D5.5 Impacts of future scenarios on the resilience of farming systems across the EU assessed with quantitative and qualitative methods

For improving the sustainability and resilience of EU farming systems, it is important to assess their likely responses to future challenges under future scenarios. In the SURE-Farm project, a five-steps framework was developed to assess the resilience of farming systems. The steps are the following: 1) characterizing the farming system (resilience of what?), 2) identifying the challenges (resilience to what?), 3) identifying the desired functions (resilience for which purpose?), 4) assessing resilience capacities, and 5) assessing resilience attributes. For assessing the resilience of future farming systems, we took the same approach as for current farming systems, with the addition that future challenges were placed in the context of a set of possible future scenarios, (i.e., Eur-Agri-SSP scenarios). We evaluated future resilience in 11 case studies across the EU, using a soft coupling of different qualitative and quantitative approaches. The qualitative approach was FoPIA-SUREFarm 2, a participatory approach in which stakeholders identified critical thresholds for current systems, evaluated expected system performance when these thresholds would be exceeded, envisaged alternative future states of the systems (and their impact on indicators and resilience attributes), as well as strategies to get there. Quantitative approaches included models simulating the behavior of the systems under some specific challenges and scenarios. The models differed in assumptions and aspects of the farming systems described: Ecosystem Service modelling focused on the biophysical level (considering land cover and nitrogen fluxes), AgriPoliS considered, with an agent-based approach, socio-economic processes and interactions within the farming system, and System Dynamics, taking a holistic approach, explored some of the feedback loops mechanisms influencing the systems resilience from both a qualitative and quantitative approach. Each method highlighted different aspects of the farming systems. For each case study, results coming from different methods were discussed and compared. The FoPIA-SURE-Farm 2 assessment highlighted that most farming systems are close to critical thresholds, primarily for system challenges, but also for system indicators and resilience attributes. System indicators related to food production and economic viability were often considered to be close to critical thresholds. The alternative systems proposed by stakeholders are mostly adaptations of the current system and not transformations. In most case studies, both the current and alternative systems are moderately compatible with 'Eur-Agri-SSP1 – Agriculture on sustainable paths’, but little with other Eur-Agri-SSPs’. From the point of view of ecosystem services and nitrogen fluxes, the more resilient case studies are those able to provide multiple services at the same time (e.g., hazelnut cultivations in Italy and vegetable and fruit cultivation in Poland, able to provide good levels of both food production and carbon storage) and those well connected with other neighbouring farming systems (e.g., the Dutch case study receiving manure by the livestock sectors). The System Dynamic simulation (applied quantitatively for the Dutch and French case study) highlighted the need to develop resources that can increase farmers’ flexibility (e.g., access to cheap credit, local research and development, and local market). It also showed that innovation, networks, and cooperation contribute to building resilience against economic disturbances while highlighting the challenges for building resilience to environmental threats. From the application of AgriPoliS to the German case study it was concluded that changes in direct payment schemes not only affect the farm size structure, but also the functions of the farming system itself and therefore its resilience. The report showed complementarity between different methods and, above all, between quantitative and qualitative approaches. Qualitative approaches are needed for interaction with stakeholders, understand perceptions of stakeholders, consider available knowledge on all aspects of the farming system, including social dimensions, and perform a good basis for developing and parameterizing quantitative models. Quantitative methods allow quantifying the consequences of mental models, operationalizing the impact of stresses and strategies to tackle them and help to unveil unintended consequences, but are limited in their reach. Both are needed to assess resilience of farming systems and suggest strategies for improvement and to help stakeholders to wider their views regarding potential challenges and ways to tackle them

Training materials for different categories of users

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use, Production Economics, Teaching/Communication/Extension/Profession,

Understanding resilience of farming systems: Insights from system dynamics modelling for an arable farming system in the Netherlands

Farming systems in Europe are facing economic, social, environmental and institutional challenges. Highly intensive, climate-exposed, arable farming systems like the Veenkoloniën in the north of the Netherlands are particularly vulnerable to many of these challenges. Just in the past twenty years, the Veenkoloniën has lost half of its small and medium sized family farms specialised in cultivating starch potatoes. While starch potato production continues to be stable as the remaining farms are increasing the size of their operation, local stakeholders are concerned that the farming system in the Veenkoloniën is endangered. In this paper we investigate this issue by using a system dynamics simulation model to explore what the potential structures are that could threaten the long term future of starch potato production and to identify leverage points that can enhance the resilience of the system. Our analysis shows that, so far, farmers’ active engagement in a processing cooperative has been an important element to their resilience to cope with economic and environmental challenges. In practice, the cooperative has been able to act as a buffer and stabilise prices for farmers in the region by implementing strategies that increase the value of their products, open new markets and increase starch potato production.publishedVersio

Integrated assessment of the EU’s greening reform and feed self-sufficiency scenarios on dairy farms in Piemonte, Italy

