Learning from Research

Impact assessment toolbox of resilience‐enhancing strategies on farming systems

This is an integrated toolbox that includes a combination of quantitative and qualitative models for impact assessment of resilience strategies/actions on farming systems.

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Output Description

One of the aims of SURE-Farm is to analyse the integrated impact of resilience enhancing strategies and actions on European farming systems. More specifically, it aims to assess how the essential functions of farming systems react to challenges from the environment in terms of resilience. The analysis aims to explore farming systems at different times and conceptual scales, using statics and dynamic perspectives for a) describing the current state of a system, b) outlining its potential developments and c) exploring relationships between resilience and the broader system’s characteristics (resilience attributes). 

SURE-Farm distinguishes three types of resilience for farming systems: 

  1. Robustness: the capacity of a system to resist external perturbations and to maintain previous levels of functionality without major changes to its internal elements and processes. 
  2. Adaptability: the capacity of a system to adjust internal elements and processes in response to changing external circumstances and thereby to continue its development along the previous trajectory while maintaining all important functionalities. 
  3. Transformability: the capacity of a system to develop or incorporate new elements and processes to a degree that changes its operational logic in order to maintain important functionalities when structural changes in the ecological, economic, or social environment make the existing system untenable or dysfunctional. 

To achieve this objective, SURE-Farm has put together existing models (static and dynamic, quantitative and qualitative) into an integrated toolbox and uses them for the assessment of resilience. The reasons for using an integrated toolbox instead of a single model are: first, the multi‐scale and multi‐level nature of resilience means that assessing different dimensions and levels of the systems’ resilience with a single tool is complex and complicated; second, there is a large variety of case studies in SURE‐Farm and they differ in terms of farming systems, data availability and model expertise of the local partners, therefore building one flexible model is very difficult. 

The term ‘toolbox’ is used by SURE-Farm as a group of tools that can be used together or separately to explore different aspects of a farming system and to assess different aspects of resilience. The insights gained from the application of different models can be compared, discussed and integrated into qualitative narratives, scenarios or hypotheses for EU farming systems and their resilience. However, the model results are not quantitatively linked and the outputs of one model are not used as inputs or assumptions for any of the other models.

A number of modelling tools were selected on the basis of their relevance for the assessment of resilience, applicability to the case studies in the project and the experience that project partners have in applying them. The models used include system dynamic models, the agent‐based model of farm structural change AgriPoliS, the Farming System SIMulator (FSSIM), statistical modelling, a stochastic model, a spatially explicit model to assess ecosystem services, and a Framework for Participatory Impact Assessment (FoPIA). 

Used for simulation of structural change of different agricultural regions, particularly in response to different policies.

The FSSIM is used to assess impacts of changes in policy, technology, climate and markets on farm plans and associated economic, environmental and social impacts, for specific categories of farms. It is based on mathematical programming.

It is used to measure the impact of changes in the socioeconomic and ecological environments on the economic sustainability of the considered farms. Uncertainty conditions in which actors operate are considered. This allows to analyse the extent of the overall risk farmers face and the relative importance of the elements that characterise it.

It is used for multi‐criteria optimisation of ecosystem services, or mono‐criteria optimisation of some ecosystem services with constraints of no‐loss on other ecosystem services. Assessment of ecosystem services multifunctionality and classification in hotspots and coldspots of ecosystem services multifunctionality.

It is used to analyse past and current farm robustness and adaptability against climate, economic and policy variability, the contribution of resilience enhancing attributes, and adaptation measures.

They are used to capture high level dynamics of a system by representing the key mechanisms driving behaviour of relevant outcomes

It is used to assess current and future resilience and sustainability of farming systems in participatory workshops. The participatory assessment aims to 1) define the farming system, 2) identify challenges, 3) identify essential functions, 4) identify resilience indicators and 5) identify resilience attributes.

The models use a wide range of inputs, which are fed, with the exception of FoPIA, into mathematical equations. Each model uses different algorithmic methods to estimate optimal states of the system (e.g. FSSIM and the ecosystem services model), structural changes in the system (e.g. AgriPoliS and System Dynamics) or risk (stochastic model). 

Since the models have been built separately and for different purposes, each of them produces different outputs and provides different insights about the essential functions of the farming system under study. However, using them together provides a holistic assessment of the farming system under study by producing different economic, social and environmental indicators associated with the different essential functions of the farming system. 

