Factors Influencing the Adoption of Organic Farming in Lithuania and Poland

Abstract

The European Green Deal, including the Farm to Fork and Biodiversity strategies, assumes an increase in the area under organic farming to 25% by 2030. In order to achieve this goal, it is important to understand the factors that lead to the development of organic farming. Data from Lithuanian and Polish Farm Accountancy Data Network datasets and logistic regression was used to evaluate factors influencing the adoption of organic farming in two neighboring countries—Lithuania and Poland—that have quite different agricultural sectors. The study period was 2009–2019. The results indicated that multiple factors affected the probability of adopting organic farming in both Lithuania and Poland. However, the results were somewhat different at the beginning and at the end of the research period. The findings also demonstrated an important role of subsidies in the adoption of organic farming. These findings are particularly important for policy makers to design effective policies and programs aimed at supporting the development of organic farming in both Lithuania and Poland.

1. Introduction

In recent years, the consumption model is changing in many developed countries, which is reflected in the growing demand for high-quality food products (i.e., organic products, traditional and regional products and products produced under quality systems) produced in a way that is not harmful to the environment and climate [1,2]. This trend will likely continue in the future, as sustainable agriculture is becoming increasingly important in many countries. Sustainable agriculture also plays a central role in the European Green Deal, which includes organic farming as a key element of the transition to more sustainable agriculture.

Organic farming is a production system that minimizes pressure on the environment. It excludes the use of synthetic fertilizers and plant protection products and requires the use of complex crop rotation and soil protection measures, which positively affect the quality of soil, water and air [3,4]. Furthermore, organic farming increases soil organic matter and is beneficial for the climate [5,6,7,8]. It also has a positive impact on biodiversity [9,10,11].

Organic farming, which fits into the sustainable development paradigm of the bioeconomy, should be considered in terms of planetary boundaries. Today and in the future, the greatest threats to agriculture are and will continue to be progressive climate change and the accompanying intensification of extreme weather events, loss of biodiversity and adverse ecosystem changes and at so-called “tipping points” within planetary boundaries. These boundaries relate to Earth systems, of which there are nine: climate change; biodiversity loss; biogeochemical (anthropic nitrogen removed from the atmosphere and anthropic phosphorus entering the oceans); ocean acidification; land use (percentage of land surface converted to cropland); fresh water; ozone depletion; and chemical pollution. Critical points/thresholds, on the other hand, are the values of the above components of Earth systems beyond which a sudden, non-linear and irreversible change in the natural environment may occur on a continental or global scale, making sustainable development very difficult. Climate change and biodiversity loss are considered the most important, as they are affected by all other systems. At the end of 2023, it was estimated that only the ozone layer, chemi-pollution and ocean acidification were within established ranges [12,13,14,15,16,17,18,19,20].

The study aims to evaluate factors influencing the adoption of organic farming in Lithuania and Poland. In order to achieve the aim of this study, the following goals were set: (1) to explore selected characteristics of both organic and conventional farms in Lithuania and Poland; (2) to determine factors that have a significant effect on the decision to engage in organic farming in two neighboring countries—Lithuania and Poland—that have quite different agricultural sectors; and (3) to propose measures to encourage the development of organic farming in the countries analyzed.

To our best knowledge, this is the first such comprehensive empirical study on the identification and exploration of the determinants of the adoption of organic farming in two Central and Eastern European (CEE) countries. Specifically, this study contributes to research focused on exploring the development of organic farming in Lithuania and Poland. Despite the fact that Lithuania and Poland are neighboring countries where the agricultural sector still plays an important role, there are differences between them. First, Poland is characterized by a high number of small and medium-sized family farms, whereas in Lithuania, larger farms prevail, often relying on hired labor. Second, in Poland, organic farming policy is more closely linked to consumer demand and is focused on the domestic market, while in Lithuania, organic farming has developed primarily with an orientation towards export. Third, the domestic market for organic products in Poland is larger due to its higher population and strong culture of ecological lifestyles in major cities, whereas in Lithuania, the domestic consumption of organic products is lower, and the majority of organic products are exported. The novelty of this study is threefold: first is the recognition of the role of agri-environmental schemes (AESs), in addition to the traditional factors recognized in the literature, from the point of view of the adoption of organic farming; second is the detailed empirical analysis of the determinants of adoption of organic farming in two CEECs based on data from the FADN (Farm Accountancy Data Network) database; and third, the methodological novelty of this system is reflected in its integrated approach, which encompasses agronomic, social and economic aspects. This system of indicators can be applied to assess the factors controlling organic farming in different regional contexts. This represents a promising direction for future research.

The study is organized as follows. Section 1 presents a literature review, and then Section 2 introduces the method and the data used to evaluate the factors influencing the adoption of organic farming. Section 3 discusses the main results. The last section concludes the study with our recommendations.

2. Literature Review

2.1. Determinants of Organic Farming

When analyzing the factors motivating organic farming, various classifications can be found in the scientific literature, but they can basically be divided into two groups: internal factors that depend on a specific producer and external factors that do not depend on a specific producer. The first group includes agricultural producers’ characteristics, while the second consists of factors related to market and policy.

