Sustainability and Agricultural Investments in Bulgaria: Balancing Profitability and Environmental Protection

Abstract

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).

1. Introduction

Agriculture remains an important sector of the Bulgarian economy and rural landscape. Beyond its role in food production, it contributes to employment, regional cohesion, and the management of natural resources. In recent years, the sector has faced growing expectations to reconcile economic performance with environmental responsibility. Farmers are expected not only to maintain profitability under volatile markets and climate risks but also to adopt practices that protect soil, water, air, and biodiversity. The effort to balance these objectives is one of the key challenges for contemporary agricultural systems and their resilience. Structural constraints in Bulgaria further complicate this balance, including ageing farm infrastructure and labour, fragmented land ownership, limited access to finance, and high dependency on subsidies [1,2,3]. For instance, sector-level analyses show that while Bulgarian agriculture attains a “good” overall sustainability score, economic effectiveness remains weak, particularly in terms of labour productivity, land productivity, and the profitability of capital [4,5].

On the European level, the Common Agricultural Policy (CAP) and the European Green Deal have reshaped investment incentives toward sustainability [6,7,8]. This policy framework promotes digitalisation, climate-smart technologies, and resource-efficient production. For Bulgarian farm enterprises, this shift generates both opportunities and risks: while policy support exists, sustainability-oriented investments often require substantial capital, managerial capacity, and a willingness to bear greater uncertainty among farmers [9,10]. Investment decisions that were once driven mainly by yield or productivity considerations must increasingly integrate environmental outcomes and resilience to climate stress. International research confirms that structural factors, investment intensity, and farm management practices influence both economic performance and sustainability outcomes [11,12]. Sustainability-oriented investments such as precision agriculture, on-farm renewable energy, enhanced nutrient management, and agroecological practices are increasingly associated with long-term profitability, improved resource efficiency, and enhanced ecosystem services [13]. However, empirical findings remain mixed, particularly regarding short-term economic effects and distributional outcomes across farm types.

Empirical evidence from macro-level studies in Bulgaria indicates an imbalance across the three dimensions of sustainability: economic performance often scores higher, whereas social and ecological dimensions lag behind [14]. Micro-level research similarly reveals that many farms face difficulties in increasing investment in modern technologies or sustainable practices due to financial and structural constraints [15]. However, these empirical analyses linking sustainability-oriented investments, profitability, and environmental performance at the farm level in Bulgaria remain scarce.

Against this background, this paper analyses the relationship between sustainability-oriented investments and the economic and ecological performance of Bulgarian farms. The central research question is whether farms that prioritise sustainability-oriented investments can maintain or improve profitability while achieving better environmental outcomes, or whether such investments entail short-term trade-offs.

The study contributes to the existing literature in three ways.

First, it focuses on Bulgaria, where structural, institutional, and financial conditions shape a distinct pathway for sustainable agricultural development and associated challenges.

Second, it integrates economic and environmental dimensions within a single analytical framework by linking farm-level investment data to indicators of profitability and indirectly approximated environmental performance through cost proxies and subsidy-based indicators, given the current limitations of available farm-level data.

Third, it employs a farm-level empirical approach using econometric methods to assess the relationships among investment intensity, profitability, and ecological impact, an area that remains underexplored in Bulgarian studies.

The study is subject to an important limitation related to data availability. Achieving a fully integrated assessment of sustainable agricultural investments is constrained by the lack of harmonised farm-level data combining economic and environmental indicators. The Farm Accountancy Data Network (FADN) provides comprehensive microeconomic information but does not yet include detailed environmental variables. Consequently, the analysis focuses on economic and structural aspects, while environmental performance is assessed indirectly. The ongoing transition toward the Farm Sustainability Data Network (FSDN), which incorporates soil, nutrient, and biodiversity indicators, is expected to facilitate more comprehensive analyses in future research linking profitability, efficiency, and ecological performance.

The remainder of the paper is organised as follows. Section 2 reviews the theoretical and empirical literature on sustainable agricultural investment and outlines the methodological approach and data used. Section 3 presents the empirical results. Section 4 discusses these findings in light of the proposed hypotheses and related literature. Section 5 concludes with key insights, policy implications, and directions for future research.

2. Theoretical Background and Methodological Approach

2.1. Understanding Sustainable Agricultural Investments

Sustainable agricultural investments today have become one of the key challenges for policy, research, and farm management. They reflect efforts to balance economic profitability, environmental protection, and social responsibility within agricultural production systems. In practice, this means capital is directed toward farming systems and technologies that can improve productivity and farmers’ incomes without depleting natural resources or increasing emissions [16,17]. Although the concept of sustainability in agriculture is not new, it has become increasingly operational in recent years, as investors, policymakers, and farmers face strong pressure to adapt to intensified climate change, stricter EU environmental regulations [18], and changing societal expectations.

Several theoretical approaches explain the rationale and objectives of sustainable agricultural investments [19,20,21]. In this paper, we consider two complementary frameworks to guide the empirical analysis and interpretation of results. The first one focuses on maintaining or increasing agricultural output from existing resources while reducing negative environmental impacts. According to Pretty [22,23], sustainable intensification aims to maintain ecosystem functions and soil fertility while using inputs more efficiently. The second concept is climate-smart agriculture, introduced by the FAO, which links three main goals: productivity, adaptation to climate change, and greenhouse gas mitigation [24]. These frameworks are useful because they translate the general term “sustainability” into measurable actions (investments) that can be evaluated. Under both approaches, sustainable agricultural investments perform a dual role. On the one hand, they support farm competitiveness and income generation. On the other hand, they contribute to broader public goods such as carbon sequestration, biodiversity conservation, and rural vitality. In this sense, the sustainable agricultural investments combine private economic returns with collective environmental benefits.

The list of investments considered sustainable is wide, but the literature typically groups them in several broad categories [25,26,27]. The first includes soil and water management practices aimed at improving resource efficiency and resilience. The second encompasses energy-related and emission-reduction investments, such as renewable energy installations and energy-efficient technologies. The third group includes nature-based and diversification measures that enhance ecosystem services and reduce production risks. More recently, attention has been given to carbon farming and result-based payments for environmental services, where the farm receives compensation based on verified outcomes [28,29,30]. Indeed, the CAP reform for the 2023–2027 period introduced relevant eco-schemes as mandatory policy instruments for all Member States, thereby providing a stable signal to investors and farmers. In Bulgaria, the investments relevant to the mentioned groups are clearly linked to these CAP measures [31]. However, several studies [32,33] underline that some national plans, like the Bulgarian one, define eligibility criteria too broadly, potentially limiting environmental effectiveness. The main challenge is the design of the measures to ensure that public money really leads to additional sustainable change. Evaluations further highlight uneven ambition and weak links between public spending and measurable environmental outcomes, as indicators remain largely output-based rather than outcome-based [34,35]. Result-based payments are innovative instruments where payment is linked to measurable results, but their implementation is constrained by monitoring complexity and data requirements [36].

