Mapping and prioritising landscape feature restoration in agricultural landscapes: A case study in Brandenburg, Germany

EU agricultural landscapes are vital for biodiversity. Intensive agricultural practices constitute, however, key drivers of biodiversity loss. The EU Biodiversity Strategy for 2030 aims to restore “at least 10 % of agricultural area under high-diversity landscape features”, yet it lacks specific guidance for assessment and implementation. Here, we develop an approach to (a) map and assess agricultural landscape features (LF) cover at a landscape scale, (b) cluster agricultural landscapes by complexity using both compositional and configurational heterogeneity metrics, and (c) prioritise landscapes and sites for LF restoration by targeting areas of lower productivity and higher erosion risk, to enhance farmer acceptance and increase restoration benefits. Using Brandenburg, Germany, as a case study, we found that 94.4 % of landscapes fall short of the 10 % landscape features cover target. We categorised five agricultural landscape types ranging from simple to complex mosaics. At the local scale, in ten randomly selected landscapes, we identified an additional 11 % of agricultural areas on which LF could be restored. Our study helps advance methodologies to prioritise LF restoration. It can provide guidance for administrators and planners to assess the LF status and advance their adoption by farmers. We recommend prioritising landscapes and selecting restoration actions based on overall landscape complexity assessment. At the local scale, we recommend participatory processes involving local stakeholders. We believe our methodology is transferable to other EU regions, and highlights both the opportunities and challenges in developing a standardised, EU-wide approach for prioritising LF restoration to support agroecological transitions in Europe. • A spatially-explicit approach to evaluate and prioritise LF restoration. • Clustering landscape characteristics help identify priority areas for LF restoration. • 94.4 % of Brandenburg's landscapes fall short of the 10 % landscape feature target. • Focusing on low-production areas at local scale can help reach the 10 % target. • Farmers’ acceptance and landscape characteristics are vital for successful adoption.

Authors

Institutions

Publication Details

Journal
Land Use Policy
Published
2025-04-16
DOI
https://doi.org/10.1016/j.landusepol.2025.107531
Primary Topic
Conservation, Biodiversity, and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mapping and prioritising landscape feature restoration in agricultural landscapes: A case study in Brandenburg, Germany

Aletta Bonn, Guy Pe’er, Sonoko Dorothea Bellingrath‐Kimura, Ariani C. Wartenberg et al.
Land Use Policy
Conservation, Biodiversity, and Resource Management
article

Mapping and prioritising landscape feature restoration in agricultural landscapes: A case study in Brandenburg, Germany

Aletta Bonn, Guy Pe’er, Sonoko Dorothea Bellingrath‐Kimura, Ariani C. Wartenberg, Elizabeth A. Finch, Linn N. Schaan, Vincent S. Boettner
article en

Abstract

EU agricultural landscapes are vital for biodiversity. Intensive agricultural practices constitute, however, key drivers of biodiversity loss. The EU Biodiversity Strategy for 2030 aims to restore “at least 10 % of agricultural area under high-diversity landscape features”, yet it lacks specific guidance for assessment and implementation. Here, we develop an approach to (a) map and assess agricultural landscape features (LF) cover at a landscape scale, (b) cluster agricultural landscapes by complexity using both compositional and configurational heterogeneity metrics, and (c) prioritise landscapes and sites for LF restoration by targeting areas of lower productivity and higher erosion risk, to enhance farmer acceptance and increase restoration benefits. Using Brandenburg, Germany, as a case study, we found that 94.4 % of landscapes fall short of the 10 % landscape features cover target. We categorised five agricultural landscape types ranging from simple to complex mosaics. At the local scale, in ten randomly selected landscapes, we identified an additional 11 % of agricultural areas on which LF could be restored. Our study helps advance methodologies to prioritise LF restoration. It can provide guidance for administrators and planners to assess the LF status and advance their adoption by farmers. We recommend prioritising landscapes and selecting restoration actions based on overall landscape complexity assessment. At the local scale, we recommend participatory processes involving local stakeholders. We believe our methodology is transferable to other EU regions, and highlights both the opportunities and challenges in developing a standardised, EU-wide approach for prioritising LF restoration to support agroecological transitions in Europe. • A spatially-explicit approach to evaluate and prioritise LF restoration. • Clustering landscape characteristics help identify priority areas for LF restoration. • 94.4 % of Brandenburg's landscapes fall short of the 10 % landscape feature target. • Focusing on low-production areas at local scale can help reach the 10 % target. • Farmers’ acceptance and landscape characteristics are vital for successful adoption.

Land Use PolicyVol. 154
Helmholtz Centre for Environmental Research (DE), German Centre for Integrative Biodiversity Research (DE), Humboldt-Universität zu Berlin (DE), Leibniz Centre for Agricultural Landscape Research (DE), Freie Universität Berlin (DE), Friedrich Schiller University Jena (DE)
Zero hunger
Openalex Percentile: Top 91%
Conservation, Biodiversity, and Resource Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.