Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-05T05:47:58.523Z Has data issue: false hasContentIssue false
  • English
  • Français

Justification for site-specific weed management based on ecology and economics

Published online by Cambridge University Press:  20 January 2017

Bruce D. Maxwell  and
Edward C. Luschei
Edward C. Luschei
Affiliation:
Department of Agronomy, University of Wisconsin–Madison, Madison, WI 53706
Get access Rights & Permissions [Opens in a new window]

Abstract

One of the primary benefits of site-specific agricultural technologies is the potential to reduce the use of polluting inputs, thereby minimizing ecological damage. Weeds are often found in patches, so site-specific (field scale) management offers a straightforward opportunity to minimize ecological effects related to wasteful broadcast use of herbicides. Beyond possible efficiencies related to accurate targeting, site-specific technologies, through a process of parameterizing management decision models for each field, may improve ecological understanding of weed populations and thus encourage ecologically based management. This hypothesis was assessed with a simple model that combined economic injury–level prediction with a single parameter (growing season precipitation) to represent environmental variability. Model simulations of crop yield in response to weed density at a virtual farm and six surrounding regional experiment stations suggested that localized (on-farm field) parameter estimation may help to circumvent the variability associated with damage function extrapolation from small-plot experiments at experiment stations and thereby improve predictive accuracy for site-specific weed management (SSWM) strategies. Thus, remote sensing and SSWM technologies may allow producers to reduce the risk associated with the reduced use of purchased inputs and greater reliance on natural weed population–regulating mechanisms. Effective ecologically based weed management may be dependent on local parameterization of models.

Keywords

Ecologically based weed managementfield-scale experimentationintegrated weed managementmodel parameterizationprecision farmingweed ecology
Type
Symposium
Information
Weed Science , Volume 53 , Issue 2 , April 2005 , pp. 221 - 227
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

Boerboom, C. M., Proost, R. T., Jacobs, A. D., Peterson, M. D., and Nowak, P. J. 2003. Can a corn IPM survey guide extension programming? Proc. N. Cent. Weed Sci. Soc 58:209.Google Scholar
Carlson, H. L. and Hill, J. E. 1985. Wild oat (Avena fatua) competition with spring wheat: plant density effects. Weed Sci 33:176–181.Google Scholar
Clark, J. S., Carpenter, S. R., and Barber, M. et al. 2001. Ecological forecasts: an emerging imperative. Science 293:657–660.CrossRefGoogle ScholarPubMed
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol 107:239–252.Google Scholar
Cousens, R. 1986. The use of population models in the study of the economics of weed control. Pages 269–276 in Proceedings of the 5th EWRS Symposium, Stuttgart-Huhenheim 1986, Economic Weed Control. Doorwerth, The Netherlands: EWRS Bookshop.Google Scholar
Cousens, R., Brain, P., O'Donovan, J. T., and O'Sullivan, P. A. 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Sci 35:720–725.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
Hilborn, R. and Mangel, M. 1997. The Ecological Detective: Confronting Models with Data. Princeton, NJ: Princeton University Press. Pp. 39–93.Google Scholar
Jasieniuk, M., Maxwell, B. D., and Anderson, R. L. et al. 1999. Development of a bioeconomic model for the management of jointed goatgrass (Aegilops cylindrica) in winter wheat (Triticum aestivum). 1. Site-to-site and year-to-year variation in wheat yield loss functions. Weed Sci 47:529–537.CrossRefGoogle Scholar
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenk, R., Kells, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)-velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci 44:309–313.Google Scholar
Lindquist, J. L., Mortensen, D. A., and Westra, P. et al. 1999. Stability of corn (Zea mays)-foxtail (Setaria spp.) interference relationships. Weed Sci 47:195–200.Google Scholar
Luschei, E., Van Wychen, L., Maxwell, B., Bussan, A., Buschena, D., and Goodman, D. 2001. Implementing and conducting on-farm weed research with the use of GPS. Weed Sci 49:536–542.CrossRefGoogle Scholar
Maxwell, B. D. 1999. My view: a perspective on ecologically based pest management. Weed Sci 47:129.Google Scholar
Mortensen, D. A., Bastiaans, L., and Sattin, M. 2000. The role of ecology in the development of weed management systems: an outlook. Weed Res 40:49–62.Google Scholar
Mortensen, D. A. and Coble, H. D. 1989. The influence of soil water content on common cocklebur (Xanthium strumarium) interference in soybean (Glycine max). Weed Sci 37:76–83.Google Scholar
[NRC] National Research Council. 1996. Ecologically Based Pest Management: New Solutions for a New Century. Washington, DC: National Academy Press. Pp. 33–53.Google Scholar
O'Donovan, J. T. 1996. Weed economic thresholds: useful agronomic tool or pipe dream? Phytoprotection 77:13–28.Google Scholar
O'Donovan, J. T., De St. Remy, E. A., O'Sullivan, P. A., and Dew, D. A. 1985. Influence of the relative time of emergence of wild oat (Avena fatua) on yield loss of barley (Hordeum vulgare) and wheat (Triticum aestivum). Weed Sci 33:498–503.Google Scholar
Oriade, C. A., King, R. P., Forcella, F., and Gunsolus, J. L. 1996. A bioeconomic analysis of site-specific management for weed control. Rev. Agric. Econ 18:523–535.Google Scholar
Paice, M. E. R., Day, W., Rew, L. J., and Howard, A. 1998. A stochastic simulation model for evaluating the concept of patch spraying. Weed Res 38:373–388.Google Scholar
Van Wychen, L. R., Luschei, E. C., Bussan, A. J., and Maxwell, B. D. 2002. Accuracy and cost effectiveness of GPS-assisted wild oat mapping in spring cereal crops. Weed Sci 49:120–129.Google Scholar
Wolfram Research Inc. 1999. Mathematica. Version 4.0. Champaign, IL: Wolfram Research.Google Scholar