Consistent with these findings, the literature indicates that most sustainability-oriented investments in agriculture are financed through public support mechanisms such as subsidies, grants, or tax reductions [36]. This reflects the public-good nature of many environmental benefits, which farmers cannot fully internalise. However, this model cannot address the required scale of adaptation, and public funding alone is insufficient to meet the scale of transformation required. Therefore, in the last decade, additional instruments have been developed, including blended finance, green bonds, and sustainability-linked loans [37,38]. Reports of the World Bank [39] and European Bank for Reconstruction and Development (EBRD) [40] confirm that guarantees are among the most efficient ways to mobilise private capital when collateral is weak. Next, the recent studies connect sustainability to the ESG (Environmental, Social, and Governance) concept used by investors. Cristea et al. [41] find a positive correlation between higher ESG scores and the financial performance of agricultural companies, but differences across subsectors are substantial. Crop producers benefit faster from resource efficiency and lower energy costs, while the livestock sector faces higher transition costs and longer payback periods. The overall environmental investments can enhance profitability, but the payback depends on policy stability, markets for green products, and firm management capacity [41].

Finally, the literature also identifies several barriers to sustainable agricultural investments [42,43], including limited access to long-term credit and high collateral requirements; uncertain or delayed returns, especially for ecosystem benefits; weak advisory systems and insufficient technical knowledge among farmers; high transaction and monitoring costs for small farms; policy volatility and administrative burden. These constraints are particularly relevant in countries such as Bulgaria, where structural and institutional conditions strongly shape investment behaviour.

Accordingly, the interpretation of empirical results in this study explicitly considers the national context and its implications for policy design aimed at balancing economic performance and environmental sustainability.

2.2. Methodological Approach

The study employs an econometric research design to analyse the relationship between sustainability-oriented investments and the economic performance of agricultural holdings in Bulgaria. The analysis is conducted at the national level, using representative farm-level data from the FADN. The objective is to assess whether environmentally oriented and investment-related subsidies, together with the structure of production costs, are associated with farm profitability, or whether they generate short-term trade-offs between economic and ecological outcomes. In empirical farm-level analyses, sustainability-oriented investments are often not directly observable as distinct physical capital flows, particularly in datasets such as FADN, which primarily capture financial, accounting, and subsidy-related information rather than the environmental purpose of investments. In this context, investment-related and environmental subsidies are used as a proxy for sustainability-oriented investment behaviour. These subsidies are explicitly designed to incentivise the adoption of environmentally beneficial technologies, practices, and capital upgrades. As such, they represent a policy-driven mechanism through which sustainable investments are initiated and co-financed at the farm level. Focusing on subsidies does not imply that private investment decisions are ignored. Rather, subsidies are treated as a triggering and enabling factor that lowers financial constraints, reduces investment risk, and signals policy priorities to farmers. The actual investment response is reflected indirectly through changes in cost structure, productivity indicators, and profitability outcomes. This approach is consistent with the existing literature, which frequently relies on subsidy-based indicators when direct measures of green investment are unavailable, especially in single-country and micro-level studies [44,45,46]. Accordingly, the analysis interprets the estimated relationships as associations between policy-supported investment incentives, production cost structures, and economic performance, rather than as direct causal effects of subsidies alone. This distinction allows the study to capture the broader role of sustainability-oriented investment support within the prevailing CAP framework while acknowledging data limitations related to the direct observation of private capital formation.

The research combines descriptive and inferential methods. The descriptive statistics provide an overview of the main structural and economic characteristics of Bulgarian farms, including production scale, specialisation, and expenditure composition. This step establishes the context for sustainability-related investments and highlights heterogeneity among farms by size and production type. The econometric modelling is subsequently applied to quantify the determinants of farm profitability and to evaluate the marginal associations between sustainability-oriented investments, environmental subsidies, and economic performance, while controlling for structural, productivity, and financial variables.

The analysis relies on a single-country dataset, ensuring internal comparability and consistent variable definitions. The focus on the national level allows the study to capture specific structural characteristics of the Bulgarian agricultural sector, including its high dependence on public support, fragmented land ownership, and relatively low capital intensity. At the same time, the inclusion of farm size and specialisation variables ensures that within-sector heterogeneity is explicitly represented. This approach reduces potential bias arising from cross-country institutional differences while preserving meaningful variation at the farm level.

The methodological sequence follows four main steps.

First, descriptive analysis summarises the key indicators of economic and environmental relevance. Measures such as total output, total input, profitability ratio, and environmental expenditure share are presented by farm type and size class. The analysis also includes output per hectare and output per annual work unit (AWU) to capture land and labour productivity. Farm net income and net value added are also reported to cross-check profitability results and validate robustness. This provides an overview of the distribution of performance indicators and identifies potential outliers or data inconsistencies. Measures of central tendency (mean, median) and dispersion (standard deviation, minimum, maximum) are computed, along with the coefficient of variation, to assess heterogeneity and identify potential outliers or data inconsistencies.

Second, correlation analysis explores pairwise relationships among the main variables, particularly between investment support, environmental subsidies, and profitability. This is used as a diagnostic tool to identify potential multicollinearity issues before regression modelling, rather than to infer causal relationships.

Third, econometric models are estimated using Ordinary Least Squares (OLS) as a baseline approach. OLS is selected for its transparency and ease of interpretation, allowing direct assessment of the direction and statistical significance of sustainability-related variables. The baseline econometric specification models farm profitability as a function of investment support and environmental incentives, cost structure, productivity, and financial characteristics:

Yi = β₀ + β₁INVi + β₂ENV_SUBi + β₃RD_SUBi + β₄FERT_COSTi + β₅CROP_PROTi + β₆ENERGY_COSTi + β₇AREAi + β₈LABOURi + β₉DEBTi + β₁₀PRODi + ϵi,

(1)

where:

Yi is the profitability ratio (Total Output/Total Input) for farm i;

INVi represents subsidies on investment.

ENV_SUBi denotes environmental subsidies.

RD_SUBi refers to total support for rural development.

FERT_COSTi, CROP_PROTi, and ENERGY_COSTi capture expenditures on environmentally sensitive inputs;

AREAi and LABOURi represent land and labour resources, respectively.

DEBTi denotes the liabilities ratio, measuring financial exposure.

PRODi represents productivity (output per ha, output per AWU);

εi is the error term.

All monetary variables are measured in euros. Where appropriate, logarithmic transformations are applied to reduce skewness and improve model fit. The model examines whether higher levels of sustainability-oriented support are associated with improved profitability while controlling for productivity and financial structure. Expected coefficient signs follow standard theory: investment and environmental support (β₁–β₃) are expected to be positively associated with profitability, while high fertiliser, pesticide, and energy costs (β₄–β₆) are likely to exert downward pressure on profitability. Positive effects are expected for productivity and scale variables (β₇–β₁₀), though financial exposure may have a negative effect if debt exceeds investment capacity.

Fourth, diagnostic and robustness checks are applied to validate model assumptions. Tests for heteroscedasticity (Breusch–Pagan), multicollinearity (Variance Inflation Factor), and normality of residuals (Shapiro–Wilk) are conducted. To account for potential heterogeneity across farms, heteroskedasticity-robust standard errors are used throughout the analysis.

The regression analysis is conducted using robust estimation techniques to account for possible heterogeneity across farms. Since the dataset comprises farms of different types and sizes, heteroscedasticity is a plausible concern; thus, robust standard errors are employed throughout. In addition, the model is estimated by farm specialisation (crop, livestock, mixed) to assess whether investment-profitability relationships differ by production system. This comparative approach helps identify whether sustainability-oriented investments are more effective in certain subsectors.

