Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-25T04:07:13.941Z Has data issue: false hasContentIssue false
  • English
  • Français

A model to predict the influence of temperature on rhizome johnsongrass (Sorghum halepense) development

Published online by Cambridge University Press:  20 January 2017

Enrique Rosales-Robles ,
James M. Chandler ,
Hsin-i Wu ,
Scott A. Senseman  and
Jaime Salinas-García
Enrique Rosales-Robles
Affiliation:
Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474
Hsin-i Wu
Affiliation:
Center for Biosystems Modeling, Department of Industrial Engineering, Texas A&M University, College Station, TX 77843-3131
Scott A. Senseman
Affiliation:
Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474
Jaime Salinas-García
Affiliation:
Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474
Get access Rights & Permissions [Opens in a new window]

Abstract

Research was conducted to formulate a temperature-dependent population-level model to predict rhizome johnsongrass development to the four-leaf stage. A nonlinear poikilotherm rate equation was used to describe development rates as a function of temperature. Development rate was highest at 36 C and declined at higher temperatures. A temperature-independent Weibull function adequately distributed development times for the population. Coupling the poikilotherm rate equation and the Weibull distribution function yielded a model suitable for characterizing rhizome johnsongrass development to the four-leaf growth stage. The model was tested and validated against independent data sets. Model predictions of 80% of rhizome johnsongrass population at the four-leaf stage were used as the central point of a 4-d application window for using reduced rates of herbicides in johnsongrass management programs. This application window included an average interval of 85 to 99% of johnsongrass population at the desired growth stage in field validation experiments.

Keywords

Johnsongrass, Sorghum halepense (L.) Pers. SORHAMechanistic phenology modelpoikilotherm rate equationWeibull functiontemperature
Type
Research Article
Information
Weed Science , Volume 51 , Issue 3 , June 2003 , pp. 356 - 362
Copyright
Copyright © 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

