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The SafeConsume educational suite was designed to improve knowledge about undertaking safer food practices and avoiding food-borne illnesses among young people. The resources were designed to support educators and members of the community who teach young people (aged 11–18 years) and include lesson plans and supporting teacher training resources. To assess the efficacy and suitability of the resources, an evaluation of the central lesson, the 'user journey', was conducted within four European countries. The mixed-methods evaluation included the following elements: a pre- and post-scenario-based questionnaire, a satisfaction questionnaire, focus groups with students; and interviews with teachers. Data from the scenario-based questionnaires were analysed using a mixed effects normal linear regression model. Qualitative data were thematically analysed, and the main themes were discussed. A total of 171 students and 9 educators took part from schools based in Portugal, Hungary, France and England. The results indicated a significant improvement in students' knowledge and understanding of appropriate food hygiene practices overall, although this varied among countries. The resources were found to be well-suited to help teachers deliver the lesson, being considered by teachers to be both informative and flexible. Minor alternations were suggested, including alterations to lesson delivery or breaking the lesson into smaller sections, and increasing the lesson's interactivity. [ABSTRACT FROM AUTHOR]

This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas' rotation, especially the eucalypt's short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations' characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales. [ABSTRACT FROM AUTHOR]