Search Results (121)

The growth of the Italian craft beer sector has renewed interest in domestic hop cultivation, presenting a promising opportunity for farm diversification, despite challenges such as structural fragmentation and limited economic data. The study examines the structural and economic characteristics of Italian hop farms using harmonised microdata from the Farm Accountancy Data Network (FADN) for the years 2021 to 2023. The sample includes 13 farms (selected from an initial sample of 14 after outlier detection) with 32 validated farm-year observations, representing approximately 19% of Italy’s total hop-growing area. A multivariate analysis—combining Principal Component Analysis (PCA) and fuzzy C-means clustering—was performed using five key economic indicators: gross margin (GM), variable costs (VCs), hop production (Q_HOP), specialisation ratio (SH), and the coefficient of variation in the gross margin (GM_cv) as a proxy for income stability. The results identify three distinct farm profiles: (i) resilient specialised farms with high margins but significant income volatility; (ii) intermediate emerging farms; and (iii) diversified units where hops represent a secondary crop. The findings of this study provide an in-depth understanding of the economic strategies underpinning hop cultivation in Italy, which may be of interest to all organisations where hops are grown as an alternative crop. They offer concrete guidelines to policymakers to support the sector’s development through targeted measures that address issues relating to farm size, technical capabilities, and supply chain integration. Full article

The Common Agricultural Policy (CAP), as one of the most prominent European Union policies, has increased concerns about the environmental sustainability of farms, particularly since its major reform in 1992. The changes implemented since this reform have intended to promote more integrated rural development, with deeper interrelationships between the agricultural sector and other rural activities, including agritourism, from the perspective of diversification of the activities that can be developed on farms and in rural areas. The idea of this strategy is to bring more income for farmers by changing the policy measures and enhancing a more sustainable agricultural and rural development. Nonetheless, the interrelationships between the diversification of activities in the agricultural sector and the characteristics of the farms have not yet been fully explored. In this context, this research aims to bring more insight into how agritourism revenues can be predicted by the farm characteristics in the European Union (UE) agricultural regions, considering data from FADN (Farm Accountancy Data Network), for 2023, using machine learning algorithms (following IBM SPSS Modeler Version 18.4 procedures). The results obtained show that agritourism output is higher in EU countries with larger farms (Slovakia and Czechia) and that are more economically dynamic (Netherlands and Denmark). Slovenia, Austria, Italy, and Finland are countries in which farms have a higher part of agritourism revenues in the total output. There is space to better explore agritourism potentialities and to improve the availability of data. When the total crop output increases by 1%, agritourism revenue grows by 0.719%. Full article

Agriculture in Bulgaria faces increasing pressure to balance profitability with environmental sustainability under the evolving framework of the Common Agricultural Policy (CAP) and the European Green Deal. This study analyses the relationship between sustainability-oriented investment support, production cost structure, and farm profitability using farm-level data from the Farm Accountancy Data Network (FADN). The analysis integrates investment-related subsidies, input intensity, productivity indicators, and structural characteristics into an econometric framework to examine their associations with economic performance. Results show that environmental payments, when aligned with efficient management, enhance profitability, whereas conventional investment and rural development support display limited or delayed effects. Higher crop protection expenditure is associated with lower profitability, suggesting cost inefficiencies in chemically intensive production systems. In contrast, fertiliser expenditure shows no significant association, while energy-related spending exhibits a positive but statistically insignificant relationship, likely reflecting mechanisation and technological modernisation effects. Structural factors, particularly farm size and land productivity, remain key determinants of profitability for balancing economic and environmental goals. Overall, the findings suggest that sustainable profitability in Bulgarian agriculture is achievable but unevenly distributed, shaped by structural conditions, managerial capacity, and the design of support instruments. The study offers empirical evidence for aligning sustainable investment incentives with farm-level competitiveness and supports the transition toward integrated economic-environmental monitoring within the forthcoming Farm Sustainability Data Network (FSDN). Full article

