Conservation auctions are increasingly being used to procure public environmental goods on private land. In the absence of demand-side price information, the majority of conservation auctions in Australia have been designed without a reserve price. In these instances bids have been accepted in order of cost-effectiveness until the budget constraint binds. It is widely recognised that in situations where auctions are run repeatedly a reserve price strategy could allow for a more efficient allocation of funds across multiple rounds, both spatially and temporally. This paper provides a brief overview of methods for determining a reserve price for application in conservation auctions. It is concluded that information deficiencies and the high transaction costs involved in the application of these methods to conservation auctions often render them unsuitable for application to real-world auctions. This paper presents an empirical approach to determining a reserve price using data obtained during an auction - the supply curve. The approach stems from the C4.5 algorithm, developed in the field of data mining to construct decision trees from training data using the concept of information entropy. The algorithm establishes a reserve price by determining the cut-off price that results in the ”best fit” of two normal distributions to the frequency distribution of bid-price per unit environmental benefit. Empirical data from conservation auctions in Victoria is used to demonstrate the algorithm and compare auction results obtained using the algorithm and traditional ”budget” methods. The paper presents a discussion on the situations where the algorithm could be appropriately used, and advantages and limitations of the approach are identified. The paper concludes that the use of the algorithm can result in efficiency gains over the traditional budget method in situations where alternative reserve price strategies are impractical.