Next Article in Journal
Pore Pressure Analysis for Distinguishing Earthquakes Induced by CO2 Injection from Natural Earthquakes
Next Article in Special Issue
Trends and Future Directions in Crop Energy Analyses: A Focus on Iran
Previous Article in Journal
Nuclear Hazard and Asset Prices: Implications of Nuclear Disasters in the Cross-Sectional Behavior of Stock Returns
Previous Article in Special Issue
Factors Affecting the Use of Organic Seed by Organic Farmers in Europe
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Factors Influencing Adoption of Sustainable Farming Practices in Europe: A Systemic Review of Empirical Literature

Teagasc Food Research Center, D15 DY05 Dublin, Ireland
Center for Research in Agrofood Economy and Development (CREDA-UPC-IRTA), 08860 Castelldefels, Spain
Author to whom correspondence should be addressed.
Sustainability 2020, 12(22), 9719;
Received: 9 October 2020 / Revised: 16 November 2020 / Accepted: 19 November 2020 / Published: 21 November 2020
(This article belongs to the Special Issue Environmental and Economic Performance of Farming and Food Systems)


Modern practices of industrial farming, such as mineral fertilization, caused a widespread degradation of agricultural land and water bodies in Europe. Different farm management strategies exist to reduce the impact of mineral fertilization while preserving soil productivity. The aim of this paper is to provide a thorough systemic review of contemporary literature exploring factors and conditions affecting EU farmers’ adoption of sustainable farming practices. The specific focus is on widely adopted and empirically explored measures, such as organic farming, manure treatment technologies and manure fertilization, as well as soil and water conservation methods. In total, 23 peer-reviewed studies were extracted by means of Google Scholar covering the time period between 2003 and 2019. The main findings show that farmers’ environmental and economic attitudes in addition to their sources of information have a strong effect on the adoption of organic farming, although there is a lack of evidence of their impact on adopting manure treatment and conservation measures. Similarly, farmers’ age and education are found to systemically influence organic farming adoption, but not adoption of other reviewed technologies. While other factors, such as farm physical characteristics or technological attributes, may be important determinants of adoption, it is hard to recognize definite patterns of their impact across technologies given a shortage of empirical evidence. More research utilizing standardized surveys and methods of analysis is needed to formulate qualified guidelines and recommendations for policymakers.

1. Introduction

Intensification of agricultural production in the past century across EU countries has seriously undermined the sustainability of the farming sector, resulting in widespread degradation of key environmental resources, such as land and water. One pernicious side effect of intensified farming is a nutrient surplus, which is defined as a positive difference between the amount of nutrients added to the soil (e.g., via fertilization) and the amount of nutrients taken or removed [1]. Despite some recent reductions in fertilizer application, the total inputs of key nutrients, such as nitrogen and phosphorus, still considerably exceed the soil absorption limits [1,2]. Additionally, the efficiency of the application of these nutrients remains extremely low [3]. Due to the high nutrient concentration, part of that surplus may be washed off from soil by heavy rains, causing environmental problems [4]. Another risk arising from high nutrient levels in soil relates to mineralization and an increased loss of organic carbon. It is reported that 45% of European soils experience infertility due to severe carbon deficits [5].
Reducing nutrient surplus to environmentally sustainable levels is crucial for achieving the aims of the Common Agricultural Policy [6]. The long-term effects of controlling soil nutrient levels may include improved agricultural productivity, soil fertility and biodiversity, which are important to ensure the stability of future food supply. The nutrient surplus reduction strategies are based on a combination of measures aimed at the preservation of soil structure and increasing nutrient and water use efficiency [7]. In addition, an important part of optimizing nutrient flows, stocks and emissions is through improved recyclability and reduced waste of animal by-products, such as manure [8].
There is ample evidence of the positive effects of organic farming, manure treatment and manure-based fertilizers and soil and water conservation on agricultural production and soil qualities. For example, conservation measures are shown to increase plant fertilizer uptake [9] and reduce surface water run-off [10], which is crucial to retain nutrients in soil and reduce the need for imported minerals [11]. Similarly, nutrients from recycled manure may substitute for chemically produced analogues [10]. Organic farming is an alternative farm management system combining various conservation measures and manure-based fertilization, while prohibiting application of mineral fertilizers. As a side effect of reduced reliance on chemical fertilizers, farmers may increase their resistance to external economic shocks (e.g., volatility in energy prices).
Previous literature reviews exploring factors of farmers’ adoption of sustainable practices focused primarily on conservation technologies. For example, Knowler and Bradshaw [12] reviewed adoption studies of conservation tillage, crop rotation and cover crops in developing countries and North America, summarizing both the direction and significance of statistical effects of adoption factors. In a similar paper, Wauters and Mathijs [13] performed a meta-analytic review of factors affecting the adoption of conservation measures in the U.S., Canada and Australia. Furthermore, Carlisle [14] narratively reviewed determinants of adoption of various soil health practices in application to agricultural commodity production in the U.S. We extend these reviews by focusing on recent adoption studies of soil and water conservation in Europe, while also complementing them with a review of factors affecting adoption of organic farming and manure treatment technologies. Adoption of agricultural technologies in Europe might differ from other locations due to specific regulations, customs and natural conditions.
To the best of our knowledge, there are just a few examples of literature reviewing factors of adoption of sustainable farming in Europe. For example, Lahmar et al. [15] provided a general (non-critical) overview of drivers and constraints of conservation agriculture development in such countries as Italy, Spain, France and Czech Republic. Separately, Prager and Posthumus [16] reviewed determinants of adoption of soil conservation measures in several EU countries, using responses to standardized farmer surveys. Liu et al. [17] also mentioned several European studies in their review of factors of farmers’ adoption of best management practices aimed at the reduction in non-point source pollution. In the most recent case, Dessart et al. [18] provided a policy-oriented review of behavioral factors influencing the adoption of various sustainable farming practices in Europe, such as organic farming and conservation agriculture. While this paper covers several studies also mentioned in our review, it focuses exclusively on behavioral factors of adoption such as farmer attitudes while disregarding other common determinants of adoption (e.g., farmer demographic attributes). Additionally, it covers studies on adoption of agri-environmental schemes, which is beyond the scope of the given review.
Given this background, our study pursues two main goals. First, it is to provide a brief overview of key regulations stipulating adoption of sustainable agriculture practices in Europe. Most of these regulations are in the form of mandatory directives, which might have a strong influence on the adoption and development of sustainable farming strategies. The knowledge of actual legislation is important to understand the differences in approaches to sustainable agriculture in Europe versus other areas. The second goal is to provide a comprehensive and systemic review of factors influencing farmers’ decision to adopt three specific technologies directly contributing to a reduction in mineral fertilization rates and normalization of soil nutrient balance, namely organic farming, manure treatment and manure-based fertilizers, and soil and water conservation (we ignore other popular themes of sustainable agriculture, such as integrated pest management, precision agriculture, genetically modified crops or agro-environmental schemes). To achieve this goal, a systemic and up-to-date review of empirical literature in Europe at the farm level is provided. We initially review key theories explaining fundamental principles behind farmers’ adoption behavior. Exercising this knowledge, we categorize all adoption factors into thematic groups and analyze their effects on farmers using reported summaries of statistical analysis or general survey results. We retrieve the direction of effects and their statistical significance from all studies based on regression analysis. Additionally, we aggregate reported marginal effects from studies based on non-linear regression analysis to estimate an overall size of the statistical effects of key adoption factors across different technologies.

2. Methodology

2.1. General Structure

The structure of this paper is organized as follows. It starts from the Methodology section which includes information about the selection of empirical literature and aggregation of marginal effects of the main adoption factors extracted from the literature. Subsequently, a review of relevant regulations at the EU level affecting farmers’ adoption strategies is provided to give the political and legislative context in which farmers make adoption decisions. This is followed by a general overview of the theoretical foundations of technology adoption behavior. A section reviewing empirical studies of adoption factors in Europe for each technology group—a central part of this paper—is presented next. In the end, conclusions are given to summarize the main findings of the empirical review.

2.2. Search Terms and Selection Criteria

To identify relevant studies for the empirical review, we initiated a search through Google Scholar using various combinations of key English words depending upon the technology in question. For example, to find studies on organic farming, we typed “Organic farming adoption” in the search field, while for conservation agriculture studies, we successively tried two search combinations: “Farmer conservation adoption” and “Conservation agriculture technology”. Additionally, we used three combinations of specific terms to find studies on manure treatment: “Manure treatment adoption farm survey”, “Manure treatment technology adoption” and “Manure utilization technology”. The choice of search words was based on the vocabulary used in previous reviews and industrial reports.
As a result, 200 different items were identified for each search combination used, yielding initially 1200 titles in total. To capture recent trends in the technology adoption literature, we imposed strict time constraints focusing specifically upon those items published in between 2003 and 2019. All the titles that passed through the time filter were further checked for compliance with the geographical criteria and the type of literature. As a result, we dropped all studies unrelated to the European Union or the EFTA area (the European Free Trade Association states are Switzerland, Norway, Liechtenstein and Iceland; these are not EU members, but it is fair to treat them as part of a common agricultural area) and only selected peer-reviewed journal publications while ignoring conference papers, industrial reports and other types of gray academic literature, which narrowed the selection pool to 33 items. At the final stage, a cursory review of the publications was performed to understand if they pertained to relevant technologies and if the analysis was based on farmer survey data. After removing items not conforming with the above-mentioned filters, we arrived at the pool of 23 publications that formed the basis of the empirical literature review. Figure 1 provides a summary of the selection process.
Overall, 11 selected studies explore adoption of organic farming, 7 studies focus on manure treatment technologies and 5 studies cover conservation farming (Table S1). Most of the selected studies use econometric techniques to analyze data from farmer surveys, while a handful of studies provide a general summary of survey results without applying any quantitative methodology. While not all EU countries are covered by the selected studies, this review is still geographically diversified, representing all main areas except for some countries of Central and Eastern Europe. Nordic (also known as Scandinavian) and Benelux countries are mentioned 11 times, while there are 7 studies from the south and 4 from the west-central part of the EU. Furthermore, there are 6 studies from the UK and Ireland, and 2 studies from Baltic countries (Figure 2).

