@article{SchwarzBapstHolingeretal.2020, author = {Schwarz, Katharina and Bapst, Beat and Holinger, Mirjam and Th{\"u}er, Susann and Schleip, Inga and Werne, Steffen}, title = {Potentials of using milk performance data and FAMACHA score as indicators for Targeted Selective Treatment in Lacaune dairy sheep in Switzerland}, series = {Veterinary Parasitology}, journal = {Veterinary Parasitology}, number = {277}, publisher = {Elsevier}, issn = {0304-4017}, doi = {10.1016/j.vpoa.2020.100030}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-5698}, year = {2020}, abstract = {Targeted Selective Treatment (TST) is one approach to slow down the development of anthelmintic resistance. Its success is closely linked to the correct identification of animals in need of treatment. In dairy goats it has been proposed to use milk yield as TST indicator and to focus treatments on high yielding dairy goats. In dairy sheep the relationship between milk performance and infection with gastrointestinal nematodes (GIN) is not well known. The aim of this study was to investigate the relationship between milk yield and GIN infection in dairy sheep and based on this, to evaluate milk performance data as a potential TST indicator. Overall 1159 Lacaune ewes of 15 dairy sheep farms in Switzerland were included in the study. The ewes were phenotyped once between August and December 2019, when they were at least 70 days in milk (DIM). Individual faecal samples were taken from every ewe to determine the nematode egg concentration per gram faeces (EPG). In addition, the clinical parameters FAMACHA score and packed cell volume (PCV) were measured. Linear mixed models were used to analyse the effects of the collected parameters on EPG. EPG increased significantly with increasing test day milk yields (P = 0.002), indicating high yielding ewes to be less resistant to GIN infections than low yielding ewes. The effect was most pronounced in earlier lactation but remained within a moderate range. Overall, our results indicated the potential of using milk yield data of rather early lactation as TST indicator in dairy sheep. On farms with predominantly H. contortus the combination with FAMACHA might improve the correct identification of highly infected ewes, as FAMACHA was correlated with EPG (r = 0.37, P < 0.001).}, language = {en} } @article{HirlOgeeOstleretal.2021, author = {Hirl, Regina T. and Og{\´e}e, J{\´e}r{\^o}me and Ostler, Ulrike and Sch{\"a}ufele, Rudi and Baca Cabrera, Juan C. and Zhu, Jianjun and Schleip, Inga and Wingate, Lisa and Schnyder, Hans}, title = {Temperature-sensitive biochemical 18O-fractionation and humidity-dependent attenuation factor are needed to predict δ18O of cellulose from leaf water in a grassland ecosystem}, series = {New Phytologist}, volume = {229}, journal = {New Phytologist}, number = {6}, publisher = {Wiley}, issn = {0028-646X}, doi = {10.1111/nph.17111}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-5700}, pages = {3156 -- 3171}, year = {2021}, abstract = {We explore here our mechanistic understanding of the environmental and physiological processes that determine the oxygen isotope composition of leaf cellulose (δ18Ocellulose) in a drought-prone, temperate grassland ecosystem. A new allocation-and-growth model was designed and added to an 18O-enabled soil-vegetation-atmosphere transfer model (MuSICA) to predict seasonal (April-October) and multi-annual (2007-2012) variation of δ18Ocellulose and 18O-enrichment of leaf cellulose (Δ18Ocellulose) based on the Barbour-Farquhar model. Modelled δ18Ocellulose agreed best with observations when integrated over c. 400 growing-degree-days, similar to the average leaf lifespan observed at the site. Over the integration time, air temperature ranged from 7 to 22°C and midday relative humidity from 47 to 73\%. Model agreement with observations of δ18Ocellulose (R2 = 0.57) and Δ18Ocellulose (R2 = 0.74), and their negative relationship with canopy conductance, was improved significantly when both the biochemical 18O-fractionation between water and substrate for cellulose synthesis (εbio, range 26-30 per mille) was temperature-sensitive, as previously reported for aquatic plants and heterotrophically grown wheat seedlings, and the proportion of oxygen in cellulose reflecting leaf water 18O-enrichment (1 - pexpx, range 0.23-0.63) was dependent on air relative humidity, as observed in independent controlled experiments with grasses. Understanding physiological information in δ18Ocellulose requires quantitative knowledge of climatic effects on pexpx and εbio.}, language = {en} } @inproceedings{BasavegowdaMosebachSchleipetal.2022, author = {Basavegowda, Deepak Hanike and Mosebach, Paul and Schleip, Inga and Weltzien, Cornelia}, title = {Indicator plant species detection in grassland using EfficientDet object detector}, series = {42. GIL-Jahrestagung, K{\"u}nstliche Intelligenz in der Agrar- und Ern{\"a}hrungswirtschaft}, booktitle = {42. GIL-Jahrestagung, K{\"u}nstliche Intelligenz in der Agrar- und Ern{\"a}hrungswirtschaft}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Bonn}, isbn = {978-3-88579-711-1}, issn = {1617-5468}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-6656}, pages = {57 -- 62}, year = {2022}, abstract = {Extensively used grasslands (meadows and pastures) are ecologically valuable areas in the agricultural landscape and part of the multifunctional agriculture. In Germany, the quality of these grasslands is assessed based on the occurrence of certain plant species known as indicator or character species, with indicators being defined at regional level. Therefore, the recognition of these indicators on a spatial level