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A methodology for evaluating risk and efficacy of weed management technologies

Published online by Cambridge University Press:  20 January 2017

Randall E. Jones  and
Richard W. Medd
Richard W. Medd
Affiliation:
Cooperative Research Centre for Australian Weed Management, NSW Department of Primary Industries, Orange Agricultural Institute, Forest Road, Orange 2800, NSW, Australia
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Abstract

A stochastic simulation modeling framework was developed for measuring the impact of weed management technologies in terms of their risk and efficacy. The framework explicitly accounted for the variability of environmental conditions, which underpins risk, and its effect upon the weed population dynamics and crop yields. It was applied to wild oat and wild radish in spring wheat as case studies. Technologies considered included a postemergence herbicide, preseeding tillage, increased crop density, and a selective spray-topping (seed-head sterilizing) herbicide. All stages of the weed life cycle were specified as random variables on the basis of triangular probability distributions, which either were derived from daily environmental conditions or specified as a subjective probability distribution. By using probability density functions the study identified the risks to changes in the weed seed bank and weed density associated with various integrated weed management strategies. This approach not only quantified the probabilities associated with the different outcomes, but also identified how the probability distributions of outcomes were changed as a result of different technology combinations used in weed management strategies. For instance, strategies involving a selective spray-topping herbicide to control seed rain not only resulted in lower seed banks, but the range in possible values was considerably reduced, implying lower likelihood of a population increase. The use of such a risk framework not only benefits weed scientists in terms of designing more effective weed management technologies, but can also assist farmer adoption by being able to quantify the probabilities of success and failure of a technology.

