Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-15T21:42:27.562Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatial Distribution of Broadleaf Weeds in North Carolina Soybean (Glycine max) Fields

Published online by Cambridge University Press:  12 June 2017

Lori J. Wiles,
Glenn W. Oliver,
Alan C. York,
Harvey J. Gold and
Gail G. Wilkerson
Lori J. Wiles
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Glenn W. Oliver
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Alan C. York
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Harvey J. Gold
Affiliation:
Dep. Statistics, North Carolina State Univ., Raleigh, NC 27695
Gail G. Wilkerson
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Get accessRights & Permissions [Opens in a new window]

Abstract

Spatial distribution of broadleaf weeds within 14 North Carolina soybean fields was characterized by fitting negative binomial distributions to frequency distributions of weed counts in each field. In most cases, the data could be represented by a negative binomial distribution. Estimated values of the parameter K of this distribution were small, often less than one, indicating a high degree of patchiness. The data also indicated that the population as a whole was patchy. Counts of individual species were positively correlated with each other in some fields and total weed count could be represented by a negative binomial for 12 of the 14 fields.

Keywords

SoybeanGlycine max (L.) MerrWeed patchinessweed spatial distributionnegative binomialspatial dispersion

Information

Type
Weed Biology and Ecology
Information
Weed Science , Volume 40 , Issue 4 , December 1992, pp. 554 - 557
Copyright
Copyright © 1992 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.)

Article purchase

Temporarily unavailable

References

Literature Cited

1. Auld, B. A. and Tisdell, C. S. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Quart. 31:81.Google Scholar
2. Bliss, C. I. and Owen, A.R.C. 1958. Negative binomial distributions with a common k. Biometrika 45:37–58.CrossRefGoogle Scholar
3. Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735–742.CrossRefGoogle Scholar
4. Cousens, R., Peters, N.C.B., and Marshall, C. J. 1984. Models of yield loss—weed density relationships. Page 367–374 in ed., Proc. 7th Int. Symp. on Weed Biology, Ecology and Systematics, Paris.Google Scholar
5. Dent, J. B., Fawcett, R. H., and Thornton, P. K. 1989. Economics of crop protection in Europe with reference to weed control. Brighton Crop Prot. Conf.—Weeds. 3:917–927.Google Scholar
6. Fehr, W. R. and Caviness, C. E. 1980. Stages of soybean development. Special Rep. 80, Coop, Ext. Serv., Iowa State Univ., Ames, IA 50011.Google Scholar
7. Gates, C. E. and Ethridge, F. G. 1972. A generalized set of discrete frequency distributions with FORTRAN program. Math. Geology 4(1):1–24.Google Scholar
8. Hollander, M. and Wolfe, D. A. 1973. Nonparametric statistical methods. John Wiley and Sons, New York. 503 pp.Google Scholar
9. Hughes, G. 1989. Spatial heterogeneity in yield-loss relationships for crop loss assessment. Crop Res. 29(2):87–94.Google Scholar
10. Ives, P. M. and Moon, R. D. 1987. Sampling theory and protocol for insects. Pages 49–74 in Teng, P. S., ed. Crop Loss Assessment and Pest Management, APS Press, St. Paul. Google Scholar
11. Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.Google Scholar
12. Mortensen, D. A., Von Bargen, K., Meyer, G. E., and Shropshire, G. J. 1991. Plant imaging sensors for intermittent sprayer control. Weed Sci. Soc. Am. Abstr. No. 239. Page 80.Google Scholar
13. Pannell, D. J. 1990. Responses to risk in weed control decisions under expected profit maximization. J. Agric. Econ. 41:391–403.Google Scholar
14. Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. 1986. Numerical Recipes: The Art of Scientific Computing. Cambridge Univ. Press, Cambridge, England. 702 pp.Google Scholar
15. Richter, O. and Sondgerath, D. 1990. Parameter Estimation in Ecology. VCH Publishers, Inc., New York. 218 pp.Google Scholar
16. Southwood, T.R.E. 1976. Ecological Methods with Particular Reference to the Study of Insect Populations. 2nd ed. John Wiley and Sons, New York. 524 pp.Google Scholar
17. Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9:337–342.Google Scholar
18. Van Groenendael, J. M. 1988. Patchy distribution of weeds and some implications for modelling population dynamics: A short literature review. Weed Res. 28:437–441.Google Scholar
19. Walker, P. T. 1987. Measurements of insect populations and injury. Pages 19–29 in Teng, P. S., ed. Crop Loss Assessment and Pest Management. APS Press, St. Paul. Google Scholar
20. Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: Decision model for postemergence weed control in soybeans. Agron. J. 83:413–417.Google Scholar