Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-05T16:27:28.079Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling the integrated management of giant foxtail in corn–soybean

Published online by Cambridge University Press:  20 January 2017

Alvin J. Bussan  and
Chris M. Boerboom
Chris M. Boerboom
Affiliation:
Department of Agronomy, University of Wisconsin, Madison, WI 53706
Get access Rights & Permissions [Opens in a new window]

Abstract

The objectives of this study were to use a computer simulation model to predict the influence of herbicides and mechanical treatments on giant foxtail population dynamics, annualized net return (ANR), and the giant foxtail economic optimum threshold (EOT) in a corn–soybean rotation over 20 yr. Mechanical treatments were interrow cultivation in corn and rotary hoe in soybean. Herbicides at full (1 ×) and half (½ ×) rates applied alone reduced giant foxtail seedbank 95% within 4 and 8 yr, respectively. Predicted seedbank dynamics had more variability when managed with herbicides at ½ × than at 1 × rates applied alone. Mechanical treatments integrated with herbicide at ½ × rates resulted in giant foxtail seedbank and variability similar to herbicides at 1 × rates applied alone. ANR was maximized when herbicides were applied between ⅜ × and 9/16 × rates applied alone. As initial giant foxtail density increased from 100 to 10,000 seeds m−2, the herbicide rate that maximized ANR increased. Economic optimum thresholds (EOTs) did not vary when herbicides were applied at different rates, but integrating mechanical treatment with herbicides increased the EOT from 0.1 to 0.7 seedlings m−2. Sensitivity analysis determined that giant foxtail seedbank demographics, seedling survival, and seed production per plant had the most influence on model predictions. Model sensitivity varied little between 1 × and ½ × rates. Integrating herbicides and mechanical treatment decreased the sensitivity of the model to perturbations in parameter estimates. Herbicides at reduced rates were more profitable over the long term than 1 × rates, but risk of herbicide failure increased as rate decreased. Integration of herbicides applied at reduced rates with mechanical treatments increased ANR and minimized the risk of herbicide failure compared to herbicides applied at 1 × rates alone.

Keywords

Corn, Zea mays L.giant foxtail, Setaria faberi Herrm. SETFAsoybean, Glycine max (L.) MerrIntegrated weed managementnicosulfuronoptimization of inputssethoxydim
Type
Research Article
Information
Weed Science , Volume 49 , Issue 5 , October 2001 , pp. 675 - 684
Copyright
Copyright © 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

