Motivational Factors, Job Satisfaction, and Economic Performance in Romanian Small Farms

Abstract

The agricultural sector ensures food security and is a major source of employment, income, and economic activity in rural areas. The Food and Agriculture Organization of the United Nations (FAO) considers that family farms are the key to a sustainable future in Europe and Central Asia. In Romania, small farms represent the pillar on which Romanian society has been developed. Although the trend has been a reduction in the number of small farms and an increase in the number of large farms, the Government of Romania understands the importance of small farms and therefore supports them through policies involving direct payments, rural development instruments, special initiatives, and loans and outstanding obligations, among others, which focus on increasing their economic performance. The aim of our research was to determine the relationship between farmers’ motivation, their job satisfaction, and the farm economic performance in the case of small Romanian farms. The research sample consisted of 900 small farms (utilized agricultural area (UAA): under 20 ha; standard output (SO): under EUR 15,000). The data obtained after applying the questionnaires were analyzed using SPSS 20.0 and Amos 24.0. For the exploratory factor analysis, values of Bartlett’s test of sphericity, the Kaiser–Meyer–Olkin test, and Cronbach’s alpha coefficient were calculated for each dimension of the proposed model. The hypothesis that motivation, job satisfaction, and farm economic performance directly and positively influence each other was confirmed. An important finding was that the correlation coefficient between farmers’ motivation and farm economic performance was ρ = 0.78, while that for the relation between farmers’ job satisfaction and farm economic performance was ρ = 0.53, which was similar to the correlation coefficient calculated for the relationship between farmers’ motivation and farmers’ job satisfaction. This result allows us to conclude that the influence of farmers’ motivation factors on farm economic performance is stronger than the influence of job satisfaction in the case of Romanian farmers on small farms. This might explain why, although work in agriculture is considered to be worse than an office job and the people that work in agriculture are sometimes stigmatized and receive lower incomes, there are still very strong motivators for Romanian farmers to continue their work in agriculture. This is proven by the fact that Romania has the highest number of small farms in Europe, and this number is not decreasing.

1. Introduction

Agriculture plays a significant socio-economic role in Romania, and its transformation into an advanced, dynamic, and market-oriented sector is central in fighting against poverty, promoting social inclusion, and reducing the urban/rural development disparities.

According to Eurostat, in 2016, the European Union had 10.3 million agricultural holdings. Of this number, Romania accounted for 33.3% of the total number, while Poland accounted for 13.7% and Italy for 11.1%. As for the dimensions of the farms, two-thirds of the EU’s farms were less than 5 hectares in size in 2016. Although the average mean size of an agricultural holding in the EU was 15.2 hectares in 2016, only about 16% of farms were this size or larger. In Romania, the average size of a farm is 3.6 hectares, which is considerably smaller than the EU average of 16.6 hectares [1].

In 2016, about 60% of the standard output generated by agriculture across the EU 27 was from farms in France (18.1%), Italy (15.3%), Germany (14.5%), and Spain (11.3%). Although Romania accounted for one-third of the EU’s farms, it accounted for only 3.6% of the EU’s standard output.

While EU farms used 156.7 million hectares of land for agricultural production in 2016, a little over two-thirds (68.5%) of the utilized agricultural area of the EU was based in just six member states: France used 27.8 million hectares for agricultural purposes in 2016, Spain used 23.2 million hectares, Germany used 16.7 million hectares, Poland used 14.4 million hectares, Italy used a further 12.6 million hectares, and Romania used 12.5 million hectares. In 2018, Romania ranked fifth in the EU in terms of utilized agricultural area, with approximately 13.4 million hectares (7.98% of the EU’s total UAA), after France, Spain, Germany, and Poland.

Sustainable agricultural development involves the incorporation of the goals of the 2030 Agenda into countries’ national development strategies (e.g., Goal 2, achieving food security, improving nutrition, and promoting sustainable agriculture). Romania, similar to other countries that support the 2030 Agenda objectives, has set out to develop a sustainable and competitive agri-food sector to improve the quality of life in rural environments (i.e., through the Romanian National Sustainable Development Strategy 2030) [2].

In our opinion, the importance of small farms to the Romanian economy must be studied from at least the following perspectives: (i) the specific context of Romanian agriculture [3], (ii) transition (i.e., in the early 1990s) and enterprise restructuring [4,5], (iii) the impact of agriculture performance on foreign trade [6], and finally (iv) subsistence farming [7,8,9].

Romania is one of the 10 largest exporters of cereals in the world (ninth in the world for crops exports and sixth for corn exports in 2015). Although most of the cultivated area is used for cereals, these generate less than 25% of the value of the total agricultural production (worth EUR 14.2 billion in 2015). Other uses of the utilized agricultural area include animal pastures, hayfields, etc. [10].

In 1993, the agricultural sector accounted for 22.6% of the GDP; in 2009, it was 6.7%; in 2015, it decreased to only 4.8%; and, in 2019, it reached 4.5%. This evolution is largely explained by the increasing importance of industry and the service sector in the national economy as a whole (the service sector increased from about 25% of GDP in 1990 to about 60% in 2015, reaching 64.3% in 2019) [11]. Although the contribution of agriculture to GDP is declining, Romania still has the largest share of the agricultural sector in the structure of its GDP among all countries in the European Union, three and a half times higher than the European average [10].

On average, in the EU 27, 4.4% of the total employed population works in agriculture, with higher percentages being found in Central and Eastern European countries, while in Western European countries, the share of the population in agriculture is below the European average. Compared to other EU member states, Romania has the largest share of its population employed in agriculture but most workers are not employed as such. Approximately 84% of agricultural workers in Romania fall into the category of unpaid staff (employers, self-employed, unpaid family workers, and members of cooperative associations); by contrast, the average share of salaried workers in the agricultural sector in the EU is 72%, and in some countries, it is even lower (Spain: 50%, France: 63.1%, Germany: 55.8%).

The share of the population working the agricultural sector compared to the total employed population has decreased slightly in the last 20 years. In 2014, the share of people employed in agriculture in Romania was about 27% of the total employed population. In 2019, out of the total number of employed persons, 21.2% worked in the agricultural sector. The share of qualified workers in agriculture, forestry, and fishing accounted for only 18.1% of the total employed population. Compared to 2018, more significant decreases in the number of employed people were registered in agriculture, forestry, and fishing (−94.4 thousand people). Out of the total number of people employed in 2019, 619,000 people worked part-time (7.1%). Most of the part-time workers worked in the agricultural sector (85.8%) [12].

