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The objective of this study was to quantify the effects of supplementing early-lactation cows with a dry pure glycerol product, delivered through the automated milking system (AMS) concentrate, in the first 21 DIM on metabolic markers, milking behavior, and milk production. In 5 commercial AMS dairy herds, 389 dairy cows were randomly assigned, controlling for parity, 21 d before expected calving to 1 of 2 treatments, within farm: (1) the control group (CON) which received the standard AMS pellet (n = 213) from 1 to 150 DIM; or (2) the glycerol group (GLY), which received the treatment AMS pellet (n = 176) formulated to additionally deliver 250 as fed g/d of glycerol product from 1 to 21 DIM, followed by the standard AMS pellet from 22 to 150 DIM. Across all farms, cows were fed partial mixed rations that were similar in ingredient and nutrient composition. One prepartum blood sample and 5 postpartum blood samples were collected from each cow to determine serum nonesterified fatty acids (NEFA), blood BHB, and blood glucose concentrations. Cow BCS was recorded every 21 d from −21 to 63 DIM. Data were collected and analyzed for the treatment period (1–21 DIM) and a follow-up period (22–150 DIM). We detected no treatment effect on serum NEFA concentrations in the first week of lactation. We detected a treatment by time interaction for blood BHB and blood glucose, where GLY cows tended to have increased BHB concentrations at 5 DIM and had decreased glucose concentrations at 9 and 12 DIM. We detected an interaction of BCS with treatment on the incidence of BHB ≥1.2 mmol/L, whereby over-conditioned CON cows (BCS ≥3.5) were 3.5 times more likely to have a high BHB test than CON cows with normal prepartum BCS. During the treatment period, GLY cows had 0.1 ± 0.05 more successful milkings per day, were delivered 0.27 ± 0.05 DM kg/d more AMS concentrate, and tended to yield 0.8 ± 0.47 kg/d more milk. During the follow-up period, GLY cows had 0.1 ± 0.04 more successful milkings per day, were delivered 0.18 ± 0.06 DM kg/d more AMS concentrate, and yielded 1.5 ± 0.53 kg/d more milk than CON cows. Glycerol supplementation allowed cows to maintain better BCS, as GLY cows lost less BCS from calving to 63 DIM than CON cows. Overall, the results of this study demonstrate that supplementing pure glycerol through the AMS concentrate for the first 21 DIM can reduce BCS loss in early lactation, improve milking behavior, and increase milk yield to mid-lactation. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [ABSTRACT FROM AUTHOR]

ABSTRACT: The objective of this study was to describe the nutritional strategies used on Canadian dairy farms with automated milking systems (AMS), both at the feed bunk and the concentrate offered at the AMS, as well as to determine what dietary components and nutrients, as formulated, were associated with milk production and milking behaviors on those farms. Formulated diets (including ingredients and nutrient content) and AMS data were collected from April 1, 2019, until September 30, 2020, on 160 AMS farms (eastern Canada [East] = 8, Ontario [ON] = 76, Quebec [QC] = 22, and western Canada [West] = 54). Both partial mixed ration (PMR) and AMS concentrate samples were collected from May 1 to September 30, 2019, on 169 farms (East = 12, ON = 63, QC = 42, West = 52). We collected AMS milking data for 154 herds. For each farm (n = 161), milk recording data were collected and summarized by farm to calculate average milk yield and components. Multivariable regression models were used to associate herd-level formulated nutrient composition and feeding management practices with milk production and milking behavior. Milk yield (mean ± SD = 37.0 ± 0.3 kg/d) was positively associated with the PMR ether extract (EE) concentration (+0.97 kg/d per percentage point [p.p.] increase) and with farms that fed barley silage as their major forage source (n = 16; +2.18 kg/d) as compared with haylage (n = 42), whereas farms that fed corn silage (n = 96; +1.23 kg/d) tended to produce more milk than farms that fed haylage. Greater milk fat content (4.09 ± 0.28%) was associated with a greater PMR-to-AMS concentrate ratio (+0.02 p.p. per unit increase) and total diet net energy for lactation (+0.046 p.p. per 0.1 Mcal/kg increase), but a lesser percentage of NFC of the PMR (−0.016 p.p. per p.p. increase of NFC percentage). Milk protein content (3.38 ± 0.14%) was positively associated with the forage percentage of the PMR (+0.003 p.p. per p.p. increase of forage percentage) and the total diet starch percentage (+0.009 p.p. per p.p. increase of starch percentage), but was negatively associated with farms feeding corn silage (−0.1 p.p. compared with haylage) as their major forage. Greater milking frequency (2.77 ± 0.40 milkings/d) was observed on farms with free-flow cow traffic systems (+0.62 milkings/d) and was positively associated with feed push-up frequency (+0.013 milkings/d per additional feed push-up), but negatively associated with PMR NFC content and forage percentage of the total ration (−0.017 milkings/d per p.p. increase of forage percentage). Lastly, greater milking refusal frequency (1.49 ± 0.82 refusals/d) was observed on farms with free-flow cow traffic systems (+0.84 refusals/d) and farms feeding barley silage (+0.58 refusals/d) than with guided flow and farms feeding either corn silage or haylage, respectively. These data give insight into the ingredients, nutrient formulations and type of diets fed on AMS dairy farms across Canada and the association of those factors with milk production and milking behaviors.

