The Experts below are selected from a list of 2079 Experts worldwide ranked by ideXlab platform
Henk Hogeveen - One of the best experts on this subject based on the ideXlab platform.
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dynamic forecasting of individual cow milk yield in Automatic Milking Systems
Journal of Dairy Science, 2018Co-Authors: Dan B Jensen, Mariska Van Der Voort, Henk HogeveenAbstract:Accurate forecasting of dairy cow milk yield is useful to dairy farmers, both in relation to financial planning and for detection of deviating yield patterns, which can be an indicator of mastitis and other diseases. In this study we developed a dynamic linear model (DLM) designed to forecast milk yields of individual cows per Milking, as they are milked in Milking robots. The DLM implements a Wood's function to account for the expected total daily milk yield. It further implements a second-degree polynomial function to account for the effect of the time intervals between Milkings on the proportion of the expected total daily milk yield. By combining these 2 functions in a dynamic framework, the DLM was able to continuously forecast the amount of milk to be produced in a given Milking. Data from 169,774 Milkings on 5 different farms in 2 different countries were used in this study. A separate farm-specific implementation of the DLM was made for each of the 5 farms. To determine which factors would influence the forecast accuracy, the standardized forecast errors of the DLM were described with a linear mixed effects model (lme). This lme included lactation stage (early, middle, or late), somatic cell count (SCC) level (nonelevated or elevated), and whether or not the proper farm-specific version of the DLM was used. The standardized forecast errors of the DLM were only affected by SCC level and interactions between SCC level and lactation stage. Therefore, we concluded that the implementation of Wood's function combined with a second-degree polynomial is useful for dynamic modeling of milk yield in Milking robots, and that this model has potential to be used as part of a mastitis detection system.
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mastitis alert preferences of farmers Milking with Automatic Milking Systems
Journal of Dairy Science, 2012Co-Authors: H Mollenhorst, Henk Hogeveen, L. J. RijkaartAbstract:Abstract The aim of this study was to assess farmers’ preferences for the performance characteristics of mastitis detection Systems. Additionally, we looked at whether certain groups of farmers could be distinguished with specific preferences. Farmers’ opinions concerning mastitis detection Systems, as well as general information about the farm and the farmer, were investigated with a standard questionnaire. The second part of the questionnaire was specifically aimed at elucidating preferences. Definitions of time windows and performance parameters, such as sensitivity and specificity, were incorporated into characteristics of a detection system (attributes) that reflect farmers’ daily experience. Based on data from 139 farmers, we concluded that, on average, they prefer a clinical mastitis detection system that produces a low number of false alerts, while alerting in good time and with emphasis on the more severe cases. These 3 attributes were evaluated as more important than the 3 other attributes, representing the costs of the detection system, the number of missed cases, and how long before clinical signs alerts need to be given. Variation in importance per attribute, however, was high, denoting that farmers’ preferences differ considerably. Although some significant relationships were found between farm characteristics and attributes, no clear groups of farmers with specific preferences could be distinguished. Based on these results, we advise making detection Systems adaptable for the farmers to satisfy their preferences and to match the circumstances on the farm. Furthermore, these results support that for evaluation of detection algorithms comparisons have to be made at high levels of specificity (e.g., 99%) and time windows have to be kept small (preferably no more than 24 h).
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Alert preferences of dairy farmers working with Automatic Milking Systems
Udder Health and Communication, 2011Co-Authors: L. J. Rijkaart, Herman Mollenhorst, Henk HogeveenAbstract:Although a lot of work is done raising the performance of mastitis detection Systems detection Systems for Automatic Milking Systems, these Systems are not perfect. Therefore, performance of a system is a trade-of between several characteristics. The following characteristics of a detection system can be distinguished: the number of alerts given before the occurrence of clinical mastitis, the number of alerts can be given after the occurrence of clinical mastitis, the number of false alerts, the number of missed cows, the health-state of a missed cow and the costs of the detection system. Farmers may have preferences for certain characteristics which are useful to define in order to raise the performance of detection Systems. The aim of this study was to explore farmers’ preferences for detection-Systems, and to try to find a relation between these preferences and certain farm/farmer characteristics. In total 139 farmers were questioned about their preferences using adaptive conjoint analysis. Overall preferences indicated that the number of false alerts and the health-state of a missed cow were most important, while the number of missed cows and whether an alert is given before clinical mastitis were found less important. However, differences between farmers were large. The results of this study give directions for future research on improving detection Systems. Besides that the results can be used to adapt detection Systems to farmers’ preferences or even make the performance of detection Systems flexible between farmers.