As we know, there are two factors that directly influence agricultural labor productivity, namely: the net income generated from agricultural goods and services in real terms, and the amount of work carried out in farming activities [13].

A great majority of the EU’s farms (96.3%) are family farms (family holdings) according to the Eurostat Agriculture, forestry and fishery statistics, 2020 edition. The term “family farm” defines any farm that is managed by a family member and where family members perform more than half of the agricultural labor [1].

In the literature, the importance of small farms has been debated and analyzed from several perspectives. Here, we provide a summary of studies from five perspectives—in particular, scientific contributions—from over the past 20 years: (i) From the perspective of food systems and security, small farms play key roles in transforming these systems and providing food and nutrition, respectively. The reorganization of the factors that contribute to the transformation of food systems involves large investments in specialized infrastructure, institutional change, and regulatory reform [14,15,16,17]. Small farms are acknowledged as an incentive for stimulating the rural economy and, more necessarily, are seen as a key factor for addressing food security in a global sense [17,18,19,20]. (ii) From the perspective of the future of small farms, small farm development is desirable for its effects on poverty and also viable even in changed circumstances. Comprehensive policies that support small farms should cover providing public goods and services to rural areas, including roads, health services, clean water, and schools, as well as investing in research on agricultural extension. Small farms exist in a wide variety of important situations. Consequently, policies and programs need to adapt to these situations, to the local conditions (e.g., Europe, Asia, etc.), and somewhat with respect to the technical concerns of the program [21,22,23,24,25,26]. (iii) From the perspective of the role of small farms for biodiversity, sustainable agriculture, and socioeconomic sustainability, economists and environmentalists should appreciate and recognize that agricultural biodiversity is valuable and, therefore, market and political forces should consider small farms and agricultural biodiversity [27,28]. At a higher level, there is a strong connection between general macroeconomic conditions and the rural economy. During economic recessions, labour demand in agriculture increases and farming can counter non-agricultural unemployment [29,30]. (iv) From the perspective of agricultural lending, the longer tenure of farm operators encourages more bank lending [31] and the level of loans for small farms can only increase if there are institutions capable of economically resolving the information problems presented by the small farm sector to profit-maximizing banks [32]. (v) From the perspective of the typology of small farms, there is widespread knowledge on small farms around the developing world. Some studies have highlighted that numerous small farms exhibit a strong market orientation, profitable business models, specialization, innovation and diversification of family income, or a lifestyle motivation, or that they may also generate positive sustainability outcomes [33,34].

There is no globally accepted definition for the “family farm” concept, but there have been a lot of discussions about it. Some authors consider family farms as activities based on traditional technology and with inefficient use of very poor resources [35], and they link it to the impediments for the growth of rural areas [36] and poverty [9]. Using old traditional inefficient techniques for farming, small family farms have a low efficiency and productivity [9]. This makes it even harder for the small farms to become competitive and to integrate into the markets and, in the end, brings insufficient household income. On the other hand, there are authors that have demonstrated through their research that in order to increase a country’s agriculture sustainability, it can be recommended to support small farms’ profitability and productivity [37].Usually, small farms are defined considering criteria such as structural size (e.g., farm area, number of animals and number of people working on the farm), economic size (standard output, farm income revenue, annual sales or turnover, etc.), and market participation (e.g., purchased inputs and food sales) [38,39].

There have been several attempts in the literature to define very small farms as those with an agricultural area of less than 2 or 5 ha [40], while small farms are considered as those with an area of up to 20 [41] or even 30 ha [42].

Considering the economic dimension—standard output (SO)—classification, Eurostat and the Farm Accountancy Data Network (FADN) have presented a methodology in which the upper limit for small farms is EUR 25,000 [43].

As for Romania, the classification of farms and agricultural holdings is regulated by Law no. 37/2015, published in Monitorul Oficial of Romania, Part 1, no. 172, from 12th March 2015. The main purpose of this law was to create a standard framework for implementing financing programs from the national budget and community funds [44].

According to the abovementioned law, depending on their economic dimension, farms and agricultural holdings are classified and defined as follows: (a) below EUR 1999—subsistence farms which produce entirely for their own consumption; (b) EUR 2000–7999—semi-subsistence farms which ensure their own consumption and include a small part of the agricultural production that they commercialize; (c) EUR 8000–49,999—small commercial farms which commercialize more than 50% of the agricultural production that they realize; (d) EUR 50,000–999,999—commercial farms/medium agricultural holdings which commercialize the entire agricultural production that they realize; and (e) over EUR 1,000,000—commercial farms/large agricultural holdings which commercialize the entire agricultural production that they realize [44].

Romania has a long history in family farming. For Romanian people, the farm is the pillar upon which the old Romanian society was founded. Here, a family farm encompasses not only agricultural activities but also other activities aimed at preserving traditions and crafts, stimulating rural tourism and agritourism, and facilitating the protection of the environment through extensive agricultural practices. A Romanian village is typically comprised of traditional farmhouses and courtyards. A farmhouse and barns and sheds for cattle, sheep, pigs, chickens, and hay; a vegetable patch for household use; an apple, plum, and pear orchard; and sometimes even other structures comprise a traditional family farm [45].

The spread of communism made people very reluctant to join any form of association; families individually worked on the small and fragmented parcels of land inherited from their pre-war ancestors. The persistence of the fragmented land structure of Romania throughout the last 30 years, despite the expectations of many land consolidation experts, is largely due to the important role that subsistence and semi-subsistence farming plays in providing livelihoods where pension and welfare payments are extremely low, food prices are similar to those in developed countries, and access to credit is very difficult. These small-scale farm landscapes, strongly associated with family farming, are still under increasing pressure due to loss of economic viability, failure to provide adequate living conditions for young farmers, and resulting abandonment.

Smallholding-based production has persisted, especially in the Romanian mountains and upland regions. However, livestock numbers have fallen since 1990, initially as a result of the dissolution of state and cooperative farms and later due to rises in input costs and loss of market shares as a result of cheap imports after Romania’s accession to the European Union (EU) in 2007. The sharpest decrease in cattle numbers began in 2009 when the milk market failed. Many small farmers sold their cattle because the milk price was too low. Both worldwide and in Romania, the trend has been to develop a well-performing agricultural system. Subsidies mostly help large farms to achieve good performance, but they have almost no impact on the poverty of rural areas. This bipolarity has been increasing over time. It is painful to accept and understand the damage caused by the shift away from the foundation on which rural development was based: small-scale family farming [45].

