The Socio-Economic and Environmental Determinants of Organic Farming Expansion in EU: A Panel Data Analysis ^†

Abstract

This study investigates the factors influencing the expansion of organic farming in Europe between 2000 and 2022. Driven by consumer demand and EU support through the Common Agricultural Policy, organic farming has grown significantly. The research uses panel data and linear regression to assess the impact of socio-economic, agronomic, and environmental variables, including GDP, HDI, population density, education, broadband access, pesticide use, and biodiversity indicators. Data sources include FAOSTAT, FiBL, Eurostat, and the World Bank. The analysis also incorporates crop-specific organic farming data and environmental metrics such as ammonia emissions. The results show that expansion is shaped simultaneously by environmental pressures and socio-economic conditions: greater pesticide use, larger land availability, higher human development, and agricultural employment support organic adoption, while intensive livestock-related emissions and indicators of urbanization, such as broadband access, tend to constrain it.

1. Introduction

Organic farming, as a sustainable agricultural system, has become an increasingly significant element of today’s agriculture and food production, as it simultaneously responds to the environmental, social, and economic concerns of consumers, farmers and the actors of the agricultural and food chain.

In its core, the system excludes synthetic fertilizers and pesticides, instead relying on ecological processes and biodiversity to maintain soil fertility and crop protection [1,2]. Numerous studies have demonstrated its benefits for biodiversity, soil quality, and climate change mitigation, as well as its contribution to healthier diets and rural development [3,4,5,6]. Within the European Union, organic agriculture is not only an available option or preference for farmers and consumers as it regards the prosses of agricultural production and the consumption of fruits, vegetables and other food products, but also a policy priority. It is integrated to the objectives of the Common Agricultural Policy (CAP) and at the epicenter of the European Green Deal and the Farm to Fork Strategy. Its importance is highlighted with the ambitious objective of reaching 25% of agricultural land under organic management by 2030 [7]. specifically, part of the European Green Deal, specifically under its Farm to Fork Strategy, and is implemented through the EU Organic Action Plan launched in 2021 [8].

Beyond its environmental impact, organic farming is increasingly understood as a pathway for rural development, income diversification, and the preservation of traditional knowledge and cultural heritage in agriculture [9,10,11]. Recent assessments highlight that the global area under organic farming has more than doubled in the past two decades, underscoring its rapid integration into agri-food systems [2]. At the same time, empirical studies demonstrate that organic farming contributes not only to environmental objectives but also to rural income diversification and resilience in farming communities [12]. The academic literature has extensively examined both the opportunities and the challenges associated with the development of organic farming. From an environmental perspective, research has consistently shown that organic systems deliver significant benefits for biodiversity, soil fertility, and water quality, while reducing greenhouse gas emissions and reliance on chemical inputs [3,4,6]. At the same time, socio-economic studies underline the importance of income levels, consumer preferences, institutional support, and policy incentives in shaping the pace of organic adoption [5,10,13]. Nevertheless, the literature also highlights persistent barriers, including higher production costs, yielding gaps relative to conventional farming, and uneven market development, which may limit the capacity of organic agriculture to expand at the desired scale [14,15]. These findings indicate that organic farming operates at the intersection of environmental sustainability and socio-economic dynamics, making it sensitive to both the ecological conditions and structural characteristics of national agricultural systems. Recent evidence further stresses the importance of policy frameworks: targeted support measures and CAP implementation have been shown to substantially accelerate adoption rates, whereas institutional barriers and insufficient farmer support mechanisms continue to constrain organic transitions [14,16].

