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A bioeconomic model for determining the optimal response strategies for a new weed incursion

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Journal of Bioeconomics

Abstract

Invasions by non-indigenous plant species pose serious economic threats to Australian agricultural industries. When a new invader is identified a rapid response is critical, particularly if the invasive species has the ability to spread rapidly. An early decision is required whether to attempt to eradicate or contain the infestation, or do nothing and leave it to landholders to manage. These decisions should be based on economic considerations that account for long term benefits and costs. This paper describes a bioeconomic simulation framework with a mathematical model representing weed spread linked to a dynamic programming model to provide a means of determining the economically optimal weed management strategies over time, from the government’s perspective. The modelling framework is used to evaluate hypothetical case study invasive weed control scenarios in the Australian cropping systems. The benefit–cost ratios of invasion control are shown to be generally very high and clearly, there are significant benefits to be achieved by controlling highly invasive weeds when initial infestations are at a low level. Even if the invasion cannot be eradicated due to its high invasiveness or budget constraints, it still pays to maintain invasions at low levels.

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References

  • Aldrich R. J. (1984) Weed-crop ecology: Principles in weed management. Breton Publishers, North Scituate, MA

    Google Scholar 

  • Auld B. A., Coote B. G. (1980) A model of a spreading plant population. Oikos 34: 287–292

    Article  Google Scholar 

  • Auld B. A., Coote B. G. (1981) Prediction of pasture invasion by Nassella trichotoma (Gramineae) in south-east Australia. Protection Ecology 3: 271–277

    Google Scholar 

  • Auld B. A., Coote B. G. (1990) INVADE: Towards the simulation of plant spread. Agriculture, Ecosystems and Environment 30: 121–128

    Article  Google Scholar 

  • Bellman R. E. (1957) Dynamic programming. Princeton University Press, Princeton, NJ

    Google Scholar 

  • Cacho O.J. (2004) When is it optimal to eradicate a weed invasion?. In: Sindel B.M., Johnson S.B. (eds) 14th Australian Weeds Conference papers & proceedings, 6–9 September 2004. Wagga Wagga, New South Wales, Australia, pp 49–54

    Google Scholar 

  • Cacho O. J., Spring D., Pheloung P., Hester S. (2006) Evaluating the feasibility of eradicating an invasion. Biological Invasions 8: 903–917

    Article  Google Scholar 

  • Clark C. W. (1990) Mathematical bioeconomics: The optimal management of renewable resources, (2nd ed.). Wiley, New York, USA

    Google Scholar 

  • Cousens R. (1985) An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. The Journal of Agricultural Science 105: 513–521

    Article  Google Scholar 

  • Cousens R., Mortimer M. (1995) Dynamics of weed populations. Cambridge University Press, Cambridge, UK

    Book  Google Scholar 

  • Crawley M. J. (1986) The population biology of invaders. Philosophical Transactions of the Royal Society London B 314: 711–731

    Article  Google Scholar 

  • Diggle A. J., Neve P. B., Smith F. P. (2003) Herbicides used in combination can reduce the probability of herbicide resistance in finite weed populations. Weed Research 43: 371–382

    Article  Google Scholar 

  • Diggle A. J., Salam M. U., Thomas G. J., Yang H. A., O’Connell M., Sweetingham M. W. (2002) Anthracnose tracer: A spatiotemporal model for simulating the spread of anthracnose in a lupin field. Phytopathology 92(10): 1110–1119

    Article  Google Scholar 

  • Doyle C. J. (1997) A review of the use of models of weed control in integrated crop protection. Agriculture, Ecosystems and Environment 64: 165–172

    Article  Google Scholar 

  • Fisher B. S., Lee R. R. (1981) A dynamic programming approach to the economic control of weed and disease infestations. Review of Marketing and Agricultural Economics 49: 175–187

    Google Scholar 

  • Gorddard R. J., Pannell D. J., Hertzler G. (1995) An optimal control model for integrated weed management under herbicide resistance. Australian Journal of Agricultural Economics 39(1): 71–87

    Google Scholar 

  • Gorddard R. J., Pannell D. J., Hertzler G. (1996) Economic evaluation of strategies for management of herbicide resistance. Agricultural Systems 51: 281–298

    Article  Google Scholar 

  • Hanski I. (1998) Metapopulation dynamics. Nature 396: 41–49

    Article  Google Scholar 

  • Higgins S. I., Richardson D. M. (1996) A review of models of alien plant spread. Ecological Modelling 87: 249–265

    Article  Google Scholar 

  • Higgins S. I., Richardson D. M. (1999) Predicting plant migration rates in a changing world: The role of long-distance dispersal. American Naturalist 153: 464–475

    Article  Google Scholar 

  • Higgins S. I., Richardson D. M., Cowling R. M. (1996) Modelling invasive plant spread: The role of plant-environment interactions and model structure. Ecology 77: 2043–2054

    Article  Google Scholar 

  • Jayasuriya, R. T., & Jones, R. E. (2008). A bioeconomic model for determining the optimal response to a new weed incursion in Australian cropping systems. Paper presented to the 52nd Annual Conference of the Australian Agricultural and Resource Economics Society, 5–8 February, Canberra, ACT, Australia. Available: http://ageconsearch.umn.edu/bitstream/6015/2/cp08ja01.pdf. Accessed 12 May 2009.