Simple Summary: The increasing use of automated milking systems (AMS) in the dairy industry has led to the necessity for changes in management practices implemented by producers. This study investigated the productive and metabolic impact of three versus six milkings per day (MPD) during early lactation on multiparous Holstein cows managed in an AMS. Mature cows (third and greater parity) with six MPD had greater milk fat production during the experimental phase (4 to 29 days in milk; DIM) and milk production during the carryover phase (30 to 90 DIM) than mature three MPD cows. The composition of milk fatty acids differed between MPD groups with cows, with three MPD having greater short-, medium-, odd- and branched-chain fatty acids during the experimental phase compared to cows with six MPD. Based on greater blood fatty acid and β-hydroxybutyrate concentrations, the mature six MPD cows may have been in a greater postpartum nutrient deficit and at greater risk of metabolic illness. Our findings suggest a need to strategically manage the MPD or nutrition of mature, early lactation dairy cows in an AMS. Automated milking systems (AMS) are increasingly adopted for dairy cow production, promoting individualized cow management dependent on factors like lactation stage, age, and productivity. The study objective was to investigate the effects of early lactation milking frequency on cows milked via AMS. Multiparous Holstein cows blocked by parity and due date were randomly assigned to treatments (n = 8 per treatment): three (3X) or six (6X) milkings per day (MPD). The experimental phase (EXP) was defined as 4 to 29 days in milk (DIM). The AMS settings were programed so 3X cows were limited to three MPD while 6X cows were allowed six MPD. Afterwards was the carry over phase (CO) ranging from 30 to 90 DIM; all cows were allowed up to six MPD. Measurements by the AMS included bodyweight, milk yield (MY), and pellet intake. Weekly composite milk samples were analyzed for macronutrient composition and fatty acid (FA) profile. Coccygeal blood was sampled at 3, 8 ± 1, and 13 ± 1 DIM; concentrations of blood plasma analytes were quantified. Greater MPD was achieved for 6X cows versus 3X cows during EXP, but similar during the CO. Daily MY was non-separable during the EXP while 6X cows in their third or greater lactation group (3 + LG) had greater MY than 3X cows of the same LG during the CO. Milk fat content and 4% fat-corrected MY were both greater for 6X, 3 + LG cows during the EXP compared to 3X, 3 + LG cows. Milk FA methyl esters (FAME) proportions were different between MPD groups, with 6X, 3 + LG cows having the lowest short, even-chain FA from de novo or post-absorptive origin. Differences in analytes indicated that 6X, 3 + LG cows experienced metabolic stress and incorporated greater FA from adipose tissue. Greater early lactation MPD in AMS may shift cow nutrient partitioning to support greater production in 3+ parity cows. [ABSTRACT FROM AUTHOR]

