Bojan Blažica

Stakeholder workshops often produce diverse qualitative and ordinal data that are difficult to process consistently, transparently, and reproducibly, particularly in multilingual settings. To address these challenges, we developed an end-to-end workflow for systematic processing of multilingual participatory workshop data. The workflow integrates preprocessing, structured data management, computational analysis, automated reporting, and interactive dissemination. It incorporates a range of data analysis methods, including large language models (LLMs), and supports both qualitative exploration and quantitative comparison of stakeholder perspectives. We also propose an LLM-based approach for topic extraction and intensity scoring, which transforms qualitative workshop inputs into quantitative representations. The workflow is demonstrated in the EU BENCHMARKS project, which involves multiple workshops, stakeholder groups, land-use contexts, and languages. The main contribution of this work is a transparent and adaptable workflow for systematic processing of multilingual participatory workshop data, supporting reproducible analysis, scalable dissemination, and cross-workshop comparison.

Decision support systems (DSSs) enable the optimisation of pesticide application timing to increase pesticide efficacy and thus reduce pesticide use without compromising yield quality and quantity. Limited access to information about available DSSs for use in integrated pest management (IPM) is a major barrier to the uptake of DSSs for IPM across Europe. To overcome this barrier, a typology for DSSs for IPM in Europe was developed, introducing a systematic approach to describe the ever-growing number of DSSs for IPM. The developed IPM-DSS typology was implemented in the free web tool “IPM Adviser”, where currently 79 IPM DSSs are described with over 50 attributes describing their structural and performance characteristics. The information about IPM DSSs, which was previously scattered on different websites and difficult to compare, is now standardised and presented in a uniform way, so that it is possible to compare different IPM DSSs on the basis of all the attributes described. The presented IPM-DSS typology implemented in the web tool IPM Adviser facilitates the dissemination and uptake of DSSs for IPM and thus contributes to the achievement of the EU targets for the sustainable use of pesticides.

French beans are tender, immature, edible pods that are harvested early in the plant’s growth cycle and are usually eaten cooked. The growth habits of French beans were studied for the first time in a Citizen Science experiment, and 19 pod samples were collected for further nutritional analysis. Various macronutrients (e.g., protein, ash, fat, carbohydrates, amino acids) and multi-element profiles were determined. A survey of their growing habits revealed that beans are usually planted once or twice a year in May and June at a length of 5–10 m, with a predominance of dwarf beans cultivation over climbing varieties, and pest resistance and stringless pods are the most important characteristics when deciding on a bean. Homogenised freeze-dried pod samples contained 16.1–23.1% protein, 4.5–8.2% ash, 0.1–1.1% fat, and 62.0–70.6% carbohydrates and had a caloric value of 337–363 kcal/100 g. Of the 17 free amino acids identified, 8 were essential (histidine, threonine, methionine, valine, lysine, isoleucine, leucine, phenylalanine) and 9 were non-essential (cysteine, aspartic acid, serine, glutamic acid, glycine, arginine, alanine, proline, tyrosine); meanwhile, of the 12 elements, 5 were macroelements and 7 were microelements. The predominant free amino acids were aspartic acid, glutamic acid, and serine. In the multiple comparisons (Box and Whisker plot), the parameters caloric value and iron showed the strongest response. A very strong positive significant Pearson correlation (≥0.95) was found for five pairs of variables within the free amino acids. Comparison of the nutrient data obtained in the pods showed near-perfect or high complementarity (85.2–103.4%) with the food composition databases for half of the parameters, suggesting that the home-grown French beans from the Citizen Science experiment are a highly nutritious vegetable.

Background: Consumers generally lack access to information on alcoholic beverages, in spite of it being readily available for food and non-alcoholic beverages. Given the rights of consumers, and as with other products harmful to the population, there have been increasing calls for health warnings to be placed on alcoholic beverages, similar to those implemented on tobacco products. The aim of our research was to assess whether knowledge and awareness of the risks and harms associated with alcohol can be improved with a mobile app. Methods: Intervention was conducted using VKJ mobile app, which enables users to scan the barcode of an alcoholic beverage and receive feedback on its labelled alcohol content and estimated energy value. At each search, eleven different health messages/warnings about the risks and harms of alcohol are also displayed randomly, rotating on the screen. A survey was conducted before and after the intervention, to assess the knowledge and awareness of the risks and harms associated with drinking alcohol. Results: Significant differences were found for eight of the twelve tested statements. The improvement was seen to a greater extent in the group of high-risk drinkers. The results also showed that the vast majority of participants (78%) who were exposed to the health messages supported mandatory labelling of alcoholic beverages with information on ingredient listing and energy value, and 72% would like to have health warnings on alcohol products. Conclusions: The use of a mobile app can be an option to improve knowledge and raise awareness of the risks and harms related to alcohol.