Anonymous. 1992. Wisconsin Agricultural Statistics. Madison, WI: Wisconsin Department of Agriculture Trade Consumer Protection. 98 p.Google Scholar
Anonymous. 1994. Wisconsin Agricultural Statistics. Madison, WI: Wisconsin Department of Agriculture Trade Consumer Protection. 98 p.Google Scholar
Anonymous. 1996. Wisconsin Agricultural Statistics. Madison, WI: Wisconsin Department of Agriculture Trade Consumer Protection. 74 p.Google Scholar
Bauer, T. A. and Mortensen, D. A. 1992. A comparison of economic and economic optimum thresholds for two annual weeds in G. max . Weed Technol. 6:228–235.Google Scholar
Boerboom, C. M., Doll, J. D., Flashinski, R. A., Grau, C. R., and Wedberg, J. L. 1997. Field Crops Pest Management in Wisconsin. Madison, WI: University of Wisconsin Cooperative Extension Bull. A3646. pp. 182–185.Google Scholar
Buhler, D. D., Doll, J. D., Proost, R. T., and Visocky, M. R. 1994. Interrow cultivation to reduce herbicide use in Z. mays following alfalfa without tillage. Agron. J. 86:66–72.Google Scholar
Buhler, D. D., Gunsolus, J. L., and Ralston, D. F. 1992. Integrated weed management techniques to reduce herbicide inputs in G. max . Agron. J. 84:973–978.CrossRefGoogle Scholar
Burnside, O. C., Wilson, R. G., Weisberg, S., and Hubbard, K. G. 1996. Seed longevity of 41 weed species buried 17 years in eastern and western Nebraska. Weed Sci. 44:74–86.Google Scholar
Bussan, A. J. 1997. Predicted Population Dynamics of Pigweed, S. faberi, and Velvetleaf as Influenced by Herbicides at Reduced Rates in Z. mays and G. max . Ph.D. dissertation. University of Wisconsin, Madison, WI. 287 p.Google Scholar
Bussan, A. J., Boerboom, C. M., and Stoltenberg, D. E. 2000. Response of S. faberi (Setaria faberi) demographic processes to herbicide rates. Weed Sci. 48:445–453.CrossRefGoogle Scholar
Bussan, A. J., Boerboom, C. M., and Stoltenberg, D. E. 2001. Response of velvetleaf (Abutilon theophrasti) demographic processes to herbicide rates. Weed Sci. 49:22–30.Google Scholar
Carey, J. B. and Kells, J. J. 1995. Timing of total postemergence herbicide applications to maximize weed control and corn (Zea mays) yield. Weed Technol. 9:356–361.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
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. 17:1–12.Google Scholar
DeFelice, M. S., Brown, W. B., Aldrich, R. J., Sims, B. D., Judy, D. T., and Guethle, D. R. 1989. Weed control in G. max (Glycine max) with reduced rates of postemergence herbicides. Weed. Sci. 37:365–374.CrossRefGoogle Scholar
Devlin, D. L., Long, J. H., and Maddux, L. D. 1991. Using reduced rates of postemergence herbicides in G. max (Glycine max). Weed Technol. 5:834–840.Google Scholar
Egley, G. H. and Chandler, J. M. 1978. Germination and viability of weed seeds after 2.5 years in a 50-year buried seed study. Weed Sci. 31:264–270.Google Scholar
Forcella, F., Wilson, R. G., Renner, K. A., Dekker, J., Harvey, R. G., Alm, D. A., Buhler, D. D., and Cardina, J. 1992. Weed seedbanks of the U.S. Z. mays belt: magnitude, variation, emergence, and application. Weed Sci. 40:636–644.CrossRefGoogle Scholar
Fuller, E., Lazarus, W., and Peterson, A. 1995. Minnesota Farm Machinery Economic Cost Estimates for 1995. St. Paul, MN: University of Minnesota. Cooperative Extension Bull.Google Scholar
Gonzalez-Andujar, J. L. and Fernandez-Quintanilla, C. 1991. Modeling the population dynamics of Avena sterilis under dry-land cereal cropping systems. J. Appl. Ecol. 28:16–27.Google Scholar
Griffin, J. L., Reynolds, D. B., Vidrine, P. R., and Saxton, A. M. 1992. Common cocklebur (Xanthium strumarium) control with reduced rates of soil and foliar-applied imazaquin. Weed Technol. 6:847–851.Google Scholar
Hanson, J. E., Stoltenberg, D. E., Lowery, B., and Binning, L. K. 1997. Influence of application rate on atrazine fate in a silt loam soil. J. Environ. Qual. 26:829–835.CrossRefGoogle Scholar
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. 34:972–979.CrossRefGoogle Scholar
Knake, E. L. and Slife, F. W. 1962. Competition of Setaria faberi with Z. mays and G. max . Weeds 10:26–29.Google Scholar
Krausz, R. F., Kapusta, G., and Matthews, J. L. 1993. The effect of S. faberi (Setaria faberi) plant height on control with six postemergence herbicides. Weed Technol. 7:491–494.Google Scholar
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
Lindquist, J. L., Maxwell, B. D., Buhler, D. D., and Gunsolus, J. L. 1995. Modeling the population dynamics and economics of velvetleaf (Abutilon theophrasti) control in a corn (Zea mays)-soybean (Glycine max) rotation. Weed Sci. 43:269–275.Google Scholar
Mulder, T. A. and Doll, J. D. 1993. Integrating reduced herbicide use with mechanical weeding in corn (Zea mays). Weed Technol. 7:382–389.Google Scholar
Mulugeta, D. and Stoltenberg, D. E. 1997. Weed and seedbank management with integrated methods as influenced by tillage. Weed Sci. 45:706–715.Google Scholar
Prostko, E. P. and Meade, J. A. 1993. Reduced rates of postemergence herbicides in conventional soybean (Glycine max). Weed Technol. 7:365–369.Google Scholar
Rabaey, T. L. and Harvey, R. G. 1994. Efficacy of corn (Zea mays) herbicides applied at reduced rates impregnated in dry fertilizer. Weed Technol. 8:830–835.Google Scholar
Reuss, S. 1997. Viability and distribution of weed seed within a Waukegon silt loam soil. Pages 16–52 In Viability and Distribution of Weed Seed Within Soil Aggregates of Three Minnesota Soils. . University of Minnesota, St. Paul, MN.Google Scholar
[SAS] Statistical Analysis Systems. 1988. SAS/STAT User's Guide. Version 6.03. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Steckel, L. E., DeFelice, M. S., and Sims, L. D. 1990. Integrating reduced rates of postemergence herbicides and cultivation for broadleaf weed control in soybean (Glycine max). Weed Sci. 38:541–545.Google Scholar
Wilson, R. G. 1993. Effect of preplant tillage, post-plant cultivation, and herbicides on weed density in corn (Zea mays). Weed Technol. 7:728–734.Google Scholar
Zoschke, A. 1994. Toward reduced herbicide rates and adapted weed management. Weed Technol. 8:376–386.Google Scholar