  • Jayasuriya R.T., van de Ven R., Jones R.E. (2008) Spatial modelling of new weed incursions in cropping systems. In: Klinken R.D., Osten V.A., Panetta F.D., Scanlan J.C. (eds) 16th Australian Weeds Conference Proceedings, 18–22 May 2008. Cairns, North Queensland, Australia, pp 54–56

    Google Scholar 

  • Jayasuriya, R. T., Jones, R. E., & van de Ven, R. (2008b). An economic decision tool for responding to new weed incursion risks in the Australian grains industry. Technical Series No 14, CRC for Australian Weed Management, Adelaide, Australia. ISBN: 978-1-920932-67-1. Available: http://catalogue.nla.gov.au/Record/4503490. Accessed 12 May 2009.

  • Jones R. (2004) The economic benefits of IWM: the role of risk and sustainability in farming systems. In: Sindel B.M., Johnson S.B. (eds) 14th Australian Weeds Conference papers & proceedings, 6–9 September 2004. Wagga Wagga, New South Wales, Australia, pp 576–579

    Google Scholar 

  • Jones R., Medd R. (1997) Economic analysis of integrated management of wild oats involving fallow, herbicide and crop rotational options. Australian Journal of Experimental Agriculture 37: 683–691

    Article  Google Scholar 

  • Jones R. E., Medd R. W. (2000) Economic thresholds and the case for longer term approaches to population management of weeds. Weed Technology 14: 337–350

    Article  Google Scholar 

  • Martin A. R., Mortensen D. A., Lindquist J. L. (1998) Decision support models for weed management: In-field management tools. In: Hatfield J. L., Buhler D. D., Stewart B. A. (eds) Integrated weed and soil management. Ann Arbor Press, Michigan, IN, pp 363–369

    Google Scholar 

  • Monjardino M., Diggle A., Moore J. (2004) What is the impact of harvesting technology on the spread of new weeds in cropping systems?. In: Sindel B.M., Johnson S.B. (eds) 14th Australian Weeds Conference papers & proceedings, 6–9 September 2004. Wagga Wagga, New South Wales, Australia, pp 580–583

    Google Scholar 

  • Okubo A. (1980) Diffusion and ecological problems: Mathematical models. Springer-Verlag, Berlin

    Google Scholar 

  • Pandey S., Medd R. W. (1990) Integration of seed and plant kill tactics for control of wild oats: An economic evaluation. Agricultural Systems 34(1): 65–76

    Article  Google Scholar 

  • Pandey S., Medd R. W. (1991) A stochastic dynamic programming framework for weed control decision making: An application to Avena fatua L. Agricultural Economics 6(2): 115–128

    Article  Google Scholar 

  • Possingham H. P. (1996) Decision theory and biodiversity management: How to manage a metapopulation. In: Floyd R. B., Sheppard A. W., De Barro P. J. (eds) Frontiers of population ecology. CSIRO Publishing, Melbourne, pp 391–398

    Google Scholar 

  • Schweizer E. E., Lybecker D. W., Wiles L. J. (1998) Important biological information needed for bioeconomic weed management models. In: Hatfield J. L., Buhler D. D., Stewart B. A. (eds) Integrated weed and soil management. Ann Arbor Press, Michigan, IN, pp 1–24

    Google Scholar 

  • Sharov A. A., Liebhold A. M. (1998) Bioeconomics of managing the spread of exotic pest species with barrier zones. Ecological Applications 8(3): 833–845

    Google Scholar 

  • Sinden, J., Jones, R., Hester, S., Odom, D., Kalisch, C., James, R., & Cacho, O. (2004). The economic impact of weeds in Australia. CRC for Australian Weed Management, Technical Series, No 8, CRC for Australian Weed Management, Adelaide, Australia. ISBN: 978-1-920932-67-1. Available: http://catalogue.nla.gov.au/Record/3107287. Accessed 10 March 2009.

  • Tayler C. R., Burt O. R. (1984) Near optimal management strategies for controlling wild oats in spring wheat. American Journal of Agricultural Economics 66: 50–60

    Article  Google Scholar 

  • Woolcock J. L., Cousens R. (2000) A mathematical analysis of factors affecting the rate of spread of patches of annual weeds in an arable field. Weed Science 48: 27–34

    Article  Google Scholar 

  • Zimdahl R. L. (1980) Weed-crop competition: A review. International Plant Protection Center, Oregon State University, Corvallis, OR

    Google Scholar 

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Correspondence to Rohan T. Jayasuriya.

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Jayasuriya, R.T., Jones, R.E. & van de Ven, R. A bioeconomic model for determining the optimal response strategies for a new weed incursion. J Bioecon 13, 45–72 (2011). https://doi.org/10.1007/s10818-010-9097-2

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  • DOI: https://doi.org/10.1007/s10818-010-9097-2

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