Research Ideas and Outcomes :
Grant Proposal
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Corresponding author: Guy Ziv (g.ziv@leeds.ac.uk), Pavel Stoev (projects@pensoft.net)
Received: 13 Mar 2020 | Published: 13 Mar 2020
© 2020 Guy Ziv, Michael Beckmann, James Bullock, Anna Cord, Ruth Delzeit, Cristina Domingo, Gunnar Dreßler, Nina Hagemann, Joan Masó, Birgit Müller, Markus Neteler, Anna Sapundzhieva, Pavel Stoev, Jon Stenning, Milica Trajković, Tomáš Václavík
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Ziv G, Beckmann M, Bullock J, Cord A, Delzeit R, Domingo C, Dreßler G, Hagemann N, Masó J, Müller B, Neteler M, Sapundzhieva A, Stoev P, Stenning J, Trajković M, Václavík T (2020) BESTMAP: behavioural, Ecological and Socio-economic Tools for Modelling Agricultural Policy. Research Ideas and Outcomes 6: e52052. https://doi.org/10.3897/rio.6.e52052
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Half of the European Union (EU) land and the livelihood of 10 million farmers is threatened by unsustainable land-use intensification, land abandonment and climate change. Policy instruments, including the EU Common Agricultural Policy (CAP) have so far failed to stop this environmental degradation. BESTMAP will: 1) Develop a behavioural theoretical modelling framework to take into account complexity of farmers’ decision-making; 2) Develop, adapt and customize a suite of opensource, flexible, interoperable and customisable computer models linked to existing data e.g. LPIS/IACS and remote sensing e.g. Sentinel-2; 3) Link economic, individual-farm agent-based, biophysical ecosystem services and biodiversity and geostatistical socio-economic models; 4) Produce a simple-to-use dashboard to compare scenarios of Agri-Environmental Schemes adoption; 5) Improve the effectiveness of future EU rural policies’ design, monitoring and implementation.
Behavioral change theory, ecosystem services, agricultural economics, rural policy impact assessment, agent-based modelling, biophysical modelling, farming systems archetypes
Nearly 50% of European Union (EU) land is used for agriculture. These landscapes have been shaped by centuries of large-scale human impacts through traditional land-use systems (i.e. practices that are not part of modern, intensive agriculture;
BESTMAP will design, demonstrate and operationalize a suite of open-sourced, flexible, interoperable and customizable computer models overcoming two critical shortcomings in state-of-the-art PIAMs – first, PIAMs over-simplify farmers’ behaviour and predict land-use change based only on economic factors; and second, current models cannot evaluate the impacts of alternative farming options on the environment, climate system, delivery of ESS and rural socio-economical status. BESTMAP will link economic modelling, agent-based models grounded in behavioural theory and established ESS models. Utilizing a sophisticated geographic framework of farming system archetypes, empirical social science, machine learning algorithms, assimilation of remotely sensed data and online platforms, BESTMAP will empower policy makers and stakeholders to use PIAMs more effectively. In the long term, BESTMAP will improve the effectiveness of EU policies and transform European policy design, impact assessment and monitoring.
By developing a new modelling framework, BESTMAP aims to improve and contribute to the existing tools used in policy impact assessment, and strengthen PIAM capacity for the EC, national and regional decision-makers and expert personnel. To do this, BESTMAP will use the emerging science of behavioural theory and link economic modelling with individual-farm Agent-Based Models (ABM). BESTMAP will quantitatively model, monitor and map policy change impacts on the environment, climate system, delivery of ESS, as well as socio-economic metrics (e.g. jobs, demographics, local markets). Finally, BESTMAP will use a range of external communication and dissemination activities to build capacity for researchers, national and EU Directorate-General staff and parliamentarians to model policy impacts and improve policy design and monitoring.
Specific objectives of the project include:
BESTMAP addresses the topic RUR-04-2018 “Analytical tools and models to support policies related to agriculture and food”, the “Socio-economic science and humanities” cross-cutting priority and the “Rural Renaissance” call as detailed in Table
BESTMAP's relation to the work programme.
Specific challenge |
How BESTMAP will address the challenge |
Development and maintenance of appropriate instruments for use in the [evidence-based policy] design and for the monitoring of their effects |
BESTMAP’s use of farm-level ABM and spatially-explicit biophysical and geostatistical ESS models is ideal to assess policy impact, while the planned Case Study Base Layer and European Base Layer collated data from e.g. CORINE, Eurostar, Copernicus Land Services and farmer data will support their monitoring at multiple scales. |
Taking advantage of new socio-economic approaches and increased possibilities opened up by progress in the ICT area |
BESTMAP’s ABM social sciences approach is based on the recent “Reasoned Action Approach” (RAA) theory of behavioural change, while the Case Study Base Layer/European Base Layer will build on cloud computing and big-data analysis. |
Scope of topic |
How BESTMAP will address the scope |
Modelling policies - from regional to global |
BESTMAP includes detailed modelling in five regional CSs (Mulde, Germany; South Moravia, Czech Republic; Catalonia, Spain; Humber, UK; Vojvodina Province, Serbia), as well as EU scale analysis. In all cases, a global economic model provides input to finer scale models. |
Agriculture and the related management of renewable resources |
BESTMAP considers different agricultural production systems for food and biofuels (renewable resources) as well as other landscape elements contributing to ESS, e.g. forests, wetlands, hedgerows. |
Modelling, environmental and climatic impact of farming, delivery of ESS, modelling of aspects ranging from product/sector to farming systems, structural change including the transfer of production factors such as land, the integration of agriculture in rural society |
BESTMAP will model nutrient runoff and Greenhouse Gas emissions (environment and climate system), crop and livestock yields, water provision, avoided sedimentation, nutrient retention (delivery of ESS), biodiversity indicators, farming system archetype patterns and dynamics (modelling of farming systems), changes in field size, farmers’ demographics, mechanisation (structural change), jobs, and participation in quality schemes and local markets (integration in rural society). |
Establishment of links with biophysical models and geo-referenced datasets |
BESTMAP builds on the large number of existing geo-referenced datasets (e.g. Integrated Administration and Control System (IACS), Farm Accountancy Data Network (FADN), INSPIRE, Eurostat) and remote sensing datasets (e.g. Copernicus Land Monitoring Services). The InVEST biophysical models (developed by the Natural Capital Project; Stanford University) will be used in BESTMAP to estimate policy impact on ESS and habitat quality. |
Build a highly modular and customisable suite of tools and allow flexible use and further improvements as needs arise |
BESTMAP will build and share publicly a set of ESS, biodiversity, socio-economic and ABM models built upon open-source platforms (InVEST, R, Repast/NetLogo/MASON) including documentation to improve these in the future. The policy dashboard will be built with on open Application Programming Interface (API) and will be extendable to include other and/or improved models. |
Be compatible with and improve the tools used at the EC |
BESTMAP will improve modelling tools used at the EC, e.g. in the Joint Research Centre (JRC). To ensure integration of the system in existing PIAM approaches, guidance on using the architecture with other Computable General Equilibrium (CGE) or Partial Equilibrium (PE) model and integration guidance to other models used by the EC (e.g. MAGNET, CAPRI, Aglink-COSIMO) will be developed. BESTMAP will also improve non-equilibrium model (E3ME) used by the EC to assess energy-environment-economy linkages. To ensure BESTMAP is useful to the EC, one of our partners (CE) regularly perform policy analyses for EC, and two members of the Science Advisory Board (Peter Witzke (EuroCARE) and Ignacio Perez-Dominguez (JRC Seville)) are leading experts in these models. |
Socio-economic science and humanities |
How BESTMAP will include social science |
Take into account the social, economic, behavioural, institutional, historical and/or cultural dimensions, as appropriate, of a societal issue |
The RAA theory incorporates social, economic, behavioural, institutional, historical and cultural dimensions, which BESTMAP includes through focus groups and interviews and scales-up using IACS, FADN, INSPIRE, and other survey data. |
Contributions from the socio-economic Science and Humanities (SSH) are integrated at various stages |
BESTMAP includes SSH researchers from political sciences, environmental and agricultural economics and socio-ecology, who will be an integral part across all work-packages of BESTMAP. |
Rural Renaissance call |
How BESTMAP will address the call |
The design of innovative policy instruments/approaches and governance models |
BESTMAP will explore policy changes that may follow from the UK’s exit and Serbian’s accession of the EU, to help inform, design and monitor changes to the EU Common Agricultural Policy (CAP). |
Focus Areas "Digitising and transforming European industry and services" and "Building a low-carbon, climate resilient future". The actions are expected to support…Sustainable Development Goals (SDGs) in particular SDG 9, 11, 12, 13 and 15 |
BESTMAP will depend on Big Data, cloud, high-performance computing and artificial intelligence (Digitising and transforming) and improve policy impact analysis on greenhouse gas emissions (Low-carbon). In addition, BESTMAP will synthesize existing knowledge and produce indicators addressing the CAP, the SDGs, COP 21 Paris Climate Agreement, EU Bioeconomy Strategy, Circular Economy Package and other national policies. |
BESTMAP builds on five concepts:
C1. Modelling approaches employed in BESTMAP
Agriculture is one of the most complex sectors to model. Besides having a large number of economic and human actors operating at different scales, agriculture is also driven by biophysical and ecological processes from global to sub-meter scales (Fig.
