Improvements in productivity and efficiency, together with agricultural modernization, are crucial in the process of future sustainable development. As Western Balkan (WB) countries are in the process of integration into the European Union (EU), the importance of agricultural efficiency in an economic and environmental context and the actuality of the problems of the agricultural sector are very important. In that context, the paper’s main goal is to examine agriculture’s technical efficiency in the EU and WB. The additional goal is to group analyzed countries by agricultural performances. A stochastic frontier analysis (SFA) is used to calculate the technical efficiency of agriculture. Results have shown a significant difference in technical efficiency between WB and the EU. Furthermore, the cluster analysis has indicated the connection between overall economic development and agricultural development, partially “deformed” by agri-environmental and climate conditions. The exogenous factors do not have a crucial influence on the overall technical efficiency of agriculture in observed countries, indicating that the endogenous factors must be improved. The paper impacts recommendations for optimizing the use of inputs and improving the educations of farmers in WB countries to achieve economic and environmental goals.

Maize is one of the leading export products in the Republic of Serbia. As a country where economic development depends on agriculture, maize production plays a critical role as a crop of strategic importance. Potential aflatoxin contamination of maize poses a risk to food and feed safety and tremendous economic losses. No aflatoxin contamination of maize samples harvested in 2019 and 2020 in different localities in the Republic of Serbia was detected by the Enzyme-Linked Immunosorbent Assay (ELISA) test and High-Performance Liquid Chromatography (HPLC) method. On the other hand, the Cluster Amplification Patterns (CAP) analyses of the isolated Aspergillus flavus strains from 2019 maize samples confirmed the presence of key biosynthesis genes responsible for aflatoxin production. Artificial inoculation and subsequent HPLC analysis of the inoculated maize samples confirmed the high capacity of the A. flavus strains for aflatoxin production, pointing to a high risk of contamination under favorable conditions. Prevention of aflatoxin contamination is primarily based on A. flavus control, where biocontrol agents play a significant role as sustainable disease management tools. In this study, antagonistic activity screening of the novel strains belonging to the Bacillus genus indicated superior suppression of A. flavus strains by two Bacillus strains isolated from the rhizosphere of Phaseolus vulgaris.

The sector of milk and dairy products in Serbia along the entire supply chain has been under significant challenges in recent years, especially in current crises. In this direction, this research looked at the supply chain of milk and dairy products by analyzing the primary production, the situation in the processing industry, and the international market’s competitiveness. Indicators of technical efficiency were used to analyze the situation on agricultural farms, while for the processing industry, the impact of various variables on profitability was evaluated using panel models. Furthermore, an examination of these products’ standings in the global market was conducted by assessing their revealed comparative advantages and integration index. The results of the research determined the level of technical efficiency of the producer, while the statistically significant influence of certain variables on profitability was determined within the processing industry. Additionally, the analysis showed modestly revealed comparative advantages in the international market but good integration with the international market. Finally, the sustainability of the milk and dairy products supply chain is highly questionable in crises, which will be a challenge for producers and policymakers.

Accurate and timely wheat yield prediction is valuable globally for enhancing agricultural planning, optimizing resource use, and supporting trade strategies. Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Aerial Vehicle (UAV) multispectral (MS) and Red-Green-Blue (RGB) imagery. Research analyzes five European wheat cultivars across 400 experimental plots created by combining 20 nitrogen, phosphorus, and potassium (NPK) fertilizer treatments. Yield variations from 1.41 to 6.42 t/ha strengthen model robustness with diverse data. The ML approach is automated using PyCaret, which optimized and evaluated 25 regression models based on 65 vegetation indices and yield data, resulting in 66 feature variables across 400 observations. The dataset, split into training (70%) and testing sets (30%), was used to predict yields at three growth stages: 9 May, 20 May, and 6 June 2022. Key models achieved high accuracy, with the Support Vector Regression (SVR) model reaching R² = 0.95 on 9 May and R² = 0.91 on 6 June, and the Multi-Layer Perceptron (MLP) Regressor attaining R² = 0.94 on 20 May. The findings underscore the effectiveness of precisely measured MS indices and a rigorous experimental approach in achieving high-accuracy yield predictions. This study demonstrates how a precise experimental setup, large-scale field data, and AutoML can harness UAV and machine learning’s potential to enhance wheat yield predictions. The main limitations of this study lie in its focus on experimental fields under specific conditions; future research could explore adaptability to diverse environments and wheat varieties for broader applicability.

Efforts to improve agricultural sustainability have increasingly focused on enhancing productivity while minimizing environmental impact. In the Republic of Serbia, dairy farming remains a critical sector due to its dual role in food production and environmental pressure. This study aims to evaluate the technical and eco-efficiency of dairy farms in the Republic of Serbia using FADN data and the Stochastic Frontier Analysis (SFA) method. Specifically, the SFA methodology was applied, which enables a separate assessment of time-invariant and time-variant efficiency, with the aim of clearly identifying the factors that shape milk production in the Republic of Serbia. It was found that the technical efficiency for the 2015–2023 period was at a level of 58.7%, while the eco-efficiency was estimated to be 13.1%. Observing the relationship between the estimated technical and eco-efficiency, it can be concluded that they share similar mechanisms for improvement. In both cases, time-invariant inefficiency dominated, indicating that factors under the control of farms, such as the characteristics of agricultural producers and farms, play a key role in shaping production efficiency. In this context, adopting circular economy principles, such as nutrient recycling, the use of renewable energy, and optimized input utilization, offers an additional opportunity to enhance both economic and environmental performance.

The transition toward sustainable, resource-efficient production has become a key challenge for agri-food systems, particularly in emerging economies, where profitability and environmental goals must be balanced. This study aimed to examine the relationship between financial structure, macroeconomic conditions, and circular economy (CE) indicators in determining the profitability of Serbian agri-food enterprises. Using panel data for 625 firms from 2014 to 2021, a two-step system GMM model was applied to control for endogeneity and firm-specific effects. The results indicate that in agriculture, moderate leverage enhances profitability, while excessive debt reduces it. Recycling and efficiency-oriented circular practices have a positive and significant effect on financial performance, suggesting that resource-efficient management supports long-term profitability. In the food industry, profitability shows strong persistence but remains mainly driven by internal and economic factors, with CE indicators exerting weaker short-term effects. Robustness tests confirm the validity and stability of the estimates. Overall, the findings highlight that integrating circular economy principles into business strategies can contribute to both financial sustainability and more efficient resource use in the agri-food sector.