4 results on '"Querengässer, Friederike"'
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2. Estimating uncertainty in milk yield reduction associated with elevated somatic cell counts
- Author
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Bartel, Alexander, Gass, Eva, Onken, Folkert, Baumgartner, Christian, Querengässer, Friederike, and Doherr, Marcus G
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RMY ,milk testing ,quantile regression ,DHI ,mastitis - Abstract
The reduction in milk yield (RMY) due to impaired health of dairy cows is a common parameter used in economic modelling. Hortet et al. (1999) showed the association between RMY and somatic cell count (SCC) levels in French Holstein cows and proposed a polynomial model to estimate the expected average lossOn the individual cow level RMY can differ, but so far no estimates for the variation around the average RMY are available. Additionally, estimates are based on only one breed. The aim of our study is to investigate the expected variation around the average RMY and to examine the influence of different breeds on the RMY estimation. Combining DHI data from over 900,000 cows from two German federal states (Bavaria and North Rhine Westphalia) over the course of two years yields more than 10 million measurements with high representation of the four most common breeds (Brown Swiss, German Fleckvieh, Holstein-Fresian, Red-Holstein). A quantile regression model with 10%, 50% and 90% bounds stratified for breed was used. Milk yield was calculated using the estimated lactation curve based on laction number, days in milk (DIM) and SCC of all animals, while adjusting for farm size and seasonal effects. Similar to Hortet et al., the SCC was set to 50,000 cells/ml to calculate unimpaired milk yield as a reference. The quantile regression was performed as a generalized additive model with penalized cubic splines using the package "qgam" (Fasiolo et al., 2017) for R (version 3.53). The High Performance Cluster of ZEDAT (Freie Universität Berlin) kindly provided us with computing time. Our results show the absolute reduction in milk yield is dependent on the breed. The highest performing breed “Holstein-Friesian” has a comparably low median absolute RMY at 100 DIM and in the first and second lactation. In contrast the variation is one of the highest of all breeds, with a 90% quantile which is one of the lowest compared to the other breeds and a 10% quantile which shows high losses especially for higher lactation numbers. The relative milk loss curves are shaped similarly for all breeds. Especially at the beginning of the lactation the median RMY closely tracks the 90% quantile with most of the variation within the lower 50% of cows. Towards the end of the lactation the relative RMY increases since healthy milk yield is dropping and absolute RMY is rising. The observed milk loss and it’s variation is similar and only slightly higher for 500,000 and 1 Mio. SCC/ml. Quantile regression allows us to quantify the variation around the estimated median RMY depending on the SCC. We can show that the variation in RMY at 300,000 SCC/ml can be higher than 1.5 liters for Holstein cows. It has already been shown that different pathogens can result in highly different RMY (Gröhn et al., 2010). Further investigation is needed to determine how much of the variation can be explained by a combination of SCC and pathogen. Our model assumes that individual cows stay on their respective milk yield quantiles with increasing SCC. This implies that low performing cows on the 10% quantile will experience the highest RMY, while high performing cows (90% quantile) will experience the lowest RMY. We believe this is unrealistic. If the model assumption is not true and cows change their respective quantiles due to rising SCCs, the observed variation in RMY can only be higher. Our approach therefore provides a lower limit for the total variation of the RMY. We hope to improve the estimation of uncertainty of RMY in the future by better modelling the disease-free milk performance of individual cows., {"references":["Hortet, P., Beaudeau, F., Seegers, H., & Fourichon, C. (1999). Reduction in milk yield associated with somatic cell counts up to 600 000 cells/ml in French Holstein cows without clinical mastitis. Livestock Production Science, 61(1), 33–42. https://doi.org/10.1016/S0301-6226(99)00051-2","Fasiolo, M., Goude, Y., Nedellec, R., & Wood, S. N. (2017). Fast calibrated additive quantile regression. Arxiv Preprint, (2000). https://doi.org/10.1097/MD.0000000000007529","Gröhn, Y. T., Wilson, D. J., González, R. N., Hertl, J. A., Schulte, H., Bennett, G., & Schukken, Y. H. (2010). Effect of Pathogen-Specific Clinical Mastitis on Milk Yield in Dairy Cows. Journal of Dairy Science, 87(10), 3358–3374. https://doi.org/10.3168/jds.s0022-0302(04)73472-4","R Core Team. (2019). R: A Language and Environment for Statistical Computing. Vienna, Austria. Retrieved from https://www.r-project.org/"]}
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- 2019
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3. ZellDiX - A new approach to assess udder health by using DHI results and cell differentiation
- Author
