This study assessed the damage and the potential economic threat of pine wilt disease, which is the most common disease caused by forest-integrated pests in Korea. To estimate the rate of damage by pine wilt disease, a structural damage function was implemented. The nonlinear panel probit model and the generalized estimated equation (GEE) were used for the estimation. The estimated damage function and representative concentration pathways (RCP)8.5 data were used to predict the future damage rate by pests caused by climate change. In the assessment of the economic impact on forests, the dynamic optimization model was introduced. The concept of environmental payment was introduced to consider the economic value of non-timber benefits. For the economic analysis, three scenarios were established, i.e., no pest outbreak (baseline), pest infestation (no control), and pest infestation (prevention and control), and the forest management revenues that included the wood and non-wood materials for each scenario were compared. On the basis of the results of the analysis, a simulation was conducted to investigate the changes in forest management revenues according to changes in timber market prices, environmental payments, and climate change. The prediction results confirmed that the future damage by pine wilt disease and the extent of the damaged areas will increase as a consequence of climate change. In addition, the analysis of the economic impact showed that the increase of pest damage caused by climate change will worsen the forest management revenues. As pest damage brought on by climate change is expected to increase uncertainties and economic losses, there is a marked need to review the policies that so far have been focusing only on post-response tasks. In addition to a proper post-incident management, it is necessary to secure the sense of control and stability over the matter through the reinforcement of pre-incident management.

This study examines the statistical association of wildfire risk with climatic conditions and non-climate variables in 48 continental US states. Because the response variable "wildfire risk" is a fractional variable bounded between zero and one, we use a non-linear panel data model to recognize the bounded nature of the response variable. We estimate the non-linear panel data model (fractional probit) using the Generalized Estimating Equation (GEE) approach to ensure that the parameter estimation is efficient. The statistical model, coupled with the future climates projected by Global Climate Models (GCMs), is then employed to assess the impact of global climate change on wildfire risk. Our regression results show that wildfire risk is positively related to spring, summer, and winter temperatures and human population density whereas it is negatively associated with precipitation. The simulation results based on GCMs and the regression model indicate that climate change will intensify wildfire risk throughout the entire US, especially in the South Central region, posing an increasing wildfire threat and thus calling for more effective wildfire management strategies.

Purpose: The purpose of this study was to examine the effect of lifestyle intervention on the development of fatigue, nutritional status and quality of life of patients with gynecologic cancer. Methods: A nonequivalent control group quasi-experimental design was used. Participants were 49 patients with gynecologic cancer. They were assigned to the experiment group (n=24) or the control group (n=25). The lifestyle intervention for this study consisted of physical activity, nutritional education, telephone call counseling, health counseling, monitoring for lifestyle, and affective support based on Cox's Interaction Model of Client Health Behavior and was implemented for six weeks. Results: Significant group differences were found for fatigue (p = .037), nutritional status (p = .034) and social/family well-being (p = .035) in these patients with gynecologic cancer. Conclusion: Results indicate that this lifestyle intervention is effective in lessening fatigue, and improving nutritional status and social/family well-being. Therefore, nurses in hospitals should develop strategies to expand and provide lifestyle interventions for patients with cancer.

This paper examines forest management planning and its possible outcomes using linear programing (LP). More specifically, the most appropriate forest harvesting schedule was selected that can maximize the carbon sequestration in the current forest areas considering forest manager's income. The LP model allows the managers to segment forests into cutting units under rotation basis logging activities. Through harvest prescription from LP, we derived the balanced age-class distribution that constitutes improved conditions for sustainable use of forest resource. However, the solutions from LP did not achieve normal forests with perfectly even aged distribution. Instead, it produced a left-skewed age-class distribution due to the cost restriction of management ruling out the achievement of a normal forest as an optimal solution. The results from our LP model also confirm that the forest management activities will enhance yearly carbon sequestration in forests for all scenarios compared to baseline, and the shorter rotation ages tend to call for more carbon sequestration and economic profit. However, it is difficult to ensure that 50 years rotation is the optimal rotation age for the target forests, since we do not consider the benefit of biodiversity conservation. ARTICLE HISTORY

Abstract:While a large sum of timber stock in private forests, especially broadleaved forests, has been ignored by their owners, a rising global concern about climate change and ecosystems has led to a renewed interest in natural broadleaved forest management strategies. This study establishes the forest growth model for the natural broadleaved forest of Gangwon-do based on the matrix model developed by Buongiorno and Michie. The matrix model by Buongiorno and Michie has been widely applied to study forest population dynamics, especially for uneven-aged forests. To develop an existing matrix model, our approach applies transitional probabilities of forest stands which are calibrated using National Forest Inventory data. Both long and short-term predicted simulation results show that the predicted average tree density and diameter distribution from our model are very close to the stand density and diameter distribution from observed data. Although the model simplifies reality, the results from our study confirm that our models are valid enough to predict the average stand status of the broadleaved forests in Gangwon-do.