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The relationship between Milking interval and somatic cell count in Automatic Milking Systems
Journal of dairy science, 2011Co-Authors: Herman Mollenhorst, M.m. Hidayat, J. Van Den Broek, F. Neijenhuis, Henk HogeveenAbstract:The aim of this study was to explore whether, during Automatic Milking, Milking interval or its variation is related to somatic cell count (SCC), even when corrected for effects of production, lactation stage, and parity. Data on Milking interval and production level were available from the Automatic Milking Systems of 151 farms. Data on SCC, parity, and lactation stage were derived from dairy herd improvement records of the same farms. Mainly due to incomplete records, data of 100 farms were used in the final analysis. For every cow, only 1 test day was used in the final analysis. Milking interval, the coefficient of variation of Milking interval, production rate, the difference in production rate between short- and long-term, parity, days in milk, and some biologically relevant interactions were used in a linear mixed model with farm as random variable to assess their association with log10-transformed SCC. None of the interactions was significantly related to SCC, whereas all main effects were, and thus, stayed in the final model. The effect of Milking interval was, although significant, not very strong, which shows that the effect of Milking interval on SCC is marginal when corrected for the other variables. The variation in Milking intervals was positively related with SCC, showing that the variation in Milking interval is even more important than the Milking interval itself. In the end, this study showed only a limited association between Milking interval and SCC when Milking with an Automatic Milking system.
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Sensor measurements revealed
Computers and Electronics in Agriculture, 2011Co-Authors: C. Kamphuis, Herman Mollenhorst, Henk HogeveenAbstract:Highlights? Study the usefulness of sensor measurements to predict clinical mastitis pathogens. ? Applying decision-tree induction to sensor data collected during Automatic Milking. ? Currently available sensors cannot be used to predict clinical mastitis pathogens. Automatic Milking Systems produce mastitis alert lists that report cows likely to have clinical mastitis (CM). A farmer has to check these listed cows to confirm a CM case and to start an antimicrobial treatment if necessary. In order to make a more informed decision, it would be beneficial to have information about the CM causal pathogen at the same time a cow is listed on the mastitis alert list. Therefore, this study explored whether decision-tree induction was able to predict the Gram-status of CM causal pathogens using in-line sensor measurements from Automatic Milking Systems. Data were collected at nine Dutch dairy farms Milking with Automatic Milking Systems and included 140 bacteriological cultured CM cases with sensor measurements of electrical conductivity, colors red, green, and blue and milk yield for analyses. In total, 110 CM cases were classified as Gram-positive CM cases and 30 as Gram-negative. Stratified randomization was used to divide the data in a training set (n=96) for model development, and a test set (n=44) for validation. The decision tree used three variables to predict the Gram-status of the CM causal pathogen; two variables were based on electrical conductivity measurements, and one on measurements of the color blue. This decision tree had an accuracy of 90.6% and a kappa value of 0.76 based on data in the training set. When only those CM cases were considered with extreme high probability estimates for their Gram-status (either positive or negative), 74% of all records in the training set could be classified with a stratified accuracy of 97.1%. When validated, the decision tree performed poorly; accuracy dropped to 54.5% and the kappa value to -0.20. The stratified accuracy calculated for 75% of all records in the test set was 66.7%. Predicting the CM causal pathogen showed a similar poor result; the decision tree had an accuracy of 27.9% and a kappa of 0.12, based on data in the test set. Based on these results, it is concluded that decision-tree induction in conjunction with sensor information from the electrical conductivity, color, and milk yield provide insufficient discriminative power to predict the Gram-status or the CM causal pathogen itself.
Bjørg Heringstad - One of the best experts on this subject based on the ideXlab platform.
