Hostname: page-component-7dd5485656-glrdx Total loading time: 0 Render date: 2025-10-31T01:40:03.831Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling Weed Distribution for Improved Postemergence Control Decisions

Published online by Cambridge University Press:  12 June 2017

Lori J. Wiles ,
Gail G. Wilkerson ,
Harvey J. Gold  and
Harold D. Coble
Lori J. Wiles
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Gail G. Wilkerson
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Harvey J. Gold
Affiliation:
Dep. Statistics, North Carolina State Univ., Raleigh, NC 27695
Harold D. Coble
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Get access Rights & Permissions [Opens in a new window]

Abstract

Broadleaf weeds apparently have patchy distributions within a field while POST control decisions are made assuming a regular spatial distribution. As a result, yield loss from weed competition may be overestimated, possibly leading to mistakes in choosing the optimal control treatment. Data on distribution of broadleaf weeds in 14 soybean fields were used in simulation experiments to investigate the potential for improving decision making with information about weed patchiness. The feasibility of modeling weed distribution in individual fields was also examined. Overall, the cost of assuming a regular distribution when making POST decisions was found to be low. Errors that occurred most often involved recommending more intensive control than was actually required, although in a few cases less intensive control was recommended. Error in the yield loss estimated for the uncontrolled population did not indicate the potential for a mistake in decision making for a field. Accurately modeling distribution of weeds within fields may be difficult as a result of correlations between distributions of individual species within a field and variation in distributions between fields.

Keywords

SoybeanGlycine max (L.) MerrSimulationdecision modelsweed managementweed competitionGlycine max

Information

Type
Weed Biology and Ecology
Information
Weed Science , Volume 40 , Issue 4 , December 1992 , pp. 546 - 553
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., Menz, K. M., and Tisdell, C. A. 1987. Weed Control Economics. Academic Press, New York. 177 pp.Google Scholar
2. Auld, B. A. and Tisdell, C. S. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Q. 31:81.Google Scholar
3. Brain, P. and Cousens, R. 1990. The effect of weed distribution on prediction of yield loss. J. Appl. Ecol. 27:735–742.Google Scholar
4. Coble, H. D. 1986. Development and implementation of economic thresholds for soybean. Pages 295–307 in Frisbie, R. E. and Adkisson, P. L., eds. CIPM: Integrated Pest Management in Major Agricultural Systems. Texas A&M Univ. Google Scholar
5. Cousens, R., Peters, N.C.B., and Marshall, C. J. 1984. Models of yield loss—weed density relationships. Page 367–374 in Proc. 7th Int. Symp. on Weed Biol., Ecol., and Systematics, Paris.Google Scholar
6. 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
7. Gerowitt, B. and Heitefuss, R. 1990. Weed economic thresholds in cereals in the Federal Republic of Germany. Crop Prot. 9:323–331.Google Scholar
8. Hughes, G. 1989. Spatial heterogeneity in yield-loss relationships for crop loss assessment. Crop Res. 29(2):87–94.Google Scholar
9. King, R. P., Lybecker, D. W., Schweizer, E. E., and Zimdahl, R. L. 1986. Bioeconomic modeling to simulate weed control strategies for continuous corn (Zea mays). Weed Sci. 34:972–979.Google Scholar
10. Law, A. M. and Kelton, W. D. 1982. Simulation Modeling and Analysis. McGraw-Hill Book Co., New York. 400 pp.Google Scholar
11. Marra, M. C. and Carlson, G. A. 1983. An economic threshold model for weeds in soybeans (Glycine max). Weed Sci. 37:84–92.Google Scholar
12. Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.Google Scholar
13. Mumford, J. D. 1987. Analysis of decision making in pest management. Pages 201–208 in Teng, P. S., ed. Crop Loss Assessment and Pest Management. APS Press, St. Paul. Google Scholar
14. Pannell, D. J. 1990. Responses to risk in weed control decisions under expected profit maximization. J. Agric. Econ. 41:391–403.Google Scholar
15. Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. 1986. Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, Cambridge, England. 702 pp.Google Scholar
16. Roberts, R. K. and Hayes, R. M. 1989. Decision criterion for profitable johnsongrass (Sorghum halepense) management in soybeans. Weed Technol. 3:44–47.Google Scholar
17. Shribbs, J. M., Lybecker, D. W., and Schweizer, E. E. 1990. Bioeconomic weed management models for sugarbeet (Beta vulgaris) production. Weed Sci. 38:436–444.CrossRefGoogle Scholar
18. 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
19. 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
20. 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
21. Tukey, J. W. 1977. Exploratory Data Analysis. Addison-Wesley Publishing Co., Reading, MA. 688 pp.Google Scholar
22. Wiles, L. J. 1991. A decision analytic investigation of the influence of weed spatial distribution on postemergence herbicide decisions for soybeans (Glycine max). Ph.D. Dissertation, North Carolina State Univ., Raleigh, NC. 143 pp.Google Scholar
23. 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.CrossRefGoogle Scholar