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ispartof: EUROCHOICES vol:21 issue:3 pages:21-26 status: published

In light of the ambitions of the European Union (EU) to achieve an ecological transition of its agricultural sector it is crucial to assess and continuously monitor (i) the uptake of main ecological approaches by farms and (ii) associated effects on farm performance, considering all sustainability dimensions (economic, environmental, social) jointly. Given these needs, in the present deliverable D5.1 of the LIFT project, we develop a novel indicator system, which combines the LIFT farm typology and farm performance data, covering all sustainability dimensions. The approach compares performance of farms in five ecological groups (referred to as ecological farming approaches or ecological farming systems) from the LIFT farm typology (Conservation Agriculture, Low-Input farming, Integrated/Circular farming, Organic farming, Agroecology) as well as possible combinations of these groups with a less ecological group, referred to as Standard farming. This allows us to depict whether ecological farms perform differently or have different trade-offs and synergies than standard farms. Based on this system, we carry out a farm sustainability performance assessment with the two main data sources in the LIFT project, namely Farm Accountancy Data Network (FADN) data and data from the LIFT large-scale farmer survey, covering main farm types present in the European Union (EU) in several case study regions/countries. Additionally, we present in-depth analyses of further specific aspects, namely (i) the extension of the developed indicator framework to bio-economic models, (ii) the integration of the consumption and provision of ecosystem services into the developed indicator system through composite agri-environmental performance (AEP) indicators, derived from the body of secondary literature and region-specific stakeholder input, and (iii) working conditions and employment on farms in the context of an ecological transition. Overall, our results show the importance of considering trade-offs and synergies both within and between farm sustainability dimensions, in the assessment of farm level sustainability performance of ecological farming approaches. Our results also highlight that in many cases the effects of an increasing uptake of ecological approaches are heterogenous and need to be investigated further. We clearly point out the assumptions associated with our approach as well as its limitations. Given these limitations, the LIFT farm sustainability performance assessment developed here is nevertheless well suited for large-scale and long-term monitoring. This is based on readily available FADN data and, in the near future, could be based on Farm Sustainability Data Network (FSDN) data, providing an in-depth exploratory view for policy makers and researchers regarding farm level sustainability performance of ecological approaches in the EU farming sector. We outline several possible avenues for further research, namely (i) the inclusion of other data sources, (ii) the usage of econometric methods to facilitate causal inference, (iii) the broader usage of the developed composite AEP indicators, and (iv) further in-depth studies regarding the social sustainability dimension. Finally, in terms of policy recommendations we point out the importance of (i) flexible policy measures, able to properly address region-specific needs of farms, (ii) sound data as a basis for evidence-based policy, and (iii) investigating the ecological transition of the EU farming sector in more detail also at regional level, e.g. via living labs.

Presentation 20503

Increasing food production to meet rising global demand while minimising negative environmental impacts such as agricultural greenhouse gas (GHG) emissions is one of the greatest challenges facing the agri‐food sector. Sustainable food production relates not only to primary production, but also has wider value chain implications. An input‐output (IO) model is a modelling framework which contains information on the flow of goods and services across a value chain at a regional or national economy level. This paper provides a detailed description of the development of a Bioeconomy IO (BIO) model which is disaggregated across the subs‐sectors of the agri‐food value chain and environmentally extended (EE) to examine environmental outputs, including GHG emissions, We focus on Ireland, where emissions from agriculture comprise 33% of national GHG emissions and where there has been a major expansion and transformation in agriculture supported by national and EU policy. In a substantial Annex to this paper, we describe the modelling assumptions made in developing the BIO model. Breaking up the value chain into components, we find that most value is generated at the processing stage of the value chain, with greater processing value in more sophisticated value chains such as dairy processing. On the other hand, emissions are in general highest in primary production, albeit emissions from purchased animal feed are higher for poultry than for other value chains, given the lower animal based emissions from poultry than from cows or sheep. The level of disaggregation also shows that the sub‐sectors are themselves discrete value chains. The analysis highlights that emissions per unit of output are much higher for beef and sheep meat value chains than for pig and poultry. The analysis facilitated by the BIO model also allows for the mapping of emissions along the agri‐food value chain using the adapted IO EE approach. Such analysis is valuable in identifying emissions ‘hot‐spots’ along the value chains and analysing potential avenues for emission efficiencies., International Journal on Food System Dynamics, Vol 10, No 1 (2019)

