Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).
ObjectivesSomatic cell count (SCC) is a reliable and approved parameter for the estimation of udder health. The maincell types regarding somatic cells in the udder are lymphocytes, macrophages, and polymorph nuclearleucocytes (PMN). The differential somatic cell count (DSCC) represents the proportion of lymphocytes andPMN to total SCC, the remaining percentages to SCC are macrophages. So far, the effects of milk yield,season, parity, milking frequency, days in milk, and major pathogens on the DSCC are already described. Afurther known effect on udder health and SCC is the milking interval (MI). On farms with automatic milkingsystems (AMS) the MI can vary for each cow compared to conventional milking systems. Regarding DSCCand SCC, cows milked by AMS systems showed higher values compared to cows milked by conventionalmilking systems. Therefore, the aim of this study was to evaluate the effect of MI on DSCC.Materials and methodsData from 27 dairy herds from Germany including 6,500 dairy cows and 43,229 recordings were evaluated.The data resulting from milk yield performance testing were collected between January to December 2020. Allherds used automatic milking systems combined with free cow traffic. Milk yield performance testing data wasrecorded 11-times per year on each farm and included the DSCC measured using the FOSS methoddescribed by Damm et al. (2017). Date and time from each milking at the farms were available and used tocalculate each individual MI between milkings. MI ranged from 1 h minimum to 24 h maximum. Data of milkinginterval >24 h were excluded. Means were compared using Wilcoxon test. P-values were Bonferroni adjusted;the threshold for significance was set after adjusting to α < 0.05. A linear mixed model was used to estimatethe effect on DSCC including MI, milk yield, lactation, days in milk, and season as fixed effects and herd,individual cow, and residuals as random effects.ResultsMean MI was 10.6 h (±0.04 h SE). MI of ≤4 h resulted in the highest DSCC (52.3 ±1.0%). The DSCCdecreased significantly for cows showing a MI >4 and ≤6 h (39.0 ±0.6%) and had its minimum between MI >6and ≤8 h (37.9 ±0.4%). MI between >8 - ≤10 h resulted in a DSCC of 40.5% (±0.4%). The DSCC increased forMI >10 - ≤12 h and for >12 h MI (42.8 ±0.4%; 46.6 ±0.3%, respectively; all P-values < 0.001). Therefore, themost frequently milked cows showed higher DSCC compared to cows between 4 and 8 hours MI. Consideringnatural behavior, the suckling interval of calves from their mothers ranges between 4 to 6 times per day, whichresults in a MI of 6 to 4 hours, representing the MI of the second lowest DSCC found in this dataset.The standard deviation of the MI (MISD) expresses the irregularity of milkings. Data evaluation showed the lower the MISD, the lower the DSCC. For MISD ≤2 h the DSCC was lowest (38.8 ±0.7%), compared to MISD >2- ≤4 h (41.0 ±0.5%), MISD >4 - ≤6 h (43.2 ±0.7%), and MISD >6 h (48.1 ±1.1%). Irregular milking is also knownto impair udder health and increase the SCC of cows.ConclusionsMilking interval between 4 to 8 hours minimizes DSCC, which aims the natural MI of suckling calves. A moreregular milking interval in AMS farms could reduce DSCC and therefore improve udder health. AMS farmsshould strive their management and settings of the AMS to encourage cows to visit the AMS more regularly.AcknowledgementWe kindly acknowledge the QNETICS GmbH, Erfurt, Germany, for providing the dataset of DSCC values andmilk yield recording data for this study.