Keywords

Wild oat, Avena spp.wild radish, Raphanus raphanistrum L.wheat, Triticum aestivum L.Modelingprobability density functionpopulation dynamicsrisksimulation
Type
Weed Management
Information
Weed Science , Volume 53 , Issue 4 , August 2005 , pp. 505 - 514
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anderson, J. R. 1991. Agricultural research in a variable and unpredictable world. Pages 103–128 in Pardey, P. G., Roseboom, J., and Anderson, J. R. eds. Agricultural Research Policy: International Quantitative Perspectives. Cambridge, Great Britain: Cambridge University Press.Google Scholar
Anderson, J. R., Dillon, J. L., and Hardaker, J. B. 1977. Agricultural Decision Analysis. Ames, IA: Iowa State University Press. P. 344.Google Scholar
Archer, D., Eklund, J., Walsh, M., and Forcella, F. 2002. WEEDEM: a user-friendly software package for predicting annual ryegrass and wild radish emergence. Pages 252–253 in Proceedings of the 13th Australian Weeds Conference. Perth, Australia.Google Scholar
Auld, B. A., Menz, K. M., and Tisdell, C. A. 1987. Weed Control Economics. London: Academic. P. 177.Google Scholar
Cook, T., Storrie, A., and Medd, R. 1999. Selective spray-topping: Field testing of a new technique for reducing wild oat seed production. Pages 53–56 in Proceedings of the 12th Australian Weeds Conference. Hobart, Australia.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol 107:239–252.CrossRefGoogle Scholar
Fitzpatrick, E. A. and Nix, H. A. 1970. The climatic factor in Australian grassland ecology. Pages 1–26 in Milton Moore, R. ed. Australian Grasslands. Canberra: ANU.Google Scholar
Forcella, F. 1993. Seedling emergence model for velvetleaf. Agronomy J 85:929–933.CrossRefGoogle Scholar
Grundy, A. C. 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Res 43:1–11.Google Scholar
Gunsolus, J. L. and Buhler, D. D. 1999. A risk management perspective on integrated weed management. Pages 167–188 in Buhler, D. D. ed. Expanding the Context of Weed Management. New York: Haworth.Google Scholar
Hardaker, J. B., Huirne, R. B. M., Anderson, J. R., and Lien, G. 2004. Coping with Risk in Agriculture. 2nd ed. Wallingford, CT: CABI.Google Scholar
Jones, R. E. and Medd, R. W. 2000. Economic thresholds and the case for longer term approaches to population management of weeds. Weed Tech 14:337–350.Google Scholar
Kingwell, R. S. 1994. Effects of tactical responses and risk aversion on farm wheat supply. Rev. Market. Agric. Econ 62:29–42.Google Scholar
Kingwell, R. S., Pannell, D. J., and Robinson, S. D. 1993. Tactical responses to seasonal conditions in whole-farm planning in Western Australia. Agric. Econ 8:211–226.Google Scholar
Lemerle, D., Gill, G. S., Murphy, C. E., Walker, S. R., Cousens, R. D., Mokhtari, S., Peltzer, S. J., Coleman, R., and Luckett, D. J. 2001. Genetic improvement and agronomy for enhanced wheat competitiveness with weeds. Aust. J. Agric. Res 52:527–548.CrossRefGoogle Scholar
Littleboy, M., Freebairn, D. M., Silburn, D. M., Woodruff, D. R., and Hammer, G. L. 1999. PERFECT Version 3.0:. A Computer Simulation Model of Productivity Erosion Runoff Functions to Evaluate Conservation Techniques (http://www.apsru.gov.au/apsru/Products/HowLeaky/Downloads.html).Google Scholar
Madafiglio, G. P. 2002. Population Management of Raphanus raphanistrum L. (Wild Radish) by Regulating Seed Production. . University of Western Sydney, Penrith South, Australia. P. 208.Google Scholar
Madafiglio, G. P., Medd, R. W., and Cornish, P. S. 1999. Selective spray-topping, a new technique for controlling broadleaved weeds in cereals. Pages 269–272 in Proceedings of the 12th Hobart: Australian Weeds Conference.Google Scholar
Madafiglio, G. P., Medd, R. W., Cornish, P. S., and Van de Ven, R. 2000. Temperature-mediated responses of flumetsulam and metolsulam on Raphanus raphanistrum . Weed Res 40:387–395.CrossRefGoogle Scholar
Medd, R. W., McMillan, M. G., and Cook, A. S. 1992. Spray-topping of wild oats (Avena spp.) in wheat with selective herbicides. Plant Prot. Quart 7:62–65.Google Scholar
Medd, R. W., Nicol, H. I., and Cook, A. S. 1995. Seed-kill and its role in weed management systems: A case-study of seed production, seed banks and population growth of Avena species (wild oats). Pages 627–632 in Proceedings of the Ninth European Weed Research Society Symposium, Volume 2. Budapest, Hungary: European Weed Research Society.Google Scholar
Medd, R. W., Van de Ven, R., Pickering, D. I., and Nordblom, T. 2001. Determination of environment-specific dose response relationships for clodinafop-propargyl on Avena spp. Weed Res 41:351–368.CrossRefGoogle Scholar
Monjardino, M., Pannell, D. J., and Powles, S. B. 2003. Multispecies resistance and integrated management: a bioeconomic model for integrated management of rigid ryegrass (Lolium rigidum) and wild radish (Raphanus raphanistrum). Weed Sci 5:798–809.Google Scholar
Nix, H. A. 1981. Simplified simulation model based on specified minimum data sets: The CROPEVAL concept. Pages 151–169 in Berg, A. ed. Application of Remote Sensing to Agricultural Production Forecasting. Rotterdam: Commission of the European Communities.Google Scholar
Powles, S. B. and Bowran, D. G. 2000. Crop weed management systems. Pages 287–306 in Sindel, B. M. ed. Australian Weed Management Systems. Melbourne, Australia: RG and FJ Richardson.Google Scholar
Preston, C. 2003. Reasons and underlying principles for IWM in grain cropping systems of southern Australia. Pages 20–21 in Weed Society of Victoria First Biennial Conference ‘Developments in Weed Management’ August 2003.Google Scholar
Shaw, W. C. 1982. Integrated weed management systems technology for pest management. Weed Sci 30:(Supplement 1). 2–12.CrossRefGoogle Scholar
Young, K. 2001. Germination and emergence of wild radish (Raphanus raphanistrum L.). . University of Melbourne, Melbourne, Australia. P. 205.Google Scholar
Young, K. and Cousens, R. 1999. Factors affecting the germination and emergence of wild radish (Raphanus raphanistrum) and their effect on management options. Pages 179–182 in Proceedings of the 12th Australian Weeds Conference, Hobart, Australia: Tasmanian Weed Society.Google Scholar
Young, K., Kriticos, D., and Gallagher, R. 2002. Towards a process based emergence model for wild radish. Pages 266–269 in Proceedings of the 13th Australian Weeds Conference. Perth, Australia: Australian Weed Society.Google Scholar
Zhang, J., Weaver, S. A., and Hamill, A. S. 2000. Risk and reliability of using herbicides at below-label rates. Weed Technol 14:106–115.CrossRefGoogle Scholar