Sustainable intensification is one of the greatest challenges facing the agri-food sector which needs to produce more food to meet increasing global demand, while minimising negative environmental impacts such as agricultural greenhouse gas (GHG) emissions. Sustainable intensification relates not just to primary production, but also has wider value chain implications. An input-output model is a modelling framework which contains the flows across a value chain within a country. Input-output (IO) models have been disaggregated to have finer granular detail in relation to agricultural sub-sectoral value chains. National IO models with limited agricultural disaggregation have been developed to look at carbon footprints and within agriculture to look at the carbon footprint of specific value chains. In this paper we adapt an agriculturally disaggregated IO model to analyse the source of emissions in different components of agri-food value chains. We focus on Ireland, where emissions from agriculture comprise nearly 30% of national emissions and where there has been a major expansion and transformation in agriculture since the abolition of milk quota restrictions. In a substantial Annex to this paper, we describe the modelling assumptions made in developing this model. Breaking up the value chain into components, we find that most value is generated at the processing stage of the value chain, with greater processing value in more sophisticated value chains such as dairy processing. On the other hand, emissions are in general highest in primary production, albeit emissions from purchased animal feed being higher for poultry than for other value chains, given the lower direct emissions from poultry than from ruminants or sheep. The analysis highlights that emissions per unit of output are much higher for beef and sheep meat value chains than for pig and poultry meat value chains., Proceedings in Food System Dynamics, Proceedings in System Dynamics and Innovation in Food Networks 2018