ABSTRACT: Various management practices can influence milk quality traits in dairy cattle. As an example, an increasing investment in automatic milking system to substitute milking parlors has been observed in the last 2 decades in dairy farms which could have affected certain bulk milk quality traits. What is more, milking practices can also affect certain milk parameters; as an example, teat disinfectants containing I are used in commercial farms where pre- or postdipping is performed, leading to presence of some I in the bulk milk. However, this trace mineral is also supplied in cows' diet to fulfill their nutritional requirements, partly contributing to the milk I final concentration. Therefore, the aim of this study was to evaluate the sources of variation of milk I along with other traditional milk quality traits. A total of 91 dairy farms in northeastern Italy were enrolled in the study. In each farm, diet and bulk milk samples were collected on the same day for chemical analysis. Concentration of I, in particular, was determined in both milk and feed with gold standard. Pearson correlations were calculated among the traits available for milk and diet, and a general linear model was used to test significance of fixed effects (feeding system, milking system, farming system, herd size, herd stage of lactation, and sampling month) on milk quality traits including the I concentration. In the case of milk I, diet I and presence of I-based predipping and postdipping teat disinfect application were also tested as fixed effects. Results showed a positive linear correlation between milk and diet I content (correlation coefficient [r] = 0.78). Although milk I was also positively correlated with lactose content (r = 0.25), dietary I was not correlated with other milk traits. Milk I content was significantly affected by dietary I, I-based predipping teat disinfectant application, and herd composition. Compared with conventional farms, organic farms showed lower protein content and greater somatic cell score (SCS) but similar milk I. Milking system significantly affected only lactose content and SCS of milk. Sampling month was only significant for milk urea nitrogen and herd composition, feeding system, herd size, and herd average days in milk did not modify milk gross composition and SCS. In conclusion, dietary supply of I is the main factor affecting milk I concentration and findings suggest that I level in milk can be naturally improved in dairy cows by modulating the I content in the diet administered. However, further research is needed to evaluate the effect of I-based sanitizers on milk I.

ABSTRACT: Automatic milking system (AMS) adoption in the United States is trending upward, with issues such as lower availability and increased cost of labor being factors frequently listed as motives for AMS implementation. In addition, more interest in precision dairy farming by the new generation of farmers may also help increase AMS adoption. The objective of this scoping review was to characterize the nature of the literature investigating non-pasture-based AMS and the opportunities and challenges for future research. The eligibility criteria included studies published in or after the year 2000, with full text in English, of at least 500 words, examining various outcomes related to AMS in non-pasture-based dairy farms. Six electronic databases were searched: Biosis (Web of Science), CAB Abstracts (CAB Direct), Medline (PubMed), PubAg, AGRIS (FAO), and Scopus (Elsevier). The review focused on studies with objectives, characteristics, farms, and AMS information. A total of 4,292 titles and abstracts were screened, and 536 studies were finally included. Most of the studies were conducted in Europe (73.5%), among commercial herds (67.9%), comprising Holstein cows (57.7%), using Lely and DeLaval brands (45.4% vs. 39.7%), with free-flow traffic (52.7%). The main research topics investigated were milk production, milk composition, and AMS efficiency, followed by behavior and welfare, health disorders (especially mastitis), and nutrition in Europe and other regions. At the same time, in the United States, trends were similar, except for nutrition. Since 2016, there has been an increased interest in studies on energy and water consumption, technological development, environment (enteric emissions), reproduction, genetics, and longevity or culling. However, the small number of studies and unclear characterization of what is optimum for reproductive management, other health disorders, economics, and water and energy consumption suggest a need for future research.