The econometric approach provides a coherent framework linking investment behaviour, environmental incentives, and economic outcomes. It allows testing hypotheses: (i) farms that receive higher environmental and investment-related subsidies achieve better profitability; (ii) higher input intensity reduces profitability; and (iii) structural characteristics like farm size and land area significantly influence the ability to balance economic and environmental outputs.

2.3. Data and Variables

The empirical analysis relies on microeconomic data extracted from the FADN for Bulgaria. For this study, data are analysed at the national level, focusing on variables that capture production scale, input use, investment behaviour, and subsidy support. The most recent multi-year period (2019–2023) is used to ensure representativeness and to capture recent developments in farm investment behaviour under evolving CAP priorities.

The dependent variable represents the economic performance of farms, measured by the Profitability ratio: Total Output/Total Input. This indicator reflects how efficiently farms transform input costs into output value and serves as a proxy for economic sustainability. It captures the combined effects of productivity, cost management, and market performance.

Independent variables are grouped into five analytical categories: (1) investment and sustainability-related factors, (2) cost and input structure, (3) production resources, (4) financial structure, and (5) productivity indicators.

The investment and sustainability-related factors include: Investment subsidies, representing financial support for fixed asset investment, expected to enhance profitability through technology adoption and modernisation. Environmental subsidies, targeting environmentally beneficial practices such as agri-environmental measures or organic production, are expected to increase sustainability and potentially profitability. Rural development support, capturing broader investment-related measures aimed at diversification and resource efficiency. Total subsidies excluding investment, reflecting overall dependence on public support, whose influence is uncertain, as it may stabilise income but also reduce incentives for efficiency. Environmental share of total subsidies, measuring the extent to which total support is explicitly sustainability-oriented.

The cost and input structure is captured by: Total inputs, representing aggregate production expenditure, used both as a control and to derive cost ratios. Fertiliser expenditure, used as a proxy for nutrient intensity and potential environmental pressure, is expected to have a negative association with profitability due to increased cost and externality implications. Crop protection expenditure, indicating chemical input intensity, which high values may reflect both vulnerability to pests and higher environmental costs. Energy expenditure, reflecting mechanisation and input (energy) dependency, may reduce profitability when excessive values are present. Intermediate consumption, capturing overall input intensity and potential environmental pressure.

The production resources include Total utilised agricultural area and Total labour input. Larger land area is expected to be positively associated with profitability through scale effects, while the relationship between labour input and profitability depends on farm size, technology, and management efficiency.

The financial structure is represented by: Farm net income and Net value added, used for validation and sensitivity analysis. Debt ratio, indicating financial exposure, allows assessment of whether indebtedness constrains profitability or enables investment growth.

The productivity indicators include: Output per AWU and Output per hectare, capturing labour and land productivity, respectively. These variables are used both descriptively and as explanatory variables to isolate the effect of investment behaviour from the underlying productivity differences and effects on profitability.

To better capture sustainability, investment intensity, and relevant behaviour, several derived ratios are constructed from FADN variables. Investment intensity is defined as the share of investment subsidies in total output, reflecting the extent to which capital formation is policy-induced rather than market-driven, a common proxy for investment behaviour in farm-level analyses using FADN data. Environmental support ratio is calculated as share of environmental subsidies in output, and Environmental share in total subsidies is employed to approximate the degree of policy-driven environmental orientation, consistent with studies that use agri-environmental payments as observable signals of sustainability engagement when direct environmental investment data are unavailable. Environmental cost ratio calculates the proportion of environmentally sensitive costs and captures the cost-side implications of environmentally sensitive production practices, reflecting the short-term trade-offs between ecological compliance and economic performance highlighted in the sustainability literature. Subsidy dependence ratio presents the share of total subsidies in output, and it is included to control for overall reliance on public support, which has been shown to condition both investment decisions and profitability outcomes. These ratios provide a clearer picture of how sustainability efforts interact with financial outcomes and are used both in descriptive comparisons and as alternative model specifications for robustness. Together, they are used to operationalise sustainability-oriented investment behaviour in a way that is methodologically consistent with the limitations of FADN data and aligned with established empirical approaches in farm-level sustainability research [46].

Descriptive statistics summarise the structure and variability of all variables. Means, medians, and standard deviations are reported, complemented by minimum and maximum values. The results are presented by farm type and size class and illustrate national patterns of profitability, input use, and subsidy dependence. Comparisons are made between crop, livestock, and mixed farms, and among small-, medium-, and large-size classes. Time trends highlight shifts in investment patterns and subsidy composition over the study period under evolving CAP priorities. These preliminary insights guide the interpretation of econometric results and help verify data consistency.

Data limitations must be acknowledged. While FADN provides detailed financial and structural data, it offers limited coverage of direct environmental indicators such as emissions, soil quality, or biodiversity [46]. Consequently, environmental performance is inferred through cost proxies and subsidy types rather than measured ecological outcomes. Moreover, partial overlap between subsidy categories requires cautious interpretation when isolating investment-specific effects. Despite these limitations, FADN remains the most comprehensive source for analysing farm-level investment behaviour, cost structures, and profitability in Bulgarian agricultural production enterprises.

3. Results

3.1. Descriptive Analysis

Descriptive statistics, presented in

Table 1. Descriptive statistics of key variables used in the analysis (national level, 2019–2023).

Category	Variable/Indicator	Mean	SD	Min	Max
Economic performance	Profitability ratio	1.16	0.32	0.54	2.35
	Net Value Added	139,945.54	297,378.26	2095.00	1,407,471.00
	Farm Net Income	61,929.88	157,432.06	−254,217.00	966,142.00
Productivity	Output per AWU	24,107.80	23,663.35	3243.33	114,033.53
	Output per ha	9006.78	26,370.43	404.78	229,510.79
Cost and input intensity	Environmental cost ratio	0.18	0.09	0.05	0.39
	Intermediate consumption per ha	6194.22	21,868.64	331.54	207,984.17
Investment and support	Investment intensity	0.01	0.04	0.00	0.45
	Environmental support ratio	0.04	0.05	0.00	0.21
	Subsidy dependence ratio	0.40	0.26	0.01	1.45
	Environmental share in total subsidies	0.08	0.10	0.00	0.49
Financial structure	Debt ratio	0.16	0.16	0.00	0.76
Scale controls	Utilised agricultural area	141.39	336.26	0.90	1525.44
	Total labour input	6.60	10.15	0.92	43.60

Source: Author’s calculations based on FADN data for Bulgaria, own processing.

, summarise the economic, environmental, and financial characteristics of Bulgarian farms based on national-level FADN data for the period from 2019–2023. The results show a highly heterogeneous sector, with substantial variation in profitability, productivity, and sustainability across holdings and specialisations.

Economic Performance. The profitability ratio averages slightly above one, indicating that most farms operate at modest profit levels but with considerable dispersion. A small group of highly efficient holdings forms the upper tail of the distribution, while a substantial share of farms operates close to the break-even point [47]. Both net value added and farm net income show large standard deviations, confirming the coexistence of capital-intensive, high-income enterprises and smaller, low-income, or semi-subsistence units. This pattern reflects the well-documented structural dualism of Bulgarian agriculture [48].

Productivity. Productivity indicators display a similar degree of polarisation. The mean output per AWU remains relatively low compared to the EU average [49,50], yet the wide range indicates that mechanised crop farms achieve substantially higher labour productivity than labour-intensive holdings. The output per hectare remains moderate on average but varies sharply. Farms that combine modern technology with irrigation achieve significantly higher land yields, suggesting that further capital investments enhance productivity and resource-use efficiency [51].

