Dr. Robert Pazur

Current agricultural practices in Europe are increasingly aggravating societal and environmental safety concerns. This creates social and regulatory pressures on farmers, which can lead to declining material and social status of farmers, farmer discontent, and anti-regulation protests. These tensions are rooted in conflicting value systems for agricultural development, which can range from productivist pathways (i.e. valuing production above all else) to increasing multifunctionality pathways (i.e. valuing agriculture for its contribution to multiple economic, environmental and societal needs). It is largely unknown to what degree individual farms and agricultural landscapes are transitioning towards increasing productivism or multifunctionality in practice. Here, we mapped landscape changes and interviewed farmers (n = 274) to examine the diversity of agricultural development pathways in 17 study sites across Europe over the last 20 years (2000–2020). We also assessed the associations between the development pathways and farmers’ perceptions of socio-economic outcomes, namely job satisfaction, societal valuation, and economic performance. Farm-level development was largely aligned with productivist pathways, while landscape-level changes aligned more closely with an increasing multifunctionality pathway. Farmers on pathways of increasing multifunctionality did not perceive improved outcomes on livelihood indicators as compared to productivist farmers. Furthermore, farms on increasing multifunctionality pathways were concentrated in sites with very high management intensities that face strong pressure from environmental regulations, as well as low-intensity, mountainous sites, where opportunities for intensification are limited. These results suggest that current pathways that increase multifunctionality arise mostly by necessity. Successful agricultural transformation will therefore require policy to create enabling environments that provide socioeconomic benefits for farmers to increase multifunctionality, and a civil society and market conditions that value sustainable agriculture.

Context: Anthropogenic landscape change is an important driver shaping our environment. Historical landscape analysis contributes to the monitoring and understanding of these change processes. Such analyses are often focused on specific spatial scales and single research methods, thus covering only limited aspects of landscape change. Objectives: Here, we aim to assess the potential of combining the analysis of historical aerial imagery and local stakeholder interviews for landscape change studies using a standardized mapping and interviewing approach. Methods: We compared six agricultural landscapes across Europe and mapped land-cover using historical aerial imagery (starting between 1930 and 1980, depending on data availability, until recent years) with an object-based image analysis and random forest classification. For local perspectives of landscape change, we conducted oral history interviews (OHIs) with (almost) retired farmers. Comparing recorded landscape changes from both approaches provided insight into advantages of combining these two methods. Results: Object-based analysis enabled the identification of high-resolution land-cover dynamics, with scale enlargement and cropland/grassland expansion being the most commonly recurring trends across European landscapes. Perceived landscape changes identified in the OHIs included changes in farm management, landscape structure, and infrastructure. Farmers also reported drivers and personal values associated with landscape change. Combining the two historical landscape analysis tools resulted in a qualitative and quantitative understanding of changes in land-cover, land use, and land management. Conclusions: Comparing physical land-cover change with local farmer perspectives is key to a comprehensive understanding of landscape change. There are different ways the two methods can be combined, leading to different venues for science and policy making.

Grasslands are one of the most widespread terrestrial biomes. In Europe, the majority of grasslands depends on human management, and as a result, grassland areas are facing different dynamics and transitions to other land cover types. This study identifies the trajectories in grassland cover in Europe from 1990 to 2018. Using a 1 km × 1 km grid spanning most European countries, we identified increasing, decreasing and mixed development trajectories of grassland cover. For each development trajectory, we selected four representative hotspots and identified the land cover and its changes over different periods. Our results reveal that the decreasing development trajectory occurred on 35% of the aggregated grids with hotspots in Eastern Europe, the Mediterranean region and the UK. The increasing development trajectory occurred on 20% of the aggregated grids, centred in Central Europe, and the mixed development trajectory occurred on 3% of the aggregated grids. Within the selected hotspots, the development trajectories were linked to transitions among agricultural land cover classes or to transitions with shrubs and urban areas. The development trajectories also indicated strong country-specific differences, suggesting a strong impact of national policies. The generated outcomes of this study can be used to support decision-making on future pathways for grassland habitats on a European scale.

