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To explore potential changes in behavior and productivity useful for early detection of health disorders in cows milked with automated milking systems (AMS), we collected longitudinal data throughout lactation of 57 dairy cows housed in a freestall barn with an AMS. Health problems were recorded, including subclinical ketosis (SCK; n = 19), metritis (n = 11), hoof disorders (n = 14), pneumonia (n = 7), and displaced abomasum (DA; n = 5). Data on rumination, activity, milking frequency and yield, and lying behavior were recorded electronically. Using repeated-measures mixed linear regression models, these data were analyzed for the days before the day of diagnosis/treatment (d 0) for each disorder separately, controlling for days in milk and parity. Analyses were performed between the day on which each outcome variable deviated significantly from baseline (up to d −14) and the day before diagnosis (nadir at d −1, before treatment and recovery). Outcomes tested were 3-d rolling averages of milk yield, milking frequency, and AMS supplement intake, in addition to daily rumination time (DRT), body weight, milk temperature, activity (measure of head/neck motion), and 3 lying behavior variables. From d −8, −6, and −5 before diagnosis of DA, SCK, or pneumonia, respectively, DRT declined by 45, 25, and 50 min/d. From d −14 to −1 before diagnosis of hoof disorders, DRT declined by 3 min/d. Body weight declined from d −4 before pneumonia (−14 kg/d) and metritis (−13 kg/d), from d −6 before SCK (−10 kg/d), and from d −5 before hoof disorders (−5 kg/d). Milk yield declined by 4.4 and 4.1 kg/d from d −4 before DA and pneumonia diagnoses, respectively, and by 1.2 kg/d from d −5 before SCK diagnosis. Activity levels declined before diagnosis of DA, pneumonia, SCK, or metritis. Lying behavior changed before diagnosis of DA, pneumonia, or metritis. Our results provide evidence that rumination behavior often deviated before milk yield and that several variables could contribute to earlier or automated identification of disorders. Behavior and productivity changed differently in association with various health disorders, suggesting the potential to distinguish among health problems. These variables merit further investigation in larger studies of cows milked with AMS. [ABSTRACT FROM AUTHOR]

This study evaluated differences in behavior and productivity between lame and nonlame cows in herds with automated milking systems (AMS). We monitored 30 cows per herd on 41 farms with AMS in Canada (26 herds in Ontario and 15 herds in Alberta). During a 6-d period, milking data (n = 1,184) and lying behavior data (n = 1,209) were collected from cows on 41 farms. Rumination behavior (n = 569) and activity (n = 615) data were available for cows at 22 farms. Locomotion was scored using a numerical rating system (NRS; 1 = sound; 5 = extremely lame). Cows were defined as clinically lame with NRS ≥ 3 (n = 353, 29%) and nonlame with NRS < 3 (n = 865, 71%). Greater parity, lower body condition, and lower environmental temperature were factors associated with lameness. When accounting for other factors, lame cows produced 1.6 kg/d less milk in 0.3 fewer milkings/d. Lame cows were 2.2 times more likely to be fetched more than 1 time during the 6-d period and spent 38 min/d more time lying down in bouts that were 3.5 min longer in comparison with nonlame cows. As the number of cows per AMS unit increased, the frequency of milkings and refusals per cow per day decreased and cow activity increased. For each 13.3-percentage-point increase in freestall stocking density (cows per stall), daily lying time decreased by 13 min/d and cows were 1.6 times more likely to be fetched more than 1 time during the 6-d period. There was no difference in daily rumination or activity between lame and nonlame cows or in night:day rumination time, but lame cows had greater night:day activity ratios. This study supports the growing knowledge that lameness has negative effects on milk production, voluntary milking behavior, and lying behavior of cows in herds with AMS. These results may help dairy producers gain a better appreciation of the negative effects of even moderate cases of lameness and may help motivate them to improve their lameness monitoring and treatment protocols. [ABSTRACT FROM AUTHOR]