2.3. Marginal Effects of Adoption Factors

To summarize the impact of the selected adoption factors across different technologies, we aggregated their marginal effects, using the reported information from empirical studies based on non-linear regression analysis, such as probit or logit models. In contrast to regression coefficients, which cannot be directly compared due to scale issues, it should be possible to compare the impact of adoption factors from different studies using their marginal effects, which measure the marginal probability of a change in an adoption variable with respect to a change in a covariate (adoption factor) [19]. To show how some key factors affect the probability of adoption of sustainable technologies, we constructed two simple statistics based on the reported information: mean and range. The former measure is a simple average of the collection of marginal effects, estimated as 1 N   i = 1 N x i , where x i is a value of a marginal effect from study (population) i, while the latter represents an interval of extreme values of these effects for a specific covariate. All data manipulations and measurements were performed using Microsoft Excel and Stata, which is a popular statistical software for data science [20].

3. EU Regulations Relating to Sustainable Agriculture

Technology adoption in the EU agricultural sector takes place in a highly regulated environment. The EU-wide regulations are complemented and enhanced with national and regional laws and policies. Research provides ample evidence of the impact of various policies and regulations on technology adoption and innovation diffusion in agriculture [21,22,23]. To provide a background for technology adoption by farmers, we review key EU-level regulations affecting the adoption of organic farming and manure treatment technologies, as well as soil and water conservation practices.
A critical set of rules and regulations, the Common Agricultural Policy (CAP), outlines the conditions of public support to EU farmers in the form of direct subsidies (Pillar 1). An additional function of CAP is support of rural development known as Pillar 2 [24]. Initially designed to stimulate agricultural output, the policy underwent a substantial transformation in the last twenty years towards a more environmentally oriented legislation. Specifically, the last big reform in 2013 introduced “greening payments” linking the allocation of subsidies to farmers’ compliance with certain conservation rules. These rules include maintaining permanent grassland, crop diversification and maintaining ecological focus areas such as field margins, hedges, trees, fallow land and buffer strips [25].
General priorities of rural development in the EU, including actions affecting agricultural ecosystems, are outlined in Regulation No 1305 [26]. The specific focus of this regulation is on restoring, preserving and enhancing biodiversity and high-value farming, as well as improving water management and fertilizer and pesticide management, and preventing soil erosion and improving soil management. A related objective of this regulation concerns the promotion of resource efficiency and support of the shift towards a low-carbon and climate-resilient economy. A special section outlines the terms of financial support for the farmers who convert to or maintain organic farming practices.
Another piece of legislation affecting organic farmers is Regulation No 834 [27], which explicitly defines the objectives, principles and rules of the production, processing and distribution of organic products. The rules of organic production affect both crop and livestock farmers. The former, according to the regulation, must employ cultivation practices that enhance soil stability and biodiversity while preventing soil compaction and erosion. No mineral fertilizers should be used to maintain soil productivity. The latter are prescribed to implement housing conditions and maintain stocking densities observing the developmental, psychological and ethological needs of animals. Specifically, livestock density should be restricted to minimize overgrazing, poaching and soil erosion. The correct conversion rules also prescribe the reliable separation of organic and conventional agricultural practices [27].
A separate set of directives regulate soil and water conservation measures. The directive No 676 [28] was enacted to promote the identification of vulnerable zones accumulating nitrogen from fertilization. The directive specifies recommendations to reduce fertilization on steep slopes, as well as on water-saturated, flooded or snow-covered ground and near the watercourse. To prevent water pollution from livestock manure, it is recommended for farmers to consider construction of storage vessels. The directive’s mandatory action program introduced limits on the amount of nitrogen applied to the soil. The directive No 278 [29] regulates the application of sewage sludge in agriculture. According to this directive, it is prohibited to use sewage sludge if the accumulated levels of heavy metals in it exceed the permitted limits. Application of sewage sludge is also prohibited on certain types of land, such as grassland.
Finally, the directive No 60 [30] provides a general framework for maintaining and improving the aquatic environment in the EU. Concerning its impact on agriculture and farming, this directive contains the indicative list of main water pollutants of agricultural origin including biocides and plant protection products, in addition to substances contributing to eutrophication. The recent Thematic Strategy for Soil Protection [31] provides a framework for a future directive aimed at the protection and sustainable use of soils in the EU. The strategy is based on two guiding principles: preventing further soil degradation and preserving its functions, as well as restoring the functionality of degraded soils.
As of manure treatment regulations, the above-mentioned directive No 676 [26] contains rules regarding the capacity of storage vessels for livestock manure. Specifically, it is prescribed to build larger vessels in excess of the required storage capacity if immediate discharge is prohibited due to the presence of a vulnerable zone. According to the directive, exemptions to this rule may apply when farmers can demonstrate that manure disposal causes no harm to the environment. The regulation No 1069 [32] stipulates rules for the collection, transport and disposal of animal manure. The disposal rules specify that animal manure can be disposed of as waste by incineration, recovered or disposed of by co-incineration, disposed of in an authorized landfill or used as an input to manufacture organic fertilizers or soil improvers, as well as being composted or transformed into biogas. According to the regulation, farm operators must seek approval from the competent authority for the construction of plants to manufacture organic fertilizers and soil improvers, or to transform animal by-products into biogas or compost. The regulation also specifies criteria for putting organic fertilizers and soil improvers on the market.

4. Theoretical Foundations of Technology Adoption

The conceptual foundations of technology adoption in agriculture are rooted in the social psychology theory. One prominent example is the theory of planned behavior (TPB), originally developed from the theory of reasoned action [33]. TPB defines human actions as a direct function of people’s intentions and the degree of their perceived behavioral control [34]. According to Ajzen [34], people’s intentions to perform behaviors result from both their personal attitudes towards the planned action and from the subjective norms representing the perceived social pressure to perform or not to perform the behavior. Behavioral control refers to people’s perception of the ease or difficulty of performing the behavior of interest. Ajzen [35] noted that perceived control can define people’s behavior directly or through the formation of intentions. According to the theory, people regularly revise their salient beliefs underlying the formation of attitudes, subjective norms and behavioral control in response to the outcomes of behavior. While people’s intentions may predict their actions, they often diverge, leading to a so-called intention–behavior gap, which was recently explored in the context of farmers’ production plans in the EU [36].
A conceptually similar model to TPB is the transactional model of human behavior adapted by Willock et al. [37] to study farmers’ behavior. The three building blocks of this model are antecedents, mediating variables and outcome variables [37]. Antecedents refer to the basic qualities of personal character, human attitudes and the attributes of the environment. Mediating variables represent cognitive constructs describing people’s aspirations, objectives or intentions, while outcome variables are closely associated with actual behaviors. In contrast to TPB, the antecedent variables of the transactional model can directly affect people’s behavior while simultaneously contributing to the formation of people’s objectives. Willock et al. [37] argued that farmers can recursively modify their objectives in response to the arrival of information about the outcomes of their behaviors.
Both afore-mentioned theories indicate that people’s attitudes and preferences might be critical determinants of their behavior. Fishbein and Ajzen [33] described attitude as a learned proclivity to respond in a consistent way towards a given object. A broad set of environmental, social and economic attitudes may direct farmers’ behavior. In a comprehensive study of farmer behavior in Scotland, Willock et al. [37] showed that such attitudes as openness in farming and the attitude towards chemical use correlate well with the farmers’ propensity to adopt an environmentally sustainable behavior. Empirical studies show that attitudes relating to profit orientation and environmental concerns are two forces driving farmers’ adoption of organic production [38,39].
A separate theory describes the farmers’ technology adoption process through the acceptance criteria proposed by Esser [40]. According to this approach [41], farmers’ technology acceptance is determined by the subject of acceptance, the object of acceptance and the surrounding context or framing conditions. The subject of acceptance refers to the farmers’ personal characteristics and their attitudes and preferences regarding the object of acceptance. The object of acceptance concerns the attributes of the technology itself such as the cost of implementation, estimated benefit, time requirements and risks. Finally, framing conditions represent various circumstances surrounding technology adoption such as the farm’s financial state, the political environment and governmental policies and regulations.
Technological adoption results from a constant innovation process transforming ideas into new or improved products, services or processes [42]. Based on the theory of diffusion of innovations, technological attributes are crucial to the adoption process. Within this approach, Rogers [43] classified innovation attributes consisting of relative advantage, compatibility, complexity, trialability and observability. Relative advantage refers to the superiority of the current innovation over its past version. Compatibility defines the degree to which the innovation is consistent with socio-cultural values and beliefs, previously introduced ideas or client needs for innovations. Complexity of innovation determines if it is perceived as difficult for understanding and further implementation. Trialability shows if the innovation’s implementation can be divided in separate instalments. Finally, observability can be defined as the visibility of the results of an innovation.
Both trialability and observability are important attributes defining the dynamic nature of an adoption process. Emphasizing this idea, Pannel et al. [44] argued that technology adoption can be broken down into a set of consecutive stages: awareness of the problem or opportunity, non-trial and trial evaluation, adoption and non-adoption or dis-adoption. According to Pannel et al. [44], farmers go through these stages as they continuously learn and update their information on the technology adopted. Chatzimichael et al. [45] identified two ways through which farmers can acquire new information. In the first case, they learn by implementing the adoption process itself, while in the second case, learning occurs because of communication with other farmers, researchers and extension agents. Pannel et al. [44] argued that in the early adoption stages, uncertainty surrounding the new technology is often high, prompting farmers to rely upon their communication networks. Upon the start of trialing, farmers gain a unique hands-on experience of technology adoption which affects their future decisions.
Whilst the theory provides a useful framework for the classification and analysis of the technology adoption process, it may be too rigid to account for subtle differences in farmers’ behavior across various localities and practices. To shed more light on the specific determinants of farmers’ adoption behavior, it is important to consider empirical studies exploiting flexible instruments of data collection such as surveys and questionnaires. Using theory as a basis, we will classify the reported adoption factors into six thematic groups, namely farmer and household characteristics, farm structure and financial state, farmer individual attitudes and beliefs and their sources of information and communication channels, as well as technological attributes and attributes of the legal/institutional environment (which includes laws, regulations and incentives). Based on this classification, we will provide a systemic summary of the impacts of individual factors on the adoption of selected farming technologies.