Several studies have found an association between the adoption of organic farming and the age of farm operators. Studies by Liu et al. [21], López and Requena [22] and Malá and Malý [23] showed that the adoption of organic farming was higher among younger farm operators. A possible explanation is that younger operators are more educated, are open to innovations and better understand the advantages of organic farming. Furthermore, older operators have a lower ability to find additional employees, who are often necessary for organic farming. Organic farming also may not be attractive to older operators because the financial benefits of organic production usually are obtained only after a longer period. However, studies by Karki et al. [24], Khaledi et al. [25] and Xie et al. [26] showed that older farm operators were more likely to adopt organic farming than younger operators. This might be due to more experience in farming.

The adoption of organic farming is also influenced by farm location. A study by Kujala et al. [27] revealed that organic farming was mainly developed in regions where soil quality was lower and farms achieved worse productivity indicators. Similarly, a study by Heinrichs et al. [28] stated that organic farming in Germany was more frequent in regions characterized by a lower intensity of agricultural production and higher degree of land fragmentation. A study by Lu and Cheng [29] suggested that organic farming in Taiwan was more likely to be adopted in agriculturally less favored regions. A study by Zieliński et al. [30] showed that in Poland most organic farms operated in less favorable agricultural areas. A possible explanation is that farms located in agriculturally less favored regions achieve worse productivity indicators; therefore, organic farming offers excellent opportunities to improve their income.

Among the factors that influence the adoption of organic farming are various farm production and economic indicators. Studies by Genius et al. [31], Kafle [32], Karki et al. [24], Koesling et al. [33] and Xia et al. [34] suggested that larger farms had an increased probability of converting to organic farming. It is possible that larger farms often charge higher prices with lower production costs. Furthermore, due to easier access to credit, larger farms can introduce new technologies. Contrary to the results above, studies by Bartulović and Kozorog [35], Khaledi et al. [25], Liu et al. [21] and Malá and Malý [23] suggested that small farms were more likely to adopt organic farming. A possible explanation is that small farms are very often family farms, where a substantial majority of laborers are from the family, which is necessary for organic production. Furthermore, studies by Wollni and Andersson [36] and Xie et al. [26] argued that an increase in the number of family members was a stimulus for organic farming. Also, studies by Läpple [37] and Läpple and van Rensburg [38] pointed out that higher number of livestock units was an obstacle to the adoption of organic farming. This is mainly due to lower support for animal production.

Regarding farm economic indicators, a study by Wiśniewski et al. [39] claimed that agricultural producers’ decisions to convert to organic farming were not driven by environmental conditions but rather economic (income) reasons. Studies by Hoque et al. [40] and Li et al. [41] stated that farms with lower income levels were more likely to adopt organic farming. Studies by Heinze and Vogel [42] and Sriwichailamphan and Sucharidtham [43] also mentioned that income from off-farm sources encouraged agricultural producers to participate in organic farming. A study by Pornpratansombat et al. [44] found that low costs were a decision driver for the adoption of organic farming. A study by Mrinila et al. [45] showed that interest in organic farming decreased when agricultural producers were in debt. All these indicators are important determinants for adoption because they directly affect the profitability of farms.

Finally, the adoption of organic farming is related to some external factors, including financial support and factors related to the market. A study by Verburg et al. [46] showed that, among other factors considered, an important factor for the development of organic farming in the Netherlands was governmental support. A study by Ambrosius et al. [47] revealed that organic farming in the Netherlands was also affected by the demand for organic products. Similar results were obtained by Ferreira et al. [48]. According to them, the main factors determining the expansion of organic farming in Portugal were public support, especially during the conversion period; prices for organic products; and the existence of sales channels. A study by Serra et al. [49] showed that price premiums and subsidies were important factors influencing the adoption of organic farming in Spain. A study by Malá and Malý [23] suggested that organic payments were one of the key determinants leading to the adoption of organic farming practices in the Czech Republic. According to them, through agricultural policy it is possible to influence the development of organic farming. Similarly, a study by Palšová [50] argued that financial support was the key driver for the adoption of organic farming in the Slovak Republic. A study by Rozman et al. [51] found that subsidies were also the main motivation for the adoption of organic farming in Slovenia. A study by Łuczka and Kalinowski [52] revealed that most Polish agricultural producers, especially those having mixed and grazing livestock farms, intend to continue organic farming only if support is provided. As noted by Siepmann and Nicholas [53], one way to develop organic farming in Germany is to increase financial support. As stated by Yanakittkul and Aungvaravong [54], support was also a decision driver for the adoption of organic farming in Thailand. However, a study by Kerselaers et al. [55] suggested that the current support system motivates only crop farms to adopt organic farming. Therefore, according to them, in order to ensure the development of organic farming, it is important to increase the variety of support measures.

2.2. Public Policies vs. Organic Farming and Their Theoretical Background (Including Behavioral Approaches)

The issue of agri-environmental schemes (AESs) is important from the perspective of the adoption of organic farming. Most of the subsidies going to European Union (EU) farmers are direct payments. In general, they are quite easy to obtain and generate relatively low administrative and transaction costs [56,57]. Last but not least, their beneficiaries are fully free to spend the funds they receive. This further means that these funds for farms are subject to volatility (the fungibility of money) and can therefore crowd out money from other sources. In this context, the relationship between direct payments, bank loans and economic insurance is particularly important [58]. Following this, neoclassical economic theory suggests that direct payments in models can be unreservedly related to utility maximization and expected utility problems [59]. However, the case is different for subsidies from Pillar II of the Common Agricultural Policy (CAP) and, for example, the reimbursement of excise duties contained in agricultural fuel. We will examine this problem more closely using the example of EU agri-environmental payments (agri-environmental schemes, AESs).

