Using constructed value of information to evaluate research needs in conservation strategy assumptions

Abstract

The foundation of any learning-based management process is a clear justification for the need to reduce uncertainty. A research team at The Nature Conservancy used constructed value of information analysis (CVOI) to prioritize which sources of uncertainty to reduce for a conservation strategy that offers conservation practices through farming industry advisors in the Chesapeake Bay watershed, USA. Seven causal assumptions related to human behavior were developed for the strategy. The team implemented synthesis reviews of three CVOI metrics. The evidence metric measured the magnitude and quality of uncertainty associated with the assumption. The relevance metric measured the degree to which actions that might reduce uncertainty would improve desired outcomes. The reducibility metric measured the degree to which uncertainty could be reduced through time, resource investment, and inference reliability. The team applied constructed ratio scales for evidence and relevance and a constructed ordinal scale for reducibility to the assumptions individually. CVOI was calculated as the product of evidence and relevance metrics, and the assumptions were graphically displayed based on their CVOI and reducibility scores. Results indicated that learning-based management should focus on promoting conservation incentives in advisor business models, seeking the best incentive that farmers are willing to accept, and assuring that farmers implement conservation practices over time. This study demonstrated decision analysis methods, and we highlighted several advantages and challenges of using the CVOI methodology to guide future research.