The majority of Romania’s poor people live in rural areas and rely on agriculture for their livelihood. The causes of poverty in Romanian rural areas are the subject of very long and controversial debates, although there are studies that demonstrate that agriculture can ensure food security [46] and represents a major source of employment, income, and economic activity. According to Page and Popa (2013) [47], the large number of small-scale holdings is an important source of economic, cultural, social, and natural strength for Romania. Otiman (2013) [48] argues that there is definitely a strong correlation between the dimensions of farms—more precisely, the existence of large farms (over 2000 ha) in certain areas—and the phenomenon of severe rural poverty. This has also been confirmed by other authors, such as Stanton [49], who points out that the eastern and southern regions of Romania are the best regions for agriculture and contain the largest farms, but, at the same time, these are the poorest regions, with the lowest levels of welfare, while small farms are located in the northern and central parts of Romania, regions that are known as the richest parts of Romania.

It is true that in Romania there is an excessive fragmentation of the agrarian structure (also known in other countries such as Poland, Italy, Bulgaria, and Greece). Very small farms (achieving a standard output below EUR 8000 per year) constitute nearly 95% of all farms in Romania, but they manage only 38% of the utilized agricultural area. Medium-sized farms (standard output between EUR 8000 and 249,999) constitute 5.3% of all farms and occupy an area of over 32%, while large farms (with standard output of EUR 250,000 and more) constitute only 0.1% of all farms, but they occupy nearly 30% of the utilized agricultural area [1].

When taking into account the EU 27, 67.6% of very small farms use 10.6% of the total utilized agricultural area. Medium-sized farms are dominant in European agriculture—their share of the number of farms is 29.5% and they use 55.8% of the total arable land, while large farms, with a share of 2.9% (of all farms), use 33.6% of the total farmland [50].

The National Programme for Rural Development (NPRD) for the period 2014–2020 aimed to support the economic and social development of rural areas sustainably and intelligently in Romania. There is a stringent need to improve environmental conditions and to promote sustainable practices in agriculture (including small farms) and the economy in general [43].

In order to ensure the viability of Romania’s farms, and since their majority is small, support tools have been addressed to them (i.e., the NPRD). It has been found that, by supporting family farms, social problems related to unemployment can be solved, and it is possible to find connections between people working in agricultural production and the domestic market (through increasing the number of farms selling their products and not only producing for own consumption). Furthermore, if Romanian farms were able to standardize their production, they could sell some agricultural and food products on the European and world markets as well. This could help Romanian farms improve their economic outputs and increase the productivity of factors in agriculture, as well as increasing agricultural incomes. Great importance has been given to the diversification of production in order to ensure revenue all year round. Three main priorities have been identified by the Government of Romania in order to better support family farms: (1) promoting the family farm as a sustainable and inclusive growth model; (2) creating an institutional framework to implement support measures; and (3) including family farms in the food supply chain [51].

In Romanian agriculture, it is common for a farm to use its own labor, i.e., when it accounts for over 50% of the total labor input on the farm. In 2016, about 99.3% of farms followed this trend in the country; therefore, only a relatively small number of farms (i.e., about 23,000) were based on hired labor (more than 50% of the total labor input on the farm), and these farms operated more as enterprises than as small farms functioning at the household level and producing mainly for internal needs. In 2016, in the European Union, a significant number of farms also used their own labor resources, which confirms that EU agriculture is based on small- and medium-sized farms. However, in the EU, the share of farms obtaining more than 50% of their labor from family members was lower than that in Romania and amounted to 94.7% [50].

Both agricultural lands as well as the people from small farms working on them were exploited for their economic potential for centuries, a phenomenon that diminished agricultural output and led Romania to become a net importer of farm products [52,53,54,55,56].

In this context, the aim of the present study was to identify the nature of the correlations between the motivation that Romanian farmers have for working in agriculture and their job satisfaction, despite their low incomes and the physical hardness of working in the agricultural sector, as well as the way farmers’ motivations and job satisfaction influence the economic performance of farms. The present article represents part of a larger international research project that aims to compare different agricultural indicators among five different Central and Eastern European countries, namely Poland, Romania, Lithuania, Moldavia, and Serbia.

Therefore, as no similar research has been conducted in Romania, this article brings an important contribution to the highlighted correlations and supports the idea that, in the agricultural sector, although the income for the workers might be lower, the conditions of work are mostly worse than those of an office job, and the people that work in agriculture are sometimes stigmatized, there are very strong motivators for farmers, and their level of job satisfaction is sometimes higher than that for an office job. All of these factors have a direct effect on the performance of a farm.

This work contributes to the existing literature on the correlations between motivation, job satisfaction, and farm performance through the following: as there are over 500 million farms in the world and most of them are small farms, of which only 4% are located in developed countries [57], most research concerns either small farms from areas such as Africa or Asia or, when talking about EU countries, large-scale farms and their economic efficiency, while we believe that small farms remain the best tool in poor agricultural areas.

The novelty and the originality of the article are given by the fact that we present a model based on the data that we collected from 900 small Romanian farms from different regions of the country. This is the first research of this kind conducted in Romania on such a large sample.

Every country has different social realities. In Romania, the reality is that more than 95% of farms are small farms (UAA of less than 5 ha and SO of less than EUR 8000) that use only 38% of the national UAA, and 99.3% of farms use their own family members as farm workers [1]. In 2019, 21% of Romanian employees were working in agriculture and most part-time workers worked in the agricultural sector (85.8%) [12]. Considering this, the importance of our study arises from the fact that it represents a good starting point for formulating good employment and human resource management policies for the agriculture sector, as we prove that motivated and satisfied workers in agriculture can improve farm economic performance.

Human resources, in any type of business, represent the form of capital that contributes the most to the future economic performance of a business. In the agriculture sector, human resources remain the most important form capital. Since motivation and job satisfaction positively influence each other, and both influence economic performance, improving human resource management policies, which can increase the level of job satisfaction of the personnel should be a priority, especially during periods of financial crisis [58].

