Assessing the Economic Sustainability of the EU and Romanian Farming Sectors

Abstract

In the context of increasing pressures on European agriculture, the economic sustainability of farming sectors is becoming a key strategic objective, especially for Member States with structural vulnerabilities such as Romania. This study proposes an integrated assessment of the economic sustainability of farming sectors in the European Union and Romania for the period 2013–2022 using an analytical framework based on composite indicators built on data from the FADN network. The seven indicators used are grouped into three dimensions: (i) economic performance—profitability, capitalization, and liquidity (FESI, FCI, PCFI); (ii) subsidy dependence and efficiency (FSDI, SEI); and (iii) technical–economic efficiency in the use of resources (FEPI, COEI). The results indicate accelerated economic growth of Romanian farms, but it is associated with structural vulnerabilities, such as low capitalization, high liquidity volatility, and high dependence on public support. In contrast, farms in the EU show superior financial resilience and a steady investment capacity. This study underlines the need for differentiated agricultural policies aimed at strengthening financial autonomy, increasing investment efficiency, and reducing the gap between Romania and the EU, helping to inform policy interventions for the transition towards a more competitive and resilient farming sector.

1. Introduction

Farming sectors plays a key role in the economic and social development of a country, influencing not only food security, but also financial stability and economic sustainability, thus in a global context marked by climate change, population growth and pressures on natural resources, the concept of economic sustainability in agriculture has become increasingly relevant [1]. Thus, Marchand et al. [2] emphasize that sustainability assessments have provided significant support in this process, and Slätmo et al. [3] note that an increasing number of sustainability assessment tools and frameworks have been developed to support decision-making in agriculture. Talukder and Blay-Palmer [4] note that these developments have led to the creation of diverse tools that are applicable at both the farm level and internationally. These sources focus on different levels of analysis and have therefore introduced different sustainability indicators depending on the objectives and perspective of each study. In other words, the factors influencing the choice of an assessment tool can vary; thus, analyzing the literature of the different tools allows us to identify some of these factors, and the choice of assessment tool by researchers and governments is usually determined by data availability, time, and budget constraints [5].

Pretty [6] says, “At the farm level, actors can assess, adjust and agree on criteria for measuring sustainability trends, but as we move to higher levels of the hierarchy, such as regional, national and international, it becomes increasingly difficult to do this in a meaningful way”. He points out that once certain parameters or criteria have been selected, it is possible to determine whether certain trends are maintaining, increasing, or decreasing. For example, at the farm level, practices that lead to soil erosion may be considered less sustainable than those that protect the soil, and practices that eliminate insect predator habitats or directly destroy them are less sustainable than those that do not. In practice, spatial scales and boundaries must be directly relevant to farmers’ activities and perceptions. Hayati et al. [7] report that ‘scale’ or ‘level of analysis’ is a distinct principle in measuring and monitoring agricultural sustainability, and similar observations are made by Hubeau et al. [8]. On the other hand, the farm is the basic economic unit in the agricultural system hierarchy and even though the use of a single field may sometimes be unprofitable or unsustainable, the farm as a whole may remain economically viable, at the same time, fields within a farm may be agro-economically productive but unprofitable due to low commodity prices or high production costs. In order to ensure socio-economic viability, it is essential that the economic and social needs of the farmer are met and that the farmer has access to relevant information on management, production inputs, and markets.

Sustainability indicators at the farm level should therefore include profitability, economic uncertainty, access to inputs and markets, the skills and information available to the farmer, the farmer’s ability to plan, and incentives for sustainable land management. These incentives may include guidelines for good management practices, additional off-farm income, access to credit, government support, and land tenure rights [9]. At regional, national, and international levels, macroeconomic constraints, especially economic policies, influence the direction of national economies and ultimately the ability of a country’s agricultural system to feed its population [10].

The aim of this study is to assess the economic sustainability of farming sectors in the EU and Romania using a set of seven economic sustainability indicators. These indicators are constructed to reflect in a multidimensional way profitability, investment efficiency, financial liquidity, dependence on public support, resource use efficiency, and value-adding capacity within farming sectors.

By applying these analytical tools, this study aims to carry out the following:

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Identify the structural strengths and vulnerabilities of farming sectors in Romania compared to the EU average;

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Provide a solid empirical basis for the formulation of sustainable agricultural policies adapted to the national context;

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Highlight the degree of convergence or divergence in terms of economic sustainability over an extended time frame (2013–2022).

To support the analysis, this study proposes the following research hypotheses:

 H1:

The economic performance of the farming sector in Romania is lower than the EU average in terms of profitability, investment capacity, and financial liquidity.

 H2:

The farming sector in Romania is less dependent on subsidies compared with the EU average, but it uses these funds less efficiently to generate added value.

 H3:

The Romanian farming sector makes efficient use of production resources, but it still has a significantly lower economic productivity per hectare than the EU average.

The comparative choice between Romania and the European Union in the framework of this study is based primarily on the fact that Romania, as a member state of the European Union, is an integral part of the Common Agricultural Policy, benefiting from instruments and funding similar to the other member states. However, Romanian agriculture retains distinctive characteristics, such as an excessive fragmentation of farms, a higher dependence on family labor, and a low level of capitalization compared to the EU average. From this perspective, the analysis of economic sustainability in Romania becomes a representative case study for understanding the structural disparities and challenges faced by the new Member States in the process of agricultural economic convergence. Secondly, comparing the performance of farming sectors in Romania with that of the European average makes it possible to highlight the differences in efficiency, profitability, and dependence on subsidies by applying a standardized set of indicators.

In comparison with the existing literature, this study makes a significant contribution to the understanding of economic sustainability in agriculture by taking a comparative approach between Romania and the EU average. While most research focuses either on the farm level or on isolated regions, this study analyzes the Romanian agricultural sector in the context of European policies and performance, providing a clear picture of structural convergences and divergences. An innovative element of this study is the use of a unified set of seven economic indicators, selected to reflect in a multidimensional manner key aspects such as profitability, investment efficiency, financial liquidity, dependence on public support, resource efficiency, and value-added capacity. This complex integration allows for a coherent and comparable assessment of economic sustainability in line with current requirements for informing sustainable agricultural policies.

In contrast to other works that rely on limited time periods or qualitative methods, this study uses rigorous quantitative data from the FADN network, covering an extended period of ten years (2013–2022), which provides a robust framework for identifying relevant economic trends and assessing the impact of agricultural policies over time. Moreover, the analysis implemented in this study is anchored in the particular context of Romanian agriculture, characterized by fragmentation of holdings, predominant use of family labor, and low capitalization. Through this focus, Romania becomes a representative case study for understanding the challenges faced by the new Member States in the process of agricultural economic convergence at the European level.

2. Literature Approach to Assessing the Economic Sustainability of the Farming Sectors

2.1. Tools and Methodologies for Assessing the Sustainable Development of the Farming Sectors

The factors influencing the choice of an assessment tool can vary, so reviewing the literature on the differences between tools allows us to identify some of these factors. The choice of assessment tool by researchers and governments is usually driven by data availability, time, and budget constraints [2]. So, within the literature, many authors have developed different indicators to measure agricultural sustainability (

Table 1. Sustainability indicators in EU agriculture: a synthesis of studies.

