The management of biological invasions is a worldwide conservation priority. Unfortunately, decision‐making on optimal invasion management can be impeded by lack of information about the biological processes that determine invader success (i.e. biological uncertainty) or by uncertainty about the effectiveness of candidate interventions (i.e. operational uncertainty). Concurrent assessment of both sources of uncertainty within the same framework can help to optimize control decisions. Here, we present a Value of Information (VoI) framework to simultaneously analyse the effects of biological and operational uncertainties on management outcomes. We demonstrate this approach with a case study: minimizing the long‐term population growth of musk thistle The ranking of biocontrol agents was sensitive to differences in the target weed's demography and also to differences in the effectiveness of the different biocontrol agents. This finding suggests that accounting for both biological and operational uncertainties is valuable when making management recommendations for invasion control. Furthermore, our VoI analyses show that reduction of all uncertainties across all combinations of demographic model and biocontrol effectiveness explored in the current study would lead, on average, to a 15.6% reduction in musk thistle population growth rate. The specific growth reduction that would be observed in any instance would depend on how the uncertainties actually resolve. Resolving biological uncertainty (across demographic model combinations) or operational uncertainty (across biocontrol effectiveness combinations) alone would reduce expected population growth rate by 8.5% and 10.5% respectively.
Farming decisions in a complex and uncertain world: Nitrogen management in Midwestern corn agriculture
Excess agricultural nitrogen (N) in the environment is a persistent problem in the United States and other regions of the world, contributing to water and air pollution, as well as to climate change. Efforts to reduce N from agricultural sources largely rely on voluntary efforts by farmers to reduce inputs and improve uptake by crops. However, research has failed to comprehensively depict farmers' N decision-making processes, particularly when engaging with uncertainty. Through analysis of in-depth interviews with US corn (Zea mays L.) growers, this study reveals how farmers experience and process numerous uncertainties associated with N management, such as weather variability, crop and input price volatility, lack of knowledge about biophysical systems, and the possibility of underapplying or overapplying. Farmers used one of two general decision-making management strategies to address these uncertainties: heuristic-based or data-intensive decision-making. Heuristic-based decision-making involves minimizing sources of uncertainty and reliance on heuristics and personal previous experiences, while data-intensive decision-making is the increased use of field- and farm-scale data collection and management, as well as increased management effort within a given growing season.
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- Award ID(s):
- 1832042
- NSF-PAR ID:
- 10170319
- Date Published:
- Journal Name:
- Journal of Soil and Water Conservation
- ISSN:
- 0022-4561
- Page Range / eLocation ID:
- jswc.2020.00070
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2489/jswc.2020.00070