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a genetic study of new udder health indicator traits with data from Automatic Milking Systems
Journal of Dairy Science, 2020Co-Authors: Karoline Bakke Wethal, M Svendsen, Bjørg HeringstadAbstract:ABSTRACT The current study aimed to investigate new udder health traits based on data from Automatic Milking Systems (AMS) for use in routine genetic evaluations. Data were from 77 commercial herds; out of these, 24 had equipment for measuring online cell count (OCC), whereas all had data on electrical conductivity (EC). A total of 4,714 Norwegian Red dairy cows and 2,363,928 Milkings were included in the genetic analyses. Electrical conductivity was available on quarter level for each Milking, whereas OCC was measured per Milking. The AMS traits analyzed were log-transformed online cell count (lnOCC), maximum conductivity (ECmax), mean conductivity (ECmean), elevated mastitis risk (EMR), and log-transformed EMR (lnEMR). In addition, lactation mean somatic cell score (LSCS) was collected from the Norwegian dairy herd recording system. Elevated mastitis risk expresses the probability of a cow having mastitis and was calculated from smoothed lnOCC values according to individual trend and level of the OCC curve. The udder health traits from AMS were analyzed as repeated Milkings from 30 to 320 DIM, and LSCS as repeated parities. In addition, both ECmax and lnOCC were analyzed as multiple traits by splitting the lactation into 5 periods. (Co)variance components were estimated from bivariate mixed linear animal models, and investigated traits showed genetic variation. Estimated heritabilities of ECmean, ECmax, and lnEMR were 0.35, 0.23, and 0.12, respectively, whereas EMR and lnOCC both showed heritabilities of 0.09. Heritability varied between periods of lactation, from 0.04 to 0.13 for lnOCC and from 0.12 to 0.27 for ECmax, although standard errors of certain periods were large. Genetic correlations among the AMS traits ranged from 0 to 0.99. The genetic correlations between EC-based traits and OCC-based traits in AMS were 0. Genetic correlations with LSCS were favorable, ranging from 0.37 to 0.80 (±0.11–0.22). The strongest correlation (0.80 ± 0.13) was found between LSCS and lnEMR. Results question the value of ECmax and ECmean as indicators of udder health in genetic evaluations and suggest OCC to be more valuable in this manner. This study demonstrates a potential of using AMS data as additional information on udder health for genetic evaluations, although further investigation is recommended before these traits can be implemented.
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genetic analyses of novel temperament and milkability traits in norwegian red cattle based on data from Automatic Milking Systems
Journal of Dairy Science, 2019Co-Authors: Karoline Bakke Wethal, Bjørg HeringstadAbstract:ABSTRACT The number of dairy cows milked in Automatic Milking Systems (AMS) is steadily increasing in Norway. Capacity and efficiency of AMS are highly dependent on the individual cow's Milking efficiency, such as Milking speed and occupation time in the Milking robot. Cows meet new challenges in herds utilizing AMS. Consequently, new or revised traits may be needed for genetic evaluation of dairy cattle. The AMS records relevant information on an individual cow basis. The aims of this study were to estimate genetic parameters of new Automatically recorded milkability and temperament traits. Data from 77 commercial herds with Norwegian Red dairy cattle were analyzed by mixed linear animal models. The final data set contained 1,012,912 daily records from 4,883 cows in first to ninth lactation. For variance component estimation, univariate and bivariate models were used. Daily records of box time (BT), average flow rate (FR), kilograms of milk per minute of box time (MEF), handling time (HT), log-transformed HT, Milking frequency, and Milking interval were analyzed with repeatability models. Among these traits, FR, BT, and MEF showed the highest heritabilities of 0.48, 0.27, and 0.22, respectively, whereas heritability of log-transformed HT, HT, Milking frequency, and Milking interval was low (0.02–0.07). Unsuccessful Milkings expressed as rejected Milkings, incomplete Milkings (IM), Milkings with kick-offs (KO), and teat not found also showed low heritabilities (0.002–0.06). Due to low frequency, KO, rejected Milkings, IM, and teat not found were also analyzed as proportions per lactation, which resulted in slightly higher heritability estimates. Genetic correlations were favorable and intermediate to strong between BT, HT, MEF, and FR with absolute values above 0.50. Intermediate and favorable correlations were found for IM and KO with BT, HT, MEF, and FR. Cow milkability in AMS can be improved by selection for reduced number of unsuccessful Milkings, faster FR, increased MEF, and shorter BT and HT. Our results confirm that Automatically recorded data on milkability and temperament can be valuable sources of information for routine genetic evaluations and that Milking efficiency in AMS can be genetically improved.
