Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T06:46:13.907Z Has data issue: false hasContentIssue false
  • English
  • Français

Interference and Seed Production by Common Lambsquarters (Chenopodium album) in Soybeans (Glycine max)

Published online by Cambridge University Press:  12 June 2017

S. Kent Harrison
S. Kent Harrison*
Affiliation:
Dep. Agron., Ohio State Univ., Columbus, OH 43210
Get access Rights & Permissions [Opens in a new window]

Abstract

Multiple regression and response surface plots were used to analyze the effects of common lambsquarters population density and interference duration on weed growth and soybean seed yield. Under favorable growing conditions in 1986, weed biomass production at all population densities and interference durations was four to five times that produced in 1987, under less favorable conditions. However, there was no significant treatment by year interaction for soybean seed yield reduction by common lambsquarters, and production of each kg/ha weed biomass resulted in an average soybean yield reduction of 0.26 kg/ha. Utilizing 5% yield loss as an arbitrary threshold level, the regression equation predicted a common lambsquarters density threshold of 2 plants/m of row for 5 weeks of interference after crop emergence and 1 plant/m of row for 7 weeks. Seed production by individual common lambsquarters plants was highly correlated (r=0.92) with weed dry weight, and seed production ranged from 30 000 to 176 000 seeds/plant.

Keywords

Common lambsquarters, Chenopodium album L. ♯ CHEALsoybean, Glycine max L. Merr., ‘Century 84’Competitionweed lossesCHEAL
Type
Weed Biology and Ecology
Information
Weed Science , Volume 38 , Issue 2 , March 1990 , pp. 113 - 118
Copyright
Copyright © 1990 by 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.)

References

Literature Cited

1. Auld, B. A. 1987. Modeling weed spread. Abstr. Weed Sci. Soc. Am. 27:96.Google Scholar
2. Bandeen, J. D., Stephenson, G. R., and Cowett, E. R. 1982. Discovery and distribution of herbicide-resistant weeds in North America. Pages 9–30 in LeBaron, H. M. and Gressel, J., eds. Herbicide Resistance in Plants. John Wiley & Sons, Toronto.Google Scholar
3. Baskin, J. M. and Baskin, C. C. 1985. The annual dormancy cycle in buried weed seeds: a continuum. Bioscience 35:492–498.CrossRefGoogle Scholar
4. Bowerman, B. L., O'Connell, T. T., and Dickey, D. A. 1986. Linear Statistical Models–An Applied Approach. Duxbury Press, Boston. Pages 335–347.Google Scholar
5. Brown, D. M. 1960. Soybean ecology. I. Development-temperature relationships from controlled environment studies. Agron. J. 52: 493–496.CrossRefGoogle Scholar
6. Chu, C., Sweet, R. D., and Ozbun, J. L. 1978. Some germination characteristics in common lambsquarters. Weed Sci. 26:255–258.CrossRefGoogle Scholar
7. Crowley, R. H., Keisling, T. C., and Oliver, L. R. 1981. Applied weed population management in soybeans using weed competition data to define yield response surfaces. Abstr. Weed Sci. Soc. Am. 21:1.Google Scholar
8. Fischer, R. A. and Miles, R. E. 1973. The role of spatial pattern in the competition between crop plants and weeds. A theoretical analysis. Math. Biosci. 18:335–350.CrossRefGoogle Scholar
9. Gomez, K. A. and Gomez, A. A. 1984. Statistical Procedures for Agricultural Research. John Wiley & Sons, New York. Page 422.Google Scholar
10. Holm, L. G., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1977. The World's Worst Weeds, Distribution and Biology. Univ. Press of Hawaii, Honolulu. Pages 84–91.Google Scholar
11. Keisling, T. C., Oliver, L. R., Crowley, R. H., and Baldwin, F. L. 1984. Potential use of response surface analyses for weed management in soybeans (Glycine max). Weed Sci. 32:352–357.CrossRefGoogle Scholar
12. Lewis, J. 1973. Longevity of crop and weed seeds: survival after 20 years in soil. Weed Res. 13:179–191.CrossRefGoogle Scholar
13. Norris, R. F. and Johnson, B. R. 1987. Demographic/population dynamics models: barnyardgrass. Abstr. Weed Sci. Soc. Am. 27:96.Google Scholar
14. Ross, M. A. and Harper, J. L. 1972. Occupation of biological space during seedling establishment. J. Ecol. 66:77–88.CrossRefGoogle Scholar
15. Saini, H. S., Bassi, P. K. and Spencer, M. S. 1986. Use of ethylene and nitrate to break seed dormancy of common lambsquarters (Chenopodium album). Weed Sci. 34:502–506.CrossRefGoogle Scholar
16. Schoney, R. A., Bay, T. F., and Moncreif, J. F. 1981. Use of computer graphics in the development and evaluation of response surfaces. Agron. J. 73:437–442.CrossRefGoogle Scholar
17. Schreiber, M. M. 1987. Weed growth/phenology models: SETSIM and AMSIM. Abstr. Weed Sci. Soc. Am. 27:95.Google Scholar
18. Shepard, D. 1968. A two-dimensional interpolation function for computer mapping of irregularly spaced data. Harvard Papers in Theoretical Geography, No. 15.Google Scholar
19. Steel, R.G.D. and Torrie, H. S. 1980. Principles and Procedures of Statistics. McGraw-Hill Book Co., New York. Pages 258–261.Google Scholar
20. Stoller, E. W., Harrison, S. K., Wax, L. M., Regnier, E. E., and Nafziger, E. D. 1987. Weed interference in soybeans (Glycine max). Rev. Weed Sci. 3:155–181.Google Scholar
21. Wiese, A. M. and Binning, L. K. 1987. Calculating the threshold temperature of development for weeds. Weed Sci. 35:177–179.CrossRefGoogle Scholar
22. Wilkerson, G. G., Coble, H. D., and Modena, S. A. 1987. A postemergence decision model for soybeans. Abstr. Weed Sci. Soc. Am. 27:95.Google Scholar
23. Williams, J. T. 1963. Biological flora of the British Isles. Chenopodium album L. J. Ecol. 51:711–725.CrossRefGoogle Scholar
24. Wilson, R. G., Kerr, E. D., and Nelson, L. A. 1985. Potential for using weed seed content in the soil to predict future weed problems. Weed Sci. 33:171–175.CrossRefGoogle Scholar