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  • 1
    Language: English
    In: SOIL, 2018, Vol.4(1), pp.83-92
    Description: The central importance of soil for the functioning of terrestrial systems is increasingly recognized. Critically relevant for water quality, climate control, nutrient cycling and biodiversity, soil provides more functions than just the basis for agricultural production. Nowadays, soil is increasingly under pressure as a limited resource for the production of food, energy and raw materials. This has led to an increasing demand for concepts assessing soil functions so that they can be adequately considered in decision-making aimed at sustainable soil management. The various soil science disciplines have progressively developed highly sophisticated methods to explore the multitude of physical, chemical and biological processes in soil. It is not obvious, however, how the steadily improving insight into soil processes may contribute to the evaluation of soil functions. Here, we present to a new systemic modeling framework that allows for a consistent coupling between reductionist yet observable indicators for soil functions with detailed process understanding. It is based on the mechanistic relationships between soil functional attributes, each explained by a network of interacting processes as derived from scientific evidence. The non-linear character of these interactions produces stability and resilience of soil with respect to functional characteristics. We anticipate that this new conceptional framework will integrate the various soil science disciplines and help identify important future research questions at the interface between disciplines. It allows the overwhelming complexity of soil systems to be adequately coped with and paves the way for steadily improving our capability to assess soil functions based on scientific understanding.
    Keywords: Soil Stability ; Evaluation ; Agricultural Production ; Modelling ; Agricultural Management ; Biodiversity ; Soil Stability ; Food Production ; Water Quality ; Raw Materials ; Biological Activity ; Decision Making ; Soil Improvement ; Soil Science ; Terrestrial Environments ; Interactions ; Water Quality ; Soil Management ; Modelling ; Raw Materials ; Raw Materials ; Soil Sciences ; Water Quality ; Soils ; Framework ; Stability ; Nutrient Cycles ; Mathematical Models ; Agricultural Production ; Biodiversity ; Nutrients (Mineral) ; Soils ; Decision Making ; Water Quality ; Biodiversity ; Biodiversity;
    ISSN: SOIL
    E-ISSN: 2199-398X
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  • 2
    Text Resource
    Text Resource
    Universitäts- und Landesbibliothek Sachsen-Anhalt
    Description: Braunrost und Echter Mehltau an Winterweizen gehören zu den gefährlichsten Weizenkrankheiten weltweit und verursachen teilweise katastrophale Ernteeinbußen. Die Quantifizierung klimatischer Einflüsse auf die Befallshäufigkeit beider Krankheiten und die Berechnung von Befallsszenarios unter Klimawandel mit Hilfe von Klimasimulationen waren die Hauptziele dieser Arbeit. Der Einfluss meteorologischer Faktoren und der Wirtsanfälligkeit auf die Befallshäufigkeit beider Schadorganismen in Sachsen-Anhalt wird mittels „window pane“ Korrelationen untersucht und logistischer Regressionsmodelle quantifiziert. Die empirischen Modelle werden durch eine genestete Kreuzvalidierung ausgiebig validiert und mit Klimaszenariodaten des STARS-Modells betrieben. Die Ergebnisse zeigten, dass beide Schaderreger sehr unterschiedlich von meteorologischen Faktoren beeinflusst werden. In Folge von Klimaveränderungen nahm die Bedeutung von Braunrost in Sachsen-Anhalt zu, die von Echtem Mehltau ab.... ; Leaf rust and powdery mildew of winter wheat belong to the most hazardous global wheat diseases and are responsible for severe crop yield losses. Main aims of this work were the quantification of the influence of meteorological variables on the occurrence of both diseases and the projection of the results into the future using climate simulations. The influence of meteorological factors and host plant susceptibility on leaf rust and powdery mildew incidence of winter wheat in Saxony-Anhalt is studied by using “window pane” correlations and quantified utilizing logistic regression models. The empirical models are extensively validated by a nested cross-validation and run with climate scenario data provided by the STARS model, to generate disease scenarios under changed climatic conditions. Results showed that meteorological variables affected both pathogens very differently, leading to an increase in leaf rust and decrease in powdery mildew incidence projected for the state....
    Keywords: Online-Publikation ; Hochschulschrift ; Puccinia Triticina; Blumeria Graminis F.Sp. Tritici; Pflanzenkrankheiten; Pilzliches Pathogen; Klimawandel; Witterung; Korrelation; Logistische Regression;Kreuzvalidierung; Modellierung ; Puccinia Triticina; Blumeria Graminis F.Sp. Tritici; Plant Diseases; Fungal Pathogen; Climate Change; Weather; Correlation; Logistic Regression; Cross Validation; Modelling ; Ddc::600 Technik, Medizin, Angewandte Wissenschaften::630 Landwirtschaft::630 Landwirtschaft Und Verwandte Bereiche
    Source: DataCite
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