Agroecology and food sovereignty: How many people can be fed through agroecology? A geospatial modeling of the food system of the Padova province

In a time of increasing environmental pressures, climate uncertainty, and global food insecurity, the question of whether local food systems can sustainably meet dietary needs has gained new urgency. This thesis presents a spatially explicit framework to evaluate the food self-sufficiency potential of the Province of Padova under two contrasting agricultural and dietary systems: a conventional, industrial-based model and an agroecological alternative aligned with the EAT-Lancet Planetary Health Diet. By integrating demographic data, national food consumption statistics, agricultural yield records, and environmental datasets, the study assesses how dietary choices and farming methods influence land use demands and sustainability outcomes. Land footprint calculations were carried out for major food groups using yield-based and coefficient-based estimations, while a geospatial Multi-Criteria Decision Analysis (MCDA) was applied in QGIS to generate an agricultural land suitability index. This spatial model informed the estimation of current, potential, and agroecological production capacities across crop groups. The study also compares local food demand with available agricultural production to determine self-reliance levels, supported by a sensitivity analysis that accounts for land set-asides required by ecological infrastructure in agroecological systems. The findings reveal substantial differences in land requirements and self-sufficiency outcomes between the two scenarios, highlighting the importance of dietary transitions, local resource optimization, and spatial planning in fostering resilient food systems. The integrative and modular methodology developed here is data-driven, adaptable, and replicable across different regional contexts, offering a valuable tool for urban and rural planners, policymakers, and researchers committed to sustainable food system transformation.