Benech Arnold, R. L., Ghersa, C. M., Sanchez, R. A., and Insausti, P. 1990. Temperature effects on dormancy release and germination rate in Sorghum halepense (L.) Pers. seeds: a quantitative analysis. Weed Res. 30:81–89.Google Scholar
Black, C. C., Chen, T. M., and Brown, R. H. 1969. Biochemical basis for plant competition. Weed Sci. 17:338–343.Google Scholar
Bridges, D. C. 1987. Techniques for modeling phenological development of weed populations. Ph.D. dissertation. Texas A&M University, College Station, TX. 148 p.Google Scholar
Bridges, D. C. and Chandler, J. M. 1987. Influence of johnsongrass (Sorghum halepense) density and period of competition on cotton yield. Weed Sci. 35:63–67.Google Scholar
Bridges, D. C. and Chandler, J. M. 1989. A population level temperature-dependent model of seedling johnsongrass (Sorghum halepense) flowering. Weed Sci. 37:471–477.Google Scholar
Burt, G. W. and Wedderspoon, J. M. 1971. Growth of johnsongrass selections under different temperatures and dark periods. Weed Sci. 19:419–423.CrossRefGoogle Scholar
Elmore, C. D. and Paul, R. N. 1983. Composite list of C4 weeds. Weed Sci. 31:686–692.Google Scholar
Ghersa, C. M. and Holt, J. S. 1995. Using phenology prediction in weed management: a review. Weed Res. 35:461–470.CrossRefGoogle Scholar
Ghersa, C. M. and Martinez-Ghersa, M. A. 1991. A field method for predicting yield losses in maize caused by johnsongrass (Sorghum halepense). Weed Technol. 5:279–285.CrossRefGoogle Scholar
Ghersa, C. M., Satorre, E. H., Van Esso, M. L., Pataro, A. M., and Elizagaray, R. 1990. The use of thermal calendar models to improve the efficiency of herbicide applications in Sorghum halepense (L.) Pers. Weed Res. 30:153–160.Google Scholar
Ghosheh, H. Z., Holshouser, D. L., and Chandler, J. M. 1996. The critical period of johnsongrass (Sorghum halepense) control in field corn (Zea mays). Weed Sci. 44:944–947.Google Scholar
Holm, L. G., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1977. The World's Worst Weeds. Distribution and Biology. Honolulu: University Press of Hawaii. pp. 54–61.Google Scholar
Holshouser, D. L. and Chandler, J. M. 1996. Predicting flowering of rhizome johnsongrass (Sorghum halepense) populations using a temperature-dependent model. Weed Sci. 44:266–272.Google Scholar
Holshouser, D. L., Chandler, J. M., and Wu, H. 1996. Temperature-dependent model for non-dormant seed germination and rhizome bud break of johnsongrass (Sorghum halepense). Weed Sci. 44:257–265.Google Scholar
Horowitz, M. 1972. Seasonal development of established johnsongrass. Weed Sci. 20:392–395.Google Scholar
Jordan, D. L., Vidrine, P. R., Griffin, J. L., and Reynolds, D. B. 1996. Influence of adjuvants on efficacy of clethodim. Weed Technol. 10:738–743.Google Scholar
McWhorter, C. G. 1974. Water-soluble carbohydrates in johnsongrass. Weed Sci. 22:159–163.Google Scholar
McWhorter, C. G. 1989. History, biology, and control of johnsongrass. Rev. Weed Sci. 4:85–121.Google Scholar
McWhorter, C. G. and Jordan, T. N. 1976. Comparative morphological development of six johnsongrass ecotypes. Weed Sci. 24:270–275.Google Scholar
Obrigawitch, T. T., Kenyon, W. H., and Kuratle, H. 1990. Effect of application timing on rhizome johnsongrass (Sorghum halepense) control with DPX-V9360. Weed Sci. 38:45–49.Google Scholar
Orwick, P. L., Schreiber, M. M., and Holt, D. A. 1978. Simulation of foxtail (Setaria viridis var. robusta-alba, Setaria viridis var. robusta-purpurea) growth: the development of SETSIM. Weed Sci. 26:691–699.Google Scholar
Rosales-Robles, E., Chandler, J. M., Senseman, S. A., and Prostko, E. P. 1999. Influence of growth stage and herbicide rate on postemergence johnsongrass (Sorghum halepense) control. Weed Technol. 13:525–529.Google Scholar
[SAS] Statistical Analysis Systems. 1989. SAS/STAT User's Guide. Version 6, 4th ed. Volume 2. Cary, NC: Statistical Analysis System Institute. 846 p.Google Scholar
Satorre, E. H., Ghersa, C. M., and Pataro, A. M. 1985. Prediction of Sorghum halepense (L.) Pers. rhizome sprout emergence in relation to air temperature. Weed Res. 25:103–109.Google Scholar
Schoolfield, R. M., Sharpe, P.J.H., and Magnuson, C. E. 1981. Non-linear regression of biological temperature dependent rate models based upon absolute reaction rate theory models. J. Theor. Biol. 88:719–731.Google Scholar
Schreiber, M. M. 1982. Modeling the biology of weeds for integrated weed management. Weed Sci. 30 (Suppl.): 13–16.Google Scholar
Sharpe, P.J.H. and DeMichele, D. W. 1977. Reaction kinetics of poikilotherm development. J. Theor. Biol. 64:649–670.Google Scholar
Vitta, J. I., Satorre, E. H., and Leguizamon, E. S. 1994. Using canopy attributes to evaluate competition between Sorghum halepense and soybean. Weed Res. 34:89–97.Google Scholar
Wagner, T. L., Wu, H., Feldman, R. M., Sharpe, P.J.H., and Coulson, R. N. 1985. Multiple-cohort approach for simulating development of insect populations under variable temperatures. Ann. Entomol. Soc. Am. 78:691–704.Google Scholar
Wagner, T. L., Wu, H., Sharpe, P.J.H., and Coulson, R. N. 1984a. Modeling distributions of insect development time: a literature review and application of the Weibull function. Ann. Entomol. Soc. Am. 77:475–487.Google Scholar
Wagner, T. L., Wu, H., Sharpe, P.J.H., Schoolfield, R. M., and Coulson, R. N. 1984b. Modeling insect development rates: a literature review and application of a biophysical model. Ann. Entomol. Soc. Am. 77:208–225.Google Scholar
Wang, J. Y. 1960. A critique of the heat unit approach to plant response studies. Ecology. 41:785–790.Google Scholar