5. Empirical Studies on Technology Adoption at Farm Level in the EU

Many innovative practices are available for EU farmers to improve soil nutrient cycles, reduce pollution and ensure sustainable food production. However, many factors and characteristics determine the adoption of such measures. The goal of this empirical review is to analyze key factors and conditions affecting the adoption of organic farming, manure treatment technologies and manure-based fertilizers, in addition to soil and water conservation measures. A brief summary of the reviewed studies is available in Table S1, while the impact of adoption factors across various technologies is summarized in Table S2.

5.1. Organic Farming Adoption

Organic farming is a prolific topic in the technology adoption literature. It is defined as a method of agricultural production encouraging application of natural substances and processes to limit the industry’s negative impact on the environment and society [46]. To be certified as organic, EU farmers must adopt certain measures to maintain and improve the quality of soil and water and ensure a high standard of animal welfare. For example, the EU organic rules prescribe the application of livestock manure and multi-annual crop rotation to maintain the fertility and biological activity of soil [47]. Based on 2016 statistics, more than 185,000 farms across the EU converted to organic farming, which amounted to 6.2% of the bloc’s total utilized agricultural area in 2016 [48]. It is expected that this figure will increase to 25% by 2030 as part of the new EU green deal [49].
Altogether, this review summarizes findings from eleven studies covering organic farming adoption in ten countries, which represent various climatic zones and agricultural conditions in Europe (Table 1 and Table S1). Among the countries in Table 1, only Benelux states, in addition to Hungary, are not covered in this section. Most studies use variations of probit or logit models to analyze survey-based data, with just a few examples applying duration analysis or agent-based modeling. In general, these models allow determining the conditional probability of adoption or the extent of diffusion of organic farming, given a variety of covariates. While the exact specifications may vary depending upon the geographic location, institutional context or researchers’ choice of questionnaire, it is possible to aggregate the most common factors into several groups according to the theory of technology adoption.

5.1.1. Farmers’ Demographic and Household Characteristics

The first group encompasses farmer demographic characteristics, in addition to household attributes. For example, age of the farm operator was found to be a significant determinant of organic farming adoption by five out of eight studies that included this covariate in the analysis. As the results show, older farmers have fewer chances of becoming organic relative to their younger colleagues, which might be an indication of dominating risk aversion among farmers nearing their retirement. Conforming to this perspective, the study by Chatzimichael et al. [45] empirically proved that age may have a non-linear impact on farmers’ adoption behavior by showing that, up to a certain level, it may improve the odds of organic farming conversion. Another factor of adoption is the level of farmers’ education. Similarly, eight studies included this variable in their analyses and five found a statistically significant effect on the likelihood of adoption. While most studies found a positive influence of education on adoption [45,57,58,59], one example showed the opposite effect [60]. In fact, Chatzimichael et al. [45] showed that, initially, education might be indeed beneficial to farmers, but more years of it result in the deterioration of their chances to turn organic. Similarities between the effects of age and education on farmers might be a direct result of how these covariates are constructed. The records of reviewed studies show that both factors are often proxied in terms of the number of years, which may cause collinearity between the two and also explain why, in the presence of education, the impact of age might be effectively muted and vice versa [39,60].
Apart from age and education, some studies also tested the impact of farmers’ marital status, gender and household size on adoption decision. Both Burton et al. [60] and Tiffin and Balcombe [61] found that female farm operators have better chances of adopting organic farming compared to their male fellows, while Mzoughi [58] did not find the effect of gender significant. Marital status was analyzed by the only study and its effect turned out to be insignificant [59]. Four studies additionally analyzed the effect of household size on the propensity to become organic, but only one found a significant positive effect on it [38,39,60,61].

5.1.2. Farm Structural Characteristics

A second group of determinants of organic farming adoption concerns farm structural characteristics including such factors as farm size, livestock density, farm specialization and geographical location. For example, farm size has a significant impact on adoption, based on the results of four studies, while eight studies reported this aspect of adoption [38,39,45,57,59,60,61,62]. Regarding the direction of its effect, it was shown that farm size may both positively and negatively affect chances of adoption, which might reflect two contradicting views in the literature. According to one of them, farm size is just a proxy for farm financial strength, thus bigger farms have more resources to invest in sustainable technologies. An alternative view would imply that larger and financially more powerful farms will be reluctant to adopt organic farming to avoid potential negative consequences of this technology, such as yield uncertainty. Additionally, four studies reported the presence of off-farm income or an off-farm activity as adoption contributors [38,39,57,62], but only one study revealed a significant impact of such an activity on adoption [62], limiting possible broader generalizations. Further, two studies found evidence that proximity to urban areas increases chances for organic farming adoption [45,59], while two separate studies identified no significant impact of distance on the probability of adoption [39,57]. Occasional studies showed that such factors as aridity [57] or livestock density [38,39] might also affect adoption. Specifically, Genius et al. [57] found a negative effect of drier weather on organic farming adoption in Crete, which is a Greek island located in the Mediterranean Sea. Greece is one of the most arid countries in our sample, with an estimated cross-country aridity index of 1.56 (Table 1). In addition, 51.4% of its agricultural land is subject to a moderate-to-severe risk of erosion by water [51]. Given such an unfavorable climate, this area might benefit from subsidized cultivation and conservation initiatives, which are an important part of organic farming.

5.1.3. Farmers’ Attitudes and Beliefs

A third group of factors includes farmers’ beliefs and attitudes. In reliance with theory, there is a variety of attitudes affecting the probability of organic farming adoption. While interpretations of attitudes vary across studies, it is possible to categorize many of them in two large groups, namely environmental and economic attitudes. In the former case, “environmental attitude” [38,39] is also termed as “environmental awareness” [57], “environmental commitment” [58] or “environmental concern” [60], which all essentially imply a strong preference for environmental sustainability. One study provided no explicit definition of environmental attitude, but it is possible to derive similar meaning from its formulations of farmers’ stated preferences for organic farming [59]. Overall, seven studies reported statistically significant evidence of some sort showing a positive relationship between environmental attitude and the inclination to develop organic production [38,39,57,58,59,60,62], while one study found no such effect [61]. Regarding economic attitude, it was similarly provided under various disguises, such as “profit orientation”, “profit maximization”, “cost cutting attitude” and others. Despite semantic differences, five studies showed that farmers’ economic attitude associated with a preference for increasing the farm’s profit and/or income negatively affects their odds to become organic [38,39,58,59,62]. In contrast, just one study found a statistically significant reverse effect [63].
Apart from these two attitudinal groups, some studies report the effect of farmers’ risk preferences on their adoption chances. While some of them perceive risk attitude as part of the economic group of attitudes, the majority puts it in a separate category. In all cases, risk attitude is formulated either as risk aversion or risk hunger, which typically predetermines the direction of the effect. For example, three studies testing the effect of risk aversion found a negative impact of risk on adoption [38,39,58], while one study using risk hunger found an increased probability of becoming organic [62]. This generally confirms the widespread notion that riskier farmers are more prone to adopting new technologies.

