. R. 1993. Interference in soybeans (Glycine max). Weed Technol. 7:220–225.Google Scholar
Ver Hoef, J. M. and Boveng, P. L. 2007. Quasi-Poisson and negative binomial regression: how should we model overdispersed count data? Ecol. 88:2766–2772.Google Scholar
Webster, T. M., Cardina, J., and Norquay, H. M. 1998. Tillage and seed depth effects on velvetleaf (Abutilon theophrasti) emergence. Weed Sci. 46:76–82.Google Scholar
Wiese, A. M. and Binning, L. K. 1987. Calculating the threshold temperature of development for weeds. Weed Sci. 35:177–179.Google Scholar
Young, B. G. 2006. Changes in herbicide use patterns and production practices resulting from glyphosate-resistant crops. Weed Technol. 20:301–307.Google Scholar