European grasslands face strong declines in extent and quality. Many grassland types are priority habitats for national and European conservation strategies. Countrywide, high spatial resolution maps of their distribution are often lacking. Here, we modelled the spatial distribution of 20 permanent grassland habitats at the level of phytosociological alliances across Switzerland at 10x10 m resolution. First, we applied ensemble models to provide distribution maps of the individual habitat types, using training data from various sources. Copernicus Sentinel satellite imagery and variables describing climate, soil and topography were used as predictors. The performance of these models was assessed based on the true skill statistics with a split-sampling of the data. Second, the individual maps were combined into countrywide maps of the most and second most likely habitat type, respectively, using an expert-based weighting approach. The performance of the combined map for the most likely habitat type was assessed via an independent testing dataset and a comparison of the predicted habitat-type proportions with extrapolations from field surveys. Most individual maps had useful to excellent predictive performance (TSS ≥ 0.6). For most grid cells in the combined maps, the most and second most likely habitat types were either ecologically closely related or representing two grassland types along a nutrient gradient. The same was true for omission errors. We found good agreement between the predicted and estimated proportions from field surveys. The area of raised bogs appears to be underestimated, while dry grasslands showed highest agreement. This work highlights the potential of earth observation data at fine spatial and temporal resolution to map habitats at broad scales, thereby providing the foundation for diverse conservation applications. A particular challenge remains in capturing the transition from nutrient-poor to nutrient-rich grasslands, which is highly important for biodiversity conservation.

Farming in Europe has been the scene of several important socio-economic and environmental developments and crises throughout the last century. Therefore, an understanding of the historical driving forces of farm change helps identifying potentials for navigating future pathways of agricultural development. However, long-term driving forces have so far been studied, e.g. in anecdotal local case studies or in systematic literature reviews, which often lack context dependency. In this study, we bridged local and continental scales by conducting 123 oral history interviews (OHIs) with elderly farmers across 13 study sites in 10 European countries. We applied a driving forces framework to systematically analyse the OHIs. We find that the most prevalent driving forces were the introduction of new technologies, developments in agricultural markets that pushed farmers for farm size enlargement and technological optimisation, agricultural policies, but also cultural aspects such as cooperation and intergenerational arrangements. However, we find considerable heterogeneity in the specific influence of individual driving forces across the study sites, implying that generic assumptions about the dynamics and impacts of European agricultural change drivers hold limited explanatory power on the local scale. Our results suggest that site-specific factors and their historical development will need to be considered when addressing the future of agriculture in Europe in a scientific or policy context.

Over the past 25 years, the study of driving forces of landscape change has developed into a central theme in land change science by contributing to theory development, promoting the analysis of causation of change and gaining insights into how landscape development could be steered into a societally more desirable direction. Based on this progress, we designate important research avenues, reviewing critical challenges forming the base for advancing the study of driving forces of landscape change and addressing the question on how the study of driving forces can contribute to system transformative research. For each of the research avenues, we describe the current dominant approach and provide some specific ways of advancing both the conceptualization and the research methods. Together, advancing on these research avenues will promote a more social-ecological systems perspective to the study of driving forces of landscape change.

It has been shown that the COVID-19 pandemic affected some agricultural systems more than others, and even within geographic regions, not all farms were affected to the same extent. To build resilience of agricultural systems to future shocks, it is key to understand which farms were affected and why. In this study, we examined farmers’ perceived robustness to COVID-19, a key resilience capacity. We conducted standardized farmer interviews (n = 257) in 15 case study areas across Europe, covering a large range of socio-ecological contexts and farm types. Interviews targeted perceived livelihood impacts of the COVID-19 pandemic on productivity, sales, price, labor availability, and supply chains in 2020, as well as farm(er) characteristics and farm management. Our study corroborates earlier evidence that most farms were not or only slightly affected by the first wave(s) of the pandemic in 2020, and that impacts varied widely by study region. However, a significant minority of farmers across Europe reported that the pandemic was “the worst crisis in a lifetime” (3%) or “the worst crisis in a decade” (7%). Statistical analysis showed that more specialized and intensive farms were more likely to have perceived negative impacts. From a societal perspective, this suggests that highly specialized, intensive farms face higher vulnerability to shocks that affect regional to global supply chains. Supporting farmers in the diversification of their production systems while decreasing dependence on service suppliers and supply chain actors may increase their robustness to future disruptions.

