Hostname: page-component-848d4c4894-xm8r8 Total loading time: 0 Render date: 2024-06-29T08:51:30.898Z Has data issue: false hasContentIssue false

A Model of Competition for Light Between Peanut (Arachis hypogaea) and Broadleaf Weeds

Published online by Cambridge University Press:  12 June 2017

James C. Barbour
Affiliation:
Dep. Crop and Soil Sciences, Univ. Georgia, Griffin, GA 30223-1797
David C. Bridges
Affiliation:
Dep. Crop and Soil Sciences, Univ. Georgia, Griffin, GA 30223-1797

Abstract

A model of competition for light between peanut and three broadleaf weed species has been developed to run with the PNUTGRO model. The model simulates shading of the peanut canopy by reducing the total daily PAR received by the peanuts in a manner that realistically represents timing and quantity of light capture by the weeds. Data were collected in nursery plots of Florida beggarweed, sicklepod, and wild poinsettia in 1989, 1990, and 1991. These data provided the values for the critical parameters: maximum attenuation of PAR by the weed, time when the weed overtops the peanut canopy, time when maximum attenuation is reached, and the distance of influence of the weed. Florida beggarweed overtopped the peanut canopy 52 DAP, and reduced PAR reaching the peanuts 45% by 73 DAP. Sicklepod overtopped the peanut canopy 42 DAP and reached an attenuation of 41% 79 DAP. Wild poinsettia overtopped the peanut canopy 44 DAP, and had an attenuation value of 39% 85 DAP. The distances of influence were 162, 150, and 192 cm for Florida beggarweed, sicklepod, and wild poinsettia, respectively. Observed yield losses in the distance of influence were 26, 27, and 22%, respectively. The model predictions accounted for at least 90% of the yield losses observed in field studies. The model also proved capable of simulating competitive differences between morphologically and phenologically different populations of Florida beggarweed. Simulation models will play an important role in reducing the expenditure of time and resources required to document yield losses due to weeds in peanuts.