ABSTRACT: The objectives of this study were to determine how dairy cow personality traits affect their adaptation to an automated milking system (AMS) upon parturition, as well as whether these personality traits are consistent across the transition from gestation to lactation. Sixty Holstein dairy cows (19 primiparous and 41 multiparous) were assessed for personality traits using a combined arena test conducted at 24 d before parturition and 24 d after first introduction to an AMS, which occurred ∼3 d after parturition. The combined arena test comprised 3 parts: a novel arena test, a novel object test, and a novel human test. Principal component analysis of the behaviors recorded during the personality assessment revealed 3 factors interpreted as personality traits (75% cumulative variance) in the pre-calving test, interpreted as explore, active, and bold. The post-calving test revealed 2 factors (78% cumulative variance), interpreted as active and explore. Data from d 1 to 7 after introduction to the AMS were summarized by cow and associated with the pre-calving factors, and data from d 21 to 27 after introduction to the AMS were summarized by cow and associated with the post-calving factors. The active trait had a moderate positive correlation between the pre- and post-calving tests, whereas exploration had a weak positive correlation between tests. Cows that scored high for activeness in the pre-calving test tended to have fewer fetching events and a higher coefficient of variation of milk yield in the first 7 d after introduction to the AMS, whereas bolder cows tended to have higher milk yield during that period. In the post-calving test, more active cows tended to have more frequent milkings and voluntary visits per day, as well as a lower cumulative milk yield from d 21 to 27 after introduction to the AMS. Overall, these results indicate that personality traits of dairy cows are associated with adaptation and performance in an AMS, and that personality traits are consistent across the transition period. Specifically, cows that scored high for boldness and activeness adapted better to the AMS immediately after calving, whereas cows that scored low for activeness and high for boldness performed better in terms of milk yield and milking activity in early lactation. This study demonstrates that personality traits affect milking activity and milk yield of dairy cows milked with an AMS and, therefore, may be useful for selection of cows who might best adapt to and use an AMS.

Dairy farms have become more reliant on technology. The overall aim of this study was to better understand how dairy farmers view technology and its effects on animal care, including their views on the prospect of integrating gene-editing technology in the future. Virtual-semistructured interviews were conducted with dairy farmers (n = 11) from British Columbia and Alberta. To facilitate discussion, the participants were asked to develop and discuss a timeline describing when and why various technologies were adopted on their farm. Although farmers defined technology broadly and affecting multiple aspects of farm management, this paper focuses on their views regarding how technology can affect animal care. Following thematic analysis of the data, the following 3 themes emerged: (1) the changing role of the farmer (including intergenerational considerations and learning new technology), (2) the effect of technology on the cow and her relationship with the farmer and, (3) technology as the future of the farm. The discussions also highlight the concerns that some farmers have regarding challenges associated with reduced human-animal interactions and effective use of the large amounts of data that are collected through technology. We also specifically asked the participants their views about gene editing as a potential future technology. Most of the participants did not specifically address their views on gene editing, but they spoke about the effect on genetic technologies more generally, often making references to genomic testing. However, some questioned how this technology may affect farmers more generally and spoke about how it could affect human-animal relationships. These results illustrate differences among farmers in the way they view technology and how this can affect the dairy cattle they care for. [ABSTRACT FROM AUTHOR]

