Local producers constitute a crucial segment of the local economy, playing a pivotal role in driving rural development and the progress of tourism. Their avenues for showcasing products extend beyond markets, bazaars, or food events, often being integrated into a destination’s tourism offerings. Moreover, they contribute to the culinary progress within tourism. Originating from wider rural areas or nearby villages, local producers significantly impact everyday migrations, services, and financial transactions in relationships spanning suburban–urban, village–town, and cross-border cooperation. This study aims to scrutinize the social facets of the organization and work of local producers, offering insights into contemporary market processes. It also serves to illustrate cross-border cooperation and the role of culinary tourism in local business. Through qualitative data processing, we will delve into the outcomes of cross-border projects, emphasizing ethical and sustainable values rooted in territory, landscape, local culture, authenticity, and the application of culinary elements in tourism. The results will shed light on the economic, social, and cultural ramifications on markets in border regions, influencing daily life and the economy. This study will define key aspects of rural development. These research findings can inform local governments, the economy, and communities in future strategic planning for developing this market segment. Tourism, especially in hospitality, will empower rural communities to enhance financial inflow and create local employment opportunities, such as roles for vineyard tour guides or local chefs. Simultaneously, it will bolster other sectors of the local economy, such as agriculture.

The evolution of daily extreme precipitation from 1966 to 2013 in Vojvodina Region (Serbia) was investigated. We calculated trends of ten precipitation indices and tested their corresponding significances using the Student’s t-test for seven locations. The obtained results suggest that the climate of the northern and central parts of Vojvodina region becomes wetter in terms of precipitation magnitude and frequency, reflecting the characteristic of the central European regime, while the southernmost part of the region is drier, reflecting the characteristic of the Mediterranean regime. In addition, the results indicate an increase in the amount of precipitation in short time intervals. Positive annual trends are strongly influenced by the significant increase of autumn frequency and intensity of extreme precipitation. According to the correlation between extreme precipitation indices and atmospheric teleconnection patterns, it was found that the NAO has the strongest influence on precipitation intensity indices in spring and winter, while during winter it also affects the frequency of dry conditions. The EAWR pattern has a strong influence on the statistically significant positive autumn trends. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. III 43007: Studying Climate Change and its Influence on the Environment: Impacts, Adaptation and Mitigation and “Geography of Serbia”]

The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989–2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow, the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant differences between mean values of four clusters, confirming that the choice of the number of clusters was suitably done; and (iii) from the clustering we found that the predictability of streamflow data of the Brazos River given by the Lyapunov time (LT), corrected for randomness by Kolmogorov time (KT) in days, lies in the interval from two to five days.