ARPHA Preprints, doi: 10.3897/arphapreprints.e82404

Deliverable D2.2 BESTMAP Conceptual Framework Design & Architecture

Guy Ziv, Jodi Gunning^‡, Tomáš Václavík^§, Michael Beckmann^|, Anne Paulus^¶, Birgit Mueller^#, Meike Will^¤, Anna Cord^«, Stephanie Roilo^», James Bullock^˄, Paul Evans^˅, Cristina Domingo-Marimon^¦, Joan Masó Pau^¦

‡ University of Leeds, Leeds, United Kingdom§ Palacký University Olomouc, Olomouc, Czech Republic| Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany¶ Helmholtz Centre for Environmental Research - UFZ (Helmholtz-Zentrum Umweltforschung GmbH UFZ), Leipzig, Germany# UFZ, Leipzig, Germany¤ Helmholtz-Centre for Environmental Research - UFZ, Leipzig, Germany« Technische Universität Dresden, Dresden, Germany» Dresden University of Technology (Technische Universität Dresden), Dresden, Germany˄ UK Centre for Ecology & Hydrology, Lancaster, United Kingdom˅ UK Centre for Ecology & Hydrology (UKCEH), Wallinford, United Kingdom¦ Centre for Ecology Research & Forestry Applications, Barcelona, Spain

Corresponding author: Guy Ziv ( g.ziv@leeds.ac.uk )

This is an open access preprint 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, Gunning J, Václavík T, Beckmann M, Paulus A, Mueller B, Will M, Cord A, Roilo S, Bullock J, Evans P, Domingo-Marimon C, Masó Pau J (2022) Deliverable D2.2 BESTMAP Conceptual Framework Design & Architecture . ARPHA Preprints. https://doi.org/10.3897/arphapreprints.e82404

Abstract

This deliverable provides a General Framework for the BESTMAP Policy Impact Assessment Modelling (BESTMAP-PIAM) toolset. The BESTMAP-PIAM is based on the notion of defining (a) a typology of agricultural systems, with one (or more) representative case study (CS) in each major system; (b) mapping all individual farms within the case study to a Farm System Archetype (FSA) typology; (c) model the adoption of agri-environmental schemes (AES) within the spatially-mapped FSA population using Agent Based Models (ABM), based on literature and a survey with sufficient representative sample in each FSA of each CS, to elucidate the non-monetary drivers underpinning AES adoption and the relative importance of financial and non-financial/social/identity drivers; (d) linking AES adoption to a set of biophysical, ecological and socio-economic impact models; (e) upscaling the CS level results to EU scale; (f) linking the outputs of these models to indicators developed for the post-2020 CAP output, result and impact reports; (g) visualizing outputs and providing a dashboard for policy makers to explore a range of policy scenarios, focusing on cost-effectiveness of different AES.

Keywords

policy assessment, modelling, agriculture, ecological, socio-economic