# Evaluating Pest Management Strategies: A Robust Method and its Application to Strawberry Disease Management

## TL;DR

This paper proposes a new simulation-based methodology incorporating disease pressure effects via quantile regression to evaluate pest management strategies with limited observational data, demonstrated through strawberry disease control in Florida.

## Contribution

It introduces a robust evaluation method that addresses limited data and includes disease pressure impacts, improving pest management decision-making.

## Key findings

- Method effectively evaluates treatments with few observations
- Incorporates disease pressure into yield and profit analysis
- Applied successfully to strawberry disease management in Florida

## Abstract

Farmers use pesticides to reduce yield losses. The efficacies of pesticide treatments are often evaluated by analyzing the average treatment effects and risks. The stochastic efficiency with respect to a function is often employed in such evaluations through ranking the certainty equivalents of each treatment. The main challenge of using this method is gathering an adequate number of observations to produce results with statistical power. However, in many cases, only a limited number of trials are replicated in field experiments, leaving an inadequate number of observations. In addition, this method focuses only on the farmer's profit without incorporating the impact of disease pressure on yield and profit. The objective of our study is to propose a methodology to address the issue of an insufficient number of observations using simulations and take into account the effect of disease pressure on yield through a quantile regression model. We apply this method to the case of strawberry disease management in Florida.