In recent years, new settlement mapping products have become available at the global and continental scale. Although accuracy assessments have indicated the high quality of these products, assessments were performed mainly on urban areas. However, there is also a need to monitor rural settlement development, as it is often located in proximity to biodiversity hotspots. In this paper, we verified the suitability of three settlement products (i.e., Global Urban Footprint - GUF, European Settlement Map - ESM and Open Street Map - OSM) to detect rural settlements in the Carpathian ecoregion. Two independent accuracy assessments indicated that the GUF captures rural settlements most effectively (overall accuracy - OA - 65.4% and 92.5% depending on the procedure). In contrast, the ESM overestimated settlements (OA - 49.5% and 90.8%), while the OSM (OA - 61.2% and 90.2%) was the most inconsistent source of settlement data. A regional comparison indicated some deviations from these accuracies, reflecting the variability of settlement structures within the study area. This study highlights that although the GUF was the best-performing product in mapping rural settlements across the whole study area, the settlement information it provided was rather conservative, and rural settlements are still insufficiently represented in all tested datasets.

Shrub encroachment is one of the major phenomena of grassland degradation throughout the world. One of the largest grassland areas is found in Central Asia. This area is playing an important role for biodiversity and food security, especially in a context of climate change. While sustainable land management is critical to avoid shrub encroachment, the dynamics of such encroachment are often unclear, especially in remote areas. We assessed the spatial and temporal dynamics of shrub encroachment in Suusamyr valley in Kyrgyzstan and analysed how these patterns relate to abiotic and social factors. To assess the extent of shrub encroachment we performed a random forest classification with Google Earth Engine to depict shrub encroachment between 2000 and 2019. By using a combination of vegetation indices and digital elevation derivatives in the classification model, the overall classification accuracy reaches above 90%. The mapping of temporal changes revealed an increase in shrub encroachment of 48% within the valley, adding up to an overall coverage of 2% of the grassland area. The spatial patterns of shrub encroachment are more strongly associated with anthropogenic factors, such as locations of roads and settlements, than with variations in precipitation and amount of organic carbon in soils. Around 45% of shrub encroachment occurs close to main roads and local paths, and within around 5 km distance from temporal settlements. This degradation is probably caused by overgrazing near settlements and the loss of grasslands in those areas puts additional pressure on local livestock farming, potentially leading to further degradation in neighboring areas. A shrub encroachment susceptibility map was created to identify areas vulnerable to further shrub expansion, affecting valuable grassland area. The identification of these areas at risk may inform sustainable grassland management by better allocating pastures according their capacity and reducing pressures on those places most susceptible to degradation.

This paper proposes a framework for identifying management practices on permanent grasslands based on a time series of remote sensing imagery. We assume that by using such an approach it is possible to check compliance with the legal conditions for granting CAP support for agricultural practices. We evaluated grassland management in the Medzilaborce region with Sentinel-2 optical satellite images. We proposed a set of indicators of mowing and grazing based on a rule-based assessment developed over a visual inspection of true color imagery as well as a phenological time series. We further used a land block database and evaluated the management using a specific traffic light system. We found that the grasslands in the studied area are managed extensively, not within time limits for granting CAP subsidies. Results suggest that this framework underestimates the proportion of the managed area of studied land blocks.

Agricultural landscapes support multiple functions and are of great importance for biodiversity. Heterogeneous agricultural mosaics of cropland and grassland commonly result from variable land use practices and ecosystem service demands. Switzerland's agricultural land use is considerably spatially heterogeneous due to strong variability in conditions, especially topography and climate, thus presenting challenges to automated agricultural mapping. Nationwide knowledge of the location of cropland and grassland is necessary for effective conservation and land use planning. We mapped the distribution of cropland and permanent grassland across Switzerland. We used several indices largely derived from Sentinel-2 satellite imagery captured over multiple growing seasons and parcel-based training data derived from landholder reporting. The mapping was conducted within Google Earth Engine using a random forest classifier. The resulting map has high accuracy in lowlands as well as in mountainous areas. The map will act as a base agricultural land cover dataset for researchers and practitioners working in agricultural areas of Switzerland and interested in land cover and landscape structure. The map as well as the training data and calculation algorithms (using Google Earth Engine) are freely available for download on the EnviDat platform https://doi.org/10.16904/envidat.205 (Pazúr et al., 2021).