AESs in the CAP became compulsory from 1992 onwards; nevertheless, the interest of farmers in this type of support was not high. This was due to a number of reasons. First, it is a complex instrument on the structural implementation and settlement design side [60]. Second, farmers’ use of it is often associated with high opportunity costs, which significantly reduce the profitability of their participation in applied programs [61]. This problem particularly affects my farm. Third, the multiplicity of economic–financial, social and psychological determinants of farmers’ participation in an ES makes it a major challenge to make the right decision, and ESs for agricultural policy makers and intervention designers are also a major problem that additionally generates one of the highest relative administrative and transaction costs. Finally, it should be mentioned that despite the use of a great number of complex econometric tools for the evaluation of AESs (e.g., laboratory experiments, randomized experiments and elections), researchers have so far not offered convincing procedures that would lead to a significant increase in farmers’ interest in this type of subsidy and at the same time rationalize its spending [62]. Increasingly, there is therefore a call to start drawing more extensively on the work of behavioral economics [63].

Agricultural interventions underpinned by the theoretical underpinning of the findings of behavioral economics should primarily appeal to the loss aversion effects of sponsorship and having mental accounts and reference points [64]. Loss aversion is a tendency in which people give greater weight in their choices to expected losses than to equal amounts of gains [65]. Consequently, they keep financial instruments on which they incur losses for too long because they fear their materialization. This is how the disposition effect manifests itself.

Another form of aversion to losses is the overly rapid divestment by stock market investors in instruments that are gaining value. In part, this is also due to the presence here of the holding effect, i.e., prescribing a higher value to what one already has.

In agriculture, loss aversion and the above two effects explain quite well the low demand of farmers for index insurance. The term “mental accounting” or mental bookkeeping was introduced into scientific circulation by Thaler [66]. Economic agents treat the sources and directions of spending money differently, using peculiar mental accounts for these purposes, and this implies a lack of the already mentioned fungibility of funds and a feeling among people of gaining more control over their money and assets. One important implication of mental accounting in relation to AES is that farmers, under certain conditions, may even agree to a lower cost for the amount of support in exchange for a radical simplification of its rules [64].

Reference points are one of the main concepts in Tversky and Kahneman’s theory of perspective [65]. In their article, they also call them adaptation levels. In general, they are used to determine the zeros on the value scales against which gains and losses are measured. This is more about the difference than their absolute values. In a broader sense, these points can be seen as the context of measurement. It should be added here that Tversky and Kahnnemann [65] introduced this concept based on an experimental approach to behaviorism [67]. The novelty of prospect theory, on the other hand, lies in the fact that Tversky and Kahneman very sensibly documented the relevance of introducing these points with results from psychological research from the field of neuroscience. The reference points are usually current asset and wealth states and various categories of well-being. In more advanced work, expected values are also used.

A very interesting study among British farmers oriented towards improving AES after Brexit was conducted by Ocean and Howley, who drew on the work of behavioral economics [64]. They used the logic of a randomized experiment, the primary advantage of which is to invoke counterfactuals, thus constructing specific control conditions, and this makes it possible to estimate the causal effects of proposed changes to the AES. The study technically consisted of sending questionnaires electronically to a randomly drawn group of 138,379 beneficiaries who were to determine responses to various issues and events from the years 1999–2013. Overall, 860 questionnaires were returned, which corresponded to 7.6% of the documents sent. The results obtained are summarized below.

• The majority of farmers regarded the AES as a difficult subsidy to obtain and as labor-intensive and unpleasant to apply for. Some of them would actually be prepared to agree to a reduction in payments for sure, as long as the labor input and overall costs of applying for them were reduced at the same time. On average, the maximum reduction could be as high as 31% if the labor intensity of applications fell from 10 to 1 h. The recommended strategy, on the other hand, would be to integrate payments and costs into a single account, so that farmers can see the net cost-effectiveness of participating in the AES.
• If the name of the AES contains unambiguous references to environmental issues that farmers understand and offers significant benefits for society, the chances increase that they would be more likely to spend the money thus obtained on improving the environmental friendliness of their farms. This means that the assumption about the fungibility of funds is thus undermined. This therefore confirms the validity of mental accounting.
• In line with loss aversion and the logic of reference points, farmers pay very close attention to cost-effectiveness and also to environmental aspects in their decision-making when applying for AES support. It may be that the marginal utility of cost-effectiveness is on average rated higher than the marginal utility of environmental benefits. Therefore, if a new AES instrument is to be introduced, it must be done in such a way that farmers do not perceive it as less beneficial compared to existing ones. What is also important is that agricultural policy should first ensure satisfactory profitability of agricultural activities before encouraging farmers to participate more widely in AESs.

In the research on the transition of conventional/conventional farms, a strand of research using the theory of planned behavior (TPB) can be distinguished. The essence of the TPB is to present the link between an individual’s beliefs and their behavior. The theory was developed by Icek Ajzen (1991) [68] as an extension of the earlier theory of reasoned action (TRA). A key improvement was the addition of the concept of perceived behavioral control to the model, which was not a component of the TRA. The TPB is an improvement of the TRA because it includes behaviors that an individual does not have full control over. The TPB assumes that an individual’s behavioral intentions are influenced by three main factors: (1) Attitude towards the behavior—this is the individual’s positive or negative appraisal of the behavior in question; moreover, it is shaped by the individual’s beliefs about and evaluation of the consequences of the behavior. (2) Subjective norms—these involve the perception of social expectations from important people in the environment, such as parents or friends; moreover, they reflect social pressure. (3) Perceived behavioral control—this is the subjective feeling of ease or difficulty in performing the behavior in question. This is conceptually linked to self-efficacy. People are much more likely to intend to perform certain behaviors when they feel they can do so successfully. Perceived behavioral control is determined by beliefs about factors that may facilitate or hinder the performance of a behavior (control beliefs). Together, these three components affect behavioral intention, which is considered the most direct determinant of actual social behavior. When an individual has a positive attitude towards a behavior, perceived social norms favor the behavior, and perceived behavioral control is high, so a strong intention to perform a given action is expected.