Despite the substantial body of literature on organic agriculture, important gaps persist in the systematic understanding of its expansion across the European Union. A considerable share of existing studies examines specific dimensions in isolation, either environmental or socio economic—without capturing the combined influence of multiple drivers. In addition, many analyses rely on cross-sectional data or case studies, which do not fully capture the temporal dynamics of organic farming transitions [5,15]. Comparative research at the EU level remains relatively limited, and the evidence base is often fragmented across national or regional contexts, reducing the generalizability of findings [14,17]. This knowledge gap is particularly critical in the current policy context. The Farm to Fork Strategy and the European Green Deal have placed organic farming at the core of the EU’s sustainability agenda, with the ambitious objective of reaching 25% of agricultural land under organic management by 2030 [17,18]. However, the absence of systematic, long-term, cross-country analyses limits the ability of policymakers to design targeted strategies that account for both structural constraints and enabling conditions. Without a more integrated and comparative perspective, it is difficult to assess why some countries advance more rapidly than others and how environmental pressures and socio-economic conditions interact to shape these trajectories. Although studies, such as Casolani et al. (2021) [19], have examined the impact of CAP measures, these analyses remain limited in scope and often neglect broader structural dynamics [14]. Similarly, debates on the so-called conventionalization of organic farming [20] illustrate the need for systematic, long-term assessments that go beyond individual case studies [21].

2. Objectives

Following the identification of the need for cross-country analyses as it regards the determinants of organic agriculture expansion in EU, the main objective of the present study is to try to investigate the socio-economic and environmental factors of organic farming expansion in the European Union utilizing an extensive panel dataset the latest available agronomic, socioeconomic and environmental variables that ultimately span from 2000 to 2022. For the realization of the analysis, a multiple linear regression framework is employed that allows for the simultaneous examination of several explanatory variables, including economic indicators, demographic and institutional-policy factors, as well as measures of environmental pressure or quality. This approach makes it possible to capture both cross-country heterogeneity and temporal variation, thereby offering a comprehensive assessment of the drivers of organic farming adoption. The spiration of this research is to enrich existing literature by employing longitudinal data and multiple explanatory factors to reveal the complexity of organic farming transitions. From a policy perspective, the results are expected to inform ongoing debates surrounding the Common Agricultural Policy and the Farm to Fork Strategy, offering insights into the differentiated strategies that may be required to achieve the EU’s sustainability objectives.

3. Methods

3.1. Data and Sample

This study is based on a panel dataset covering all 27 European Union countries over the period 2000–2022. The panel structure allows the study to exploit both the cross-sectional and temporal variation in the data, providing robust evidence on the socio-economic and environmental determinants of organic farming expansion. Data were sourced from widely acknowledged international databases to ensure consistency and comparability, including the Food and Agriculture Organization of the United Nations (FAOSTAT), the Research Institute of Organic Agriculture (FiBL), European Environment Agency (EEA), Eurostat, and the World Bank. When gaps were identified in the annual series, linear interpolation was applied to ensure continuity and maintain comparability across countries and years.

3.2. Variables

The dependent variable is the area under organic farming, expressed in hectares. This measure captures the absolute scale of organic adoption and allows cross-country comparison of long-term dynamics. The independent variables reflect both socio-economic and environmental drivers. Economic development is proxied by GDP per country and the Human Development Index (HDI), while population density (inhabitants per square kilometer) and employment in agriculture (% of total employment) capture demographic and labor structures. Environmental pressures are represented by pesticide use (tons of active ingredients) and ammonia emissions (kilotons), as well as biodiversity and ecosystem vitality indicators. Country area (1000 hectares) is included as a structural determinant of land availability. Finally, proxies for modernization and infrastructure, such as fixed broadband subscriptions (per 100 people), are considered to capture the degree of technological influence. All variables described above were included in the regression model. Section 4 focuses on coefficients that are statistically significant, while non-significant estimates are not discussed further.

3.3. Econometric Specification

In this study, a multiple linear regression model has been employed, since more than one independent variable is evaluated simultaneously. The use of this statistical method allows the estimation of the relationship between the dependent variable—area under organic farming—and a set of socio-economic and environmental explanatory variables [22]. The general form of the multiple linear regression equation is Equation (1):