Context Agricultural land abandonment across the steppe belt of Eurasia has provided an opportunity for the restoration of steppe landscapes in recent decades. However, global food demands are about to revert this trajectory and put restored steppe landscapes at risk.
Objectives We analysed steppe development in southern Russia in the last 40 years, assessed its spatial patterns and drivers of change for several periods.
Methods Using Landsat imagery, we mapped the permanent steppe and steppe restoration from 1990 to 2018. Based on regression tree models, we evaluate and explain its dynamics. Results were compared with district-level trends in land-use intensities of cropland.
Results We found 70% of the steppe in 2018 represented permanent steppe and 30% of former cropland dominantly abandoned in the postsocialism (1990–2000). The permanent steppe and steppe restored in the postsocialism (1990–2000) were located far from settlements, on rough terrain and in districts of the Virgin Land Campaign (1954–1963). In recent decades, the patterns of steppe restoration (2000–2018) were mostly determined by unfavourable agroclimatic conditions and distance from grain storage facilities. The restoration pattern reflects regional differences in land-use intensities, e.g., isolated steppe patches mostly appeared in areas of intensive agricultural land-use.
Conclusions Steppe restoration has appeared in areas marginal for agricultural production, with poor natural conditions and little human footprint. Consequently, the permanent steppe became less fragmented and a more continuous steppe landscape resulted. The remaining isolated steppe patches require attention in restoration programs as they are mostly located in areas of intensive agricultural land-use.

Context: Open data policies and accessible computation platforms allow efficient extraction of information from remote sensing data for landscape research. Landscape ecology is strongly influenced by remote sensing, and the value of fine resolution temporal information for characterising landscapes is under-explored.
Objectives: We highlighted the importance of temporal information extracted from remote sensing data gathered over a period of time for landscape research. A case study approach was used to show how time-series information can benefit the mapping of land cover and landscape elements in a heterogeneous landscape dominated by agricultural land use.
Methods: We constructed four composite images of the study area, each incorporating different levels of temporal information. The images either represent a single date or summarise temporal information into single values as the median of spectral bands or vegetation indices. Random forest and k-means clustering methods were used to classify the images. Results: The overall accuracy of the landscape classifications ranged between 0.3 to 0.8, increasing substantially when including temporal information, for mapping both land cover and small landscape elements. Using temporal information and a RF-based classification it was generally possible to map crop and forest types. The size of landscape elements was overestimated, although the clustering model predicted elements close to their true size and complexity.
Conclusions: The approach highlights the importance of temporal resolution for landscape ecology research. The easy-to-implement methodology offers an opportunity for landscape ecologists to increase the accuracy of landscape mapping and identify ecologically important landscape elements that might otherwise be missed.

Landslide inventory data sets are required for any landslide susceptibility mapping and prediction approaches. However, generating accurate landslide inventory data sets depends on applied methods and quality of input data, for example spatial resolution for satellite imagery. Therefore, the accuracy and availability of inventories vary in different studies. This study evaluated a strategy of sudden landslide identification product (SLIP) for landslide detection using Bi-Temporal Sentinel 2 Imagery and ALOS Digital Elevation Model (DEM). The resulting landslide detection map was then compared with an improved version of SLIP based on a fuzzy overlay. The resulting probability map was classified into three classes using the natural breaks method; the third class with the highest probability was extracted as the final map. The accuracy assessment stage demonstrated that using the improved version increased the accuracy by 16% compared to the SLIP method.

In der Schweiz gibt es zahlreiche unterschiedliche Lebensräume. Die WSL hat eine digitale Verbreitungskarte erstellt.