Food waste remains a critical sustainability challenge for the European Union (EU), with significant negative impacts on environmental resources, economic efficiency, and social equity. This paper presents a comprehensive analysis of food waste across EU member states during the 2020–2023 period, examining waste generation across five key sectors: households, food service (restaurants and catering), retail, food manufacturing, and primary agriculture. The study uses Eurostat statistical data, standardising measurements to kilograms per capita and absolute tonnage to enable cross-country comparisons. Particular attention is devoted to the impacts of the COVID-19 pandemic, which disproportionately affected the service and retail sectors. Beyond descriptive analysis, the research investigates potential relationships between major economic indicators (Gross Domestic Product [GDP], median income, and material deprivation) and food waste rates, employing Kruskal–Wallis statistical tests to examine sectoral and cross-national patterns. Contrary to conventional assumptions, analyses reveal no statistically significant direct correlation between economic prosperity and waste generation, suggesting that institutional design, infrastructure availability, consumer awareness, and education exert greater determinative influence than aggregate wealth. Results demonstrate that households are the largest source of food waste across the EU, accounting for approximately 50% of food waste. At the same time, sectoral variations reflect country-specific structural and regulatory factors rather than levels of economic development. The research concludes with actionable policy recommendations targeting three intervention levels: individual behaviour change (consumer education, digital tools, and purchase planning), community infrastructure (food redistribution networks and collective composting), and institutional reform (regulatory harmonisation, circular economy incentives, and extended producer responsibility). These recommendations align with EU strategic priorities, including the European Green Deal, Farm to Fork Strategy, and 2030 Circular Economy Action Plan, with the specific objective of halving food waste by 2030 to enhance both environmental sustainability and food security. Full article

The study analyzes agricultural labour productivity in the context of the economic dimension of sustainability and the idea of European Union (EU) cohesion. This idea constitutes a central principle of European integration. The basis for implementing the concept of cohesion in European agriculture is the convergence of labour productivity levels. Convergence in this area forms the foundation of economic sustainability and serves as a prerequisite for the social dimension of sustainability, while often also being an underlying factor in environmental sustainability. The analysis concerns the productivity of labour in farms by the economic size, both at the national and regional levels, based on Farm Accountancy Data Network (FADN) data for the years 2007–2022. The β and σ-convergence methods were used. The results indicate that processes of labour productivity convergence occur in EU agriculture. This phenomenon was manifested by a decline in the heterogeneity of labour productivity levels among agricultural holdings. The fastest reduction in regional diversity was observed among the group of the largest economically farms (GE6). However, the dispersion of labour productivity levels remains considerable, and the rate of convergence continues to be slow. The convergence of labour productivity in agriculture will not accelerate without widespread and comprehensive structural changes in the sector, extending beyond mere changes in land use patterns. Full article

The article analyses the direction and intensity of changes occurring in agriculture in mountain areas in Europe between 2000 and 2022. For the calculations, the ESA CCI Land Cover global land-use map set was used. This dataset was established by the European Space Agency (ESA) through the classification of satellite images from sources (MERIS, AVHRR, SPOT, PROBA, and Sentinel-3). In the next step, the organisational features and economic performance of farms located in mountain areas of the European Union were determined for the period 2004–2022. For this purpose, data from the European Farms Accountancy Data Network (FADN-FSDN) were used. Subsequently, using Poland as a case study, the capacity of mountain agriculture to implement key environmental interventions under the Common Agricultural Policy (CAP) 2023–2027 was assessed. The results highlight the varying directions and intensity of organisational changes occurring in mountain agriculture across Europe. They also show that farms can operate successfully in these areas, although their economic situation varies between EU countries. The findings indicate the need for further adaptation of CAP instruments to better reflect the ecological and economic conditions of mountain areas. Strengthening support mechanisms for these regions within the current and future CAP is of crucial importance for protecting biodiversity, promoting sustainable land use, and maintaining the socio-environmental functions of rural mountain landscapes. Our study highlights that the CAP for mountain farms should be targeted, long-term, and compensatory, so as to compensate for the naturally unfavorable farming conditions and support their multifunctional role. The most important assumptions of CAP for mountain farms are a fair system of compensatory payments (LFA/ANCs), support for local and high-quality production, income diversification, and investments adapted to mountain conditions. Full article