5.1.4. Farmers’ Sources of Information

A fourth group of factors broadly concerns farmers’ sources of information. In contrast to attitudinal effects, it is hard to classify informational factors given their heterogenous constructions across studies. For example, Läpple and van Rensburg [38] and Läpple and Kelley [39], two studies sharing similar data, found that various media sources such as TV and internet significantly depress farmers’ chances to convert to organic production, while knowing other farmers may on the contrary help with this endeavor. The positive effect of communication with fellow farmers was in various forms, also identified by Burton et al. [60], Chatzimichael et al. [45] and Tiffin and Balcombe [61]. Five studies additionally reported the influence of an advisory service on farmers, with two showing the negative effect [60,61], one identifying the positive relationship [64] and the rest stating a lack of significant evidence of any relationship [38,39]. Moreover, Tiffin and Balcombe [61] and Burton et al. [60] both found that communication with buyers deteriorates farmers’ chances for organic conversion. Professional membership was found to be negatively associated with technological adoption by Tiffin and Balcombe [61], while Burton et al. [60] did not find this relationship significant.

5.1.5. Institutional Environment

The last group of factors combines conditions of the institutional environment. Overall, three studies found an increased probability of starting organic production as a result of conversion subsidies [45,57,64]. Additionally, Kallas et al.’s [62] somewhat counterintuitive finding demonstrates that difficulty to get loans might actually induce adoption of organic farming. Another factor that this study found should positively affect adoption chances is farm location in a disfavored (less favored) area. A less favored area (LFA) is a legally defined area with conditions unsuitable for productive cultivation, such as mountains or low-soil productivity areas [65]. While this result was not properly discussed in the study, it is possible to attribute the impact of LFA on farmers’ behavior to the allocation of compensatory allowances, which was a mechanism of farm income support in the less favored regions of the EU. This is in line with similar research, showing the positive impact of compensatory payments on farmers’ participation in the environmentally sensitive areas schemes [66]. Moreover, Kallas et al.’s [62] study focused on organic winemaking in Catalonia, where 56% of the region’s agricultural area was classified as less favored due to its extremely dry climate [67]. Deserted soils in semi-arid regions of Spain were shown to benefit from various organic farming activities [68].
In summary, these studies show that farmer demographic characteristics, such as age and education, and household attributes, such as household size, might be strong determinants of switching to organic farming. Additionally, farm structural characteristics, including its own size, location relative to urbanized areas or specialization may also induce or discourage adoption. It is important to note that factors describing farm financial state were not adequately covered in surveys on organic farming. Likewise, farmers’ personal attitudes about the environment and financial bottom line are found to be strong determinants of adoption, so are various sources of their information about organic farming. While the institutional environment might affect organic conversion through various financial mechanisms, it is not clear from the reviewed studies how laws and regulations shape the conversion process regionally.

5.2. Adoption of Manure-Based Fertilizers and Manure Treatment Technologies

Structural changes, such as the emergence of large-scale farming and the intensification of livestock production, have resulted in an increased accumulation of animal by-products, including manure, on farms [69]. In terms of benefits to farmers, animal manure has potential as a valuable source of many macro- and micronutrients, including nitrogen and phosphorus. Within the EU, the amount of nitrogen in excreted manure was 5.65 million tons in 2017, and just roughly half of this amount was applied to soil [70]. Apart from yielding farmer benefits, the increased manure concentration on farms may result in detrimental effects for the environment. One such effect is air pollution in the form of emissions of methane, which is a strong greenhouse gas [71]. The detrimental effects of manure on soil and water are primarily related to the nitrogen and phosphorus run-off resulting from over-fertilization. Various technologies are implemented in the EU to mitigate the negative impact of animal manure on the environment, including manure separation, anaerobic digestion and slurry acidification, in addition to pelletizing and composting [72,73,74].
This review covers seven studies addressing the adoption of manure treatment technologies in ten countries of the EU. These countries cover large swaths of agricultural land in areas with diversified climatic conditions such as Scandinavia, Benelux, Western and Southern Europe, as well as Hungary. There were no peer-reviewed studies found on this topic from the UK or Ireland (Table 1 and Table S1). While five studies applied econometric models to analyze survey data, two others took a more qualitative approach by simply summarizing questionnaire responses from adopters and non-adopters. Four analytical studies used variations of probit or logit models to determine the probability of technological applications, and one study applied linear regression to measure the impact of the chosen covariates on the predicted variables. Since qualitative studies cannot be directly compared to those applying prediction techniques, we explicitly separate their results in this report. As before, all common factors of adoption are aggregated according to basic theory.

5.2.1. Farmers’ Demographic Characteristics, Household Attributes and Farm Structure

In general, there is limited empirical evidence of the effect of demographic attributes and household characteristics on the adoption of manure treatment across the EU. Two studies reported a significant impact of age on adoption of manure separation and manure-based fertilization [72,75]. However, in the former case, older farmers are less likely to adopt the technology, while in the latter case, the opposite is true. Gebrezgabher et al. [72] also tested the impact of farmer education and the presence of farmer successors on the propensity to implement manure separation, but this attempt yielded an insignificant outcome. Similarly scant is evidence of the effects of farm structural characteristics on adoption. Gebrezgabher et al. [72] and Case et al. [75] reported the effect of farm size, which was determined an insignificant predictor of adoption in both studies. Additionally, these studies came to the conclusion that livestock density may significantly affect adoption chances, and adoption is more likely given an increase in density [72,75]. A third study reporting the effect of density on adopting various innovations in the horse sector found an insignificant result [76]. Further, Zemo and Termansen [77] showed that a farm’s proximity to a bio-based plant increases farmers’ willingness to invest in the plant, once again underlying the role of physical distance in defining adoption behavior.

5.2.2. Farmers’ Attitudes and Beliefs, and Sources of Information

Farmer attitudes and beliefs as adoption factors went essentially unnoticed by manure treatment researchers. Only the study by Rantala et al. [71] identified an impact of farmer individual valuations on technological adoptions by horse operators. Counterintuitively, their results show a negative significant effect of environmental valuations, but a positive effect of economic valuations on adoption decisions, which stands in contrast to the findings of organic farming researchers. This observation may point to the apparent disagreement between the definitions of most relevant attitudes in empirical surveys. Regarding the impact of sources of information on adoption, only Zemo and Termansen [77] explicitly showed that a start-up consultancy might positively affect the willingness of farmers to collectively invest in manure-based biogas facilities. Gebrezgabher et al. [72] additionally explored the effect of farmer knowledge of the relevant technology in general but found no evidence of statistical significance of this effect.

5.2.3. Technological Attributes

Another group of adoption criteria deals with attributes of manure treatment technologies. According to this review, four studies in total mentioned multiple technological attributes as determinants of adoption. For example, Tur-Cardona et al. [78] found that the reduction in the price of organic fertilizer stimulates its dissemination on farms, whereas the uncertainty over the nitrogen content on the contrary reduces the likelihood of adoption. In a similar way, this study found that such attributes of organic fertilizer as a solid form and fast nutrient release in the soil motivate farmers’ widespread application. In a separate study, Gebrezgabher et al. [72] identified a positive causal relationship between the potential adoption of manure separation and the factor combining various attributes of this technology, such as cost or fraction thickness. Zemo and Termansen [77] identified the terms of agreement between farmers participating in a collective investment in a manure-based biogas plant. Their findings demonstrate that farmers’ willingness to participate in this project increases if they can prematurely leave the partnership. Finally, Hou et al. [74], using a simple summary of a farmer survey, showed that that the increased price of a chemical fertilizer may stimulate farmers to switch to a bio-based alternative.

5.2.4. Institutional Environment

There are also a few studies reporting the influence of institutional characteristics on the adoption of manure treatment technologies. For example, Zemo and Termansen [77] found that the subsidy to support the construction of a biogas plant should stimulate farmers into being willing to invest collectively in this project, while Hou et al. [74] reported pressure from policies and regulations as a possible determinant of adoption of various manure treatment technologies.
In the end, it is possible to conclude that there is little evidence showing the impact of basic demographic characteristics, household attributes, attributes of farm structure and farmer individual attitudes and preferences on adoption of manure treatment technologies, signifying the presence of an obvious gap in the empirical literature. On the other hand, it should be clear that more studies are desirable to further pinpoint which technological attributes, apart from those defined, drive farmers to adopt specific technologies of manure treatment.