Farming systems provide public and private goods. Public good provision includes: 

  • Maintaining natural resources in a good condition (water, soil, air); 
  • Protecting the biodiversity of habitats, genes, and species; 
  • Ensuring that rural areas are attractive places for residence and tourism (country side, social structures); 
  • Ensuring animal health and welfare. 

Private good provision includes: 

  • Delivering healthy and affordable food products; 
  • Delivering other bio‐based resources for the processing sector; 
  • Ensuring economic viability; 
  • Improving the quality of life in farming areas. 

Combinations of these different models/approaches included in the toolbox were applied in the SURE-Farm case study regions to assess current and future (scenario analysis) impacts of resilience strategies/actions on farming systems. Additionally, they were applied to identify resilience attributes and strategies which contribute to the robustness, adaptability and transformability of the farming systems. 

Relevance for monitoring and evaluation of the CAP

The SURE-Farm integrated toolbox is useful for impact evaluations, notably of the resilience of farming systems. This is not only very relevant for the CAP Specific Objective 1, which focuses on economic resilience, but the SURE-Farm toolbox also allows for the assessment of a farms' numerous functions that encompass economic, environmental and the quality of life dimensions of the CAP Specific Objectives. 

This diversity among models, their calculation approaches and their outputs are the main strength of the SURE‐Farm integrated toolbox because it enables the analysis of the farming systems and their resilience from different perspectives. 

In the integrated toolbox, each model uses different analytical lenses to assess each particular function. For instance, FSSIM and the Ecosystem Services Model use mathematical optimisation to assess impacts of changes in policy, technology, climate on farming systems, while System Dynamics uses systems thinking to examine changes and evolution over time. The aggregation used in each model is also different. For example, AgriPoliS assesses individual farms and their individual interactions. At the same time, System Dynamics aggregates farms into big groups and focuses on the aggregated dynamics between the different groups and their environment. 

By using an integrated toolbox rather than a single model, one can get more benefits resulting from different modelling approaches without having to integrate all of the models into one single interconnected modelling system. 

The outputs of the models and assessment of the resilience indicators were used on the SURE-Farm case studies to study the relationships between resilience attributes and the resilience indicators. Causal relationships, statistical correlations and stakeholders’ inputs were also used to identify key attributes shaping the identity of farming systems and contributing to specific resilience indicators. 

Knowledge of the different tools, including strong mathematical and econometric skills is required for using the toolbox. 

Relevance of the output per CAP Objectives

  • Specific Objective 1 - Ensure a fair income for farmers 
  • Specific Objective 2 - Increase competitiveness
  • Specific Objective 4 - Climate change action
  • Specific Objective 6 - Preserve landscape and biodiversity
  • Specific Objective 9 - Protect food and health quality

Additional output information

Data collection systems used:

  • FADN(FSDN)
  • Ad-hoc data collection
  • National statistical office

Type of output:

  • Methodology
  • Forecasting model/tool

 

Associated evaluation approaches:

  • Impact evaluation ex ante
  • Impact evaluation ex post
  • Impact evaluation ongoing

Spatial scale:

  • Regional
  • National

Project information

Sure Farm logo

Towards SUstainable and REsilient EU FARMing systems 

To analyse, assess and improve the resilience and sustainability of farms and farming systems in the EU. 

Specific objectives: 

  • Develop a framework to measure the determinants of the resilience of current and future EU agricultural systems. 
  • Understand farmers’ risk behavior and risk management decisions in a comprehensive way in order to develop and test a set of effective and usable risk management strategies and decision support tools. 
  • Develop an improved farm demographic assessment tool. 
  • Develop a policy resilience assessment tool to evaluate the strengths and weaknesses of the existing policy framework (in particular the CAP). 
  • Develop an integrated impact assessment tool to make long-term projections towards the effective delivery of private and public goods. 
  • Identify pathways towards resilience and non-resilience, and synthesise lessons learned to design an enabling environment and construct roadmaps for implementation, co-created with public and private actors. 

Project’s timeframe: 2017 – 2021

Contacts of project holder: Miranda Meuwissen, Professor of risk management in food supply chains, Wageningen University & Research miranda.meuwissen@wur.nl 

Website: SURE-Farm: https://www.surefarmproject.eu/

CORDIS database : https://cordis.europa.eu/project/id/727520  

Territorial coverage: Belgium, Bulgaria, France, Germany, Italy, Poland, Romania, Spain, Sweden, The Netherlands

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