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Gass, Eva, Bartel, Alexander, Onken, Folkert, Baumgartner, Christian, Querengässer, Friederike, and Doherr, Marcus G
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milk testing ,food and beverages ,DHI ,CDI ,differential somatic cell count ,mastitis - Abstract
Management of udder health is a challenging aspect on every dairy farm. Checking the somatic cell count (SCC) routinely in the course of DHI testing is the best way to indirectly assess udder health of dairy cows. In Germany, six udder health scores are being computed based on the monthly cell count results. Provided in a monthly report, they have been proven to be a useful tool in order to reflect the current udder health status. However, to our knowledge, there are no standardized indicators available based on DHI results to predict the individual status of udder health in the future. The German ZellDiX project aims to enhance the informative value of DHI results by evaluating the additional value of differential somatic cell count (DSCC) and by establishing prognostic key figures for udder health. Since the introduction of a new generation of high throughput devices, SCC and DSCC can be analyzed simultaneously, allowing the assessment of the so called cell differentiation index (CDI). The CDI essentially reflects the proportion of macrophages of the total SCC. Throughout the project, cell differentiation was routinely performed from DHI samples taken over a period of 1.5 years from approximately 627,000 animals in Bavaria as well as from 139,000 animals partly from robot farms in North Rhine-Westphalia. Additionally, an experiment including 2,800 animals in Berlin-Brandenburg was conducted over a period of 5 months: DHI samples were analyzed with regard to SCC and CDI. Simultaneously, SCC, CDI, and the bacteriological status were assessed from udder quarter level samples of the same animals. Based on the collected DHI data, two key figures were established in regard to different initial SCC. In the case of currently > 100,000 cells/ml, the probability for elevated cell counts in the next two months can be predicted. Whereas in the case of currently < 100,000 cells/ml, the probability for stable udder health with low cell counts in the next two months is predicted. By providing the probability for different outcome scenarios, farmers would be able to rank their animals according to high or low risk and prioritize their effort. Results from the current data evaluation of quarter milk samples, will serve as reference to DHI samples and give detailed insight into actual processes in the udder and the value of CDI., {"references":["R Core Team. (2019). R: A Language and Environment for Statistical Computing. Vienna, Austria. Retrieved from https://www.r-project.org/"]}
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- 2019
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4. Potentielle Risikofaktoren für das Auftreten der Infektion mit dem Schmallenberg-Virus in deutschen Rinder- und Schafbetrieben
- Author
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Querengässer, Friederike
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sheep ,600 Technik, Medizin, angewandte Wissenschaften::630 Landwirtschaft::630 Landwirtschaft und verwandte Bereiche ,cattle ,animal housing ,risk analysis ,farm surveys ,epidemiology ,schmallenberg virus - Abstract
Seit dem erstmaligen Auftreten der Infektion mit dem neuartigen Orthobunyavirus, dem Schmallenberg-Virus, im Spätsommer 2011 wurde eine rasche Ausbreitung der Infektion in Deutschland und vielen anderen europäischen Ländern beobachtet. Viele Aspekte der Epidemiologie der SBV-Infektion waren anfangs unerforscht. Wie bei anderen zunächst unbekannten Infektionserkrankungen ist die Bestimmung und Bewertung von Risikofaktoren für die Verbreitung dieser Erkrankung erforderlich, um effiziente Maßnahmen für die Reduzierung des Infektionsrisikos von SBV entwickeln zu können. Im Rahmen dieser deutschlandweiten Fall-Kontroll-Studie zur SBV-Infektion wurden von November 2011 bis Februar 2013 in 7 Bundesländern retrospektiv Daten erfasst, um Risikound auch Schutzfaktoren für das Vorkommen von SBV-Infektionen bei Rindern und Schafen auf Betriebsebene zu bestimmen. Bei Betriebsbesuchen wurden mittels eines standardisierten Fragebogens Angaben zum Betriebsmanagement, zur tierärztlichen Betreuung und Überwachung, zu Bestandsbesuchen von Personen mit engem Tierkontakt, zum Krankheitsgeschehen sowie zur potentiellen Einschleppungsursache und Weiterverbreitung gesammelt. Darüber hinaus wurden Proben für die Ermittlung der Seroprävalenz entnommen. Die nach Abgleich der serologischen Ergebnisse falsch eingestuften Kontrollen machten eine Anpassung der Falldefinition für eine bessere Trennschärfe zwischen Fall- und Kontrollbetrieben notwendig. Der schnelle Einzug und die Verbreitung dieser neuen Viruserkrankung innerhalb Deutschlands spiegelten sich auch in der Schwierigkeit des Auffindens SBV-freier Betriebe und schließlich in der Verteilung der Fälle und Kontrollen in Untersuchungsgebiet wider. Folglich wurden in den Betrieben im Zentrum der Epidemie hohe Intraherdenprävalenzen festgestellt, während in peripheren Gebieten weniger SBV-exponierte Betriebe gefunden werden konnten. Im Rahmen der Risikofaktoren- Analyse wurden 73 auswertbare Variablen aus 7 