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Modeling the Population Dynamics and Economics of Velvetleaf (Abutilon theophrasti) Control in a Corn (Zea mays)-Soybean (Glycine max) Rotation

Published online by Cambridge University Press:  12 June 2017

John L. Lindquist
Affiliation:
Dep. Agron. Plant Gen., Univ. Minnesota, St. Paul, MN 55108
Bruce D. Maxwell
Affiliation:
Plant, Soil, and Environ., Sci. Dept., Montana State Univ., Bozeman, MT 59717
Douglas D. Buhler
Affiliation:
U.S. Dept. Agric., Agr. Res. Serv., National Soil Tilth Lab., Ames IA 50011
Jeffrey L. Gunsolus
Affiliation:
Dept. Agron. and Plant Gen., Univ. Minnesota, St. Paul, MN 55108

Abstract

A simulation model was developed to predict the population dynamics and economics of velvetleaf control in a corn-soybean rotation. Data compiled from the literature were used to parameterize the model for two situations, one in which velvetleaf was infected by a Verticillium spp. wilt and one without infection. Verticillium was assumed to have no effect on corn or soybean yield. In the absence of control, simulated seedbank densities of a Verticillium-infected velvetleaf population were 5 to 50 times lower than for an uninfected velvetleaf population. The model was used to evaluate a threshold weed management strategy under the assumption that velvetleaf was the only weed and bentazon the only herbicide available for its control. In the absence of Verticillium, an economic optimum threshold of 2.5 seedlings 100 m−2 afforded the highest economic returns after 20 yr of simulation. Simulations in which velvetleaf was infected in 8 out of 20 randomly assigned years indicated a 6% increase in annualized net return and an 11 % reduction in the number of years that control was necessary. Sensitivity analysis indicated the parameter estimates having the greatest impact on economic optimum threshold were seedling emergence and survival, maximum seed production, and herbicide efficacy. Under an economic optimum threshold of 2.5 seedlings 100 m−2, management practices that manipulate the most sensitive demographic processes increased annualized net return by up to 13% and reduced long-term herbicide use by up to 26%. Results demonstrate that combining an economic optimum threshold with alternative weed management strategies may increase economic return and reduce herbicide use.

Type
Weed Biology and Ecology
Copyright
Copyright © 1995 by the Weed Science Society of America 