ObjectivesTail injuries and pathological alterations have been reported in many species. In cattle, they were investigatedmainly in fattening bulls and feedlot cattle. In dairy cows high prevalences for different tail alterations werefound. However, aetiology and pathogenesis of this health trait are still unclear and need further investigation.Out of 4443 phenotypes of different tail alterations we assorted seven groups common in dairy cows: 1. verytip of the tail , 2. ring-like, 3. scurf, 4. swelling, 5. thinning, 6. axis anomaly, and 7. verruca-like mass. Theobjective of this study was to identify genomic regions that may influence the occurrence of different tailalterations in dairy cows, which could be useful for a potential implementation of a genomic selection tool formore robust and healthy cows in the future.Material and methodsData collection started in December 2019 from a German 75 German Holstein (HOL) cows dairy herd. All cows wereexamined every two weeks during six months regarding any kind of tail alterations. The findings were described andphotographed. Data analysis resulted in seven different kinds of tail alterations: 1. very tip of the tail, 2. ring-like, 3. scurf,4. swelling, 5. thinning, 6. axis anomalies, and 7. verruca-like mass.Hereinafter, prevalences for the observed tail alterations were calculated based on monthly data collection from fivedifferent dairy herds: 3 HOL herds, counting average herd sizes of 75, 300, and 1300, respectively; 2 German Fleckvieh(FV) herds, counting 60 cows, each. All cows were housed in free stall barns with conventional (HOL, FV) or automaticmilking systems (FV).In total, 4443 Dairy Cows' Tail Scores were recorded. Data preparation and analysis were performed using R version4.1.2. Prevalences for tail alterations were calculated by dividing the number of observations within by the total number ofobservations of each kind of tail alteration and was given in percent. For calculating the total prevalence per breed andfarm, the occurrence of at least one tail alteration counted as an observation, was divided by the total number of cowsunder investigation and given in percent.ResultsThe overall prevalence for any kind of tail alteration was 88% in German Holstein and 99% in Fleckvieh cows; it variedbetween farms from 74% to 99%. Prevalences for HOL and FV regarding alterations of the very tip of the tail were 26%and 71%, ring-like alterations 24% and 30%, swelling 26% and 42%, scurf 55% and 60%, thinning combined with axisanomalies 16% and 21%, and verruca-like mass 10% and 21%, respectively. Number per tail ranged for ring-likealterations and thinning/axis anomalies from 1 to 5 and for verruca-like mass from 1 to 3.ConclusionsDuring this study, high prevalences for different tail alterations in HOL and FV dairy cows were found out. The grouping ofdifferent alterations as described above can be useful to phenotype tail alterations in dairy cows. However, furtherinvestigations regarding pathogenesis, aetiology, and genetics of the observed alterations in dairy cows' tails are neededto understand their origin and impact on animal health and welfare.FundingThis research was funded by the Tönnies Forschung, Rheda, Germany.
Mehr
Prof. Dr. Prisca Kremer-Rücker,
Lukas Volkert,
Kim F. Schubert,
Dr. Saskia Meier
ObjectivesTail injuries and pathological alterations have been reported in many species. In cattle, they were investigatedmainly in fattening bulls and feedlot cattle. In dairy cows high prevalences for different tail alterations werefound. However, aetiology and pathogenesis of this health trait are still unclear and need further investigation.Out of 4443 phenotypes of different tail alterations we assorted seven groups common in dairy cows: 1. verytip of the tail , 2. ring-like, 3. scurf, 4. swelling, 5. thinning, 6. axis anomaly, and 7. verruca-like mass. Theobjective of this study was to identify genomic regions that may influence the occurrence of different tailalterations in dairy cows, which could be useful for a potential implementation of a genomic selection tool formore robust and healthy cows in the future.Material and methodsOccurrence data of each tail alteration group were collected monthly from 167 German Holstein cows. Thecows originated from a German 1300 cows dairy herd. Data collection was performed from May to December2021, since calving of all included cows was from April to May. The cows were in their first to seventh lactation.The phenotype was encoded binary, where 0 means the absence and 1 the presence of a tail alteration groupwithin the whole timespan.For 118 cows, Illumina EuroG10k genotypes were available and imputed up to 45k (FImpute). The remainingcows were genotyped with the Illumina EuroG MD (V1, V1.1, V2) with 45613 SNPs. After quality check (onlysegregating SNPs, at least two groups with a minimum of 10 observations, no duplicated markers, a minorallele frequency of 1%, and within Hardy-Weinberg-Equilibrium P>0.01), 41062 SNPs remained.A genome-wide association study was performed using the software GEMMA and the univariate linear mixedmodel. Each tail alteration group was treated as a separate phenotype. A standardized relatedness matrix wasincluded in the model and calculated on SNP chip data to consider the population stratification, since manyhalf-sib groups were present. The lactation (1st, 2nd, ≥3rd) was included as covariate. The genotype matrix wasincluded in the model and the effect size per marker was estimated and tested for significance using a Waldtest.For positional