$$\begin{array}{c}\mathit{y}={\mathsf{\beta }}_{\mathbf{0}}+{\mathsf{\beta }}_{\mathbf{1}}\mathit{G}\mathit{D}\mathit{P}+{\mathsf{\beta }}_{\mathbf{2}}\mathit{H}\mathit{D}\mathit{I}+{\mathsf{\beta }}_{\mathbf{3}}\mathit{P}\mathit{O}\mathit{P}+{\mathsf{\beta }}_{\mathbf{4}}\mathit{E}\mathit{M}\mathit{P}\mathit{L}+{\mathsf{\beta }}_{\mathbf{5}}\mathit{E}\mathit{D}\mathit{U}\mathit{C}+{\mathsf{\beta }}_{\mathbf{6}}\mathit{B}\mathit{R}\mathit{B}.\mathit{S}\mathit{U}\mathit{B}\mathit{S}\mathit{C}.+{\mathsf{\beta }}_{\mathbf{7}}\mathit{C}.\mathit{A}\mathit{R}\mathit{E}\mathit{A}+{\mathsf{\beta }}_{\mathbf{8}}\mathit{P}\mathit{E}\mathit{S}\mathit{T}\\ +{\mathsf{\beta }}_{\mathbf{9}}\mathit{B}\mathit{I}\mathit{O}\mathit{D}+{\mathsf{\beta }}_{\mathbf{10}}\mathit{E}\mathit{C}\mathit{O}\mathit{V}\mathit{I}\mathit{T}+{\mathsf{\beta }}_{\mathbf{11}}\mathit{A}\mathit{M}\mathit{M}\mathit{O}\mathit{N}\mathit{I}\mathit{A}+{\mathsf{\beta }}_{\mathbf{12}}\mathit{A}\mathit{I}\mathit{R}+{\mathsf{\beta }}_{\mathbf{13}}\mathit{W}\mathit{A}\mathit{T}\mathit{E}\mathit{R}\end{array}$$

(1)

where

• Y indicates the dependent variable that accounts for the area under organic farming in each EU country for the period 2000–2022;
• β0, β1, β2… β13 indicates the estimated parameters;
• GDP indicates the gross domestic product;
• HDI indicates the human development index;
• POP indicates the population density;
• EMPL indicates employment in agriculture;
• EDUC indicates the education attainment;
• BRB.SUNSC. indicates the fixed broadband subscriptions;
• C.AREA indicates the total country area;
• PEST indicates the pesticide use;
• BIOD indicates biodiversity and habitat;
• ECOVIT indicates the ecosystem vitality;
• AMMONIA indicates the ammonia emissions;
• AIR indicates the air pollution from agriculture;
• WATER indicates the water and sanitation indicators.

Equation (1) presents the general specification; all variables are defined below the equation. Several variables (area under organic farming, GDP, pesticide use) were log-transformed to correct skewed distributions and to allow elasticities to be interpreted directly.

The variables employed in this study were obtained from internationally recognized databases to ensure data reliability and comparability. The area under organic farming was sourced from FiBL, representing the total hectares under certified organic cultivation. GDP, HDI, and population density were taken from Eurostat, capturing key socio-economic dimensions of national development. Employment in agriculture was drawn from the World Bank, reflecting the structural importance of the agricultural sector in the labor force. Fixed broadband subscriptions were retrieved from the World Bank and serve as a proxy for technological development and modernization, while educational attainment was collected from Word Bank to indicate the level of higher education within the population. Structural characteristics were captured through country area, obtained from FAOSTAT, representing the total land area of each country. Environmental pressures were included through pesticide use, sourced from FAOSTAT, and ammonia emissions from agriculture, taken from the European Environment Agency (EEA). Broader sustainability aspects were captured with indicators from the Environmental Performance Index (EPI), developed by Yale University, CIESIN (Columbia University), the World Economic Forum, and the European Commission’s Joint Research Centre, specifically biodiversity and habitat, ecosystem vitality, and air quality. Finally, water and sanitation indicators were obtained from the World Bank, reflecting access to basic environmental services. Collectively, these variables provide a comprehensive framework for analyzing the socio-economic and environmental drivers of organic farming development.

3.4. Data Treatment and Software

Linear interpolation was applied to occasional gaps to preserve a panel dataset; while this approach enhances comparability, it may smooth short-term fluctuations and should be interpreted accordingly. All estimations were conducted in SPSS Statistics (v.29), which was also used for model diagnostics, including residual distribution, heteroscedasticity checks, and overall model fit, to ensure the robustness of the regression specification.