In Table S1, we present the results of a review of studies on the conversion of conventional to organic farms based on the TPB [34,69,70,71,72,73,74,75,76,77]. We also added the empirical study of Singh and Saiwan (2023) [75], which is based on a neoclassical approach to agricultural economics (without the TPB).

The empirical study of Polish agroeconomists B. Czyżewski et al. (2025) [77] significantly extends the widely used theory of planned behavior (TPB) in the context of farmers’ intentions to convert to organic farming. This study neither negates nor rejects previous research but rather innovatively enriches it. A key contribution is the integration of embeddedness theory, which differentiates in detail between network-level and farm-level embeddedness. This deepens our understanding of how social connections and farm-specific factors impact farmers’ decisions. Embeddedness influences the traditional TPB components (attitudes, subjective norms and perceived behavioral control) and the moral commitments included in the model. This is consistent with other studies that emphasize the significance of ethical norms in pro-environmental behavior. This study also distinguishes itself by focusing on small-scale farms in Romania, enabling the consideration of a distinctive socialist legacy that influences trust and social relations. Furthermore, this work contributes to our understanding of resistance to change among certain groups of farmers (e.g., older, more experienced farmers) by incorporating these factors into an extended embeddedness model.

All research on organic farming can be accommodated in two problem strands: the supply-side-oriented one, which includes analyses of farmers’ decision-making processes related to the transition from a conventional to an organic farming model, and the one focusing on the supply side [78]. The full picture, of course, could be obtained if these two approaches could be integrated in a single stadium. In practice, this is very difficult to achieve, at least given the complexity of the design of such an analysis and its costs.

Researchers in the field of farmers’ choice of organic farming increasingly model the associated decision-making process using the concept of implementation and diffusion of innovations, the basis of which was presented by M.E. Rogers (2003) [79]. It is a five-phase process of knowledge, persuasion, decision, implementation and confirmation. Rogers began to develop his concept by first referring to agricultural innovation, dividing the entire population of agricultural producers into five groups: early innovators, early adopters, early majority, late majority and laggards.

The implementation of organic farming practices, like the process of the adoption and diffusion of innovations, on the other hand, is usually seen in terms of four phases: (1) deciding whether a conventional or organic model is chosen; (2) adapting the farm to the chosen model (here, organic); (3) transporting and processing organic farming raw materials; and (4) selling organic products to consumers [80]. However, most publications deal with the first two phases. The same is also true for this article. Again, it can be reiterated that covering all four phases in a reasonably thorough analysis would be an organizationally very difficult and very costly undertaking.

For several years, the transtheoretical model by J.O. Prochaska and W.F. Velicer (1997) [81], which originally explained the recovery of sick people, was also used to model the implementation and diffusion of innovations in agriculture. In acronymic terms, here we have TTM. The original version of recovery consisted of five phases: precontemplation, contemplation, preparation, action and maintenance. Later, Prochaska, Redding and Valicer (2015) [82] promoted a version in which phase one was no longer present. This narrower approach is currently used in the study of the implementation and diffusion of innovations in agriculture. For example, the following applications of TTM can be mentioned: plant selection [83]; drone use [84]; sustainable process innovation [85]; and digital risk management [86]. To the best of the knowledge of the authors of this article, no work has yet been published on the TTM convention, i.e., showing the step-by-step process of innovation in the form of a transition from conventional to organic farming.

The conversion to and operation of organic farming always takes place within a specific institutional, environmental, socio-economic and cultural context in time and space, which makes the processes involved non-linear, dynamic and stochastic [87]. This is a very serious challenge in selecting quantitative tools for researchers, although indeed indispensable in order to fully capture the determinants governing this type of innovation and then make policy recommendations based on sound scientific evidence.

Möhring et al. (2024) [88] conducted a systematic review of the global literature on the implementation of organic farming. In total, there were 120 appropriately selected papers covering the years 2000–2021. Let us now cite only selected general statistics on the methodological approaches used. The largest number of papers (N = 75) used linear regression and binary models and cross-sectional and observational data from agricultural holdings. The present paper can be included in this class. Only seven papers were based on the theory of planned behavior, which was combined with structural equation modeling (SEM). Unfortunately, it is not possible to deduce from the article by Möhring et al. (2024) [88] which papers are involved here; ergo, it is not possible to analyze the exact methodology used in them. Instead, the article reiterates several times that, in each case, researchers must satisfactorily resolve the trilemma: depth and comprehensiveness of the analysis—organization and complexity of the research program—time, cost and ability to raise adequate funds.