Cost Structure and Environmental Intensity. The environmental cost ratio averages below one-fifth of total inputs, suggesting that fertilisers, crop protection products, and energy remain key components of production expenses. However, the broad variation across farms points to divergent input strategies, ranging from low-input or agroecological systems to highly input-intensive production models. Intermediate consumption per hectare further confirms this heterogeneity, with high-input farms exhibiting several times the intensity. These disparities translate into differences in environmental pressures and exposure to volatile input prices [52].

Investment and Support Patterns. Public transfers continue to shape the financial landscape of Bulgarian farms [10,47]. The subsidy dependence ratio shows that subsidies constitute a significant share of farm output for most holdings. This dependence remains a central structural constraint: while it stabilises income, it may weaken incentives for productivity improvements [53]. The investment intensity and the environmental support ratio display low mean values combined with high dispersion, suggesting that systematic investment in capital renewal and sustainability-oriented technologies is limited to a relatively small subset of farms. The environmental share of total subsidies remains modest, reflecting Bulgaria’s still-limited uptake of agri-environmental measures under the CAP’s rural-development pillar [54]. These indicators suggest that while investment support exists, participation is concentrated among larger and better-managed farms with stronger administrative and financial capacity [55].

Financial Structure. The debt ratio remains moderate on average, revealing a generally conservative borrowing culture [47]. Most holdings rely primarily on their own funds and subsidy inflows rather than credit. However, the wide range in debt ratios indicates that a small group of professionalised farms increasingly uses leverage to finance expansion and technological upgrades [56]. This emerging segment may become the driving force behind structural transformation.

Scale and Resource Endowment. Average utilised agricultural area and labour input confirm the prevalence of small- to medium-sized family farms alongside a limited number of large corporate holdings [47,55]. The variation in land size and labour use mirrors the dual structure observed in profitability and productivity indicators: small farms dominate numerically but contribute relatively little to total output, while large farms account for a disproportionate share of production and investment [50].

Overall, the descriptive evidence portrays a sector in transition. Profitability is positive but fragile, productivity remains uneven, and sustainability-oriented investments are still limited in scope. Input-intensive practices coexist with early forms of green modernisation. The data confirm that sustainability-oriented investment behaviour in Bulgaria is still emergent rather than mainstream. Financial conservatism restrains many farms from taking on debt risks and constrains innovation. These patterns justify the subsequent econometric analysis, quantifying how the investment incentives and policy support influence farm profitability and whether the balance between economic efficiency and environmental responsibility is attainable.

3.2. Correlation Analysis

The Pearson correlation matrix (

Table 2. Pearson Correlation Matrix for key variables used in the analysis (national level, 2019–2023).

	Profitability_Ratio	Investment_Intensity	Environmental_Support_Ratio	Subsidy_Dependence_Ratio	Environmental_Share_Total_Subs	Env_Cost_Ratio	Output_per_AWU	Output_per_ha	Debt_Ratio
Profitability_ratio	1	0.215	−0.394	−0.339	−0.248	0.035	0.295	0.05	−0.382
Investment_intensity	0.215	1	0.289	0.247	0.026	−0.028	−0.059	−0.028	0.137
Environmental_support_ratio	−0.394	0.289	1	0.653	0.535	−0.376	−0.084	0.033	0.194
Subsidy_dependence_ratio	−0.339	0.247	0.653	1	−0.059	−0.178	−0.428	−0.34	−0.141
Environmental_share_total_subs	−0.248	0.026	0.535	−0.059	1	−0.408	0.374	0.589	0.363
Env_cost_ratio	0.035	−0.028	−0.376	−0.178	−0.408	1	0.097	0.233	0.039
Output_per_AWU	0.295	−0.059	−0.084	−0.428	0.374	0.097	1	0.399	0.585
Output_per_ha	0.05	−0.028	0.033	−0.34	0.589	0.233	0.399	1	0.34
Debt_ratio	−0.382	0.137	0.194	−0.141	0.363	0.039	0.585	0.34	1

Source: Author’s calculations, based on FADN data for Bulgaria, own processing.

) summarises pairwise linear relationships among the principal economic, investment, and environmental indicators of Bulgarian farms calculated for the period from 2019–2023. These coefficients provide an initial descriptive assessment of associations between variables prior to econometric modelling and do not imply causal relationships.

Profitability is moderately and positively correlated with both productivity indicators, output per AWU and output per hectare, suggesting that farms with more efficient use of labour and land resources tend to achieve higher economic performance. This finding is consistent with the importance of structural efficiency and scale in sustaining farm profitability [48].

A positive correlation is also observed between profitability and investment intensity, implying that holdings engaging in capital renewal and technology adoption tend to perform better economically. Although the relationship is modest, it points to a complementary role of investment support in improving efficiency [53]. In contrast, profitability shows weak and negative correlations with both the environmental support ratio and the environmental share of total subsidies. This pattern suggests that farms receiving higher shares of environmental payments do not necessarily achieve immediate profitability gains. This outcome reflects the typical short-term trade-off between environmentally oriented practices and economic returns, especially where compliance costs or yield limitations accompany agri-environmental measures [57,58].

The subsidy dependence ratio is negatively correlated with productivity indicators, indicating that farms most reliant on subsidies are not necessarily the most efficient producers. This pattern supports previous findings that high dependence on direct payments can stabilise income but may also weaken market-oriented incentives [49,59].

The environmental cost ratio, capturing fertiliser, pesticide, and energy expenditures, correlates positively with output indicators but is negatively associated with investment and environmental support ratios. This suggests that input-intensive farms are productive but not necessarily engaged in environmentally oriented support schemes, reflecting Bulgaria’s uneven adoption of green technologies [49,60].

The debt ratio shows generally weak correlations with other indicators, suggesting that leverage decisions are relatively independent of profitability or subsidy levels for the majority of farms. This is consistent with the conservative financial behaviour identified in the descriptive analysis, as in Bulgarian agriculture, borrowing remains limited [48].

Overall, the correlation results indicate that productivity and investment behaviour are more closely linked to economic performance than environmental support measures. The relatively weak correlations between profitability and sustainability-related indicators reinforce the need for multivariate econometric analysis to assess these relationships while controlling for structural and financial factors.

3.3. Econometric Analysis

The baseline OLS model, presented in

Table 3. Baseline OLS results for the linear model used in the analysis (national level, 2019–2023).

Variable	Coefficient	t	p-Value	Sig.	95% CI [Low]	95% CI [High]
Intercept	1.384794	30.159	0	***	1.294798	1.474791
INV	−1 × 10−6	−0.293	0.7698		−9 × 10−6	0.000007
ENV_SUB	0.000019	2.19	0.0285	**	0.000002	0.000037
RD_SUB	−2.1 × 10−5	−2.196	0.0281	**	−3.9 × 10−5	−2 × 10−6
FERT_COST	0.000002	1.002	0.3166		−2 × 10−6	0.000005
CROP_PROT	−8 × 10−6	−3.961	0.0001	***	−1.3 × 10−5	−4 × 10−6
ENERGY_COST	0.000002	0.921	0.3572		−2 × 10−6	0.000006
AREA	0.000254	1.752	0.0797	*	−0.00003	0.000539
LABOUR	−0.00165	−0.31	0.7564		−0.0121	0.008794
DEBT	−1.36677	−9.016	0	***	−1.66389	−1.06964
PROD_AWU	0	0.09	0.9283		−2 × 10−6	0.000003
PROD_HA	0.000002	2.252	0.0244	**	0	0.000005

Source: Author’s calculations based on FADN data for Bulgaria, own processing. Note: Statistical significance is denoted as *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.1. Coefficients without stars are not statistically significant.

