The adoption of climate-smart agriculture to address wildfires in the Maya Golden Landscape of Belize: Smallholder farmers' perceptions.

Ecosystems around the globe are enduring wildfires with greater frequency, intensity, and severity and this trend is projected to continue as a result of climate change. Climate-smart agriculture (CSA) has been proposed as a strategy to prevent wildfires and mitigate climate change impacts; however, it remains poorly understood as a strategy to prevent wildfires. Therefore, the authors propose a multimethod approach that combines mapping of wildfire susceptibility and social surveys to identify priority areas, main factors influencing the adoption of CSA practices, barriers to their implementation, and the best CSA practices that can be implemented to mitigate wildfires in Belize's Maya Golden Landscape (MGL). Farmers ranked slash and mulch, crop diversification, and agroforestry as the main CSA practices that can be implemented to address wildfires caused by agriculture in the MGL. In order to reduce wildfire risk, these practices should, be implemented in agricultural areas near wildlands with high wildfire susceptibility and during the fire season (February-May), in the case of slash and mulch. However, socio-demographic and economic characteristics, together with a lack of training and extension services support, inadequate consultation by agencies, and limited financial resources, hinder the broader adoption of CSA practices in the MGL. Our research produced actionable and valuable information that can be used to design policies and programs to mitigate the impacts of climate change and wildfire risk in the MGL. This approach can also be used in other regions where wildfires are caused by agricultural practices to identify priority areas, barriers and suitable CSA practices that can be implemented to mitigate wildfires.

Spatiotemporal distribution, trend, forecast, and influencing factors of transboundary and local air pollutants in Nagasaki Prefecture, Japan.

The study of PM2.5 and NO2 has been emphasized in recent years due to their adverse effects on public health. To better understand these pollutants, many studies have researched the spatiotemporal distribution, trend, forecast, or influencing factors of these pollutants. However, rarely studies have combined these to generate a more holistic understanding that can be used to assess air pollution and implement more effective strategies. In this study, we analyze the spatiotemporal distribution, trend, forecast, and factors influencing PM2.5 and NO2 in Nagasaki Prefecture by using ordinary kriging, pearson's correlation, random forest, mann-kendall, auto-regressive integrated moving average and error trend and seasonal models. The results indicated that PM2.5, due to its long-range transport properties, has a more substantial spatiotemporal variation and affects larger areas in comparison to NO2, which is a local pollutant. Despite tri-national efforts, local regulations and legislation have been effective in reducing NO2 concentration but less effective in reducing PM2.5. This multi-method approach provides a holistic understanding of PM2.5 and NO2 pollution in Nagasaki prefecture, which can aid in implementing more effective pollution management strategies. It can also be implemented in other regions where studies have only focused on one of the aspects of air pollution and where a holistic understanding of air pollution is lacking.