3. Materials and Methods

3.1. Data Sources

Data from the period from 2009 to 2019 was used in this study. This research period of eleven years was chosen because, since 2020, the approach of Lithuania and Poland to promoting the development of organic farming has fundamentally changed. This shift is largely related to the European Green Deal and the new CAP for 2023–2027, which sets an ambitious goal of dedicating 25% of EU agricultural land to organic farming by 2030. In addition, the situation of agricultural product markets has changed, as it was affected by the COVID-19 pandemic, which began in 2020, and the war in Ukraine, which started in 2022.

The data was obtained from Lithuanian and Polish Farm Accountancy Data Network (FADN) databases. FADN is a standardized European data collection system designed to gather detailed information on the economic and production performance of professional farms across EU Member States. It provides harmonized, annually updated micro-level data that allows for cross-country and longitudinal comparisons. Only farms that produce marketable agricultural output and maintain proper bookkeeping are included in the sample.

The samples used in this study cover all administrative regions in both countries and represent a wide range of agro-climatic zones and farming systems. This geographical and structural diversity ensures the representativeness of the data at a national level.

Given its scope, reliability and harmonization, the FADN database serves as an appropriate and robust data source for analyzing socio-economic factors influencing farm performance and agricultural practices, including the adoption of organic farming.

In order to identify the socio-economic features of organic and conventional farms and compare them, the main characteristics of the farm sample were analyzed: age of the farm operator, family labor force, total utilized agricultural area, total costs, income, total liabilities, total subsidies and share of crop output in total output. These indicators were assessed based on average values.

3.2. Model

Based on the analysis of existing literature, the following variables suggested as factors influencing the adoption of organic farming were selected: age of farm operator (in years), total costs per 1 ha of utilized agricultural area (UAA) (in EUR and PLN), family labor force (in family work units (FWU), total utilized agricultural area (in hectares), share of crop output in total output (in %), income per 1 ha of UAA (in EUR and PLN), income from off-farm sources (dummy variable(equal to one if the farm has income from off-farm sources, and zero if not), total liabilities per 1 ha of UAA (in EUR and PLN), location in agriculturally less favoured areas (dummy variable (equal to one if the farm is located in agriculturally less favoured areas, and zero if not), total subsidies (excluding on investment) per 1 ha of UAA (in EUR and PLN).

The selected characteristics were presented to compare organic and conventional farms in both Lithuania and Poland. Logistic regression was applied to investigate the factors influencing the adoption of organic farming:

$$P\left(Y|x_1,\dots , x_n\right)={\displaystyle \frac{e^{b_0+b_1{x}_1+\cdots +b_n{x}_n}}{1+e^{b_0+b_1{x}_1+\cdots +b_n{x}_n}}},$$

(1)

where ${ x}_1,\dots , x_n$ are explanatory variables, $Y$ is a binary dependent variable that represents whether or not a farm is organic and $P$ is the conditional probability that $Y=1$ (a farm is organic) given the values of $x_1,\dots , x_n$.

Figure 1 summarizes the methodological approach used in this study. Logistic regression models were developed using EViews 14 software.

4. Results and Discussion

4.1. Overview of Organic and Conventional Farming Systems

Increased demand for high-quality products creates an attractive market space for Lithuanian and Polish organic producers. However, there is still a gap in organic farming between Lithuania and Poland and highly developed EU countries (Figure 2). This is due to both supply and demand factors.

In 2019, there were 2429 certified organic farms in Lithuania. A total of 246.6 thousand ha was dedicated to organic production (8.3% of the total agricultural area). During the research period, the production of organic wheat, oats and cereal mixtures prevailed, i.e., products that can be sold unprocessed. The production of organic livestock products was little developed. The area of certified organic production increased by 82.8% during 2009–2019, whereas the number of organic farms changed unevenly, and since 2016, it was constantly decreasing (organic farms were getting bigger) [90].

In 2019, there were 18,637 organic farms operating in Poland. The area of organic agricultural land amounted to a total of 507.6 thousand ha. Organic farms covered 3.5% of the total agricultural area. In the structure of organic agricultural land, the largest part was occupied by cereals, forage crops and meadows and pastures [91].

Looking at the dynamics of the area of organic agricultural land in Poland, the share of organic agricultural land in relation to the total UAA in individual years ranged from 2.7% in 2009 to 4.6% in 2013. In 2013, there was both the largest number of organic farms (26,598) as well as the largest area of organic agricultural land (670 thousand ha). Since 2013, there has been a downward trend in terms of the number of organic farms and the area of organic agricultural land. In 2019, the area of organic agricultural land started to grow again despite a further decline in the number of farms [91,92].

Table 1. Selected characteristics of Lithuanian organic and conventional farms.

Variable	2009		2019		Change 2019 Compared to 2009 (%)
	Organic Farms	Conventional Farms	Organic Farms	Conventional Farms	Organic Farms	Conventional Farms
Age of farm operator (years)	44	45	48	48	9.1	6.7
Total costs per 1 ha of UAA (EUR)	448	654	632	951	41.1	45.4
Family labor force (FWU)	1.4	1.6	1.3	1.3	−7.1	−18.7
Total utilized agricultural area (hectares)	113	137	105	166	−7.1	21.2
Share of crop output in total output (%)	74	68	61	69	−17.6	1.5
Income, per 1 ha of UAA (EUR)	294	601	405	836	37.8	39.1
Total liabilities per 1 ha of UAA (EUR)	218	326	462	607	2.1 *	86.2
Total subsidies (excluding on investment) per 1 ha of UAA (EUR)	359	163	361	223	0.6	36.8

* Times. Source: own elaborations on FADN data.

and

Table 2. Selected characteristics of Polish organic and conventional farms.