4. Results

The empirical analysis was conducted using a panel dataset covering all the European countries for the period 2000–2022. Before proceeding to regression estimations, descriptive statistics were calculated for the main variables of interest. Referring to

Table 1. Descriptive statistics of the main variables used in the analysis.

Variable	Mean	Std. Dev.	Min	Max
GDP	26,736.46	19,089.82	3350.0	101,170.0
HDI	0.87	0.05	0.72	0.98
Population Density (people per sp. km of land area)	173.07	256.57	16.99	1660.97
Employment in agriculture (% of total employment) (modeled ILO estimate)	6.39	6.08	0.68	45.18

, the results indicate substantial heterogeneity across European countries, reflecting structural differences in their agricultural sectors. For instance, GDP per capita ranged from as low as 3350 to more than 101,000 USD, with a mean of 26,736, while the Human Development Index (HDI) varied between 0.72 and 0.98 with an average of 0.87. Population density displayed large variation as well, from 16.9 to 1660 inhabitants per square kilometer. Employment in agriculture represented on average 6.4% of total employment, though in some countries it reached levels above 40%, highlighting the persistence of more traditional agricultural structures. Finally, the share of organic farming in total agricultural area exhibited strong cross-country variation, ranging from marginal levels in the early years to more than 25% in countries with the largest areas under organic farming, confirming the diverse pace of transition within the EU.

Model fit indices confirmed the robustness of the regression specification. The model achieved an excellent explanatory capacity, with R² = 0.912 (Adjusted R² = 0.911), while the overall significance was validated by the ANOVA test (F(12, 608) = 528.176, p < 0.001).

Diagnostic checks, including the histogram of standardized residuals and the normal P–P plot, showed that the residuals were approximately normally distributed, thus satisfying the basic assumptions of linear regression. The scatterplot of standardized predicted values versus standardized residuals further indicated no major heteroscedasticity issues.

Table 2. Panel regression results for the determinants of area under organic farming in EU countries, 2000–2022.

Model	Unstandardized Coefficients		Standardized Coefficients	t	p-Value
	B	Std. Error	Beta
(Constant)	10.060 ***	2.319		4.34	0.000
GDP_Ln	−0.042	0.090	−0.012	−0.47	0.637
HDI_Ln	7.191 ***	1.347	0.168	5.34	0.000
PopulationDensity_Ln	−0.017	0.061	−0.007	−0.28	0.776
Employmentinagriculture_Ln	0.186 *	0.064	0.062	2.90	0.004
SMEAN(ShareofTotalCountryArea_Ln)	0.892 ***	0.032	0.501	27.73	0.000
SMEAN(PesticideUse_Ln)	0.773 ***	0.020	0.534	38.01	0.000
SMEAN(Fixedbroadbandsubscriptions_Ln)	−0.167 ***	0.027	−0.097	−6.23	0.000
SMEAN(Educationalattainment_Ln)	−0.027	0.030	−0.011	−0.90	0.368
SMEAN(EcosystemVitality_Ln)	0.461	0.418	0.014	1.10	0.270
SMEAN(AirQuality_Ln)	−0.445	0.292	−0.021	−1.52	0.128
SMEAN(WaterandSanitation_Ln)	−0.307	0.318	−0.014	−0.97	0.335
SMEAN(AmmoniaEmissions_Ln)	−1.019 ***	0.094	−0.284	−10.88	0.000
Adjusted R2	0.911
N	621
F-test	528.176

Notes: Panel data linear regression for the area under organic farming across EU countries, 2000–2022. Statistical significance: *** 0.001, * 0.05. N: number of observations.

presents the regression estimates for the socio-economic and environmental determinants of organic farming expansion across EU countries between 2000 and 2022. The model confirms a very high explanatory capacity (adjusted R² = 0.911), while the F-test indicates that the joint effect of the explanatory variables is highly significant (F(12, 608) = 528.176, p < 0.001).

