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Integrated Weed Management: Knowledge-Based Weed Management Systems

Published online by Cambridge University Press:  20 January 2017

Clarence J. Swanton*
Affiliation:
Department of Plant Agriculture, Crop Science Building, University of Guelph, 50 Stone Road E., Guelph, ON, N1G 2W1, Canada
Kris J. Mahoney
Affiliation:
Department of Plant Agriculture, Crop Science Building, University of Guelph, 50 Stone Road E., Guelph, ON, N1G 2W1, Canada
Kevin Chandler
Affiliation:
Department of Plant Agriculture, Crop Science Building, University of Guelph, 50 Stone Road E., Guelph, ON, N1G 2W1, Canada
Robert H. Gulden
Affiliation:
Department of Plant Agriculture, Crop Science Building, University of Guelph, 50 Stone Road E., Guelph, ON, N1G 2W1, Canada
*
Corresponding author's E-mail: cswanton@uoguelph.ca

Abstract

The fundamental role of integrated weed management (IWM) is to provide a source of scientifically based knowledge from which growers can make informed weed-management decisions. The objectives of this article include (1) highlighting the essential knowledge base required for the success of an IWM cropping system, (2) identifying the barriers to acceptance of IWM, and (3) discussing the future research opportunities for IWM. The minimum knowledge base consists of four key components: the effect of tillage on weed population dynamics, the time of weed emergence relative to the crop, the critical period for weed control, and the concept of a harvest window. There are substantial barriers, however, that reduce the willingness of growers to adopt the components of an IWM cropping system. IWM systems can be perceived as unreliable resulting in increased risk to management. No direct economic benefit can be defined clearly nor has there been sustained support for the adoption of IWM. In the future, IWM must change from a descriptive to a predictive science. As new markets evolve for agricultural products, new quality issues will arise that may influence weed management. Environmental auditing of IWM systems in terms of ISO 14000 accreditation, total carbon credits, or energy use will provide an important template from which comparisons of alternative weed-control strategies can be assessed. IWM strategies must be developed to reduce the risk to management and to gain broader support from the crop-protection industry, growers, and government.