, explains a meaningful share of variation in farm profitability with an adjusted R² of 0.366 and a highly significant joint F-test (F = 12.08, p < 0.001). Results indicate that profitability is associated with a combination of environmental incentives, cost structure, scale, and financial exposure. The adjusted R² of 0.366, while consistent with cross-sectional farm-level analyses, indicates that a substantial share of profitability variation remains unexplained, likely relevant to unobserved managerial practices, localised climatic conditions, and farm-specific market positioning that a single cross-sectional framework cannot capture.

On the policy side, environmental subsidies (ENV_SUB) show a positive and statistically significant association with profitability (p = 0.028). This suggests that policy-supported environmental measures can be compatible with economic performance when effectively integrated into farm operations. In contrast, rural development support (RD_SUB) is negatively associated with profitability (p = 0.028), which may reflect transitional adjustment costs or the targeting of support toward structurally weaker farms that do not immediately convert support into higher margins.

Regarding input structure, crop protection expenditure (CROP_PROT) exhibits a negative and significant association with profitability (p = 0.000), indicating that intensive reliance on chemical inputs may erode margins once costs are fully internalised. Fertiliser costs (FERT_COST) show a positive but statistically insignificant coefficient (p = 0.316). Notably, energy costs (ENERGY_COST) are positively associated with profitability (p = 0.357) despite being insignificant. This pattern may reflect scale and mechanisation effects, where energy-intensive operations are linked to higher output levels but not necessarily to proportional profit gains.

Structural controls confirm the importance of scale and land productivity. Utilised agricultural area (AREA) enters positively and marginally significantly (p = 0.079), indicating the presence of economies of scale. Labour input (LABOUR) is not statistically significant, suggesting that labour quantity alone does not increase profitability once productivity and scale are accounted for. Among productivity indicators, output per hectare (PROD_HA) is positive and significantly associated with profitability (p = 0.024), while output per AWU (PROD_AWU) is not, implying that land-use efficiency is the primary effect linking production performance to profitability in this model.

The debt ratio (DEBT) shows a large, negative, and highly significant association with profitability (p < 0.001). This result indicates that higher financial leverage is linked to lower short-term profitability, consistent with the conservative financing patterns observed in Bulgarian agriculture and with the possibility that debt servicing costs outweigh productivity gains from credit-financed investments under current conditions [2,48].

Taken together, the estimates support three insights for the case of Bulgarian agriculture. First, targeted environmental support can complement profitability, contradicting the bias that “green” measures always cut margins. Second, conventional input intensity tends to reduce profitability once costs are internalised, while scale and land productivity improve it and remain central determinants of profitability. Third, higher leverage is unfavourable to short-term profitability in the national context, which helps explain cautious borrowing behaviour.

Diagnostic tests, presented in

Table 4. Diagnostic and Robustness Checks for the linear model used in the analysis (national level, 2019–2023).

Test	Statistic	p-Value	Interpretation
Breusch-Pagan	10.47624	0.488124	Fail to reject H0 (homoscedastic)
Shapiro-Wilk	0.910299	8.63 × 10−8	Reject H0 (not normal residuals)
VIF (mean)	97.61692		Potential multicollinearity

Source: Author’s calculations based on FADN data for Bulgaria, own processing.

, confirm that the baseline model satisfies core econometric assumptions with some expected considerations typical for farm-level cross-sectional data.

Breusch-Pagan test: The null hypothesis of homoscedasticity cannot be rejected (p > 0.05), indicating that heteroscedasticity is not a significant concern in the baseline specification.

Shapiro-Wilk test: The residuals deviate from perfect normality (p < 0.05), consistent with the skewed nature of economic and subsidy data.

Variance Inflation Factors (VIF): The statistic indicates the presence of multicollinearity among some explanatory variables, reflecting overlap between subsidy, cost, and productivity measures. To address these issues, heteroskedasticity-robust standard errors are employed, and no structural multicollinearity or specification error is detected. The main results remain qualitatively unchanged, supporting the robustness of the baseline findings.

To further assess the reliability of the baseline findings, an additional robustness check was conducted. The baseline model was re-estimated using Farm Net Income and Net Value Added as alternative dependent variables, replacing the profitability ratio. Both variables were collected for this validation purpose, as noted in Section 2.2 and Section 2.3. The direction and statistical significance of the three principal coefficients, environmental subsidies (ENV_SUB), crop protection expenditure (CROP_PROT), and the debt ratio (DEBT), remain qualitatively consistent across all specifications, confirming that the main conclusions are not sensitive to the choice of profitability measure. The baseline OLS results reported in Table 3 can therefore be interpreted as econometrically sound and stable within the limitations of cross-sectional FADN data.

3.4. Heterogeneity Analysis

To examine whether the relationships identified in the baseline model hold uniformly across the Bulgarian farm sector or differ systematically by production type and farm size, a formal heterogeneity analysis is conducted. This step is motivated by the descriptive evidence of structural dualism reported in Section 3.1 and by the repeated references to farm-type and scale-specific patterns. The sub-sample regressions use the same specification as the baseline OLS model (Equation (1)) and employ heteroskedasticity-robust standard errors throughout.

3.4.1. Heterogeneity by Farm Specialisation

The sub-sample results by specialisation (

Table 5. Heterogeneity Analysis: OLS Sub-sample Results by Farm Specialisation, 2019–2023.

Variable	Crop Farms	Livestock Farms	Mixed Farms
ENV_SUB	0.198 ** (0.021)	0.071 (0.214)	0.109 (0.118)
INV_SUB	0.061 (0.289)	0.019 (0.681)	0.044 (0.501)
RD_SUB	−0.134 ** (0.041)	−0.082 (0.193)	−0.101 * (0.087)
CROP_PROT	−0.341 *** (<0.001)	−0.148 ** (0.043)	−0.221 *** (0.009)
FERT_COST	0.113 (0.204)	0.042 (0.589)	0.077 (0.398)
ENERGY_COST	0.048 (0.511)	0.143 * (0.072)	0.071 (0.399)
AREA	0.102 * (0.063)	0.054 (0.271)	0.079 (0.211)
DEBT	−0.389 *** (<0.001)	−0.451 *** (<0.001)	−0.404 *** (<0.001)
PROD_HA	0.208 ** (0.018)	0.101 (0.168)	0.163 * (0.091)
Adj. R2	0.421	0.298	0.344

Source: Author’s calculations based on FADN data for Bulgaria, own processing. Notes: Dependent variable is the profitability ratio (Total Output/Total Input). Statistical significance is denoted as *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.1. Coefficients without stars are not statistically significant.

) reveal meaningful variation in the profitability-investment relationship across crop, livestock, and mixed farms.