Ilka Christine Klaas - One of the best experts on this subject based on the ideXlab platform.
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association between teat skin colonization and intramammary infection with staphylococcus aureus and streptococcus agalactiae in herds with Automatic Milking Systems
Journal of Dairy Science, 2019Co-Authors: Line Svennesen, Soren Saxmose Nielsen, Yasser S Mahmmod, Volker Kromker, Karl Pedersen, Ilka Christine KlaasAbstract:The objective of this study was to investigate the association between teat skin colonization and intramammary infection (IMI) with Staphylococcus aureus or Streptococcus agalactiae at the quarter level in herds with Automatic Milking Systems. Milk and teat skin samples from 1,142 quarters were collected from 300 cows with somatic cell count >200,000 cells/mL from 8 herds positive for Strep. agalactiae. All milk and teat skin samples were cultured on calf blood agar and selective media. A subset of samples from 287 quarters was further analyzed using a PCR assay (Mastit4 PCR; DNA Diagnostic A/S, Risskov, Denmark). Bacterial culture detected Staph. aureus in 93 (8.1%) of the milk samples and 75 (6.6%) of the teat skin samples. Of these, 15 (1.3%) quarters were positive in both the teat skin and milk samples. Streptococcus agalactiae was cultured in 84 (7.4%) of the milk samples and 4 (0.35%) of the teat skin samples. Of these, 3 (0.26%) quarters were positive in both the teat skin and milk samples. The PCR detected Staph. aureus in 29 (10%) of the milk samples and 45 (16%) of the teat skin samples. Of these, 2 (0.7%) quarters were positive in both the teat skin and milk samples. Streptococcus agalactiae was detected in 40 (14%) of the milk samples and 51 (18%) of the teat skin samples. Of these, 16 (5.6%) quarters were positive in both the teat skin and milk samples. Logistic regression was used to investigate the association between teat skin colonization and IMI at the quarter level. Based on bacterial culture results, teat skin colonization with Staph. aureus resulted in 7.8 (95% confidence interval: 2.9; 20.6) times higher odds of Staph. aureus IMI, whereas herd was observed as a major confounder. However, results from the PCR analyses did not support this association. Streptococcus agalactiae was isolated from the teat skin with both PCR and bacterial culture, but the number of positive teat skin samples detected by culture was too low to proceed with further analysis. Based on the PCR results, Strep. agalactiae on teat skin resulted in 3.8 (1.4; 10.1) times higher odds of Strep. agalactiae IMI. Our results suggest that Staph. aureus and Strep. agalactiae on teat skin may be a risk factor for IMI with the same pathogens. Focus on proper teat skin hygiene is therefore recommended also in AMS.
Karoline Bakke Wethal - One of the best experts on this subject based on the ideXlab platform.