Authors	Year	Study Area	Research Methods	Research Data
Rigby et al. [11]; Hayati et al. [7]	2001	on-farm studies	development of sustainability indices	constructed indices based on yield, profit, crop failure, soil depth, certification, and soil cover
von Wirén-Lehr [12]	2001	conceptual/theoretical	evaluation of goal-oriented sustainability concepts	assessed 7 goal-oriented concepts using theoretical frameworks; emphasized practical transfer of sustainability paradigms
López-Ridaura et al. [13]	2002	natural resource systems	attribute-based framework	defined sustainability through attributes like productivity, stability, adaptability, equity, self-empowerment
Zhen and Routray [14]; Dale and Beyeler [15]	2003	theoretical framework	literature synthesis	proposed criteria for effective sustainability indicators
Binder et al. [16]	2010	multiple regions	conceptual framework	indicator selection influenced by system characteristics and farm level context
Sala et al. [17]	2013	sustainability assessment theory	philosophical-methodological analysis	criteria based on ontology, epistemology, methodology; use of CBAs, risk analysis, indicator systems
Meulen et al. [18]	2016	innovation in agriculture	qualitative assessment	studied innovation as an indicator of competitiveness and sustainability
Asseldonk et al. [19]	2016	EU agriculture	empirical study using FLINT and FADN	analyzed risk management indicators and integrated FADN and FLINT data
De Olde et al. [20]	2017	tool development context	indicator evaluation	identified factors influencing indicator choice including user beliefs and institutional compatibility
Kelly et al. [21]	2017	EU farm level	FADN data analysis	assessed sustainability using FADN data; identified gaps in environmental and social indicators
Roesch et al. [22]	2021	general farm context	SALCAsustain methodology	developed indicators for profitability, liquidity, and stability using multiple metrics
Kryszak et al. [23]	2021	EU regions	ROA-based profitability analysis	FADN 2007–2018 data; analyzed equity turnover, debt, specialization and subsidy effect
Vrolijk and Poppe [24]	2021	EU FADN extension	cost–benefit analysis	analyzed FADN to FSDN cost implications; proposed sample size adjustments for sustainability data
Andrejovská and Glova [25]	2022	EU countries	linear regression analysis	used FADN 2009–2018 data; analyzed economic/environmental factors on farm income indicators
Martinho [26]	2022	EU regional/national	descriptive, spatial autocorrelation, regression	FADN 2004–2018 data; examined financial indicators affecting ROA and ROE
Hloušková et al. [27]	2022	Czech Republic	economic viability index analysis	FADN 2016–2020 data; integrated opportunity costs into economic viability assessment
Xavier et al. [28]	2022	Portugal	trade-off programming, multivariate analysis	used 2009–2019 census data with HJ-Biplot and cluster analysis to create composite indicators
Staniszewski et al. [29]	2023	EU regions	cluster analysis	EU agricultural data 2005–2018, structured by 4 clusters based on farming characteristics
Robling et al. [30]	2023	Sweden (dairy farms)	five-step indicator evaluation	identified 20 indicators across 12 themes; major measurement issues from lack of secondary data
Bathaei and Štreimikienė [31]	2023	systematic literature review	SALSA and PRISMA methodologies	reviewed 2010–2022 literature; identified 31 economic indicators including productivity and profitability

Source: authors’ contribution based on literature studies.

).

In practice, spatial scales and boundaries should be directly relevant to farmers’ activities and perceptions. In this regard, OECD (2001) [32] reports that ‘scale’ or ‘level of analysis’ is a distinct principle in measuring and monitoring economic sustainability, and similar observations are made by von Wiren-Lehr [12] and Staniszewski et al. [29]. Thus, it examines the sustainable intensification of agriculture in EU regions, focusing on the impact of structural features on this process. The study identified four main clusters of EU regions with distinct structural features: Cluster 1: regions with small farming sector and mixed production, predominantly in eastern Poland, Romania, Croatia, Greece, southern Italy and Bulgaria; Cluster 2: regions with large farming sector, predominantly oriented towards livestock production, located in the UK, France, the Benelux countries (Belgium, The Netherlands and Luxembourg), Scandinavia and some regions in Italy and Spain; Cluster 3: postcommunist industrial farming sector, located in East Germany, the Czech Republic and Slovakia; and Cluster 4: regions with small, unspecialized and polarized farming sector, predominantly in the Baltic States, Western Poland, Austria, Hungary and central and southern Spain. The results of the analysis show that the process of sustainable intensification was uneven between 2005 and 2018, being significantly influenced by the global financial crisis of 2008–2013. An important aspect highlighted by the study is that farmers in regions with a large and specialized farming sector (cluster 2) had the smallest decline in sustainable intensification, suggesting that farm concentration and specialization are key factors to achieve progress in sustainability.

Zhen and Routray [14] and Dale and Beyeler [15] suggest that sustainability indicators should fulfill the following criteria: (i) be easy to measure; (ii) be responsive to system stresses; (iii) respond predictably to stress; (iv) be anticipatory, indicating impending changes in the system; (v) be able to predict changes that can be managed preemptively; (vi) be integrated, providing comprehensive coverage of different agricultural systems such as crops, livestock, and pasture; (vii) respond reliably to natural disturbances, anthropogenic stresses, and changes over time; and (viii) be relatively stable in the face of variation. Rigby et al. [11] and Hayati et al. [7] have constructed on-farm sustainability indices covering six aspects: yield, profit, crop failure frequency, soil depth, organic certification, and permanent soil cover [33].

Binder et al. [16] emphasize that the selection of indicators is closely related to the above-mentioned normative and systemic issues, being influenced by the characteristics of the field, farm, or region and the specific problems of the system under analysis [34,35]. De Olde et al. [20] identify other important criteria for the selection of agricultural sustainability indicators, namely the values and beliefs of the model users (advisors and farmers) about sustainability issues; the degree to which the design and proposed use of the tool are compatible with the data systems and institutional structure of the agencies involved; the flexibility and ease of use of the FSI tool; the accessibility of the data needed to calculate the indicators; the clarity of the model used; the ability of the tool to support discussions on sustainability, both through the model itself and through its use in farmer groups; the degree of complexity of the SIA tool; and the practical aspects to be taken into account for the successful organization of discussion sessions with farmers.

Sala et al. [17] approach the criteria for the selection of indicators from the perspectives of ontology, epistemology, and methodology; in other words, sustainable agriculture cannot be reduced to a simple set of abstract principles and theories. Thus, to support the decision-making process, it is essential to assess the current state of the environment, which is complex and dynamic, and to define the processes of change. Additionally, the measurement and description of sustainable agriculture can be achieved through various methods such as cost–benefit analysis, risk analysis, ecosystem diagrams, and the use of indicator systems [36]. López-Ridaura et al. [13] define sustainability by a set of general attributes of natural resource management systems, such as productivity, stability (combining reliability and resilience), adaptability, equity, and self-empowerment, while Asseldonk et al. [19] emphasize the importance of risk management strategies for the sustainability of agriculture in Europe. So, in their study, the risk management indicators and other indicators collected by the FLINT project were combined with the FADN database.

Meulen et al. [18] have studied the role of innovation in promoting the sustainability of agriculture, noting that the adoption of innovations is an important indicator of competitiveness and sustainability, and Kelly et al. [21] provide a comprehensive review of indicators used to measure sustainability in agriculture. The authors discuss the use of indicators organized according to the three pillars of sustainability, environment, economy, and society, emphasizing that although there is a proliferation of indicators for the environmental dimension, there are still difficulties in integrating them to provide a comprehensive assessment at the farm level. The paper emphasizes that the selection of indicators is essential to ensure credible sustainability assessments, and that indicators need to be relevant, practical, and meet stakeholder expectations. The authors discuss the challenges of aggregating indicators into a single sustainability index and suggest using multiple methods to ensure a comprehensive assessment.

Xavier et al. [28] explore the recent dynamics of agricultural sustainability in Portugal using data from the 2009 and 2019 agricultural censuses. The study proposes a trade-off programming approach to analyze and create a composite indicator of agricultural sustainability, using multivariate techniques such as HJ-Biplot analysis and cluster analysis.

