Fakultät Agrarwissenschaften

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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.
Biomonitoring via DNA metabarcoding and light microscopy of bee pollen in rainforest transformation landscapes of Sumatra
(2022) Carneiro de Melo Moura, Carina; Setyaningsih, Christina A.; Li, Kevin; Merk, Miryam Sarah; Schulze, Sonja; Raffiudin, Rika; Grass, Ingo; Behling, Hermann; Tscharntke, Teja; Westphal, Catrin; Gailing, Oliver; Carneiro de Melo Moura, Carina; Department of Forest Genetics and Forest Tree Breeding, University of Göttingen, Göttingen, Germany; Setyaningsih, Christina A.; Department of Palynology and Climate Dynamics, Albrecht-von-Haller-Institute for Plant Sciences, University of Göttingen, Göttingen, Germany; Li, Kevin; Agroecology, Department of Crop Sciences, University of Göttingen, Göttingen, Germany; Merk, Miryam Sarah; Statistics and Econometrics, University of Göttingen, Göttingen, Germany; Schulze, Sonja; Agroecology, Department of Crop Sciences, University of Göttingen, Göttingen, Germany; Raffiudin, Rika; Department of Biology, IPB University ID, Bogor, Indonesia; Grass, Ingo; Department of Ecology of Tropical Agricultural Systems, University of Hohenheim, Stuttgart, Germany; Behling, Hermann; Department of Palynology and Climate Dynamics, Albrecht-von-Haller-Institute for Plant Sciences, University of Göttingen, Göttingen, Germany; Tscharntke, Teja; Agroecology, Department of Crop Sciences, University of Göttingen, Göttingen, Germany; Westphal, Catrin; Functional Agrobiodiversity, Department of Crop Sciences, University of Göttingen, Göttingen, Germany; Gailing, Oliver; Centre of Biodiversity and Sustainable Land Use, University of Göttingen, Göttingen, Germany
Background: Intense conversion of tropical forests into agricultural systems contributes to habitat loss and the decline of ecosystem functions. Plant-pollinator interactions buffer the process of forest fragmentation, ensuring gene flow across isolated patches of forests by pollen transfer. In this study, we identified the composition of pollen grains stored in pot-pollen of stingless bees, Tetragonula laeviceps , via dual-locus DNA metabarcoding (ITS2 and rbcL ) and light microscopy, and compared the taxonomic coverage of pollen sampled in distinct land-use systems categorized in four levels of management intensity (forest, shrub, rubber, and oil palm) for landscape characterization. Results: Plant composition differed significantly between DNA metabarcoding and light microscopy. The overlap in the plant families identified via light microscopy and DNA metabarcoding techniques was low and ranged from 22.6 to 27.8%. Taxonomic assignments showed a dominance of pollen from bee-pollinated plants, including oil-bearing crops such as the introduced species Elaeis guineensis (Arecaceae) as one of the predominant taxa in the pollen samples across all four land-use types. Native plant families Moraceae, Euphorbiaceae, and Cannabaceae appeared in high proportion in the analyzed pollen material. One-way ANOVA (p > 0.05), PERMANOVA (R² values range from 0.14003 to 0.17684, for all tests p-value > 0.5), and NMDS (stress values ranging from 0.1515 to 0.1859) indicated a lack of differentiation between the species composition and diversity of pollen type in the four distinct land-use types, supporting the influx of pollen from adjacent areas. Conclusions: Stingless bees collected pollen from a variety of agricultural crops, weeds, and wild plants. Plant composition detected at the family level from the pollen samples likely reflects the plant composition at the landscape level rather than the plot level. In our study, the plant diversity in pollen from colonies installed in land-use systems with distinct levels of forest transformation was highly homogeneous, reflecting a large influx of pollen transported by stingless bees through distinct land-use types. Dual-locus approach applied in metabarcoding studies and visual pollen identification showed great differences in the detection of the plant community, therefore a combination of both methods is recommended for performing biodiversity assessments via pollen identification.
Effects of different ground segmentation methods on the accuracy of UAV-based canopy volume measurements
(2024) Han, Leng; Wang, Zhichong; He, Miao; He, Xiongkui; Han, Leng; College of Science, China Agricultural University, Beijing, China; Wang, Zhichong; Tropics and Subtropics Group, Institute of Agricultural Engineering, University of Hohenheim, Stuttgart, Germany; He, Miao; College of Science, China Agricultural University, Beijing, China; He, Xiongkui; College of Science, China Agricultural University, Beijing, China
The nonuniform distribution of fruit tree canopies in space poses a challenge for precision management. In recent years, with the development of Structure from Motion (SFM) technology, unmanned aerial vehicle (UAV) remote sensing has been widely used to measure canopy features in orchards to balance efficiency and accuracy. A pipeline of canopy volume measurement based on UAV remote sensing was developed, in which RGB and digital surface model (DSM) orthophotos were constructed from captured RGB images, and then the canopy was segmented using U-Net, OTSU, and RANSAC methods, and the volume was calculated. The accuracy of the segmentation and the canopy volume measurement were compared. The results show that the U-Net trained with RGB and DSM achieves the best accuracy in the segmentation task, with mean intersection of concatenation (MIoU) of 84.75% and mean pixel accuracy (MPA) of 92.58%. However, in the canopy volume estimation task, the U-Net trained with DSM only achieved the best accuracy with Root mean square error (RMSE) of 0.410 m 3 , relative root mean square error (rRMSE) of 6.40%, and mean absolute percentage error (MAPE) of 4.74%. The deep learning-based segmentation method achieved higher accuracy in both the segmentation task and the canopy volume measurement task. For canopy volumes up to 7.50 m 3 , OTSU and RANSAC achieve an RMSE of 0.521 m 3 and 0.580 m 3 , respectively. Therefore, in the case of manually labeled datasets, the use of U-Net to segment the canopy region can achieve higher accuracy of canopy volume measurement. If it is difficult to cover the cost of data labeling, ground segmentation using partitioned OTSU can yield more accurate canopy volumes than RANSAC.

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