5.3. Adoption of Soil and Water Conservation Practices

One of the implications of the intensification of agricultural production is soil degradation and water quality deterioration. Typical forms of soil degradation are linked to intensive agricultural practices including compaction, salinization, acidification, decline in organic matter and the loss of biodiversity [5]. Due to the interrelations of biological, physical and chemical processes in water and soil, it is common to observe the negative impact of soil degradation on water. An example is eutrophication resulting from the leaching of soil nutrients to water bodies. In response to the problem of soil degradation, various conservation practices are implemented across the EU such as reduced tillage, no-tillage and direct seeding, as well as soil covering, crop rotation and others [15]. Regarding water conservation, the implemented technologies include permanent grass, sediment ponds and riparian buffer zones, among others [79,80].
Despite the existing cases of conservation agriculture in Europe, the overall scope of technology adoption remains limited as compared to the U.S., Australia and New Zealand [81]. As a result, there are a few examples of academic research addressing the problem of farmers’ adoption of conservation technologies. In the most recent review of literature covering farmers’ adoption of soil conservation in Europe, Prager and Posthumus [16] concluded that a wide variety of environmental, economic, institutional and personal factors can motivate farmers to adopt conservation technologies. They noted that depending on the political and cultural contexts, adoption might be either fully voluntary or result from mandatory policies and incentive programs. Further to this review, we contribute to the literature by including more recent empirical studies exploring the adoption of soil as well as water conservation practices in the EU.
Specifically, this review comprises five studies covering four EU states, such as Ireland, Greece, Denmark and Spain (Table S1). These countries represent three areas with distinct environmental and socio-demographic characteristics (Table 1). No relevant publications were found with respect to Nordic states, Western Europe, the Baltics or Central Europe. Non-linear econometric models are implemented in four studies, while one study exploits a general farmer survey to retrieve qualitative information about factors of adoption. Three studies explore various adoptions of soil conservation and two studies focus on water conservation. Given the limited amount of empirical evidence, the results of this review should be treated with caution when making generalized statements or providing policy recommendations. Following the earlier adopted approach, all factors are aggregated in groups in accordance with technology adoption theory.

5.3.1. Farmer Demographic Factors and Household Characteristics

In terms of demographic factors and household attributes, three studies explored the effect of farmer age on the probability of adoption [80,82,83], with only one study showing that older farmers have significantly lower chances to adopt a soil protection strategy [82]. Giovanopoulou et al. [82] also discovered a positive significant effect of higher education on adoption, while Rodriguez-Entrena and Arriaza [83] found that education in general has no impact on farmers but those farmers with special agricultural training might be more willing to adopt selected technologies. Additionally, the latter study identified a positive role of descendants in stimulating farmers to develop conservation agriculture [83].

5.3.2. Farm Structural Characteristics

Several studies reported farm structural characteristics influencing farmer adoption decisions. For example, farm size was found to be a positive predictor of adoption by two studies [80,83]. Gachango et al. [80] also found that farms located on mid-steep slopes are more prone to adopting conservation schemes than those established on flat ground, while Rodriguez-Entrena and Arriaza [83] found that location in irrigation districts significantly increases the odds of implementing conservation technologies. Additionally, several studies explicitly identified a significant impact of farm financial indicators on the likelihood of adoption. For example, Buckley et al.’s [79] finding shows a negative impact of higher gross margin on farmers’ willingness to adopt a water conservation scheme, whereas Rodriguez-Entrena and Arriaza [83] demonstrated that more profitable farms are more likely to adopt soil conservation. While this result seems to be surprising at first sight, it might reflect distinctive environmental and climatic conditions dominating sustainable agriculture development in the countries under review. For example, Buckley et al.’s [79] study covered certain areas in Ireland, which is a humid country with a relatively flat terrain and good soil quality (Table 1). Conversely, Rodriguez-Entrena and Arriaza [83] focused on southern Spain (Andalusia), known for its arid climate contributing to high levels of soil degradation (the estimated proportion of soil under moderate-to-severe risk of erosion by water is 48%, which is well above the average rate in the country, see Table 1). Under such circumstances, Irish farmers might be unwilling to adopt conservation measures, fearing low returns on their investments (because more conservation does not seem to be improving soil quality much), while in Spain, adopting conservation strategies might be a sure option to increase both soil productivity and farm profitability.

5.3.3. Farmer Attitudes and Beliefs

Based on this review, farmer attitudes and beliefs may also affect their chances of adopting conservation measures. For example, two studies found that a strong preference for environmental protection stimulates adoption behavior [79,82]. Buckley et al. [79] additionally showed that a positive attitude over environmental regulation makes adoption of water conservation more likely. Further, Giovanopoulou et al. [82] found a similar effect of problem awareness on the probability of adopting a soil protection measure. On the contrary, the latter study reported that farmers perceiving conservation subsidies to be low are less likely to begin adoption. The effect of farmers’ perception of subsidies as well as their attitude to fines and penalties was studied by Gachango et al. [80], but their results provide no significant evidence of the impact of these attitudes on adoption.

5.3.4. Farmers’ Information Sources and Technological Attributes

There is limited evidence of the impact of farmers’ information sources on the adoption of conservation practices in the area. Giovanopoulou et al. [82] found that membership in professional cooperatives makes it rather unlikely, while Gachango et al. [80] showed that farmers’ awareness of ecological status and measures to reduce nitrogen and phosphorus run-off has no effect on adoption. Similarly, there is insufficient information on the effects of conservation technology attributes on adoption. For example, Rodriguez-Entrena and Arriaza [83] found that only applying a certain fertilization method makes adoption of selected soil conservation practices more likely, while other characteristics, such as number of plant varieties, plantation age and plantation density, are determined irrelevant for this purpose. In a separate survey, Carmona et al. [81] identified weed presence, pest incidence and lack of zero-till technology as inhibitors of adoption of conservation technologies, but they did not test the statistical significance of these effects on farmers. Concerning institutional factors, only one study explored this issue by testing the effect of conservation subsidies on adoption, which was found positive but insignificant [82].
Overall, it was found that basic characteristics of farmer household, farm structure, demographic attributes and farm financial results may affect decisions to adopt conservation measures, but the existing empirical evidence is rather scant and inconclusive. A similar argument is applicable to attitudinal factors shown to affect farmers in one case but producing no effect in the other. Finally, sources of information, technological attributes and institutional factors are covered very superficially in studies on conservation farming, which complicates analysis of their roles in farmers’ decision making. Further research is necessary to fill this gap in the literature and provide qualified guidelines for policymakers.

5.4. Aggregation of Marginal Effects

To analyze the impact of adoption factors across various technologies, we aggregate their marginal probabilities of adoption from multiple studies to construct two simple measures of an overall size effect: mean and range. Breen et al. [19] noted that marginal effects (probabilities) often reported alongside the main coefficients of probit or logit models are free from scale issues and can be used to compare the effects of typical covariates across different models and populations.
As shown in Table 2, we report aggregate statistics for marginal effects related to several adoption factors, such as environmental attitude, age, general education and farm size. Age and education are typically measured by the number of years or coded as a dummy variable (yes/no), which makes it possible to compare their effects on the probability of adoption across different studies. A similar argument applies to farm size, measured in hectares, and environmental attitude, which typically comes in two forms: as a dummy variable (important/not important) and as a principal component (which combines effects of different but related factors).
To construct aggregated measures, we primarily extracted results from studies using non-linear regression models, such as probit or logit, while discarding those applying duration analysis, linear regression analysis, agent-based modeling or qualitative approaches, which initially yielded 17 studies. Further, five studies using logit or probit models were also discarded due to marginal effects not being reported. As a result, there are between 6 and 14 extracted values of marginal effects depending on the covariate. Since some studies report marginal effects for several adoption populations (for example, one study does that for full and partial organic converters), the total number of extracted values sometimes exceeds the overall number of studies that reported them.
The summary results in Table 2 show that the average (mean) marginal effect of environmental attitude on the probability of adoption across six different population groups is 0.135, which means that a single standard deviation change in environmental attitude leads to a 13.5% increase in the likelihood of adoption. However, the range of reported effects is quite wide from relatively small (0.022) to substantial (0.422). Regarding the effect of age, it follows from Table 2 that the likelihood of adoption increases as farmers get older, and the average effect of age is even higher for significant cases. Additionally, there is a very wide interval covering both negative and positive values of marginal effects. To explore this result further, it should be noticed that the mean effect of age on adoption is spoiled due to the presence of an outlier from the study of Giovanopoulou et al. [82], who expressed age as a dummy variable increasing for younger farmers. After removing this study from aggregation, the upper limit of the interval reduces to 0.016, while the mean effect becomes −0.0136 for both significant cases and the entire pool, which means that a unit increase in age makes adoption of sustainable agriculture 1.4% less probable. Furthermore, the average effect of education is positive and even higher for significant cases, while the interval for the significant pool is also free from negative values, meaning a positive effect of education on adoption. Finally, the average effect of farm size on adoption is quite small and negative for the entire pool but positive for significant cases. The interval of significant cases is also much narrower compared to the range of all cases. Basically, this result reflects existing confusion in the literature about the effect that farm size exerts on adoption of innovations. Visually, it can be further shown that both environmental attitude and education exert a substantially higher influence on farmers’ adoption chances than age or farm size (Figure 3). The multiple comparison test using one-way ANOVA also shows a significant difference between these factors at the 5% level (see the bottom of Table 2).

