Suchergebnisse

The Common Agricultural Policy (CAP) is one of the most complex and also the most costly of all EU policies. It comprises over 40 financing streams, including Pillar I and Pillar II measures which are highly regulated. In the case of Poland, these are directed to all 16 NUTS2 regions. We are modelling here the regional and thus national consequences of the CAP's most costly Pillar II measure in Poland, so called Less Favoured Areas support (LFA). It is complex, when we recognise the multipurpose of this measure and significant amount of funds directed at a large number of regions. To handle the regional complexity of this problem, we require a multi-regional model. Such a model must be detailed in its disaggregation of industries, commodities and households if it is to be capable of reflecting the complexity of this measure. As such, we use a large-scale multi-regional CGE model. The model is tailored to reflect the complexity of the rural development policy (Pillar II), of which LFA is the largest part in Poland. Of the 82 region-specific sectors in the model, over 20 is related to agricultural production. The model distinguishes rural and urban households in each region and is based on the most recent IO tables of 2005. We propose a framework for mapping the individual financing stream of the LFA to the specific structural variables relating to specific type of land (LFA and nonLFA) in each region. As to our best knowledge such an approach was never conducted before with respect not only of LFA but also Pillar II measures in general.

L'efficacité technique et d'échelle des exploitations agricoles polonaises est calculée par la méthode Data envelopment analysis. L'étude porte sur les différences selon l'orientation productive, en grandes cultures ou élevage, à deux dates de la transition, en 1996 et en 2000. L'article examine de plus la variabilité statistique des résultats d'efficacité. Ceux-ci sont revus à la lumière des intervalles de confiance construits grâce au bootstrapping. En moyenne, les exploitations d'élevage ont une efficacité technique et d'échelle supérieure aux exploitations de grandes cultures. L'efficacité d'échelle est toutefois très élevée pour les deux orientations. L'inefficacité technique semble donc due principalement à une inefficacité technique pure, c'est-à-dire à une gestion inefficace. La faible éducation des agriculteurs en est une raison majeure. En 2000, 64% des exploitations d'élevage et 86% des exploitations de grandes cultures présentaient des retours d'échelle croissants. Ce résultat suggère des recommandations politiques afin de supprimer les incitations à garder une structure opérationnelle fragmentée et de stimuler le marché de la terre. Il s'agit en particulier d'améliorer la législation concernant la location des terres et de modifier le système de retraite agricole.

This deliverable investigates the employment effects of ecological farming by analysing both the differences in the intensity of labour use and rewards to skills. It contributes to one of the main outcomes/achievements of the LIFT project, i.e. to "identify the sources of performance and sustainability differences between farms of different types, size and output complexity as observed in the landscape of the European Union (EU)". It is based on collaborative research on Task 3.5 'Employment effects of ecological farming at the farm level' of LIFT between UNIKENT (England) (Task Leader), INRAE (France), DEMETER (Greece), MTA KRTK (Hungary) and IRWiR PAN (Poland). The analyses reported consider the differences between farms on a scale from the most conventional farming systems to the most ecological as measured by the intensities of use of external inputs and labour, the receipt of agri-environmental payments (AEP), capital and involvement in organic production. This approach was used since, at the time of working on the deliverable the LIFT typology protocol on how to classify farms according to the different degrees of adoption of ecological approaches to farming has not been completed. The approach nevertheless makes use of the LIFT conceptual typology in Deliverable 1.1 (Rega et al., 2018). In the analysis, intensity of labour use is studied as a function of farming inputs (fuel, fertiliser, crop protection chemicals), capital and AEP. Dependent and independent variables have been standardised by dividing them by the output value and the resulting ratios named intensities of use. The standardisation by dividing by the overall output value was done to remove the effect of different farm sizes and to focus the discussion on the way output is produced, rather than how land is used. In order to reduce the impact of quality differences in the inputs, we make use of monetary value of input used to take account of quality differences that would be reflected in price differentials. Five EU Member States (MSs) are ...