Variable	2009		2019		Change 2019 Compared to 2009 (%)
	Organic Farms	Conventional Farms	Organic Farms	Conventional Farms	Organic Farms	Conventional Farms
Age of farm operator (years)	45.1	43.8	47.4	45.2	5.1	3.2
Total costs per 1 ha of UAA (EUR)	3199	12,636	3722	7868	16.3	−37.7
Family labor force (FWU)	1.6	1.7	1.4	1.6	−12.5	−5.9
Total utilized agricultural area (hectares)	34	35	29	34	−14.7	−2.9
Share of crop output in total output (%)	49	50	60	59	22.4	18.0
Income, per 1 ha of UAA (EUR)	1811	4236	2594	3719	43.2	−12.2
Total liabilities per 1 ha of UAA (EUR)	1481	6352	1532	2736	3.4	−56.9
Total subsidies (excluding on investment) per 1 ha of UAA (EUR)	1782	1180	2155	1535	20.9	30.1

Source: own elaborations on FADN data.

explore some characteristics of Lithuanian and Polish organic and conventional farms. As can be seen in Table 1, during the research period, Lithuanian conventional farms were, on average, larger in terms of utilized agricultural area than organic farms. On average, conventional farms generated higher income per 1 ha of UAA. Those farms also had higher costs and liabilities than organic farms. Nevertheless, during 2009–2019, organic producers received, on average, more subsidies than their conventional counterparts. This is an outcome of the possibility of benefiting from different measures under the CAP. Other indicators, such as the age of farm operators and share of crop output among total output, were rather similar between these two groups. Table 1 also shows that most indicators showed an increase during the research period.

As shown in Table 2, similar trends were observed for Polish farms. During the research period, most of the analyzed economic indicators, such as costs per 1 ha of UAA, income per 1 ha of UAA and liabilities per 1 ha of UAA, were higher for traditional farms. However, organic farms received, on average, more subsidies per 1 ha of UAA. Other indicators, such as family labor force, farm area and share of crop output in total output, were quite similar between these two groups. It should be also emphasized that the analyzed indicators changed unevenly during 2009–2019. This might be due to the development of technologies, policy and market changes and other factors.

Note that multicollinearity was detected among total costs per 1 ha of UAA and income per 1 ha of UAA after calculating the correlation coefficients among explanatory variables. Thus, in order to satisfy the assumptions of regression analysis, income per 1 ha of UAA was not used for further analysis.

4.2. Factors Influencing the Adoption of Organic Farming

Table 3. Factors influencing the adoption of organic farming in Lithuania.

Variable	Years
	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Age of farm operator	−0.017 (0.012)	−0.039 (0.013) ***	−0.015 (0.011)	−0.015 (0.012)	−0.004 (0.012)	−0.012 (0.012)	0.022 (0.009) **	0.007 (0.011)	0.022 (0.010) **	0.027 (0.010) ***	0.014 (0.009)
Total costs	−0.006 (0.001) ***	−0.005 (0.001) ***	−0.004 (0.001) ***	−0.002 (0.001) ***	−0.006 (0.001) ***	−0.006 (0.001) ***	−0.005 (0.001) ***	−0.005 (0.001) ***	−0.004 (0.000) ***	−0.003 (0.000) ***	−0.003 (0.000) ***
Family labor force	−0.268 (0.246)	−0.009 (0.254)	0.122 (0.253)	−0.300 (0.300)	0.147 (0.285)	−0.065 (0.292)	−0.035 (0.226)	−0.509 (0.290) *	−0.640 (0.299) **	−0.601 (0.275) **	−0.519 (0.261) **
Total utilized agricultural area	0.002 (0.001) *	0.002 (0.001) ***	0.001 (0.001) *	0.001 (0.001) *	0.003 (0.001) ***	0.003 (0.001) ***	0.002 (0.001) ***	0.003 (0.001) ***	0.002 (0.001) ***	0.001 (0.001) *	0.001 (0.001)
Share of crop output in total output	0.002 (0.003)	0.000 (0.005)	0.008 (0.006)	0.017 (0.006) ***	0.011 (0.006) *	0.036 (0.007) ***	0.018 (0.005) ***	0.018 (0.006) ***	0.001 (0.005)	0.001 (0.004)	−0.002 (0.003)
Income from off-farm sources	0.268 (0.325)	0.356 (0.323)	0.178 (0.279)	0.246 (0.301)	0.227 (0.314)	0.399 (0.333)	1.013 (0.274) ***	1.295 (0.320) ***	1.227 (0.289) ***	0.653 (0.278) **	0.916 (0.252) ***
Total liabilities	0.001 (0.000) *	0.000 (0.000)	0.000 (0.000)	−0.000 (0.000)	0.001 (0.000) *	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000) ***
Location in agriculturally less favored areas	0.034 (0.277)	−1.777 (0.357) ***	−0.776 (0.308) ***	−0.317 (0.316)	−1.304 (0.354) ***	−1.593 (0.371) ***	−0.498 (0.262) *	−0.676 (0.297) **	−0.489 (0.293) *	−0.687 (0.252) ***	−0.677 (0.235) ***
Total subsidies (excluding on investment)	0.027 (0.002) ***	0.037 (0.003) ***	0.029 (0.002) ***	0.034 (0.002) ***	0.037 (0.003) ***	0.045 (0.003) ***	0.026 (0.002) ***	0.026 (0.002) ***	0.023 (0.002) ***	0.021 (0.002) ***	0.017 (0.001) ***

Note: cells contain binary logistic regression coefficients with standard errors in parentheses (*** p < 0.01; ** p < 0.05; * p < 0.1). Source: own elaboration on FADN data.

and

Table 4. Factors influencing the adoption of organic farming in Poland.

