This paper examines the evolution of food security in Slovakia during the pandemic and post-pandemic years (2020-2023), with a focus on economic access to food. Using household data from the Statistical Office of the Slovak Republic, the study examines changes in income, the share of food expenditures, and income elasticity across regions and demographic groups. The findings show that while household incomes remained relatively stable during the pandemic, the post-pandemic period was marked by rising food prices that outpaced wage growth. As a result, the share of income spent on food increased from 14% in 2020 to 17% in 2023. Data suggests differences between regions: Bratislava recorded the lowest share (12%), whereas Nitra reached 20%. Vulnerability was greatest among retirees, while rural households demonstrated slightly better resilience, likely due to their higher levels of self-sufficiency. Although income elasticity improved overall, disparities widened in 2023 between urban and rural areas. These results underline the need for targeted policies to address affordability and regional inequalities in order to strengthen food security under conditions of economic stress.
A booming field known as "smart agriculture" has emerged because of the rapid revolution and transformation of agriculture. It makes use of cutting-edge agricultural technologies, like the internet of things and machine learning (ML) algorithms, to enhance farm management tasks, boost agricultural productivity, and lessen environmental impact by empowering farmers to react swiftly and efficiently to climate change. A key component of the shift to smart farming is the Internet of Things (IoT), which makes it easier for sensors and devices to connect and share data. It also gives farmers access to actionable information and knowledge, enabling them to make well-informed decisions using machine learning algorithms to enhance crop management and boost yields. Numerous tasks, including weed identification, fertilization requirement analysis, irrigation adjustment, and pest and soil management, are made possible by the automation made possible by these technologies. They allow farmers to make targeted interventions by continuously monitoring crop health, which lowers labor costs and the environmental impact. The effects of technological advancements, such as machine learning and the Internet of Things, on the agriculture industry are thoroughly examined in this paper. It draws attention to the useful applications of these instruments, which help farmers better, more automatically, and more precisely manage their resources and output.
Digital transformation in livestock enterprises creates managerial value only when digital tools are embedded in business processes and translated into measurable sustainability effects. The paper develops a digital maturity and portfolio prioritization approach for sustainable livestock process management. The research uses a structurally balanced pilot sample of 20 Ukrainian livestock en-terprises, documentary evidence, expert-documentary scoring, KPI baseline comparison and multi-criteria normalization. The results show uneven maturity across strategy, process automation, data analytics, digital architecture and cyber-resilience. The highest priority is assigned to BI and data warehouse initiatives, followed by ERP-based feed logistics integration and operational control solutions.
We examine the impact of rising food prices on poverty in Indonesia by considering regional characteristics. Using panel data from 34 provinces in Indonesia from 2013 to 2024, we apply the Generalized Method of Moments (GMM) to estimate the relationship between food price inflation and poverty rates. We find that rising food prices significantly increase poverty levels. The results are consistent across models. The impact is more severe in rural areas compared to urban areas, indicating greater vulnerability of rural households to food price fluctuations. Moreover, we find that in Western Indonesia which are a more developed regions experiences a lower poverty impact from rising food prices compared to Eastern Indonesia. These findings highlight the need to stabilize food prices to support inclusive development. Strengthening regional food security, ensuring targeted funding, and implementing effective food subsidies are essential to mitigate the impact of food price inflation on low-income populations
The growing digitalisation of agriculture is leading to a rapid increase in the volume of data produced by agricultural enterprises; however, its practical use is often limited by fragmented data sources, varying data quality, insufficient metadata, and inconsistent storage and management practices. The aim of this article is to propose a practical framework for the systematic management of data in agricultural enterprises to support its long-term availability, reuse, and integration into decision-making processes. The proposed approach is based on four interlinked steps: identifying data needs and issues, creating and recording a data inventory, drawing up a Data Management Plan (DMP), and implementing it in practice. The framework links technical aspects of data management – such as data formats, metadata, quality, integrity, security, backup and archiving – with the definition of responsibilities for individual staff members and farm (agricultural enterprise) management. For practical implementation, two basic approaches to data storage are distinguished: file systems and data platforms or repositories. The more advanced platform-based approach enables the centralisation of heterogeneous data sources, the automation of their transfer and processing, and subsequently the integration of the acquired data with analytical tools and decision-support systems. The proposed framework thus represents a path from the isolated collection of operational data to its systematic management and utilisation as a long-term information source for both operational and strategic decision-making within an agricultural enterprise.