2.2. Economic Indicators and Determinants of Farming Sectors’ Sustainability in the EU

Bathaei and Štreimikienė [31] review a systematic review of agricultural sustainability indicators, and the main aim of the research is to identify and organize relevant indicators for assessing agricultural sustainability, taking into account economic, environmental, and social dimensions. The study applies the SALSA and PRISMA methodologies to search, assess, and synthesize relevant indicators from academic literature published between 2010 and 2022. At the level of economic sustainability indicators, they identified 31 indices such as agricultural labor productivity, farm profitability, market accessibility, income diversification, and production costs. The study emphasizes the importance of continuously updating the list of indicators to reflect new trends and requirements in sustainable agriculture.

Kelly et al. [21] explore the potential of the European Union Farm Accountancy Data Network (FADN) to assess sustainability at the farm level using economic, environmental, and social indicators. The study highlights that while the FADN provides a solid basis for economic assessment, there are significant gaps in environmental and social data, limiting the ability to make comprehensive sustainability assessments at the farm level and develop a set of indicators to improve the assessment of agricultural sustainability at the EU level. The study emphasizes that, while the FADN is a valuable platform for assessing the economic performance of farms, it is insufficient to meet current policy requirements for sustainability, and the FADN needs to be extended to include relevant data on environmental impacts and the social dimensions of sustainability, which would allow the effects of agricultural policies to be assessed in a way that better reflects the EU’s objectives of promoting sustainable and environmentally and socially responsible agriculture.

Andrejovská and Glova [25] explore the sustainability of agricultural holdings in European Union countries by analyzing income indicators using a new classification. The main aim is to identify and quantify the statistically significant determinants of the main income indicators of EU farm holdings, such as net value added per farm work unit (FNVA/AWU), Family Farm Income per family work unit (FFI/FWU), and net farm income (FNI). The study uses linear regression analysis to assess the impact of different economic and environmental factors on these indicators, using data from the FADN network for the period 2009–2018. The main conclusion of the study is that the sustainability of farms in the EU largely depends on the balance between economic and environmental factors, as well as on the economic size of the farm and the efficiency of resource use.

Hloušková et al. [27] explore the economic sustainability of farms in the Czech Republic by assessing economic viability, including opportunity costs. The authors propose an economic viability index that integrates entrepreneurial income and economic profit, considering opportunity costs of labor, land, and capital. The study uses data from the FADN database over a five-year period (2016–2020) to analyze differences in economic viability between different farm types and economic sizes. The study emphasizes the importance of considering opportunity costs in the assessment of economic viability in order to obtain a complete picture of the long-term sustainability of farms. The authors also recommend the use of this economic viability index to optimize agricultural policies, especially in terms of support to vulnerable farms, in order to increase their resilience and promote economic sustainability in the Czech Republic’s agriculture sector.

Kryszak et al. [23] investigate the determinants of farm profitability in different regions of the European Union, emphasizing the influence of the economic size of the farm. The study uses data from the FADN network for the period 2007–2018, analyzing the profitability of agricultural holdings through the prism of return on assets (ROA). The results of the study show that equity turnover is a strong determinant of farm profitability, especially for small and medium-sized farms, where the impact of this indicator is more pronounced, and that high debt levels have a negative effect on profitability in almost all farm categories, especially for large farms. Farm specialization is a positive determinant of profitability, but only for medium and large farms, and investments, although important, did not have a significant impact on profitability, suggesting that EU farms are already saturated in terms of capital. Another important finding of the study is that subsidies have a positive impact on profitability, especially for small farms, but this impact becomes negative for very large farms due to the degressivity mechanism applied in the Common Agricultural Policy.

Martinho [26] explores the profitability and financial performance of agricultural holdings in the European Union at regional and national levels, analyzing how different financial variables influence these aspects. The study uses data from the FADN network for the period 2004–2018, applying descriptive analysis, spatial autocorrelation methods, and regressions. The author examines the influence of financial indicators, such as current assets-to-total assets ratio, current debt-to-total assets ratio, and total debt-to-total assets ratio, on return on assets (ROA) and financial performance, measured by return on equity (ROE). The results show that return on assets (ROA) is positively influenced by the current assets-to-total assets ratio and the current debt-to-total assets ratio, but negatively affected by leverage. These results suggest that the effective management of current assets and current debt is essential to improve farm profitability, and similar patterns were observed for ROE, with more pronounced positive influences from current assets and current debt ratios, emphasizing the importance of managing these variables for the economic success of farms.

Robling et al. [30] address the essential role of data in constructing indicators for assessing sustainability at the farm level, exploring the limitations imposed by the availability of secondary data. The study uses an example applied to dairy farms in Sweden, using a five-step approach that includes a literature review, an inventory of data sources, an expert consultation, a fit analysis, and a critical appraisal. The results show that 20 indicators in 12 sustainability themes have measurement problems, mainly due to a lack of adequate data, the need for additional data, or the transient nature of the data. The study identified numerous challenges related to the use of secondary data to construct sustainability indicators. In particular, the authors found that no single data source is sufficient to measure all of the sustainability themes identified. For example, the indicators for pesticide use, non-renewable energy, and soil quality had major measurement problems due to a lack of adequate data. The authors also emphasized that there are trade-offs between data availability and the analytical validity of some of the indicators.

Roesch et al. [22] present the SALCAsustain methodology, a set of indicators developed to assess the sustainability of farms, covering environmental, economic, and social dimensions. At the level of economic indicators, the authors analyzed profitability: income per family labor unit and return on assets; liquidity: cash flow rate and dynamic debt ratio; and stability: ratio of fixed assets to total assets and ratio of equity to fixed assets.

Vrolijk and Poppe [24] explore the costs associated with extending the Farm Accountancy Data Network (FADN) to include a broader set of sustainability indicators, thus forming a new network called the Farm Sustainability Data Network (FSDN). This extension is motivated by the increasing demands imposed by the Farm to Fork strategy of the European Green Deal. The results show that adding sustainability data to the FSDN would lead to an increase in costs of around 40%, raising the average cost per farm from EUR 750 to EUR 1040, and the differences between Member States are significant, with cost increases ranging from 10% in Ireland and the Netherlands to 124% in France and even 225% in Malta. To keep costs at an acceptable level, the authors suggest two options: either reduce the size of the FADN sample to 55,000 farms or collect sustainability data from a sample of 15,000 farms. This second option would imply a reduction in the number of FSDN farms to 75,000, while still allowing the collection of relevant data for the main farm types in each Member State. The overall conclusion is that, although the extension of the FADN to the FSDN entails additional costs, these are justified by the urgent need for sustainability data for better evaluation and monitoring of agricultural policies at the EU level.

3. Materials and Methods

3.1. Methodological Foundations of the Economic Sustainability Assessment of the Farming Sectors

The assessment of the economic sustainability of farming sectors in the EU and Romania is based on a set of indicators derived from the FADN database, covering the economic dimension of sustainability, such as farm profitability, the impact of financial support policies, and the techno-economic efficiency of agricultural production. A fundamental aspect of this analysis is the use of data from the Farm Accountancy Data Network (FADN), which provides a standardized set of economic indicators at the EU level [37,38].

Figure 1 highlights the methodological design developed to assess the economic sustainability of farming sectors in the European Union and Romania; thus, the conceptual structure is based on a system of seven synthetic indicators, grouped into three analytical dimensions.

The first dimension, economic indicators, integrates three fundamental components: Farm Economic Sustainability Index (FESI), which allows a synthetic estimate of economic sustainability by aggregating net income, value added, and family labor remuneration, providing an overview of real profitability; Farm Capitalization Index (FCI), which expresses the ability of farms to accumulate and use fixed capital for development, reflecting the degree of modernization and investment potential of the sector; and Profitability and Cash Flow Index (PCFI), which assesses the balance between operating and total cash flows, providing key clues on the liquidity and financial resilience of farming sectors in the face of economic risks.

