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Modelling root uptake processes in heterogeneous soils at the plant scale with a specific attention to biopores and rhizosphere impact on nutrient acquisition

The pattern of plant nutrient uptake in a soil profile is the result of complex processes occurring at the cellular or sub-cellular levels but affecting the whole-plant behaviour in function of the plant environment that varies strongly in time and space. The plant nutrient acquisition depends on root architecture and growth, on soil properties and heterogeneity, and on the 3-D distribution of nutrients and water. Equally important is how these parameters interact, as for instance how the nutrient distribution and soil properties and heterogeneity impact root growth or how nutrient and water limitation affect assimilate allocation. Mathematical modelling using a spatial resolution that resolves the spatial structure of the root structure and the nutrient and water distribution is therefore needed to quantitatively account for these complex and interacting processes and to predict plant nutrient uptake behaviour under environmental constraints. The main goal of the project is to build a modelling platform able to describe 3-D flow and transport processes in the soil to individual roots of an entire root system (WP1). Model parameters will be derived from specific experiments performed at the plant scale in the research group (WP3) and stored in a specific data warehouse (WP2). The impact of different parameters, which describe root growth and nutrient uptake at the single root scale, on nutrient uptake at the soil profile scale, will be investigated based on scenario analyses (WP4). Data on water and nutrient uptake and root growth from plant and field scale experiments will be compared with model predictions to validate the model. Simulations with the 3-D root scale model will be used to validate hypotheses and parameterizations of larger scale 1-D models that do not describe processes at the scale of individual roots (WP5 and SP10).

Komplementaritaeten der Siedlungsstruktur als Bedingung fuer Verkehr

Ziel der Forschung ist die Formulierung von Bewertungsmodellen des Raumzusammenhangs im weitesten Sinne. Verkehr als Mittel der Raumordnungspolitik ist quantitativ zu erfassen und im Zusammenhang aller Raumstrukturen zu bewerten. Die Raumstruktur (mit den Bereichen 'Siedlungsstruktur' und 'Verkehrsinfrastruktur') weist Komplementaritaeten auf, die Bedingung fuer Kommunikation und, in Verbindung mit der Raumlage, fuer Verkehr sind. Diese gilt es zu durchleuchten und in allgemeinen, verifizierbaren Modellen zu formulieren. Dazu gehoeren nicht nur die ueblicherweise behandelten Wirkungen in der Verkehrsinfrastruktur, sondern auch die Rueckwirkungen des Verkehrsgefueges auf die Raumstruktur.

Analysis of dairy production systems differentiated by location

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.

Regionales Raumordnungsprogramm 2020 Landkreis Rotenburg (Wümme) - WMS

WMS des rechtskräftigen Regionalen Raumordnungsprogramms des Landkreises Rotenburg (Wümme) in zeichnerischer Darstellung. Das Regionale Raumordnungsprogramm ist aus dem Landesraumordnungsprogramm (LROP) 2017 entwickelt und legt die angestrebte räumliche und strukturelle Entwicklung des Planungsraumes fest.

Regionaler Entwicklungsplan 2018

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.

Hydrogeologische Raumgliederung BB

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.

Regionaler Entwicklungsplan 2018

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.

Regionalplanung Sachsen - Raumstruktur

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.

Repository der KI-Ideenwerkstatt: faszination_naechtlicher_vogelzug

# 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) |

INSPIRE: Information system salt structures: planning basis, selection criteria and estimation of the potential for the construction of salt caverns for the storage of renewable energies (hydrogen and compressed air) (InSpEE) (WMS)

The WMS InSpEE (INSPIRE) provides information about the areal distribution of salt structures (salt domes and salt pillows) in Northern Germany. Contours of the salt structures can be displayed at horizontal cross-sections at four different depths up to a maximum depth of 2000 m below NN. The geodata have resulted from a BMWi-funded research project “InSpEE” running from the year 2012 to 2015. The acronym stands for "Information system salt structures: planning basis, selection criteria and estimation of the potential for the construction of salt caverns for the storage of renewable energies (hydrogen and compressed air)”. Taking into account the fact that this work was undertaken at a scale for providing an overview and not for investigation of single structures, the scale of display is limited to a minimum of 1:300.000. Additionally four horizontal cross-section maps display the stratigraphical situation at a given depth. In concurrence of maps at different depths areal bedding conditions can be determined, e.g. to generally assess and interpret the spread of different stratigraphic units. Clearly visible are extent and shape of the salt structures within their regional context at the different depths, with extent and boundary of the salt structures having been the main focus of the project. Four horizontal cross-section maps covering the whole onshore area of Northern Germany have been developed at a scale of 1:500.000. The maps cover the depths of -500, -1000, -1500, -2000 m below NN. The four depths are based on typical depth requirements of existing salt caverns in Northern Germany, mainly related to hydrocarbon storage. The shapes of the structures show rudimentary information of their geometry and their change with depths. In addition they form the starting point for rock mechanical calculations necessary for the planning and construction of salt caverns for storage as well as for assessing storage potentials. The maps can be used as a pre-selection tool for subsurface uses. It can also be used to assess coverage and extension of salt structures. Offshore areas were not treated within the project. All horizontal cross-section maps were adjusted with the respective state geological survey organisations. According to the Data Specification on Geology (D2.8.II.4_v3.0) the WMS InSpEE (INSPIRE) provides INSPIRE-compliant data. The WMS InSpEE (INSPIRE) contains two group layers: The first group layer “INSPIRE: Salt structures in Northern Germany“ comprises the layers GE.Geologic.Unit.Salt structure types, GE.GeologicUnit.Salt pillow remnants, GE.GeologicUnit.Structure-building salinar and GE.GeologicUnit.Structural outlines. The layer GE.GeologicUnit.Structural outlines contains according to the four depths four sublayers, e.g. GE.GeologiUnit.Structural outlines 500 m below NN. The second group layer „INSPIRE: Horizontal cross-section maps of Northern Germany“ comprises according to the four depths four layers, e.g. Horizontal cross-section map – 500 m below NN. This layer, in turns, contains two sublayers: GE.GeologicFault.Relevant fault traces and GE.GeologicUnit.Stratigraphic Units. Via the getFeatureInfo request the user obtains additional information on the different geometries. In case of the GE.Geologic.Unit.Salt structure types the user gets access to a data sheet with additional information and further reading in German for the respective salt structure via the getFeatureInfo request.

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