An Application of Fuzzy of Decision Theory to cost-benefit analysis of pest management strategies

This paper deals with pest metapopulations and the problem of selecting the best strategy in a set of proposed treatments for their control. A multi-criteria decision making approach is followed. On the one hand, a spatially explicit metapopulation model is applied to describe pest metapopulation dynamics and predict the consequences of management strategies. An index derived from Information Theory, the Kullback-Leibler divergence, is then applied to evaluate the efficacy of the control strategies on a regional basis. A strategy is effective if able to make the distance from the target state of global eradication lower than the distance obtained by renouncing to the application of any control strategy. On the other hand, a simple cost function is introduced. A cost-benefit analysis is finally carried out by Fuzzy Decision Making to rank strategies according to their capability in being effective and economically advantageous for pest control. The proposed method can be applied even in presence of scarce data; the involved quantities can be easily computed and more generality can be included. All these features make it a promising tool in an adaptive framework for pest metapopulation management. The methodology is exemplified by the case of Pine processionary moth Traumatocampa pityocampa metapopulations.