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Eine verbesserte Zusammenarbeit und Abstimmung von Fachwissenschaft, Fachdidaktik, Bildungswissenschaften und schulpraktischen Lernorten ist einer der Förderbereiche der „Qualitätsoffensive Lehrerbildung“. Zahlreiche der geförderten Projekte haben für dieses Handlungsfeld im Bereich Vernetzung von Fachwissenschaft, Fachdidaktik und Bildungswissenschaften hochschulspezifische Maßnahmen konzipiert und umgesetzt.
Der vorliegende Tagungsband beinhaltet Beiträge von 15 entsprechenden Projekten, von denen sieben anlässlich von zwei standortübergreifenden Projekt-Tagungen in Hannover und Potsdam vorgestellt und in Workshops diskutiert wurden. Insgesamt geben die Beiträge einen theoretisch fundierten und gleichzeitig praxisorientierten Überblick über aktuelle Ansätze und Konzepte zur besseren Vernetzung fachwissenschaftlicher, fachdidaktischer und bildungswissenschaftlicher Studienanteile im Lehramtsstudium. Dargestellt werden Projektarbeiten der Hochschulen, die auf verschiedenen Ebenen wirksam werden (curricular-inhaltliche Ebene, kollegiale Ebene, hochschul-strukturelle Ebene). Die Maßnahmen sind so beschrieben, dass sie als Grundlage für einen Transfer auf andere Fächer bzw. andere Standorte genutzt werden können.
Die Beiträge richten sich an alle Lehrenden im Bereich der Lehramtsbildung sowie sonstige Akteure im Bereich der Lehr- und Qualitätsentwicklung an den Universitäten. Sie alle können den beschriebenen Konzepten und Umsetzungsformaten transferierbare Ideen und Impulse entnehmen.
Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. While regional studies allow detailed insights into land use and ecosystem service interactions, information on a national scale can aid biodiversity assessments. However, for most European countries this information is not yet widely available. We used an analysis-ready-data cube that contains dense time series of co-registered Sentinel-2 and Landsat 8 data, covering the extent of Germany. We propose an algorithm that detects mowing events in the time series based on residuals from an assumed undisturbed phenology, as an indicator of grassland use intensity. A self-adaptive ruleset enabled to account for regional variations in land surface phenology and non-stationary time series on a pixelbasis. We mapped mowing events for the years from 2017 to 2020 for permanent grassland areas in Germany. The results were validated on a pixel level in four of the main natural regions in Germany based on reported mowing events for a total of 92 (2018) and 78 (2019) grassland parcels. Results for 2020 were evaluated with combined time series of Landsat, Sentinel-2 and PlanetScope data. The mean absolute percentage error between detected and reported mowing events was on average 40% (2018), 36% (2019) and 35% (2020). Mowing events were on average detected 11 days (2018), 7 days (2019) and 6 days (2020) after the reported mowing. Performance measures varied between the different regions of Germany, and lower accuracies were found in areas that are revisited less frequently by Sentinel-2. Thus, we assessed the influence of data availability and found that the detection of mowing events was less influenced by data availability when at least 16 cloud-free observations were available in the grassland season. Still, the distribution of available observations throughout the season appeared to be critical. On a national scale our results revealed overall higher shares of less intensively mown grasslands and smaller shares of highly intensively managed grasslands. Hotspots of the latter were identified in the alpine foreland in Southern Germany as well as in the lowlands in the Northwest of Germany. While these patterns were stable throughout the years, the results revealed a tendency to lower management intensity in the extremely dry year 2018. Our results emphasize the ability of the approach to map the intensity of grassland management throughout large areas despite variations in data availability and environmental conditions.
The dynamics of grassland ecosystems are highly complex due to multifaceted interactions among their soil, water, and vegetation components.
Precise simulations of grassland productivity therefore rely on accurately estimating a variety of parameters that characterize different processes of these systems.
This study applied three calibration schemes - a Single-Objective (SO-SUFI2), a Multi-Objective Pareto (MO-Pareto), and, a novel Uncertainty-Based Multi-Objective (MO-SUFI2) - to estimate the parameters of MONICA (Model for Nitrogen and Carbon Simulation) agro-ecosystem model in grassland ecosystems across Germany.
The MO-Pareto model is based on a traditional Pareto optimality concept, while the MO-SUFI2 optimizes multiple target variables considering their level of prediction uncertainty.
We used measurements of leaf area index, aboveground biomass, and soil moisture from experimental data at five sites with different intensities of cutting regimes (from two to five cutting events per season) to evaluate model performance.
Both MO-Pareto and MO-SUFI2 outperformed SO-SUFI2 during calibration and validation.
The comparison of the two MO approaches shows that they do not necessarily conflict with each other, but MO-SUFI2 provides complementary information for better estimations of model parameter uncertainty.
We used the obtained parameter ranges to simulate grassland productivity across Germany under different cutting regimes and quantified the uncertainty associated with estimated productivity across regions.
The results showed higher uncertainty in intensively managed grasslands compared to extensively managed grasslands, partially due to a lack of high-resolution input information concerning cutting dates. Furthermore, the additional information on the quantified uncertainty provided by our proposed MO-SUFI2 method adds deeper insights on confidence levels of estimated productivity.
Benefiting from additional management data collected at high resolution and ground measurements on the composition of grassland species mixtures appear to be promising solutions to reduce uncertainty and increase model reliability.