Top-level conceptual framework of BESTMAP. BESTMAP combining existing global/EU scale (green) models used by the EC, in particular Partial Equilibrium (PE) and Computable General Equilibrium (CGE) models with regional analyses (yellow) by
Most agricultural PIAM take a top-down approach, explicitly or implicitly describing drivers at global, continental, national and (less frequently) regional scales – often with very simplistic assumptions or missing details on the accompanying biophysical and socio-economic drivers’ side. Those top-down models are used, however, to predict aggregated changes made at farm level. BESTMAP innovation is to combine these models with a bottom-up approach, describing the large-scale economic, social and environmental impacts via aggregation of farm level units, considering statistical distributions and correlations at each scale among the economic/human and biophysical drivers. In this sense, BESTMAP argues that large scale phenomena such as regional/national food production, employment in agricultural sector or flood risk are emergent properties of the individual farms multi-actor system.
Fig.
C1.1. Economic modelling for agricultural impact assessment
Global economic models, either PE or CGE, solve for balance between supply and demand with flexible prices. Their structure allows analysis of the impact of changes in supply (e.g. due to yield changes under climate change) or demand (e.g. biofuel targets) on prices, trade, production, and land allocation (e.g.
C1.2. Agent-based modelling (ABM) in agricultural decision making
In BESTMAP, the outputs of the DART-BIO (Sec. C1.1) will be linked to an ABM to model farmers’ decision making, including the influence of exogenous factors (from the CGE model) such as prices but also endogenous factors such as social interactions between farmers. In recent years, ABMs have been proposed as an alternative tool to predict impacts of agricultural policies (
Agricultural ABMs such as AGRIPOLIS or MP-MAS are grounded in agricultural economics and assume that socio-economic factors drive farmers’ decisions (cf.
C1.3. Reasoned Action Approach (RAA) to human decision
A wide range of theories on human behaviour have been developed in the social sciences, psychology and economics, focussing on diverse aspects such as individual and group decision-making, learning or social influence. Whereas some theories are more generic (e.g. bounded rationality), others focus on specific aspects of decision-making processes. Table
Overview of selected behavioural theories, their origin and field of application. BESTMAP will use the ‘Reasoned Action Approach’, adapted from
Name of theory | Field of application | Theory considers... |
Reference (see |
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Utility maximization | Limited cognitive capacity | Aspiration level/ satisfaction | Reinforcement/ social influence | Risk aversion | |||
Rational choice theory Actors are self-interested, goal-oriented utility maximizers. They possess perfect knowledge and unlimited cognitive capacity to determine optimal choice. | Economics; Political science; Psychology; International relations; Natural resource use | Yes | No | No | No | Yes |
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Bounded rationality Rationality of actors is limited by available information and cognitive capacity. Behavioural choice may be realized through utility maximization, but also by reaching an aspiration level or following a heuristic. | Economics; Political science; Psychology; International relations; Natural resource use | Yes | Yes | Yes | No | No |
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Reinforcement learning Positive behavioural outcomes reinforces the behaviour of an actor who will deliberate about alternative behaviours if need satisfaction drops below a certain level. | Psychology; Neuroscience | No | No | Yes | Yes | No |
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Descriptive norms Actors observe the behaviour of others, which can have an impact to the actor’s own behaviour. | Psychology; Environmental sciences | No | No | No | Yes | No |
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Prospect theory Actors have a degree of risk aversion and bias a rational decision towards avoiding loss over a possible gain. | Economics; Environmental management | Yes | No | No | No | Yes |
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Reasoned Action Approach (RAA) Intention towards a behaviour depend on its attitudes towards the behaviour, perceived norm and perceived behavioural control. |
Environmental psychology; Environmental management | Yes | Yes | Yes* | Yes | Yes* |
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* can be modelled through attitudes of ABM agents
For the implementation of ABMs, BESTMAP will use the RAA, an extension of the ‘Theory of Planned Behaviour’ (cf.
In BESTMAP, we will operationalize the RAA in the form of specific quantitative and qualitative (categorical/ordinal) attributes in the ‘Farmer’ agents which determine their decisions (Sec. M3.5). These attributes will take role in ‘perceiving’ external drivers such as policy change and economic price shocks etc. They will also determine other rules that affect ‘Field’ agents, such as his/her crop rotation etc.
C1.4. Ecosystem Services (ESS) modelling
ESS are a useful framework to communicate and analyse the ecological and socio-economic impacts of land-use decisions. ESS approach at a European scale is driven by Action 5 of the EU Biodiversity Strategy to 2020, which calls for Member States to map and assess the state of ecosystems and their services before 2020. A commonly used European classification of ESS is CICES (https://cices.eu). In Table
Key ESS for rural agricultural land, using the CICES classification.
CICES Section & Division | Description | Important processes affecting ESS |
Provisioning - Biomass | Cultivated crops, livestock, fibres and other materials from plants, animals | Crops grown/breeds used, agricultural management methods |
Regulation & Maintenance - Regulation | Control of erosion rates | Cultivation methods, cover crops, livestock densities |
Regulating water flows | Water use, attenuating surface flows, water infiltration and holding | |
Pollination of cultivated plants | Habitat for pollinators, pesticide use | |
Habitat protection | Providing habitats for wild plants and animals | |
Pest control | Habitat for predators of agricultural pests, pesticide use | |
Quality of freshwaters | Use and runoff of pesticides, herbicides, phosphorous, nitrogen, etc. soil erosion | |
Climate regulation | Greenhouse gas storage, emissions & sequestration | |
Air quality | Pollution from fertiliser use, livestock, etc (Ammonia, Nitrogen oxides) | |
Cultural - Direct | Recreation. observing & studying nature. cultural values, aesthetic appreciation | Land use, accessibility, non-agricultural features |
Cultural - Indirect | Biotic components with non-use value | Conserving biodiversity on farmed & non-farmed areas |
Some economic models have been linked with global ESS models (e.g. EUCLIMIT, SEAMLESS-IF, MACSUR projects). However, these ESS models are typically of coarse scale, often based on weighted indirect proxies/indicators, poorly validated (i.e. with in-situ or remote sensing data) and focus on emissions/pollution with little consideration of the capacity of the natural environment to mitigate these (via regulating services). ESS modelling approaches vary greatly in complexity, resources requirements and accuracy – from the Capacity Matrix approach (
C2. Typology and mapping of agricultural Farming System Archetypes (FSAs)
Unlike many models based on land use/land cover, BESTMAP-PIAM will build on a new typology of FSAs. A new typology/framework is needed because farming systems and their changes are characterized by
Understanding land use, land use intensities and socioeconomic factors is crucial to design and implement effective policy measures tailored for the specific farming system and to assess their impact on the agro-ecosystems and the ESS they provide. Therefore, a system approach that identifies archetypical farming patterns, considering both the natural and social potential of rural areas (
Fig.