Simple Summary: Automatic milking box systems are becoming more common in USA dairy farms despite being costlier than traditional methods. This study investigated farmers' perceptions on why farms with at least seven milking boxes opted for them, focusing on technological changes, farm structure, management, labor, cow health, and milk production. The primary reasons for choosing these systems were labor cost reduction, improved cow welfare, and enhanced milk production. Most farms constructed new barns with open stalls and easy cow movement to facilitate use of these systems. Farmers perceived that the adoption led to higher water and energy usage, while the operational costs remained manageable. Farmers reported labor savings, better working conditions, and more effective management of cow health issues like mastitis and foot problems. The impact on milk production and quality was mixed, with some farms experiencing improvements. Similarly, farmers perceived an improvement in reproductive performance, supported by activity data. Overall, the shift to Automatic Milking Systems was viewed positively by more than half of farmers, who recommended it to others, although they advised careful consideration of specific farm needs and conditions. These are initial experiences rather than conclusive evidence. Automatic Milking System (AMS) installations are increasing in the USA despite the higher investment cost than conventional systems. Surveys on AMSs conducted outside the USA focused on small–medium herds, specific regions, or aspects of AMS milking. This study described farmers' perceptions about the decision-making process of adopting an AMS in the USA's large dairies (≥7 AMS boxes) regarding changes in technology, housing, management practices, labor, herd performance, and health. After being contacted, 27 of 55 farmers from large AMS herds completed the survey. The main reasons for adopting an AMS were labor costs, cows' welfare, and herd performance. Most farms constructed new barns, used a free-flow traffic system, and changed their feed management. Increases in water and energy use were perceived by 42% and 62% of farmers, respectively. Farmers estimated decreases in labor costs of over 21%, and AMS employees worked 40–60 h/week. Milk production increases were reported by 58%, with 32% observing higher milk fat and protein content. Easier sick cow detection, better mastitis management, and improvements in pregnancy rates were reported. Thus, farmers transitioning to AMSs perceived altered resource utilization, labor cost savings, and improvements in employee quality of life, animal welfare, and farm management. While 54% of respondents would recommend an AMS to other farms, 38% suggested considering additional aspects prior to adoption. [ABSTRACT FROM AUTHOR]

Copyright of Krmiva is the property of Croatian Society of Agronomists and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study is to determine the effect of first-calving age (FCA) on yield parameters and productive life in dairy farms using a robotic milking system in Turkey. The cows (n = 1579) were divided into five groups (24, 25, 26, 27, 28 months and above FCA. The average milk yield was highest in 24 months of FCA (9140.31 ± 145.55 kg) and was lowest in 27 months of FCA (8534.55 ± 131.00 kg) (p

Copyright of Eurasian Journal of Veterinary Sciences is the property of Eurasian Journal of Veterinary Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study evaluated the effects of pellet starch concentration and amount of pellet delivered in an automated milking system (AMS) on AMS pellet and partial mixed ration (PMR) intake, milk and milk component yield, and milking and feeding behavior for Holstein cows at different stages of lactation. Twentyfour Holstein cows at peak [n = 8; 85 ± 25.7 days in milk (DIM)], mid (n = 8; 185 ± 35.1 DIM) or late (n = 8; 290 ± 69.5 DIM) lactation (3 squares/DIM category), housed in a free-stall barn with a feed-first guided traffic flow AMS, were used. Treatments included low (LS; 24% DM) or high (HS; 34% DM) starch pellets that were provided at low (LA; 2 kg/d DM) or high (HA; 6 kg/d DM) quantities within replicated 4 × 4 Latin square design. Each period lasted 21 d including 16 d of adaptation and 5 d of data and sample collection to evaluate feed intake and behavior, milking characteristics, milk and milk component yields. Pellet starch did not affect the amount of pellet consumed, but HA cows consumed more pellet than LA (4.3 vs. 1.8 kg/d; P < 0.01). Relative to LA, HA had greater pellet refused in the AMS (P < 0.01) and decreased PMR intake (P = 0.04). Total DMI was 1.3 kg/d greater for HA than LA cows (P = 0.05). Pellet starch and DIM did not affect PMR intake or DMI. Neither pellet starch nor allocation affected the number of PMR meals; however, LA increased PMR eating time by 20 min/d (P < 0.01) and PMR meal length by 2 min/meal (P = 0.03). Milking frequency was not affected by pellet starch or DIM, but HA tended to increase total milking frequency (2.7 vs. 2.6 no/d; P = 0.06) over LA, with greater voluntary milkings (2.5 vs 2.3 no/d; P < 0.01). Milk yield/ visit and milking duration were not affected by pellet starch or allocation. Milk yield was not affected by pellet starch, or the amount allocated averaging 43.2, 41.3 and 43.2 kg/d for peak, mid, and late lactation cows (P ≥ 0.19). Compared with LS, HS decreased milk fat concentration (4.1 vs 3.9%; P < 0.01) and providing the HA reduced milk fat concentration (4.1 vs 3.96%; P < 0.01) when compared with LA; however, neither pellet starch nor allocation affected fat yield, averaging 1.67 kg/d (P ≥ 0.15). True protein yield was not affected. Cows fed the LA had greater milk urea nitrogen (14.8 mg/dL) relative to HA (14.3 mg/dL; P = 0.03). Increasing the amount of pellet allocated in the AMS reduced PMR intake, while increasing total DMI and resulted in greater amounts of pellet refusals by cows; however, greater amounts of pellet may increase attendance at the AMS without affecting milk or milk component yields. Starch concentration of the pellet had little effect on productivity. [ABSTRACT FROM AUTHOR]

