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Applications of an Ecophysiological Model for Irrigated Rice (Oryza sativa)-Echinochloa Competition

Published online by Cambridge University Press:  12 June 2017

John L. Lindquist  and
Martin J. Kropff
John L. Lindquist
Affiliation:
Dep. Agron. Plant Gen., Univ. Minnesota, St. Paul, MN 55108
Martin J. Kropff
Affiliation:
Systems Agron., International Rice Research Institute, P.O. Box 933, 1099, Manila, The Philippines
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Abstract

A simulation model of rice-barnyardgrass competition for light was used for two management applications. First, simulations using 47 weather data sets from four locations in Asia were conducted to evaluate the influence of weather variation on single year economic threshold densities of barnyardgrass. Second, rapid leaf area expansion and leaf area index were evaluated as potential indicators of improved rice competitiveness and tolerance to barnyardgrass. Influence of weather variation on single year economic thresholds was small under the assumption that competition was for light only. Increasing early leaf area expansion rate reduced simulated barnyardgrass seed production and increased single year economic thresholds, suggesting that the use of competitive rice cultivars may reduce the need for chemical weed control. The model predicted that rice leaf area index 70 to 75 d after planting was a good indicator of early leaf area expansion rate.

Keywords

Barnyardgrass, Echinochloa crus-galli (L.) Beauv., ♯ ECHCGrice, Oryza sativa L. ‘IR72.’Economic thresholdintegrated weed managementweed ecologyIPMweed-crop interferenceECHCG
Type
Weed Biology and Ecology
Information
Weed Science , Volume 44 , Issue 1 , March 1996 , pp. 52 - 56
Copyright
Copyright © 1996 by the Weed Science Society of America 

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References

Literature Cited

1. Bauer, T. A., Mortensen, D. A., Wicks, G. A., Hayden, T. A., and Martin, A. R. 1991. Environmental variability associated with economic thresholds for soybeans. Weed Sci. 39: 564–569.CrossRefGoogle Scholar
2. Callaway, M. B. 1992. A compendium of crop varietal tolerance to weeds. Amer. J. Alt. Agric. 7: 169–180.Google Scholar
3. Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6: 191–195.CrossRefGoogle Scholar
4. Cousens, R. 1985. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105: 513–521.Google Scholar
5. Forcella, F. 1987. Tolerance of weed competition associated with high leaf area expansion rate in tall fescue. Crop Sci. 27: 146–147.CrossRefGoogle Scholar
6. Graf, B., Gutierrez, A. P., Rakotobe, O., Zahner, P., and Delucchi, V. 1990. A simulation model for the dynamics of rice growth and development: Part II—The competition with weeds for nitrogen and light. Agric. Syst. 32: 367–392.CrossRefGoogle Scholar
7. Hill, J. E., De Datta, S. K., and Real, J. G. 1989. Echinochloa competition in rice: a comparison of studies from direct-seeded and transplanted flooded rice. Pages 115–129 in Auld, B. A., Umaly, R. C., and Thitrosomo, S. S., eds. Proc. Symp. on Weed Man., Biotrop Spec. Publ. No. 38. Bogor, Indonesia.Google Scholar
8. IRRI. 1990. IRRI Rice Facts, International Rice Research Institute, Los Banos, Philippines. 10 pages.Google Scholar
9. Jordan, N. 1993. Prospects for weed control through crop interference. Ecol. Applic. 3: 84–91.Google Scholar
10. Keen, R. E. and Spain, J. D. 1992. Page 299 in Computer simulation in biology: A basic introduction. Wiley-Liss Inc., New York NY.Google Scholar
11. Kropff, M. J., van Keulen, N. C., van Laar, H. H., and Schnieders, B. J. 1993. The impact of environmental and genetic factors. Pages 137–147 in Kropff, M. J. and van Laar, H. H., eds. Modelling Crop-Weed Interactions. CAB International and the International Rice Research Institute.Google Scholar
12. Kropff, M. J. and van Laar, H. H. (eds.). 1993. Modelling crop-weed interactions. CAB International, Wallingford, UK and the International Rice Research Institute.Google Scholar
13. Kropff, M. J. and Lotz, L. A. P. 1992. Optimization of weed management systems: The role of ecological models of interplant competition. Weed Technol. 6: 462–470.Google Scholar
14. Kropff, M. J., Moody, K., Lindquist, J. L., Migo, T., and Fajardo, F. F. 1994. Models to predict yield loss due to weeds in rice ecosystems. Phil. J. Weed Sci., Special Issue: 29–44.Google Scholar
15. Kropff, M. J. and Spitters, C.J.T. 1991. A simple model of crop loss by weed competition from early observations on relative leaf area of weeds. Weed Res. 31: 97–105.Google Scholar
16. Kropff, M. J. and Spitters, C.J.T. 1992. An ecophysiological model for interspecific competition, applied to the influence of Chenopodium album L. on sugar beet. I. Model description and parameterization. Weed Res. 32: 437–450.Google Scholar
17. Kropff, M. J., Weaver, S. E., Lotz, L.A.P., Lindquist, J. L., Joenje, W., Schnieders, B. J., van Keulen, N. C., Migo, T. R., and Fajardo, F. F. 1993. Understanding crop-weed interaction in field situations. Pages 105–136 in Kropff, M. J. and van Laar, H. H., eds. Modelling Crop-Weed Interactions. CAB International and the International Rice Research Institute.Google Scholar
18. Moody, K. 1988. Developing appropriate weed management strategies for small-scale farmers. Pages 319–320 in Altieri, M. A. and Liebmann, M., eds. Weed management in agroecosystems: Ecological approaches. CRC Press Inc., Boca Raton FL, USA.Google Scholar
19. Noda, K., Ozawa, K., and Ibaraki, K. 1968. Studies on the damage on rice plants due to weed competition (effect of barnyardgrass competition on growth, yield, and some ecophysiological aspects of rice plants). Bull. Kyushu Agric. Exper. Sta., Vol. XIII, nos. 3&4, 345–367.Google Scholar
20. Spitters, C.J.T. and Aerts, R., 1983. Simulation of competition for light and water in crop weed associations. Asp. Appl. Biol. 4: 467–484.Google Scholar
21. Swinton, S. M., Buhler, D. D., Forcella, F., Gunsolus, J. L., and King, R. P. 1994. Estimation of crop yield loss due to interference by multiple weed species. Weed Sci. 42: 103–109.Google Scholar
22. Wiles, L. J. and Wilkerson, G. G. 1991. Modeling competition for light between soybean and broadleaf weeds. Agric. Syst. 35: 37–51.CrossRefGoogle Scholar
23. Wilkerson, G. G., Jones, J. W., Coble, H. D., and Gunsolus, J. L., 1990. SOYWEED: A simulation model of soybean and common cocklebur growth and competition. Agron. J. 82: 1003–1010.CrossRefGoogle Scholar
24. Zanin, G. and Sattin, M. 1988. Threshold level and seed production of velvetleaf (Abutilon theophrasti Medicus) in maize. Weed Res. 28: 347–352.Google Scholar