6. Discussion

Reviewing empirical studies on farmers’ technology adoption in the EU revealed a variety of factors having an impact on the adoption and proliferation of such technologies as organic farming, manure treatment and conservation measures. Following the theory of technology adoption, we classified all adoption factors into several groups, such as farmer demographic and household characteristics, farm structural and financial attributes, farmer attitudes and beliefs and sources of farmer information, as well as technological attributes and conditions of institutional environment. Altogether, 23 studies were recovered and reviewed, with eleven studies on organic farming, seven studies on manure treatment technologies and five studies about conservation agriculture. While all studies are based on farmer surveys, they used various methods to analyze data, with 16 studies applying non-linear regressions to estimate the probability of technological adoption. Due to the presence of these methods, it is often possible to aggregate the results of these studies based on both the significance and the direction of a statistical effect.
In general, it is hard to compare the effects of various adoption factors across the three technologies, given the obvious shortage of empirical evidence on manure treatment and conservation farming in the EU. While it is possible to track the effect of selected demographic attributes, like age or education, on organic farming adoption, their impact on the adoption of manure treatment and conservation farming remains rather dubious and inconclusive. Additionally, the impact of household characteristics on the adoption of sustainable agriculture remains largely under-investigated, with just a few studies exploring the effect of household size or that of descendants. With respect to farm structural characteristics, it is shown that farm size might be a strong predictor of adoption of organic farming, with rather scant evidence of its impact on other technological adoptions. Other factors of this group, like distance to urbanized areas, might also influence farmer behavior, as follows from the selected studies covering various technologies. It is worth noting the evidence of the impact of farm financial state on conservation farming, which is absent from the studies on organic farming and manure treatment adoption.
Furthermore, farmer attitudes and beliefs are shown to clearly determine the adoption of organic farming in the EU, with environmental attitude stimulating adoption and economic attitude discouraging it. On the other hand, there is an absence of regular evidence of their impacts on both manure treatment and conservation farming adoption. Specifically, there is only one recent study exploring the impact of farmer attitudes on manure treatment and three other studies that investigate this aspect of adoption in application to conservation agriculture. Apart from attitudes and preferences, farmers may also formulate their adoption strategies under the influence of their sources of information, like media, an advisory service or other farmers. This conclusion follows mostly from the studies on organic farming adoption, although limited evidence exists proving the impact of informational sources on adoption of other technologies too.
Finally, this review found no systemic evidence of the impact of technological attributes on organic farming adoption, save for one study showing the positive effect of the market price of organic production. In a similar way, the price of organic fertilizer might be a predictor of adoption of manure fertilization methods, in addition to other physical and chemical characteristics of manure. While conservation farming attributes are mentioned in a few studies, they provide no systemic evidence of a statistically significant impact. Regarding the impact of institutional conditions, it was shown that adoption subsidies may induce the development of organic farming and manure treatment technologies in the area, but this effect is missing for conservation farming adoption.

7. Conclusions

Sustainable management of natural resources is one of the long-term aims of the Common Agricultural Policy, along with improved productivity and a stable supply of affordable food [6]. Given the challenge of attaining these aims, it is essential to maintain an efficient and circular production system based on the principles of increased recyclability of resources and waste minimization [8]. In agriculture, the decades of intensive use led to the degradation of critical resources such as land and water. Halting and reversing land degradation is crucial for mitigating hunger and achieving sustainable living [84]. Additionally, the efforts of sustainable resource management in agriculture should be focused on the treatment of animal manure to reduce waste and recover valuable nutrients [8].
Nutrient surplus is a common cause of land degradation in Europe, resulting from over-fertilization of soil with basic nutrients. The negative effects of excessive nutrient use include reduction in soil organic matter, stalled land productivity and water pollution. To deploy efficient nutrient reduction strategies, it is important to maintain a complex approach to land management based on the minimization of soil mechanical disturbance and measures improving nutrient cycles. Various conservation measures, such as reduced tillage, are shown to benefit soil and prevent water pollution. Additionally, technologies aiming to increase treatment and reduce waste of animal manure contribute to resource efficiency while reducing the need for mineral fertilizers, which is crucial for the development of alternative farm management systems like organic farming.
This review paper focused on various technological systems of sustainable farming in Europe, utilizing information and numerical data from peer-reviewed journal publications. While this methodological approach has its advantages, it also constrains the research to a narrow pool of selected results, which may not be fully representative of an entire research landscape (an issue known as publication bias). To mitigate this problem, it might be instructive for future reviews to consider other types of relevant output, such as conference papers or book chapters, covering different geographical locations. Another problem emanating from the increased selectivity of literature sources is a shortage of numerical data, which limits the potential for a more profound statistical analysis. Provided more data on the statistical effects of adoption factors are available, it would be useful to recommend trying a meta-analysis involving regressions to disentangle the effects of geography or climate, for example. Finally, future research efforts may also consider reviewing other technologies of sustainable farming related to effective nutrient management, such as precision agriculture.

Supplementary Materials

The following are available online at, Table S1: Summary of empirical studies on adoption of sustainable farming practices in the EU, Table S2: Impact of adoption factors across various technologies.

Author Contributions

D.S. introduced a review plan, wrote a first draft and consummated necessary revisions. S.N.M. contributed to a review plan and substantially modified the initial draft. F.T. and Z.K. both substantially revised the initial draft. All authors have read and agreed to the published version of the manuscript.


This review forms part of the Circular Agronomics projects funded from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement No 773649. The APC has also been funded from this project.

Conflicts of Interest

The authors declare no conflict of interest.