Lameness is problematic for herds with automated milking systems (AMS) due to negative effects on milking frequency and productivity. The objective of this study was to evaluate how management, barn design, and the prevalence of lameness relate to productivity and behavior at the herd level in AMS. Details about barn design, stocking density, and management were collected from 41 AMS farms in Canada (Ontario: n = 26; Alberta: n = 15). We collected milking data for all cows on each farm, plus lying behavior data for 30 cows/farm during a 6-d period. Farms averaged 105 ± 56 lactating cows and 2.2 ± 1.3 AMS units. Forty cows/farm were gait scored (or 30% of cows for herds with >130 cows) using a numerical rating system (NRS; 1 = sound to 5 = extremely lame). Cows were defined as clinically lame with NRS ≥3 (mean = 26.2 ± 13.0%/herd) and severely lame with NRS ≥4 (mean = 2.2 ± 3.1%/herd). The prevalence of both clinical and severe lameness were negatively associated with environmental temperature. Clinical lameness tended to be less prevalent with more frequent scraping of manure alleys. The prevalence of severe lameness was positively associated with stocking density and curb height of the lying stalls. Milking frequency/cow per day was negatively related to the ratio of cows to AMS units. Doubling the prevalence of severe lameness (i.e., from 2.5 to 5%) was associated with reductions in milk production of 0.7 kg/cow per day and 39 kg/AMS per day. Milk/AMS was positively associated with more cows/AMS (+32 kg/cow). Fewer cows were fetched to the AMS with more frequent alley scraping. Lying behavior was associated with the frequency of feed push-ups, stall base, and environmental temperature. These results highlight the need for AMS producers to identify and reduce clinical lameness because 26% of cows/herd were clinically lame. Further, the results indicate that more frequently scraped alleys and optimal stocking densities are associated with improved cow mobility, productivity, and voluntary milking behavior. [ABSTRACT FROM AUTHOR]

The objective of this work was to assess the effect of timing of feed delivery on the behavior and productivity of cows milked 3 times per day. Twelve lactating Holstein dairy cows (4 primiparous and 8 multiparous), milked 3 times per day (at 1400, 2100, and 0700 h), were individually assigned and exposed to each of 2 treatments (over 21-d periods) in a replicated crossover design. Treatments were the manipulation of timing of TMR delivery, 2 times per day, in relation to milking time: (1) feeding at milking time (at 1400 and 0700 h), and (2) feeding halfway between milking times (at 1730 and 1030 h). Milk production, feeding, sorting, and rumination behavior were monitored for each animal for the last 7 d of each treatment period. Milk samples were collected for 2 of the last 4 d of each period for milk component analysis. With a feed delivery delay, dry matter intake (DMI) tended to be lower (26.5 vs. 27.2 kg/d). Although no difference was found in feeding time (224.2 min/d), cows fed with a delay consumed their feed more slowly (0.12 vs. 0.13 kg of dry matter/min) in more frequent meals (10.0 vs. 9.1 meals/d), which were smaller in size (2.8 vs. 3.1 kg/meal) and tended to be shorter in duration (26.7 vs. 30.1 min/ meal). Cows fed at milking sorted for long particles (102.3%), whereas cows fed with a delay did not sort for or against those particles. Cows sorted for medium particles to a similar extent (102.5%) on each treatment. Cows did not sort for or against short particles on either treatment. Sorting against fine particles was observed, to a similar extent (97.1%), on both treatments. Rumination time (8.9 h/d) and lying time (9.5 h/d) were similar between treatments. Cows without fresh feed at the 1400 h milking tended to stand for less time following that milking (71.0 vs. 94.0 min), whereas cows without fresh feed at the 0700 h milking stood for less time following that milking (66.3 vs. 87.8 min). No difference in this latency to lie down was seen at the 2100 h milking. Milk yield (48.0 kg/d), milk fat content (3.64%), and milk protein content (2.86%) were similar between treatments. Given the tendency for a difference in DMI and no change in yield, efficiency of production was improved with a feed delay (1.93 vs. 1.80 kg of milk/kg of DMI). These data suggest that moving the timing of feed delivery resulted in cows consuming their feed more slowly in smaller, more frequent meals, contributing to an improvement in efficiency of production. [ABSTRACT FROM AUTHOR]

Impaired locomotion (lameness) may negatively affect the ability and desire of cows to milk voluntarily, which is a key factor in success of automated milking systems (AMS). The objective of this study was to identify factors associated with herd-level lameness prevalence and associations of lameness and other farm-level factors with milking activity, milk yield, and milk quality in herds with AMS. From April to September 2019, 75 herds with AMS in Ontario, Canada, were visited, and data on barn design and farm management practices were collected. Data from AMS were collected, along with milk recording data, for the 6-mo period before farm visits. Farms averaged 98 ± 71 lactating cows, 2.3 ± 1.5 robot units/farm, 43.6 ± 9.4 cows/robot, 36.4 ± 4.9 kg/d of milk, a milking frequency of 3.01 ± 0.33 milkings/d, and a herd average geometric mean SCC of 179.3 ± 74.6 (× 1,000) cells/mL. Thirty percent of cows/farm (minimum of 30 cows/farm) were scored for body condition (1 = underconditioned to 5 = over conditioned) and locomotion (1 = sound to 5 = lame; clinically lame ≥3 out