Specialised dairy farms are challenged to be competitive and yet respect environmental constrains. A tighter integration of cropping and livestock systems, both in terms of feed and manure flows, can be beneficial for the farm economy and the environment. The greening of the direct payments, which was introduced in the European Union’s greening reform in 2013, is assumed tostimulate the transition towards more sustainable systems. The aim of this study was to quantitatively assess the impacts of greening policies on important economic and environmental indicators of sustainability, and explore potential further improvements in policies. The Farm System SIMulator (FSSIM) bioeconomic farm model was used to simulate the consequences of scenarios of policy change on three representative dairy farms in Piedmont, Italy, i.e., an ‘intensive’, an ‘extensive’, and an ‘organic’ dairy farm. Results showed that in general, there is a large potential to increase the current economic performance of all of the farms. The most profitable activity is milk production, resulting in the allocation of all of the available farm land to feed production. Imposing feed self-sufficiency targets results in a larger adaptation of current managerial practice than the adaptations that are required due to the greening policy scenario. It was shown that the cropping system is not always able to sustain theactual herd composition when 90% feed self-sufficiency is imposed. Regarding the greening policies, it is shown that extensive and organic farms already largely comply with the greening constrains, and the extra subsidy is therefore a bonus, while the intensive farm is likely to sacrifice the subsidy, as adapting the farm plan will substantially reduce profit. The introduction of nitrogen (N)-fixingcrops in ecological focus areas was the easiest greening strategy to adopt, and led to an increase in the protein feed self-sufficiency. In conclusion, it is important to note that the greening policy in its current form does not lead to reduced environmental impacts. This implies that in order to improveenvironmental performance, regulations are needed rather than voluntary economic incentives

Assessing the resilience and sustainability of a hazelnut farming system in central Italy with a participatory approach

European agriculture is facing increasing economic, environmental, institutional, and social challenges, from changes in demographic trends to the effects of climate change. In this context of high instability, the agricultural sector in Europe needs to improve its resilience and sustainability. Local assessments and strategies at the farming system level are needed, and this paper focuses on a hazelnut farming system in central Italy. For the assessment, a participatory approach was used, based on a stakeholder workshop. The results depicted a system with a strong economic and productive role, but which seems to overlook natural resources. This would suggest a relatively low environmental sustainability of the system, although the actual environmental impact of hazelnut farming is controversial. In terms of resilience, we assessed it by looking at the perceived level of three capacities: robustness, adaptability, and transformability. The results portrayed a highly robust system, but with relatively lower adaptability and transformability. Taking the farming system as the focal level was important to consider the role of different actors. While mechanisation has played a central role in enhancing past and present system resilience, future improvements can be achieved through collective strategies and system diversi?cation, and by strengthening the local hazelnut value chain.</p

What are the prospects for citizen science in agriculture? Evidence from three continents on motivation and mobile telephone use of resource-poor farmers

As the sustainability of agricultural citizen science projects depends on volunteer farmers who contribute their time, energy and skills, understanding their motivation is important to attract and retain participants in citizen science projects. The objectives of this study were to assess 1) farmers’ motivations to participate as citizen scientists and 2) farmers’ mobile telephone usage. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. The questionnaire was applied in three communities of farmers, in countries from different continents, participating as citizen scientists. We used statistical tests to compare motivational factors within and among the three countries. In addition, the relations between motivational factors and farmers characteristics were assessed. Lastly, Principal Component Analysis (PCA) was used to group farmers based on their motivations. Although there was an overlap between the types of motivations, for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. While fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest. Two major groups of farmers were distinguished: one motivated by sharing information (egoistic intrinsic), helping (altruism) and contribute to scientific research (collectivistic) and one motivated by egoistic extrinsic factors (expectation, expert interaction and community interaction). Country and education level were the two most important farmers’ characteristics that explain around 20% of the variation in farmers motivations. For educated farmers, contributing to scientific research was a more important motivation to participate as citizen scientists compared to less educated farmers. We conclude that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. Citizen science does have high potential, but easy to use mechanisms are needed. Moreover, gamification may increase the egoistic intrinsic motivation of farmers

European Research for Impact Assessment Tools

This is the 6th issue of the LIAISE ('Linking Impact Assessment Instruments to Sustainability Expertise') Innovation Report. The aim of this series is to shed light on the science-policy interface of policy Impact Assessment (IA). The application of analytical tools in policy IA is a means to include scientific knowledge in IA exercises and the policy process. Tools are used to capture the causal relationship between planned policies and its likely social, economic and environmental impacts and hence inform the analytical process of the assessment. The development of analytical tools which are readily applicable for IA is an emergent field of research. The European Commission, in its Framework Programmes (FP) on research funding, has also invested in research promoting those tools

Wheat yield gaps across smallholder farming systems in Ethiopia

Wheat 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

Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes

Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences on other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimisation algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Context Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used