2. Theoretical Framework and Hypothesis Development

Even though there is quite a lot of research in the area of motivation and job satisfaction, not much of it specifically focuses on the agricultural sector. Good research on work in agriculture requires special attention to be paid to the employees, as their working conditions are not comparable with those of employees working in any other field. The main characteristics of agricultural work are seasonality, compulsory overtime during the busy season, and the irregularity of work during the day. Sometimes, during seasonal working, the workers in agriculture do not have days off. This is one of the reasons why agricultural work is considered physically more demanding than other types of work; furthermore, the productivity of work is much lower and there is also an adverse effect from climatic conditions on the workers [59].

Several studies have presented the positive associations of motivation and job satisfaction [60,61,62], both of which are management tools in the form of effective motivation and job satisfaction. They significantly affect both individual [63,64] and organizational performance [62].

According to Aziri (2011), “many studies have demonstrated an unusually large impact of the job satisfaction on the motivation of workers, while the level of motivation has an impact on productivity, and hence also on the performance of business organizations”. In his work he mentions that there is a difference between motivation and job satisfaction. Motivation is a pay-out for an individual to do something to satisfy his or her needs, whereas job satisfaction is a simple positive emotional state in relation to the job. While motivation considers the employees’ future expectations, satisfaction represents the result of past events and experiences [65].

The relationship between motivation and job satisfaction is not clearly defined but, as Brown and Peterson (1994) suggest, it can be outlined by means of the motivational theories classified by these two authors into two categories: “content” and “process” theories [66]. According to Brown and Peterson, content theories are particularly concerned with job satisfaction and assume a direct relationship between job satisfaction and improved performance, while process theories look in more detail at the relationship between motivation, satisfaction, and performance [67]. Nel et al. (2004) refer to content theories as the “what” of motivation and to process theories as the “how” of motivation [68].

Businesses that intend to achieve good performance (including sustainability) require happy, motivated, and satisfied human resources [58], and it is the task of organizations to analyze the factors that influence their employees’ motivation, job satisfaction, and work performance [69]. Job satisfaction may vary according to human resource management practices, including those relating to motivation, job recognition, rewards and career prospects, and organizational commitment [70].

After analyzing the literature, we considered the appropriate relationships between motivation, job satisfaction, and economic performance in the form of the model proposed in Figure 1.

Motivation influences job satisfaction and, together with the latter, contributes to the development of economic performance. Motivation can be considered an independent variable. To measure it, we took into consideration relationships with the other farmers/workers; work conditions/the work itself; educational possibilities and responsibilities; and benefits. Job satisfaction is a variable is dependent on motivation and, at the same time, is able affect the economic performance variable.

2.1. Motivation

The aim of farm management can be taken beyond the restraints set by the belief that motivations have their foundation only in economic gain, and workers can take account of the effects of individual farmers’ different managerial skills, managerial objectives, and resources.

A farmer owning a small family farm can also be considered a manager. They also have experiences, motivations, and capabilities. These create managerial behavior which, in turn, generates an outcome [71]. However, understanding farmers’ decision-making processes remains incomplete as difficulties in dealing with diversified managerial abilities have limited progress [72].

Early work describes three factors relating to motivation and one relating to ability that are mirrored in the performance of a farm. The motivation factors are “interest”, “need”, and “ambition or will”, while “knowledge” represents the factor for ability. Some research has indicated the importance of variations in the interest factor. When the reasons for undertaking farm work were classified based on the degree of interest in or liking for the vocation, a positive relationship with earnings was apparent. There was also a positive relation between the size and efficiency of the different farm enterprises and the operator’s interest in or liking for the enterprise. The scores for this characteristic were also positively correlated with earnings. The motive considered for explaining why an individual undertakes economic enterprises is their desire for personal gain or the betterment of their economic position. This use of the word “need” may be thought of as describing the relation of the individual’s economic accumulations to their probable personal and family requirements [73].

The different attitudes of farmers toward their work can also be explained in part by the influence that the environment has on the farm management, the mentors they had during their learning period and early work time, their age and experience in dealing with practical farm decisions, and the economic experience they have [74].

Other authors consider that a worker’s motivation arises in relation to the connection they have with their supervisors and colleagues. Motivation that continues even during the supervisor’s absence is not based on pay scale or other maintenance factors, commonly known as “fringe benefits”. Motivation is more directly related to a worker’s sense of achievement, their feeling of responsibility, opportunity for growth and recognition. The factors that contribute most to good employer–employee relationships do not cost money and the obstacles to good relations do not reduce costs [75].

Ruth Gasson’s classic study in 1973 identified four broad “value orientations” that are important for farmers. These orientations are “instrumental” (making money, expanding the business), “social” (maintaining a tradition), “expressive” (creativity), and “intrinsic” (enjoyment of work tasks, lifestyle preference) [76]. Following on from this work, much recent research suggests that there are distinct behavioral categories or typologies of farmers, with some being driven more by economic motives while others are driven more by social, lifestyle, or family objectives, with varying degrees of interaction between them [77].

Guither (1963) described research in which farmers were questioned about their real motives for farming as a business or as an occupation. The respondents mentioned several different reasons that they liked farming activity: (1) they can “have a sense of accomplishment; (2) a farm is a better place to raise children; (3) it is a challenging occupation; (4) farming gives more opportunity to work outside in the open air; (5) a farmer runs his own business; (6) a farmer likes to operate machinery; (7) a farmer can spend more time with his family; and (8) living in the country is more enjoyable than living in town” [78].

Other motivators refer to the fact that farm families struggle to obtain the most that they can out of the resources they have. Usually, it is not only net income that matters to this kind of family, but rather a combination of things, including survival, net income over time, increasing the number of resources controlled by the family, and increased prestige within the local social system. In most cases, family resources seem to consist mostly of the family workforce and entrepreneurship, there being not so much capital or land to use [49].

Work motivation also implies factors that push employees to work harder and more efficiently. These factors would be the existence of a remuneration system and the presence of facilities and incentives. According to Liu and Wu, using Maslow’s hierarchy of needs, farmers’ needs can be divided into three levels: survival needs, interpersonal harmony, and social responsibility towards and endorsement of the fact that farmers have rationality, which exist in a dynamic condition. They also state that farmers pay attention to cost–benefit calculations and make decisions according to the results [79], as farmers seek to balance economic, social, and lifestyle goals [80,81]. At the same time, farmers may place profit maximization low on their list of priorities and, for many of them, farming is considered to be a vocation that is valued in itself [82,83].