The second dimension, financial support indicators, is the Farm Subsidy Dependency Index (FSDI), which measures the degree of income dependence on subsidies, highlighting the economic vulnerability of farming sectors in the absence of such support, and the Subsidy Efficiency Index (SEI), which analyzes the relationship between farm income and the financial support received through agricultural policies. In addition, the SEI quantifies the efficiency of the use of public funds by relating direct payments to the value added generated, contributing to the analysis of the quality of public intervention.

The third dimension, techno-economic efficiency indicators, comprises the Farm Efficiency and Productivity Index (FEPI), which investigates the efficiency of resource use by relating total output to specific costs, and the Crop Output Efficiency Index (COEI), which analyzes productivity per unit of agricultural area. Together, these indices make it possible to quantify the technical performance of farming sectors and to identify the potential for optimizing the inputs used.

For the analysis of the indices, the evaluation was based on the sample of farms in the FADN database [38], which applies its own sampling methodology (Figure 2).

This methodology aims at national and regional representativeness in terms of the economic size of agricultural holdings. Thus, the data used are considered sufficiently robust to support relevant comparisons between Romania and the European Union in the period 2013–2022 on the economic sustainability of farming sectors.

Statistical and econometric methods are used to interpret the data, such as time series analysis to identify trends and fluctuations in indicators over the period under analysis (2013–2022), comparative analysis to highlight differences between the European Union and specific trends in Romania, and econometric modeling to identify relationships between economic, production, and social variables and to assess the impact of agricultural policies on the performance of farming sectors, and the main data sources used in the analysis include Farm Accountancy Data Network (FADN), Eurostat, and national databases.

The economic size of agricultural holdings reflects the economic strength of farming sectors and allows a comparison between the situation in Romania and that in the European Union, which is assessed by means of economic indicators that highlight the profitability and financial sustainability of farming sectors. Thus, the economic performance of farming sectors can be measured by analyzing farmers’ incomes and investments in infrastructure and equipment, as well as cash flow (Annex 1 Appendix A).

Linking financial support to the sustainability of farming sectors is essential to assess the effectiveness of these policies. Studies show that subsidies have a positive impact on the financial stability of farmers [39], but there are also concerns about their uneven distribution and possible negative effects on the market and competition. In this context, a balanced approach is needed to ensure that subsidies support innovation and sustainability without creating excessive dependence on EU funds (Annex 3 Appendix A). EU agricultural policies are strongly influenced by subsidy mechanisms, which play a crucial role in supporting farmers and promoting the sustainability of agriculture. European subsidies are granted through the Common Agricultural Policy and are mainly aimed at maintaining farmers’ incomes, encouraging sustainable practices, and developing rural areas [40]. One of the main objectives of the subsidies is to reduce economic disparities between agricultural regions of the European Union, thus ensuring a balanced development of farming sectors.

Assessing the efficiency of agricultural production is important for determining the sustainability of European agriculture by analyzing crop yields and agricultural land use, which are influenced by several factors, including soil quality, climatic conditions, farming practices, and the use of modern technologies, all of which influence the economic performance of farming sectors and their environmental impact (Annex 3 Appendix A).

3.2. Analytical Model Based on Indicators for Assessing the Economic Sustainability of Farming Sectors

The assessment of the economic sustainability of farming sectors in the EU and Romania takes into account the economic aspects based on the data provided by the FADN [37], developing a set of seven indicators that allow a comprehensive assessment of farming sectors for the period 2013–2022, grouped into three tiers:

I. Economic indicators: Reflecting the profitability of farming sectors, their investment capacity, and financial liquidity, based on the following indices:

• Farm Economic Sustainability Index (FESI), reflecting the net income, value added, and profitability of farming sectors, calculated on the basis of the formula

FESI = ((FFI/FWU) + FNVA + FNI)/3

(1)

where the following are used:

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FFI/FWU—Family Farm Income is expressed per family labor unit and reflects differences in the amount of family labor to be remunerated at the level of each holding;

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FNVA—Farm Net Value Added in agriculture represents the remuneration of fixed factors of production (labor, land, and capital), whether they are family or external. Thus, holdings can be compared regardless of the origin (family or non-family) of the resources used;

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FNI—Farm Net Income includes the payment of family-type factors of production (labor, land, capital), but also the compensation for the risks assumed by the entrepreneur (profit or loss) during an accounting year.

A low Farm Economic Sustainability Index may signal a high dependence on agricultural subsidies, which raises questions about the long-term sustainability of farming sectors, and the analysis of this index in relation to the level of subsidies received can provide valuable information for the development of policies aimed at reducing the economic vulnerability of farming sectors and encouraging diversification of agricultural activities.

2.

The Farm Capitalization Index (FCI) measures the capacity of farming sectors to accumulate and use capital for investment, with the following formula:

FCI = AFC/GIFA

(2)

where the following are used:

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AFC—Average Farm Capital is the average amount of capital used on the farm, calculated as the arithmetic average between the opening and closing values of the year. This capital includes livestock, permanent crops, land improvements, buildings, machinery and equipment, and circulating capital. Quotas and other regulated rights are not taken into account as their value cannot always be separated from that of the land;

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GIFA—Gross Investment in Fixed Assets is determined by the following formula: purchases of fixed assets minus sales of fixed assets plus change in value of breeding livestock.

The Farm Capitalization Index is an analytical tool for assessing the financial structure of farming sectors, allowing a better understanding of the degree of capitalization and the potential for long-term development. Thus, a high Farm Capitalization Index reflects a competitive farming sector that is able to invest steadily in technology, modern equipment, and infrastructure, which is essential for increasing productivity and reducing operational costs, contributing to a sustainable and efficient agriculture.

3.

Profitability and Cash Flow Index (PCFI) assesses liquidity management and financial risks, calculated according to the following formula:

PCFI = (Cash Flow 1/Cash Flow 2) × 100

(3)

where the following are used:

-

Cash Flow (1) operating reflects the farm’s ability to economize and self-finance. It is calculated as the difference between receipts and payments during the accounting year, excluding capital transactions or loans and debts. The indicator is similar to the one used by Eurostat [41] in macroeconomic accounts. The detailed formula is as follows:

Cash Flow (1) = Sales of products + Other income + Livestock sales − All costs paid − Livestock purchases + Agricultural subsidies − Agricultural taxes + Balance VAT + Investment subsidies − Investment taxes

(4)

-

Cash Flow (2) total also expresses the farm’s ability to save and self-finance, but also includes capital and debt transactions. The extended formula is

Cash Flow (2) = Net receipts from agriculture and other sources + Balance of farm subsidies and taxes + Balance related to investments + Balance related to capital transactions + Balance related to changes in debt and loans

(5)

The Profitability and Cash Flow Index (PCFI) is an analytical tool that assesses the ability of farming sectors to generate cash and effectively manage financial risks, reducing exposure to long-term debt. A Profitability and Cash Flow Index above 100% indicates a well-managed farm with balanced cash flows, while an index below 100% suggests high financial risks and possible dependence on subsidies and loans. The application of this indicator in agricultural analysis and financing strategies contributes to the stability of farming sectors, allowing the identification of farming sectors in need of support to improve economic performance and reduce financial vulnerability.

II. Indicators of the impact of financial support: Assess the dependence of farming sectors on subsidies and their effectiveness in the development of agriculture:

4.

Farm Subsidy Dependency Index (FSDI), calculated according to the following formula:

FSDI = (TS_(exc_l.inv)/TDP) × 100

(6)

where the following are used:

-

TS_(exc_1.inv) = Total Subsidies, excluding investment subsidies, represents subsidies granted for current operations related to production, excluding support for investment, also not including payments for cessation of farming activity;

-

TDP = Total Direct Payments include EU and national subsidies, both decoupled (not directly linked to production) and coupled (linked to production), excluding those for rural development, cost coverage, or livestock purchase.

