Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-23T21:34:51.710Z Has data issue: false hasContentIssue false
  • English
  • Français

Model of rice (Oryza sativa) yield reduction as a function of weed interference

Published online by Cambridge University Press:  12 June 2017

Karl W. VanDevender ,
Thomas A. Costello  and
Roy J. Smith Jr.
Karl W. VanDevender
Affiliation:
Cooperative Extension Service, University of Arkansas, Little Rock, AR 72202
Roy J. Smith Jr.
Affiliation:
Agricultural Research Service, USDA, Stuttgart, AR 72160
Get access Rights & Permissions [Opens in a new window]

Abstract

Economic assessment of weed management strategies in rice is dependent upon a quantitative estimate of the yield impact of a given weed population. To assist rice producers in making such assessments, a mathematical model was developed to predict rice yield reduction as a function of weed density and duration of interference. The nonlinear empirical model was a unique 3-dimensional adaptation of the Richards equation with 4 parameters. Using published data, individual parameter values were fitted for each of 6 weed species interfering with either conventional or semi-dwarf statured rice cultivars. The functional form of the equation produced surfaces that were qualitatively consistent with available data and experience regarding rice-weed biology. Hence, predictions from the model should be useful and reliable in assessing the economic impact of weeds and in determining the feasibility of alternative weed control treatments for various field scenarios.

Keywords

RiceOryza sativa L.Weed densityduration of interferenceweed control economicsmathematical modelresponse surfaceRichards equation
Type
Weed Biology and Ecology
Information
Weed Science , Volume 45 , Issue 2 , April 1997 , pp. 218 - 224
Copyright
Copyright © 1997 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.)

Footnotes

Published with the approval of the Director, Arkansas Agricultural Experiment Station, Manuscript No. 95055.

References

Literature Cited

Carey, V.F. III. 1990. Barnyardgrass (Echinochloa crus-galli) and bearded sprangletop (Leptochloa fascicularis) interference and control in rice (Oryza sativa). M.S. thesis. University of Arkansas, Fayetteville, AR. 108 p.Google Scholar
Cousens, R., Peters, N.C.B., and Marshall, C. J. 1984. Models of yield loss—weed density relationships. Proc. 7th Int. Symp. on Weed Biol., Ecol. and Systematics:367–374.Google Scholar
Diarra, A., Smith, R. J. Jr., and Talbert, R. E. 1985. Interference of red rice (Oryza sativa) with rice (Oryza sativa). Weed Sci. 33: 644–649.CrossRefGoogle Scholar
France, J. and Thornley, J.H.M. 1984. Mathematical Models in Agriculture. London: Butterworth. pp. 85–87.Google Scholar
Keisling, T. C., Oliver, L. R., Crowley, R. H., and Baldwin, F. L. 1983. Modeling of weed management in applied determinant soybean production. St. Joseph, MI: ASAE Microfiche 83-4040, ASAE.Google Scholar
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: 552–557.Google Scholar
Kwon, S. L., Smith, R. J. Jr., and Talbert, R. E. 1991a. Interference of red rice (Oryza sativa) densities in rice (O. sativa). Weed Sci. 39: 169–174.Google Scholar
Kwon, S. L., Smith, R. J. Jr., and Talbert, R. E. 1991b. Interference durations of red rice (Oryza sativa) in rice (O. sativa). Weed Sci. 39: 363–368.Google Scholar
Lloyd, F. T. and Harms, W. R. 1986. An individual stand growth model for mean plant size based on the rule of self-thinning. Ann. Bot. 57: 681–688.CrossRefGoogle Scholar
McFadden, G. and Oliver, C. D. 1988. Three dimensional forest growth model relating tree size, tree number, and stand age; relation to previous growth models and to self-thinning. For. Sci. 34: 662–676.Google Scholar
McGregor, J. T. Jr., Smith, R. J. Jr., and Talbert, R. E. 1988a. Interspecific and intraspecific interference of broadleaf signalgrass (Brachiaria platyphylla) in rice (Oryza sativa). Weed Sci. 36: 589–593.CrossRefGoogle Scholar
McGregor, J. T. Jr., Smith, R. J. Jr., and Talbert, R. E. 1988b. Broadleaf signalgrass (Brachiaria platyphylla) duration of interference in rice (Oryza sativa). Weed Sci. 36: 747–750.Google Scholar
Richards, F. J. 1959. A flexible growth function for empirical use. J. Exp. Bot. 10: 290–300.Google Scholar
Slaton, N. A., Helms, R. S., and Stuart, C. A. Jr. 1992. Results of the Rice Research Verification Trials, 1991. Little Rock, AR: University of Arkansas Cooperative Extension Service Publication AG90-3–92. 18 p.Google Scholar
Smith, R. J. Jr. 1968. Weed competition in rice. Weed Sci. 16: 252–255.Google Scholar
Smith, R. J. Jr. 1983. Competition of bearded sprangletop (Leptochloa fascicularis) with rice (Oryza sativa). Weed Sci. 31: 120–123.CrossRefGoogle Scholar
Smith, R. J. Jr. 1988. Weed thresholds in southern U. S. rice, Oryza sativa. Weed Technol. 2: 232–241.Google Scholar
Stauber, L. G., Smith, R. J. Jr., and Talbert, R. E. 1991. Density and spatial interference of barnyardgrass (Echinochloa crus-galli) with rice (Oryza sativa). Weed Sci. 39: 163–168.CrossRefGoogle Scholar
VanDevender, K. W. 1992. A weed management support system for Arkansas rice production based on expert system recommendations and weed control economics. Ph.D. dissertation. University of Arkansas, Fayetteville, AR. 530 p.Google Scholar
VanDevender, K. W., Costello, T. A., Ferguson, J. A., Huey, B. A., Slaton, N. A., Smith, R. J. Jr., and Helms, R. S. 1994. Weed management support system for rice producers. Appl. Engr. in Agric. 10: 573–578.Google Scholar
Von Bertanlanffy, L. 1957. Quantitative laws for metabolism and growth. Q. Rev. Biol. 32: 217–231.Google Scholar
Zimdahl, R. L. 1980. Weed-Crop Competition: A Review. International Plant Protection Center. Corvallis, OR: Oregon State University. 196 p.Google Scholar