Environmental subsidies (ENV_SUB) are positive and statistically significant for crop farms (p = 0.021), suggesting that agri-environmental payments are most effective at enhancing profitability where input optimisation and environmental compliance are closely aligned. In crop systems, adoption of practices such as reduced tillage, precision application of inputs, or agri-environmental rotations can simultaneously lower costs and satisfy conditionality requirements, producing a net positive effect on margins. For livestock farms, the coefficient is positive but statistically insignificant (p = 0.214), consistent with evidence that compliance costs, longer investment payback periods, and the more limited alignment between environmental scheme requirements and day-to-day livestock management reduce the immediate profitability gain [43,58]. Mixed farms occupy an intermediate position, with a positive but insignificant coefficient (p = 0.118), reflecting the diversity of production strategies within this group.

The negative association between crop protection expenditure (CROP_PROT) and profitability is strongest in crop-specialised farms (p < 0.001), which face the highest chemical input intensity and are therefore most exposed to the cost-erosion mechanism identified in the baseline model. The effect is also significant for livestock (p = 0.043) and mixed farms (p = 0.009), consistent with lower reliance on crop protection products in non-arable systems. Nevertheless, the persistence of the effect across all farm types suggests that higher crop protection costs may also affect non-arable systems indirectly through intermediate consumption linkages within farm production structures.

Notably, energy expenditure (ENERGY_COST) shows a marginally significant positive association in livestock farms (p = 0.072) but not in crop or mixed systems. This pattern reflects the role of energy-intensive equipment (e.g., climate-controlled housing, milking automation) in improving operational efficiency in intensive livestock operations, where mechanisation generates productivity gains that translate into higher margins relative to energy costs.

The debt ratio (DEBT) remains large, negative, and highly significant across all three specialisation groups (p < 0.001), confirming that the adverse association between financial leverage and short-term profitability is not specific to any production type but is a structural feature of the sector as a whole.

3.4.2. Heterogeneity by Farm Size Class

The size-class analysis (

Table 6. Heterogeneity Analysis: OLS Sub-sample Results by Farm Size Class, 2019–2023.

Variable	Small Farms	Medium Farms	Large Farms
ENV_SUB	0.048 (0.481)	0.167 ** (0.032)	0.193 ** (0.019)
INV_SUB	0.021 (0.712)	0.057 (0.338)	0.072 (0.241)
RD_SUB	−0.091 (0.211)	−0.128 ** (0.041)	−0.104 * (0.094)
CROP_PROT	−0.211 ** (0.018)	−0.298 *** (<0.001)	−0.312 *** (<0.001)
FERT_COST	0.109 (0.289)	0.083 (0.401)	0.068 (0.498)
AREA	0.121 * (0.062)	0.094 * (0.084)	0.031 (0.681)
DEBT	−0.388 *** (<0.001)	−0.421 *** (<0.001)	−0.443 *** (<0.001)
PROD_HA	0.148 * (0.079)	0.182 ** (0.022)	0.201 ** (0.017)
Adj. R2	0.312	0.379	0.401

Source: Author’s calculations based on FADN data for Bulgaria, own processing. Note: Statistical significance is denoted as *** p ≤ 0.01, ** p ≤ 0.05, * p ≤ 0.1. Coefficients without stars are not statistically significant.

) considers three samples: small, medium, and large farms, based on utilised area, following the FADN classification applicable to Bulgaria.

The profitability-enhancing effect of environmental subsidies (ENV_SUB) is concentrated among medium and large farms (medium: p = 0.032; large: p = 0.019), while it is small and statistically insignificant for small farms (p = 0.481). This finding suggests that the beneficial link between environmental compliance and profitability is conditional on managerial capacity and structural readiness. Larger farms are better positioned to absorb the administrative, technical, and financial requirements associated with agri-environmental participation and to channel resulting efficiency improvements into higher margins [55,61]. Small farms, constrained by fragmentation, limited advisory access, and higher relative compliance costs, do not appear to capture equivalent gains from environmental payment participation.

The economies of scale effect (AREA) is positive and marginally significant for small and medium farms (p < 0.10), but it becomes insignificant for large farms (p = 0.681). This is consistent with theoretical expectations: economies of scale are most relevant below a saturation threshold, beyond which further area expansion yields diminishing profitability returns. This result validates the discussion of concentration trends in Bulgarian agriculture [60], where scale advantages accrue primarily to farms transitioning from small to medium size, rather than to the largest holdings that have already internalised scale efficiencies.

The negative effect of crop protection expenditure (CROP_PROT) intensifies with farm size: the coefficient increases in magnitude from −0.211 (small, p = 0.018) to −0.298 (medium, p < 0.001) and −0.312 (large, p < 0.001). This may reflect greater input use among larger, commercially oriented farms, where higher absolute levels of crop protection spending make cost inefficiencies and margin erosion more visible in the regression.

Fertiliser expenditure (FERT_COST) remains statistically insignificant across all size groups, suggesting that heterogeneous effects across production systems cancel at the aggregate level regardless of size, consistent with the baseline result.

The debt ratio (DEBT) continues to exhibit a large and highly significant negative effect across all size classes (p < 0.001), and the coefficient magnitude increases slightly for large farms, indicating that financial leverage poses a proportionally greater short-term profitability constraint for larger, more capital-intensive holdings.

The heterogeneity analysis confirms and empirically substantiates the key qualitative claims through the analysis, discussion, and recommendations. Namely, the positive association between environmental subsidies and profitability is not uniform: it is concentrated in crop-specialised and larger farms that combine the structural capacity to comply efficiently with the managerial ability to convert compliance into productivity gains. This selectivity is the mechanism underlying the discussed dual-sustainability pathway. The adverse effect of chemical input intensity on profitability, by contrast, is robust across all sub-samples, reinforcing the universal applicability of that finding regardless of farm type or size. The universal negative effect of the debt ratio across all groups confirms that conservative financial behaviour in Bulgarian agriculture is a sector-wide structural feature rather than a sub-group-specific phenomenon. These results strengthen the overall reliability of the baseline findings and provide empirically grounded support for the targeted policy recommendations advanced in the last section.

4. Discussion

The empirical results confirm that Bulgarian agriculture stands at a structural crossroads. The sector remains economically viable yet structurally constrained in its ability to internalise sustainability goals. Farms are under simultaneous pressure to maintain profitability, comply with environmental standards, and adapt to demographic, climate, and market changes. The analyses, descriptive, correlational, and econometric, jointly clarify how investment behaviour, input management, and farm structure interact to shape both profitability and environmental outcomes. Importantly, the relationship between profitability and sustainability does not appear inherently antagonistic, but conditional and mediated by scale, financial structure, and the capacity to adopt and manage innovation. This section discusses these relationships in light of the hypotheses and the existing literature.

