Semi-natural meadow conservation under pressure

Despite their high nature conservation value and protection through the EU Habitats Directive (92/43/EEC), extensively managed semi-natural grasslands are often disappearing rapidly from European landscapes. Given the limited success of agricultural policy interventions in curbing grassland loss, a better understanding of farmers’ decision-making regarding grassland management is needed. This study explored the determinants of extensively managed meadow conservation in two lowland regions in Slovenia using mixed methods – qualitative analysis of interviews with farmers and quantitative modelling using machine learning. The results of our quantitative model indicate that the most influential predictors of extensive meadow preservation are increased plot size, greater distance from roads, higher share of rented land and share of meadows on the farm, as well as close proximity to water. These meadows thus often seem to be productively marginalised due to flooding, remoteness or low drainage. While farmers in our interviews valued extensive meadows for their aesthetic beauty, it is the productive value and social norms that typically drive their decisions favouring grassland intensification. Although some farmers appreciated the possibility of being compensated for extensive grassland management under agri-environmental measures, these measures do not act as a sufficient motivator for extensive meadow conservation.

Authors

Institutions

Publication Details

Journal
Repository of the University of Ljubljana (University of Ljubljana)
Published
2026-03-31
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Semi-natural meadow conservation under pressure

Tanja Šumrada, Živa Alif, Ana Novak
Repository of the University of Ljubljana (University of Ljubljana)
Land Use and Ecosystem Services
article

Semi-natural meadow conservation under pressure

Tanja Šumrada, Živa Alif, Ana Novak
article en

Abstract

Despite their high nature conservation value and protection through the EU Habitats Directive (92/43/EEC), extensively managed semi-natural grasslands are often disappearing rapidly from European landscapes. Given the limited success of agricultural policy interventions in curbing grassland loss, a better understanding of farmers’ decision-making regarding grassland management is needed. This study explored the determinants of extensively managed meadow conservation in two lowland regions in Slovenia using mixed methods – qualitative analysis of interviews with farmers and quantitative modelling using machine learning. The results of our quantitative model indicate that the most influential predictors of extensive meadow preservation are increased plot size, greater distance from roads, higher share of rented land and share of meadows on the farm, as well as close proximity to water. These meadows thus often seem to be productively marginalised due to flooding, remoteness or low drainage. While farmers in our interviews valued extensive meadows for their aesthetic beauty, it is the productive value and social norms that typically drive their decisions favouring grassland intensification. Although some farmers appreciated the possibility of being compensated for extensive grassland management under agri-environmental measures, these measures do not act as a sufficient motivator for extensive meadow conservation.

Repository of the University of Ljubljana (University of Ljubljana)
University of Ljubljana (SI)
Life in Land
Openalex Percentile: Top 20%
Land Use and Ecosystem Services
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.