Type
Weed Biology and Ecology
Copyright
Copyright © 1995 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. Acock, B. 1991. Modeling canopy photosynthetic response to carbon dioxide, light interception, temperature, and leaf traits. Pages 4155 in Boote, K. J., and Loomis, R. S., eds. Modeling crop photosynthesis—from biochemistry to canopy. Crop Science Society of America, Madison, WI.Google Scholar
2. Akey, W. C., Jurik, T. W., and Dekker, J. 1990. Competition for light between velvetleaf (Abutilon theophrasti) and soybean (Glycine max). Weed Res. 30:403411.CrossRefGoogle Scholar
3. Aldrich, R. J. 1986. Interference between crops and weeds. Pages 300312 in Waller, G. R., ed. Allelochemicals: Role in agriculture and forestry. ACS Symposium Series 330. American Chemical Society, Washington, D.C. Google Scholar
4. Aldrich, R. J. 1987. Predicting crop yield reductions from weeds. Weed Technol. 1:199206.Google Scholar
5. Begonia, G. B., Aldrich, R. J., and Salisbury, C. D. 1991. Soybean yield and yield components as influenced by canopy heights and duration of competition of velvetleaf (Abutilon theophrasti Medik.). Weed Res. 31:117124.Google Scholar
6. Boote, K. J., Jones, J. W., Hoogenboom, G., Wilkerson, G. G., and Jagtap, S. S. 1989. PNUTGRO v1.02 peanut growth simulation model user's guide. Univ. Florida and IBSNAT, Gainesville, FL.Google Scholar
7. Bridges, D. C., Brecke, B. J., and Barbour, J. C. 1992. Wild poinsettia (Euphorbia heterophylla) interference with peanut (Arachis hypogaea). Weed Sci. 40:3742.Google Scholar
8. Brown, S. M. and Monks, C. D. 1991. Approximate costs of herbicide treatments for agronomic and horticultural crops and noncropland sites—1991. Univ. Georgia Cooperative Extension Service, Athens, GA.Google Scholar
9. Byrd, J. D. and Coble, H. D. 1991. Interference of selected weeds in cotton (Gossypium hirsutum). Weed Technol. 5:263269.CrossRefGoogle Scholar
10. Cardina, J. and Brecke, B. J. 1989. Growth and development of Florida beggarweed (Desmodium tortuosum) selections. Weed Sci. 37:207210.CrossRefGoogle Scholar
11. Cardina, J. and Brecke, B. J. 1991. Florida beggarweed (Desmodium tortuosum) growth and development in peanuts (Arachis hypogaea). Weed Technol. 5:147153.CrossRefGoogle Scholar
12. Cousens, R. 1985. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci., Camb. 105:513521.Google Scholar
13. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.Google Scholar
14. Cousens, R., Peters, N. C. B., and Marshall, C. J. 1988. Models of yield loss weed density relationships. Proc. 7th Intl. Symp. on Weed Biology, Ecology and Systematics, pp. 367374.Google Scholar
15. Fellows, G. M. and Roeth, F. W. 1992. Shattercane (Sorghum bicolor) interference in soybean (Glycine max). Weed Sci. 40:6873.CrossRefGoogle Scholar
16. 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. 18:335350.Google Scholar
17. Holt, J. S. and Orcutt, D. R. 1991. Functional relationships of growth and competitiveness in perennial weeds and cotton (Gossypium hirsutum). Weed Sci. 39:575584.CrossRefGoogle Scholar
18. Kropff, M. J., Weaver, S. E., and Smits, M. A. 1992. Use of ecophysiological models for crop-weed interference: Relations amongst weed density, relative time of weed emergence, relative leaf area, and yield loss. Weed Sci. 40:296301.Google Scholar
19. Landsberg, J. J. 1977. Some useful equations for biological studies. Expl. Agric. 13:273286.Google Scholar
20. Lydolph, P. E. 1985. The climate of the earth. Rowman & Allanheld, Totowa, NJ.Google Scholar
21. Munger, P. H., Chandler, J. M., Cothren, J. T., and Hons, F. M. 1987. Soybean (Glycine max)—velvetleaf (Abutilon theophrasti) interspecific competition. Weed Sci. 35:647653.Google Scholar
22. Pike, D. R., Stoller, E. W., and Wax, L. M. 1990. Modeling soybean growth and canopy apportionment in weed-soybean (Glycine max) competition. Weed Sci. 38:522527.CrossRefGoogle Scholar
23. Radosevich, S. R. 1987. Methods to study interactions among crops and weeds. Weed Technol. 1:190198.Google Scholar
24. Retzinger, E. J. Jr. 1984. Growth and development of sicklepod (Cassia obtusifolia) selections. Weed Sci. 32:608611.CrossRefGoogle Scholar
25. SAS Institute 1988. SAS/STAT user's guide, release 6.03 edition. SAS Institute, Inc., Cary, NC.Google Scholar
26. Sellers, W. D. 1965. Physical Climatology. University of Chicago Press, Chicago.Google Scholar
27. Spitters, C. J. T. 1984. A simple simulation model for crop-weed competition. Proc. 7th Ind. Symposium on Weed Biology, Ecology, and Systematics, pp. 355366.Google Scholar
28. Wiles, L. J., Wilkerson, G. G., and Gold, H. J. 1993. Value of information about weed distribution for improving postemergence control decisions. Crop Prot. 11:547554.CrossRefGoogle Scholar
29. Wilkerson, G. G., Jones, J. W., Coble, H. D., and Gunsolus, J. L. 1990. SOYWEED: A simulation model of soybean and common cocklebur growth and competition. Agron. J. 82:10031010.Google Scholar
30. Wilson, B. J. and Wright, K. J. 1990. Predicting the growth and competitive effects of annual weeds in wheat. Weed Res. 30:201211.Google Scholar