Type
Symposium
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anonymous, , 2007. The ISO 14020 Series. http://www.bsdglobal.com/markets/eco_label_iso14020.asp. Accessed: July 12, 2007.Google Scholar
Ball, D. A. 1992. Weed seedbank response to tillage, herbicides, and crop rotation sequence. Weed Sci. 40:654659.CrossRefGoogle Scholar
Beckie, H. J. 2006. Herbicide-resistant weeds: management tactics and practices. Weed Technol. 20:793814.CrossRefGoogle Scholar
Bond, W. and Grundy, A. 2001. Non-chemical weed management in organic farming systems. Weed Res. 41:383405.CrossRefGoogle Scholar
Bosnić, and Swanton, C. J. 1997a. Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays). Weed Sci. 45:276282.CrossRefGoogle Scholar
Bosnić, and Swanton, C. J. 1997b. Economic decision rules for postemergence herbicide control of barnyardgrass (Echinochloa crus-galli) in corn (Zea mays). Weed Sci. 45:557563.CrossRefGoogle Scholar
Buhler, D. D. 2002. Challenges and opportunities for integrated weed management. Weed Sci. 50:273280.CrossRefGoogle Scholar
Buhler, D. D., Liebman, M., and Obrycki, J. J. 2000. Theoretical and practical challenges to an IPM approach to weed management. Weed Sci. 48:274280.CrossRefGoogle Scholar
Bullied, W. J., Marginet, A. M., and Van Acker, R. C. 2003. Conventional- and conservation-tillage systems influence emergence periodicity of annual weed species in canola. Weed Sci. 51:886897.CrossRefGoogle Scholar
Cardina, J., Herms, C. P., and Doohan, D. J. 2002. Crop rotation and tillage system effects on weed seedbanks. Weed Sci. 50:448460.Google Scholar
Cardina, J., Regnier, E., and Sparrow, D. 1995. Velvetleaf (Abutilon theophrasti) competition and economic thresholds in conventional- and no-tillage corn (Zea mays). Weed Sci. 43:8187.Google Scholar
Chandler, K., Shrestha, A., and Swanton, C. J. 2001. Weed seed return as influenced by the critical weed-free period and row spacing of no-till glyphosate-resistant soybean. Can. J. Plant Sci. 81:877880.Google Scholar
Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci. 44:545554.Google Scholar
Clements, D. R., Benoit, D. L., Murphy, S. D., and Swanton, C. J. 1996. Tillage effects on weed seed return and seedbank composition. Weed Sci. 44:314322.Google Scholar
Clements, D. R., Weise, S. F., Brown, R., Stonehouse, D. P., Hume, D. J., and Swanton, C. J. 1995. Energy analysis of tillage and herbicide inputs in alternative weed management systems. Agric. Ecosys. Environ. 52:119128.Google Scholar
Cromar, H. E., Murphy, S. D., and Swanton, C. J. 1999. Influence of tillage and crop residue on postdispersal predation of weed seeds. Weed Sci. 47:184194.Google Scholar
Cummins, J. D. 1991. Statistical and financial models of insurance pricing and the insurance firm. J. Risk Insurance. 58:261302.Google Scholar
Czapar, G., Curry, M. P., and Wax, L. M. 1997. Grower acceptance of economic thresholds for weed management in Illinois. Weed Technol. 11:828831.Google Scholar
Debaeke, P. 1997. Integrated weed control in arable cropland: management parameters and application prospects. Cah. Agric. 6:185194. [In French with English abstract].Google Scholar
Dew, D. A. 1972. Index of competition for estimating crop loss due to weeds. Can. J. Plant Sci. 52:921927.CrossRefGoogle Scholar
Dieleman, A., Hamill, A. S., Fox, G. C., and Swanton, C. J. 1996. Decision rules for postemergence control of pigweed (Amaranthus spp.) in soybean (Glycine max). Weed Sci. 44:126132.Google Scholar
Dieleman, A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (Amaranthus spp.) interference in soybean (Glycine max). Weed Sci. 43:612618.Google Scholar
Dunan, C. M., Westra, P., Schweizer, E. E., Lybecker, D. W., and Moore, F. D. III. 1995. The concept and application of early economic period threshold: the case of DCPA in onions (Allium cepa). Weed Sci. 43:634639.Google Scholar
Green, J. M. 2007. Review of glyphosate and ALS-inhibiting herbicide crop resistance and resistant weed management. Weed Technol. 21:547558.Google Scholar
Gunsolus, J. L. and Buhler, D. D. 1999. A risk management perspective on integrated weed management. in Buhler, D.D., ed. Expanding the Context of Weed Management. New York Hawthorn. 167187.Google Scholar
Hagood, E. S. Jr., Bauman, T. T., Williams, J. L. Jr., and Schreiber, M. M. 1981. Growth analysis of soybeans (Glycine max) in competition with jimsonweed (Datura stramonium). Weed Sci. 29:500504.Google Scholar
Hall, M. R., Swanton, C. J., and Anderson, G. W. 1992. The critical period of weed control in grain corn (Zea mays). Weed Sci. 40:441447.CrossRefGoogle Scholar
Hamerschlag, K. 2007. More Integrated Pest Management Please—How USDA Could Deliver Greater Environmental Benefits From Farm Bill Conservation Programs. http://www.nrdc.org/health/pesticides/ipm/ipm.pdf. Accessed: June 10, 2007.Google Scholar
Hamill, A. S., Weaver, S. E., Sikkema, P. H., Swanton, C. J., Tardif, F. J., and Ferguson, G. M. 2004. Benefits and risks of economic vs. efficacious approaches to weed management in corn and soybean. Weed Technol. 18:723732.CrossRefGoogle Scholar
Hock, S. M., Knezevic, S. Z., Johnson, W. G., Sprague, C., and Martin, A. R. 2007. WeedSOFT: effects of corn-row spacing for predicting herbicide efficacy on selected weed species. Weed Technol. 21:219224.CrossRefGoogle Scholar
Jones, R., Cacho, O., and Sinden, J. 2006. The importance of seasonal variability and tactical responses to risk on estimating the economic benefits of integrated weed management. Agric. Econ. 35:245256.Google Scholar