candidate gene analysis, genomic regions around top markers (P < 0.0001) of 325kbp wereconsidered,since the linkage disequilibrium decay analysis gave a mean r² of >0.61 within this distance. Themarker positions are given on the ARS-UCD 1.2 Bos taurus genome assembly.ResultsIn total 51 top markers resulted for all seven tail alteration groups, whereof one marker reached Bonferronicorrectedgenome-wide significance threshold for tail alteration group “thinning” (BTA1: rs42577957, −log10(P)= 9.22). The markers were found on 18 different chromosomes. Close to these markers, 65 positionalcandidate genes reside. Among them CCDC122 (rs42421906, −log10(P) = 5.46), which was associated withthe phenotype “scurf” in our analysis. CCDC122 is one of the top differentially expressed genes in livermetabolism in pigs showing swine inflammation and necrosis syndrome (Ringseis et al., 2021). This syndromeresults in severe tail alterations in pigs as well.ConclusionsThis first genetic investigation of tail alterations in dairy cows showed the potential of finding genetic markersfor this novel health trait. Nonetheless, it is recommended to increase the sample size of cows and to furtherinvestigate the cause of tail alterations, to substantiate the reported phenotypes.LiteratureRingseis, R., Gessner, D. K., Loewenstein, F., Kuehling, J., Becker, S., Willems, H., et al. (2021). Swine inflammation and necrosis syndrome is associated with plasma metabolites and liver transcriptome in affected piglets. Animals 11, 1–14. doi:10.3390/ani11030772 Sargolzaei, M., Chesnais, J. P., and Schenkel, F. S. (2014). A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15. doi:10.1186/1471-2164-15-478 Zhou, X., and Stephens, M. (2014). Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies. Nat Methods 11, 407–409. doi:10.1038/nmeth.2848 AcknowledgementWe thank the MASTERRIND GmbH, Verden, Germany, for providing the genotypes from the investigatedcows.FundingPart of the data results from the project TINCa Dairy, which is funded by the Tönnies Forschung, Rheda,Germany.
ObjectivesSomatic cell count (SCC) is a reliable and approved parameter for the estimation of udder health. The maincell types regarding somatic cells in the udder are lymphocytes, macrophages, and polymorph nuclearleucocytes (PMN). The differential somatic cell count (DSCC) represents the proportion of lymphocytes andPMN to total SCC, the remaining percentages to SCC are macrophages. So far, the effects of milk yield,season, parity, milking frequency, days in milk, and major pathogens on the DSCC are already described. Afurther known effect on udder health and SCC is the milking interval (MI). On farms with automatic milkingsystems (AMS) the MI can vary for each cow compared to conventional milking systems. Regarding DSCCand SCC, cows milked by AMS systems showed higher values compared to cows milked by conventionalmilking systems. Therefore, the aim of this study was to evaluate the effect of MI on DSCC.Materials and methodsData from 27 dairy herds from Germany including 6,500 dairy cows and 43,229 recordings were evaluated.The data resulting from milk yield performance testing were collected between January to December 2020. Allherds used automatic milking systems combined with free cow traffic. Milk yield performance testing data wasrecorded 11-times per year on each farm and included the DSCC measured using the FOSS methoddescribed by Damm et al. (2017). Date and time from each milking at the farms were available and used tocalculate each individual MI between milkings. MI ranged from 1 h minimum to 24 h maximum. Data of milkinginterval >24 h were excluded. Means were compared using Wilcoxon test. P-values were Bonferroni adjusted;the threshold for significance was set after adjusting to α < 0.05. A linear mixed model was used to estimatethe effect on DSCC including MI, milk yield, lactation, days in milk, and season as fixed effects and herd,individual cow, and residuals as random effects.ResultsMean MI was 10.6 h (±0.04 h SE). MI of ≤4 h resulted in the highest DSCC (52.3 ±1.0%). The DSCCdecreased significantly for cows showing a MI >4 and ≤6 h (39.0 ±0.6%) and had its minimum between MI >6and ≤8 h (37.9 ±0.4%). MI between >8 - ≤10 h resulted in a DSCC of 40.5% (±0.4%). The DSCC increased forMI >10 - ≤12 h and for >12 h MI (42.8 ±0.4%; 46.6 ±0.3%, respectively; all P-values < 0.001). Therefore, themost frequently milked cows showed higher DSCC compared to cows between 4 and 8 hours MI. Consideringnatural behavior, the suckling interval of calves from their mothers ranges between 4 to 6 times per day, whichresults in a MI of 6 to 4 hours, representing the MI of the second lowest DSCC found in this dataset.The standard deviation of the MI (MISD) expresses the irregularity of milkings. Data evaluation showed the lower the MISD, the lower the DSCC. For MISD ≤2 h the DSCC was lowest (38.8 ±0.7%), compared to MISD >2- ≤4 h (41.0 ±0.5%), MISD >4 - ≤6 h (43.2 ±0.7%), and MISD >6 h (48.1 ±1.1%). Irregular milking is also knownto impair udder health and increase the SCC of cows.ConclusionsMilking interval between 4 to 8 hours minimizes DSCC, which aims the natural MI of suckling calves. A moreregular milking interval in AMS farms could reduce DSCC and therefore improve udder health. AMS farmsshould strive their management and settings of the AMS to encourage cows to visit the AMS more regularly.AcknowledgementWe kindly acknowledge the QNETICS GmbH, Erfurt, Germany, for providing the dataset of DSCC values andmilk yield recording data for this study.