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References

LITERATURE CITED

1. Bauer, T. A. and Mortensen, D. A. 1992. A comparison of economic and economic optimum thresholds for two annual weeds in soybeans. Weed Technol. 6:228235.Google Scholar
2. Bazzaz, F. A., Ackerly, D. D., Woodward, F. I., and Rochefort, L. 1992. CO2 enrichment and dependence of reproduction on density in an annual plant and a simulation of its population dynamics. J. Ecol. 80:643651.CrossRefGoogle Scholar
3. Bussler, B. H. 1993. Corn interactions with common cocklebur and velvetleaf. Ph.D. Thesis, University of Minnesota, St. Paul MN, 103 p.Google Scholar
4. Burnside, O. C. 1979. Soybean (Glycine max) growth as affected by weed removal, cultivar, and row spacing. Weed Sci. 27:562565.CrossRefGoogle Scholar
5. Cousens, R., Doyle, C. J., Wilson, B. J., and Cussans, G. W. 1986. Modelling the economics of controlling Avena fatua in winter wheat. Pestic. Sci. 17:112.Google Scholar
6. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.Google Scholar
7. Cousens, R. 1986. The use of population models in the study of the economics of weed control. Proc. European Weed Res. Soc. Symp., Economic Weed Control, 269276.Google Scholar
8. Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Quart. 2:1320.Google Scholar
9. Doyle, C. J., Cousens, R., and Moss, S. R. 1986. A model of the economics of controlling Alopecurus myosuroides Huds. in winter wheat. Crop Prot. 5:143150.Google Scholar
10. Firbank, L. G. and Watkinson, A. R. 1986. Modelling the population dynamics of an arable weed and its effects upon crop yield. J. Appl. Ecol. 23:147159.CrossRefGoogle Scholar
11. Fuller, E. I., Lazarus, B., and Nordquist, D. 1990. Minnesota farm machinery economic cost estimates. Univ. Minnesota Extension Service. AG-FO-2380.Google Scholar
12. Fuller, E., Lazarus, B., and Carrigan, L. 1991. What to grow in 1991. Crop budgets for soil area 4. Univ. Minnesota Extension Service. AG-FS-0937-A.Google Scholar
13. Gonzalez-Andujar, J. L. and Fernandez-Quintanilla, C. 1991. Modelling the population dynamics of Avena sterilis under dry-land cereal cropping systems. J. Appl. Ecol. 28:1627.Google Scholar
14. Green, R. J. and Wiley, G. L. 1987. Verticillium dahliae as a biocontrol agent of velvetleaf, Abutilon theophrasti . Can. J. Plant Path. 9:81.Google Scholar
15. Gunsolus, J. L., Durgan, B. R., Becker, R. L., Dexter, A. G., Buhler, D. D., and Wyse, D. L. Cultural and chemical weed control in field crops—1992. University of Minnesota Extension Service. AG-BU-3157-S.Google Scholar
16. Harrison, S. K., Wax, L. M., and Bode, L. E. 1986. Influence of adjuvants and application variables on postemergence weed control with bentazon and sethoxydim. Weed Sci. 34:462466.Google Scholar
17. Jordan, N. 1992. Weed demography and population dynamics: Implications for threshold management. Weed Technol. 6:184190.Google Scholar
18. King, R. P., Lybecker, D. W., Schweizer, E. E., and Zimdahl, R. L. 1986. Bioeconomic modeling to simulate weed control strategies for continuous corn (Zea mays). Weed Sci. 34:972979.CrossRefGoogle Scholar
19. Lindquist, J. L. 1994. Population dynamics and economics of velvetleaf (Abutilon theophrasti Medik.) in a corn-soybean rotation. M.S. Thesis, University of Minnesota, St. Paul MN 55108.Google Scholar
20. Lindquist, J. L. Maxwell, B. D., Buhler, D. D., and Gunsolus, J. L. 1995. Velvetleaf (Abutilon theophrasti) recruitment, survival, seed production, and interference in soybean (Glycine max). Weed Sci. 43:226232.Google Scholar
21. Maxwell, B. D., Wilson, M. V., and Radosevich, S. R. 1988. Population modeling approach to evaluating leafy spurge (Euphorbia esula) development and control. Weed Technol. 2:132138.Google Scholar
22. McWhorter, C. G. and Hartwig, E. E. 1972. Competition of johnsongrass and cocklebur with six soybean varieties. Weed Sci. 20:5659.Google Scholar
23. Norris, R. F. 1992. Ecological perspectives on utility of thresholds for weed management. Weed Technol. 6:182183.Google Scholar
24. Pacala, S. W. and Silander, J. A. Jr. 1985. Neighborhood models of plant population dynamics. I. Single-species models of annuals. Amer. Nat. 125:385411.CrossRefGoogle Scholar
25. Pacala, S. W. and Silander, J. A. Jr. 1987. Neighborhood interference among velvetleaf, Abutilon theophrasti, and pigweed, Amaranthus retroflexus . Oikos 48:217224.Google Scholar
26. Pacala, S. W. and Silander, J. A. Jr. 1990. Field tests of neighborhood population dynamic models of two annual weed species. Ecol. Monogr. 60:113134.Google Scholar
27. Rose, S. J., Burnside, O. C., Specht, J. E., and Swisher, B. A. 1984. Competition and allelopathy between soybeans and weeds. Agron. J. 76:523528.Google Scholar
28. Sattin, M., Zanin, G., and Berti, A. 1992. Case history for weed competition/population ecology: Velvetleaf (Abutilon theophrasti) in corn (Zea mays). Weed Technol. 6:213219.Google Scholar
29. Sickinger, S. M. 1981. The effects of Verticillium dahliae (Kleb.) on velvetleaf (Abutilon theophrasti) and crops. M.S. Thesis, University of Wisconsin, Madison. 141 pp.Google Scholar
30. Steckel, L. E., DeFelice, M. S., and Sims, B. D. 1990. Integrating reduced rates of postemergence herbicides and cultivation for broadleaf weed control in soybeans (Glycine max). Weed Sci. 38:541545.Google Scholar
31. Stoller, E. W., Harrison, S. K., Wax, L. M., Regnier, E. E., and Nafziger, E. D. 1987. Weed interference in soybeans (Glycine max). Rev. Weed Sci. 3:155181.Google Scholar
32. Thrall, P. H., Pacala, S. W., and Silander, J. A. Jr. 1989. Oscillatory dynamics in populations of an annual weed species Abutilon theophrasti . J. Ecol. 77:11351149.Google Scholar
33. Warwick, S. I. and Black, L. D. 1988. The biology of Canadian weeds. 90. Abutilon theophrasti . Can. J. Plant Sci. 68:10691085.Google Scholar
34. Watkinson, A. R., Lonsdale, W. M., and Andrew, M. H. 1989. Modelling the population dynamics of an annual plant Sorghum intrans in the wet-dry tropics. J. Ecol. 77:162181.CrossRefGoogle Scholar
35. 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. 82:10031010.Google Scholar
36. Wiles, L. J. and Wilkerson, G. G. 1992. Modeling competition for light between soybean and broadleaf weeds. Agric. Syst. 35:3751.CrossRefGoogle Scholar
37. Wright, L. (ed.). 1993. Minnesota agriculture statistics 1993. USDA National Statistics Service and the Minnesota Department of Agriculture.Google Scholar
38. Zanin, G. and Sattin, M. 1988. Threshold level and seed production of velvetleaf (Abutilon theophrasti Medicus) in Maize. Weed Res. 28:347352.Google Scholar