John Rolfe, Juliana McCosker, Jill Windle

1. Identifying the incentives that graziers in central-western Queensland need to manage land more conservatively John Rolfe, Juliana McCosker, Jill Windle

2. Managing rangelands areas • Substantial evidence that there are some impacts of pastoral activities on rangelands areas • Substantial impacts emerged in western NSW in 1880s and 1890s • Varied impacts since then by region and time period • Problems of imperfect knowledge and lags between cause and effect

3. Better knowledge • Improvements in knowledge by land managers, scientists, and policy makers • Still gaps between what public wants and incentives that landholders face to maximise production • Gaps in knowledge about how changes in land management will have ecological impacts • General view that reductions in livestock pressure will have beneficial outcomes for environment

4. How to quantify the economic impacts of reduced grazing pressure? • Important to assess economic impacts of reduced grazing at property level • Three main options to do this • Farm production models • Analysis of land prices (expectations about future profitability) • Experimental auctions (assessing expectations of landholders)

5. Case study of interest • Desert Uplands region of central-western Queensland • About size of Tasmania • Beef cattle, extensive grazing • Low productivity country, but generally good condition • Some fragmentation from clearing • Fragile in many areas • Increased pressure from grazing

6. Scenario of interest • Landholders enter voluntary agreement to have minimum level of biomass – 40% - over certain areas • Could be over particular area or for corridor across property • Expect that lower stocking rates would be needed to achieve condition

7. Used two approaches to assess economic impacts • Simple production models • Estimated returns per acre • Multiplied by change in stocking rate • Multiplied by area involved • Experimental auctions • Asked landholders to design conservation areas and submit bids • Assessed bids to identify drivers of bid values

8. Simple production models

9. Experimental workshops • Held 3 hour workshop with small group of landholders in Barcaldine and Jericho • Each allocated a ‘dummy property’ to treat as their own • Had to indicate the area that they would manage, and a bid for being paid • Several rounds held in each workshop • Small cash prizes awarded to most efficient bids • Efficiency estimated by calculating environmental benefits and dividing by price • Like BushTender process with single management action

10. Dealing with hypothetical bias • Put pressure on workshop participants to deliver cost-effective bids • Provided cash prizes after each round for the most cost-effective bids • Repeated the rounds 3 or 4 times • Tried to guard against artificially low bids • Asked participants to base bids on their own property operations • Said that our results might be used by government to allocate funding to the area

13. Cost effectiveness of pooled bids

14. Cost-effectiveness of cumulative bids

15. Outcomes of experimental auctions • Experimental auctions allowed the MBI process to be trialled • Efficiency of multiple bidding rounds • Auction design to link corridors across properties • Also provided feedback about the incentives that landholders would need to engage in conservation actions • Analysis of bids allows key drivers to be identified

16. Regression analysis of bids

17. Comparison to simple production model - 1 • Comparison shows that in experimental auction process: • Area of yellowjacket and ironbark not significant • Value/acre of other vegetation types much higher than in simple model • A number of other factors important • Property characteristics (size, % of vegetation) • Interest in being paid for providing services • Bidding round (effect of competition)

18. Comparison to simple production model - 2 • Both approaches used to estimate the value of conserving an option: • 1000 acres of gidgee scrub • 1000 acres of box • 1000 acres of ironbark • 1000 acres of yellowjacket • 1000 acres of cleared country (regrowth) • Value under simple model = $3440 • Value from experimental auction / regression model = $15, 028

19. Why did the experimental auctions predict higher values than simple production models? • Production models too simple • Did not take into account location factors (creek lines, water points, fences) • Did not account for risk and uncertainty • Did not consider extra management costs (extra mustering, fire breaks) • Experimental auction results included more factors • transaction costs (for negotiating and monitoring agreements) • Engagement costs (pain and suffering for dealing with the government)

20. Conclusions • Experimental auctions can be used to estimate the cost to landholders of taking up new management practices • Results give values of changed management that are much higher than predicted from simple production models • Simple production models not accurate • Experimental auctions includes more factors – such as attitudes of landholders.