Variable	Years
	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Age of farm operator	0.020 (0.006) ***	0.010 (0.006)	0.016 (0.006) ***	0.016 (0.005) ***	0.017 (0.005) ***	0.017 (0.005) ***	0.028 (0.005) ***	0.032 (0.005) ***	0.022 (0.005) ***	0.024 (0.005) ***	0.027 (0.005) ***
Total costs	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***	−0.000 (0.000) ***
Family labor force	−0.491 (0.120) ***	−0.500 (0.127) ***	−0.585 (0.118) ***	−0.571 (0.110) ***	−0.442 (0.102) ***	−0.441 (0.109) ***	−0.380 (0.115) ***	−0.235 (0.011) **	−0.128 (0.105)	−0.161 (0.102)	−0.204 (0.104) *
Total utilized agricultural area	−0.001 (0.002)	−0.002 (0.002)	−0.002 (0.002)	−0.001 (0.001)	−0.002 (0.001)	−0.001 (0.001)	−0.005 (0.002) **	−0.008 (0.002) ***	−0.005 (0.002) **	−0.006 (0.002) ***	−0.004 (0.002) **
Share of crop output in total output	−0.763 (0.219) ***	−0.651 (0.225) ***	−0.601 (0.219) ***	−0.417 (0.202) **	−0.535 (0.176) ***	−0.249 (0.188)	0.079 (0.193)	0.109 (0.193)	−0.012 (0.183)	−0.384 (0.167) **	−0.445 (0.174) **
Income from off-farm sources	0.122 (0.193)	−0.017 (0.183)	−0.134 (0.200)	0.086 (0.174)	0.294 (0.146) **	0.237 (0.158)	0.230 (0.169)	0.113 (0.149)	0.067 (0.152)	−0.120 (0.157)	0.139 (0.138)
Total liabilities	0.000 (0.000) ***	0.000 (0.000) ***	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000) ***
Location in agriculturally less favored areas	0.659 (0.144) ***	0.917 (0.157) ***	0.949 (0.149) ***	0.933 (0.141) ***	0.967 (0.131) ***	1.117 (0.141) ***	1.003 (0.147) ***	1.108 (0.152) ***	1.013 (0.142) ***	1.017 (0.151) ***	0.444 (0.350)
Total subsidies (excluding on investment)	0.001 (0.000) ***	0.001 (0.000) ***	0.001 (0.000) ***	0.000 (0.000) ***	0.001 (0.000) ***	0.000 (0.000) ***	0.000 (0.000) ***	0.001 (0.000) ***	0.000 (0.000) ***	0.000 (0.000) ***	0.001 (0.000) ***

Note: cells contain binary logistic regression coefficients with standard errors in parentheses (*** p < 0.01; ** p < 0.05; * p < 0.1). Source: own elaboration on FADN data.

present factors influencing the adoption of organic farming in Lithuania and Poland. As can be seen in Table 3, during the research period, multiple factors affected the probability of adopting organic farming in Lithuania. At the beginning of the research period, the probability of adopting organic farming was higher among producers who operate larger farms. The estimated model shows that a one-hectare increase in farm size is associated with an increase in the odds ratio of adopting organic farming by 0.2%. This finding corroborates studies by Genius et al. [31] and Karki et al. [24], which showed that larger farms were more likely to adopt organic farming. As noted by Genius et al. [31], larger farms have greater potential to adopt organic farming due to high costs of conversion. Furthermore, larger farms have less financial pressure to search for ways to improve their income and introduce new technologies. In addition, the adoption of organic farming was more likely for farms having higher liabilities. The results indicate that each additional euro in liabilities is associated with an increase in the odds ratio of adopting organic farming by 0.1%.

At the end of the research period, the effect of liabilities on the adoption of organic farming remained unchanged. In addition, the adoption of organic farming was more likely for producers who had income from off-farm sources. It was found that farms with income from off-farm sources had a 2.5-fold higher odds ratio of adopting organic farming than those without such income. The same results were achieved by Heinze and Vogel [42] and Sriwichailamphan and Sucharidtham [43], who suggested that income from off-farm sources was important in encouraging producers to shift towards organic farming. A possible explanation for this is that off-farm income provides financial resources, thus creating incentives to bear risk situations, which include, for instance, higher input costs and low market demand.

The probability of adopting organic farming was also higher among farms having fewer family laborers. The estimated model shows that each additional unit of farm labor is associated with a decrease in the odds ratio of adopting organic farming by 40.5%. Finally, the adoption of organic farming was more likely for farms that operate outside less favored areas. The results indicate that farms that operate outside less favored areas have an approximately 50% higher odds ratio of adopting organic farming than farms located in less favored areas. This is mainly due to higher yield variability in less favored areas.

Note that two factors, namely costs and subsidies, were significant throughout the research period. As can be seen in Table 3, during 2009–2019, the probability of adopting organic farming was higher among farms that had lower costs. Regarding costs, such results can be explained by the fact that lower costs can allow producers to allocate more funds to investment, since switching to organic farming often requires some investment. In addition, the adoption of organic farming was stimulated by higher subsidies. The estimated model indicates that, over the study period, each additional euro in subsidies was associated with an increase in the odds ratio of adopting organic farming of between 1.7% and 4.6%. Similar results were achieved, for instance, by Ferreira et al. [48] and Malá and Malý [23], who suggested that subsidies were one of the key determinants leading to the adoption of organic farming practices.