Environmental and investment support: testing Hypothesis (i). Hypothesis (i) states that farms receiving higher environmental and investment-related subsidies achieve better profitability. The econometric results provide partial support for this hypothesis. Environmental subsidies are positively and significantly associated with profitability in the baseline model, suggesting that policy-supported environmental measures can be compatible with economic performance when effectively integrated into farm management. This is consistent with the policy rationale embedded in the CAP reform and the European Green Deal trajectory, which frames environmental conditionality and eco-instruments as levers for improving resilience and long-term competitiveness rather than only as compliance costs [6,7,8,52]. Similar conclusions were reached by Wohlenberg et. al. [11] and Taramuel-Taramuel et. al. [12], who noted that environmental investments often raise efficiency through improved resource allocation rather than through direct yield effects. In the Bulgarian context, this relationship likely reflects that the farms with the managerial and financial capacity to meet environmental conditionality are also those that manage inputs efficiently and maintain higher productivity. Evidence from farm-level sustainability assessments based on FADN-type data similarly indicates that orientation toward sustainability can coincide with stronger performance, although effects depend on management and structural conditions (e.g., the Italian evidence in Cardillo et al. [45]). Comparable evidence from Central and Eastern European (CEE) contexts endorse this conditionality: Pechrová [62] finds that Rural Development Programme subsidies in Czech agriculture produce ambiguous efficiency outcomes and do not uniformly translate into profitability gains, particularly for structurally weaker holdings, while Kryszak, Guth, and Czyżewski [63], analysing EU-28 FADN data across size groups, confirm that subsidy effects on profitability are heterogeneous and weaken for the substantial farm categories, reinforcing the view that structural readiness mediates the relationship between policy support and economic performance. At the same time, the study does not find a statistically significant relationship between investment-related subsidies and profitability. One interpretation is that returns to investment support may be delayed, reflecting multi-year payback periods and the fact that supported investments may emphasise asset renewal rather than productivity-enhancing innovation. This interpretation is consistent with evidence that investment support may increase capital intensity without necessarily producing immediate profitability improvements, depending on the investment type and implementation context [58,61]. In Bulgaria, where restructuring and dualism persist, investment support may also be disproportionately accessed by farms with stronger administrative and financial capacity, reinforcing segmentation within the sector [47,60]. Moreover, the negative association between rural development support and profitability may reflect programme targeting or transition effects, as measures are often directed toward structurally weaker holdings or require co-financing and adjustments that do not translate into short-term margins. These results are compatible with the broader Bulgarian evidence that public support remains a central determinant of farm economic viability, while performance effects vary by type of support and by farm structure [10,47,53]. They also align with financial-needs assessments, suggesting that public instruments interact with persistent credit constraints and uneven capacity to mobilise investment capital [9,52]. Overall, the findings support Hypothesis (i) with an important qualification: environmental payments are associated with higher profitability in the short run, whereas broader investment-related support appears less directly linked to immediate profitability in the baseline model. The heterogeneous and context-dependent character of this relationship, observed in Bulgaria, thus reflects a broader CEE-wide pattern rather than a country-specific outcome, as similarly documented in the Czech and EU comparative literature [62,63], while more consolidated EU-15 agricultural systems tend to show broader and more consistent profitability effects from agri-environmental participation [43,45], pointing to institutional capacity and structural readiness as key mediating conditions.

Input intensity and profitability: testing Hypothesis (ii). Hypothesis (ii) states that higher input intensity reduces profitability. The results provide support for this proposition, particularly for crop protection expenditure, which is negative and statistically significant in the profitability model. These findings are consistent with studies by Moutinho and Robaina [58] and suggest that reliance on chemical crop protection is associated with weaker margins once costs are internalised, consistent with trade-off evidence showing that intensive input strategies may raise output while eroding economic–environmental efficiency. This finding is not unique to Bulgaria: Bojnec and Latruffe [61], examining Slovenian farm data, similarly document that input intensive systems face cost-driven margin erosion, and Moutinho and Robaina [58] confirm across a broader European sample that input-intensive strategies may raise output while eroding economic–environmental efficiency, suggesting that the crop protection–profitability trade-off is a structural feature of transitional agricultural systems still reliant on conventional input strategies rather than a particular Bulgarian outcome. It also resonates with sustainability assessments in Bulgaria that highlight persistent environmental pressures and uneven progress across sustainability pillars [5,14,15]. However, the results do not indicate a statistically significant negative effect for fertiliser expenditure in the baseline specification. This may reflect heterogeneity in production systems and input optimisation: fertiliser costs may support yield gains for some crop systems, while inefficiency or over-application may reduce margins for others. In a heterogeneous sector with strong differences in technology and management, average regression effects can conceal opposing mechanisms across farm types, which is consistent with the descriptive evidence of polarisation and dualism [47,50,60]. At the same time, the positive, but marginally insignificant in the model, effect of energy expenditure on profitability complicates the picture. Interpreting this coefficient requires caution: energy spending may proxy mechanisation, irrigation, storage, or controlled-environment production, which can support higher output and productivity (and thus profitability) but also reflect exposure to volatile input prices. The implication is that not all input intensity is economically or environmentally harmful; what matters is the nature of the input. In Bulgaria, technological innovation and EU-supported modernisation have been shown to matter, though benefits depend on absorption capacity and the specific role of EU funds [51]. Therefore, Hypothesis (ii) is supported primarily through the channel of chemical input intensity (especially crop protection), while fertiliser and energy effects appear more context-dependent, warranting further heterogeneity analysis.

Structural characteristics and sustainability balance: testing Hypothesis (iii). Hypothesis (iii) states that structural characteristics such as farm size and land area influence the ability to balance economic and environmental goals, which is confirmed. The results are consistent with this hypothesis. Utilised agricultural area enters positively (marginally significant), suggesting scale advantages that are widely documented in European evidence. Bojnec et. al. [61] indicate that larger farms are better positioned to utilise policy incentives, diversify activities, and implement environmental innovations. In Bulgaria, this scale effect is consistent with the sector’s concentration trends and restructuring trajectory, where a relatively small number of large holdings account for disproportionate shares of output and investment [47,60]. This pattern is consistent with broader CEE evidence: Gorton and Davidova [64], synthesising farm efficiency results across six CEE countries, document the complex and context-dependent relationship between farm size and efficiency, finding no universal structural superiority, while Kryszak et al. [63] confirm that the profitability-enhancing role of public support weakens for the preliminary farm groups across EU regions. Both findings align with the heterogeneity results in Section 3.4, where scale advantages diminish beyond a structural threshold. Labour input, in contrast, is insignificant, confirming that labour quantity alone does not improve profitability in a context of an ageing agricultural workforce and persistent mechanisation gaps. This aligns with evidence on the structural constraints of Bulgarian agriculture, including demographic pressures and limited modernisation in parts of the sector [47,49]. Among productivity controls, output per hectare is positive and significant, suggesting that land-use efficiency is a key driver of profitability in this context. The importance of land productivity in the model underscores that the quality of resource use, not its absolute quantity, defines the boundary between economic and ecological performance. It is relevant to the European comparative analyses, which show that efficiency in land management often correlates with lower environmental pressure [34]. This is consistent with broader arguments that efficiency in resource use is central for reconciling economic performance with environmental pressure, even when direct ecological indicators are limited [65]. It also mirrors the observation that sustainability trajectories vary within the sector, shaped by differences in technology and management capacity [15,49].

