Postharvest water loss critically determines the shelf life and quality of pepper fruits. This study investigated how three alternative soil amendments—rhyolite tuff (RT), composted and pelletized poultry litter (CPPL), and clarifying agent (CA)—affect water loss, structural traits, and their interrelationships across three ripening stages (immature, mature, and overripe) in the Hungarian wax pepper cultivar ‘Tizenegyes’. A completely randomized design (CRD) was applied in a small-plot field experiment. Macro- and micromorphometrical analyses included pericarp, cuticle, epidermis, collenchyma, hypodermis, parenchyma, and endocarp thickness; fruit fresh weight; surface area; and the surface area-to-weight ratio (SA/W). Daily weight measurements were used to quantify water loss, while cuticle function was assessed by comparing wax-removed and intact fruits. The water loss rate (WLR) was strongly influenced by pericarp tissue structure—particularly cuticle thickness, hypodermal cell layer number and thickness, and collenchyma cell wall thickness—as well as fruit size at ripening stage. Among treatments, RT was the most effective in reducing postharvest water loss and extending fruit shelf life. Fruits from the control plots lost 26% more water than RT samples, 12.8% more than CPPL, and 14.2% more than CA. Although RT-treated fruits were smaller, they had thicker pericarp and hypodermis layers, more collenchymatous cell rows, and thicker cell walls, all of which contributed to lower water loss and prolonged freshness. These findings highlight RT as a promising alternative nutrient source for sustainable pepper production, with significant potential to improve postharvest quality.

Common bean (Phaseolus vulgaris L.) is the most widely consumed grain legume in Europe, yet dry bean production in Hungary remains below domestic demand. This study aimed to identify optimal cultivation strategies for improving yield by evaluating the combined effects of sowing time, plant density, and fertiliser dose across three dry bean varieties under field conditions in Nyíregyháza (Hungary) from 2015 to 2017. The results revealed that all three agronomic factors significantly influenced yield and its components, with varietal differences in response patterns. Sowing time and plant density consistently affected pod number, thousand-seed weight, and yield per plant. Fertiliser application had a more variable impact, depending on the variety and year. The findings underline the importance of variety-specific recommendations to optimise yield under varying environmental and soil conditions.

The European sunflower moth (Homoeosoma nebulellum Den. et Schiff.) is one of the earliest documented pests of sunflower. The literature considers it a two-generation pest, but a third partial generation may appear depending on weather conditions. However, due to global warming, the emergence of a third generation is becoming more frequent and more complete in east-central Europe. In the years 2021-2022-2023, we observed the swarming dynamics of the pest. Our study compared the infection rates among the Kisvárdai breed, the widely cultivated confection hybrid, Jaguar, and the oil hybrid NK Neoma, which is extensively grown in the region. Our results show that the sunflower moth is now a three-generation species in east-central Europe, likely due to global warming. There is significant variation in susceptibility among the studied species. Varieties lacking a protective pericarp (like Kisvárdai) should be excluded from cultivation, and instead, resistant hybrids such as Jaguar and Neoma should be grown.

In the context of the current socio-economic and technological transformations, plant-based diets have gained an increasing importance. The present comparative study, conducted on a sample of 70 participants (35 vegans and 35 omnivores), investigated the influence of education level and socio-economic conditions on the adoption of a plant-based diet in Romania. Statistical analysis, based on the Pearson correlation coefficient and logistic regression, did not reveal significant associations between the studied variables, highlighting the need for further exploration of cultural and ideological factors. Limitations of the study, including the small sample size, are acknowledged and this preliminary analysis opens directions for future research. Soon, emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) will provide new tools for analyzing nutritional behaviours and personalizing dietary recommendations.