This thesis describes the development of an integrated, farm level, bioeconomic systems model of Irish sheep production using nationally representative data from the National Farm Survey (NFS). A systems approach is applied to develop three sub-model components which enable an integrated assessment of the impact of policy reform and farm management practice on the financial, technical and environmental performance of Irish sheep farms. The framework is bioeconomic in that, alongside financial analysis it seeks to capture the biophysical attributes of livestock and crops and the variability in farm environmental conditions that are an inherent feature of agriculture production systems. The framework is subsequently applied to provide input to the Irish Bioeconomy Input-Output model (BIO) in order to simulate the economy wide economic and environmental impact of achieving Food Wise 2025 (FW 2025) National policy objectives in terms of economic output, and greenhouse gas (GHG) emissions. This systems approach, using NFS data, enables the disaggregation of the agriculture sector and the extension of I-O tables to an environmental account of GHG emissions. It is proposed that the linking of micro and macro analysis is necessary for integrated systems assessment in the context of national policy, which straddles both farm level production targets and national macroeconomic targets. A number of economic models of production are specified to analyse the distribution in technical management performance and associated financial performance across the distribution of sheep farms and to examine the farm level effects of a policy reform. In the context of the growing emphasis on production efficiency per unit output, as promoted by recent EU Common Agriculture Policy (CAP) reform, World Trade Organisation (WTO) agreements, and international climate change legislation, Chapter 2 describes the “Animal Nutrition” component of the systems model. This is applied to assess the impact of flock nutrition management practices on financial and technical performance across all sheep farms. Results from a single equation econometric input demand model finds concentrate demand on Irish sheep farms to be elastic and thus sensitive to price changes. A second model specification indicates the presence of spatially heterogeneous effects of lambing date on concentrate demand across regions. Chapter 3 describes the ‘animal demographics’ subcomponent which is applied to estimate the impact of an improved efficiency simulation on farm income. Results indicate the potential impacts on farm output and gross margins for a series of improved animal performance scenarios which are achievable through specific technology adoptions and which are in-line with national policy objections for the sector as set down under Food Harvest 2020 (FH 2020). Chapter 4 describes the ‘environmental component’ of the model by performing a Life Cycle Assessment (LCA) of Irish sheep farms to account for GHG emissions and land occupation. Results provide an estimate of the farm level carbon footprint and land occupation of sheep farms. The distribution in performance witnessed across farms points towards higher technical performance and increased production intensity as a means of mitigating GHG emissions from sheep production systems. This is in line with previous `hypothetical’ or average production systems LCAs for Ireland. Chapter 5 takes data generated from the systems model developed previously and scales results to be representative at the national economy level. This information is used as input to the Bioeconomy Input-Output (BIO) model for Ireland, adapted here to simulate the environmental and economic impacts of meeting FW 2025 growth targets. This is achieved through an extension of the BIO model to include an environmental account of GHG emissions and land occupation. In the context of potentially conflicting economic and environmental policies for Irish Agriculture, a scenario analysis is undertaken which assesses the potential increase in GHG emissions arising from the achievement of agriculture sector expansion plans. This thesis informs the current production literature through an analysis of the full distribution of Irish sheep producers. Detailed farm level production data not previously used in applied economic research provides information here on animal and crop performance, and the technical proficiency and management choices of the range of producers. This new information illuminates the management behaviour of these agents in response to policy and environmental stimulus. This provides a unique contribution to knowledge by establishing a framework under which the economic and environmental impacts of policy and farm management for the full distribution of sheep farms can be assessed. Unlike previous systems models applied to Irish agriculture, which modelled `representative’, average farms or `hypothetical’ farms based on experiment from research farms, this thesis models actual farms. Using NFS data means results are representative of the national population of farms and inference can be made on distribution of performance taking account of site specific environmental and agronomic conditions. Furthermore, results can be scaled to the national level to incorporate an integrated assessment of the impact of policy shocks on economic and environmental outputs across the entire value chain from `cradle to grave’.

In this paper we have taken an Innovation Systems approach to examine the structure and function of the Irish Agri-Environmental Knowledge and Innovation System with the aim of improving water quality in Ireland. Utilising a methodology due to Hekkert et al., (2007), we described and analysed the Innovation System under a number of headings, particularly focusing on specific incentives and features. A key part in changing the regulatory or public incentive system is to change the behaviour not only of the farmers but also of the policy makers to facilitate the movement to a more localised approach. The fundamental message of this paper is that improving a complex local environmental externality • Requires local solutions and information and incentives • Taking an Innovation System perspective to the problem solution • Means that changing the behaviour of farmers may involve changing the behaviour of others upstream within the innovation system, requiring an examination of their incentives and motivations • Local information is necessary to facilitate local decisions • While solutions are local, one must by mindful of transaction costs. Where transaction costs higher than the cost of implementation locally, then it may make sense to focus on less targeted measures, particularly in areas with lower risk.

Increasing food production to meet rising global demand while minimising negative environmental impacts such as agricultural greenhouse gas (GHG) emissions is one of the greatest challenges facing the agri‐food sector. Sustainable food production relates not only to primary production, but also has wider value chain implications. An input‐output (IO) model is a modelling framework which contains information on the flow of goods and services across a value chain at a regional or national economy level. This paper provides a detailed description of the development of a Bioeconomy IO (BIO) model which is disaggregated across the subs‐sectors of the agri‐food value chain and environmentally extended (EE) to examine environmental outputs, including GHG emissions, We focus on Ireland, where emissions from agriculture comprise 33% of national GHG emissions and where there has been a major expansion and transformation in agriculture supported by national and EU policy. In a substantial Annex to this paper, we describe the modelling assumptions made in developing the BIO model. Breaking up the value chain into components, we find that most value is generated at the processing stage of the value chain, with greater processing value in more sophisticated value chains such as dairy processing. On the other hand, emissions are in general highest in primary production, albeit emissions from purchased animal feed are higher for poultry than for other value chains, given the lower animal based emissions from poultry than from cows or sheep. The level of disaggregation also shows that the sub‐sectors are themselves discrete value chains. The analysis highlights that emissions per unit of output are much higher for beef and sheep meat value chains than for pig and poultry. The analysis facilitated by the BIO model also allows for the mapping of emissions along the agri‐food value chain using the adapted IO EE approach. Such analysis is valuable in identifying emissions ‘hot‐spots’ along the value chains and analysing potential avenues for emission efficiencies., International Journal on Food System Dynamics, Vol 10, No 1 (2019)

