Consumer knowledge, attitudes, and behaviour regarding food safety - A perspective from Laos.

Primary research data on food chain safety at consumer level is needed to help policymakers to decrease the risk of foodborne infections in the Lao People's Democratic Republic. Therefore, the objectives of this study are to 1) determine Lao people's food safety knowledge, attitudes, and intentions and identify the most influential factors that shape their food safety behaviour, and 2) to support the government in the development of more efficient risk communication programmes. A paper-based consumer survey was implemented to collect quantitative data on the selected topics about Laotian consumers, as well as on the equipment of households regarding the conditions and tools necessary for safe food preparation and storage. A model was developed based on the extended Theory of Planned Behaviour (TPB), and partial least squares structural equation modelling (PLS-SEM) was applied to analyse the factors that affect food safety behaviour. The elements of Knowledge and Perceived Behavioural Control positively influence behaviours, such as using separate chopping boards for raw and ready-to-eat foods. According to the model, only 10 % of the Intentions would be translated into Behaviours, which gap can be explained by the lack of a hygienic environment. Although the survey indicated that Laotian people intend to follow food safety guidelines, participants felt they lacked sufficient knowledge. This perception was validated by the knowledge test and behavioural part of the study. In the light of these findings, it is essential to raise awareness and promote food safety knowledge and interventions, such as enhancing food safety education in childhood. This would help to facilitate and encourage adopting safe practices to improve overall food safety at the consumer level, delivering positive impacts also to the food business sector.

A dataset on the geographical distribution, bounds, and reed cover of Hungarian fishponds.

This paper presents geospatial datasets, figures, and tables illustrating i) the location and total area of fish farms under cultivation; and ii) the spatiotemporal dynamics of reed cover in Hungarian fishponds generated from the published study of Sharma et al., [1]. Preliminary data for fish farm locations were obtained from the Institute of Agricultural Economics (AKI), followed by significant refinement based on high-resolution Google Earth Pro-imagery. The fishpond area dataset was validated against the values reported in annual statistical reports on aquaculture. In order to map reed vegetation freely available Sentinel-2 imagery (between 2017 and 2021) was accessed from the Copernicus Open Access Hub [2] and emergent macrophyte cover was classified using the NDVI-based threshold values [1]. Scientists, policymakers, and fish farmers can all benefit from such geospatial datasets. It could be used to monitor the extent of fishponds in Hungary and to design farm-level reed management plans to optimize the provision of ecological and production services.