ObjectivesTail injuries and pathological alterations have been reported in many species. In cattle, they were investigatedmainly in fattening bulls and feedlot cattle. In dairy cows high prevalences for different tail alterations werefound. However, aetiology and pathogenesis of this health trait are still unclear and need further investigation.Out of 4443 phenotypes of different tail alterations we assorted seven groups common in dairy cows: 1. verytip of the tail , 2. ring-like, 3. scurf, 4. swelling, 5. thinning, 6. axis anomaly, and 7. verruca-like mass. Theobjective of this study was to identify genomic regions that may influence the occurrence of different tailalterations in dairy cows, which could be useful for a potential implementation of a genomic selection tool formore robust and healthy cows in the future.Material and methodsData collection started in December 2019 from a German 75 German Holstein (HOL) cows dairy herd. All cows wereexamined every two weeks during six months regarding any kind of tail alterations. The findings were described andphotographed. Data analysis resulted in seven different kinds of tail alterations: 1. very tip of the tail, 2. ring-like, 3. scurf,4. swelling, 5. thinning, 6. axis anomalies, and 7. verruca-like mass.Hereinafter, prevalences for the observed tail alterations were calculated based on monthly data collection from fivedifferent dairy herds: 3 HOL herds, counting average herd sizes of 75, 300, and 1300, respectively; 2 German Fleckvieh(FV) herds, counting 60 cows, each. All cows were housed in free stall barns with conventional (HOL, FV) or automaticmilking systems (FV).In total, 4443 Dairy Cows' Tail Scores were recorded. Data preparation and analysis were performed using R version4.1.2. Prevalences for tail alterations were calculated by dividing the number of observations within by the total number ofobservations of each kind of tail alteration and was given in percent. For calculating the total prevalence per breed andfarm, the occurrence of at least one tail alteration counted as an observation, was divided by the total number of cowsunder investigation and given in percent.ResultsThe overall prevalence for any kind of tail alteration was 88% in German Holstein and 99% in Fleckvieh cows; it variedbetween farms from 74% to 99%. Prevalences for HOL and FV regarding alterations of the very tip of the tail were 26%and 71%, ring-like alterations 24% and 30%, swelling 26% and 42%, scurf 55% and 60%, thinning combined with axisanomalies 16% and 21%, and verruca-like mass 10% and 21%, respectively. Number per tail ranged for ring-likealterations and thinning/axis anomalies from 1 to 5 and for verruca-like mass from 1 to 3.ConclusionsDuring this study, high prevalences for different tail alterations in HOL and FV dairy cows were found out. The grouping ofdifferent alterations as described above can be useful to phenotype tail alterations in dairy cows. However, furtherinvestigations regarding pathogenesis, aetiology, and genetics of the observed alterations in dairy cows' tails are neededto understand their origin and impact on animal health and welfare.FundingThis research was funded by the Tönnies Forschung, Rheda, Germany.
