A framework for evaluating the value of agricultural pest management decision support systems

Abstract

Disease management decision support systems (DSS) are typically prediction algorithms that help farmers assess the risk of an epidemic, to guide whether, and to what extent, fungicide treatment is needed. However, there is frequently little information presented to quantify the value of using the DSS, i.e. the likely increased profit or reduced impact to the environment, and the risks of failing to control the pest. Validation of DSS is often limited to a small number of sites and seasons, as extensive field testing is prohibitively expensive. It would therefore be beneficial to have a method to estimate the value of a DSS using existing data sets gathered for other purposes.

We present a theoretical framework for evaluating the value of DSS, and then describe how this can be applied in practice using four case studies of contrasting DSS under different data constraints. The four case studies include DSS that guide (i) the total dose of pesticide applied; (ii) the number of sprays required; (iii) the timing of the first fungicide application in a spray programme; and (iv) infection risk alerts. We demonstrate how our theoretical framework can be used to evaluate DSS, using existing field and literature data to infer the benefits and risks associated with their use. The limitations of using existing data are explored.