Kontrollund 33 Fallbetrieben bei den Rindern und 63 auswertbare Variablen aus 16 Kontroll- und 29 Fallbetrieben bei den Schafen auf ihre Signifikanz hin zunächst bivariat und dann multivariat getestet. Die statistische, bivariate Analyse mittels des exakten Tests nach Fisher ergab für 7 Variablen bei den Rinderbetrieben und 5 Variablen bei den Schafbetrieben signifikante Unterschiede zwischen den Kontroll- und Fallbetrieben. Aus der multivariaten Auswertung gingen schließlich je Tierart drei finale Modelle hervor, deren Variablen sowohl in der bivariaten Analyse als auch im logistischen Regressionsmodell einen Erklärungsbeitrag zur Zielvariablen leisteten. Bei den Rinderbetrieben ergaben sich für die Variablen „zeitweise Stallhaltung“, „eigener Bulle“ und „Zukauf von Tieren“ positive Assoziationen zur SBV-Infektion. Für die Variablen „Wanderschafherden im Gebiet“ und „ganzjährige Stallhaltung“ konnte anhand der statistischen Signifikanz ein protektiver Effekt festgestellt werden. In der multivariaten Modell-Analyse erklären „Zukauf von Tieren“, „Wanderschafherden im Gebiet“ und „ganzjährige Stallhaltung“ kombiniert am besten das Vorkommen der SBV-Infektion. Bei den Schafbetrieben zeigten innerhalb der bivariaten Auswertung „Haltung von Geflügel“, „Haltung von Haarschafen“ und „Ganzjährige Bedeckung“ statistisch signifikante Unterschiede zwischen den Fall- und Kontrollbetrieben auf. In der multivariaten Analyse leistete das Zusammenspiel aus „Fruchtbarkeitsstörungen der Muttertiere“, „Regelmäßige Tierarztbetreuung“ und „Haltung von Geflügel“ den höchsten Erklärungsbeitrag zur Zielvariablen. Im Rahmen der vorliegenden Fall-Kontroll-Studie konnten keine Hinweise auf einen Zusammenhang zwischen einer SBV-Infektion und der Betriebsnähe zu feuchten Gebieten oder zur Behandlung mit Repellentien und Insektiziden zwischen Fall und Kontrollbetrieben festgestellt werden., Since the first occurrence of infection with this new orthobunyavirus in late summer 2011, the Schmallenberg virus (SBV) infection has spread rapidly in Germany and in many other European countries. A multitude of epidemiologic aspects of the SBV infection had initially been unexplored. As for many other firstly unexplored infectious diseases, an identification and estimation of risk factors for the spread of this very disease is required in order to develop efficient measures to decrease the risk of infection. In the context of this case-control study of SBV infection in Germany, data has been analyzed retrospectively from November 2011 to February 2013 from 7 federal states in order to identify potential risk and protective factors of SBV infection on cattle and sheep farms. During farm visits, standardized surveys with questionnaires were used to generate data on farm management, veterinary visits and monitoring, herd visits by people with close animal contacts, occurring diseases and potential causes of invasion or retransmission. Furthermore, samples were taken to determine the seroprevalence. After the match of the serologic results had led to falsely categorised control cases, the case definitions had to be adjusted to achieve a better distinction between cases and controls. The fast spread and retransmission of this new disease in Germany was also reflected in the difficulty of finding SBV-free farms at the time of the visits and in the distribution of cases and controls within the investigation area. Accordingly, farms close to the epidemiologic centre showed high intraherd prevalence, whereas farms in peripheral regions were less exposed to SBV. In the bivariate and multivariate analyses of risk factors of this study, 73 variables were tested from 7 control and 33 case farms in the cattle sector, while 63 variables were checked from 16 control and 29 case farms in the sheep sector. Bivariate analyses based on the Fisher exact test resulted in 7 variables for cattle and 5 variables for sheep showing statistically significant differences between case and control farms. From the multivariate analyses, 3 models were derived for each of the two species. Its underlying variables played an important role to explain the target variable by bivariate analysis and using logistic regression models. For cattle farms, the variables “temporary indoor housing”, “own bull” and “purchase of new animals” revealed positive associations with SBV infection. The variables “migrating sheep herds” and “all-year indoor housing” showed a protective effect on a statistically significant level. The best multivariate model explaining the occurrence of SBV infection consisted of the variables “purchase of new animals”, “migrating sheep herds” and “all-year indoor housing”. For sheep farms, the variables „keeping poultry“, “keeping hair sheep” and “all-year coverage” revealed statistically significant differences between case and control farms in the bivariate analysis. In the multivariate analysis, the combination of “fertility disturbances of ewes”, “regular veterinary care” and “keeping of poultry” explained the target variable in the best way. This case-control study did not indicate a correlation between SBV infection and the proximity of the farms to wetlands or to the use of repellents and insecticides.
- Published
- 2016
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