On the opposite side, motivating factors for the case of large-scale private farms are considered to include a high level of wages, promotion opportunities, a good pension scheme, job security, and the recognition of staff. The demotivating factors are considered to be their non-participation in decision-making processes and the unencouraging reward system [84].

Taking into account the above considerations, we propose the first research hypothesis:

Hypothesis 1 (H1).

Farmers’ motivation positively influences job satisfaction.

2.2. Job Satisfaction

In recent decades, there have been several attempts at defining job satisfaction. Some consider it to be a combination of psychological, physiological, and environmental factors, which result in an internal sense of satisfaction [85], while others define it as a feeling of enthusiasm and contentment in relation to one’s job, which brings personal accomplishment [86], or as the extent to which an employee expresses a positive orientation towards their job or the quality of life at work as experienced by the employee, with these being understood as conditions that could be promoted by social responsibility programs executed by the employer [87].

Another definition of the job satisfaction concept refers to it as an affective or emotional response towards various aspects of an individual’s work [88]. It may influence various work aspects within an organization, such as efficiency, productivity, absenteeism, turnover rates, and human resource wellbeing [89,90]. Job satisfaction at work can vary in relation to several parameters, such as the expectations of the worker, their personality, and cultural differences [91].

A low level of job satisfaction may affect mental health by increasing the level of stress, anxiety, depression, and burnout and leads to a low level of self-esteem [92], or even suicide, with many depressive symptoms and suicide cases being related to the level of job satisfaction [93].

Developing human capital through continuous training may increase the productive output of each employee, either through improvements in skill level or in morale and job satisfaction [94]. In fact, the development of human capital is strongly correlated with the development of the intellectual capital of the enterprise, with a higher level of intellectual capital meaning a higher level of competitive advantages in the long run [95].

According to Morgan (2014), the factors that directly impact the level of job satisfaction, in the case of non-farming jobs, are: appreciation of the work, work–life balance, good relationships with colleagues [96], interesting job content, and a good salary level [97]. These are considered to be the route to independence and personal growth [98].

There is not much research on the job satisfaction of employees working in the agricultural sector, as little attention has been given to workers from agricultural businesses. This neglect means that there is a lack of research data and a knowledge gap regarding the levels of job satisfaction [99].

With regard to job satisfaction while working on a farm, the main factors that are considered by specialists to have a direct influence are: family and colleagues relationships, maintenance of family values, an interesting diversity of job tasks, and the autonomy of work [97], while the reasons for dissatisfaction are considered to be the bad image that agricultural jobs have compared to all other jobs—as well as the very low wages compared to other sectors—the high physical stress, the seasonal work peaks, and the limited opportunities for leisure and holiday activities [100].

While examining the relationship between farmers’ perceptions of the nonfinancial benefits from farming resulting from different behaviors such as disinvestment, production, diversification, and off-farm labor market participation, researchers found that nonfinancial benefits have an important impact on a broad spectrum of farmer activities. Even though farm costs and returns are important, nonfinancial benefits can transform some of the choices of farmers into much more attractive ones, even more attractive than other options that could be more appealing from a financial perspective [101].

While some research indicates that farmers are affected by both nonmonetary and monetary factors, there is little information on the relative contribution of beliefs about nonmonetary benefits to individual farmer behaviors. Economists have long been interested in determining the importance that employees give to different nonmonetary aspects of their employment [102].

Authors such as Howley think that nonfinancial benefits from farming have multidimensional structures and different dimensions can be used to better understand farmers’ behaviors. Howley identified two kinds of nonfinancial benefits from the point of view of their importance for farmers. The first are supplementary benefits that farmers obtain from farm work, which can be considered intrinsic benefits and include personally enjoying the benefits of their own work. The other type originate in the wider social and lifestyle benefits of farming and are considered extrinsic benefits; they include the greater autonomy of choice that comes with being self-employed, the lifestyle benefits of living in a rural area, and social interaction with other farmers [97].

The main reasons for discontentment/dissatisfaction in the case of farmers who carry out only one type of activity are usually related to their leisure time and the level and stability of income, mostly because they compare themselves with office workers, who, they assume, receive a secure and sometimes even higher income for fewer hours of work. They would also like more time to relax, to be seen as successful farmers, given the long time spent working on the farm, and for the farm to remain in the family [103]. However, they rarely think about looking for a job outside the farm, and this is because they appreciate the independence of self-employment and the ability to make daily decisions for themselves on the farm, which, in the end, makes them satisfied with their lives [83].

Farm life means long hours of work, usually involving several different activities, which can be more or less demanding depending on the type of work. This is the reason why, some years ago, farming was called a “greedy occupation”, as it asks for the fulltime commitment of farm family members. However, doing farm work may actually make a farmer feel a lower level of stress. Women from farms state that their main activity is household work, which they consider as being demanding but satisfying, and they do not wish to have an off-farm job. Usually, these people see farming as a way of life rather than a profit-making business and that is why they tend to accept a satisfying lifestyle as compensation for a lower level of financial benefits [104].

Most farmers see farming as a family business, as all members of the family are involved in working and in making the most important decisions for the farm. When needed, all family members participate in determining the goals of the farm [103].

On the other hand, a high level of job satisfaction has been observed among self-employed people. Studies have found that self-employed people generally tend to be happier with their jobs than people working for a business [98,105,106].

Benz and Frey (2008) compared the level of satisfaction for both self-employed persons and those working for a business in more than 20 countries and they observed that the self-employed people were significantly more satisfied than those who were employed in a business. According to their research, people value self-employment not because it is correlated to higher earnings but because of the freedom it offers [102,106].

Self-employed people are, on average, more satisfied with their jobs than those employed in organizations, as most of them enter and persist in business despite both lower initial earnings and lower earnings growth, and there are benefits for many workers, such as being their own boss [102,105,107].

In conclusion, self-employed people tend to be more satisfied with their work as this status offers them more autonomy, flexibility, and skill utilization, greater job security, and even a good effect on psychological health [97].

A correlation analysis between job satisfaction and other parameters demonstrated positive effects of participation in the decision-making process, age, salary, and the productivity of the farm on the job satisfaction of the managers [108].

Finally, recent research evidence has indicated that job satisfaction may not necessarily lead to individual performance improvement, but it does lead to department- and organization-level improvements [87].