This deliverable (D2.3) of the LIFT project presents the results of a series of investigations around up-take of ecological approaches across the value chain. We use primary and secondary data collected utilising a number of methods, built on conceptual frameworks developed within LIFT and elsewhere. This provides a range of empirical investigations for an overview of farming, supply chains and consumption drivers which may constrain or enable uptake of ecological approaches. Both exogenous and endogenous drivers were considered for these studies. The report is presented as a set of summaries from academic paper outputs - to show the individual exercises across farmers, value chains and consumers – and to understand both the barriers and enablers for transition to more ecological approaches within European farming. A summary table is provided to show these investigations, as well as the approach used and the type of data collected. Specifically the following sets of studies are presented: Typologies of farm activity and farmer perceptions towards ecological practices.This allows us to partition a large scale farmer dataset (from the LIFT large-scale farmer survey) with multiple variables of interest [papers 1,2]. Examination of the exogenous and endogenous drivers behind ecological uptake, such as gender, the farm family life-cycle, neighbouring farms and supply chains.These papers take either a quantitative approach, through the application of behavioural models, or a qualitative approach to understand what drives this decision to adopt ecological practices [papers 3,4,5,6]. Examination of the value chain, collaboration and cooperation.These are explored through quantitative and qualitative routes to understand how value chains operate for ecological practices, compared to conventional practices, and how actors engage within specific value chains [papers 7,8,9,10,11]. Finally the role of consumption is explored, through market segmentation, labels or specific traits of food products that offer opportunities to promote ecological practices [papers 12,13,14]. Overall, we find much heterogeneity in both practice and attitudes towards production and consumption of ecological approaches. The investigations presented here provide illustrations of how these approaches and perceptions are driven by both personal, informal and formal institutional influences, such as the support from local market conditions or sharing of knowledge. This leads to us understand the relationships between economic and non-economic goals, which are key to eventual uptake of ecological approaches. Incentives were considered to balance the conflicts between endogenous and exogenous drivers, such as labelling and support for social incentives, but also as a means to overcome perceived or real barriers through mechanisms which support further collaboration between farmers.

This document presents the results of Task 3.2 (farm technical-economic performance) in workpackage (WP) 3 (farm performance of ecological agriculture) of the LIFT project. The overall aim of Task 3.2 is to assess and compare technical-economic farm performance across the European Union (EU) depending on the degree of ecological approaches adopted by farms and analyse drivers, affecting their performance. This requires an approach, which allows to consider regional specifics, while still allowing comparisons between different regions and countries. The deliverable thus consists of several academic papers, focussing on a range of different case studies, applying a wide range of methods, which can most generally be divided into empirical econometric approaches and bio-economic models. At the same time, all case studies follow a similar structure and include some common elements in terms of the applied methods, in particular a set of common indicators of technical-economic farm performance was implemented in several papers. Various approaches to differentiate farms according to the degree of ecological approaches adopted were explored, including the LIFT farm typology developed in WP1 and other strategies. Overall, our results show that the wide variety of farm types and biophysical, socio-economic and political framework conditions present in the EU matter: results of comparing technical-economic farm performance depending on the degree of ecological approaches adopted, as well as with respect to drivers of farm technical-economic performance, are heterogenous and vary between the different analyses. Therefore, this heterogeneity needs to be considered by policy makers and can most likely best be addressed by providing a policy framework, which provides the necessary flexibility to adjust measures to region-specific framework conditions in order to foster economic viability of farms in the context of an ecological transition of EU agriculture. Building on the results of this deliverable and the other deliverables within WP3, Task 5.1 will in a next step undertake an integrative assessment of all performance dimensions jointly (technical-economic, environmental and private-social performance as well as employment effects at the farm level), uncovering associated trade-offs and synergies of an increasing uptake of ecological approaches in the EU farming sector, while WP6, in particular Task 6.2 and Task 6.3, will further investigate the role of policies in the development of ecological agriculture.

