In order to succeed to feed the global population in upcoming period, additionally pressed by climate change, or economic shocks and environmental issues, agriculture is forced to be based on innovation and improved technologies. Furthermore, although it mainly works with living organisms and complex chemical processes, it rapidly becomes a part of a digital world. So, current agrifood production ensures food safety and security by the wider use of various drones, robots, sensors, software packages, IoT, etc., enabling business optimization, sustainability and profitability. On other side, in some segments, operating with advanced mechanization, equipment and technology requires specific skills and knowledges form farmers, exposing the user-friendly approach as certain challenge for contemporary science and practice.

Soybean (Glycine max L.) is one of the leading cultivated crops globally. Although the region of Vojvodina is favorable for soybean production, the climate, especially high temperatures and uneven distribution of precipitation, represents a major limiting factor. The aim of this study is to investigate the correlation between soybean yield, water stress levels, and vegetation indices obtained using a handheld multispectral sensor and a drone under natural moisture conditions on a test plot in Čenej, Vojvodina. The results showed a significant correlation between vegetation indices with evapotranspiration, soil moisture changes, and soybean yield. During the intensive growth phase (V4), NDVI-UAV, EVI-UAV, and GNDVI-UAV showed highly significant positive correlations with yield (r=0.96**, r=0.94**, r=0.86*). During the flowering phase (R1), GNDVI-POM had significant positive correlations with all analyzed parameters, while GNDVI-UAV had significant correlations with evapotranspiration and soil moisture. During the pod formation phase (R3), GNDVI-UAV again showed a significant correlation with yield (r=0.86*), while NDVI-POM had significant correlations with evapotranspiration and soil moisture. During the pod filling phase (R4), EVI-UAV showed highly significant positive correlations with evapotranspiration, soil moisture, and yield (r=0.94**, r=0.96**, r=0.89**). These results are useful for the application of multispectral sensors in detecting soybean water availability and improving production under natural moisture conditions

The goal of writing the chapter is to analyze the distribution of agricultural mechanization, which is the most common in the Republic of Serbia (single-axle tractors, two-axle tractors, universal combine harvesters). The purpose of this chapter is to analyze the used mechanization by regions and areas in Serbia during 2012 and 2018, as well as by the number of holdings at its disposal. Principal component analysis (PCA) was used in the research, and the results were presented both tabularly and graphically (loading plot and biplot). The results of the PCA method showed that the situation has not changed in the observed years when it comes to the distribution of mechanization by regions. The PCA method was also applied to mechanization that were used at the level of 25 areas in Serbia only in 2012 because the Statistical Office of the Republic of Serbia (SORS) does not have data for 2018. Based on the biplot presentation, the authors concluded the dominant representation of certain types of mechanization in certain regions and areas of Serbia.

One of the biggest problems faced by the world business is the constant struggle to stay competitive in the market. The acquired knowledge and satisfied numerous technical and organizational conditions have resulted in the development of a whole range of modern management techniques whose meaningful application should enable the acquisition and presentation of relevant management models. Namely, in the light of these new circumstances, in addition to strategic and operational management, companies introduce controlling into the basic activities of corporate management. The aim of this article is to understand the importance of the controlling introduction, its role in the business process, its place in the organizational structure of the company, and the potential benefits that controlling brings. This study addresses a research gap in the literature by examining the implementation of financial controlling practices in the energy sector of a non-EU transition economy. While extensive research has been conducted in the Western European contexts, empirical studies focusing on Southeast European countries, particularly Serbia, remain limited. The article contributes to filling this gap by providing survey-based insights into managerial perceptions, controlling functions, and risk management tools in a transitional regulatory and institutional environment. The added value of this study lies in its empirical examination of financial controlling within a transition economy context, offering sector-specific insights from Serbia’s energy industry. Unlike existing studies focusing on developed markets, this article highlights managerial attitudes, applied tools, and risk-related practices in an underexplored regulatory environment. The aim of this work is to determine the influence of controlling in Serbia based on the results of practical research. Testing of statistical hypotheses proved the importance of controlling in the company. The value of this work is that it was observed that traditional management approaches, based on information obtained from conventional systems, are not sufficient and that they need to be upgraded with modern controlling techniques.

In this chapter, the authors followed the trend of the movement of maize according to selected variables: harvested area (in ha), production (in t), and the value of maize exports (thousand USD). The movement of these variables was observed in the example of twenty-seven European countries that have available data in the period from 2010 to 2022. The aim of the research was to determine the changes that occurred in the analyzed countries during the observed time period and according to the variables in terms of growth and/or reduction of average indicator values. The basic source of data was the FAOSTAT database, but also other relevant scientific and professional literature. The results of applying descriptive statistics showed that the greatest deviation from the arithmetic mean was recorded in the value of maize exports (Cv = 209.3493), while the Pearson correlation coefficient showed that the strongest correlation was between production and area harvested of maize (.955). Finally, a conclusion was given according to the graphic presentation in the form of a trend line of maize production.