Optimal spraying strategy to combat the coffee berry borer: A dynamic approach

Abstract

The coffee berry borer (CBB), Hypothenemus hampei , is one of the most destructive pests worldwide. In Hawaii, coffee farmers have adjusted their farm management practices to deal with CBB since its introduction in 2010. This study addresses decisions coffee farmers make to combat damage from the coffee berry borer in Hawaii. The decision to spray or not spray a biological insecticide, Beauveria bassiana , is modeled during a typical coffee growing season in Kona, Hawaii. If the expected damage to the crop from not spraying is greater than the cost to spray, then it is beneficial to spray in order to mitigate that damage. To estimate economic damage, a Markov-chain tracks changes in farm-level infestation levels from month-to-month based on whether the farm decides to spray or not. The Markov-chain is incorporated into a dynamic programming model to provide a decision path for spray decisions over the season that optimizes the final net-benefit for a typical farm. The developed economic model is then used as the performance standard for alternate real-world management strategies. Integrated pest management performs well but not much better than spraying on a calendar schedule, and all do better than never spraying. An IPM-calendar hybrid could improve on both alternatives. • Estimate coffee berry borer damages based on farm-level decisions across a variety of spraying strategies. • Optimize a recursive economic model to arrive at the most desirable farm-level decisions about spraying. • Economic model performs best when compared across different strategies. • Results reveal an IPM-calendar hybrid approach could improve farm-level decisions.