In Romania, small farming has been a way of life since ancient times and involves a direct connection with the land and a strong sense of family, with family members working together. This is the reason why farmers often mention that they achieve a higher level of satisfaction from farming than from off-farm work [109]. Although farming requires many hours of hard physical work and involves a lot of uncertainty because it is a sector that is directly influenced by climate change—and, furthermore, the level of income remains rather low, especially in family farms—farmers declare that they are, in general, satisfied with their work.

In light of the above findings, we derived the second hypothesis of the conceptual model of the research:

Hypothesis 2 (H2).

Motivation positively influences farm economic performance.

2.3. Farm Economic Performance

At the farm level, performance can be measured in a variety of ways, including as economic performance [110], environmental sustainability or eco-efficiency [111], innovation, or even social and cultural performance. As Sauer and Moreddu (2020) have stated, productivity, profitability, cost effectiveness, technological or allocative efficiency, and technical progress over time, in terms of productivity growth, are all examples of economic performance indicators [112]. A total factor perspective differs from a partial perspective, which focuses on the output of specific production factors [113].

The economic performance of a farm is determined by a range of structural, process, and behavioral factors. The first important factor is the specific production structure, which is determined by the type and qualification of the labor force, as well as by the size of the farm. Second, the agro-ecological conditions and climate dynamics, as well as access to certain resources and technologies, are also important. Third, the placement of the farm is also relevant in terms of market access, policy system, and specific network effects [114].

New ways of doing business [115] have been adopted by farm families as they have integrated farming into their livelihoods and, in this way, they have created a diverse sector in terms of the sizes and types of farms, types of business organizations, and environmental performance.

In the case of agricultural businesses, an optimal mix of orientations toward profit-making, risk-taking, and motivation is needed in order to achieve good economic performance. If financial benefits are the main driving motivator behind most farming strategies, then the effort directed towards improving economic performance depends on orientations toward profit and risk. However, if motivations are directed away from profits and toward the achievement of nonfinancial benefits (such as social acceptance, prestige, respect from the community, better life, or less stress at work), then attention may be divided between obtaining profits and nonfinancial objectives.

A particularly important factor that can result in competitive advantages [116] is increased sustainability related to the environment, as markets and consumers are evolving and asking for farmers that are more involved with and motivated toward the adoption of green principles within their current business strategies [117].

Motivation is very closely related to job satisfaction, and there are several studies that appear to analyze the link between them. However, we considered that the links between these two variables and a farm’s economic performance have not been sufficiently analyzed.

Taking into consideration all of the above, we posited the third hypothesis regarding the relationship between farmers’ job satisfaction and farm economic performance:

Hypothesis 3 (H3).

Farmers’ job satisfaction directly influences farm economic performance.

3. Materials and Methods

The aim of the present study was to determine the correlation between the motivation that Romanian farmers have for working in the agricultural sector, farm performance, and the level of job satisfaction. The research is part of a larger international research project that aims to compare agricultural indicators among Central and Eastern European countries.

We consider that we contribute to the literature through the following:

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Providing better knowledge on the subject, as there are only a few studies that consider the relation between motivation, job satisfaction, and farm performance with regard to small farms; most studies consider these relations for other types of businesses.

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Using a unique set of variables, we succeeded in creating a structural model that describes the relations that appear, in the case of farmers on small farms, between farmers’ motivation, their satisfaction and, in the end, the performance of their farms.

Sample and Data Collection

Considering the study hypotheses, it was necessary to collect primary data of a quantitative nature. A questionnaire-based survey with appropriate scales was used for measuring the variables considered (motivation, job satisfaction, and economic performance), and then appropriate analysis methods were applied to test the hypotheses.

The analysis was based on surveys conducted in Romania during the summer of 2019. The sample included 900 small farms. A purposeful and random selection of the research sample was used.

In the first stage, small farms were selected. For this research, the first set of criteria adopted for the selection of farms concerned the utilized agricultural area (ha)—up to 20 ha—and the standard output (thousand euros)—EUR 15,000. The lower limit (15,000 instead of 50,000) was chosen for several reasons: first, because of the high level of fragmentation of the agrarian structure; second, according to the Romanian National Institute of Statistics, in Romania in 2016, 3,053,088 farms (91.58%) had a UAA of under 5 ha and 3,188,660 farms (93.18%) had an SO below EUR 8000, while only 114,168 farms (3.34%) had an SO between EUR 8000 and 15,000 [46].

These criteria of small farms are most often met by family farms, the most common type of Romanian farm, although this is a very heterogeneous group. Family farms, apart from owning agricultural land and running agricultural activities, are characterized by agricultural work using the labor force from the farm (family members).

In the second stage, we addressed the County Agricultural Directorate, which is appointed by the Romanian Ministry of Agriculture to cooperate with farmers to gather data for the Farm Accountancy Data Network (FADN), and randomly selected farms from its database, according to the criteria established previously. The representativeness of the sample was maintained by evenly dividing the population by region/sub-region, type of production, land area, and production scale (in terms of criteria). Data were collected in the form of direct interviews by collectors and interview operators from the County Agricultural Directorate.

As regards the questionnaire, it was a complex one. The interview questions concerned different areas, including general farm features, economic and social sustainability, environmental sustainability, connections with the market, motivation, and job satisfaction. As we mentioned earlier, this study was just a small part of a larger research project conducted among five different countries; as such, in this study, we only used the variables from the questionnaire that addressed farmers’ motivation, job satisfaction, and economic performance. Pilot studies were carried out before the main study, first in Poland, as this was the country coordinating the research project, and then in each partner country, in order to avoid misunderstandings during the actual survey.

At the end, after eliminating questionnaires that were incomplete, incorrectly completed, or that contained outliers, 900 valid questionnaires were included.

According to

Table 1. Basic descriptive statistics for the analyzed farms.

Variable	Category	Frequency	Percentage
Type of farm	Crops	468	52.0
	Animals	121	13.4
	Mixed	311	34.6
Farm manager gender	Male	658	73.1
	Female	242	26.9
Agricultural education	Yes	509	56.6
	No	391	43.4
Farm area	Under 5 ha	390	43.3
	5–10 ha	242	26.9
	Over 10 ha	268	29.8
Region	Moldova	260	28.9
	Transylvania	270	30.0
	Dobrogea	280	31.1
	Oltenia	90	10.0

Source: authors’ calculations based on survey data.

, from the total number of 900 small farms included in the sample, more than half (468 farms) were crop farms, 311 were mixed farms, and the rest of them (121 farms) were animal farms. Out of the total number of farms, 43.3% had an area under 5 ha, 26.9% had an agricultural area between 5 and 9 ha, and the remaining 29.8% were over 10 ha.

