Farm structures are often characterized by regional heterogeneity, agglomeration effects, sub-optimal farm sizes and income disparities. The main objective of this study is to analyze whether this is a result of path dependent structural change, what the determinants of path dependence are, and how it may be overcome. The focus is on the German dairy sector which has been highly regulated and subsidized in the past and faces severe structural deficits. The future of this sector in the process of an ongoing liberalization will be analyzed by applying theoretical concepts of path dependence and path breaking. In these regards, key issues are the actual situation, technological and market trends as well as agricultural policies. The methodology will be based on a participative use of the agent-based model AgriPoliS and participatory laboratory experiments. On the one hand, AgriPoliS will be tested as a tool for stakeholder oriented analysis of mechanisms, trends and policy effects. This part aims to analyze whether and how path dependence of structural change can be overcome on a sector level. In a second part, AgriPoliS will be extended such that human players (farmers, students) can take over the role of agents in the model. This part aims to compare human agents with computer agents in order to overcome single farm path dependence.
Dairy farming across Germany displays diverse production systems. Factor endowment, management, technology adoption as well as competitive dynamics in the local or regional land, agribusiness and dairy processing sectors contribute to this differentiation on farm level. These differences impact on the ability of dairy farms and regional dairy production systems to successfully respond to pressures arising from future market and policy changes. The overall objective of the research activities of which this project is a part of, is to develop a thorough understanding of the processes that govern the spatial dynamics of dairy farm development in different regions in Germany. The central hypothesis of this research project is that management system and technological choices differ systematically across local production and market conditions. The empirical approach will focus on the estimation of farm specific nonparametric cost functions for dairy farms located in across Germany differentiated by time and location. A spatially differentiated data base with information on input use, resource availability, as well as local market conditions for land and output markets will be compiled. The nonparametric approach is specifically suited to disclose a more accurate representation of dairy production system heterogeneity across locations and time compared to parametric concepts as it provides the necessary flexibility to accommodate non-linearities relevant for a wide domain of explanatory variables. The methodology employed goes beyond the state of the art of the literature as it combines kernel density estimation with a Bayesian sampling approach to provide theory consistent parameters for each farm in the data sample.The specific methodological hypothesis is that the nonparametric approach is superior to current parametric techniques and this hypothesis is tested using statistical model evaluation. Regarding the farm management and technological choices, we hypothesize that land suitability for feed production determines the farm intensity of dairy production and thus management and technological choices. With respect to the ability of farms to successfully respond to market pressures we hypothesize that farms at the upper and lower tail of the intensity distribution both can generate positive returns from dairy production. These last two hypotheses will be tested using the estimated spatially differentiated farm specific costs and marginal costs.The expected outcomes are of relevance for the agricultural sector and the food supply chain economy as a whole as fundamental market structure changes in the dairy sector are ongoing due to the abolition of the quota regulation in the years 2014/2015. Thus, exact knowledge about differences and development of dairy cost heterogeneity of farms within and between regions are an important factor for the actors involved in the market as well as the political support of this process.
Inhaltsverzeichnis des Kartenteils der ersten Gesamtfortschreibung des Regionalpla-nes für die Anhörung entsprechend Sächsisches Landesplanungsgesetz (SächsLPlG): Karte 1 Raumnutzung (1:100 000) Karte 2 Siedlungswesen (1:280 000) Karte 3 Raumstruktur (1:280 000) Karte 4 Tourismus (1:280 000) Karte 5 Landschaftsbereiche mit besonderen Nutzungsanforderungen (1:200 000) Karte 6 Sanierungsbedürftige Bereiche der Landschaft (1:200 000) Karte 7 Tierhaltungsstandorte (1:280 000) Karte 8 Bergbauumgang (1:280 000)
Seit den 60er und 70er Jahren praegen umweltrelevante Fehlentwicklungen des Suburbanisierungsgeschehens das Bild der Siedlungsentwicklung. Dieser Suburbanisierungsprozess einerseits und andererseits die grossraeumigen Verflechtungen fuehren in wachsendem Mass zu oekosystemaren Beeintraechtigungen in einzelnen Verflechtungsraeumen, aber auch in weniger hoch verdichteten staedtischen Gebieten und Gebieten mit ueberwiegend laendlicher Raumstruktur entspricht die Siedlungsentwicklung oft nicht dem Ziel schonenden Umgangs mit den natuerlichen Ressourcen.