The Farm Subsidy Dependency Index (FSDI) is a tool for assessing the economic dependency of farming sectors on subsidies, identifying the most vulnerable farms, and allowing more effective agricultural policies to be designed. A high FSDI indicates increased economic vulnerability, while a low index reflects a higher capacity of farming sectors to generate their own income, and the application of this indicator in agricultural policies and support strategies can contribute to the transition towards a more self-reliant and sustainable farming sector, reducing the financial risks associated with subsidy dependency.

5.

Subsidy Efficiency Index (SEI) measures how efficiently subsidies are used to generate value added in farming sectors and is calculated according to the following formula:

SEI = (TDP/FNVA) × 100

(7)

where the following are used:

-

TDP = Total Direct Payments, represents subsidies granted by both the European Union and national authorities, including decoupled (not linked to production) and coupled (linked to production) support. It does not include payments related to rural development, overheads, or animal purchase;

-

FNVA = Farm Net Value Added, reflects the remuneration of the fixed factors of production—labor, land, and capital, whether they are owned by the household or external. This indicator makes it possible to compare agricultural holdings irrespective of the family or non-family structure of the resources used.

This indicator allows a comparative analysis between farms in different EU member states, highlighting the level of efficiency of the use of public funds, thus a high SEI indicates an economic vulnerability of farming sectors, as they fail to generate sufficient added value without major financial support, while a low SEI suggests high economic sustainability, reflecting a more efficient use of resources and greater autonomy of farming sectors.

III. Techno-economic efficiency indicators: Analyze the capacity of farms to transform inputs into agricultural output by the ratio between the output obtained and the specific costs associated with it, in order to underpin balanced policies adapted to the European agricultural reality:

6.

The Farm Efficiency and Productivity Index (FEPI) shows how efficiently resources are used for agricultural production, with the following calculation formula:

FEPI = TOC/TSC

(8)

where the following are used:

-

TOC = Total Output—Crops and Crop Production is calculated as the sum of: realized sales, on-farm internal consumption, and household consumption, plus the difference between the value of stocks at the end and beginning of the accounting year;

-

TSC = Total Specific Costs include costs directly related to crop production (seeds and planting material, fertilizers, plant protection products, other crop-specific costs), costs specific to animal husbandry (fodder for grazing livestock and monogastric livestock, other animal-specific costs), and costs specific to secondary agricultural activities.

7.

Crop Output Efficiency Index (COEI) measures the economic efficiency of agricultural land use in crop production, expressing the economic value generated per unit of agricultural area used, with the following calculation formula:

COEI = TCO/UAA

(9)

where the following are used:

-

TCO = Total Crop Output = sales + on-farm consumption + household consumption + (closing value − opening value);

-

UAA = Utilized Agricultural Area, in hectares

Thus, the use of these seven indicators allows a comprehensive and detailed assessment of the economic sustainability of farming sectors, providing a clear perspective on the economic performance, the impact of subsidies and the social stability of farmers, and by integrating these indices into the analysis of agricultural development, weaknesses and opportunities for improvement can be identified, facilitating the design of effective policies to support the transition towards a more competitive, self-reliant and sustainable farming sectors in the long term.

4. Results

4.1. The Economic Dimension of Sustainability: Analysis of Profitability, Investment Capacity, and Liquidity of Farming Sectors

The economic dimension examines the fundamentals of economic sustainability in agriculture through the direct financial performance of agricultural holdings, using summary indicators of profitability, capitalization, and cash flow efficiency. The assessment of these components not only makes it possible to quantify the ability of farms to generate income and sustain productive investments, but also to assess their financial performance in the face of the variability of agricultural markets and climatic conditions.

In line with these considerations, this study tests the following hypothesis:

 H1:

The economic performance of the Romanian farming sector is lower than the EU average in terms of profitability, investment capacity, and financial liquidity, as reflected by the Farm Economic Sustainability Index (FESI), the Farm Capitalization Index (FCI), and the Profitability and Cash Flow Index (PCFI). This hypothesis aims to capture the persistent structural disparities that affect Romania’s farming sector and its ability to achieve sustainable economic growth comparable with other EU Member States.

The comparative analysis between Romania and the EU average reveals the levels of competitiveness and the potential for sustainable economic development of the national farming sectors, highlighting significant differences in both the general trends and the rate of growth of this indicator (Figure 3).

In the case of the EU, the economic size of agricultural holdings has been steadily increasing over the period analyzed. From 49 thousand EUR in 2013, this indicator progressively increased to 66 thousand EUR in 2022, thus the annual increase was gradual, with an inflection point in 2018, when the value increased from 54 thousand EUR (2017) to 64 thousand EUR (2018). This development suggests a structural consolidation of the European farming sectors, driven by favorable agricultural policies, improved access to finance, and technological modernization of farming sectors. In contrast, Romania exhibited a significantly different growth pattern, characterized by an initial stagnation followed by steep growth. Between 2013 and 2017, the economic size of farming sectors fluctuated in the range of 15–18 thousand EUR, indicating a relative stability without significant improvements; however, in 2018, there was an exceptional increase from 16 thousand EUR (2017) to 38 thousand EUR (2018), which represents an advance of 137.5% in a single year. The results are also confirmed by Ionitescu [42], who shows that although the absolute contribution of agriculture to GDP has doubled in the last decade, its share has decreased (from 5.4% in 2013 to 4.5% in 2022), signaling a slower evolution compared to other economic sectors. In addition, gross fixed capital investment in agriculture accounts for only 0.7% of total national investment, reflecting a chronic underfunding of the sector. At the same time, Popescu et al. [43] show that Romania ranks last in the EU in terms of the concentration of agricultural production (GSC = 0.1597), but first in terms of the fragmentation of the number of farms (GSC = 0.7004), which confirms the persistence of a low-efficiency, poorly integrated and difficult to consolidate agricultural model.

Throughout the entire period analyzed, Romania maintained substantially lower values compared to the EU average, even after the rapid increase in 2018. While in 2013–2017 the economic size of the Romanian farming sectors was about 30% of the EU average, after 2018, this ratio improved, reaching about 58% in 2022. However, the gap remains significant, indicating the persistence of structural differences between Romanian and European agriculture. Thus, the differences in the dynamics of this indicator can be attributed to factors such as national agricultural policies, access to finance, degree of modernization, and the structure of farming sectors. Florea et al. [44] show that the development of the Romanian farming sector is predominantly influenced by structural and geographical factors, while economic and capitalization indicators have a limited contribution to the sector’s performance. The authors emphasize that only a small number of well-equipped and capitalized farms have the potential to make a significant contribution to economic convergence with the EU, while the majority remain dependent on subsistence and public support.

Over the analyzed interval, the Farm Economic Sustainability Index in the EU shows an upward trend, registering an increase of 108.3% from 2013 (19,940.51 EUR) to 2022 (41,528.59 EUR). In contrast, the index in Romania increased by 206.4%, from 9549.45 EUR in 2013 to 29,262.12 EUR in 2022, which shows a significant convergence with the EU average, thus the strong increase in 2018–2021 reflects a significant improvement in Romanian farmers’ incomes, due to more efficient access to EU funds and modernization of farming sectors (Figure 4).

During the period analyzed, the average value of the FESI in the European Union was around EUR 23,973, while in Romania, the average was significantly lower, at EUR 15,598, reflecting a persistent gap in the level of economic sustainability between the two regions. Also, the dispersion of FESI values is higher in Romania, with a standard deviation of EUR 9131, compared with EUR 8051 in the EU, indicating a higher volatility in the economic performance of Romanian farms. In terms of minimum and maximum values, Romania recorded a minimum of EUR 5620 in 2015 and a maximum of EUR 29,262 in 2022, while in the EU, the values ranged between EUR 16,432 and EUR 41,529, confirming the upward trend in economic performance in both regions, but also the existence of a significant structural gap.

