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Predicting Flowering of Rhizome Johnsongrass (Sorghum halepense) Populations Using a Temperature-dependent Model

Published online by Cambridge University Press:  12 June 2017

David L. Holshouser
Affiliation:
Dep. Agron., Northeast Res. & Ext. Cen., University of Nebraska, Concord, NE 68728
James M. Chandler
Affiliation:
Dep. Soil & Crop Sci., Texas Agric. Exp. Stn., College Station, TX 77843

Abstract

Research was conducted to formulate a temperature-dependent population-level model for rhizome johnsongrass flowering. A nonlinear poikilotherm rate equation was used to describe development as a function of temperature and a temperature-independent Weibull function was used to distribute development times for the population. Johnsongrass flowering data were collected under constant temperature conditions to parameterize the poikilotherm rate equation and Weibull function. Coupling the poikilotherm rate equation with the Weibull function resulted in a population level temperature-dependent model. The model was validated against independent field data sets. The model accurately predicted rhizome johnsongrass flowering from plants emerging in the spring. The model performed poorly for plants emerging in summer. Adjustments to the high-temperature inhibition parameter of the poikilotherm rate equation improved model performance in the summer without affecting spring predictions.

Type
Weed Biology and Ecology
Copyright
Copyright © 1996 by the Weed Science Society of America 

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References

Literature Cited

1. Baxendale, F. P. and Teetes, G. L. 1983. Factors influencing adult emergence from diapausing sorghum midge, Contarinia sorghicola (Diptera: Cecidomyiidae). Environ. Entomol. 12: 10641067.Google Scholar
2. Baxendale, F. P. and Teetes, G. L. 1983. Thermal requirements for emergence of overwintered sorghum mid mge (Diptera: Cecidomyiidae). Environ. Entomol. 12: 10781082.Google Scholar
3. Baxendale, F. P., Teetes, G. L., and Sharpe, P.J.H. 1984. Temperature-dependent model for sorghum midge (Diptera: Cecidomyiidae) spring emergence. Environ. Entomol. 13: 15661571.Google Scholar
4. Baxendale, F. P., Teetes, G. L., Sharpe, P.J.H., and Wu, H. 1984. Temperature-dependent model for development of nondiapausing sorghum midges (Diptera: Cecidomyiidae). Environ. Entomol. 13: 15721576.Google Scholar
5. Benech Arnold, R. L., Ghersa, C. M., Sanchez, R. A., and Insausti, P. 1990. A mathematical model to predict Sorghum halepense (L.) Pers. seedling emergence in relation to soil temperature. Weed Res. 30: 9199.CrossRefGoogle Scholar
6. 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: 8189.Google Scholar
7. Bridges, D. C. 1987. Techniques for modeling phenological development of weed populations. ; Texas A&M University, College Station, Texas.Google Scholar
8. Bridges, D. C. and Chandler, J. M. 1989. A population level temperature-dependent model of seedling johnsongrass (Sorghum halepense) flowering. Weed Sci. 37: 471477.Google Scholar
9. Bridges, D. C., Wu, H., Sharpe, P.J.H., and Chandler, J. M. 1989. Modeling distributions of crop and weed seed germination time. Weed Sci. 37: 724729.Google Scholar
10. Brown, R. F. and Mayer, D. G. 1988. Representing cumulative germination. 2. The use of the Weibull function and other empirically derive curves. Ann. Bot. 61: 127138.Google Scholar
11. Burt, G. W. and Wedderspoon, I. M. 1971. Growth of johnsongrass selections under different temperatures and dark periods. Weed Sci. 19: 419423.Google Scholar
12. Gagne, J. A., Wagner, T. L., Sharpe, P.J.H., Coulson, R. N., and Fargo, W. S. 1982. Reemergence of Dendroctonus frontalis (Coleoptera: Saolytidae) at constant temperatures. Environ. Entomol. 11: 12161222.Google Scholar
13. Ghersa, C. M., Satorre, E. H., Van Esso, M. L., Pataro, A., and Elizagaray, R. 1990. The use of thermal calendar models to improve the effeciency of herbicide applications in Sorghum halepense Pers. Weed Res. 30: 153160.Google Scholar
14. Holm, L. G., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1977. Sorghum halepense (L.) Pers. Pages 5466 in The world's worst weeds—distribution and biology. Univ. Press of Hawaii, Honolulu.Google Scholar
15. Hodges, T. 1991. Predicting Crop Phenology. CRC Press, Boca Raton, Florida, 233 pp.Google Scholar
16. Hodges, T. 1992. Modeling the effects of stress on plant phenology. Abst. 22nd Ann. Workshop Crop Simul. 22: 3.Google Scholar
17. Holshouser, D. L. 1993. Modeling the phenological development of johnsongrass [Sorghum halepense (L.) PERS.] populations. , Texas A&M Univ., College Station, TX.Google Scholar
18. Horowitz, M. 1972. Early development of johnsongrass. Weed Sci. 20: 271273.Google Scholar
19. Ingle, M. and Rogers, B. J. 1961. The growth of a midwestern strain of Sorghum halepense under controlled conditions. Am. J. Bot. 48: 392396.Google Scholar
20. Kigel, J. and Rubin, B. 1985. Sorghum halepense L., in Handbook of Flowering Vol. 4, Halevy, A., ed. CRC Press, Boca Raton, Florida.Google Scholar
21. McWhorter, C. G. and Jordan, T. N. 1976. The effect of light and temperature on the growth and development of johnsongrass. Weed Sci. 24: 8891.Google Scholar
22. NeSmith, D. S. and Bridges, D. C. 1992. Modeling chilling influence of cumulative flowering: a case study using ‘Tifblue’ rabbiteye blueberry. J. Amer. Soc. Hort. Sci. 117: 698702.Google Scholar
23. Palmer, W. A., Bay, D. E., and Sharpe, P.J.H. 1981. Influence of temperature on the development and survival of the immature stages of horn fly, Haematoba irritans irritans (L.). Prot. Ecol. 3: 299309.Google Scholar
24. 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: 103109.Google Scholar
25. Sharpe, P.J.H. and DeMichele, D. W. 1977. Reaction kinetics of poikilotherm development. J. Theo. Biol. 64: 649670.Google Scholar
26. Vitta, J. I. and Leguizamon, E. S. 1991. Dynamics and control of Sorghum halepense (L.) Pers. shoot populations: a test of a thermal calendar model. Weed Res. 31: 7379.Google Scholar
27. 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: 691704.CrossRefGoogle Scholar
28. Wagner, T. L., Wu, H., Sharpe, P.J.H., and Coulson, R. N. 1984. Modeling distributions of insect development time: A literature review and application of the Weibull function. Ann. Entomol. Soc. Am. 77: 475487.Google Scholar
29. Wagner, T. L., Wu, H., Sharpe, P.J.H., Schoolfield, R. M., and Coulson, R.N. 1984. Modeling insect development rates: A literature review and application of a biophysical model. Ann. Entomol. Soc. Am. 77: 208225.Google Scholar
30. Wang, J. Y. 1960. A critique of the heat unit approach to plant response studies. Ecology 41: 785790.Google Scholar
31. Warwick, S. I. and Black, L. D. 1983. The biology of Canadian weeds. Sorghum halepense (L.) Pers. Can. J. Plant Sci. 63: 9971014.Google Scholar