Hostname: page-component-745bb68f8f-lrblm Total loading time: 0 Render date: 2025-01-12T00:52:53.372Z Has data issue: false hasContentIssue false
  • English
  • Français

Spreading Dogbane (Apocynum androsaemifolium) Development in Wild Blueberry Fields

Published online by Cambridge University Press:  20 January 2017

Lin Wu ,
Nathan S. Boyd ,
G. Christopher Cutler  and
A. Randall Olson
Lin Wu
Affiliation:
Department of Environmental Sciences, Nova Scotia Agricultural College, Truro, B2N 4V9, NS, Canada
Nathan S. Boyd*
Affiliation:
Department of Environmental Sciences, Nova Scotia Agricultural College, Truro, B2N 4V9, NS, Canada
G. Christopher Cutler
Affiliation:
Department of Environmental Sciences, Nova Scotia Agricultural College, Truro, B2N 4V9, NS, Canada
A. Randall Olson
Affiliation:
Department of Environmental Sciences, Nova Scotia Agricultural College, Truro, B2N 4V9, NS, Canada
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Spreading dogbane is a common perennial weed in wild blueberry fields. It is highly competitive and spreads rapidly once established. Herbicides can provide effective control of spreading dogbane, but application timing is important. The emergence pattern, ramet height, and flowering time of spreading dogbane were observed in 2008 and 2009, and thermal-based emergence, growth, and development models were developed and used to estimate optimum herbicide application timing. Spreading dogbane emergence and height were described with a three-parameter, sigmoid, nonlinear regression model, whereas flowering was described with a four-parameter, Weibull, nonlinear regression model. Spreading dogbane ramets initiated emergence soon after the biofix date of April 1. Peak emergence tended to occur at 420 growing degree days (GDD). Spreading dogbane reached its peak height by about 558 GDD. The maximum number of flowers per plant was reached at approximately 750 GDD. This study suggested that POST herbicides should be applied between 486 and 535 GDD to maximize efficacy. This time frame occurs after peak emergence and during early floral bud development.

Keywords

Spreading dogbane, Apocynum androsaemifolium Lwild blueberry, Vaccinium angustifolium AitDegree daysplant growth modelsnonlinear regressionweed management
Type
Weed Biology and Ecology
Information
Weed Science , Volume 61 , Issue 3 , September 2013 , pp. 422 - 427
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