The results reveal a nuanced set of relationships. Among the positive and statistically significant drivers, pesticide use (β = 0.53, p < 0.001) and country area (β = 0.50, p < 0.001) stand out as the strongest predictors. This indicates that organic farming tends to expand more rapidly in contexts where intensive pesticide reliance creates environmental and social pressure for alternatives, and where sufficient land resources allow for broader conversion. The Human Development index (β = 0.17, p < 0.001) also exerts a notable positive influence, suggesting that higher levels of income, education, and institutional development provide fertile ground for the uptake of organic practices. Employment in agriculture (β = 0.06, p = 0.004), although weaker, remains significant, implying that rural labor availability and the persistence of traditional farming knowledge may still facilitate organic transitions.

Conversely, two variables exhibit significant negative effects. Fixed broadband subscriptions (β = –0.10, p < 0.001) are inversely correlated with organic adoption, which may reflect the role of urbanization and technologically intensive conventional systems in constraining the expansion of organic alternatives. Even more substantial is the effect of ammonia emissions (β = –0.28, p < 0.001), underscoring the incompatibility of livestock-intensive production models with organic farming principles. These findings resonate with broader debates on structural barriers to sustainable agricultural transitions.

Other factors, such as GDP, population density, educational attainment, ecosystem vitality, air quality, and water and sanitation indicators, did not yield statistically significant effects in this specification. This result highlights that the expansion of organic farming is not necessarily linked to aggregate economic growth or broader environmental quality, but rather to specific pressures (pesticide use, livestock intensity) and enabling socio-economic conditions (human development, rural employment).

Overall, the regression results indicate that the expansion of organic farming in the EU is primarily associated with specific environmental pressures, such as pesticide use and ammonia emissions, as well as socio-economic conditions including human development levels, agricultural employment, and land availability. In contrast, broader indicators such as GDP, population density, education, ecosystem vitality, and air or water quality does not appear to exert significant effects within this specification.

The validity of these results is supported by the diagnostic analysis of the regression residuals, which indicates no major deviations from normality or homoscedasticity assumptions (Figure 1).

The regression coefficients shed light on the socio-economic and environmental drivers of organic farming expansion. Pesticide use emerged as the strongest positive determinant (β = 0.534, p < 0.001), confirming that higher reliance on chemical inputs is directly associated with a stronger demand and policy push for organic alternatives. Country area was the second most influential factor (β = 0.501, p < 0.001), reflecting structural constraints and opportunities linked to land availability. The Human Development Index was also positively associated with organic adoption (β = 0.168, p < 0.001), suggesting that higher levels of human development, education, and awareness contribute to more rapid uptake. Employment in agriculture exerted a weaker yet statistically significant effect (β = 0.062, p = 0.004), pointing to the role of labor intensity and traditional knowledge in supporting organic farming.

In contrast, two variables displayed significant negative effects. Marked cross-country differences in fixed broadband subscriptions and ammonia emissions are observed across the EU (Figure 2). Fixed broadband subscriptions were inversely correlated with organic share (β = –0.097, p < 0.001). This may imply that the increased urbanization and the corresponding adoption of technologically intensive conventional practices constraints the organic uptake alternatives.

Ammonia emissions showed a strong negative effect (β = –0.284, p < 0.001), highlighting the detrimental role of livestock intensification and synthetic fertilizers in hindering the organic transition. These relationships are clearly illustrated in Figure 3 which presents the standardized regression coefficients and highlights both the positive and negative drivers of organic farming expansion.

The estimated elasticities allow for a clearer interpretation of the magnitude of effects. Specifically, a 1% increase in the Human Development Index is associated with a 7.19% increase in the area under organic farming, underscoring the role of social development in facilitating sustainability transitions. Similarly, a 1% rise in agricultural employment corresponds to a 0.19% increase in the organic farming area, suggesting that stronger rural labor structures contribute positively to organic adoption. Regarding structural drivers, a 1% increase in total country area is linked to a 0.89% expansion in organic farmland, reflecting the importance of land availability.