Farming systems (FS) operate in biophysical, political, social, economic and cultural environments which are often far from stable. Frequently or unfavourably changing conditions can affect FS performance, i.e., the delivery of FS functions (such as food production or ecosystem services). The aim of task 6.1 is to identify principles for an enabling environment to foster (rather than hinder) resilient farming systems in Europe. Task 6.2 will translate these principles into roadmaps that will contain recommendations for both public and private actors and institutions in the enable environment on how to support farming system resilience. A farming system is a system hierarchy level above the farm at which properties emerge resulting from formal and informal interactions and interrelations among farms and non-farm actors to the extent that these mutually influence each other. The environment can then be defined as the context of a farming system on which farming system actors have no or little influence. Hence, actors belonging to the environment may be food processors, retailers, financial institutions, technology providers, consumers, policy makers, etc. Fostering FS resilience is done through (re)designing institutions and building and mobilising resources in order to enhance resilience enabling attributes of FS (and remove resilience constraining attributes). These institutions can be both part of the FS and part of an enabling environment, consisting of private actors (such as food processors, retailers, banks, etc.) and public actors (government agencies). Four archetypical patterns according to which challenges are insufficiently addressed to foster FS resilience have been identified. Six general principles underpinning patterns that enable FS resilience have been formulated. An important challenge is that FS and enabling environments should always find a good balance between addressing challenges in the short run and dealing with challenges in the long run.

An increasing variety of stresses and shocks provides challenges and opportunities for EU farming systems. This article presents findings of a participatory assessment on the sustainability and resilience of eleven EU farming systems, to inform the design of adequate and relevant strategies and policies. According to stakeholders that participated in workshops, the main functions of farming systems are related to food production, economic viability and maintenance of natural resources. Performance of farming systems assessed with regard to these and five other functions was perceived to be moderate. Past strategies were often geared towards making the system more profitable, and to a lesser extent towards coupling production with local and natural resources, social self‐organisation, enhancing functional diversity, and facilitating infrastructure for innovation. Overall, the resilience of the studied farming systems was perceived as low to moderate, with robustness and adaptability often dominant over transformability. To allow for transformability, being reasonably profitable and having access to infrastructure for innovation were viewed as essential. To improve sustainability and resilience of EU farming systems, responses to short‐term processes should better consider long‐term processes. Technological innovation is required, but it should be accompanied with structural, social, agro‐ecological and institutional changes.

The Common Agricultural Policy (CAP) of the European Union is essential to enhance the resilience of Europe's farming systems along three capacities: robustness, adaptability and transformability. The SURE-Farm project conducted the first systematic assessment how the CAP performs in this regard. The findings show that hitherto the CAP has been overly focused on supporting the robustness of an increasingly fragile status quo, with uneven effects, while neglecting adaptability and even constraining transformability. The future CAP needs to allow for a better balance with policy mixes that are tailored to regional needs, based on a shared long-term vision. This impliesreplacing direct payments with measures that specifically address resilience needs, e.g. points-based eco-schemes, agro-environmental programs, coordinated adaptation to shifting markets, ample support for cross-sectoral cooperation, innovation and advice to integrate production and provision of public goods, and participatory and integrative foresight to develop transformation pathways.

The SURE-Farm project aims to analyse, assess and improve the resilience and sustainability of farming systems in Europe. Farming systems face a whole range of social, ecological, economic and political disturbances and changes, such as sharp market fluctuations, severe weather events, climate change, new technologies, changes in consumer preferences and in governance structures and so forth, operating at a range of scales (local, regional, national and global). Some stresses on the farm system can be predicted (e.g. retirement of farmers), while other shocks are more uncertain and unpredictable (e.g. flooding, sudden price drop, illness). Project's WP2 aims to comprehensively understand farmers' risk behaviour and risk management (RM) decisions, and to develop and test RM strategies and decision support tools that farmers can use to cope with increasing economic, environmental and social uncertainties and risks. WP2 contributes to the development of RM in EU farming systems by understanding and eliciting farmers' risk perceptions and preferences; learning about farmers' adaptive behaviour; learning capacity and preferred improvements of current RM tools; designing and analysing improved strategies to deal with extreme weather; and co-creating improved RM tools and map-related institutional challenges.

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 ...