  1. European Environment Agency. Agricultural Land: Nitrogen Balance. 2018. Available online: (accessed on 26 March 2020).
  2. Eurostat. Agri-Environmental Indicator—Risk of Pollution by Phosphorus. 2018. Available online: (accessed on 26 March 2020).
  3. Buckwell, A.; Nadeu, E. Nutrient Recovery and Reuse (NRR) in European Agriculture. A Review of the Issues, Opportunities, and Actions. RISE Foundation, Brussels. 2016. Available online: (accessed on 2 October 2020).
  4. Chislock, M.F.; Doster, E.; Zitomer, R.A.; Wilson, A.E. Eutrophication: Causes, consequences, and controls in aquatic ecosystems. Nat. Educ. Knowl. 2013, 4, 10. [Google Scholar]
  5. Jeffery, S.; Hiederer, R.; Lükewille, A.; Strassburger, T.; Panagos, P.; Hervás, J.; Barcelo, S.; Jones, A.; Yigini, Y.; Erhard, M.; et al. The State of Soil in Europe. A Contribution of the JRC to the European Environment Agency’s Environment State and Outlook Report—SOER 2010; Publications office of the European Union: Luxembourg, 2012; Available online: (accessed on 23 January 2020).
  6. European Commission. The Common Agricultural Policy at a Glance. 2020. Available online: (accessed on 26 March 2020).
  7. FAO. Conservation Agriculture. 2016. Available online: (accessed on 26 March 2020).
  8. European Commission. Closing the Loop—An EU Action Plan for the Circular Economy. 2015. Available online: (accessed on 26 March 2020).
  9. Alam, M.K.; Bell, R.W.; Salahin, N.; Pathan, S.; Mondol, A.T.M.A.I.; Alam, M.J.; Rashid, M.H.; Paul, P.L.C.; Hossain, M.I.; Shil, N.C. Banding of fertiliser improves phosphorus acquisition and yield of zero tillage maize by concentrating phosphorus in surface soil. Sustainability 2018, 10, 3234. [Google Scholar] [CrossRef][Green Version]
  10. Edmeades, D.C. The long-term effects of manures and fertilisers on soil productivity and quality: A review. Nutr. Cycl. Agroecosyst. 2003, 66, 165–180. [Google Scholar] [CrossRef]
  11. Corsi, S.; Muminjanov, H. Conservation Agriculture: Training Guide for Extension Agents and Farmers in Eastern Europe and Central Asia; FAO: Rome, Italy, 2019; Available online: (accessed on 26 March 2020).
  12. Knowler, D.; Bradshaw, B. Farmers’ adoption of conservation agriculture: A review and synthesis of recent research. Food Policy 2007, 32, 25–48. [Google Scholar] [CrossRef]
  13. Wauters, E.; Mathijs, E. The adoption of farm level soil conservation practices in developed countries: A meta-analytic review. Int. J. Agric. Resour. Gov. Ecol. 2014, 10, 78–102. [Google Scholar] [CrossRef]
  14. Carlisle, L. Factors influencing farmer adoption of soil health practices in the United States: A narrative review. Agroecol. Sustain. Food Syst. 2016, 8, 583–613. [Google Scholar] [CrossRef]
  15. Lahmar, R. Adoption of conservation agriculture in Europe: Lessons of the KASSA project. Land Use Policy 2010, 27, 4–10. [Google Scholar] [CrossRef]
  16. Prager, K.; Posthumus, H. Socio-economic factors influencing farmers’ adoption of soil conservation practices in Europe. In Human Dimensions of Soil and Water Conservation. A Global Perspective; Napier, T., Ed.; Nova Science Publishers: New York, NY, USA, 2010; pp. 203–223. [Google Scholar]
  17. Liu, T.; Bruins, R.J.F.; Heberling, M.T. Factors Influencing Farmers’ Adoption of Best Management Practices: A Review and Synthesis. Sustainability 2018, 2018, 432. [Google Scholar] [CrossRef][Green Version]
  18. Dessart, F.G.; Barreiro-Hurlé, J.; van Bavel, R. Behavioural factors affecting the adoption of sustainable farming practices: A policy-oriented review. Eur. Rev. Agric. Econ. 2019, 46, 417–471. [Google Scholar] [CrossRef][Green Version]
  19. Breen, R.; Karlson, K.B.; Holm, A. Interpreting and understanding logits, probits, and other nonlinear probability models. Annu. Rev. Sociol. 2018, 44, 39–54. [Google Scholar] [CrossRef][Green Version]
  20. Stata. Stata: Software for Statistics and Data Science. 2020. Available online: (accessed on 16 November 2020).
  21. Bosch, D.J.; Cook, Z.L.; Fuglie, K.O. Voluntary versus mandatory agricultural policies to protect water quality: Adoption of Nitrogen testing in Nebraska. Rev. Agric. Econ. 1995, 17, 13–24. [Google Scholar] [CrossRef]
  22. Tait, J. How are governments influencing innovation and uptake of technologies for sustainable farming systems: Pesticides and biotechnology. In Adoption of Technologies for Sustainable Farming Systems. Wageningen Workshop Proceedings; OECD (Organisation for Economic Cooperation and Development): Paris, France, 2001; Available online: (accessed on 26 March 2020).
  23. Zilberman, D.; Khanna, M.; Lipper, L. Economics of New Technologies for Sustainable Agriculture. Aust. J. Agric. Resour. Econ. 2002, 41, 63–80. [Google Scholar] [CrossRef][Green Version]
  24. Spence, T. Cap 2014–2020: A Long Road to Reform; Euroactiv: Brussels, Belgium, 2013; Available online: (accessed on 26 March 2020).
  25. European Commission. CAP Reform—An Explanation of the Main Elements. 2013. Available online: (accessed on 26 March 2020).
  26. Council Regulation 1305/2013 on support for rural development by the European Agricultural Fund for Rural Development (EAFRD) and repealing Council Regulation (EC) No 1698/2005. Off. J. 2013, L347, 487–548. Available online: (accessed on 26 March 2020).
  27. Council Regulation 834/2007 on organic production and labelling of organic products and repealing Regulation (EEC) No 2092/91. Off. J. 2007, L189/1, 1–22. Available online: (accessed on 26 March 2020).
  28. Council Directive 91/676/EEC concerning the protection of waters against pollution caused by nitrates from agricultural sources. Off. J. 1991, L375, 1–13. Available online: (accessed on 26 March 2020).
  29. Council Directive 86/278/EEC on the protection of the environment, and in particular of the soil, when sewage sludge is used in agriculture. Off. J. 1986, L181, 6–12. Available online: (accessed on 26 March 2020).
  30. Council Directive 2000/60/EC on establishing a framework for community action in the field of water policy. Off. J. 2000, L327, 1–72. Available online: (accessed on 26 March 2020).
  31. European Commission. Thematic Strategy for Soil Protection. 2006. Available online: (accessed on 26 March 2020).
  32. Council Regulation 1069/2009 on laying down health rules as regards animal by-products and derived products not intended for human consumption and repealing Regulation (EC) No 1774/2002 (Animal by-products Regulation). Off. J. 2009, L300, 1–33. Available online: (accessed on 26 March 2020).
  33. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research; Addison-Wesley: Boston, MA, USA, 1975. [Google Scholar]
  34. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  35. Ajzen, I. From intentions to actions: A theory of planned behavior. In Action Control: From Cognition to Behaviour; Kuhl, J., Beckmann, J., Eds.; Springer: Berlin/Heidelberg, Germany; New York, NY, USA, 1985; pp. 11–39. [Google Scholar] [CrossRef]
  36. Hennessy, T.; Kinsella, A.; Thorne, F. Planned intentions versus actual behaviour: Assessing the reliability of intention surveys in predicting farmers’ production levels post decoupling. Int. J. Agric. Manag. Inst. Agric. Manag. 2016, 5, 70–77. [Google Scholar] [CrossRef]
  37. Willock, J.; Deary, I.; Edwards-Jones, G.; Gibson, G.; McGregor, M.; Sutherland, A.; Dent, J.; Morgan, O.; Grieve, R. The role of attitudes and objectives in farmer decision-making: Business and environmentally oriented behaviour in Scotland. J. Agric. Econ. 1999, 50, 286–303. [Google Scholar] [CrossRef]
  38. Läpple, D.; and Van Rensburg, T. Adoption of organic farming: Are there differences between early and late adoption. Ecol. Econ. 2011, 70, 1406–1414. [Google Scholar] [CrossRef]
  39. Läpple, D.; Kelley, H. Spatial dependence in the adoption of organic drystock farming in Ireland. Eur. Rev. Agric. Econ. 2015, 42, 315–337. [Google Scholar] [CrossRef][Green Version]
  40. Esser, P. Akzeptanz—Was Steckt Dahinter? Überlegungen zur Akzeptanzdebatte in Naturschutz und Landschaftsplanung. Master’s Thesis, Technical University Berlin, Berlin, Germany, 1999. [Google Scholar]
  41. Sattler, C.; Nagel, U.J. Factors affecting farmers’ acceptance of conservation measures—A case study from north-eastern Germany. Land Use Policy 2010, 27, 70–77. [Google Scholar] [CrossRef]
  42. Baregheh, A.; Rowley, J.; Sambrook, S. Towards a multidisciplinary definition of innovation. Manag. Decis. 2009, 47, 1323–1339. [Google Scholar] [CrossRef]
  43. Rogers, E.M. (Ed.) Diffusion of Innovations; The Free Press: New York, NY, USA, 1995. [Google Scholar]
  44. Pannell, D.J.; Marshall, G.R.; Barr, N.; Curtis, A.; Vanclay, F.; Wilkinson, R. Understanding and promoting adoption of conservation technologies by rural landholders. Aust. J. Exp. Agric. 2006, 46, 1407–1424. [Google Scholar] [CrossRef][Green Version]
  45. Chatzimichael, K.; Genius, M.; and Tzouvelekas, V. Informational cascades and technology adoption: Evidence from Greek and German organic growers. Food Policy 2014, 49, 186–195. [Google Scholar] [CrossRef][Green Version]
  46. European Commission. Organics at Glance. 2020. Available online: (accessed on 26 March 2020).
  47. European Commission. Frequently Asked Questions on Organic Rules. 2020. Available online: (accessed on 26 March 2020).
  48. European Commission. Facts and Figures on Organic Agriculture in the EU. 2016. Available online: (accessed on 26 March 2020).
  49. European Commission. European Green Deal: Commission Prepares New Initiatives to Boost the Organic Farming Sector. 2020. Available online: (accessed on 9 October 2020).
  50. Index Mundi. Average Precipitation in Depth in 2014 (Provided by FAO). 2020. Available online: (accessed on 6 November 2020).
  51. Kaggle. Climate Change: Earth Surface Temperature Data (Provided by Berkeley Earth). 2020. Available online: (accessed on 6 November 2020).
  52. Terraclimate. Global, High-Resolution Gridded Temperature, Precipitation, and Other Water Balance Variables. 2020. Available online: (accessed on 6 November 2020).