The conceptualization of Farming System Archetypes (FSAs) in BESTMAP is an extension of land-use intensity framework from
The Land Systems Archetypes mapped by
C3. BESTMAP-PIAM – an improved architecture for Policy Impact Assessment Modelling (PIAM)
BESTMAP proposes a new architecture – the BESTMAP-PIAM - for integrating economic, biophysical and statistical/machine learning models for improved policy impact assessment (Fig.
BESTMAP conceptual framework for policy impact assessment modelling, combining actions at EU scale (green), and activities within a representative set of case studies (orange; demonstrated in five areas within BESTMAP). The framework combines workshops (ovals), modelling (rounded rectangles) and interviews (ovals) producing several datasets (curved rectangles) and an online interactive dashboard (barrel shape). At the EU level, BESTMAP will co-design with policy-makers and stakeholders policy scenarios which, with existing scenarios of climate change and other global events, will input into the global economic models (here DART-BIO CGE model, but also MAGNET, CAPRI etc.). Georeferenced data layers (”Case Study Base Layers”; CBL) will be collated from existing sources including the Land Parcel Identification System (LPIS), Farm Accountancy Data Network (FADN), CORINE land-use/land cover, INSPIRE Geoportal, Copernicus Land Monitoring, national/regional datasets and existing remote sensing products. This will be used to define ‘prototype’ Farm System Archetypes (FSAs) which help stratify and design the interview campaign in each of the case studies. BESTMAP will collect demographic, behavioural characteristics and socio-economic information using semi-structured interviews (with harmonized protocols). Ecosystem services models (here the InVEST biophysically based models, but simpler e.g. capacity matrix or more complex e.g. SWAT models can in be used in the future) will estimate ESS, environmental/climatic impacts and biodiversity provided by different farming units. The ‘bundle’ of ESS, geo-statistically modelled socio-economics and behavioural characteristics will add to the CBL and define the final typology of FSAs in the case study. A generic ABM template based on the RAA behavioural theory will be locally adapted by the same interviews/surveys, and driven by the outputs (prices and costs of commodities, energy etc.) from the global economic model (DART-BIO) as well as narratives and drivers arising from a national/regional workshop interpreting the EU-level policy. The change in FSAs resulting from those scenarios via the ABM will translate to change in ESS and socio-economics defining the FSA ‘bundles’ including their uncertainty. Those impacts at the case study level will be translated into stakeholders-defined policy indicators (e.g. of the SDGs) and visualized using a standard interactive web-based data portal policy dashboard, the use of which will be the focus of training and dissemination activities.
BESTMAP demonstration case studies: overview of CS locations in the context of Land Systems Archetypes mapped by
The implementation of BESTMAP-PIAM in BESTMAP can, in principle, be extended to cover the entire EU with FSAs and ABMs developed at NUTS-2 scale. Given the resources such an endeavour will require, it is much more efficient to use representative sample of case studies in each Member State – and BESTMAP will develop a metric to estimate how representative the case studies in BESTMAP are and how many are needed to cover larger geographic regions (Sec. M4). In addition, BESTMAP includes a task to develop an initial European-wide model by scaling up the ABMs developed in the CSs. A roadmap to explore the research, capacity, regulatory and financing challenges and options to scale up the project will be developed.
The conceptual architecture of BESTMAP-PIAM is highly flexible. In particular it can:
C3.1. Indicators to measure policy impact
To make use of the modelled impacts of policy on different FSAs, BESTMAP will link ESS, biodiversity and socio-economic outputs models to existing or currently developed indicators, in particular those related to SDGs and CAP monitoring. The 2030 Agenda on Sustainable Development describes 17 Goals. SDGs such as No Poverty (SDG 1), Zero Hunger (SDG 2), Reduced Inequalities (SDG 10), Climate Action (SDG 13) and Life on Land (SDG 15) have direct relation to the new CAP (
C3.2. Interactive dashboard for policy-makers
Existing PIAMs are complex, requiring specialist expertise to run, analyse and report. At present, policy-makers (and their aides and supporting EC staff) rely on impact assessment reports developed early on in the life-cycle of new rural policy. As a result, they have no control over the scenarios explored, and changes made to the policy during parliamentary debates and due to the involvement and consultation of other political actors and stakeholders are not reflected in their impact assessment and underlying models. Interactive dashboards aim to alleviate those constrains, by allowing stakeholders (as well as scientists and the public) to explore a much larger number of options (pre-computed or computed on-the-fly) and visualize the impacts of such changes on different dimensions (e.g. indicators) and, in the case of large areas, on their spatial heterogeneity. The dashboard approach helps with monitoring the overall evolution of some variables and numerical indicators as well as visualizing their geospatial distribution to determine the hot spots where new policies are having negative impacts. Importantly, the underlying models and their assumptions, the data used etc. will be made transparent within those dashboards. These dashboards will allow policy makers and expert practitioners to simulate future scenarios and compare policy alternatives leading to new recommendations. Such decision-supporting tools will become the cockpit of decision-making, by radically transforming policy-making, democratizing impact assessment and providing much greater control, transferability and accountability of the policy process as a whole.
How BESTMAP may work in practice? Hypothetical example in a CS region
The following example demonstrates how BESTMAP will work within each of the individual CS areas (Sec. M2). In our initial EU-level stakeholders workshop in Brussels (Sec. M1), a need to better understand the impact of including the agricultural sector’s role in achieving Paris agreement commitments is raised. This is brought to a CS stakeholders workshop where a plausible scenario of new Agri-Environmental Scheme options for setting aside agricultural land to become permanent meadow and accumulate carbon in soil is seen as a way to accomplish this EU-level goal in the local CS context, and a hypothetical monetary value of the subsidy is agreed on. This (or other options mentioned by stakeholders) may have impacts across the agricultural sector and wider economic system—which BESTMAP will explore using a CGE model to get net effect on producers’ revenues and subsidies (Sec. M3.1). We will use this information in interviews with a sample of farmers in the CS area (Sec. M3.2). Farmers will be asked about the likelihood they will adopt this option (e.g. permanent meadows) given different levels of economic incentives. We will ask them also other demographic, socio-economic and behavioural questions. The latter will follow the RAA regarding their attitudes, perceived norms, and behavioural control. Questions on how they would be influenced by peers will also be asked. Combining data harmonized from existing geo-referenced datasets (Sec. M2.1) e.g. IACS, FADN, and the field interviews, we will generate a typology of farms or FSAs using a mixed hypothesis-driven and data-based approach (Sec. M3.4). Biophysical models, calibrated and validated using existing in-situ datasets (Sec. M3.3), and geostatistical socio-economic models of e.g. labour and demographics will link each FSA to a ‘typical’ bundle of ESS, socio-economics and behavioural characteristics. The transition probability among FSAs, e.g. from farm without permanent meadows, to a farm with on average 10% land area as permanent meadows, will be established from interviews and regression analyses using FSA characteristics. These dynamic rules will populate an ABM (Sec. M3.5) which predicts the distribution of FSAs for different levels of subsidy for permanent meadows. Based on the ‘typical’ bundles per FSA, the ‘average’ change (and uncertainty thereof) in a set of ecosystem services (e.g. carbon storage, water quantity and quality, wildlife habitat quality) and socio-economic aspects (e.g. jobs, demographics, quality schemes) from introducing this new EU level policy will be derived for each CS. These outputs, and existing policy-indicators derived from them (Sec. M5.1), will be visualized in an interactive online policy dashboard (M5.2), showing impacts within a CS and across CS regions using a geospatial framework. Workshops and training with decision-makers will support implementation and dissemination, leading to increased transparency and accountability in policy-making process.
