Hostname: page-component-848d4c4894-pjpqr Total loading time: 0 Render date: 2024-06-19T01:02:58.240Z Has data issue: false hasContentIssue false
  • English
  • Français

Simulation of Foxtail (Setaria viridis var. robusta-alba, Setaria viridis var. robusta-purpurea) Growth: The Development of SETSIM

Published online by Cambridge University Press:  12 June 2017

P. L. Orwick ,
M. M. Schreiber  and
D. A. Holt
P. L. Orwick
Affiliation:
Res. Agron., Sci. Ed. Admin., U.S. Dep. Agric.
M. M. Schreiber
Affiliation:
Dep. Bot. and Plant Pathol
D. A. Holt
Affiliation:
Purdue Univ., West Lafayette, IN 47907
Get access Rights & Permissions [Opens in a new window]

Abstract

We developed a model and subsequently simulated robust white foxtail (Setaria viridis var. robusta-alba Schreiber) or robust purple foxtail (Setaria viridis var. robusta-purpurea Schreiber) growth. SETSIM (SETaria SIMulation) uses the GASP IV simulation language which allows for both continuously changing variables and discrete events. GASP IV provides the necessary integrations and automatic time-stepping essential in simulation. SETSIM uses the materials-flow concept to simulate foxtail growth and development on a population basis. Carbohydrate flow among six compartments (leaf, stem, and root total nonstructural carbohydrate pools; leaf, stem, and root tissue) is governed by nine physiological rates. Each rate is dependent on the physiological state of the foxtail population and the environmental conditions prevailing. Simulation of carbohydrate flow in and out of each compartment results in the net growth of that compartment. By considering all six compartments simultaneously, the vegetative growth and development of a foxtail population can be simulated. Validation of SETSIM with field data recorded over a 2-yr period has shown that this simulator can accurately predict foxtail growth parameters such as dry matter accumulation, plant height, leaf area index, and leaf to stem ratio. SETSIM could serve as a framework for other weed models because of its modular structure. Such models can benefit weed science by predicting the active stage of weed growth, predicting whether a weed could become a problem under different climatic conditions, interfacing with existing crop models to predict yield and harvest restriction, pointing out gaps in our present knowledge in weed biology, and serving as teaching aids.

Keywords

GASP IVcarbohydrate flowleaf area indexphysiological rates
Type
Research Article
Information
Weed Science , Volume 26 , Issue 6 , November 1978 , pp. 691 - 699
Copyright
Copyright © 1978 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. Arkin, G. F., Vanderlip, R. L., and Ritchie, J. T. 1974. A dynamic grain sorghum growth model. Trans. ASAE. 19:622–626, 630.Google Scholar
2. Beadle, C. L., Stevenson, K. R., Newmann, H. H., Thurtell, G. W., and King, K. M. 1973. Diffusive resistance, transpiration, and photosynthesis in single leaves of corn and sorghum in relation to leaf water potential. Can. J. Plant Sci. 53:537–544.Google Scholar
3. Brouwer, R. and de Wit, C. T. 1969. A simulation of plant growth with special attention to root growth and its consequences. Pages 224–244 in Whitington, W. J., ed. Root Growth. Butterworths, London.Google Scholar
4. Chen, T. M., Brown, R. H., and Black, C. C. 1970. CO2 compensation, photosynthesis rate, and carbonic anhydrase activity of plants. Weed Sci. 18:399–402.Google Scholar
5. Curry, R. B. 1971. Dynamic simulation of plant growth. Part I. Development of a model. Trans. ASAE. 14:945–949, 959.Google Scholar
6. Curry, R. B. and Chen, L. H. 1971. Dynamic simulation of plant growth. Part II. Incorporation of actual daily weather and partitioning of net photosynthate. Trans. ASAE. 14:1170–1174.Google Scholar
7. de Wit, C. T., Brouwer, R., and Penning de Vries, R. W. T. 1970. The simulation of photosynthetic systems. Pages 47–70 in Prediction and Measurement of Photosynthetic Productivity. Proc. IBP/PP Technical Meeting. Trebon. September, 1969.Google Scholar
8. Duncan, W. G. 1972. SIMCOT: A simulator of cotton growth and yield. Pages 115–118 in Murphy, C., Hesketh, J. D., and Strain, B. R., eds. Proceedings of the Workshop on Tree Growth Dynamics and Modeling. Duke Univ., October 1971.Google Scholar
9. Duncan, W. G. 1975. Maize. Pages 23–50 in Evans, L. T., ed. Crop Physiology – Some Case Histories. Cambridge Univ. Press, New York.Google Scholar
10. El-Sharkawy, M. A. and Hesketh, J. D. 1964. Effects of temperature and water deficit on leaf photosynthesis rates of different species. Crop Sci. 4:514–518.Google Scholar
11. Fick, G. W., Williams, W. A., and Loomis, R. S. 1973. Computer simulation of dry matter distribution during sugar beet growth. Crop. Sci. 13:413–417.Google Scholar
12. Hesketh, J. D., Baker, D. N., and Duncan, W. G. 1971. Simulation of growth and yield in cotton: respiration and the carbon balance. Crop Sci. 11:394–398.Google Scholar
13. Hesketh, J. D., Baker, D. N., and Duncan, W. G. 1972. Simulation of growth and yield in cotton. II. Environmental control of morphogenesis. Crop Sci. 12:436–439.Google Scholar
14. Holt, D. A., Bula, R. J., Miles, G. E., Schreiber, M. M., and Peart, R. M. 1975. Environmental physiology, modeling and simulation of alfalfa gtowth. I. Conceptual development of SIMED. Purdue Univ. Agric. Exp. Stn. Res. Bull. 907. 26 pp.Google Scholar
15. Holt, D. A., Miles, G. E., Bula, R. J., Schreiber, M. M., and Peart, R. M. 1976. SIMED, a crop simulation model, as a tool for teaching crop physiology. J. Agron. Ed. 5:53–56.Google Scholar
16. McKinnion, J. M., Baker, D. N., Hesketh, J. D., and Jones, J. W. 1975. SIMCOT II: A simulation of cotton growth and yield. Pages 27–82 in Computer Simulation of a Cotton Production System. ARS-S-52, Agric. Res. Serv., U.S. Dep. Agric.Google Scholar
17. Neales, T. F. and Incoll, L. D. 1968. The control of leaf photosynthesis rate by the level of assimulate concentration in the leaf: a review of the hypothesis. Bot. Rev. 34:107–125.Google Scholar
18. Pritsker, A. A. B. 1974. The GASP IV Simulation Language. John Wiley and Sons, Inc., New York. 451 pp.Google Scholar
19. Rickman, R. W., Ramig, R. E., and Allmaras, R. R. 1975. Modeling dry matter accumulation in dryland winter wheat. Agron. J. 67:283–289.CrossRefGoogle Scholar
20. Schreiber, M. M. and Oliver, L. R. 1971. Two new varieties of Setaria viridis . Weed Sci. 19:424–427.Google Scholar
21. Shearman, L. L., Eastin, J. D., Sullivan, C. Y., and Kinbacher, E. J. 1972. CO2 exchange in water-stressed sorghum. Crop Sci. 12:406–408.CrossRefGoogle Scholar
22. Stapleton, H. N. and Meyers, R. P. 1971. Modeling subsystems for cotton. The cotton plant simulation. Trans. ASAE. 14:950–953.Google Scholar
23. Stapleton, H. N., Buxton, D. R., Watson, F. L., Nolting, D. J., and Baker, D. N. 1973. Cotton: A computer simulation of cotton growth. Univ. Arizona Agric. Exp. Tech. Bull. 206, Univ. of Arizona, Tucson. 124 pp.Google Scholar
24. Stephenson, R. A., Brown, R. H., and Ashley, D. A. 1976. Translocation of 14C-labeled assimilate and photosynthesis in C3 and C4 species. Crop Sci. 16:285–288.Google Scholar
25. Wilson, G. L. and Ludlow, M. M. 1970. Net photosynthesis of tropical grass and legume leaves. Pages 534–538 in Proc. XI Int. Grassland Congress, Australia.Google Scholar
26. Winter, S. R. and Pendleton, J. W. 1970. Results of changing light and temperature regimes in a corn field and temperature effects on the apparent photosynthesis of individual leaves. Agron. J. 62:181–184.Google Scholar