  53. U.S. Central intelligence agency. The World Factbook: Elevation. 2020. Available online: (accessed on 6 November 2020).
  54. The World Bank. Agricultural Land (% of Land Area). 2016. Available online: (accessed on 6 November 2020).
  55. Eurostat. Estimated Soil Erosion by Water, by Erosion Level, land Cover and NUTS 3 Regions. 2016. Available online: (accessed on 6 November 2020).
  56. Eurostat. CAP Context Indicators—2019 Update. 2016. Available online: (accessed on 6 November 2020).
  57. Genius, M.; Pantzios, C.J.; Tzouvelekas, V. Information acquisition and adoption of organic farming practices. J. Agric. Resour. Econ. 2006, 31, 93–113. [Google Scholar]
  58. Mzoughi, N. Farmers adoption of integrated crop protection and organic farming: Do moral and social concerns matter. Ecol. Econ. 2011, 70, 1536–1545. [Google Scholar] [CrossRef]
  59. Koesling, M.; Flaten, O.; Gudbrand, L. Factors influencing the conversion to organic farming in Norway. Int. J. Agric. Resour. Gov. Ecol. 2008, 7, 78–95. [Google Scholar] [CrossRef]
  60. Burton, M.; Rigby, D.; Young, T. Modelling the adoption of organic horticultural technology in the UK using duration analysis. Aust. J. Agric. Resour. Econ. 2003, 47, 29–54. [Google Scholar] [CrossRef][Green Version]
  61. Tiffin, R.; Balcombe, K. The determinants of technology adoption by UK farmers using Bayesian model averaging: The cases of organic production and computer usage. Aust. J. Agric. Resour. Econ. 2011, 55, 579–598. [Google Scholar] [CrossRef][Green Version]
  62. Kallas, Z.; Serra, T.; Gil, J.M. Farmers’ objectives as determinants of organic farming adoption: The case of Catalonian vineyard production. Agric. Econ. 2010, 41, 409–423. [Google Scholar] [CrossRef]
  63. Läpple, D.; Kelley, H. Understanding the uptake of organic farming: Accounting for heterogeneities among Irish farmers. Ecol. Econ. 2013, 88, 11–19. [Google Scholar] [CrossRef]
  64. Kaufmann, P.; Stagl, S.; Franks, D.W. Simulating the diffusion of organic farming practices in two new EU member states. Ecol. Econ. 2009, 68, 2580–2593. [Google Scholar] [CrossRef]
  65. Dax, T. The redefinition of Europe’s less favoured areas. In Proceedings of the 3rd Annual Conference Rural Development in 2007–2013, London, UK, 15–16 November 2005; Available online: (accessed on 6 November 2020).
  66. Wilson, G.A. Factors influencing farmer participation in the environmentally sensitive areas scheme. J. Environ. Manag. 1997, 50, 67–93. [Google Scholar] [CrossRef]
  67. Department of Agriculture and Food of the Republic of Ireland. Expenditure Review of the Compensatory Allowance Scheme. 2005. Available online: (accessed on 6 November 2006).
  68. Achuelo, M. Crops for Better Soil—Profitable Organic Farming Techniques Based on Traditional Crops: Contrasting Soil Degradation in the Mediterranean. 2016. Available online: (accessed on 6 November 2006).
  69. Szogi, A.A.; Vanotti, M.B.; Ro, K.S. Methods for Treatment of Animal Manures to Reduce Nutrient Pollution Prior to Soil Application. Curr. Pollut. Rep. 2015, 1, 47–56. [Google Scholar] [CrossRef]
  70. Faostat. Livestock Manure. 2020. Available online: (accessed on 26 March 2020).
  71. Cox, D. The Planet’s Prodigious Poo Problem. The Guardian. 2019. Available online: (accessed on 4 January 2020).
  72. Gebrezgabher, S.A.; Meuwissen, M.P.M.; Kruseman, G.; Lakner, D.; Oude Lansink, A.G.J.M. Factors influencing adoption of manure separation technology in The Netherlands. J. Environ. Manag. 2015, 150, 1–8. [Google Scholar] [CrossRef]
  73. Pampuro, N.; Caffaro, F.; Cavallo, E. Reuse of Animal Manure: A Case Study on Stakeholders’ Perceptions about Pelletized Compost in Northwestern Italy. Sustainability 2018, 10, 2028. [Google Scholar] [CrossRef][Green Version]
  74. Hou, Y.; Velthof, G.L.; Case, S.D.C.; Oelofse, M.; Grignani, C.; Balsari, P.; Zavattaro, L.; Gioelli, F.; Bernal, M.P..; Fangueiro, D.; et al. Stakeholder perceptions of manure treatment technologies in Denmark, Italy, The Netherlands and Spain. J. Clean. Prod. 2016, 172, 1620–1630. [Google Scholar] [CrossRef]
  75. Case, S.D.C.; Oelofse, M.; Hou, Y.; Oenema, O.; Jensen, L.S. Farmer perceptions and use of organic waste products as fertilisers: A survey study of potential benefits and barriers. Agric. Syst. 2017, 151, 84–95. [Google Scholar] [CrossRef]
  76. Rantala, T.; Ukko, J.; Saunila, M.; Havukainen, J. The effect of sustainability in the adoption of technological, service, and business model innovations. J. Clean. Prod. 2018, 172, 46–55. [Google Scholar] [CrossRef]
  77. Zemo, K.H.; Termansen, M. Farmers’ willingness to participate in collective biogas investment: A discrete choice experiment study. Resour. Energy Econ. 2018, 52, 87–101. [Google Scholar] [CrossRef]
  78. Tur-Cardona, J.; Bonnichsen, O.; Speelman, S.; Verspecht, A.; Carpentier, L.; Debruyne, L.; Marchand, F.; Jacobsen, B.H.; Buysse, J. Farmers’ reasons to accept bio-based fertilizers: A choice experiment in seven different European countries. J. Clean. Prod. 2018, 197, 406–416. [Google Scholar] [CrossRef]
  79. Buckley, C.; Hynes, S.; Mechan, S. Supply of an ecosystem service—Farmers’willingness to adopt riparian buffer zones in agricultural catchments. Environ. Sci. Policy 2012, 24, 101–109. [Google Scholar] [CrossRef][Green Version]
  80. Gachango, F.G.; Andersen, L.M.; Pedersen, S.M. Adoption of voluntary waterpollution reduction technologies and water quality perception among Danish farmers. Agric. Water Manag. 2015, 158, 235–244. [Google Scholar] [CrossRef]
  81. Carmona, I.; Griffith, D.M.; Soriano, M.A.; Murillo, J.M.; Madejón, E.; Gómez-Macpherson, H. What do farmers mean when they say they practice conservation agriculture? A comprehensive case study from southern Spain. Agric. Ecosyst. Environ. 2015, 213, 164–177. [Google Scholar] [CrossRef][Green Version]
  82. Giovanopoulou, E.; Nastis, S.A.; Papanagiotou, E. Modeling farmer participation in agri-environmental nitrate pollution reducing schemes. Ecol. Econ. 2011, 70, 2175–2180. [Google Scholar] [CrossRef]
  83. Rodríguez-Entrena, M.; Arriaza, M. Adoption of conservation agriculture in olive groves: Evidences from southern Spain. Land Use Policy 2013, 34, 294–300. [Google Scholar] [CrossRef]
  84. United Nations. Sustainable Development Goals. 2020. Available online: (accessed on 26 March 2020).
Figure 1. Diagram of the main selection steps.
Figure 1. Diagram of the main selection steps.
Sustainability 12 09719 g001
Figure 2. Map of European areas included in the review.
Figure 2. Map of European areas included in the review.
Sustainability 12 09719 g002
Figure 3. Comparison of aggregate marginal effects (probabilities) of adoption factors
Figure 3. Comparison of aggregate marginal effects (probabilities) of adoption factors
Sustainability 12 09719 g003
Table 1. Key environmental and socio-demographic characteristics of reviewed countries.
Table 1. Key environmental and socio-demographic characteristics of reviewed countries.
AreaCountryRainfall (mm/year) 1Temperature (°C) 2Aridity 3Elevation (meters) 4Agri land (%) 5Soil Erosion Risk (%) 6Age 7Education 8
SouthSpain63614.70.96660 to 371852.526.8756.51.9
Greece65215.891.56498 to 291747.629.1360.90.6
Italy83214.11.45538 to 474843.2452.41656.1
UK/IrelandIreland11189.874.73118 to 104164.52.5754.225.2
UK12209.114.09162 to 134571.77.356217.3
Nordic statesDenmark703NA4.5734 to 171620.0854.26.8
Finland5362.216.84164 to 13287.50.1133.411.4
Norway14141.0230.15460 to 24692.7NANANA
BeneluxNetherlands77810.262.8430 to 32253.30.0847.79.4
Belgium84710.343.40181 to 69444.64.5748.821.3
WestFrance86713.771.63375 to 481052.4511.144.334.9
Germany7009.026.92263 to 296347.686.839.517
BalticsEstonia6265.813.3761 to 31823.070.0749.928.6
Latvia6416.3312.6587 to 31231.060.2756.931.3
CenterHungary58910.843.59143 to 101458.369.5758.14.4
Notes: 1 Rainfall data are the year average from 2014 records [50]. 2 Earth surface temperature is the year average over 2010–2012 records [51]. 3 Aridity is estimated as a ratio of monthly levels of precipitation to reference evaporation based on 2010–2019 data (lower values point to drier conditions) [52]. 4 Elevation (from mean to peak) [53]. 5 Agricultural land as a percentage of total land area [54]. 6 Percentage of agricultural areas and grassland subject to moderate-to-severe levels of soil erosion by water [55]. 7 Percentage of farm managers ≥55 years old [56]. 8 Percentage of farmers with full agricultural training [56].
Table 2. Summary of marginal effects (probabilities) for selected factors.
Table 2. Summary of marginal effects (probabilities) for selected factors.
Variable# of Studies Reported# of Values ExtractedMeanRange
TotalSignificant Cases
Environmental Attitude106 (6)0.1353 (0.1353)[0.022 to 0.422][0.022 to 0.422]
Age1314 (12)0.0255 (0.0339)[−0.096 to 0.556][−0.096 to 0.556]
Education (General)1111 (6)0.0637 (0.1256)[−0.25 to 0.481][0.0015 to 0.481]
Farm Size1210 (5)−0.0017 (0.0015)[−0.08 to 0.095][−0.001 to 0.0035]
Analysis of variance (between-groups comparison): F = 3.69 with p > F = 0.0258
Notes: significant results for columns 3 and 4 are given in parentheses.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Serebrennikov, D.; Thorne, F.; Kallas, Z.; McCarthy, S.N. Factors Influencing Adoption of Sustainable Farming Practices in Europe: A Systemic Review of Empirical Literature. Sustainability 2020, 12, 9719.

AMA Style

Serebrennikov D, Thorne F, Kallas Z, McCarthy SN. Factors Influencing Adoption of Sustainable Farming Practices in Europe: A Systemic Review of Empirical Literature. Sustainability. 2020; 12(22):9719.

Chicago/Turabian Style

Serebrennikov, Dmytro, Fiona Thorne, Zein Kallas, and Sinéad N. McCarthy. 2020. "Factors Influencing Adoption of Sustainable Farming Practices in Europe: A Systemic Review of Empirical Literature" Sustainability 12, no. 22: 9719.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop