Wildfire occurrences have increased and are projected to continue increasing globally. Strategic, evidence-based planning with diverse stakeholders, making use of diverse ecological and social data, is crucial for confronting and mitigating the associated risks. Prescribed fire, when planned and executed carefully, is a key management tool in this effort. Assessing where prescribed fire can be a particularly effective forest management tool can help prioritize efforts, reduce wildfire risk, and support fire-resilient lands and communities. We collaborated with expert stakeholders to develop a Bayesian network model that integrated a large variety of biophysical, socioecological, and socioeconomic spatial information for the Southeastern United States to quantify where risk is high and where prescribed fire would be efficient in mitigating risk. The model first estimated wildfire risk based on landscape-scale interactions among the likelihoods of fire occurrence and severity and the people and resources potentially exposed—accounting for socioeconomic vulnerabilities as well as key ecosystem services. The model then quantified the potential for risk reduction through prescribed fire, given the existing fuel load, climate, and other landscape conditions. The resulting expected risk estimates show high risk concentrated in the coastal plain and interior highland subregions of the Southern US, but there was considerable variation among risks to different ecosystem services and populations, including potential exposure to smoke emissions. The capacity to reduce risk through fuel reductions was spatially correlated with risk; where these diverged, the difference was largely explained by fuel load. We suggest that both risk and the capacity for risk reduction are important in identifying priorities for management interventions. The model serves as a decision support tool for stakeholders to coordinate large-landscape adaptive management initiatives in the Southern US. The model is flexible with regard to both empirical and expert-driven parameterizations and can be updated as new knowledge and data emerge. The resulting spatial information can help connect active management options to forest management goals and make management more efficient through targeted investments in priority landscapes.

Risks associated with severe wildfire are growing in forest landscapes due to interactions among climate change, fuel accumulation from fire suppression, an expanding wildland–urban interface, and additional factors. People, infrastructure, ecosystem services, and forest health all face varying degrees of risk. The spatial distributions of the many social and ecological factors that influence wildfire, its impacts, and management responses are an important landscape-level context for managing risks and fostering resilient lands and communities. Decision-support tools that integrate these varied distributions can provide a holistic and readily interpreted characterization of landscapes, helping fire management decision making be appropriate, efficient, and effective. Firescapes—landscape types defined in relation to fire, its drivers, and its effects as a socioecological system—fill this role, providing a way to organize and interpret spatial variation along multiple relevant dimensions. We describe a quantitative approach for classifying and mapping firescapes for decision support, using the southeastern United States as a case study. We worked with regional partners to compile relevant large-scale datasets and identify 73 variables for analysis. We used factor analysis to reduce the data to eight factors with intuitive interpretations relevant to fire dynamics, fire history, forest characteristics, climate, conservation and ecosystem service values, social and ecological landscape properties, and social vulnerabilities. We then used cluster analysis on the factors to generate quantitative landscape classes, which we interpreted as nine distinctive firescape classes. The firescapes provide a broad-scale socioecological information context for wildfire risk management and planning. The analytical approach can accommodate different data types at a variety of scales, incorporate new monitoring data as they are available, and can be used under data-driven scenarios to assess possible consequences of future change. The resulting firescape maps can provide decision support to forest managers, planners, and other stakeholders, informing appropriate strategies to manage fire and associated risks, build community and forest resilience to fire, and improve conservation outcomes.

Bioenergy crops are considered as potential biomass feedstocks to support the bioenergy industry in the southern US. Even though there are suitable areas to grow bioenergy crops, commercial scale production of bioenergy crops has not been established to meet the increasing energy demand. Establishing bioenergy crops in the region requires landowners’ participation and it is crucial to understand whether they intend to promote bioenergy crop production. This study evaluated landowners’ perception of bioenergy and their willingness to supply lands for bioenergy crops in northern Kentucky. A questionnaire survey of randomly selected landowners was administered in four selected counties. Results indicated that landowners’ land use decisions for bioenergy crop production were based on their current land management practices, socio-economic and environmental factors. Overall, there was a low willingness of landowners to participate in bioenergy crop production. Those who were interested indicated that a higher biomass price would be required to promote bioenergy crops on their land. This information could be useful to plan for policies that provide economic incentives to landowners for large-scale production of bioenergy crops in the study area and beyond. Further, results showed how landowners’ opinion on bioenergy affected their preferences for land use decisions. Younger landowners with positive attitude towards bioenergy were more willing to promote bioenergy crops. This information could be useful to develop outreach programs for landowners to encourage them to promote bioenergy crops in the study area.

This study used a spatially-explicit model to identify the amount and spatial distribution of economically feasible sites for establishing dedicated energy crops under various market and policy scenarios. A sensitivity analysis was performed for a biomass market with different discount rates and biomass prices as well as policy scenarios including propriety tax exemption, carbon offset payments, and the inclusion of farmland for biomass production. The model was applied to a four-county study area in Kentucky representing conditions commonly found in the Ohio River Valley. Results showed that both biomass price and discount rate have a can strongly influence the amount of economically efficient sites. Rising the biomass price by 5 $·t⁻¹ and lowering discount rate by 1% from the baseline scenario (40 $·t⁻¹ and 5%) resulted in an over fourteen fold increment. Property tax exemption resulted in a fourfold increase, a carbon payment on only 1 $·t⁻¹ caused a twelve fold increase and extending the landbase from marginal land to farmland only slightly increase the economically efficient sites. These results provide an objective evaluation of market and policy scenarios in terms of their potential to increase land availability for establishing dedicated energy crops and to promote the bioenergy industry.