As shown in Table 4, at the beginning of the research period, the adoption of organic farming was more likely for older Polish producers. The results indicate that each additional year of farm operator age was associated with a 2.0% increase in the odds ratio of adopting organic farming. Similar results were also obtained, for instance, by Khaledi et al. [25] and Xie et al. [26], who showed that older farm operators were more likely to adopt organic farming than their younger counterparts. A possible explanation is that older farm operators have more knowledge and experience in farming.

Furthermore, the probability of adopting organic farming was higher among producers with less family labor and a lower share of crop output among total output. Regarding the latter indicator, the estimated model shows that crop farms have an approximately 36% higher odds ratio of adopting organic farming than livestock farms. In addition, the adoption of organic farming was more likely for producers having higher liabilities.

This study also revealed that the adoption of organic farming was influenced by farm location. Specifically, the adoption of organic farming was more likely for producers located in agriculturally less favored areas. It was found that farms that operate in less favored areas have approximately 56% higher the odds ratio of adopting organic farming than farms that operate outside less favored areas. The same results were observed, for instance, by Lu and Cheng [29] and Zieliński et al. [30]. This can be explained by the fact that producers in these areas achieve worse productivity indicators; therefore, organic farming is an excellent opportunity to improve their income.

At the end of the research period, the effect of four indicators, namely, age of the farm operator, family labor force, share of crop output among total output and total liabilities, on the adoption of organic farming remained unchanged. Moreover, the adoption of organic farming was more likely for smaller producers. It was found that a one-hectare increase in farm size was associated with a decrease in the odds ratio of adopting organic farming by 0.4%. This finding corroborates the studies by Bartulović and Kozorog [35], Khaledi et al. [25], Liu et al. [21] and Malá and Malý [23]. The key reason for such results is that small farms are usually family farms selling directly to consumers and aiming to reduce environmental effects and ensure sustainability; therefore, they are more interested in organic farming.

Similar to findings in Lithuania, two factors, namely, costs and subsidies, were significant throughout the research period. The model estimates suggest that, throughout the study period, each additional euro in subsidies increased the odds ratio of adopting organic farming by 0.1%. Thus, this study once again confirms that support is the key factor for the development of organic farming. Therefore, in order to encourage the development of organic farming, a particular focus should be placed on policy to support organic farms. Specifically, in both Lithuania and Poland, payments for conversion to and maintenance of organic farming should be maintained at an appropriate level during further programming periods. In addition, policy measures for the development of organic farming should be targeted to improve the economic condition of organic producers (increase their market potential by investing in the development of farms, processing organic products and shortening supply chains). It is also important to support demand for organic products and build consumer confidence. Only such a complex approach can ensure the development of this sector in the future.

Based on the research results, it can also be stated that more attention should be given to specific types of farms to encourage the development of organic farming. In Lithuania, greater attention should be given to smaller farms and those located in less favored areas, as they often face more economic constraints. In contrast, in Poland, policy efforts could focus more on supporting younger farmers and livestock farms in order to ensure the sustainable development of organic farming.

We found that organic farms in both countries may be described by a complex business profile. While they may have higher revenue per unit of land, on the other hand, they incur higher costs, especially for labor, marketing and insurance [93]. To this must be added the costs of obtaining the right certificates, the uncertainty of future profitability and the problems of accessing subsidized insurance [94]. From the point of view of insurance companies, ecological farms are sometimes treated as dealing with special crops and are therefore difficult for technical-insurance modeling, consequently causing difficulties in the precision of rate calculation processes and their tariffication [95,96,97]. Organic farms may be less risky than conventional ones if risk is measured by the loss ratio, i.e., the ratio of claims to premiums [98].

5. Conclusions

During the research period, similar trends were observed on Lithuanian and Polish farms. In both countries, most of the analyzed economic indicators, such as costs, income and liabilities, were higher for traditional farms. However, organic farms received, on average, more subsidies than their conventional counterparts. Other indicators, such as family labor force and share of crop output in total output, were rather similar between these two groups.

During the period of 2009–2019, multiple factors affected the probability of adopting organic farming in both Lithuania and Poland. Moreover, these factors were somewhat different at the beginning and at the end of the research period. However, subsidies had a significant effect on the adoption of organic farming throughout the research period, thus indicating that they were one of the key factors leading to the adoption of organic farming practices in both Lithuania and Poland.

In order to encourage the development of organic farming in both Lithuania and Poland, a specific focus should be paid on policy to support organic farms. More specifically, without the effective utilization of existing measures, new policy initiatives that aim to improve the economic condition of organic producers, support demand for organic products and build consumer confidence should be developed. Also, more attention should be given to specific types of farms to encourage the development of organic farming. Such a holistic approach can ensure the sustainable development of organic agriculture, thereby achieving the intended objectives.

Organic farming in Lithuania and Poland in the near future should be linked to regenerative agriculture that emphasizes the restoration and enhancement of agroecosystem health, diverging from intensive, industrial practices. It synthesizes principles from integrated, organic and precision agriculture with influences from permaculture. Our future research should be focused on the adaptation of regenerative farming practices in organic farms.