Mehr
Prof. Dr. Prisca Kremer-Rücker,
Lukas Volkert,
Kim F. Schubert,
Dr. Saskia Meier
ObjectivesTail injuries and pathological alterations have been reported in many species. In cattle, they were investigatedmainly in fattening bulls and feedlot cattle. In dairy cows high prevalences for different tail alterations werefound. However, aetiology and pathogenesis of this health trait are still unclear and need further investigation.Out of 4443 phenotypes of different tail alterations we assorted seven groups common in dairy cows: 1. verytip of the tail , 2. ring-like, 3. scurf, 4. swelling, 5. thinning, 6. axis anomaly, and 7. verruca-like mass. Theobjective of this study was to identify genomic regions that may influence the occurrence of different tailalterations in dairy cows, which could be useful for a potential implementation of a genomic selection tool formore robust and healthy cows in the future.Material and methodsOccurrence data of each tail alteration group were collected monthly from 167 German Holstein cows. Thecows originated from a German 1300 cows dairy herd. Data collection was performed from May to December2021, since calving of all included cows was from April to May. The cows were in their first to seventh lactation.The phenotype was encoded binary, where 0 means the absence and 1 the presence of a tail alteration groupwithin the whole timespan.For 118 cows, Illumina EuroG10k genotypes were available and imputed up to 45k (FImpute). The remainingcows were genotyped with the Illumina EuroG MD (V1, V1.1, V2) with 45613 SNPs. After quality check (onlysegregating SNPs, at least two groups with a minimum of 10 observations, no duplicated markers, a minorallele frequency of 1%, and within Hardy-Weinberg-Equilibrium P>0.01), 41062 SNPs remained.A genome-wide association study was performed using the software GEMMA and the univariate linear mixedmodel. Each tail alteration group was treated as a separate phenotype. A standardized relatedness matrix wasincluded in the model and calculated on SNP chip data to consider the population stratification, since manyhalf-sib groups were present. The lactation (1st, 2nd, ≥3rd) was included as covariate. The genotype matrix wasincluded in the model and the effect size per marker was estimated and tested for significance using a Waldtest.For positional candidate gene analysis, genomic regions around top markers (P < 0.0001) of 325kbp wereconsidered,since the linkage disequilibrium decay analysis gave a mean r² of >0.61 within this distance. Themarker positions are given on the ARS-UCD 1.2 Bos taurus genome assembly.ResultsIn total 51 top markers resulted for all seven tail alteration groups, whereof one marker reached Bonferronicorrectedgenome-wide significance threshold for tail alteration group “thinning” (BTA1: rs42577957, −log10(P)= 9.22). The markers were found on 18 different chromosomes. Close to these markers, 65 positionalcandidate genes reside. Among them CCDC122 (rs42421906, −log10(P) = 5.46), which was associated withthe phenotype “scurf” in our analysis. CCDC122 is one of the top differentially expressed genes in livermetabolism in pigs showing swine inflammation and necrosis syndrome (Ringseis et al., 2021). This syndromeresults in severe tail alterations in pigs as well.ConclusionsThis first genetic investigation of tail alterations in dairy cows showed the potential of finding genetic markersfor this novel health trait. Nonetheless, it is recommended to increase the sample size of cows and to furtherinvestigate the cause of tail alterations, to substantiate the reported phenotypes.LiteratureRingseis, R., Gessner, D. K., Loewenstein, F., Kuehling, J., Becker, S., Willems, H., et al. (2021). Swine inflammation and necrosis syndrome is associated with plasma metabolites and liver transcriptome in affected piglets. Animals 11, 1–14. doi:10.3390/ani11030772 Sargolzaei, M., Chesnais, J. P., and Schenkel, F. S. (2014). A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15. doi:10.1186/1471-2164-15-478 Zhou, X., and Stephens, M. (2014). Efficient Algorithms for Multivariate Linear Mixed Models in Genome-wide Association Studies. Nat Methods 11, 407–409. doi:10.1038/nmeth.2848 AcknowledgementWe thank the MASTERRIND GmbH, Verden, Germany, for providing the genotypes from the investigatedcows.FundingPart of the data results from the project TINCa Dairy, which is funded by the Tönnies Forschung, Rheda,Germany.
Mehr
Jan D Hüwel,
Prof. Dr. Florian Haselbeck,
Prof. Dr. Dominik Grimm,
Christian Beecks
One of the major challenges in time series analysis are changing data distributions, especially when processing data streams. To ensure an up-to-date model delivering useful predictions at all times, model reconfigurations are required to adapt to such evolving streams. For Gaussian processes, this might require the adaptation of the internal kernel expression. In this paper, we present dynamically self-adjusting Gaussian processes by introducing Event Triggered Kernel Adjustments in Gaussian process modelling (ETKA), a novel data stream modelling algorithm that can handle evolving and changing data distributions. To this end, we enhance the recently introduced Adjusting Kernel Search with a novel online change point detection method. Our experiments on simulated data with varying change point patterns suggest a broad applicability of ETKA. On real-world data, ETKA outperforms comparison partners that differ regarding the model adjustment and its refitting trigger in nine respective ten out of 14 cases. These results confirm ETKA's ability to enable a more accurate and, in some settings, also more efficient data stream processing via Gaussian processes.Code availability: https://github.com/JanHuewel/ETKA
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