The sample was composed of owners of small farms from the four regions of Romania: 28.9% of the interviewed farmers were from Moldova, 30% were from Transylvania, 31.1% were from Dobrogea, and 10% from Oltenia.

To measure job satisfaction and motivation, a five-point Likert scale was used (from 1 = strongly disagree to 5 = strongly agree). After reviewing the literature, we decided to measure job satisfaction, i.e., feelings of enthusiasm and contentment in relation to a job [85] and that which brings a sense of personal accomplishment [86], using the following three items: autonomy of work and self-employment [83,96,97,98,100,101,102,105,106,107]; maintenance of family values and traditions [97,100,102,103,104]; social and lifestyle benefits [83,100,102,103,104,106].

To measure farm economic performance, three items were used: profits or losses over the last three years, the level of debt, and the situation of receipts and payments [57,110,111,117,118,119].

All data were analyzed with SPSS 20.0 and AMOS 24.0 software. Cronbach’s alpha coefficient was used to evaluate the internal consistency of the scale reliability, which is a coefficient of reliability rather than a statistical test. For each dimension of the model, Bartlett’s test of sphericity was applied as well as the Kaiser–Meyer–Olkin test, thus outlining the exploratory factor analysis. Correlation and regression coefficients for a significance level of p < 0.001 and a theoretical critical ratio (CR) of > ±1.96 were used to test and identify the hypotheses.

4. Results

The values recommended to be considered acceptable for the Kaiser–Meyer–Olkin test are 0.5 and above. Therefore, the values in the model can be considered exceptionally good. Bartlett’s test of sphericity is used in factor analysis to see if the correlation matrix resembles the identity matrix. If this is provable, we can say that each variable correlates with all the others.

The Kaiser–Meyer–Olkin (KMO) test is a measure for how suitable the data are for factor analysis. The test measures the sampling adequacy for each variable in the model and the complete model. The statistic is a measure of the proportion of variance among variables that might be common variance. The lower the proportion is, the more suited the data are to factor analysis. If p < 0.001, the null hypothesis is rejected and the test is considered significant.

All of the data demonstrated that factor analysis could be applied (see

Table 2. Values from the exploratory factor analysis (values of Bartlett’s test of sphericity, the Kaiser–Meyer–Olkin test and Cronbach’s alpha coefficient for each dimension of the model, extracted and retained based on the considered items).

Test	Motivation	Satisfaction	Farm Performance
Cronbach’s alpha	0.78	0.98	0.78
Kaiser–Meyer–Olkin measure of sampling adequacy	0.78	0.77	0.67
Approximate chi-square	974.57	4530.88	752.42
Bartlett’s test of sphericity	6	3	3
Sig.	0.00	0.00	0.00

Source: Authors’ calculation based on survey data.

). With this confirmation, we used factorial analysis as the confirmation method.

The confirmatory factor analysis was performed using version 24.0 of the IBM SPSS AMOS software.

Having observed the statistical data related to the model and compared them with the theoretical values, the model was validated.

Analyzing the data from the above table, it can be observed that the structural indices of the model that resulted from the influence of the three variables—motivation, job satisfaction, and farm economic performance—were satisfactory in terms of statistical consistency (see

Table 3. Model indices.

Model	P	GFI	RMSEA	PCLOSE	CFI	NFI
Obtained research values	0	0.96	0.06	0.00	0.98	0.97
Theoretical statistical values	<0.05	>0.90	<0.10	<0.05	>0.95	>0.95
Model	TLI	RFI	PGFI	PNFI	PCFI
Obtained research values	0.97	0.96	0.56	0.69	0.69
Theoretical statistical values	>0.95	>0.90	>0.50	>0.50	>0.50

Source: authors’ calculation based on survey data.

). The indices indicated values as follows: chi-square—CMIN = 4.89, df = 32; p = 0.00; GFI = 0.96; RMR = 0.004, IFI = 0.98, NFI = 0.97, TLI = 0.97, IFI = 0.98; and RMSEA = 0.06. The statistical values were p < 0.05 and RMSEA < 0.1.

Table 4. Parameter estimates, standard errors, critical ratios, and p-values for the measurement model.

			β	S.E.	C.R.	P
MOTIVATION	<-->	ECONOMIC_PERFORMANCE	0.08	0.006	13.234	***
JOB_SATISFACTION	<-->	ECONOMIC_PERFORMANCE	0.05	0.004	12.213	***
MOTIVATION	<-->	JOB_SATISFACTION	0.04	0.004	11.644	***

P < 0.001, S.E.: approximate standard error, C.R.: theoretical critical ratio >±1.96. Source: Authors’ calculation based on survey data.

and

Table 5. Standardized coefficients of correlation between the model’s variables.

			ρ
MOTIVATION	<-->	ECONOMIC_PERFORMANCE	0.78
JOB_SATISFACTION	<-->	ECONOMIC_PERFORMANCE	0.53
MOTIVATION	<-->	JOB_SATISFACTION	0.53

Source: authors’ calculation based on survey data.

show the correlations between the variables analyzed in the model, namely the influences that exist between motivation and economic performance and between job satisfaction and farm economic performance.

Analyzing the data for hypothesis 1, we can state that the hypothesis is confirmed based on the following values: β = 0.04 and P < 0.001. Motivation directly and positively influences job satisfaction.

Additionally, for hypothesis 2, the values of β = 0.08 and P < 0.001 show the direct link between variables; therefore, the hypothesis is confirmed. Motivation directly and positively influences the economic performance of farms.

With regard to hypothesis 3, calculated values for coefficients β = 0.05 and P < 0.001 also confirm this hypothesis. The variable job satisfaction directly and positively influences the economic performance of farms.

For the chi-square model = 157.127, degrees of freedom = 32 and probability level = 0.00.

In Figure 2, hypothesis H1—the link between motivation and job satisfaction—shows a correlation coefficient of ρ = 0.53, indicating that the hypothesis is confirmed.

The correlation coefficient between farm economic performance and motivation is ρ = 0.78; thus, we can state that hypothesis H2 is confirmed.

The link between farm performance and job satisfaction showed a correlation coefficient of ρ = 0.53, thus indicating that hypothesis H3 is also confirmed.