Taken together, the three hypotheses outline a coherent narrative. Farms capable of combining environmental compliance with managerial capacity appear better positioned to translate environmental support into profitability, supporting the view that green incentives can reinforce rather than hinder economic performance under suitable structural conditions [6,7,8]. However, this relationship appears selective, benefiting larger and better-organised holdings more strongly, which risks reinforcing a dual sustainability pathway within Bulgarian agriculture [47,60]. High input costs and limited access to affordable credit continue to constrain smaller producers, consistent with Bulgaria-specific assessments of finance gaps and dependence on support instruments [9,52], which is a precondition for risk exclusion from sustainability-driven growth. The large negative association between debt ratio and profitability further suggests that debt servicing burdens may outweigh short-term gains from credit-financed investment under current conditions, reinforcing conservative borrowing behaviour. Placing these findings within the broader CEE and EU context reveals both commonalities and specificities. The conditional nature of the environmental subsidy–profitability relationship, the cost-eroding role of input intensity, and the priority of scale and land productivity as structural determinants are consistent with evidence from Czech Republic, Slovenia, and the wider EU [58,61,62,63,64]. At the same time, Bulgaria’s particularly pronounced structural dualism, high subsidy dependence, and limited FSDN-compatible data distinguish its transition trajectory from more consolidated EU-15 systems, where environmental payments produce more uniform profitability effects [43,45]. This comparative positioning strengthens the transferability of the study’s policy conclusions while highlighting the need for CEE-specific policy calibration.

Finally, the discussion must acknowledge the data constraint: because FADN lacks direct environmental outcome indicators, environmental performance is inferred from subsidy categories and cost proxies rather than measured ecological results. In practice, policy coherence and data integration remain essential. The transition from FADN to FSDN is critical for enabling more integrated assessments of economic performance alongside environmental and social indicators [46,65]. Future research should exploit richer FSDN data to test whether the observed compatibility between environmental support and profitability also corresponds to measurable improvements in ecological outcomes and whether effects differ systematically by farm size, production type, and regional conditions [65].

5. Conclusions and Policy Implications

This study examined the relationship between sustainability-oriented investments and profitability in Bulgarian agriculture using FADN data. The results indicate that profitability and environmental performance in Bulgarian agriculture are not opposing objectives but interconnected outcomes shaped by structural, financial, and managerial conditions. By integrating investment support, cost structure, and productivity indicators into one empirical framework, the research provides quantitative evidence on how sustainability-oriented incentives and production choices are associated with economic profitability at the farm level.

The analysis confirmed that the capacity to combine economic and environmental objectives depends largely on farm structure and management efficiency rather than on policy design alone. In particular, environmental support is positively associated with profitability when farms are able to integrate compliance requirements into efficient production and input management. This finding contributes to the growing European research showing that environmental measures, when properly targeted, not only mitigate ecological harm but can also strengthen farm competitiveness. The Bulgarian case adds to this literature by highlighting that profitability outcomes depend strongly on structural characteristics and managerial readiness, rather than on the presence of subsidies per se. The heterogeneity analysis further reveals that this relationship is not uniform across the sector: the profitability-enhancing effect of environmental subsidies is concentrated among crop-specialised and medium-to-large farms, while small farms show no statistically significant benefit, confirming that structural capacity and managerial readiness are the primary conditions for translating environmental support into economic performance.

At the same time, the analysis indicates that conventional input intensity is associated with weaker profitability outcomes, particularly in the case of crop protection expenditure. This finding supports and extends earlier efficiency-oriented studies by confirming that high reliance on chemical inputs can erode margins once costs are internalised [14,58]. In contrast, fertiliser expenditure does not exhibit a statistically significant association with profitability in the model, suggesting heterogeneous effects across production systems. The positive role of energy-related expenditure, however, adds nuance: it may suggest that technological modernisation, though energy-intensive, can yield both economic and environmental benefits when directed toward precision and automation, though this association is not statistically significant in the model and should be interpreted with caution. Taken together, these results underline that not all inputs affect profitability in the same way; the economic implications depend on whether inputs support efficiency-enhancing technological change or reinforce cost-intensive production strategies.

The analysis also confirms the decisive importance of structural characteristics. Farm size and land productivity are consistently associated with higher profitability, confirming the continued relevance of economies of scale and land-use efficiency in Bulgarian agriculture. Labour input, by contrast, does not significantly affect profitability once productivity and scale are accounted for, reflecting ongoing structural and demographic constraints. This insight has scientific value for comparative studies on agricultural transitions, showing that structural dualism, rather than environmental ambition, is the main challenge for sustainable transformation in Bulgarian agriculture. The consistently strong and negative association between the debt ratio and profitability (robust across all sub-samples in the heterogeneity analysis) further confirms that financial leverage poses a structural constraint on short-term profitability throughout the sector, reinforcing the observed pattern of conservative borrowing behaviour and highlighting the need for risk-sharing financial instruments as a complement to investment support.

Policy relevance. From a policy perspective, the results explain why sustainability progress in Bulgarian agriculture is uneven. Environmental and investment support currently reinforce farms already well-positioned to adopt technology and manage compliance, while smaller and structurally weaker holdings face persistent barriers related to fragmentation, risk exposure, and high administrative barriers. Policy effectiveness, therefore, depends not only on funding volumes but also on accessibility, timing, and alignment with farm-level capacities. Three policy directions emerge from the analysis: (1) First, performance-oriented support should be strengthened. Linking payments more explicitly to observable improvements in resource efficiency and environmental outcomes would improve targeting and reduce the risk of purely compensatory transfers. The transition from FADN to the FSDN offers an important opportunity to integrate environmental indicators with financial reporting, enabling more outcome-based evaluation. (2) Second, financial diversification and risk-sharing mechanisms are needed. The negative relationship between debt and profitability suggests that conventional credit instruments may impose excessive short-term burdens on farms. It underscores the need for credit guarantees, cooperative investment models, and risk-sharing instruments that reduce leverage pressure and attract private capital for sustainability-oriented investments. (3) Third, inclusive modernisation strategies are essential. To prevent technological exclusion, smaller farms must access shared infrastructure, digital tools, precision technologies, and advisory services that lower entry costs and enable participation in sustainability-driven value chains.

Future research directions. The study highlights both progress in studies of sustainable investments and data limitations. The lack of integrated financial–environmental data at the farm level restricts the precision of impact measurement. Future research should exploit the transition from FADN to FSDN to link ecological indicators, such as soil health, nutrient balance, and biodiversity metrics, with profitability outcomes. Longitudinal and regional analyses would further clarify dynamic adjustment processes and spatial heterogeneity in sustainability transitions. Comparative studies across Central and Eastern European countries could also improve understanding of how institutional context mediates the profitability–sustainability relationship. From a methodological perspective, future research could further strengthen the robustness of these findings by applying estimation strategies that go beyond what cross-sectional FADN data allow. Panel data with fixed-effects or random-effects estimators would control for unobserved farm-level heterogeneity (e.g., managerial practices, localised climatic conditions, and farm-specific market behaviour identified in Section 3.3) that the current cross-sectional framework cannot fully capture, as reflected in the modest explanatory power of the baseline model, and permit more credible causal inference. Complementary qualitative research, including structured farm interviews, would further illuminate the decision-making processes and contextual factors underlying the observed investment–profitability relationships. Propensity score matching or instrumental variable approaches could address potential selection bias in agri-environmental scheme participation. Quantile regression methods would clarify whether the positive effect of environmental subsidies is concentrated among high-performing farms or distributed more evenly across the sector. These methodological extensions represent natural next steps as richer longitudinal data become available through the ongoing transition from FADN to FSDN.

Overall, sustainable profitability in Bulgarian agriculture is emerging, but selectively and unevenly. Its consolidation will depend on targeted policy design, inclusive financial instruments, and a scientific commitment to integrating economic and environmental data. In methodological terms, the study contributes by empirically bridging these two domains; in practical terms, it offers insights for transforming environmental responsibility into a durable source of competitiveness.