In the period 2013–2022, the Average Farm Capitalization Index (FCI) value in the European Union was 18.96, significantly higher than the average value recorded in Romania, 13.84, which highlights a higher level of capitalization of European farms compared to Romanian ones. At the same time, the variability of the FCI values was more pronounced in the EU, where the standard deviation was 4.63, compared with only 1.62 in Romania, indicating a higher dispersion between Member States or wider fluctuations in fixed-asset investments (Figure 5).

In terms of extreme values, in the EU, the index reached a low of 10.40 in 2022 and a high of 26.85 in 2013, reflecting a long-term downward trend. By contrast, in Romania, the FCI has varied in a narrower range, between 9.93 and 15.69, with a relatively stable evolution, but with a slight decrease in recent years. This difference in magnitude between the two regions suggests that Romania has a more homogeneous agricultural investment structure, but with a generally lower level of capitalization compared with the EU average.

In the European Union, the PCFI Profitability and Cash Flow Index was relatively stable, ranging between 137% and 157%, which indicates a steady and solid cash flow management in the European farming sectors indicating a solid liquidity and good profitability of farming sectors, while in Romania, the index showed higher fluctuations, ranging between 105.9% and 136.6%, which reflects a higher volatility in the cash flow management of the Romanian farming sectors (Figure 6).

During the period analyzed, the average PCFI in the European Union was 145.39%, significantly higher than the average recorded in Romania of 118.63%, suggesting that European farms have, on average, a better capacity for self-financing and liquidity management. The EU values were also more constant, with a standard deviation of 6.13 percentage points, compared with 9.37 in Romania, indicating a higher volatility in cash flow performance in Romanian agriculture.

While the PCFI values in the EU have consistently remained above the 137% threshold, reflecting strong financial sustainability, Romania has had years in which the indicator fell below 110% (2014, 2019, 2021, 2022), which may signal financial vulnerability or dependence on external financing in certain periods.

Based on the comparative analysis of the Economic Sustainability Index (FESI), the Farm Capitalization Index (FCI), and the Profitability and Cash Flow Index (PCFI), it can be concluded that Hypothesis 1 is confirmed. The farming sector in Romania consistently shows lower levels of economic performance compared to the EU average for all three indicators analyzed. Although there has been notable progress in some periods, especially after 2018, the average values remain significantly below the European level, especially in terms of investment capacity and financial liquidity. Also, the higher volatility of these indicators in the case of Romania reflects an increased vulnerability to external shocks and long-term financial instability. These results highlight the structural limitations of the Romanian farming sector in achieving a level of economic sustainability comparable to that of the more consolidated EU Member States. These results are also confirmed by Ursu and Petre [45], who emphasize the need to increase efficiency and sustainable use of resources in the context of the expanding farming sector, and Nowak et al. [46] developed a summary index of agricultural sustainability for the 28 EU Member States using the TOPSIS method. According to them, Romania ranks in Class III, thus being among the countries with a low level of agricultural sustainability, together with Portugal, Greece, Slovenia, and Italy. The low value of the synthetic measure for Romania was mainly driven by low labor productivity and insufficient investment in fixed capital, which are consistent with the low levels of FESI and FCI indices obtained in our analysis. At the same time, the study points out that the new entrants to the EU are characterized by a lower intensity of production, as well as by a lower level of structural development and access to capital.

Aceleanu [47] points out that the farming sector in Romania continues to be marked by low economic efficiency, due to land fragmentation, a low level of technologization, and underdeveloped infrastructure, which limits its capacity for sustainable development. At the same time, the data on the economic dimension of farms confirm that Romania is on an upward path, but with semi-significant gaps compared with the EU average, and Guth et al. [48] emphasize that, in the absence of CAP support, EU agriculture would become unprofitable, and in the case of Central and Eastern European Member States such as Romania, economic sustainability depends significantly on European subsidies. At the same time, the same authors point out that the current CAP policies favor large farms, which leads to the accentuation of structural disparities in the agricultural sector. And Hurduzeu et al. [49] show that Romania, along with other Eastern EU countries, faces systemic challenges in achieving sustainable development goals (SDG 2), especially in terms of increasing farm incomes and farm consolidation. At the same time, studies suggest that the sluggish economic dynamics of the Romanian farming sector are influenced by a combination of factors, such as limited access to finance, excessive land fragmentation, poor infrastructure, and a low level of technologization.

4.2. The Role of Public Support in Enhancing Sustainability: Economic Dependence on Subsidies and the Efficiency of Their Use

The role of public support in enhancing sustainability investigates the correlation between the economic sustainability of agricultural holdings and the financial support provided through Common Agricultural Policy mechanisms. Relevant indicators on the degree of dependence on direct payments and their efficiency in generating added value are analyzed, providing a critical perspective on the balance between economic autonomy and public support. The results allow for an assessment of the capacity of the subsidy system to support sustainable agricultural development and for the formulation of recommendations for redirecting financial support to areas with low yields or high structural vulnerability.

Given the central role that subsidies play in shaping the structure and performance of EU farming sectors, and acknowledging Romania’s specific context within the Common Agricultural Policy framework, it becomes essential to assess not only how dependent Romanian farming sector are on public support but also how effectively they use this support to enhance economic sustainability. While subsidies are meant to stabilize farm incomes and stimulate development, their long-term contribution to sustainable value creation varies significantly across countries and farm structures. To explore these dynamics more thoroughly, this study puts forward the following hypothesis:

 H2:

The Romanian farming sector is less dependent on subsidies than the EU farming sector, but it is also less efficient in using these funds to create added value, as measured by the Farm Subsidy Dependency Index (FSDI) and the Subsidy Efficiency Index (SEI).

This hypothesis reflects the assumption that, despite receiving relatively comparable support mechanisms through the CAP, structural and operational differences, such as farm size, investment capacity, and market access, may limit the effectiveness with which the Romanian farming sector leverages public financial aid. The analysis of FSDI and SEI over the 2013–2022 period allows us to evaluate whether the Romanian farming sector exhibits higher economic autonomy and whether it manages to convert subsidies into increased productivity and income in a way that supports long-term sustainability.

The European Union had a stable Farm Subsidy Dependency Index, between 117 and 118%, and continued to gradually increase, exceeding 120% in 2018, reinforcing the high dependence of the European farming sectors on subsidies, indicating a high and constant level of dependency, while in Romania, the FSDI increased from 106.7% to 111.4%, suggesting an increasing dependency during this period (Figure 7).

On average, the EU had an FSDI of 121.29%, suggesting a consistent level of financial support in relation to farm income, while Romania had an average of 107.49%, indicating a slightly lower dependence on subsidies than the European average. However, the values in Romania were more constant, with a smaller standard deviation (±2.60 percentage points) compared with the EU (±3.99), indicating a more stable subsidy/income ratio in Romanian agriculture.

The FSDI values in the EU increased steadily from 117.12% in 2013 to 127.75% in 2022, while in Romania, they remained in a narrower range between 103.68% and 111.40%, with a peak in 2015. This suggests that, although Romanian agriculture is financially supported, subsidy dependency has not increased significantly, which could reflect either an increase in economic self-sufficiency or a ceiling in the level of support.

The Subsidy Efficiency Index in the European Union decreased significantly from 51.14% in 2013 to 47.36% in 2020 and to 32.16% in 2022, indicating greater efficiency in the use of funds and a transition towards less subsidy-dependent agriculture. The same was the case in Romania, where the index had a similar trajectory, decreasing from 36.70% in 2013 to 27.25% in 2022, indicating that farms have diversified their income and become more sustainable (Figure 8).

On average, the SEI in the European Union was 47.17%, compared with 37.20% in Romania, indicating that EU farms were more efficient in using subsidies to generate added value. The standard deviations of 7.41% in the EU and 6.69% in Romania suggest a relatively close variability between years in both regions, but with a significantly higher level of efficiency on EU farms.

