In the context of economic crises and inflationary pressures, consumers often rely on discounted food products to manage their monthly budgets. This study aims to explore how socio-demographic factors are associated with Romanian consumers’ perception of food promotions. Data were collected from 1060 respondents, and the analysis was conducted using R version 4.4.2, applying both descriptive and inferential statistical methods, including Pearson’s chi-square test and multinomial logistic regression. The Pearson’s chi-square test revealed significant differences in consumer responses based on all socio-demographic factors examined, except for residence. Males, married individuals, those with higher education, and higher net incomes were generally more cautious about promotions, while younger consumers (aged 18–24) showed greater receptiveness. The multinomial logistic regression further identified significant predictors and estimated their impact on consumers’ perception of food promotions. We found that gender, marital status, education level, and age were strong predictors, while income had only a quadratic impact, and residence showed no statistically significant outcome. These findings offer valuable insights for shaping marketing strategies and highlight the role of socio-demographic factors in shaping consumer perceptions toward food promotions.

In recent years, people have become increasingly interested in adopting a healthy diet, which also extends to healthy snacks, such as chips. Understanding the interplay of factors that influence the preference decisions concerning food products is very helpful in market segmentation for identifying specific groups of consumers with similar needs. This study aims to obtain a better understanding of Romanian consumers’ preference for organic vegetable chips vs. classic potato chips. The research involved a transversal, cross-sectional, descriptive, exploratory, and correlational design. Data were collected based on a questionnaire (1060 participants) and processed with the SPSS 22 program, using the Pearson chi-square test and binary logistic regression as statistical procedures. Significant differences were found regarding the distribution of the respondents who prefer organic vegetable chips vs. classic potato chips based on socio-demographic characteristics. The results of the binary logistic regression analysis (χ² = 102,906, df = 22, N = 909, p < 0.001) indicate that education level and frequency of consumption have a statistically significant impact on the preference for organic vegetable chips. The obtained results can contribute to a better understanding of Romanian consumers’ preferences, acting as a knowledge stage in the adoption of a healthy eating style.

Nutrition is a fundamental aspect of consumer behavior, closely linked to the satisfaction of basic household needs and strategies for purchasing food products. This study aimed to examine how fundamental food needs—specifically survival (daily food) and food security (food stocks)—shape purchasing behaviors, enabling the identification of vulnerable consumer segments and the delineation of patterns useful for producers and retailers. Data were collected through a cross-sectional survey (N = 1060) and analyzed using the Rao & Scott-adjusted Pearson chi-square test (R, version 4.4.3), considering key socio-demographic factors including gender, age, educational level, marital status, residence, and income. Results indicate that gender, age, and education significantly influence food purchases driven by the need for food security, whereas marital status is a significant factor only for survival-related purchases. Differences observed in other contexts were not statistically significant. Additionally, two multinomial logistic regression models were developed to predict consumer food purchases driven by fundamental needs, demonstrating high explanatory power. Each socio-demographic factor emerged as a significant predictor for at least one response category on the Likert scale, and the relative influence of each predictor was quantified. These models provide actionable insights for marketing strategies, including the identification of optimal store locations and the adjustment, diversification, or optimization of product ranges based on the characteristics of specific consumer segments and geographic areas.

Accurate estimation of genetic parameters is a prerequisite for efficient genetic improvement in dairy cattle. In the Romanian Black and White (BNR) breed, the genetic covariance structure among milk production traits has not been simultaneously estimated under a Bayesian multi-trait framework. A Bayesian multi-trait animal model implemented via Markov chain Monte Carlo (MCMC) methods using MCMCglmm was applied to 8207 standard 305-day lactation records from 16,658 animals with pedigree information. Three traits were analyzed simultaneously: milk yield, fat content, and protein content. Heritabilities, genetic, phenotypic, and environmental correlations, and mean accuracy of selection were estimated, with uncertainty quantified through 95% highest posterior density (HPD) intervals. Heritability estimates were h² = 0.249 (95% HPD: 0.172–0.332) for milk yield, h² = 0.504 (95% HPD: 0.380–0.607) for fat content, and h² = 0.383 (95% HPD: 0.284–0.491) for protein content, with compositional traits showing stronger genetic determination than milk yield. Milk yield was negatively correlated with both fat content (r_g= −0.496) and protein content (r_g = −0.574), while fat and protein contents were positively correlated (r_g = 0.643). The results provide a quantitative basis for the BNR national breeding program and confirm that the negative genetic correlations between milk yield and compositional traits should be considered in any selection index targeting simultaneous improvement in production volume and milk quality.