This paper analyzes the implementation of the Geoportal SIG FESBAL–UPM, a Participatory Geographic Information System (PGIS) developed within the Master’s and Doctorate programs in Rural Development Project Planning and Sustainable Management at UPM. The study introduces a model integrated with Project-Based Learning (PBL), the Working With People (WWP) framework, and the CFS-RAI principles to address challenges in responsible food systems. The geoportal designed to be applied at the Food Bank–UPM Chair–FESBAL, acts as an innovative instrument for participation among the different stakeholders enabling the spatialization and analysis of data across social, environmental, and governance dimensions. Functionally, it offers a robust foundation for evidence-based decision-making, systematizes geographic information, and visualizes data via the web, supporting research, training, and community engagement actions. Furthermore, this study details the specific projects and activities developed under the three involved action lines: research, training, and community engagement, identifying strengths and weaknesses in each. The findings affirm that this participatory approach ensures that the proposed solutions are aligned with local needs and priorities, increasing the sustainability and long-term success of the projects implemented through the geoportal.

Pixels classifiers do not allow assessment of uncertainty due to classification errors. This is a major limitation to the direct use of crop type maps (CTM) as agricultural or environmental statistics. Here, we include the results of our recent research to overcome this limitation, using statistical models that integrate ground and CTM data to evaluate any source of uncertainty, including classification errors. We use linear models for field data that can be treated as values of a continuous variable, such as crop acreage observed at parcel level, and multinomial logit models for field data that can be treated as values of a categorical variable, such as the type of crop observed at the pixel level. Among the tasks required to produce agricultural and environmental statistics, we focus on three for which CTM are especially useful: improving the effectiveness of currently used methods, increasing the granularity of statistics by producing small area estimations, and optimizing the sample design. A prototype for integrating ground and CTM data to achieve each of them is included, together with the results of its application to data provided by two National Statistical Offices.