Soil structure determines a large part of the spatial heterogeneity in water storage and fluxes from the plot to the hillslope scale. In recent decades important progress in hydrological research has been achieved by including soil structure in hydrological models. One of the main problems herein remains the difficulty of measuring soil structure and quantifying its influence on hydrological processes. As soil structure is very often of biogenic origin (macropores), the main objective of this project is to use the influence of bioactivity and resulting soil structures to describe and support modelling of hydrological processes at different scales. Therefore, local scale bioactivity will be linked to local infiltration patterns under varying catchment conditions. At hillslope scale, the spatial distribution of bioactivity patterns will be linked to connectivity of subsurface structures to explain subsurface stormflow generation. Then we will apply species distribution modelling of key organisms in order to extrapolate the gained knowledge to the catchment scale. As on one hand, bioactivity influences the hydrological processes, but on the other hand the species distribution also depends on soil moisture contents, including the feedbacks between bioactivity and soil hydrology is pivotal for getting reliable predictions of catchment scale hydrological behavior under land use change and climate change.
In subsoils, organic matter (SOM) concentrations and microbial densities are much lower than in topsoils and most likely highly heterogeneously distributed. We therefore hypothesize, that the spatial separation between consumers (microorganisms) and their substrates (SOM) is an important limiting factor for carbon turnover in subsoils. Further, we expect microbial activity to occur mainly in few hot spots, such as the rhizosphere or flow paths where fresh substrate inputs are rapidly mineralized. In a first step, the spatial distribution of enzyme and microbial activities in top- and subsoils will be determined in order to identify hot spots and relate this to apparent 14C age, SOM composition, microbial community composition and soil properties, as determined by the other projects within the research unit. In a further step it will be determined, if microbial activity and SOM turnover is limited by substrate availability in spatially distinct soil microsites. By relating this data to root distribution and preferential flow paths we will contribute to the understanding of stabilizing and destabilizing processes of subsoil organic matter. As it is unclear, at which spatial scale these differentiating processes are effective, the analysis of spatial variability will cover the dm to the mm scale. As spatial segregation between consumers and substrates will depend on the pore and aggregate architecture of the soil, the role of the physical integrity of these structures on SOM turnover will also be investigated in laboratory experiments.
Regionaler Entwicklungsplan für die Planungsregion Anhalt-Bitterfeld-Wittenberg mit den Planinhalten „Raumstruktur, Standortpotenziale, technische Infrastruktur und Freiraumstruktur“ Die oberste Landesentwicklungsbehörde hat am 21.12.2018 die Genehmigung unter einer Maßgabe erteilt. Am 29.03.2019 trat die Regionalversammlung mit Beschluss Nr. 03/2019 der Maßgabe bei. Mit Bekanntmachung der Genehmigung trat der Regionale Entwicklungsplan am 27.04.2019 in Kraft.
Dieser Dienst enthält Daten der Planungsregionen Oberes Elbtal/Osterzgebirge, Region Chemnitz und Oberlausitz-Niederschlesien und deckt im Endausbau den gesamten Freistaat Sachsen ab. Entsprechend des Landesentwicklungsplanes 2013 als fachübergreifendes Gesamtkonzept zur räumlichen Entwicklung, Ordnung und Sicherung des Freistaates Sachsen stellen die Regionalpläne einen verbindlichen Rahmen für die räumliche Entwicklung, Ordnung und Sicherung des Raumes dar. Im Dienst sind regionalplanerische Festlegungen des Komplexes Raumstruktur enthalten. Die rechtsverbindlichen Karten Raumstruktur werden in der Regel in Maßstäben zwischen 1:300.000 und 1:450.000 erstellt. Eine Darstellung der Inhalte der Regionalpläne erfolgt in diesem Dienst nur im Maßstab kleiner 1:10.000.
