farmers of providing these services. The researchers develop a programming model for the Peak District livestock farms which optimises the farm gross margin subject to meeting varying biodiversity targets (birds were chosen as the policy-relevant indicator of biodiversity). By incrementally increasing the amount of a chosen biodiversity target that farmers are required to produce, the researchers recover trade-off curves relating maximum farm profit to biodiversity change. Some clear conclusions jump out from their analysis. The cost of achieving a given biodiversity improvement varies across locations. Further, the locations offering low-cost gains to biodiversity vary with the conservation target chosen -- some farms could increase skylark numbers at relatively low cost, but other farms would be more cost-effective in increasing linnet numbers, for example. The greater the number of conservation targets required of a farmer, the more limited the opportunities for low-cost gains. Indeed, in some cases the management actions to encourage different species might even be contradictory. Costs of policy simplification They then discuss the policy that would purchase the maximum improvement in biodiversity for a given budget. The optimal policy design involves preferentially allocating conservation contracts to regions where biodiversity enhancements can be provided relatively cheaply. It also involves setting payment rates that vary across regions and with the amount of biodiversity produced on a farm to recognise the non-linear nature of the trade-off curves. The maximum biodiversity gain possible with the optimal policy is then compared to the biodiversity gain expected in more conventional simpler AEM schemes. (Note