Chronic Diseases: Chronic Diseases and Development 2 Health, agricultural, and economic effects of adoption of healthy diet recommendations

Transition to diets that are high in saturated fat and sugar has caused a global public health concern as the pattern of food consumption is a mayor modifiable risk factor for chronic non-communicable diseases Although agri food systems are intimately associated with this transition, agriculture and health sectors are largely disconnected in their priorities policy, and analysis with neither side considering the complex inter relation between agri trade patterns of food consumption health, and development We show the importance of connection of these perspectives through estimation of the effect of adopting a healthy diet on population health, agricultural production trade the economy and livelihoods, with a computable general equilibrium approach on the basis of case studies from the UK and Brazil we suggest that benefits of a healthy diet policy will vary substantially between different populations, not only because of population dietary intake but also because of agricultural production trade and other economic factors

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.