Hostname: page-component-669899f699-tpknm Total loading time: 0 Render date: 2025-04-25T07:31:53.272Z Has data issue: false hasContentIssue false
  • English
  • Français

Apocynum cannabinum interference in no-till Glycine max

Published online by Cambridge University Press:  20 January 2017

Theodore M. Webster ,
John Cardina  and
Samuel J. Woods
John Cardina
Affiliation:
Department of Horticulture and Crop Science, Ohio Agricultural Research and Development Center, Ohio State University, Wooster, OH 44691
Samuel J. Woods
Affiliation:
Agricultural Technical Institute, Ohio State University, Wooster, OH 44691
Get access Rights & Permissions [Opens in a new window]

Abstract

Field studies were conducted in three site-years to measure no-till Glycine max yield loss in relation to Apocynum cannabinum vegetative shoot density. Apocynum cannabinum densities of 28 to 40 shoots m−2 reduced predicted G. max yield 58 to 75% and 62 to 94% with the rectangular hyperbolic and linear regression models, respectively. Differences between locations were attributed to rainfall and temperatures, with delayed G. max canopy closure and higher yield loss where soil moisture remained high and temperatures were relatively cool. Application of these predictive G. max yield loss equations to field populations of A. cannabinum showed that between 19 and 36% and 20 and 29% G. max yield loss could be expected from within A. cannabinum patches for the rectangular hyperbolic and linear regression models, respectively. The rectangular hyperbolic regression model appeared to describe the relation between G. max yield loss and A. cannabinum density accurately; however, the model appeared to be dominated by the initial linear phase. This may indicate a lack of high levels of intraspecific competition among A. cannabinum shoots. The results of this study indicate that there is a strong linear relation between G. max yield loss and A. cannabinum shoot density. We conclude that the biological basis for the use of the rectangular hyperbolic model for creeping perennial weeds is questionable.

Keywords

Apocynum cannabinum L. APCCA, hemp dogbaneGlycine max (L.) Merr., soybeanCompetitionperennial weedyield lossAPCCA
Type
Research Article
Information
Weed Science , Volume 48 , Issue 6 , December 2000 , pp. 716 - 719
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.)

Article purchase

Temporarily unavailable

References

LITERATURE CITED

Buchanan, G. A. 1974. Weed survey—southern states. South. Weed Sci. Soc. Res. Rep. 27:215–249.Google Scholar
Buhler, D. D. 1995. Influence of tillage systems on weed population dynamics and management in corn and soybean in the central USA. Crop Sci. 35:1247–1258.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
Doll, J. D. 1995. Hemp dogbane growth and control in corn and soybean. North Cent. Weed Sci. Soc. Proc. 50:79–85.Google Scholar
Doll, J. D. 1997. Hemp dogbane (Apocynum cannabinum L.) management in corn and glyphosate resistant soybeans. Weed Sci. Soc. Am. Abstr. 37:90.Google Scholar
Donald, W. W. and Khan, M. 1992. Yield loss assessment for spring wheat (Triticum aestivum) infested with Canada thistle (Cirsium arvense). Weed Sci. 40:590–598.Google Scholar
Dowler, C. C. 1997. Weed survey—southern states—grass crop subsection. Proc. South. Weed Sci. Soc. 50:227–246.Google Scholar
Frazier, J. C. 1944. Nature and rate of development of root system of Apocynum cannabinum . Bot. Gaz. 105:463–470.Google Scholar
Gerhards, R., Wyse-Pester, D. Y., Mortensen, D., and Johnson, G. A. 1997. Characterizing spatial stability of weed populations using interpolated maps. Weed Sci. 45:108–119.Google Scholar
Henn, R. L. 1998. Wildflowers of Ohio. Bloomington and Indianapolis, IN: Indiana University Press, pp. 38–39.Google Scholar
Loux, M. M. and Berry, M. A. 1991. Use of a grower survey for estimating weed problems. Weed Technol. 5:460–466.Google Scholar
Lovett Doust, L. 1981. Population dynamics and local specialization in a clonal perennial (Ranunculus repens) I. The dynamics of ramets in contrasting habitats. J. Ecol. 69:743–755.Google Scholar
Lovett Doust, L. 1989. Infiltration invasion—or dispersal and fate? Funct. Ecol. 3:379–382.Google Scholar
McIntyre, G. I. 1990. The correlative inhibition of bud growth in perennial weeds: a nutritional perspective. Rev. Weed Sci. 5:27–48.Google Scholar
Patterson, M. G., Buchanan, G. A., Street, J. E., and Crowley, R. H. 1980. Yellow nutsedge (Cyperus esculentus) competition with cotton (Gossypium hirsutum). Weed Sci. 28:327–329.Google Scholar
Schultz, M. E. and Burnside, O. C. 1979. Distribution, competition, and phenology of hemp dogbane (Apocynum cannabinum) in Nebraska. Weed Sci. 27:565–570.Google Scholar
Triplett, G. B. and Lytle, G. D. 1972. Control and ecology of weeds in continuous corn grown without tillage. Weed Sci. 20:453–457.Google Scholar
Voss, E. G. 1996. Michigan Flora. Volume 3. Dicots. Cranbook Institute Science Bull. 61 Ann Arbor, MI: Edward Brothers. 622 p.Google Scholar
Webster, T. M. and Cardina, J. 1997. Accuracy of a global positioning system (GPS) for weed mapping. Weed Technol. 11:782–786.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. and Coble, H. D. 1997. Changes in the weed species composition of the southern United States: 1974 to 1995. Weed Technol. 11:308–317.Google Scholar
Yenish, J. P., Durgan, B. R., Miller, D. W., and Wyse, D. L. 1997. Wheat (Triticum aestivum) yield reduction from common milkweed (Asclepias syriaca) competition. Weed Sci. 45:127–131.Google Scholar