Jones, R. E. and Medd, R. W. 2005. A methodology for evaluating risk and efficacy of weed management technologies. Weed Sci. 53:505514.CrossRefGoogle Scholar
Knezevic, S. Z., Evans, S. P., Blankenship, E. E., Van Acker, R. C., and Lindquist, J. L. 2002. Critical period for weed control: the concept and data analysis. Weed Sci. 50:773786.CrossRefGoogle Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568573.Google Scholar
Kudsk, P. 1999. Optimising herbicide use—the driving force behind the development of the Danish decision support system. in. Proceedings of the Brighton Crop Protection Conference: Weeds. Brighton, UK British Crop Protection Council. 737746.Google Scholar
Kudsk, P. and Kristensen, J. L. 1992. Effect of environmental factors on herbicide performance. in. Proceedings of the 1st International Weed Control Congress. Melbourne, Australia Weed Science Society of Victoria, Melbourne. 173186.Google Scholar
Liebman, M. and Gallandt, E. R. 1997. Many little hammers: ecological approaches for management of crop-weed interactions. in Jackson, L.E., ed. Ecology in Agriculture and Soil Management. San Diego, CA Academic. 291343.CrossRefGoogle Scholar
Llewellyn, R. S., Lindner, R. K., Pannell, D. J., and Powles, S. B. 2004. Grain grower perceptions and use of integrated weed management. Aust. J. Exp. Agric. 44:9931001.CrossRefGoogle Scholar
Lutman, P. J. W., Cussans, G. W., Wright, K. J., Wilson, B. J., Wright, G. McN., and Lawson, H. M. 2002. The persistence of seeds of 16 weed species over six years in two arable fields. Weed Res. 42:231241.CrossRefGoogle Scholar
Manning, W. G. and Marquis, M. S. 1996. Health insurance: the tradeoff between risk pooling and moral hazard. J. Health Econ. 15:609639.CrossRefGoogle ScholarPubMed
Minkey, D. M. and Moore, J. H. 1998. HERBIRATE: a model that predicts the performance of four herbicides with respect to environmental interactions. in Medd, R.W. and Pratley, J.E., eds. Precision Weed Management in Crops and Pastures. Adelaide, Australia CRC. 123127.Google Scholar
Murphy, S. D., Clements, D. R., Belaoussoff, S., Kevan, P. G., and Swanton, C. J. 2006. Promotion of weed species diversity and reduction of weed seedbanks with conservation tillage and crop rotation. Weed Sci. 54:6977.CrossRefGoogle Scholar
Nazarko, O. M., Van Acker, R. C., and Entz, M. H. 2005. Strategies and tactics for herbicide use reduction in field crops in Canada: a review. Can. J. Plant Sci. 85:457479.Google Scholar
O'Donovan, J. T., Blackshaw, R. E., Harker, K. N., Clayton, G. W., Moyer, J. R., Dosdall, L. M., Maurice, D. C., and Turkington, T. K. 2007. Integrated approaches to managing weeds in spring-sown crops in western Canada. Crop Prot. 26:390398.Google Scholar
O'Donovan, J. T., de St. Remy, E. A., O'Sullivan, P. A., Dew, D. A., and Sharma, A. K. 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. 33:498503.CrossRefGoogle Scholar
Piepho, H. P. 1999. Stability analysis using the SAS system. Agron. J. 91:154160.Google Scholar
Pimentel, D., Hepperly, P., Hanson, J., Douds, D., and Seitel, R. 2005. Environmental, energetic, and economic comparisons of organic and conventional farming systems. Bioscience. 55:573582.CrossRefGoogle Scholar
Pimentel, D., McLaughlin, L., Zepp, A., Lakitan, B., Kraus, T., Kleinman, P., Vancini, F., Roach, W. J., Graap, E., Keeton, W. S., and Selig, G. 1993. Environmental and economic effects of reducing pesticide use in agriculture. Agric. Ecosys. Environ. 46:273288.Google Scholar
Sammons, R. D., Heering, D. C., Dinicola, N., Glick, H., and Elmore, G. A. 2007. Sustainability and stewardship of glyphosate and glyphosate-resistant crops. Weed Technol. 21:347354.Google Scholar
Smith, R. G. 2006. Timing of tillage is an important filter on the assembly of weed communities. Weed Sci. 54:705712.Google Scholar
Swanton, C. J., Chandler, K., and Shrestha, A. 1999. Weed seed return as influenced by the critical weed-free period in corn (Zea mays L). Can. J. Plant Sci. 79:165167.CrossRefGoogle Scholar
Swanton, C. J., Clements, D. R., and Derksen, D. A. 1993. Weed succession under conservation tillage: a hierarchical framework for research and management. Weed Technol. 7:286297.Google Scholar
Swanton, C. J. and Murphy, S. D. 1996. Weed science beyond the weeds: the role of integrated weed management (IWM) in agroecosystem health. Weed Sci. 44:437445.Google Scholar
Swanton, C. J., Murphy, S. D., Hume, D. J., and Clements, D. R. 1996. Recent improvements in the energy efficiency of agriculture: case studies from Ontario, Canada. Agric. Syst. 52:399418.Google Scholar
Swanton, C. J., Shrestha, A., Knezevic, S. Z., Roy, R. C., and Ball-Coelho, B. R. 2000. Influence of tillage type on vertical weed seedbank distribution in a sandy soil. Can. J. Plant Sci. 80:455457.Google Scholar
Swanton, C. J. and Weise, S. F. 1991. Integrated weed management: the rationale and approach. Weed Technol. 5:657663.Google Scholar
Szumigalski, A. and Van Acker, R. 2005. Weed suppression and crop production in annual intercrops. Weed Sci. 53:813825.Google Scholar
Tuesca, D., Puricelli, E., and Papa, J. C. 2001. A long-term study of weed flora shifts in different tillage systems. Weed Res. 41:369382.Google Scholar
Van Acker, R. C., Swanton, C. J., and Weise, S. F. 1993. The critical period of weed control in soybean (Glycine max). Weed Sci. 41:194220.Google Scholar
Wall, E., Weersink, A., and Swanton, C. 2001. Agriculture and ISO 14000. Food Policy. 26:3548.Google Scholar
Weaver, S. E., Kropff, M. J., and Groenevald, R. M. W. 1992. Use of ecophysiological models for crop–weed interference. Weed Sci. 40:302307.Google Scholar