of 5 = 28.3 ± 11.7%, and severely lame ≥4 out of 5 = 3.0 ± 3.2%). Clinical lameness (locomotion score ≥3) was less prevalent on farms with sand bedding, with increased feed bunk space per cow, and on farms with non-Holstein breeds versus Holsteins, and tended to be less prevalent with lesser proportion of underconditioned cows (with body condition score ≤2.5). Severe lameness occurrence (farms with any cows with locomotion score ≥4) was associated with a greater proportion of underconditioned cows and in farms with stalls with greater curb heights. Herd average milk yield/cow per day increased with lesser prevalence of clinical lameness (each 10-percentage-point decrease in clinical lameness prevalence was associated with 2.0 kg/cow per day greater milk yield) and greater milking visit frequency per day, and tended to be greater with increased feed push-up frequency. Lesser herd average somatic cell count was associated with lesser clinical lameness prevalence, herd average days in milk, and proportion of overconditioned cows, and somatic cell count tended to be lesser for farms with sand bedding versus those with organic bedding substrates. The results highlight the importance of minimizing lameness prevalence, using of sand bedding, ensuring adequate feed access and feed bunk space, and maintaining proper cow body condition to optimize herd-level productivity and milk quality in AMS herds., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The objective of this study was to benchmark the herd-level housing and management strategies of automated milking system (AMS) farms across Canada and assess the associations of these herd-level housing factors and management practices with milk production and quality. Canadian AMS farms (n = 197; Western Canada: n = 50, Ontario: n = 77, Quebec: n = 59, Atlantic Canada: n = 11) were each visited once from April to September 2019, and details were collected related to barn design and herd management practices. Milk-recording data for the 6 mo before farm visits were collected. Farms averaged (± standard deviation) 110 ± 102 lactating cows, 2.4 ± 1.9 AMS units/farm, 47.5 ± 14.9 cows/AMS, 36.7 ± 5.0 kg/d of milk, 4.13 ± 0.34% fat, 3.40 ± 0.16% protein, and a herd-average somatic cell count of 186,400 ± 80,800 cells/mL. Farms mainly used freestall housing systems (92.5%), organic bedding substrates (73.6%), and free flow cow traffic systems (87.8%); farms predominantly milked Holsteins (90.4%). Multivariable regression models were used to associate herd-level housing factors and management practices with milk production and quality. At the herd level, feed push-up frequency (mean = 12.8 ± 8.3 times per day) and feed bunk space (mean = 64 ± 21.5 cm/cow) were positively associated with milk yield. Greater milk yield was associated with herds using inorganic (sand) versus organic bedding, milking Holsteins versus non-Holsteins, and using a form of mechanical ventilation versus natural ventilation alone. Milk fat and milk protein content were only associated with breed. Herds with lower somatic cell counts had more frequent alley cleaning (mean = 12.1 ± 7.5 times per day), wider lying alleys (mean = 304.5 ± 40.0 cm), and sand bedding. The results highlight the importance of using sand bedding, using mechanical ventilation, keeping feed pushed up, ensuring alleys are clean, and ensuring adequate space at the feed bunk for maintaining herd-level productivity and milk quality in farms with AMS., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to determine the effect of molasses-based liquid feed (LF) supplementation within automated milking systems (AMS) on the behavior, health, and production of early-lactation dairy cows. In 6 commercial AMS dairy herds, 390 dairy cows were randomly assigned before calving to 1 of 2 treatments, within farm, balanced by parity: (1) control group (CON) receiving a standard AMS pellet [mean = 3.9 kg/d on a dry matter (DM) basis, n = 188], or (2) same amount of standard AMS pellet (mean = 3.6 kg/d on a DM basis, n = 202) plus 1 kg/d DM of LF for multiparous cows (1.6 kg/d as fed) and 0.88 kg/d DM for primiparous cows (1.4 kg/d as fed). Across farms, cows were fed partial mixed rations similar in ingredient and nutrient composition. Cows on the LF treatment received supplementation for the first 60 d in milk (DIM). Blood samples were taken 2 times/wk for the first 3 wk postcalving to assess β-hydroxybutyrate (BHB). Samples with BHB ≥1.2 mmol/L were classified as a positive case of subclinical ketosis (SCK). Cow body condition and lameness were scored every 10 d postcalving until 60 DIM. No differences were detected between treatments for milk yield (average milk yield = 37.4 ± 1.98 kg/d) and milking frequency (average milking frequency = 3.2 ± 0.01 times/d). Similarly, no difference between treatments in daily AMS visits was detected (average AMS visits = 5.3 ± 0.3 times/d). However, CON cows experienced more AMS refusals/d than LF cows (LF = 1.6 refusals/d, CON = 2.1 refusals/d). No difference between treatments in daily rumination time was detected (average rumination time = 476 ± 10.8 min/d). Treatment was associated with the number of times cows tested positive for SCK; cows on LF had fewer repeated diagnoses of SCK, such that 15% of cows on LF had ≥3 diagnoses of SCK out of 5 tests, compared with 27% of CON cows. Cows receiving molasses tended to have lower blood BHB values at ∼15 DIM. Molasses supplementation also allowed cows to maintain a more stable body condition score, as LF cows tended to lose less body condition over the first 60 DIM compared with CON cows. Overall, the results of this study demonstrate that supplementing a molasses-based LF to AMS cows may help support the energy demands of milk production in early lactation and, thus, reduce the incidence of repeat positive SCK tests during that time period., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to evaluate the accuracy of inline milk fat-to-protein (F:P) data to detect hyperketonemia (HYK) in herds with automated milking systems (AMS). The F:P ratio has been investigated as a tool for detecting HYK with moderate accuracy in past studies, but inline F:P data in AMS may also be useful for HYK screening. To assess the accuracy of these data in commercial settings, we monitored 484 cows from 9 AMS herds for their first 3 wk of lactation, taking blood samples once per week (n = 1,427). Positive cases of HYK were defined by whole-blood β-hydroxybutyrate (BHB) concentrations ≥1.2 or ≥1.4 mmol/L. Milk data were collected from the AMS software on each farm for each cow and converted into 4 different F:P values: (1) value from the same day as the BHB test; (2) 5-d centered-moving average (CMA); (3) 5-d backward-moving average (BMA); (4) 5-d forward-moving average (FMA). In linear regression models, all 4 values were associated with BHB, but slope estimates varied and R 2 were low: same day (slope = 0.95, R 2 = 0.07), CMA (slope = 1.05, R 2 = 0.07), BMA (slope = 0.65, R 2 = 0.04), and FMA (slope = 1.23, R 2 = 0.09). In logistic regression models, the odds of having HYK (BHB ≥1.2 mmol/L) increased with every 0.1-unit increase from the mean F:P ratio (1.16) using same-day values (odds ratio = 1.35, 95% confidence interval = 1.25-1.47) and CMA (odds ratio = 1.39, 95% confidence interval = 1.27-1.51). The same increase in F:P from mean BMA (1.14) and FMA (1.17) was associated with 1.22 and 1.49 times the odds of HYK, respectively. For all 4 F:P variations, we evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of different F:P thresholds with HYK status. As the F:P threshold increased from 1.17 to 1.50, sensitivity decreased (range: 77 to 9%) but specificity increased (range: 58 to 96%). Same-day and CMA F:P cutoffs at which a balance was reached between sensitivity and specificity ranged from 1.18 to 1.22; however, even at these values we found high rates of false positives and negatives (range: 31-39%). These results suggest that inline milk F:P data from inconsistently calibrated sensors should not be used alone to detect HYK in AMS herds., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Automated milking systems (AMS) provide the capability of adjusting the amount of supplemental concentrate offered to cows based on parity, days in milk, and milk yield; in many situations, more supplement is provided to those cows with higher production demands. To determine associations of subclinical ketosis (SCK) and blood β-hydroxybutyrate (BHB) with milk yield, supplement consumed in the AMS, rumination time, and the ratios of milk yield to supplement intake and milk yield to rumination time, we monitored 605 cows from 9 AMS herds, testing blood BHB concentrations 1×/wk for the first 3 wk of lactation. Milk yield, supplement intake, and rumination data were collected from the AMS computer on each farm. For analyses, only multiparous cows from 8 herds were included (n = 172 total) and were matched, within farm, to include an equal number of cows in each of 4 health status groups (n = 43 per group): SCK (BHB ≥1.2 mmol/L at ≥1 of 3 tests, with no other disorder in the first 30 d in milk), SCK+ (BHB ≥1.2 mmol/L at ≥1 of 3 tests, with another health disorder), HLT (BHB always <1.2 mmol/L, with no other disorder), or OTH (BHB always <1.2 mmol/L, with a health disorder). Milk yield and rumination time varied by health status, but we found no difference in supplement intake by health status group. As a result, milk yield relative to supplement intake and milk yield relative to rumination time differed by health status and were both positively associated with BHB. Cows in SCK had the highest milk yield and ratio of milk yield to supplement intake, but SCK+ cows had the lowest rumination time and the highest milk yield to rumination ratio. These results highlight the differences in milk production (per day and relative to supplement consumption or rumination time) associated with blood BHB and health status. Therefore, potential exists to modify supplementation in AMS to reduce negative energy balance by accounting for milk production of fresh cows., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

