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Optimising control of an agricultural weed in sheep-production pastures


  • Chalak, Morteza
  • Pannell, David J.


Optimal integrated control strategies for the weed blackberry (Rubus anglocandicans) infesting sheep pastures in Australia are analysed for a range of different circumstances. A wide range of control strategies with moderate to high costs and efficacies are analysed, including chemicals, mowing, grazing goats and biological control. The study employs a stochastic dynamic simulation model and a stochastic dynamic programming model to find the optimal control strategies under different levels of infestation. Results show that the application of a biological control agent (Phragmidium violaceum) increases expected net present value (ENPV) by so little that it is not worth introducing. Results indicate that for higher initial infestation areas, the optimal control strategies include fewer control options, resulting in lower cost but also less effective control. This is because the control costs are proportional to the infestation area, so applying expensive control strategies in high infestation area has lower net benefits. When the labour cost of spraying chemicals increases and infestation area is high, it is optimal to replace chemicals with mowing. If the efficacy of chemicals increases it is optimal to use less effective and cheaper chemicals.

Suggested Citation

  • Chalak, Morteza & Pannell, David J., 2012. "Optimising control of an agricultural weed in sheep-production pastures," Agricultural Systems, Elsevier, vol. 109(C), pages 1-8.
  • Handle: RePEc:eee:agisys:v:109:y:2012:i:c:p:1-8
    DOI: 10.1016/j.agsy.2012.01.010

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    References listed on IDEAS

    1. Odom, Doreen I. S. & Cacho, Oscar J. & Sinden, J. A. & Griffith, Garry R., 2003. "Policies for the management of weeds in natural ecosystems: the case of scotch broom (Cytisus scoparius, L.) in an Australian national park," Ecological Economics, Elsevier, vol. 44(1), pages 119-135, February.
    2. Pannell, David J., 1988. "Weed Management: A Review of Applied Economics Research in Australia," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 56(03), December.
    3. Wu, JunJie, 2001. "Optimal weed control under static and dynamic decision rules," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 25(1), June.
    4. C. Robert Taylor & Oscar R. Burt, 1984. "Near-Optimal Management Strategies for Controlling Wild Oats in Spring Wheat," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(1), pages 50-60.
    5. Abadi Ghadim, Amir K. & Pannell, David J., 1991. "Economic trade-off between pasture production and crop weed control," Agricultural Systems, Elsevier, vol. 36(1), pages 1-15.
    6. Epanchin-Niell, Rebecca S. & Wilen, James E., 2012. "Optimal spatial control of biological invasions," Journal of Environmental Economics and Management, Elsevier, vol. 63(2), pages 260-270.
    7. repec:eee:ecomod:v:222:y:2011:i:4:p:1030-1037 is not listed on IDEAS
    8. Zhang, Wei & van der Werf, Wopke & Swinton, Scott M., 2010. "Spatially optimal habitat management for enhancing natural control of an invasive agricultural pest: Soybean aphid," Resource and Energy Economics, Elsevier, vol. 32(4), pages 551-565, November.
    9. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    10. Wu, JunJie, 2001. "Optimal weed control under static and dynamic decision rules," Agricultural Economics, Blackwell, vol. 25(1), pages 119-130, June.
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    Cited by:

    1. Chalak, Morteza, 2014. "Optimal Control for a Dispersing Biological Agent," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(2), August.

    More about this item


    Weed; Blackberry; Optimisation; Stochastic; Agriculture;


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