This document presents the results of Task 3.2 (farm technical-economic performance) in workpackage (WP) 3 (farm performance of ecological agriculture) of the LIFT project. The overall aim of Task 3.2 is to assess and compare technical-economic farm performance across the European Union (EU) depending on the degree of ecological approaches adopted by farms and analyse drivers, affecting their performance. This requires an approach, which allows to consider regional specifics, while still allowing comparisons between different regions and countries. The deliverable thus consists of several academic papers, focussing on a range of different case studies, applying a wide range of methods, which can most generally be divided into empirical econometric approaches and bio-economic models. At the same time, all case studies follow a similar structure and include some common elements in terms of the applied methods, in particular a set of common indicators of technical-economic farm performance was implemented in several papers. Various approaches to differentiate farms according to the degree of ecological approaches adopted were explored, including the LIFT farm typology developed in WP1 and other strategies. Overall, our results show that the wide variety of farm types and biophysical, socio-economic and political framework conditions present in the EU matter: results of comparing technical-economic farm performance depending on the degree of ecological approaches adopted, as well as with respect to drivers of farm technical-economic performance, are heterogenous and vary between the different analyses. Therefore, this heterogeneity needs to be considered by policy makers and can most likely best be addressed by providing a policy framework, which provides the necessary flexibility to adjust measures to region-specific framework conditions in order to foster economic viability of farms in the context of an ecological transition of EU agriculture. Building on the results of this deliverable and the other deliverables within WP3, Task 5.1 will in a next step undertake an integrative assessment of all performance dimensions jointly (technical-economic, environmental and private-social performance as well as employment effects at the farm level), uncovering associated trade-offs and synergies of an increasing uptake of ecological approaches in the EU farming sector, while WP6, in particular Task 6.2 and Task 6.3, will further investigate the role of policies in the development of ecological agriculture.