In particular, a 1% increase in pesticide use is linked to a 0.77% expansion in organic farming, pointing to the societal and institutional demand for alternatives in the face of intensive chemical reliance. Conversely, higher broadband penetration exerts a negative effect: a 1% increase in subscriptions is associated with a 0.17% decline in area under organic farming, possibly reflecting urbanization and the dominance of technologically intensive conventional practices. Finally, ammonia emissions exert the strongest negative influence, with a 1% increase leading to a 1.02% reduction in organic farming area, highlighting the incompatibility of livestock-intensive systems with organic farming principles.

Beyond the aggregate descriptive statistics, the temporal evolution of organic farming provides further insights into the dynamics of adoption across European countries. In Figure 4, Spain, France, and Germany were selected for closer examination as they consistently account for the largest organic farming areas in Europe, together representing a substantial share of the EU total. Spain experienced the strongest increase, particularly after 2010, eventually surpassing both France and Germany in terms of cultivated area under organic farming. France followed a pronounced upward trend, with marked acceleration during the last decade, while Germany displayed a steadier but more moderate growth path. Taken together, these patterns underline the diversity in the pace of organic farming expansion across European countries, shaped by structural conditions and varying policy frameworks.

Complementing the time-series evidence, a spatial overview illustrates were organic farming stands in Europe today. Figure 5 illustrates the distribution of the area under organic farming in 2022, highlighting pronounced differences across countries. Spain, France, and Italy dominate with the largest areas under organic farming, while Germany also remains among the top contributors. In contrast, several Central and Eastern European countries record comparatively lower levels, demonstrating the uneven spread of organic farming across the continent. This spatial heterogeneity reflects the combined influence of natural conditions, structural characteristics, and varying policy support.

Overall, the results provide a nuanced understanding of the determinants of organic farming in Europe. Expansion is shaped simultaneously by environmental pressures—such as pesticide use and ammonia emissions—and by socio-economic conditions, including levels of human development and agricultural employment, while land availability offers a structural basis for growth. This dual nature positions organic farming both as a response to environmental challenges and as an expression of broader development patterns, highlighting the importance of integrated policy approaches that align environmental regulation with socio-economic support.

5. Discussion

The findings of this study provide new insights into the determinants of organic farming expansion in the European Union. The regression analysis revealed that both environmental and socio-economic drivers influence the pace of organic agriculture, with pesticide use and ammonia emissions exerting the strongest effects in opposite directions. These results align with previous research emphasizing the dual role of organic farming as both a reaction to environmental pressures and an outcome of broader socio-economic transformations [9,10]. Similar conclusions have been drawn by Eyhorn et al. (2019) [5], who argued that organic adoption reflects not only consumer awareness of environmental risks but also institutional and structural conditions. More recently, Bottazzi et al. (2023) [16] and the European Commission (2024) [14] highlighted that organic expansion is often hindered by systemic barriers, such as insufficient farmer support mechanisms and inconsistencies in policy implementation, reinforcing the argument that both ecological and socio-economic dimensions must be addressed simultaneously. Other studies also support this interpretation, showing that transitions to organic systems are typically driven by the simultaneous presence of environmental stressors and enabling socio-economic contexts [6,13,17]. Beyond the European context, recent global assessments reinforce this multidimensional character of organic transitions. Reganold and Wachter (2016) [23] emphasize that organic farming systems combine environmental and social benefits while facing yield and market challenges, while Kleemann et al. (2014) [24], demonstrate how institutional frameworks such as certification schemes shape farmers’ access to markets, further underlining the importance of socio-economic drivers alongside ecological conditions.