This review synthesizes a range of research findings regarding behavioral and production responses to health disorders and subsequent illness detection for herds using automatic (robotic) milking systems (AMS). We discuss the effects of health disorders on cow behavior and production, specifically those variables that are routinely recorded by AMS and associated technologies. This information is used to inform the resultant use of behavior and production variables and to summarize and critique current illness detection studies. For conventional and AMS herds separately, we examined research from the past 20 yr and those variables recorded automatically on-farm that may respond to development of illness and lameness. The main variables identified were milk yield, rumination time, activity, and body weight, in addition to frequency of successful, refused, and fetched (involuntary) milkings in AMS herds. Whether making comparisons within cow or between sick and healthy cows, consistent reductions in activity, rumination time, and milk yield are observed. Lameness, however, had obvious negative effects on milk yield but not necessarily on rumination time or activity. Finally, we discuss detection models for identifying lameness and other health disorders using routinely collected data in AMS, specifically focusing on their scientific validation and any study limitations that create a need for further research. Of the current studies that have worked toward disease detection, many data have been excluded or separated for isolated models (i.e., fresh cows, certain lactation groups, and cows with multiple illnesses or moderate cases). Thus, future studies should (1) incorporate the entire lactating herd while accounting for stage of lactation and parity of each animal; (2) evaluate the deviations that cows exhibit from their own baseline trajectories and relative to healthy contemporaries; (3) combine the use of several variables into health alerts; and (4) differentiate the probable type of health disorder. Most importantly, no model or software currently exists to integrate data and directly support decision-making, which requires further research to bridge the gap between technology and herd health management., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Associations of electronically recorded data were examined before diagnosis of health disorders in early-lactation cows in herds with automated milking systems (AMS). Rumination time, activity, and milk yield data were collected for 8 mo for 605 early-lactation cows in 9 commercial AMS herds. Using multivariable generalized linear regression models controlling for parity and days in milk, data were examined relative to the day of diagnosis for health disorders occurring in absence of, or at least 14 d before, another disorder: mastitis (n = 13), new cases of lameness (n = 45), subclinical ketosis (SCK; n = 113), and purulent vaginal discharge (n = 49). All cases of displaced abomasum (DA; n = 8) occurred in conjunction with other disorders. Deviations from baseline among affected cows were examined, as well as differences compared with a group of healthy cows and an average group of all cows, who were given mock diagnosis days using the mean days in milk at diagnosis for each disorder. On 6 to 14 d of the 2 wk before diagnosis, cows with DA or mastitis had lower milk yield, rumination time, milking frequency, activity, and milk temperature compared with healthy cows as well as deviations from their own baseline rumination time and milk data starting 4 to 12 d before diagnosis. Cows with DA had lower AMS supplement intake than healthy cows and deviations from their baseline activity and milk temperature starting 6 and 4 d before diagnosis, respectively. Cows with mastitis had greater milk conductivity than healthy cows and deviated from their baseline milking frequency and conductivity 8 and 12 d before diagnosis, respectively. Compared with healthy cows, those with SCK or new cases of lameness generally had lower milk yield, rumination time, milk temperature, supplement intake, and milking and refusal frequencies. Only the milk temperature of lame cows deviated from baseline. Thus, acute health disorders (i.e., DA and mastitis) were associated with deviations from those cows' baseline AMS data, whereas more chronic disorders (i.e., SCK and lameness) were associated with significant but subtle longer term changes in milk production and behavior. Because cows with health disorders deviated from a group of healthy cows before they deviated from their own baseline and from the average of all other cows, including a healthy reference group in health alerts could refine the ability of detection models to identify subtle deviations in early lactation., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)