# Faszination Nächtlicher Vogelzug A web component for visualizing migratory bird detections on an interactive map. Built with React, MapLibre GL, and the BirdWeather GraphQL API. Designed for embedding into CMS platforms like Contao. ## Tech Stack - **React 19** + **TypeScript** (Vite) - **MapLibre GL** -- WebGL map rendering (Stadia Maps dark theme) - **Supercluster** -- per-species spatial clustering - **Apollo Client 4** -- GraphQL data fetching with caching - **GraphQL Code Generation** -- type-safe queries from BirdWeather schema - **SunCalc** -- astronomical day/night calculations - **Tailwind CSS 4** + **Ant Design 6** -- UI - **Vitest** -- testing ## Features - **Interactive map** with color-coded detection clusters per species - **Timeline animation** with autoplay, step controls, and throttled slider - **Night-only mode** that compresses inactive daytime hours using SunCalc sunrise/sunset calculations - **Day/night overlay** showing the terminator (day/night boundary) as a real-time GeoJSON polygon - **Species search** with autocomplete and availability checking per map viewport - **Supplementary layers** (light pollution, noise mapping via WMS) - **Web component** (`<zug-birdnet>`) for CMS embedding without routing ## Project Structure ``` src/ main.tsx Web component registration App.tsx Root component, species selection state api/ fragments.ts GraphQL fragments (DetectionItem, SpeciesItem) queries.ts GraphQL queries (detections, species, search) useDetections.ts Detection fetch hook with prefetching components/ DatesProvider.tsx Time state context (date range, animation, night mode) MapProvider.tsx MapLibre GL instance context SpeciesDropdown.tsx Species selection with search autocomplete Timeline.tsx Date picker, animation slider, playback controls LayersDropdown.tsx Toggle info layers (light pollution, noise) InfoPopup.tsx Map info marker popups map/ Map.tsx MapLibre GL initialization and rendering clusterUtils.ts Per-species Supercluster index creation colorUtils.ts MapLibre paint expression builder mapStyles.ts Map layer definitions usePersistentColors.ts Stable color assignment per species infopoints.ts Static info marker data lib/ apollo-client.ts Apollo Client with cache type policies buildAvailableSpeciesQuery.ts Dynamic aliased query generation getDayPolygon.ts Day/night terminator polygon calculation getTranslatedSpeciesName.ts i18n species name lookup isNotNull.ts, hasNonNullProp.ts Type guard utilities throttle.ts Throttle utility gql/ Auto-generated GraphQL types (do not edit) ``` ## Architecture Three React context providers compose the application: ``` ApolloProvider GraphQL caching and data fetching DatesProvider Date range, animation state, night-only time segments MapProvider MapLibre GL map instance App Species selection, filtered detections, color mapping ``` **Data flow:** Apollo fetches detections for the current bounding box and date range. Detections are filtered client-side by the visualisation time window (controlled by the timeline slider). Each species gets its own Supercluster index for independent color-coded clustering. Cluster features are rendered via MapLibre GL layers with dynamic `match` paint expressions. **GraphQL:** Queries and fragments are defined in `src/api/` and typed via `@graphql-codegen/client-preset`. Run `npm run codegen` after schema changes to regenerate `src/gql/`. ## Development ```sh npm install npm run dev ``` The dev server uses a self-signed SSL certificate via `@vitejs/plugin-basic-ssl`. Accept the browser warning on first visit. Other commands: ```sh npm run build # Production build npm run test # Run tests npm run lint # ESLint npm run codegen # Regenerate GraphQL types ``` ## Build & Integration Run `npm run build` to produce the `dist/` folder. The build outputs stable filenames (no hashes) and splits vendor dependencies into separate chunks for caching: ``` dist/ index.html assets/ index.css App styles (Tailwind + Ant Design) index.js Application code, React, Supercluster, dayjs, SunCalc maplibre.js MapLibre GL antd.js Ant Design + icons apollo.js Apollo Client + graphql ``` Only `index.js` changes on application updates. Vendor chunks are cache-stable between deploys. To embed the web component, include the built CSS and JS, then use the custom element: ```html <link rel="stylesheet" href="/assets/index.css"> <script type="module" src="/assets/index.js"></script> <zug-birdnet></zug-birdnet> ``` No routing. The component is self-contained and can be placed anywhere on the page. Third-party CMS integration (e.g., Contao) only needs to include the built assets and the custom element tag. ## Configuration App-level settings are in `src/config.ts`: | Option | Default | Description | |---|---|---| | `SHOW_DEMO_INFOPOINTS` | `false` | Show static info markers on the map (demo/development only) |
Der Datensatz beinhaltet Daten des LBGR über die Hydrogeologische Raumgliederung Brandenburgs und wird über je einen Darstellungs- und Downloaddienst bereitgestellt. Die Karte gibt einen Überblick zu den hydrogeologischen Raumgliederungen Brandenburgs. Die Gliederungseinheiten tragen den angewandten Charakter von Nutzungsräumen. Sie werden anhand von Wassereinzugsgebieten und Charakteristiken dazugehöriger Grundwasserdynamik beschrieben. Für das Territorium einer hydrogeologischen Einheit werden vergleichbare Grundwasserverhältnisse vorausgesetzt.
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