From an environmental perspective, the strong positive association between pesticide use and area under organic farming confirms the hypothesis that conventional intensification generates counter-reactions that foster the demand for organic alternatives. This result is consistent with Seufert et al. (2017) [6], who emphasize that consumer awareness of pesticide-related risks frequently stimulates market demand for organic products. At the same time, it resonates with the argument by Fess and Benedito (2018) [25], who demonstrated that organic systems are increasingly perceived as a corrective mechanism to mitigate the externalities of intensive chemical use. Conversely, the negative relationship between ammonia emissions and organic shares reflects the structural incompatibility between livestock-intensive production models and the principles of organic farming. This finding is corroborated by Seidel et al. (2019) [20] and Stein-Bachinger et al. (2021) [26], who pointed out that conventionalized livestock systems create lock-in effects that restrict transitions toward more sustainable practices. Together, these results illustrate that organic farming expands most effectively in contexts where environmental degradation creates both social and institutional pressure for systemic change. Previous research has shown that nutrient surpluses and ammonia-related air pollution pose major obstacles to sustainable farming transitions, particularly in regions with concentrated livestock production [18]. Taken together, these results confirm that organic expansion is not simply a matter of consumer preference but also reflects deeper structural constraints and trade-offs embedded in agricultural systems.

Socio-economic determinants also emerged as key factors shaping the expansion of organic farming. The positive effect of the Human Development Index suggests that organic farming is more likely to expand in contexts characterized by higher levels of education, stable incomes, and a broader culture of social engagement with sustainability issues. This finding is in line with previous research indicating that the adoption of environmentally friendly practices is facilitated in advanced economies where institutional capacity, information access, and consumer awareness are more developed [13].

Employment in agriculture, although exerting only a modest positive effect, also carries important implications. In countries where farming remains a significant source of employment, the availability of labor and the persistence of traditional knowledge can create more favorable conditions for the uptake of organic methods. This is particularly true in regions where smallholder and family farming structures dominate, as these systems are often less capital-intensive and more reliant on labor, making them more adaptable to the requirements of organic production [5,9]. At the same time, this result underscores the continuing relevance of rural labor markets in shaping agricultural transitions, despite broader processes of mechanization and rural depopulation across the EU.

The observed heterogeneity across countries highlights the importance of regional contexts. Southern European countries, such as Italy and Spain, showed strong upward trends in area under organic farming, while Northern Europe has reached comparatively higher shares. These divergences mirror the uneven diffusion of organic practices across the EU, as documented in earlier studies [7,19]. Such heterogeneity suggests that policy design must be context-sensitive, balancing financial incentives with structural reforms tailored to national agricultural systems.

At the policy level, the findings bear important implications for the Common Agricultural Policy (CAP) and the European Green Deal. The Farm to Fork Strategy sets a target of 25% of agricultural land under organic farming by 2030, yet the uneven expansion patterns observed here imply that differentiated efforts will be needed. Countries with high pesticide reliance and intensive livestock sectors require stronger regulatory frameworks, whereas in more advanced economies emphasis may need to be placed on consumer demand, value-chain development, and export opportunities [7,10].

Although the regression model is statistically sound, some limitations must nevertheless be acknowledged. The dataset, although comprehensive, does not fully capture qualitative factors such as cultural preferences, institutional effectiveness, or farmers’ attitudes towards risk. In addition, the use of aggregate national-level data may conceal significant within-country heterogeneity, as regional dynamics often diverge substantially. Future research could benefit from more granular, regional-level panel data, as well as from mixed-methods approaches combining econometric analysis with qualitative case studies [6].

6. Conclusions

This study has examined the socio-economic and environmental determinants of organic farming expansion across the European Union using a panel dataset for the period 2000–2022. The results confirm that organic growth is a multidimensional process shaped by both environmental pressures—particularly pesticide use and ammonia emissions—and socio-economic conditions such as human development, labor availability, and the availability and distribution of agricultural land. The analysis also highlights that organic farming is not entirely driven by consumer demand but reflects deeper systemic constraints and opportunities integrated in agricultural systems.

By drawing on long-term panel data and diverse explanatory factors, the study highlights the complexity of organic farming transitions in the EU. The findings suggest that achieving the Farm to Fork Strategy target of 25% organic land by 2030 could be facilitated through differentiated strategies, combining stronger regulatory interventions in countries with intensive input use and measures to enhance demand, value-chain development, and export opportunities in more advanced economies.

Despite the limitations related to aggregate data and the lack of qualitative information, the findings suggest that integrated policies combining environmental regulation and socio-economic support are essential. Future research may build on this by incorporating regional-level analyses and more complex methodological approaches.