is a prerequisite for monitoring grassland biodiversity. The identification of indicator species for the status quo of grassland using traditional methods was found to be challenging and tedious. Deep learning-algorithms applied to high-resolution UAV imagery could be the key solution, where UAV with remote sensors can map a large area of grassland in comparison to manual or ground mapping methods and deep learning-algorithms can automate the detection process. In this research work, we use an EfficientDet based algorithm to train an object detection model capable of recognizing indicators on RGB data. The experimental results show that this approach is very promising in contrast to the difficult and time-consuming manual recognition methods. The model was trained with the momentum-SGD optimizer with a momentum value of 0.9 and a learning rate of 0.0001. The model was trained and tested on 1200 images and achieves 45.7 AP (and 85.7 AP50) on test data set. The dataset includes images of four distinct indicator plant species: Armeria maritima, Campanula patula, Cirsium oleraceum, and Daucus carota}, language = {en} } @article{ErtelZahnSchleip2022, author = {Ertel, Carsten and Zahn, Nils and Schleip, Inga}, title = {Niedertrampeln erw{\"u}nscht : Mob Grazing f{\"u}r den Naturschutz}, series = {Bioland}, journal = {Bioland}, number = {3}, publisher = {Bioland-Verl.}, issn = {0173-9832}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-6801}, pages = {34 -- 36}, year = {2022}, abstract = {In Brandenburgs Dauergr{\"u}nland, das von Trockenheit bedroht ist, experimentiert ein Mutterkuhbetrieb mit dem Weidesystem Mob Grazing. Naturschutz und Landwirtschaft k{\"o}nnten profitieren.}, language = {de} } @techreport{MosebachBirrWenzletal.2024, author = {Mosebach, Paul and Birr, Friedrich and Wenzl, Franz and Schleip, Inga and Luthardt, Vera}, title = {Biodiversit{\"a}tsf{\"o}rdernde Maßnahmen und Bewirtschaftungstechnik f{\"u}r eine standortgerechte Niedermoornutzung}, organization = {Ministerium f{\"u}r Landwirtschaft, Umwelt und Klimaschutz des Landes Brandenburg (MLUK)}, doi = {10.57741/opus4-882}, pages = {65}, year = {2024}, abstract = {Standortgerechte Nutzung von Niedermooren bedeutet, sie so zu bewirtschaften, dass sie langfristig ohne weitere Qualit{\"a}tseinbußen eine an den Lebensraum angepasste Pflanzendecke tragen, die eine stabile Biomasseproduktion liefert. F{\"u}r Moorstandorte ist eine standortgerechte Nutzung nur unter nassen Verh{\"a}ltnissen gegeben. Jedoch greift jede Form der Bewirtschaftung in den Lebensraum von Tier- und Pflanzenarten ein. Um m{\"o}glichen negativen Wirkungen vorzubeugen, sollten deshalb nasse und sehr feuchte Bewirtschaftungsverfahren von Beginn an naturschutzfachlich flankiert, kontrolliert und gegebenenfalls angepasst werden. Im Teil 1 dieser Brosch{\"u}re wird ein Katalog mit naturschutzfachlichen Maßnahmen f{\"u}r Bewirtschaftungsverfahren auf Niedermoor unterschiedlicher Feuchtestufen vorgestellt. Die landwirtschaftliche Nutzung nasser und sehr feuchter Moorfl{\"a}chen ist nur mit daran angepasster Landtechnik oder Spezialmaschinen m{\"o}glich. Im Teil 2 der Brosch{\"u}re werden daher Empfehlungen zum Technikeinsatz in der moorschonenden Gr{\"u}nlandbewirtschaftung gegeben.}, subject = {Moor}, language = {de} } @article{BazzoKamaliBehrendetal., author = {Bazzo, Clara Oliva Gon{\c{c}}alves and Kamali, Bahareh and Behrend, Dominik and Hueging, Hubert and Schleip, Inga and Mosebach, Paul and Behrendt, Axel and Gaiser, Thomas}, title = {Assessing the Effect of Field Disturbances On Biomass Estimation in Grasslands Using UAV-Derived Canopy Height Models}, series = {PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science}, volume = {93}, journal = {PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science}, number = {1}, publisher = {Springer International Publishing}, address = {Cham}, issn = {2512-2789}, doi = {10.1007/s41064-024-00322-x}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-9685}, pages = {37 -- 49}, abstract = {Abstract Accurate estimation of biomass in grasslands is essential for understanding ecosystem health and productivity. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools for biomass estimation using canopy height models derived from high-resolution imagery. However, the impact of field disturbances, such as lodging and molehills, on the accuracy of biomass estimation using UAV-derived canopy height models remains underexplored. This study aimed to assess the relationship between UAV-derived canopy height and both reference canopy height measurements and dry biomass, accounting for different management systems and disturbance scenarios. UAV data were collected using a multispectral camera, and ground-based measurements were obtained for validation. The results revealed that UAV-derived canopy height models remained accurate in estimating vegetation height, even in the presence of disturbances. However, the relationship between UAV-derived canopy height and dry biomass was affected by disturbances, leading to overestimation or underestimation of biomass depending on disturbance type and severity. The impact of disturbances on biomass estimation varied across cutting systems. These findings highlight the potential of UAV-derived canopy height models for estimating vegetation structure, but also underscore the need for caution in relying solely on these models for accurate biomass estimation in heterogeneous grasslands. Future research should explore strategies to enhance biomass estimation accuracy by integrating additional data sources and accounting for field disturbances.}, subject = {-}, language = {en} }