Planetary Health: A Comprehensive View of Food in Brazil - PHFood Brazil

This dataset is part of the study “Integrating national open databases for a comprehensive view on food, environment and health in Brazil”, an initiative that integrates national open databases to provide harmonized information on food production, consumption, environmental sustainability, and public health in Brazil from 1974 to 2024.

The dataset was developed to investigate relationships between food systems, nutrient availability, agricultural production, environmental pressures, and dietary patterns over time. By aggregating and harmonizing data from multiple national sources, the platform enables the analysis of longitudinal trends associated with food production, nutrition, sustainability, and health.

The database includes information on harvested areas, food production (tons), nutrient availability (e.g., energy, protein, fiber, vitamins, and minerals), food acquisition and consumption, water use and deficit, pesticide use, maximum residue levels (MRL), residue monitoring, toxicity classifications, and environmental indicators across Brazilian regions and states.

Data were obtained from official Brazilian governmental platforms, including IBGE, ANVISA, FAOSTAT, and the Ministry of Agriculture. The datasets were integrated using Extract, Transform, Load (ETL) procedures to ensure compatibility, standardization, and interoperability across sources, years, and regions.

Food items were harmonized and classified into standardized food groups, enabling integrated analyses of food systems and environmental sustainability. The database was designed according to FAIR principles (Findable, Accessible, Interoperable, and Reusable) to support transparency and reproducibility.

This dataset can support studies in agrifood systems, nutrition, sustainability, public health, environmental exposure, and climate change. It may also contribute to future modeling approaches involving dietary scenarios, environmental footprints, and food system sustainability.

This work was initially supported by the CAPES Thesis Award (2023) and is currently being expanded through postdoctoral research funded by FAPESP (2025).