SEI values experienced a gradual decrease in both regions, especially from 2020 onwards, when Romania reached a peak of 48.99%, followed by a steep decrease to 27% in 2021. In 2022, the SEI fell to 32.16% in the EU and 27.25% in Romania, suggesting a decrease in efficiency in the use of financial support, possibly influenced by structural changes, prices, or economic instability.

Based on the results, Hypothesis 2 is confirmed. Although the farming sector in Romania shows a slightly lower degree of dependence on subsidies compared with the EU average, the efficiency of the use of these funds for value-added generation remains consistently below the European level. This situation indicates that, although Romanian agriculture has a relatively stable income structure in relation to public support, the capacity to transform subsidies into sustainable economic performance is still limited. The results underline the need for improved mechanisms to optimize the productive use of public funds and support a more efficient allocation of public funds in agricultural regions with high structural vulnerability. The results of this study are in line with those presented by Kravčáková Vozárová and Kotulič [50], who demonstrated that subsidies have a significant impact on the competitiveness of Slovak farms, but that their effectiveness is variable and depends on farm size and other structural factors. The authors emphasize that farmers in the new Member States, including Romania, are disadvantaged by an unequal support system, which negatively influences the efficient use of EU funds and the economic performance of the farming sector.

Barbosa [51] shows that subsidies are the main government support mechanism analyzed in the farming sector, but that their effectiveness varies significantly depending on the national context and the way policies are implemented. The literature also draws attention to the fact that, in the absence of coherent and well-targeted government support, negative effects such as the maintenance of unsustainable farming practices, declining organic production, or stagnation of rural development may occur.

Volkov et al. [52] provide a valuable comparative framework between old and new EU Member States, including Romania, on farming sector performance and subsidy dependency. The authors show that the new Member States, such as Romania, have better industrial performance in the farming sector than the older Member States, especially in the specialized crops sector (cereals, oilseeds, and protein crops), due to lower input costs and investments in agricultural modernization. These findings validate the observations made regarding Romania’s relatively lower dependence on subsidies compared with the EU average, as well as the lower efficiency in their use for value-added generation. Thus, the study by Heyl et al. [53] confirms that although subsidies can play an important complementary role in sustainability governance, most of them in their current form contribute to the perpetuation of unsustainable agricultural practices. The authors argue that instruments such as quantitative controls (e.g., limits or CAP and trade schemes) are significantly more effective than subsidies in addressing the drivers of unsustainability, such as fossil fuel use and intensive livestock farming. In this sense, the results of the study reinforce the idea that the high dependence on subsidies and low efficiency in their use reflect a structural vulnerability of the Romanian agricultural sector, which needs to be addressed through a strategic rethinking of the allocation of funds and by increasing the transparency and environmental conditionality of payments, as also recommended by the international literature. Additionally, Boros et al. [54] emphasize that, although subsidies have positively contributed to income and to maintaining farming sector activity, they do not always guarantee sustainable economic efficiency. In some cases, excessive dependence may lead to market imbalances and inefficient allocation of resources; moreover, the results show that the beneficial effect of subsidies is more pronounced among large farms, while small farms may suffer economic distortions and a decrease in efficiency.

4.3. Technical–Economic Efficiency as a Determinant of Sustainability: Performance Analysis in the Use of Productive Resources

Techno-economic efficiency as a determinant of sustainability aims to quantify the efficiency of the allocation and use of productive resources on farms through specific indicators that relate agricultural output to the inputs used. It analyzes technical and economic performance in relation to specific crop costs and agricultural land use in order to highlight the capacity of farms to generate value under conditions of operational efficiency. The differences between Romania and other EU Member States are essential in order to outline a functional diagnosis of farming sectors and to identify the measures needed to optimize economic performance in order to achieve real and long-term sustainability.

Given Romania’s relatively limited access to capital and lower levels of farming sector consolidation, it is particularly relevant to assess the extent to which the Romanian farming sector succeeds in using available resources efficiently. Therefore, this section investigates the third research hypothesis:

 H3:

The Romanian farming sector uses production resources more efficiently than the EU average, but its economic performance per hectare remains significantly lower, as measured by the Farm Efficiency and Productivity Index (FEPI) and the Crop Output Efficiency Index (COEI).

The Farm Efficiency and Productivity Index (FEPI) in the European Union ranged between 2.51 and 3.10, with a slightly increasing trend towards the end of the period, and in Romania, the FEPI had higher values than the EU average in most years, remaining above 3 in 2013, 2014, 2017, 2018, 2019, and 2021, indicating high economic efficiency in relation to production costs (Figure 9).

During the analyzed period, the average FEPI in Romania was 3.15, higher than the EU average of 2.74, which indicates a higher efficiency of agricultural input use on Romanian farms, in relation to the crop production obtained. Romania also showed a higher variability (standard deviation ±0.26) compared with the EU (±0.18), suggesting larger differences in performance between years and possibly between farm types.

The minimum values were 2.68 in Romania (2020) and 2.51 in the EU (2016), while the maximum values were recorded in 2021, 3.62 in Romania and 3.11 in the EU, the year in which both regions reached peak productivity. This confirms that, despite more limited resources, farms in Romania have shown an increased capacity to convert inputs into production, which is a positive sign in assessing economic sustainability.

Over the period 2013–2022, Romania recorded average COEI values of 847 EUR/ha, significantly below the EU average of 1366 EUR/ha, reflecting an average gap of about 38% in the economic efficiency of agricultural land use (Figure 10).

While the values in Romania varied between a minimum of 690 EUR/ha (2020) and a maximum of 1134 EUR/ha (2022), in the EU, the increase was steady, from 1194 EUR/ha in 2013 to 1781 EUR/ha in 2022, indicating a sustained trend of improvement in agricultural productivity, while in Romania, only in the last two years has a significant approach to the European level been observed.

On average, the EU recorded a COEI of 1268 EUR/ha, significantly higher than Romania’s average of 836 EUR/ha, indicating a higher efficiency of crop production at the European level. At the same time, the dispersion of the values is more pronounced in the EU (standard deviation: ±207) than in Romania (±142), suggesting a higher variability of yield between Member States compared with a more uniform evolution in Romanian agriculture. In the long term, Romania shows a clear upward trend, with a notable increase in the last two years: from 690 EUR/ha in 2020 to 1134 EUR/ha in 2022. This progress may signal improvements in agricultural technology, farming practices, and land use efficiency.

Based on the contrasting results between the two indicators, Hypothesis 3 is validated. Although the farming sector in Romania exceeds the European Union average in terms of input use efficiency, as reflected by higher values of the efficiency and productivity index (FEPI), it continues to record lower economic productivity per hectare, according to the crop production efficiency index (COEI). This duality highlights the capacity of the Romanian farming sector to produce cost-effectively, but it also confirms the existence of structural limitations in the economic use of agricultural land, underlining the need for targeted interventions on land use and technological modernization.

The results are also supported by the study conducted by Bumbescu [55], which emphasizes that in Romania, only small and medium-sized farms, in terms of economic size, generate positive sustainable value. These same categories of farms manage to use resources such as land, energy, and agricultural inputs more efficiently than large farms. The latter have negative sustainable values, reflecting an inefficient use of resources and a low contribution to sustainable development. And the study by García-Cornejo et al. [56] demonstrates, through an empirical analysis using the Data Envelopment Analysis (DEA) method applied to a sample of 49 dairy farms in northern Spain, that value-added diversification strategies (such as the production of varied dairy products, direct sales, organic labeling and PDO-type certifications) contribute significantly to economic efficiency and, therefore, to farm sustainability. The study shows that direct sales and the use of organic or controlled origin certifications are positively associated with efficiency, while excessive product diversification may reduce economic performance due to managerial complexity.

