The resilience of EU farming systems is perceived to be low to moderate. Many farming systems are perceived to be close to critical thresholds, with low economic viability leading to farmer exits, making it hard to maintain the social fabric, natural resources and biodiversity. There are limits to success with regard to increasing farm size and intensity, the main adaptation strategies in the past. In the future, a more balanced attention is needed for economic, social and environmental dimensions, and for an enabling environment. All involved actors inside and outside the farming system need to collaborate in order to make a change towards business models that tackle long-term challenges. ; EU; en; contact: pytrik.reidsma@wur.nl
This report presents the results of a participatory sustainability and resilience assessment of 11 farming systems in the European Union (EU). The assessments focused on 1) ranking the importance of functions and selecting representative indicators for these functions, 2) scoring the current performance of the representative indicators, 3) sketching dynamics of main representative indicators of functions, 4) linking these dynamics to challenges and resilience enhancing strategies, 5) assessing level of implementation of identified strategies and their potential contribution to the robustness, adaptability and transformability of the farming system, and 6) assessing level of presence of resilience enhancing system characteristics (resilience attributes) and their potential contribution to the robustness, adaptability and transformability of the farming system. ; EU; en; contact: pytrik.reidsma@wur.nl
Mature ecosystems are characterised by recurrent patterns of material and energy flows, intense recycling within the system and relatively little dependence from the exterior. Their resilience and productivity are sustained on (i) their diversity of flow pathways that allows buffering external shocks, and (ii) the increased efficiency of few of their flow paths that are not affected by external stressors. This study is built on analogies between natural ecosystems and food production systems, or agro-ecosystems (i.e., systems in which humans - socially and/or politically related - use resources to make a living and re-invest to ensure their future). Within agro-ecosystems, humans use resources that are available far beyond the physical boundaries of the ecosystem, by making use of different types of flows, including information (flows), negotiation and (cross-scale) links with other systems. Very often such flows are ¿hidden¿ in our analysis of farming systems, but they may be essential to sustainable food production in resource-constrained systems. Network analysis (NA) is proposed as a methodology to assess productivity and resilience of agro-ecosystems. With NA, the structure of a system is made explicit by a number of indices of system functioning: flow size, diversification, integration, connectivity and stability. Critical nodes in the organization of agro-ecosystems can be identified. Using examples from smallholder system in Africa, NA is applied at farm and community scales to analyse current strategies in the use of communally-owned resources. Through human agency, signals are sensed and management adapted by modifying flow pathways. Identifying critical nodes in current systems helps in designing more productive and resilient food production systems, and targeting policy interventions. (Texte intégral)
International audience ; AbstractWheat yields in Ethiopia need to increase considerably to reduce import dependency and keep up with the expected increase in population and dietary changes. Despite the yield progress observed in recent years, wheat yield gaps remain large. Here, we decompose wheat yield gaps in Ethiopia into efficiency, resource, and technology yield gaps and relate those yield gaps to broader farm(ing) systems aspects. To do so, stochastic frontier analysis was applied to a nationally representative panel dataset covering the Meher seasons of 2009 and 2013 and crop modelling was used to simulate the water-limited yield (Yw) in the same years. Farming systems analysis was conducted to describe crop area shares and the availability of land, labour, and capital in contrasting administrative zones. Wheat yield in farmers' fields averaged 1.9 t ha− 1 corresponding to ca. 20% of Yw. Most of the yield gap was attributed to the technology yield gap (> 50% of Yw) but narrowing efficiency (ca. 10% of Yw) and resource yield gaps (ca. 15% of Yw) with current technologies can nearly double actual yields and contribute to achieve wheat self-sufficiency in Ethiopia. There were small differences in the relative contribution of the intermediate yield gaps to the overall yield gap across agro-ecological zones, administrative zones, and farming systems. At farm level, oxen ownership was positively associated with the wheat cultivated area in zones with relatively large cultivated areas per household (West Arsi and North Showa) while no relationship was found between oxen ownership and the amount of inputs used per hectare of wheat in the zones studied. This is the first thorough yield gap decomposition for wheat in Ethiopia and our results suggest government policies aiming to increase wheat production should prioritise accessibility and affordability of inputs and dissemination of technologies that allow for precise use of these inputs.
Context: Past reviews of policy impact assessment studies using bio-economic farm models (BEFM) called for the development of a generic and modular implementation that can be maintained by a network of modellers. A main reason for these calls is the project-oriented way in which model developers receive funding. It favours the development of new models with case-study specific features over the maintenance and extension of well-tested, more generic ones which allow comparing results in a consistent way across many case-studies. The demand for more generic tools also reflects the dynamic landscape of policy measures within larger policy frameworks like the Common Agricultural Policy (CAP). These policy frameworks move increasingly away from a 'one-size-fits- all' approach of policy design towards more flexible systems, giving greater freedom to shape, implement, and target policy measures to specific regions, farm management systems and farm types. This creates new challenges for model-based impact assessment as applied models have to reflect the variety of policy measures and characteristics of targeted farmers and rural communities. Objective: The aim of this paper is to first address key questions regarding the functionality and implementation of such a modular BEFM that can be maintained and expanded by a user group, and second to develop concrete proposals of necessary model features, model design and shared development. Methods: This paper builds on literature research, including a detailed review of four models that are used extensively for impact assessment within the EU and were developed by multiple teams over a longer period of time. From there, necessary and desirable features of a generic and modular BEFM are identified and requirements for model design regarding modularity, software engineering, and shared development are discussed. Results and conclusions: This feeds into the development of concrete proposals of how modularity and flexibility can be addressed in the development, application and maintenance of a BEFM. At the end, a list of design decisions and implementation steps is proposed to build a modular BEFM that can be maintained by a network of researchers. Significance: The concept for a network-based generic and modular bio-economic farm model responds to the demand for analytical tools in agricultural policy impact analysis. The paper develops a research agenda to overcome observed limitations in the current landscape of such models.