The transition toward sustainable and circular agricultural systems is increasingly important, yet evidence linking circularity and farm profitability in transition economies remains limited. This study examines the determinants of farm profitability in Serbia by combining micro-level structural and productivity indicators with a macro-level agro-eco efficiency measure, used here as a sector-wide ecological pressure indicator rather than a direct proxy for circular practices. Using a balanced Farm Accountancy Data Network (FADN) panel of 443 farms (2015–2022) across dairy, mixed, field crop, and fruit & wine sectors, dynamic panel estimators (difference and system Generalized Method of Moments-GMM) reveal strong sectoral heterogeneity. Asset turnover is the primary driver of profitability in field crops and perennial systems, while dairy farms benefit from scale and land productivity. Energy intensity consistently reduces profitability across all sectors. Agro-eco efficiency shows a negative effect in livestock-based systems, indicating higher sensitivity to macro-ecological pressures. These findings suggest that environmental and economic vulnerabilities differ across production systems, highlighting the need for sector-specific strategies aimed at improving resilience rather than inferring the profitability of circular technologies. Full article

The objective of this paper is to describe the process of transition from the Farm Accountancy Data Network (FADN) to the Farm Sustainability Data Network (FSDN), a European survey that gathers yearly data about agricultural holdings at microeconomic level. Established initially to monitor economic aspects of farm income and support Common Agricultural Policy (CAP) evaluations, the network has now broadened its scope to integrate environmental and social aspects of farm management, in line with the EU Green Deal and the Farm to Fork strategy. The Implementing Regulation (EU) 2023/2674 formalizes this integration, adding new variables, encouraging the participation of the farms (voluntary), and supporting the improvement in interoperability to reduce the statistical burden on farmers and data collectors. The paper discusses the main challenges and opportunities of this transition, emphasizing how FSDN will deliver a more comprehensive and reliable dataset for policy evaluation and for advancing the understanding of farm-level sustainability. Full article

Within the energy system, agriculture represents a distinct sector, as it functions both as a consumer of energy derived from fossil fuels and renewable sources and as a producer of renewable energy. Since energy consumption is closely linked to production intensity and cost efficiency, energy costs have a direct impact on farm profitability and financial stability. The aim of the study is to analyze and assess the relationships between energy costs and the financial situation of farms in Poland in comparison to the European Union average, based on data from the Farm Accountancy Data Network (FADN) and its successor, the Farm Sustainability Data Network (FSDN), covering the years 2014–2023. The study focuses on differences in the structure and burden of energy costs and their implications for the economic performance and financial resilience of agricultural holdings. The comparative analysis revealed that farms in Poland are characterized by a higher share of energy costs in total production costs and a higher ratio of energy costs to total income compared to the EU average, indicating lower financial resilience to energy price volatility. These findings suggest that measures aimed at improving energy efficiency, supporting technological modernization, and encouraging the adoption of on-farm renewable energy could strengthen the long-term stability and competitiveness of Polish agriculture. Full article

Offshore wind farm projects are being promoted in the seas surrounding the Korean Peninsula to secure renewable energy. To support site selection, offshore wind resource maps were generated using deep neural networks trained on Sentinel-1 SAR imagery, numerical weather prediction data, offshore wind observations, sea surface temperature, and bathymetry. The deep neural network (DNN) framework consisted of six sub-models targeting eastward and northward wind components across three regions—the Yellow Sea, Korea Strait, and East Sea—to account for spatial heterogeneity. The proposed models outperformed existing approaches, achieving mean absolute errors (MAE) ranging from 1.31 to 1.69 m/s and correlation coefficients (CC) between 0.827 and 0.913. These DNN models were then applied to produce offshore wind energy maps at a 150 m resolution, effectively capturing seasonal and regional variability. The resulting high-resolution maps provide valuable insights for evaluating the suitability of existing wind farm sites and identifying potential new candidates. Full article