The relevance of these findings is reinforced by the study conducted by Bobitan et al. [57], which, using Data Envelopment Analysis (DEA) and the Malmquist index, highlights significant variations in financial efficiency across agricultural firms in Romania. The study emphasizes that, in the absence of effective benchmarking and strategic financial planning practices, many agricultural firms do not achieve high levels of resource management efficiency. Moreover, the authors draw attention to the need to develop dynamic capabilities of agricultural firms to cope with market volatility and sustainability pressures. These findings strengthen the argument for the existence of structural barriers in the Romanian agricultural sector, similar to those identified in the comparative analysis carried out in this research between Romania and the EU. And Radlińska [58] emphasizes that very small and very large farms have the best technical performance, while medium-sized farms show lower levels of efficiency, supporting the idea that the structure of the agricultural sector plays a key role in determining overall efficiency. Leghari et al. [59] consider that pressure on water resources is a major global challenge, accentuated by population growth, expansion of cultivated areas, and climate change. This finding is in line with the results, according to which the Romanian agricultural sector outperforms the EU average in terms of input use efficiency (FEPI) but continues to have lower economic productivity per hectare (COEI).

5. Discussion

The literature emphasizes that the economic sustainability of farming sector is closely linked to various financial management strategies and the impact of economic and environmental factors, and agricultural policies need to take these variables into account to support sustainable growth and long-term competitiveness of farming sectors, and the literature highlighting economic sustainability and farm viability provides a detailed analysis of the various studies that address economic viability and farm sustainability [60]. These studies explore various economic models [23], financial performance indicators [25], and financial management strategies to improve the economic viability and long-term sustainability of the farming sector [26,27].

The analysis of the economic dimension of the sustainability of the Romanian farming sector, carried out by comparing the FESI, FCI, and PCFI indices, reveals significant discrepancies with the EU average. Although Romania has recorded an accelerated growth of the Economic Sustainability Index (FESI), especially in the period 2018–2022, its level remains below the European average, which confirms hypothesis H1 and highlights the persistence of structural limits in achieving a comparable level of economic performance. Similarly, the Farm Capitalization Index (FCI) reflects a modest investment in fixed assets, with constant but low values, signaling difficulties in attracting and efficiently using capital. This undercapitalization is explained by both limited access to finance and excessive land fragmentation, also confirmed by Popescu et al., [43] who identify Romania as having one of the lowest concentrations of agricultural production in the EU.

The Profitability and Cash Flow Index (PCFI) has had an oscillating evolution, reflecting a high vulnerability to agricultural market fluctuations, prices, and climatic conditions. This volatility underlines the high dependence on CAP support and the absence of constant sources of self-financing, especially for small and medium-sized farms. Studies in the literature [4,7,20,42,45,46,47] support the conclusion that the Romanian farming sector faces chronic problems of economic efficiency, caused by poor infrastructure, a low degree of technologization, and insufficient investment. Although progress has been evident in recent years, the differences in economic performance compared with the EU remain substantial, which indicates the need for a coherent strategy to modernize Romanian agriculture based on investment in fixed capital, support for economically viable farms, and better integration into European value chains.

The analysis of the role of public support in sustaining the economic sustainability of the farming sector confirms hypothesis H2, according to which farms in Romania are, on average, less dependent on subsidies than those in the European Union but use these funds with lower efficiency in generating added value. Thus, the average value of the FSDI index in Romania is 107.49%, compared with 121.29% in the EU, indicating a lower degree of dependence but also a possible capping of financial support or a greater diversification of income. However, the SEI, which reflects the capacity to transform support into economic value, is significantly lower in Romania (37.2%) than the EU average (47.17%), which points to limitations in the efficient use of funds. These results are supported by the literature; thus, studies [21,39,51,52] emphasize that although subsidies contribute to the stability of farm incomes, their effectiveness is often diminished by structural deficiencies and lack of well-targeted policies. In the case of Romania, the context characterized by fragmented farms, modest investments, and limited access to markets leads to a low capacity to make strategic use of public support. Studies [53,54] also draw attention to the perverse effects of a poorly calibrated subsidy system, which can perpetuate unsustainable practices and market imbalances. It is therefore necessary to reformulate support policies that favor the efficient and sustainable use of funds through environmental conditionality, incentives for modernization, and prioritization of farms with real potential for economic development. Only through such measures can Romania turn public support into a catalyst for long-term performance and sustainability.

The analysis of the techno-economic efficiency of the farming sector in Romania confirms hypothesis H3, according to which Romanian farms utilize production resources more efficiently than the EU average but lag behind in terms of economic productivity per hectare. This contrast is evidenced by the higher values of the Farm Efficiency and Productivity Index (FEPI) in Romania, on average 3.15 compared with 2.74 in the EU, which reflects a favorable ratio between the output obtained and the costs involved. However, the crop production efficiency index per unit area (COEI) remains about 38% below the European average, suggesting an under-valorization of the agricultural potential of the land used. This duality can be explained by the structural limitations of Romanian agriculture: the excessive fragmentation of holdings, the low degree of mechanization, the limited access to high-performance technology, and the poor infrastructure. Specialized studies [55] support this conclusion by the fact that only small and medium-sized farms in Romania manage to generate sustainable value by making efficient use of resources, while large farms record negative performances. The results are also supported by the analysis of the studies in [56,57,58], which shows that farm structure, diversification of activities, and the ability to adapt to market requirements have a decisive influence on economic efficiency and sustainability. It is therefore necessary to implement measures aimed at technological modernization, rational use of agricultural land, and support for the adaptability of small and medium-sized farms. Only through policies tailored to the specific structural features of Romanian agriculture can economic efficiency be maximized and the long-term sustainability of this key sector for the national economy ensured.

The results obtained in this study confirm the existence of significant structural differences between the agricultural sector in Romania and the EU average, both in terms of economic performance, resource use efficiency, and dependence on public support. This study can be extended by including the social and environmental dimensions of sustainability to assess the aggregate impact of agricultural activities on rural development and the environment, as well as by applying panel econometric models to investigate the causal relationships between public policies and economic performance, taking into account regional specificities and long-term data dynamics.

6. Conclusions

The analysis of farming sector sustainability in the EU and Romania highlights important progress but also major challenges. Although Romania has seen rapid growth in FESI and other economic indicators, it remains vulnerable due to limited access to finance and agricultural market volatility. But in the EU, although the farming sector is better capitalized and less vulnerable to fluctuations, subsidy dependency remains a major concern. The results reflect significant differences in agricultural performance between Romania and the European Union, with the Farm Economic Sustainability Index (FESI) showing a significant improvement in economic sustainability in both regions. But Romania’s accelerated growth suggests both a convergence process and higher vulnerability to economic shocks. The Farm Capitalization Index (FCI) showed relative stability in the EU, while Romania experienced major fluctuations, suggesting a higher exposure to financial risks, and the Profitability and Cash Flow Index (PCFI) demonstrated adequate ability to finance in both regions, but Romania’s lower values reflect a higher level of economic uncertainty.

On the other hand, the Farm Subsidy Dependency Index (FSDI) highlighted a high dependency on subsidies in both the EU and Romania, with values consistently above 100%, which raises questions about the long-term sustainability of farming sector without government support while the Subsidy Efficiency Index (SEI) showed a decreasing trend in both regions, suggesting a more efficient use of public funds in generating added value. Additionally, the Farm Efficiency and Productivity Index (FEPI) confirmed that the Romanian farming sector was, on average, more efficient in the use of agricultural resources than the European farming sector, demonstrating higher competitiveness.

From an overall perspective, the results of the analysis show that the EU farming sector has higher financial stability and investment capacity, while the Romanian farming sector has experienced faster economic growth, but with persistent challenges related to subsidy dependency and financial resilience. Although Romania has partially converged towards European values since 2018, persistent differences suggest that further measures are needed to close the economic gap between the Romanian and EU farming sectors. Thus, sustainable agricultural policies focused on modernization, efficiency, and productivity growth are needed to ensure a competitive integration into the European agricultural market to ensure long-term economic, social, and environmental sustainability in the sector.