A Decision Theoretic Approach to Crop Disease Prediction and Control

TLDR: In this article, Bayesian decision theory procedures are used to arrive at optimal crop disease control practices, and subjective probabilities of disease loss intensity are measured and used in the decision model, and the optimal pesticide use actions are computed for three different objective functions, maximum subjective expected returns, mean-standard deviation of returns, and maximum expected returns with a minimum income side condition.

Abstract: The pesticide application practices of California peach growers in controlling peach brown‐rot are used to demonstrate how Bayesian decision theory procedures can be used to arrive at optimal crop disease control practices. Subjective probabilities of disease loss intensity are measured and used in the decision model. Information from an analyst (this researcher) is combined with farmers' subjective probabilities of disease loss by means of Bayes' theorem. Optimal pesticide use actions are computed for three different objective functions—maximum subjective expected returns, mean‐standard deviation of returns, and maximum expected returns with a minimum income side condition.