Intensifying urbanization in Central Europe is increasingly pushing flood retention areas onto private farmland, yet the agronomic and socio-economic trade-offs remain poorly quantified. We conducted a narrative review of published field data and post-event assessments from 2014–2023 along the transboundary Sava River. Information was collected from research articles, case studies, and environmental monitoring reports, and synthesized in relation to national and EU regulatory thresholds to evaluate how floods altered soil functions and agricultural viability. Water erosion during floods stripped up to 30 cm of topsoil in torrential reaches, while stagnant inundation deposited 5–50 cm of sediments enriched with potentially toxic elements, occasionally causing food crops to exceed EU contaminant limits due to uptake from the soil. Flood sediments also introduced persistent organic pollutants: 13 modern pesticides were detected post-flood in soils, with several exceeding sediment quality guidelines. Waterlogging reduced maize, pumpkin, and forage yields by half where soil remained submerged for more than three days, with farm income falling by approximately 50% in the most affected areas. These impacts contrast with limited public awareness of long-term soil degradation, raising questions about the appropriateness of placing additional dry retention reservoirs—an example of nature-based solutions—on agricultural land. We argue that equitable flood-risk governance in the Sava River Basin requires: (i) a trans-boundary soil quality monitoring network linking agronomic, hydrological, and contaminant datasets; (ii) compensation schemes for agricultural landowners that account for both immediate crop losses and delayed remediation costs; and (iii) integration of strict farmland protection clauses into spatial planning, favoring compact, greener cities over lateral river expansion. Such measures would balance societal flood-safety gains with the long-term productivity and food security functions of agricultural land. Full article

The concept of sustainable development has been widely analyzed in scientific literature and is understood as a process aimed at balancing human activities with the environment. Sustainable agricultural systems generate economic value, manage natural resources responsibly, and support rural development. Modern agricultural production, however, faces challenges across these dimensions, making their assessment essential for the long-term viability of farms. This paper introduces indicators of economic, ecological, and social sustainability for agricultural holdings, using the FADN (Farm Accountancy Data Network) database as a foundation. The structured nature of FADN allows for consistent analysis of sustainability, while additional indicators assess the impact of agricultural policy on farm performance. Together, these provide a comprehensive framework for understanding and improving farm sustainability. The main contribution of the study is the establishment of a set of feasible indicators that can be derived from the FADN database to support comprehensive sustainability assessments. Full article

EU production is important for global poultry markets and is concentrated in a few countries, including Italy. The aim of this study is to investigate the technical efficiency of Italian poultry farms in 2019–2022, characterized by the COVID-19 pandemic and avian influenza, which occurred almost simultaneously and presented poultry farms with important economic challenges. In particular, this study aims to observe how efficiently poultry farms utilized their inputs with regards to controllable or managerial factors and exogenous shocks and factors beyond the firm’s control. Data was retrieved from the RICA database, the Italian section of the EU Farm Accountancy Data Network. After a descriptive analysis, a stochastic frontier model was applied to the panel data to estimate production frontier and firm-specific inefficiency factors. Results reveal the relevance of certain cost categories (feed, water, fuel, and electricity) and their increase over the observed period. Current and capital costs have positive and significant impacts on the value of production. As regards the determinants of technical efficiency, a greater endowment of some inputs (labor and farm area) and the sizes of farms in terms of livestock units are correlated with an improvement in the technical efficiency of farms. Full article

Developments in agriculture is reshaping the agribusiness landscape, altering farms’ bargaining power and strategic positioning within supply chains. These dynamics raise important questions about how financial strength influences farmers’ preferences for different components of business relationships with input suppliers. The primary objective of this study is to examine the relationship between a farm’s financial power and the importance it assigns to the behavioral dimension in such relationships. To address this objective, we employ a two-stage research design. In the first stage, qualitative interviews with farmers were conducted to identify the key attributes contributing to relationship value, encompassing economic, strategic, and behavioral dimensions. In the second stage, a quantitative survey was administered to 249 farmers, supplemented with financial data from the Farm Accountancy Data Network (FADN). The Maximum Difference Scaling (MaxDiff) method was applied to assess the relative importance of these attributes, followed by statistical analysis linking the observed preferences to a composite indicator of financial power. The results indicate that financially stronger farms place greater emphasis on economic factors while attaching less importance to behavioral aspects. Among less financially powerful farms, two distinct patterns emerge: one characterized by opportunistic, price-oriented behavior, and another reflecting a relational orientation that values trust, communication, and long-term cooperation alongside economic conditions. These findings contribute to a better understanding of business relationships in agribusiness by explaining how financial power shapes the trade-off between economic and behavioral components. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article