M1. Co-designed policy scenarios driving modelling approaches in BESTMAP
The BESTMAP co-design and co-development phase will organize stakeholder/policy-maker workshops and focus groups to discuss policy scenarios. These will be at both EU level and with national and local stakeholders at each CS. At the EU level, we will identify key areas for future rural policy which can have significant influence on ESS and socio-economic aspects. The modelling platform SUPREMA had similar EU level workshops, and BESTMAP will build on the knowledge gathered by those. BESTMAP will also build on the long history of scenarios development in the Global Environmental Outlook 4/5, Millennium Ecosystem Assessment, and the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change (IPCC). More recently, the FP7 OpenNESS project developed policy scenarios based on varying levels of shared responsibility to ESS and coherence of policies impacting ESS and biodiversity (
Based on the outcomes of the workshop, BESTMAP will either adapt existing scenarios (e.g. from OpenNESS or SUPREMA) or create new scenarios. Those scenarios will estimate the extra (or reduction of) subsidies for sectors within the CS regions, as well as other factors important in the economic model (Sec. M3.1). At the national and regional scale, stakeholder’s workshops will translate those EU level policy scenarios into specific predicted changes in the rural agricultural landscape, and into narratives about the change in procedure (application, monitoring, compliance) expected to affect farmer’s decisions (i.e. non-economic drivers).
BESTMAP and Brexit – in March 2019, the UK intend to leave the EU. The implications on agriculture and trade within the UK, between the UK and remaining EU-27, between UK and third-party countries (e.g. new bilateral agreements) and the spill-over effects globally are highly uncertain. The DART-BIO model economic analysis ‘baseline’ uses the latest available GTAP 9 whose base year is 2011, before the 2016 UK Referendum. We expect that during the project, clarity on the form of Brexit will emerge. Recently, UK government documents on ‘no-deal’ Brexit (dated 23 August 2018) ensure farming subsidies to keep at their current levels. It is also unlikely that new economic base datasets implementing the post-Brexit global economy will be available during BESTMAP implementation, although this would be a topic we aim to explore in exploitation plan. For the UK case study, we will use expert-opinion from previous workshops, published studies and our own focus group to change DART-BIO outputs and account for Brexit impacts.
M2. Developing demonstration case studies
BESTMAP will model the effects of policy scenarios on the dynamics of farming systems in five regional case studies across three EU Member States (Spain, Germany, Czech Republic) and two countries possibly undergoing major rural policy transitions in the near future (UK and Serbia) (
The size of the case studies is approximately corresponding to a NUTS-2 level or is defined by a catchment boundary.
To ensure a consistent approach across regional CSs, which is key for synthesis activities, a ‘protocol’ will be developed early in the project. A national stakeholders’ workshop will inform the specific decision context implemented in the ABM for that CS, e.g. switching to organic farming, adopting soil management, converting food to biofuel crops etc. BESTMAP partners have previous research, data infrastructure and farmers’ networks within the CSs areas, which together span a range of climatic regions, institutional structures and management histories (see description below and statistics in Table
Regional case studies in BESTMAP.
Humber (UK) | Mulde (DE) | South Moravia (CZ) | Bačka (RS) | Catalonia (ES) | |
Total area (km2) | 4,664 | 5,814 | 2,089 | 8,218 | 31,206 |
Elevation (m) mean, min, max | 20, -13, 265 | 409, 71, 1213 | 298, 151, 963 | 80, 0, 641 | 350, 0, 2100 |
Precipitation (mm) mean | 626 | 828 | 657 | 613 | 575 |
Predominant terrain | Flat to hilly | Flat to hilly, including steep valleys | Mostly flat | Flat to hilly | Heterogeneous |
Temperature (ºC) mean | 9.6 | 6.9 | 8.5 | 16 | 13.9 |
Field size (ha) mean | N.D. | 6.3 | 6.1 | 5.4 | 0.7 |
Farm size (ha) mean | 90 | 157 | 96 | 10.6 | 19 |
Land in agriculture | Cropland (78.9%) | Cropland (54%); grassland (9%) | Cropland (57%), pastures (5%) | Cropland (84%) | Cropland (27%) |
Main crops (in order of prevalence) | Cereal crops, oilseeds, root crops | Winter grains, oilseed rape, maize | Cereals, maize, rape seed | Cereal, sugar beet, pastures, orchards | Grain, olives, forage |
Other agricultural uses | Livestock (pigs, sheep, cattle) | Intensive and extensive grasslands | Viticulture, orchards, extensive grasslands | Vegetables | Viticulture, orchards, rice |
Humber Catchment (UK) drains a major area of mixed agricultural land and semi-natural habitats in the northeast of England. The study area is typical for the North East region, and contains a wide range of farming systems that are present in other parts of England, Wales and Scotland including some more intensively farmed areas such as the South West. The southern section of the region drains via the River Ancholme into the Humber estuary, or further south drains through the River Eau towards the Wash. The centre of the region is an area of largely flat land, dominated by drained peatlands.
Mulde River Basin (DE) is located in the western part of Saxony and includes the so-called “Saxon Loess fields”. The study area is representative for wider regions in Central Germany that are characterized by fertile soils, relatively flat terrain and intensive agriculture use. It ranges from the Pleistocene lowlands in the north to the Ore Mountains in the south. The catchment area of the Mulde river has already been affected several times by floods, of which the event in 2002 had catastrophic proportions. The Mulde river basin is increasingly affected by land-use changes and conflicts related to bioenergy and intensification threatening ESS and biodiversity.
South Moravia (CZ) is a traditional agricultural region located in the southeast part of the Czech Republic at the border with Slovakia. The study area is representative of other areas in Central Europe that are characterized by flat terrain with fertile soils and a diversity of environmental conditions, landscape structure and farming practices. It lies in the lower Morava river basin and extends across two administration units (Jihomoravský and Zlínský kraj). The region underwent significant changes in the landscape structure, land-use intensities and property rights in the post-war period and also in the post-communist era as a result of land-use and economic changes and policy interventions. Agricultural holdings range from small family farms to very large agro-businesses.
Bačka County (RS) is located in the Autonomous Province of Vojvodina which is divided into three main counties: Banat, Bačka and Srem. The study area is an archetype of a flat, fertile agricultural area within the larger Pannonian Plain. Around 35% of all Serbian agricultural area is in Vojvodina. The county is crisscrossed by parts of the Danube–Tisza–Danube Canal system. The average age of holders of family agricultural holdings is 59 years with decreasing tendency in the last few years. The study area presents a unique case for agricultural policies modelling and its influence and significance because it belongs to a non-EU country that is currently undergoing a transitional and transformational phase.
Catalonia (ES) is located in the Northeast of the Iberian Peninsula, covering around 32 000 km² of which around 9000 km² are dedicated to agriculture. This study area is characteristic for its heterogeneous agricultural landscape, with predominance of small and medium-sized farms making it a perfect representative study site of Southern Europe. Agriculture lands are distributed in a mosaicked land cover mixed with areas of natural and semi-natural vegetation, including coniferous, sclerophyllous and deciduous forests. The rural community is organized according to irrigation communities, obligatory associations that group users with the right to use a certain concession of public water, both superficial and underground in a given concession area.
M2.1. Collecting geospatial data into a Case Study Base Layer
For all analyses to be conducted in the five CSs (M3.2 to M3.5), BESTMAP will collect a high-resolution dataset (“Case Study Base Layer” see Table
Datasets included in Case Study Base layer and European Base Layer.
Dataset name | Information included | Type of data | Covers | Target Layer | Relevant for |
Integrated Administration and Control System (IACS) | All CAP related data, including Land Parcel Identification System (LPIS) and land ownership information. New datasets including maps of Ecological Focus Areas (e.g. hedgerows) are already available for some CSs. | Polygons per individual land parcels | EU but country specific dataset | Case Study | FSAs, ABMs, ESS models |
AgroSens | Serbian data platform including information on land parcels, crop types. Meteorological data and remote sensing data | Raster, tabular and parcel polygons | RS | Case Study | FSAs, ABMs, ESS models |
INSPIRE Geoportal | Farm polygons (cadastral units) | Polygons | EU | Case Study/ European | ABMs |
EEA Waterbase | Status, quality and quantity of Europe's water resources, and on the emissions to surface waters from point and diffuse sources of pollution | Water-body specific database | EU | Case Study/ European | ESS models |
Crop type classification | Major crops mapped based on phenology per field derived from Sentinel-2 | 20m raster | DE, UK | Case Study | FSAs, ABMs, ESS models |
Farm Accountancy Data Network (FADN) | Income of agricultural holdings and the impacts of the CAP | NUTS3 / 10km | EU | Case Study/ European | FSAs, ABMs, ESS models |
CORINE Land Cover | Inventory of land cover in 44 classes | Polygons or 100m raster | EU | Case Study/ European | FSAs, ABMs, ESS models |
ESA CCI Land Cover | Annual global land cover maps 1995-2015 based on remote sensing | 20m raster | Global | European | ESS models |
EU statistics on income and living conditions (EU-SILC) | Cross-sectional and multidimensional microdata on income, poverty, social exclusion and living conditions | NUTS-1 | EU | European | socio-economic models |
Agri4Cast | Agri-Meterological and Crop data | 25 km | EU | European | ESS models |
EU-DEM | Digital elevation map for Europe | 25m raster | EU | Case Study/European | ESS models |
European Soil Database (ESDB) | Information on soils in EU | 1 km raster | EU | European | ESS models |
WorldClim | Climatic information | 30 secs | Global | European | ESS models |
Global-Aridity/Global-PET | Climatic information related to evapotranspiration processes and rainfall deficit | 30 secs | Global | European | ESS models |
Global Runoff Data Centre | Runoff data | Stations (points) | Global | European | ESS models |
Land Use and Coverage Area frame Survey (LUCAS) | Land-use and land cover, field size | points | EU | European | ESS models |
LUCAS soil database | Topsoil properties | Points (ca 20,000 points) | EU | European | ESS models |
European Soil Data Centre ESDAC | Rainfall erosivity | 30 secs | Global | European | ESS models |
Living forest biomass and carbon stock | Forest carbon stock | 1 km | EU | European | ESS models |
EUROSTAT agricultural production | Annual crop production data | NUTS-2 | EU | European | ESS models |
Butterfly monitoring data | Annual butterfly transects | Points | 15 Member States incl DE, UK | European | ESS models |
European Social Survey ESS | Surveys of individual attitudes | NUTS-3 | EU | European | European ABM |
Beneficiaries of CAP payments | Payments made to farms under CAP | Individual farms + NUTS-2 | EU | European | European ABM |
Farm Structure Survey | Information on individual farms including area, livestock heads, labour force, monetary outputs etc. | NUTS-2 | EU | European | European ABM |
M2.2. Testing case studies representativeness
Case studies conducted in a set of contrasting European agricultural landscapes (Fig.
M3. Modelling approaches employed in BESTMAP
M3.1. Economic modelling using DART-BIO
BESTMAP will use producer prices, as estimated from DART-BIO CGE model, as part of the ‘instrumental attitudes’ of individual agents in the RAA-based ABM. Alongside with developing the CS modelling, BESTMAP will consider a number of different global trends on global and European economies, and their impact on producer prices in each Member State. The DART model is a global multi-sectoral, multi-regional recursive-dynamic CGE model. It was developed at the Kiel Institute for the World Economy and has been widely applied to analyse international climate policies (e.g.
M3.2. Socio-economic and behavioural data collection and extrapolation
Potential farmers for interview in the case study regions will be identified through the LPIS/AgroSense (for RS) data (in CSs where the contact information is included); through local administrations / offices / trusts / societies; farmers associations (see Sec 4.3); land registry and from previous interviewees. The interview phase will start with contacting local farmers’ networks / key farmers, discuss with them the project aims and potential obstacles, and get them to help build the communication material for that CS. In addition, we will identify what BESTMAP can offer local farmers to motivate their engagement such as results, ‘scorecard’ comparing their farm to peers, publicity or remote sensed information on their fields (e.g. crop health indicators). Afterwards, communication materials will be developed and send out to farmers to request their consent to participate. Those communication materials will be tailored to farmers, emphasizing the ethos of BESTMAP to include “farmers’ voice” in policy analysis and go beyond pure economic decisions. Semi-structured face-to-face interviews based on a common questionnaire will be conducted in each CS. Following a pilot stage, BESTMAP aims to interview about 50-70 farmers in each CS. The timing and location of these interviews is critical – BESTMAP will aim to do these in the farmhouses or nearby public spaces, during events farmers already visit (e.g. local agricultural shows) and off peak budy periods (summer). Part of the questionnaires will be the same as in EU-wide social and socio-economic surveys, to help compare with microdata at aggregated scales (Sec. M4).
Regression models (e.g. constrained and/or regularized linear models) and/or machine-learned models (e.g. Random Forest) will up-scale the farm level data to all farm polygons within the CS. Specifically, BESTMAP will collect data on farming activities (irrigation, fertilizer use, mowing frequency, yields), labour (number of workers, income), mechanization (type of equipment used), participation in quality schemes, markets (local vs. export) and farmers’ demographics (e.g. age, education, experience). In addition, we will collect behavioural characteristics of individual farmers. These will include general questions, using a tailor made questionnaire based on the RAA (see Table
Examples of the types of behavioural related data to be collected in interviews.
RAA generic questions | |
Beliefs | How important is it for you to improve biodiversity in the agriculture areas you farm? How significant do you feel the following cultivation methods are in contributing to improving biodiversity? Setting aside land, tillage practices, organic farming, … |
Norms | Who are important people/institutions that you would consider in your farming decisions? How strongly do you agree with the following statements? Most people who are important to you think that you should adopt….Most people who are important to you think that you should only consider economic return in your decision-making. |
Control | What are barriers preventing you from adopting…? How confident are you that you could overcome the barriers that prevent you from…? How often do you feel you have control over the following outcomes? Water use efficiency / Fertilizer use efficiency / … |
M3.3. Calibrating and validating InVEST ecosystem service and biodiversity models
BESTMAP approach is to use a compromise between simplicity (e.g. Capacity Matrix approach) and accuracy (as with most detailed process-based models; see Fig.
InVEST models used in BESTMAP.
Model and short description | Model application in BESTMAP | Refs |
Carbon model: Includes above- and belowground biomass, soil, and dead organic matter. Estimates the net amount of carbon stored in a land parcel over time. | Map carbon storage densities to each FSA based on land-use type, management (including cropping types and patterns, livestock type and density, cultivation practices, cover crops) and climate, soil and topographic characteristics |
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Water quantity model: Estimating water run-off as precipitation minus evapotranspiration, not separating surface, sub-surface and shallow groundwater flows. | Using land use from CORINE. root restricting layer depth and AWC from ESDB. precipitation data from WorldClim and PET from the CGIAR-CSI Global-Aridity and Global-PET Database. EEA Waterbase and/or CS streamflow stations data will be used for calibration and validation. |
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Water quality model: Computes a long-term nutrient mass balance assuming nutrient sources associated with different FSAs and retention properties (e.g. LULC, slope) of pixels belonging to the same flow path. | Using data on each FSA (including from interviews) estimate nutrient load applied to the land and the proportional retention of that nutrient load. |
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Sediment delivery model: USLE equation-based estimated eroded sediment and proportion reaching the catchment outlet based on topography and land-use | Using similar data to water quality model. Information on rainfall erosivity and soil erodibility will be accessed from the ESDAC and LUCAS databases and literature searches. |
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Crop production model: Data-driven yield model, covering 175 crops worldwide, and a regression based model that accounts for fertiliser application rates on 12 crops based on annual precipitation & number of growing degrees days | Use local/regional data to estimate intensification level (yield percentile). Use future climate projections (e.g. from CMIP5) to project yields assuming no technological changes |
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Biodiversity (habitat quality) model: Impact of different land-uses, linear and aerial features in the quality of habitat for indicator taxa. | Use existing data of abundance / likelihood of presence for indicator species (e.g. birds or butterflies, although the decision will depend on the CS) to calibrate model |
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BESTMAP workflow to map FSAs, defined as having a characteristic bundle of ESS (and dis-services e.g. pollution), biodiversity, socio-economic and behavioural characteristics. Geospatial datasets (the Case Study Base Layer) will be used to approximate these FSAs (the “Proto-FSAs” clusters) in the first step; followed by interviews, field data collection and modelling to assign values on ESS, biodiversity, socio-economic aspects and behaviour for a stratified sample of proto-FSAs farms. The proto-FSAs will likely share “socio-economic-environmental” space and will be split/merged to minimize (but not eliminate) that overlap by changing the clustering in Case Study Base Layer space, defining the final FSAs and their spatial distribution.
M3.4. Establishing typology of Farming System Archetypes
BESTMAP will extend previous land systems typologies (Sec. C2) and apply them at the scale of individual farms. What is the best operational definition of FSAs, and which Case Study Base Layer variables should be considered in the final definition is one of the research questions of BESTMAP. This is critical as it will determine the balance between the level of detail captured by FSAs, the BESTMAP-PIAM general applicability and its scalability. First, we will generate ‘Proto-FSAs’ using either a self-organizing map (SOM) bottom-up, data-driven classification approach (
M3.4.1. Exploring state-of-art Remote Sensing for mapping Farming System Archetypes
BESTMAP will also explore how additional remote sensed data can help improve and possible scale-up the FSA mapping. Sentinel-2 satellite imagery will be radiometrically and atmospherically corrected (top-of-canopy reflectance) using partner MUNDIALIS´ automated, cloud based processing chain actinia (https://actinia.mundialis.de). Cloud free image composites will be created and based on these, suitable vegetation indices (see Table
Example of vegetation index computable from Sentinel-2. BESTMAP will explore which indices complement other data best to report on land use intensity and other dimensions of the FSAs.
Index | What does the index capture | Reference |
Greenness Index (GI) | Measure of vegetation vigour or biomass |
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Normalized Difference VI (NDVI) | Measure of healthy, green vegetation |
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Soil adjusted Vegetation Index (SAVI) | Like NDVI but soil colour, soil moisture, and saturation effects from high density vegetation |
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Enhanced Vegetation Index (EVI) | Healthy, green vegetation, less sensitive to saturation and reduce atmospheric influences |
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Green–RedEdge NDVI (GRNDVI) | Vegetation health and plant stress, biomass |
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Modified Chlorophyll Absorption Ratio Index (MCARI) | Variations in chlorophyll concentrations and leaf area index (LAI) |
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Normalized Difference Water Index (NDWI) | Measure of vegetation water content |
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M3.5. Multi-level Agent-Based Modelling
BESTMAP we will develop ABMs to investigate and represent the decision-making of farmers, including the influence of exogenous factors such as existing and new policies on farm management. The ABM specifications will create individual farmers and representing their behaviour (how they ‘perceive’ exogenous drivers, e.g. become informed on policy change and/or economic price/cost changes, how farmers interact and farmer/field agents interact, how farmers attributes change over time and the rules that govern these attributes). The model will create a realistic, spatially explicit dynamic environment (based on rules that govern diversification i.e. new fields within farm, homogenization, merge/splitting of farms, and inter-generational transitions) and embed relevant policy within the computer environment (new and existing policies implemented by farmers, water companies, advisory bodies etc.). The basic entities and their attributes are shown in Fig.
BESTMAP ABM concept. Individual farmer’s decision-making is modelled using the RAA (orange elements). Each farmer agent has an internal state that is characterized by goals & needs (profit maximization, safety first, risk aversion, etc.), knowledge (education, farming strategies, beliefs about consequences of different practices, etc.), values (social norms, strength of beliefs, value of conservation or biodiversity, etc.) and assets (monetary resources, production means, information, etc.). These shape farmer’s attitude towards different behaviours (e.g. pro or contra organic farming), perceived norm (social pressure or other farmer’s behaviour) and perceived behavioural control (agent’s capacity to perform certain behaviours). Every farmer agent can perceive the state of the farming system (e.g. yields, ESS provision), the behaviour of other farmers as well as exogenous drivers (e.g. price or policy changes). Evaluating these lead to changes in attitudes or perceived norm, which can expand or reduce its behavioural options. Each behavioural option represents an intention to perform a certain behaviour (e.g. adopting a different crop choice or setting aside land). Using a multi-objective utility function, the farmer agent will select a specific behavioural option that will change the state of its farm’s fields and provide some economic benefit to the farmer. Specifically, farmer’s decisions can lead to changes in cropping system, farm and field size, or in farm ownership, which allows us to analyse/model structural change.
An initial basic model prototype will be jointly developed for CSs that will be theoretically based on the RAA and empirically informed by knowledge from Brussels level experts and stakeholders. In the second step, the prototype will be adapted to the specific CS context, based on empirical data gathered through questionnaires/interviews (Sec. M3.2). To parameterize the model, we will use
Methods such as pattern-oriented modelling (
M4. Upscaling and testing the BESTMAP approach at EU and global scale
BESTMAP will make use of a bottom-up, data-driven approach that assesses the potential transferability of CS findings (sensu
M4.1. European-wide ecosystem services modelling
BESTMAP will try using the European-wide FSAs and a subset of the InVEST models for which EU-wide validation data currently exist, to test if these simple biophysical models, which have been successfully calibrated/validated at UK scale (
M4.2. European-wide Agent-Based Modelling
There are different approaches to upscaling of ABMs (
To build a European-wide ABM, BESTMAP will assign attributes and behavioural rules to synthetically derived representative populations within each NUTS-2 based on similarly metric to the FSAs with CS ABMs. A cross-validation approach (i.e. using four CSs for training and comparing model on the fifth) will be used to validate the model. The European-wide ABM will be calibrated to reproduce micro-scale and macro-scale properties in CS scale, as well as large scale historical trends [e.g. from Eurostar/FAOSTAT data, from CORINE land-use change, from land systems archetypes change 1990-2006 of
M5. Building capacity
Communication, dissemination and capacity building (particularly training) activities will lay a solid foundation for responding to the needs and specific requests of identified stakeholders (EU DG staff, Members of the European Parliament (MEPs) and the EC, local government agencies, other researchers). BESTMAP activities will include
Those events will take place in Brussels (organized by local partner RISE) and in each CS. Capacity building will additionally include effective use of social media, such as LinkedIn, Facebook, SlideShare and Twitter overseen by a social media officer appointed by the GA.
A variety of dissemination materials will be produced, including mini user guides, open access scientific papers, policy briefs andvideos. These will use current rural policy debates to introduce modelling innovation to non-experts. Training materials such as CS booklets and data packages, user guides, technical models descriptions, webinar and open online course (e.g. using FutureLearn) will build capacity for different audiences.
M5.1. Producing indicators for decision-makers
BESTMAP will maps the ESS, socio-economic models and biodiversity impact outputs from the ABMs to existing and under-development SDGs and CAP indicators. An initial mapping performed during proposal preparation shows that BESTMAP models are related to many of the CAP Strategic Plans output, results and impact indicators as well as 10 of the SDGs and respective indicators. The list of indicators will be presented, reviewed and discussed with stakeholders and decision makers in a Brussels workshop. This will help BESTMAP to validate, reduce or extend the list according to stakeholders’ expertise and needs. Results of this workshop are expected to impact on future list on indicators adopted by the EC, so will be elevated to interested organizations.
M5.2. Implementing an integrated modelling and dashboard system
BESTMAP will adopt a rapid application development where an initial implementation of the system is presented to the users (i.e. policy makers and modellers) and iteratively co-designed and refined. It will offer open access to the individual component for its integration into other bigger systems, possibly from SUPREMA or other RUR-04 funded projects. The diversity of data platforms and models (Case Study Base Layer, FSA mapping, DART-BIO CGE model, ABM, InVEST ESS models, geostatistical models of socio-economic aspects etc.) represents diversity in inputs, execution platforms and combinable results. Instead of imposing a common infrastructure (e.g. programming language), the policy dashboard aggregating regional CS results will use pre-computed economic scenarios (from DART-BIO), ABM runs and ESS and socio-economic analyses. The dashboard user interface will allow easy selection and comparison of different policy scenarios, their impacts within each case study area, and comparisons across CSs. It will also include data quality indicators (following ISO 19157 and QualityML - FP7 GeoViQua) and be exposed as OGC Web Map Services.
In addition, BESTMAP will develop (in collaboration with LifeWatch-ERIC) a Virtual Lab for the European-wide models (Fig.
M6. Data management and community building
It is central to BESTMAP exploitation and innovation potential that outputs produced by the project are available, discoverable, and usable by others. Those outputs include the economic model (DART-BIO) outputs, the Case Study Base Layer and European Base Layer databases (limited by data privacy and secondary data licences), the FSAs we identified, the ESS/biodiversity/socio-economical models (including their parameterization for different CSs and at European scale), the ABMs, the policy scenarios (co-developed in both Brussels and national/regional workshops), the analyses of policy scenarios impacts, and the produced policy dashboard and the Virtual Lab (for the European-wide models). To make our outputs available, the Data Management Plan will list all outputs and define how these should be deposited into permanent repositories (e.g. GitHub, Zenodo, CoMSES Net (aka OpenABM)) with sufficient meta-data to be discoverable, e.g. including JSON-LD mark-up to be listed by Google Dataset Search https://toolbox.google.com/datasetsearch. The project will adopt a single ‘OpenBESTMAP’ identity (e.g. username) across multiple community websites to develop a link to the project during and after its lifetime. Instead of creating our own community website (which will unlikely sustain after the project), BESTMAP will actively engage in existing developers communities including NetLogo, ModelingCommons (http://blog.modelingcommons.org), CoMSES Net (www.comses.net), Ecosystem Services Partnership (www.es-partnership.org), Natural Capital Project (https://forums.naturalcapitalproject.org), European Social Simulation Association (http://www.essa.eu.org), Integrated Assessment Modelling Consortium (http://www.globalchange.umd.edu/iamc) and others.
M7. Social Science and Humanities and Inter-Disciplinary issues
BESTMAP directly addresses three social issues
To address these issues, we will consider the social dimension (e.g. by exploring how the incorporation of ABMs change policy impact assessment), economic dimension (e.g. with the business model and cost analysis to up-scale the project outputs), behavioural dimension (with planned interview campaigns), institutional issue (e.g. with capacity building and training workshops to help exploitation of outputs) and cultural dimension (with communication and dissemination events aimed to bring BESTMAP objectives to stakeholders in each CS region).
BESTMAP consortium includes social science experts from multiple social and behavioural sciences. These include economics (environmental and agricultural economist R. Delzeit, IfW), political science (O. Fritsch, UNIVLEEDS), geography (A. Heppenstall, UNIVLEEDS), and sociology (Nina Hagemann, UFZ, social-ecological modeller B. Müller, UFZ). Their contribution will be essential in all stages of the proposed project, in particular in planned Brussels and national/regional workshops, building economic model (DART-BIO) scenarios and outputs, producing protocols and leading interview campaigns across five CS regions, and creating, calibrating and validating individual ABMs at the CS level and one at the European scale.
BESTMAP is multi-disciplinary, inter-disciplinary and trans-disciplinary. Beyond the listed social sciences expertise, the project involves experts in ESS/biodiversity (G. Ziv, M. Beckmann, J. Bullock, T. Václavík), agro-ecology (B. Kunin, B. Šarapatka), remote sensing (A. Cord, C. Domingo, M. Neteler), ICT and data standards (J. Masó), communication and publishing (L. Penev, PENSOFT), business development (H. Pollitt, CE), and national/international development (A. Williams). BESTMAP include experts from academia, non-profit and SMEs. Many of the tasks and activities within the project involve expertise of multiple disciplines. Furthermore, BESTMAP has strong element of co-design and co-development with policy-makers and decision-makers, as well as with other modellers.
M8. Sustainability and Climate Change issues
Agriculture contributes to 10% of the EU’s total greenhouse gas emissions. These emissions have declined by 24% since the early 1990s, while the total output of agricultural production has been maintained thanks to better land management using modern technologies, improved knowledge and specific practices to combat climate change (EC 2015). BESTMAP models strive to take into account issues related to the reduction of soil fertility due to erosion (SDG 2), water quantity and quality (SDG 6), and chemical releases into the water (SDG 12), resource-efficient agriculture (SDG 9) and climate action in general (SDG 13, UN Paris Agreement), aiming to verify the difference in barriers to adapt climate focused CAP with the usage of RAA-based ABM.
M9. Gender and geographic balance
BESTMAP project considers gender balance and other equality issues in accordance with the main EU documents on research and innovation, including the Regulation No. 1291/2013 of the European Parliament and of the Council of 11 December 2013 establishing Horizon 2020, European Strategy of Responsible Research and Innovation (RRI), and ERC Gender Equality Plan 2014-2020.
Consortium structure: BESTMAP project governance is carefully designed to achieve gender and age balance, and the basic premise of the project is to equally involve representatives of both genders to decision-making processes at all levels and phases of the project. The Deputy Coordinator and two of the Consortium Coordination Team are females. The ratio of male to female team members involved in BESTMAP is nearly 1:1 (see Sec. 4.1). In addition, partners’ teams will actively support (mentoring of) young women and men at early stage of their careers both in terms of career building and in other project activities.
Project recruitment: Recruitment of PhDs and post docs will follow announcements in the national / international press. Partners will be instructed to offer positions in gender-sensitive was. All EU partners will be required to fully respect the local legislation on awarding of maternity / paternity leaves. Non-EU partners will follow best practices and regulations in their own country. All partners will be required to offer flexible working hours as much as possible to their appointed personnel. This will be stated in the job description.
