A decision aid for estimating financial feasibility of non-lethal wolf-livestock conflict prevention practices

Abstract

U.S. reintroduction efforts of the gray wolf (Canis lupus) have contributed to negative externalities that disproportionately fall onto producers in the form of direct and indirect costs due to wolf-livestock predations. Direct costs incurred by livestock owners include market value losses due to livestock death, veterinary costs for injured livestock, and transaction costs associated with carcass management. Indirect costs also occur, such as reduced livestock weight, reduced milk production, and decreased pregnancies from livestock stress responses due to wolf presence. These losses, while not widespread, can be costly to those that experience them. However, there are non-lethal predation prevention practices that can be effective in reducing wolf-livestock conflict. Some of these practices include turbo fladry, electrified night penning, range riding, carcass composting, livestock guardian dogs and more. While there is a great deal of literature about these practices, uncertainty remains regarding their financial feasibility for producers. To help livestock producers manage their risk, this study develops a framework for determining the feasibility of these practices by combining the producer's risk probability of wolf-livestock conflict with their respective costs to implement prevention and mitigation practices of interest. To implement the framework, I develop an online decision aid that provides comparative break-even analysis to offer producers a metric to understand how effective their mitigation practice(s) must be to offset the potential costs associated with livestock predation. The tool provides an estimate of the probability of conflicts for specific properties based on local conditions such as habitat and presence of wildlife and livestock and helps users identify the cost of various mitigation tools and the value that might be lost in a conflict. Additionally, this analysis implements sensitivity analysis and Monte Carlo Simulation (MSC) to better understand how impactful varying inputs such as material costs, predation risk and state funding for practice implementation are on the financial outcomes of the model.