How Do Growers Respond to Host Resistance? A Conditional Gaussian Bayesian Network for Causal Inference of Fungicide Cost Savings

The economic value of cultivars resistant to disease is of great interest, but how growers change their fungicide use in response to host resistance may be nuanced. We draw upon a well-described data set of the incidence of hop plants with powdery mildew and associated production metadata and demonstrate the utility of Bayesian networks as a framework for quantifying causal relationships for fungicide use and cost in response to host resistance. Conditional Gaussian Bayesian network models applied to cultivars differing in race-specific resistance to powdery mildew revealed cultivar resistance to powdery mildew influenced disease levels in early spring, which had a causal effect on how often and what fungicides growers later applied. Annual costs depended on not only the number of applications made but also the specific types of fungicides growers selected. Fungicide costs were little changed on cultivars that possessed race-specific resistance to only one of two extant strains of the pathogen. For cultivars with resistance to both pathogen strains, annual costs of fungicides were reduced commensurate with the level of resistance. Predicted values from the Bayesian networks and simulation indicate that growers apply a baseline level of fungicide, independent of cultivar resistance. Fungicide cost savings result from how fungicide inputs differentially scale with the incidence of powdery mildew and the type of fungicides used. Our analyses indicate that for a high-value crop, deployment of disease resistance may cause complex and unexpected changes in growers' fungicide use patterns that may not be obvious in simplified randomized controlled trials.