Social performance is the pillar of sustainability that is the most often neglected, compared to the evaluation of environmental and economic performances of farming systems. Farmers’ working conditions are rarely studied. To understand farmers’ working conditions and to assess them, it is necessary to develop a multicriteria approach including not only quantifiable dimensions (e.g., the length of working days) but also dimensions that can explain how working conditions are experienced by workers (e.g., by understanding farmer’s reasons for acting). Multiple factors contribute to determine farmers’ working conditions such as the composition of the workforce, the region, but also the degree of uptake of ecological practices. This deliverable contributes to knowledge on this issue, explaining the work carried out in task 3.3 of the LIFT project. The main objectives of task 3.3 were: i) to describe farmers’ and farm workers’ working conditions in different farming systems characterised by different degrees of uptake or ecological practices and; ii) to identify factors explaining these working conditions (degree of uptake of ecological practices, workforce composition, country, etc.). To achieve these objectives, a set of indicators on working conditions was selected in a two-step approach: firstly, a theoretical basis from the literature, and, secondly, expert knowledge in the LIFT partners. Primary data was collected during interviews with 160 farmers in five European Union (EU) regions (Brittany in France, Puy-de-Dôme in France, Crete in Greece, Ireland, Salzburg area in Austria, Umgebung Steyr-Kirchdorf in Austria). Statistical analysis was conducted to describe the diversity of indicators (working conditions, workforce composition and farm structure) within the whole sample and in the different case study areas. A principal component analysis (PCA) was carried out on working condition indicators, using farms with complete data (123 farms). Farm characteristics and workforce composition indicators were shown on PCA factorial plans to explore relationships between working conditions and farm/workforce characteristics. Main outcomes of this comparative analysis were identified. First, an overall positive feeling expressed by the farmers may seem contradictory at first sight with some of the results showing long working hours, little time for holidays and day off, etc. Second, the comparative analysis highlighted that farmers’ working conditions differ across European regions. Working conditions in Ireland and Greece differ significantly from farms in other study areas. In the five European case studies, workers were mainly men (64.6%), and only 18.75% of farm managers were women. There was a broad diversity among case studies: female farm managers were most often present in both Austrian and in French Puy-de-Dôme case studies than in France Brittany, Greece and Ireland Third, the degree of uptake of ecological practices, defined here as organic farming practices or the livestock density, do not discriminate working conditions in the sample composed by the five European case studies. Two main factors considered in the comparative analysis explain the variability observed on farmers’ working conditions: the case study area and the production system. Considering a more homogeneous sample - the dairy farms - other factors seem to explain the variability in working conditions, such as the level of education of farmers and the workforce composition. Fourth, another contribution of this comparative analysis in five European case studies is to propose a list of indicators to analyse farmers’ working conditions based on different dimensions (work duration, work organisation, quality at work, work complexity, self-identity and attitudes, stress, satisfaction, social relations). In some case studies (Brittany in France, Puy-de-Dôme in France, Crete in Greece), a more in-deep analysis was performed. Main results were that farmers experienced an impact of the adoption of ecological practices on their working conditions. Farmers indicated various impacts on workload, work organisation and the need for special equipment, depending on the nature of the production systems and the applied ecological practices. They all expressed a positive effect with an improvement of the workload and their own perception of their job. The Scottish case study highlighted that the relationship between farm organic status and efficiency scores of labour used for both ‘traditional’ and ‘diversification’ outputs, emphasises that organic production is not only environmentally oriented but has a clear economic reasoning.

This deliverable (D2.3) of the LIFT project presents the results of a series of investigations around up-take of ecological approaches across the value chain. We use primary and secondary data collected utilising a number of methods, built on conceptual frameworks developed within LIFT and elsewhere. This provides a range of empirical investigations for an overview of farming, supply chains and consumption drivers which may constrain or enable uptake of ecological approaches. Both exogenous and endogenous drivers were considered for these studies. The report is presented as a set of summaries from academic paper outputs - to show the individual exercises across farmers, value chains and consumers – and to understand both the barriers and enablers for transition to more ecological approaches within European farming. A summary table is provided to show these investigations, as well as the approach used and the type of data collected. Specifically the following sets of studies are presented: Typologies of farm activity and farmer perceptions towards ecological practices.This allows us to partition a large scale farmer dataset (from the LIFT large-scale farmer survey) with multiple variables of interest [papers 1,2]. Examination of the exogenous and endogenous drivers behind ecological uptake, such as gender, the farm family life-cycle, neighbouring farms and supply chains.These papers take either a quantitative approach, through the application of behavioural models, or a qualitative approach to understand what drives this decision to adopt ecological practices [papers 3,4,5,6]. Examination of the value chain, collaboration and cooperation.These are explored through quantitative and qualitative routes to understand how value chains operate for ecological practices, compared to conventional practices, and how actors engage within specific value chains [papers 7,8,9,10,11]. Finally the role of consumption is explored, through market segmentation, labels or specific traits of food products that offer opportunities to promote ecological practices [papers 12,13,14]. Overall, we find much heterogeneity in both practice and attitudes towards production and consumption of ecological approaches. The investigations presented here provide illustrations of how these approaches and perceptions are driven by both personal, informal and formal institutional influences, such as the support from local market conditions or sharing of knowledge. This leads to us understand the relationships between economic and non-economic goals, which are key to eventual uptake of ecological approaches. Incentives were considered to balance the conflicts between endogenous and exogenous drivers, such as labelling and support for social incentives, but also as a means to overcome perceived or real barriers through mechanisms which support further collaboration between farmers.