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a genetic study of new udder health indicator traits with data from Automatic Milking Systems
Journal of Dairy Science, 2020Co-Authors: Karoline Bakke Wethal, M Svendsen, Bjørg HeringstadAbstract:ABSTRACT The current study aimed to investigate new udder health traits based on data from Automatic Milking Systems (AMS) for use in routine genetic evaluations. Data were from 77 commercial herds; out of these, 24 had equipment for measuring online cell count (OCC), whereas all had data on electrical conductivity (EC). A total of 4,714 Norwegian Red dairy cows and 2,363,928 Milkings were included in the genetic analyses. Electrical conductivity was available on quarter level for each Milking, whereas OCC was measured per Milking. The AMS traits analyzed were log-transformed online cell count (lnOCC), maximum conductivity (ECmax), mean conductivity (ECmean), elevated mastitis risk (EMR), and log-transformed EMR (lnEMR). In addition, lactation mean somatic cell score (LSCS) was collected from the Norwegian dairy herd recording system. Elevated mastitis risk expresses the probability of a cow having mastitis and was calculated from smoothed lnOCC values according to individual trend and level of the OCC curve. The udder health traits from AMS were analyzed as repeated Milkings from 30 to 320 DIM, and LSCS as repeated parities. In addition, both ECmax and lnOCC were analyzed as multiple traits by splitting the lactation into 5 periods. (Co)variance components were estimated from bivariate mixed linear animal models, and investigated traits showed genetic variation. Estimated heritabilities of ECmean, ECmax, and lnEMR were 0.35, 0.23, and 0.12, respectively, whereas EMR and lnOCC both showed heritabilities of 0.09. Heritability varied between periods of lactation, from 0.04 to 0.13 for lnOCC and from 0.12 to 0.27 for ECmax, although standard errors of certain periods were large. Genetic correlations among the AMS traits ranged from 0 to 0.99. The genetic correlations between EC-based traits and OCC-based traits in AMS were 0. Genetic correlations with LSCS were favorable, ranging from 0.37 to 0.80 (±0.11–0.22). The strongest correlation (0.80 ± 0.13) was found between LSCS and lnEMR. Results question the value of ECmax and ECmean as indicators of udder health in genetic evaluations and suggest OCC to be more valuable in this manner. This study demonstrates a potential of using AMS data as additional information on udder health for genetic evaluations, although further investigation is recommended before these traits can be implemented.
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genetic analyses of novel temperament and milkability traits in norwegian red cattle based on data from Automatic Milking Systems
Journal of Dairy Science, 2019Co-Authors: Karoline Bakke Wethal, Bjørg HeringstadAbstract:ABSTRACT The number of dairy cows milked in Automatic Milking Systems (AMS) is steadily increasing in Norway. Capacity and efficiency of AMS are highly dependent on the individual cow's Milking efficiency, such as Milking speed and occupation time in the Milking robot. Cows meet new challenges in herds utilizing AMS. Consequently, new or revised traits may be needed for genetic evaluation of dairy cattle. The AMS records relevant information on an individual cow basis. The aims of this study were to estimate genetic parameters of new Automatically recorded milkability and temperament traits. Data from 77 commercial herds with Norwegian Red dairy cattle were analyzed by mixed linear animal models. The final data set contained 1,012,912 daily records from 4,883 cows in first to ninth lactation. For variance component estimation, univariate and bivariate models were used. Daily records of box time (BT), average flow rate (FR), kilograms of milk per minute of box time (MEF), handling time (HT), log-transformed HT, Milking frequency, and Milking interval were analyzed with repeatability models. Among these traits, FR, BT, and MEF showed the highest heritabilities of 0.48, 0.27, and 0.22, respectively, whereas heritability of log-transformed HT, HT, Milking frequency, and Milking interval was low (0.02–0.07). Unsuccessful Milkings expressed as rejected Milkings, incomplete Milkings (IM), Milkings with kick-offs (KO), and teat not found also showed low heritabilities (0.002–0.06). Due to low frequency, KO, rejected Milkings, IM, and teat not found were also analyzed as proportions per lactation, which resulted in slightly higher heritability estimates. Genetic correlations were favorable and intermediate to strong between BT, HT, MEF, and FR with absolute values above 0.50. Intermediate and favorable correlations were found for IM and KO with BT, HT, MEF, and FR. Cow milkability in AMS can be improved by selection for reduced number of unsuccessful Milkings, faster FR, increased MEF, and shorter BT and HT. Our results confirm that Automatically recorded data on milkability and temperament can be valuable sources of information for routine genetic evaluations and that Milking efficiency in AMS can be genetically improved.
Ea Wechsle - One of the best experts on this subject based on the ideXlab platform.