En Suisse, il existe d'innombrables milieux naturels différents, dont l'Institut fédéral de recherches sur la forêt, la neige et le paysage (WSL) a dressé une carte synoptique numérique.

New technological developments modulate the light levels of LED street luminaires according to traffic volumes: light levels are increased given traffic and reduced in its absence. Such dimming of street lights reduces the level of artificial light at night (ALAN) and may thus contribute to mitigate light pollution. To quantify the impact of traffic-driven dimming of street lights on nocturnal insect abundance and bat activity in comparison to full light (i.e., dimming functions of luminaires switched off), we mounted 20 insect flight-interception traps and ten batloggers on street light poles along two dimmable street light sections. Insect abundance and bat activity were measured alternately with one week of full street lighting followed by a week with light levels modulated by traffic volumes. In total, 16 dimmed and 16 full-light days were investigated. Overall, traffic-driven dimming reduced light levels by 35%. Weather conditions (warm, dry nights) were the main drivers of insect abundance and bat activity, but traffic-driven dimming resulted in lower numbers of insects caught and reduced bat activity. Among insect groups, Heteroptera benefited most from dimming. For bats, urban exploiters (Pipistrellus spp.) benefited from increased availability of prey at brightly lit street lights, while less frequent species (Myotis spp.) did not benefit from street lighting. We conclude that street light dimming technology may contribute to mitigate negative effects of ALAN on nocturnal organisms, although the measure may not be efficient enough to support light-sensitive and threatened species.

Central and Eastern Europe has experienced fundamental land use changes since the collapse of socialism around 1990. We analyzeanalyzed the patterns and determinants of agricultural land abandonment and recultivation in Slovakia during the transition from a state-controlled economy to an open-market economy (1986 to 2000) and the subsequent accession to the European Union (2000 to 2010). We quantified agricultural land-use change based on available maps derived from 30-m multi-seasonal Landsat imagery and analyzeanalyzed the socioeconomic and biophysical determinants of the observed agricultural land-use changes using boosted regression trees. We used a scenario-based approach to assess future agricultural land abandonment and recultivation until 2060. The maps of agricultural land use analysis reveal that cropland abandonment was the dominant land use process on 11% of agricultural land from 1986 to 2000, and on 6% of the agricultural land from 2000 to 2010. Recultivation occurred on approximately 2% of agricultural land in both periods. Although most abandoned land was located in the plains, the rate of abandonment was twice as high in the mountainous landscapes. The likelihood of abandonment increased with increased distance from the national capital (Bratislava), decreased with an increase of annual mean temperatures and was higher in proximity to forest edges and on steeper slopes. Recultivation was largely determined by the opposite effects. The scenario for 2060 suggests that future agricultural land abandonment and recultivation may largely be determined by climate and terrain conditions and, to a lesser extent, by proximity to economic centers. Our study underscores the value of synergetic use of satellite data and land-use modeling to provide the input for land planning, and to anticipate the potential effects of changing environmental and policy conditions.

Künstliches Licht bei Nacht wird zunehmend als Bedrohung für die biologische Vielfalt und für Ökosysteme erkannt. Mit dimm- und vor allem individuell steuerbaren LED-Strassenbeleuchtungen kann dieLichtstärke flexibel dem aktuellen Verkehrsaufkommen angepasst werden.

This study aims to analyze and assess studies published from 1992 to 2019 and listed in the Web of Science (WOS) and Current Contents (CC) databases, and to identify agricultural abandonment by application of remote sensing (RS) optical and microwave data. We selected 73 studies by applying structured queries in a field tag form and Boolean operators in the WOS portal and by expert analysis. An expert assessment yielded the topical picture concerning the definitions and criteria for the identification of abandoned agricultural land (AAL). The analysis also showed the absence of similar field research, which serves not only for validation, but also for understanding the process of agricultural abandonment. The benefit of the fusion of optical and radar data, which supports the application of Sentinel-1 and Sentinel-2 data, is also evident. Knowledge attained from the literary sources indicated that there exists, in the world literature, a well-covered problem of abandonment identification or biomass estimation, as well as missing works dealing with the assessment of the natural accretion of biomass in AAL.