Financing methods for small-scale hardwood plantations in Queensland, Australia

Under Vision 2020, a target was set in 1997 for trebling the plantation area in Australia by the year 2020. Government subsidies and extension for plantation establishment have largely disappeared, hence forestry expansion is highly dependent on access to private finance. In the state of Queensland, plantation expansion has occurred predominantly through managed investment schemes and the joint venture scheme managed by Forestry Plantations Queensland, a government-owned corporation. Most of these plantings are relatively small-scale hardwood plantations, which are designed to replace the hardwood timber from the native forests that will be protected from further logging after 2024 under the South-East Queensland Regional Forestry Agreement. Views on financing methods for forestry expansion in Queensland were investigated through by an email survey of 12 forestry and finance professionals, followed by in-depth personal interviews of the same group of key informants. Some of the issues identified include lack of transparent information, inequitable taxation system between Managed Investment Scheme (MIS) companies and small-scale forest operators, the need for further R&D on all aspects of the industry, the potential impacts of carbon credit schemes on the industry, and the design of a strategic model for forestry investors. Participants took the view that adoption of a strategic alliance model would encourage further investment in small-scale forestry, arguing that this model could protect the interest of all the stakeholders through reducing investment risk and creating competitive advantage. The potential introduction of a carbon trading scheme also attracted interest from investors, who look for recognisable structures that may alleviate the risk of investing in an industry with which they are unfamiliar. The participants considered that further R&D should be the main focus for government participation in small-scale forestry

Análises espaciais na identificação das áreas de risco para a esquistossomose mansônica no município de Lauro de Freitas, Bahia, Brasil Identification of schistosomiasis risk areas using spatial analysis in Lauro de Freitas, Bahia State, Brazil

A disseminação da esquistossomose mansônica vem desafiando o sistema de saúde brasileiro, deixando clara a necessidade da reavaliação das estratégias do programa de controle da endemia. O objetivo deste trabalho foi delimitar as áreas geográficas de risco para a esquistossomose em Lauro de Freitas, Bahia, Brasil, e estabelecer o perfil epidemiológico e socioeconômico da doença no município. Utilizou-se o estimador de densidade de Kernel para a identificação visual de aglomerados de casos e a análise de varredura espaço-temporal de Kulldorff & Nagarwalla para a obtenção de aglomerados com significância estatística e mensuração do risco. As duas técnicas identificaram quatro áreas de risco para a doença no município, com indicadores socioeconômicos mais baixos que as áreas fora dos aglomerados. A análise de correspondência múltipla mostrou um perfil diferenciado nos pacientes positivos para a esquistossomose pertencentes ao aglomerado primário. As técnicas empregadas se configuram em uma importante aquisição metodológica para a vigilância e controle da doença no município.<br>The spread of schistosomiasis mansoni defies efforts by Brazil's Unified National Health System, thus demonstrating the need to reassess endemic control programs in the country. The aim of this study was to demarcate geographic areas at risk of schistosomiasis in Lauro de Freitas, Bahia State, Brazil, and to establish the epidemiological and socioeconomic profile of the disease in this municipality (county). Kernel density estimator exploratory analysis was used for visual identification of areas at risk. Kulldorff & Nagarwalla's spatial analysis was used to obtain statistically significant clusters and to measure risk. These technologies identified four risk areas for schistosomiasis. Clusters identified within the risk areas were characterized by lower socioeconomic conditions. Multiple correspondence analyses showed a distinct profile for positive patients in the primary cluster. The techniques employed here represent an important methodological acquisition for tracking and controlling schistosomiasis in Lauro de Freitas