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Finding the Best amongst Best Bets: A Conceptual Model

Finding the Best amongst Best Bets: A Conceptual Model. TERESO A. MORFE Landscape Protection Strategies Catchment and Agriculture Services Department of Primary Industries, Frankston Victoria 3199 Cooperative Research Centre - Australian Weed Management. Challenge.

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Finding the Best amongst Best Bets: A Conceptual Model

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  1. Finding the Best amongst Best Bets:A Conceptual Model TERESO A. MORFE Landscape Protection Strategies Catchment and Agriculture Services Department of Primary Industries, Frankston Victoria 3199 Cooperative Research Centre - Australian Weed Management

  2. Challenge Develop recommendations“Best bets” matrix (Grade using*** or ** or *)

  3. Purpose Use optimising model => Find the most cost-effective weed control plan => Examine financial implications “Best bets” matrix (assess quantitatively using index no.) Weed control not an exclusively biological problemDifficultchoices : limited budget & increasing number of competing alternatives

  4. Overview • Two approaches : find the best amongst ‘best bets’ • Features : conceptual model (demo version) • Scenarios : ‘base’ & ‘with technical change’ • Modelling results

  5. Two approaches Approach 1: Extrapolation • Use own judgment by projecting known information • Easy to apply • Weaknesses • what’s worked in the past may no longer work for us today • unsure whether we have really found the ‘best’ (most cost-effective control plan)

  6. Two approaches Approach 2: Linear Programming (LP) • A mathematical modelling technique • Has nothing to do with computer programming • Invented in WW II to optimise military “programs” • Farming application : • To determine the level of a farming activity, • to achieve a specified objective (maxi - min), • subject to a set of restrictions

  7. Restrictions? * amt, qlty of ingredients * nutritional requirements * prices ?? $$ Manager’s Objective? Two approaches LP application : dairy farming problem

  8. Overview • Two approaches : find the best amongst ‘best bets’ • Features : conceptual model (demo version) • Scenarios : ‘base’ & ‘with technical change’ • Modelling results

  9. LP model (demo version) Features: • Manager’s objective : • minimise total cost of weed management plan • Restrictions : • range of ‘best bet’ control techniques (4) for every type of infestation (5) ; • minimum level of infestation desired to be removed • Cost-efficiency index (per technique by type…) cost efficiency index (CEI) = total area controlled (ha) total cost of control ($) e.g., a CEI value of 3 being more efficient than 2, 2 better than 1, and so on… x $100

  10. LP model (demo version) Efficiency ...folding a T-shirt

  11. LP model (demo version) Efficiency index, objective, restrictions

  12. Overview • Two approaches : find the best amongst ‘best bets’ • Features : conceptual model (demo version) • Scenarios : ‘base’ & ‘with technical change’ • Modelling results

  13. ‘With technical change’ scenario Modelled scenarios Base model : ‘Before technical change’ scenario

  14. Overview • Two approaches : find the best amongst ‘best bets’ • Features : conceptual model (demo version) • Scenarios : ‘base’ & ‘with technical change’ • Modelling results

  15. Modelling results (Before technical change) ($’00)

  16. Modelling results (With technical change) ($’00)

  17. Modelling results

  18. Modelling results

  19. Modelling results Run model ...

  20. Final comments Potential to . . . • Reduce level of uncertainty, guesswork • Evolve into a robust & repeatable decision support tool to guide resource allocation decisions • Be useful in examining the effects of efficiency changes on overall budget for weed control • Generate savings to users

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