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Modeling Distributions of Crop and Weed Seed Germination Time

Published online by Cambridge University Press:  12 June 2017

David C. Bridges ,
Hsin-I Wu ,
Peter J. H. Sharpe  and
James M. Chandler
David C. Bridges
Affiliation:
Dep. Agron., Univ. Georgia, Griffin, GA 30223–1797;
Hsin-I Wu
Affiliation:
Biosystems Res. Group, Industrial Eng. Dep., Texas A&M Univ., College Station, TX 77843
Peter J. H. Sharpe
Affiliation:
Biosystems Res. Group, Industrial Eng. Dep., Texas A&M Univ., College Station, TX 77843
James M. Chandler
Affiliation:
Dep. Soil and Crop Sci., Texas Agric. Exp. Stn., College Station, TX 77843. TAES J. No. TA23651
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Abstract

Research was conducted to determine the utility of a single, temperature-independent Weibull function for describing cumulative seed germination under several temperature regimes with 14 sets of weed and crop seed germination data. A modified cumulative Weibull function was derived to distribute germination times for individuals within the population and distributed the occurrence of germination given ample sample size and appropriate sample interval. The descriptive and predictive attributes of the stochastic model component are well suited for incorporation into seed germination models and are likely applicable to models to predict distribution of times for other developmental processes of plants.

Keywords

Simulationcomputer modelstochastic germination modelcumulative Weibull function

Information

Type
Special Topics
Information
Weed Science , Volume 37 , Issue 5 , September 1989 , pp. 724 - 729
Copyright
Copyright © 1989 by the Weed Science Society of America 

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References

Literature Cited

1. Angus, J. F. and Moncur, M. W. 1977. Water stress and phenology in wheat. Aust. J. Agric. Res. 1977:177–181.Google Scholar
2. Baxendale, F. P., Teetes, G. L., and Sharpe, P.J.H. 1984. Temperature-dependent model for sorghum midge (Diptera:Cecidomyiidae) spring emergence. Environ. Entomol. 13:1566–1571.Google Scholar
3. Baxendale, F. P., Teetes, G. L., Sharpe, P.J.H., and Wu, H. 1984. Temperature-dependent model for development of non- diapausing sorghum midges (Diptera:Cecidomyiidae). Environ. Entomol. 13:1572–1576.Google Scholar
4. Beddows, A. R. 1968. Head emergence in forage grasses in relation to February—May temperatures and the predicting of early or late springs. J. Br. Grassl. Soc. 23:88–97.CrossRefGoogle Scholar
5. Bould, A. and Abrol, B. K. 1981. A model for seed germination curves. Seed Sci. Technol. 9:601–611.Google Scholar
6. Brown, R. F. 1987. Germination of Aristida armata under constant and alternating temperatures and its analysis with the cumulative Weibull distribution as a model. Aust. J. Bot. 35:581–591.Google Scholar
7. Brown, R. F. and Mayer, D. G. 1986. Problems in applying Thornley's model of germination. Ann. Bot. 57:49–53.Google Scholar
8. Brown, R. F. and Mayer, D. G. 1988. Representing cumulative germination. 2. The use of the Weibull function and other empirically derived curves. Ann. Bot. 61:127–138.Google Scholar
9. Burt, G. W. and Wedderspoon, I. M. 1971. Growth of johnsongrass selections under different temperatures and dark periods. Weed Sci. 19:419–423.Google Scholar
10. Caddel, J. L. and Weibel, D. E. 1971. Effect of photoperiod and temperature on the development of sorghum. Agron. J. 63:799–803.CrossRefGoogle Scholar
11. Eastin, E. F. 1983. Redweed (Melochia corchorifolia) germination as influenced by scarification, temperature, and seeding depth. Weed Sci. 31:229–331.CrossRefGoogle Scholar
12. Eastin, E. F. 1983. Smallflower morningglory (Jacquemontia tamnifolia) germination as influenced by scarification, temperature, and seeding depth. Weed Sci. 31:727–730.CrossRefGoogle Scholar
13. Eastin, E. F. 1984. Drummond rattlebush (Sesbania rummondii) germination as influenced by scarification, temperature, and seeding depth. Weed Sci. 31:223–225.CrossRefGoogle Scholar
14. Gaswiler, J. S. 1971. Emergence and mortality of Douglas-fir, western hemlock, and western redcedar seedlings. For. Sci. 17:230–237.Google Scholar
15. Hinckley, A. L. 1981. Climatic models predicting growth and development of two nightshade species. MS. Thesis. Soil Sci. and Biometerol., Utah State Univ., Logan, UT.Google Scholar
16. Johnson, F. H. and Lewin, I. 1946. The growth rate of E. coli in relation to temperature, quinine, and coenzyme. J. Cell. Comp. Physiol. 28:47–75.CrossRefGoogle ScholarPubMed
17. Quinby, J. R., Hesketh, J. D., and Voigt, R. L. 1973. Influence of temperature and photoperiod on floral initiation and leaf number in sorghum. Agron. J. 13:243–246.Google Scholar
18. Sharpe, P.J.H. and DeMichelle, D. W. 1977. Reaction kinetics of poikilotherm development. J. Theor. Biol. 64:649–670.Google Scholar
19. Sharpe, P.J.H. and Hu, L. C. 1980. Reaction kinetics of nutrition dependent poikilotherm development. J. Theor. Biol. 82:317–333.Google Scholar
20. Sharpe, P.J.H. and Nordheim, A. W. 1980. Distribution model of human ovulatory cycles. J. Theor. Biol. 83:663–673.Google Scholar
21. Sharpe, P.J.H., Curry, G. L., DeMichelle, D. W., and Cole, C. L. 1977. Distribution model of organism development times. J. Theor. Biol. 66:21–38.Google Scholar
22. Thornley, J.H.M. 1977. Germination of seeds and spores. Ann. Bot. 41:1363–1365.Google Scholar
23. Veerhoff, O. 1940. Time and temperature relations of germinating flax. Am. J. Bot. 27:225–231.Google Scholar
24. 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. 77:475–487.Google Scholar