5. Discussions

A positive relation between expectations and work results, which, in a simplistic form, is the essence of a certain level of job satisfaction, gives an individual the ability to motivate themselves further. In a farm business model, the positive results of work are relatively easy and quick to quantify; therefore, we can discuss their relatively fast effects on the level of motivation. Economic performance can be interpreted according to indicators that ensure comparability with other entities or with standard levels already defined. Once achieved, the performance produces immediate and visible effects for the farm, thus being an integral part of a process highlighted by the model mechanism. Performance confirmation (as compared with the indicators used) fuels a higher level of satisfaction and represents a driver for motivation, determining its growth, especially when it is maintained for several production cycles, thus resulting in self-sustainability.

The results of our study can be related to the results presented in the literature. Jankelová et al. (2020) indicated that a relationship existed between business performance and the motivation of agricultural workers in Slovakia. However, such an effect was observed indirectly (unlike in our analysis); it was mediated by the job satisfaction of the employees [59]. There was no direct significant relationship between the economic results of the analyzed farms and the effectiveness of employee motivation. In turn, there was a significant relationship between motivation and job satisfaction, mainly for the financial factors, which is understandable if we take into account the relatively low level of remuneration for workers employed in agriculture [120]. In addition, the money factor is also a creator of job satisfaction in other professional groups; for example, agricultural researchers from Romania, Austria, Germany, and Switzerland. As Mann (2018) has proven, lower job satisfaction in Romania, as compared with the three Western European countries, can be explained fully by differences in the perceived financial situations [121]. Other determinants that motivate and shape job satisfaction include work conditions on farms and health-related factors. This was confirmed by the results of a study on family-run farms in Scotland by Näther et al. (2015). The authors reported that, due to the growing shortage of skilled labour, job satisfaction was becoming increasingly important and had to be taken into account in the context of planning financial results [122].

A decent level of remuneration, a good pension scheme, and job security were also assessed as very important motivators for shaping job satisfaction on large-scale farms in Nigeria [84]. However, contrary to our conclusions, the authors noted a lack of significant relationships between socioeconomic characteristics and motivational factors. Material systems, as well as higher level needs, such as recognition of one’s own qualifications, the importance of one’s own work, and social engagement, are essential for building job satisfaction, which translates into work productivity and commitment to farm activities [97]. Similar opinions were described in a study by Nicole Mcdonald (2017), who used social cognitive career theory (SCCT) to measure farmers’ job satisfaction in Australia [123]. Referring to a study by Lent and Brown (2006), they identified many variables, such as personality and affective traits, self-efficacy, and expected and received work conditions and outcomes [124].

Regardless of the nature of the motivators, it should be assumed that a policy of supporting motivation and creating job satisfaction is of key importance for guaranteeing the resilience and viability of small-scale farm enterprises. This opinion is shared by, among others, von Braun and Mirzabaev (2015) [125] and Borychowski et al. (2020) [57]. The support policy might take the form of financial aid ensuring the conditions for profitability of agricultural production. On the one hand, economic performance is one of the key determinants of satisfaction for the farming profession in small family farms [126] and, currently, economic compensation is one of the incentives commonly used by governments to enhance farmers’ enthusiasm [127]. On the other hand, nonfinancial motivators should be used by creating a positive image of working in agriculture and adding value to the farming profession. Additionally, well-motivated and satisfied farmers are more open to adopting decisions regarding sustainable intensification practices that affect the economic performance and also the natural environment [128].

Our findings show the existence of a relationship among the examined variables and indicate that they positively influence each other. Motivation influences job satisfaction, and vice versa, and at the same time influences the economic performance of the farm. Importantly, job satisfaction is linked to a number of positive health and wellbeing outcomes and is a pivotal component that can shape overall satisfaction and quality of life for individuals [129,130]. Nevertheless, a farm’s economic performance brings a farmer more job satisfaction and motivation to continue working on the farm. It is also recognized that a key factor stimulating gains in agricultural production is a capable and motivated workforce, both throughout the supply chain and on the farms [131].

Previous studies have shown that job satisfaction and motivation directly affect the success of businesses [71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108] and the results of this study show the same thing.

6. Conclusions

This study was conducted on a sample of 900 small farms in Romania and the results confirmed that the motivation of farmers positively influences job satisfaction, and that both motivation and job satisfaction influence the economic performance of a farm.

The results of this study emphasize the importance of and need for human resource management policies, at the national level, that consider increasing farmers’ job satisfaction and motivation, as other studies have similarly proven that both can positively influence the economic performance of farms [71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108]. This performance would lead to the development of the Romanian agricultural sector, as more than 95% of the Romanian farms are small farms.

In the case of the studied small farms, an important feature is that most farmers’ families depend on the economic performance of their farm [1,12,13] and, usually, all family members work on the farm. Therefore, motivational factors and job satisfaction define the long-term development of Romanian small farms.

The management of funds distributed to small farms by the government should consider the importance of farmers’ motivation.

The results confirmed the proposed conceptual model. As can be seen, the relationship between farmers’ motivation and the economic performance of a farm is the most powerful in terms of a positive influence (β = 0.08). We can state that, in the case of Romanian small farmers, the relationship with other farmers/workers, the work itself/the type of work, educational possibilities and responsibilities, and financial benefits influence the economic performance of Romanian small farms and, in this respect, the influence is more powerful than with regard to job satisfaction (autonomy of work/self-employment; maintenance of family values/traditions; social and lifestyle benefits).

Regarding a farm’s particular situation and business model, this strong direct influence can be explained by the direct connection that can be established between the capacity of a farmer to combine resources and the ease of translating their motivation into a positive factor that can boost this process. At the same time, the other two hypotheses—that farmers’ motivation positively influences job satisfaction and that farmers’ job satisfaction directly influences the economic performance of a farm—integrate themselves into the same business model, in which the level of job satisfaction ensures continuity of the production processes. In fact, the model indicates the influence of the farmer’s motivation, on the one hand, directly in relation to the economic performance and, on the other hand, indirectly as mediated by satisfaction.

Regarding the limitations of the study, the first limitation was that the survey concerned only small farms from certain regions of Romania. However, it should be noted that these analyses can be the starting point for other studies on the same subject, but utilizing a sample considering medium and large Romanian farms. A comparative study could also be performed in several different countries and with an analysis of the dependence of these variables on different farm categories. The variables chosen to measure the economic performance of the farms was another limitation of the study, as economic results are also influenced by other factors.