Social performance is the pillar of sustainability that is the most often neglected, compared to the evaluation of environmental and economic performances of farming systems. Farmers’ working conditions are rarely studied. To understand farmers’ working conditions and to assess them, it is necessary to develop a multicriteria approach including not only quantifiable dimensions (e.g., the length of working days) but also dimensions that can explain how working conditions are experienced by workers (e.g., by understanding farmer’s reasons for acting). Multiple factors contribute to determine farmers’ working conditions such as the composition of the workforce, the region, but also the degree of uptake of ecological practices. This deliverable contributes to knowledge on this issue, explaining the work carried out in task 3.3 of the LIFT project. The main objectives of task 3.3 were: i) to describe farmers’ and farm workers’ working conditions in different farming systems characterised by different degrees of uptake or ecological practices and; ii) to identify factors explaining these working conditions (degree of uptake of ecological practices, workforce composition, country, etc.). To achieve these objectives, a set of indicators on working conditions was selected in a two-step approach: firstly, a theoretical basis from the literature, and, secondly, expert knowledge in the LIFT partners. Primary data was collected during interviews with 160 farmers in five European Union (EU) regions (Brittany in France, Puy-de-Dôme in France, Crete in Greece, Ireland, Salzburg area in Austria, Umgebung Steyr-Kirchdorf in Austria). Statistical analysis was conducted to describe the diversity of indicators (working conditions, workforce composition and farm structure) within the whole sample and in the different case study areas. A principal component analysis (PCA) was carried out on working condition indicators, using farms with complete data (123 farms). Farm characteristics and workforce composition indicators were shown on PCA factorial plans to explore relationships between working conditions and farm/workforce characteristics. Main outcomes of this comparative analysis were identified. First, an overall positive feeling expressed by the farmers may seem contradictory at first sight with some of the results showing long working hours, little time for holidays and day off, etc. Second, the comparative analysis highlighted that farmers’ working conditions differ across European regions. Working conditions in Ireland and Greece differ significantly from farms in other study areas. In the five European case studies, workers were mainly men (64.6%), and only 18.75% of farm managers were women. There was a broad diversity among case studies: female farm managers were most often present in both Austrian and in French Puy-de-Dôme case studies than in France Brittany, Greece and Ireland Third, the degree of uptake of ecological practices, defined here as organic farming practices or the livestock density, do not discriminate working conditions in the sample composed by the five European case studies. Two main factors considered in the comparative analysis explain the variability observed on farmers’ working conditions: the case study area and the production system. Considering a more homogeneous sample - the dairy farms - other factors seem to explain the variability in working conditions, such as the level of education of farmers and the workforce composition. Fourth, another contribution of this comparative analysis in five European case studies is to propose a list of indicators to analyse farmers’ working conditions based on different dimensions (work duration, work organisation, quality at work, work complexity, self-identity and attitudes, stress, satisfaction, social relations). In some case studies (Brittany in France, Puy-de-Dôme in France, Crete in Greece), a more in-deep analysis was performed. Main results were that farmers experienced an impact of the adoption of ecological practices on their working conditions. Farmers indicated various impacts on workload, work organisation and the need for special equipment, depending on the nature of the production systems and the applied ecological practices. They all expressed a positive effect with an improvement of the workload and their own perception of their job. The Scottish case study highlighted that the relationship between farm organic status and efficiency scores of labour used for both ‘traditional’ and ‘diversification’ outputs, emphasises that organic production is not only environmentally oriented but has a clear economic reasoning.