Fakultät Agrarwissenschaften

Permanent URI for this communityhttps://hohpublica.uni-hohenheim.de/handle/123456789/9

Die Fakultät entwickelt in Lehre und Forschung nachhaltige Produktionstechniken der Agrar- und Ernährungswirtschaft. Sie erarbeitet Beiträge für den ländlichen Raum und zum Verbraucher-, Tier- und Umweltschutz.

Homepage: https://agrar.uni-hohenheim.de/

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The active core microbiota of two high-yielding laying hen breeds fed with different levels of calcium and phosphorus
(2022) Roth, Christoph; Sims, Tanja; Rodehutscord, Markus; Seifert, Jana; Camarinha-Silva, Amélia
The nutrient availability and supplementation of dietary phosphorus (P) and calcium (Ca) in avian feed, especially in laying hens, plays a vital role in phytase degradation and mineral utilization during the laying phase. The required concentration of P and Ca peaks during the laying phase, and the direct interaction between Ca and P concentration shrinks the availability of both supplements in the feed. Our goal was to characterize the active microbiota of the entire gastrointestinal tract (GIT) (crop, gizzard, duodenum, ileum, caeca), including digesta- and mucosa-associated communities of two contrasting high-yielding breeds of laying hens (Lohmann Brown Classic, LB; Lohmann LSL-Classic, LSL) under different P and Ca supplementation levels. Statistical significances were observed for breed, GIT section, Ca, and the interaction of GIT section x breed, P x Ca, Ca x breed and P x Ca x breed (p < 0.05). A core microbiota of five species was detected in more than 97% of all samples. They were represented by an uncl. Lactobacillus (average relative abundance (av. abu.) 12.1%), Lactobacillus helveticus (av. abu. 10.8%), Megamonas funiformis (av. abu. 6.8%), Ligilactobacillus salivarius (av. abu. 4.5%), and an uncl. Fusicatenibacter (av. abu. 1.1%). Our findings indicated that Ca and P supplementation levels 20% below the recommendation have a minor effect on the microbiota compared to the strong impact of the bird’s genetic background. Moreover, a core active microbiota across the GIT of two high-yielding laying hen breeds was revealed for the first time.
Automatic classification of submerged macrophytes at Lake Constance using laser bathymetry point clouds
(2024) Wagner, Nike; Franke, Gunnar; Schmieder, Klaus; Mandlburger, Gottfried; Wagner, Nike; Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria;; Franke, Gunnar; Institute of Landscape and Plant Ecology (320), University of Hohenheim, Ottilie-Zeller-Weg 2, 70599 Stuttgart, Germany; (G.F.); (K.S.); Schmieder, Klaus; Institute of Landscape and Plant Ecology (320), University of Hohenheim, Ottilie-Zeller-Weg 2, 70599 Stuttgart, Germany; (G.F.); (K.S.); Mandlburger, Gottfried; Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria;; Stateczny, Andrzej
Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the classification of submerged vegetation captured with bathymetric LiDAR (Light Detection And Ranging) as a basis for monitoring their state and change, and we validated the results against established monitoring techniques. Employing full-waveform airborne laser scanning, which is routinely used for topographic mapping and forestry applications on dry land, we extended its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of bathymetric 3D LiDAR point clouds using a decision-based approach, distinguishing the three vegetation classes, (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy, based on their height and other properties like local point density. The results reveal detailed 3D representations of submerged vegetation, enabling the identification of vegetation structures and the inference of vegetation types with reference to pre-existing knowledge. While the results within the training areas demonstrate high precision and alignment with the comparison data, the findings in independent test areas exhibit certain deficiencies that are likely addressable through corrective measures in the future.

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