Bashtanova, U. B., Beckett, K. P., and Flowers, T. J. 2009. Review: physiological approaches to the improvement of chemical control of Japanese knotweed (Fallopia japonica). Weed Sci. 57:584–592.Google Scholar
Bergweiler, C. J. and Manning, W. J. 1998. Inhibition of flowering and reproductive success in spreading dogbane (Apocynum androsaemifolium) by exposure to ambient ozone. Environ. Pollut. 105:333–339.Google Scholar
Bullied, J. W., Marginet, A. M., and Van Acker, R. C. 2003. Conventional- and conservation- tillage systems influence emergence periodicity of annual weed species in canola. Weed Sci. 51:886–897.Google Scholar
Cardina, J., Herms, C. P., Herms, D. A., and Forcella, F. 2007. Evaluating phenological indicators for predicting giant foxtail (Setaria faberi) emergence. Weed Sci. 55:455–464.Google Scholar
Doll, J. D. 1997. Hemp dogbane (Apocynum cannabinum L.) management in corn and glyphosate-resistant soybean. Weed Sci. Soc. Am. Abstr. 37:90.Google Scholar
Donald, W. W. 2000. A degree-day model of Cirsium arvense shoot emergence from adventitious root buds in spring. Weed Sci. 48:333–341.Google Scholar
Forcella, F., Benech Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crops Res. 67:123–139.Google Scholar
Ghersa, C. M. and Holt, J. S. 1995. Using phenology prediction in weed management: a review. European Weed Research Society. Weed Res. 35:461–470.Google Scholar
Hacault, K. M. and Van Acker, R. C. 2006. Emergence timing and control of dandelion (Taraxacum officinale) in spring wheat. Weed Sci. 54:172–181.Google Scholar
Izquierdo, J., Gonzalez-Andujar, J. L., Bastida, F., Lezaun, J. A., and Sanchez del Arco, M. J. 2009. A thermal time model to predict corn poppy (Papaver rhoeas) emergence in cereal fields. Weed Sci. 57:660–664.Google Scholar
Lapointe, L. and Rochefort, L. 2001. Weed survey of lowbush blueberry fields in Saguenay-Lac-Saint-Jean, Quebec, following eight years of herbicide application. Can. J. Plant Sci. 81:471–478.Google Scholar
Lawson, A. N., Van Acker, R. C., and Friese, L. F. 2006. Emergence timing of volunteer canola in spring wheat fields in Manitoba. Weed Sci. 54:873–882.Google Scholar
Leblanc, M. L., Cloutier, D. C., Stewart, K. A., and Hamel, C. 2003. The use of thermal time to model common lambsquarters (Chenopodium album) seedling emergence in corn. Weed Sci. 51:718–724.Google Scholar
Martinson, K., Durgan, B., Forcella, F., Wiersma, J., Spokas, K., and Archer, D. 2007. An emergence model for wild oat (Avena fatua). Weed Sci. 55:584–591.Google Scholar
McCully, K. V., Sampson, M. G., and Watson, A. K. 1991. Weed survey of Nova Scotia lowbush blueberry (Vaccinium angustifolium) fields. Weed Sci. 39:180–185.Google Scholar
Myers, M. W., Curran, W. S., VanGessel, M. J., Calvin, D. D., Mortensen, D. A., Majek, B. A., Karsten, H. D., and Roth, G. W. 2004. Predicting weed emergence for eight annual species in the northeastern United States. Weed Sci. 52:913–919.Google Scholar
Nowland, J. L. and MacDougall, J. J. 1973. Soils of Cumberland County NS: Nova Scotia Soil Survey. Altona, MB Canada Department of Agriculture, W.W. Friesen and Sons Ltd. Report 17. 154 p.Google Scholar
Ransom, J. K., Oelke, E. A., and Wyse, D. L. 1983. Behavior of 2,4-D in common waterplantain (Alism atriviale). Weed Sci. 31:766–770.Google Scholar
Robles, E. R., Chandler, J. M., Wu, H., Senseman, S. A., and Garcia, J. S. 2003. A model to predict the influence of temperature on rhizome johnsongrass (Sorghum halepense) development. Weed Sci. 51:356–362.Google Scholar
Ross, M. A. and Lembi, C. A. 1999. Applied Weed Science. 2nd ed. Upper Saddle River, NJ Prentice Hall. 452 p.Google Scholar
Sampson, M. G., McCully, K. V., and Sampson, D. L. 1990. Weeds of eastern Canadian blueberry fields. Truro, NS Nova Scotia Agricultural College Bookstore. 229 p.Google Scholar
Satorre, E. H., Ghersa, C. M., and Pataro, A. M. 1985. Prediction of Sorghym halepense (L.) Pers. rhizome sprout emergence in relation to air temperature. Weed Res. 25:103–109.Google Scholar
Shirtliffe, S. J., Entz, M. H., and Van Acker, R. C. 2000. Avena fatua development and seed shatter as related to thermal time. Weed Sci. 48:555–560.CrossRefGoogle Scholar
Webb, K. T., Thompson, R. L., Beke, G. J., and Nowland, J. L. 1991. Soils of Colchester County, Nova Scotia: Nova Scotia Soil Survey. Ottawa, ON Research Branch, Agriculture Canada, Report 19. 201 p.Google Scholar
Webster, T. M. and Cardina, J. 1999. Apocynum cannabinum seed germination and vegetative shoot emergence. Weed Sci. 47:524–528.Google Scholar
Webster, T. M., Cardina, J., and Woods, S. J. 2000. Spatial and temporal expansion patterns of Apocynum cannabinum patches. Weed Sci. 48:728–733.Google Scholar
Woodson, R. E. 1930. Studies in the Apocynaceae, I: a critical study of the Apocynoideae. Ann. Mo. Bot. Gard. 17:1–212.Google Scholar
Wu, L. 2010. Development of a best management plan for spreading dogbane (Apocynum androsaemifolium L.) in wild blueberry fields. . Truro, NS Nova Scotia Agricultural College. 85 p.Google Scholar
Yarborough, D. E. and Bhowmik, P. C. 1989. Effect of hexazinone on weed populations and on lowbush blueberries in Maine. Acta Hortic. 241:344–349.Google Scholar
Yarborough, D. E. and Marra, M. C. 1997. Economic thresholds for weeds in wild blueberry fields. Acta Hortic. 446:293–301.Google Scholar