Methodology: Gender and equality issues will be emphasised in balanced empirical data collection (Sec. M3.2) and community and capacity building (Sec. M5). Data will be obtained via questionnaires, interviews, focus groups, and other approaches, and analysed in a gender-sensitive way. Gender, age, socio-economic specifics, accessibility and physical impairment will be taken into account also as selection principles in recruitment of interviewees (farmers, agricultural advisors, policy-makers, etc.). Employing intersectional approach, gender variables will be intersected with other variables to obtain more complex and valid results and to avoid making general conclusions based on partial data.
Geographical and socio-cultural balance: Consortium members and the CSs were selected to represent various geographic, climatic and socio-economic backgrounds, various traditions and cultures, and they also reflect different human-nature relations. Following the principles of gender and social equity, partners will contribute their specific know-how in common effort to design and test the BESTMAP-PIAM in five regional case studies throughout Europe.
BESTMAP’s ambition is to empower EU and regional policy makers to better understand the assets and long-term drivers of rural territories and land-use and establish how they are impacted by current and future policies leading to better informed decisions. The ground-breaking nature of this project lies in the sophisticated co-design of core project elements, innovative and congruent data collection and provision, ambitious modelling and synthesis activities and the plans for legacy building that goes beyond the project. A fundamental understanding of the underlying concepts of land-use and human behaviours, knowledge, tools, experiences and skills form the basis of this project.
BESTMAP draws upon what is already known in practice and science and combines this with new innovative approaches by identifying spatially related units in Europe’s agricultural landscapes through farming system archetypes (FSA) and the implementation of key stakeholder behaviours and individual decision making in PIAM. The project will model the impact of policies changes at various geographic scales allowing deducing the local effects of global and EU-wide events. Through modelling the delivery of ESS at various scales, BESTMAP will help to harmonize the possibilities with the expectations societies have on the agricultural sector and rural areas which go well beyond food production. Through the development of a highly modular and customisable suite of tools and visualizations in the BESTMAP dashboard legacy beyond the project will be ensured that will allow flexible use and further improvements as needs arise. BESTMAPs results will substantially improve the capacity to model policies dealing with agriculture in the short term and will lead to improved, evidence-based policy design, impact assessments and monitoring in the long term.
BESTMAP has strong ambition to push the state-of-art across multiple domains. Table
Scientific and technical advances expected in BESTMAP, referenced to Implementation task numbers (e.g. Task 3.2).
Gaps in the state-of-the-art | Advances beyond state-of-the-art by BESTMAP |
Farmers’ decision makingExisting PIAMs ignore farmers’ decision making and work at a spatial scale too coarse to accurately model ESS impacts. | BESTMAP will link economic models (Task 2.4), behavioural-theory based ABM (Task 4.1) and proven high-resolution ESS models (Task 3.2) to work through these shortcomings. BESTMAP will demonstrate the feasibility of such integrated system (Task 4.4), and evaluate in what way its policy predictions differ from current PIAMs. |
Underlying land-use dataMost current ESS model frameworks use simple ‘land-use/land cover’ as their geographic reference system. | BESTMAP will use a more consistent system of “Farming System Archetypes” (FSAs), combining land-use/land cover with intensity, cropping system and rotation and field size (WP3). BESTMAP will map FSAs at regional CSs and EU level (Task 5.2), including demonstrating the use of state-of-art remote sensing (Task 5.3), and explore what are the key drivers differentiating FSAs within and across CSs. |
Implementation of decision making at larger scalesCurrent behavioural model approaches are mainly restricted to localized scales. | BESTMAP will explore the feasibility of upscaling ABM results (WP5) based on European Base Layer and Case Study Base Layer data harmonization, FSA mapping and ESS and socio-economic models at European scale (Task 5.2). |
Upscaling individuals’ behaviourMost computational approaches in social science are struggling with upscaling individual behaviours to large scales. | BESTMAP will explore up-scaling of regional ABMs (Task 5.2), based on microsimulations of farm-level agents at larger scale, using wegihted data from regional CS ABM dynamical rules and a metric of similarity based on agent characteristics. This approach is readily transferable to other social science questions where the challenge is to understand the impact of aggregrated individual behaviour. |
Disparity and lack of homogenization in geospatial data sources Existing datasets are distributed in different formats and locations, and lack a consistent temporal and spatial reference system. | BESTMAP will combine (Task 3.1) multiple data sources, mostly existing geospatial datasets (e.g. IACS, LPIS, FADN, INSPIRE), and socio-economic statistics into Case Study Base Layers and a European Base Layer to ensure the unified use and analyses across all CS and at European scale. Those databases will be made publicly available as Deliverables. |
Мapping of ESS Most efforts using modelling tools such as InVEST are typically using indirect proxies and/or not rigorously validating the outputs. |
BESTMAP will follow example used in UK by UKRI ( |
Lack of knowledge on new CAP policy impacts on the environment and society Policy debates about the impact of COP21 on agriculture (and vice versa) as well as other topics (e.g. UK Brexit) necessitates increased capacity for impact assessment. | BESTMAP will explore several co-designed policy scenarios, and predict their impacts at European scale (from DART-BIO (Task 2.4) and from a European-wide model (Task 5.2) and in a set of five CSs that is typical for their countries (WP4). Developing those outputs into an online policy dashboard and a Virtual Lab (Task 6.4) will be a technical development that can help in this issue. |
Discussions on policy targets/ indicators is disconnected from data Ongoing efforts to monitor CAP and SDG goals created a large number of targets, but getting these mapped into data (including remote sensed data) and outputs (e.g. of ESS models) is lacking. | BESTMAP workshops will link existing indicators / targets to geospatial data and to ESS / socio-economic model outputs used in the project (Task 2.1). The ongoing work to operationalize that mapping will collimate with actual translation of outputs to indicators (Task 4.3) and the development of the policy dashboard (Task 6.4). |
Remote sensing algorithms of agricultural intensity lack accuracy While some remote sensing algorithms are well developed, others including mapping mowing frequency and agricultural productivity (yield/ intensity) are still at their infancy. | BESTMAP will demonstrate the use of recently launched ESA satellites Sentienl-1 and Sentinel-2 to monitor mowing frequency and crop intensity. This task (Activity 5.3.1) will be led by SME MUND working in the field of remote sensing geospatial data services. BESTMAP will improve on automated pipelines for imagery pre-processing, and on machine-learning algorithms for crop type, mowing frequency and crop yields mapping – testing new data fusion combining optical and radar data. In particular, we will explore what type of vegetation indices produced by Sentinel-2, focusing on the three Red-Edge, SWIR and NIR bands, best characterize heterogeneity within different cropping systems (i.e. as indicators of yield/intensity). |
BESTMAP has ambitious innovation potential across all aspects of the proposal. The details of the planned innovation will be elaborated in the Commercialization and Exploitation Plan (CEP) in Sec 2.2. At the long-term, there is greater potential for innovation (i.e. future exploitation leading to different types of benefits) from the project objectives, concepts, approach and new organizational and service models:
The overall goal of BESTMAP is to reach a TRL 5 or 6 (technology validated/demonstrated in relevant environment) for the proposed modelling architecture. Different components of the project will reach other TRL levels. For example, the pre-processing system for remote sensed data by MUND is at TRL 7, the InVEST platform is already at TRL 9 while for some of socio-economic models, it is likely that TRL 3 (proof of concept) or 4 (validated at lab) will be the maximum we can reach. For the online data visualization and the policy dashboard, an implementation at regional CS will reach TRL 7 (demonstrated in operational environment) or higher. The Plan for Exploitation will discuss a roadmap to increase the TRL of the project outputs during and after BESTMAP.
This project receives funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 817501.