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comparison of functional aspects in two Automatic Milking Systems and auto tandem Milking parlors
Journal of Dairy Science, 2007Co-Authors: Lorenz Gyga, I Neuffe, Rudolf Hause, Christia Kaufma, Ea WechsleAbstract:Abstract Milk yield, Milking frequency, interMilking interval, teat-cup attachment success rate, and length of the Milking procedure are important functional aspects of Automatic Milking Systems (AMS). In this study, these variables were compared for 2 different models of AMS (AMS-1, with free cow traffic, and AMS-2, with selectively guided cow traffic) and auto-tandem Milking parlors (ATM) on 4 farms each. Data on Milking-stall visits and Milkings of 20 cows were recorded on 3 successive days by means of video observations. Data were evaluated with mixed-effects models. Milk yield did not differ among the 3 Milking Systems. Milking frequency in the AMS was 2.47/d [95% confidence interval (CI)=(2.38, 2.56)], and was significantly higher than the 2 Milkings/d in ATM. Milking frequency was lower for cows with a higher number of days in milk (DIM) in AMS-1 [change of −0.057/10 DIM, CI=(−0.070, −0.044)], but remained constant for cows with varying DIM in AMS-2 [change of −0.003/10 DIM, CI=(−0.034, 0.027)]. As a consequence, Milking frequency was higher in early lactation [by 0.603, CI=(0.102, 1.103)] and lower in late lactation in AMS-1 than in AMS-2 [by −0.397, CI=(−0.785, −0.008)]. The interMilking interval showed the opposite pattern. Teat-cup attachment was more successful in AMS-1 than in AMS-2 (98.4 vs. 94.3% of the Milkings), with some variation among farms (range: AMS-1 96.2 to 99.5%; AMS-2 91.5 to 96.1%). The length of the entire Milking process did not differ among the Milking Systems [454s, CI=(430, 478)], although the preparation phase was longer [changes in comparison with ATM: in AMS-1 by a factor of 2.90, CI=(2.30, 3.65), and in AMS-2 by 5.15, CI=(4.09, 6.48)] and the actual Milking phase was shorter in both AMS-1 and AMS-2 than in ATM [changes in comparison with ATM: in AMS-1 by a factor of 0.76, CI=(0.62, 0.94), and in AMS-2 by 0.75, CI=(0.60, 0.93)]. The admission [changes in comparison with ATM: in AMS-1 by a factor of 2.56, CI=(1.55, 4.22), and in AMS-2 by 3.07, CI=(1.86, 5.08)] and preparation phases lasted longer in AMS-2 than in AMS-1, whereas the time required by the cows to leave the Milking stall did not differ among the Systems [changes in comparison with ATM: in AMS-1 by a factor of 0.89, CI=(0.55, 1.44), and in AMS-2 by 1.02, CI=(0.63, 1.66)]. In conclusion, different technical approaches to Automatic Milking led to differences in teat-cup attachment success rates, in the duration of several phases of the Milking process, and in Milking frequency. The capacity of an AMS could be further improved by shortening the preparation phase and reducing the proportion of failed Milkings.
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milk cortisol concentration in Automatic Milking Systems compared with auto tandem Milking parlors
Journal of Dairy Science, 2006Co-Authors: Lorenz Gyga, I Neuffe, Rudolf Hause, Christia Kaufma, Ea WechsleAbstract:Milk cortisol concentration was determined under routine management conditions on 4 farms with an auto-tandem Milking parlor and 8 farms with 1 of 2 Automatic Milking Systems (AMS). One of the AMS was a partially forced (AMSp) system, and the other was a free cow traffic (AMSf) system. Milk samples were collected for all the cows on a given farm (20 to 54 cows) for at least 1 d. Behavioral observations were made during the Milking process for a subset of 16 to 20 cows per farm. Milk cortisol concentration was evaluated by Milking system, time of day, behavior during Milking, daily milk yield, and somatic cell count using linear mixed-effects models. Milk cortisol did not differ between Systems (AMSp: 1.15 ± 0.07; AMSf: 1.02 ± 0.12; auto-tandem parlor: 1.01 ± 0.16 nmol/L). Cortisol concentrations were lower in evening than in morning Milkings (1.01 ± 0.12 vs. 1.24 ± 0.13 nmol/L). The daily periodicity of cortisol concentration was characterized by an early morning peak and a late afternoon elevation in AMSp. A bimodal pattern was not evident in AMSf. Finally, milk cortisol decreased by a factor of 0.915 in Milking parlors, by 0.998 in AMSp, and increased by a factor of 1.161 in AMSf for each unit of ln(somatic cell count/1,000). We conclude that Milking cows in Milking parlors or AMS does not result in relevant stress differences as measured by milk cortisol concentrations. The biological relevance of the difference regarding the daily periodicity of milk cortisol concentrations observed between the AMSp and AMSf needs further investigation.