ABSTRACT: Milk fatty acid (FA) profile can be divided into (1) de novo (C4–C14) that are synthesized in the mammary gland; (2) preformed (≥C18) that are absorbed from blood and originate from mobilized adipose tissues or dietary fat; and (3) mixed (C16), which have both origins. Our objectives were to describe the FA profile, as predicted using Fourier transform mid-infrared spectroscopy, of bulk tank milk from automated milking system (AMS) farms and to assess the association of management and housing factors with the bulk tank milk composition and FA profile of those AMS farms. The data used were collected from 124 commercial Canadian Holstein dairy farms with AMS, located in the provinces of Ontario (n = 68) and Quebec (n = 56). The farms were visited once from April to September 2019, and information were collected on barn design and herd management practices. Information regarding individual cow milk yield (kg/d), days in milk, parity, and the number of milking cows were automatically collected by the AMS units on each farm. These data were extracted for the entire period that the bulk tank milk samples were monitored, from April 2019 to April 2020 in Quebec and from August 2019 to May 2020 in Ontario. Across herds, milk yield averaged (mean ± standard error) 35.9 ± 0.4 kg/d, with 3.97 ± 0.01% fat and 3.09 ± 0.01% protein, whereas FA profile averaged 26.2 ± 0.1, 33.1 ± 0.1, and 40.7 ± 0.2 g/100 g of FA for de novo, mixed, and preformed, respectively. The FA yield averaged 0.34 ± 0.01, 0.44 ± 0.01, and 0.54 ± 0.01 kg/d for de novo, mixed, and preformed, respectively. Multivariable regression models were used to associate herd-level housing factors and management practices with milk production, composition, and FA profile. Milk yield was positively associated with using a robot feed pusher (+2.1 kg/d) and the use of deep bedding (+2.6 kg/d). The use of a robot feed pusher, deep bedding, and greater stall raking frequency were positively associated with greater yield (kg/d) of de novo, mixed, preformed, and de novo + mixed FA. Use of deep bedding was negatively associated with concentration of fat, de novo FA, mixed FA, and de novo + mixed FA, expressed in grams per 100 g (%) of milk. A wider lying alley width (≥305 cm) was associated with a greater concentration (g/100 g of milk) of de novo and de novo + mixed FA. Greater frequency of partial mixed ration delivery (>2×/d vs. 1 and 2×/d) was positively associated with a greater proportion (g/100 g of FA) of de novo, mixed, and de novo + mixed FA and negatively associated with the proportion of preformed FA. Overall, these associations indicated that bulk tank FA profile can be used as a tool to monitor and adjust management and housing in AMS farms.

ABSTRACT: Milk production may be reduced before dry-off to decrease the risk of cows developing intramammary infections during the dry period. Such reductions in milk may be possible in automated milking systems (AMS) where milking frequency and feed allocation at the AMS can be controlled at the cow level. This study investigated the effect of dry-off management of cows milked in AMS on milk yield, milking behavior, and somatic cell count (SCC). Using a 2 × 2 factorial arrangement of treatments, applied from d 14 to 1 before dry-off, 445 cows from 5 commercial dairy farms in Quebec, Canada, were assigned within farm to either (1) reduced feed [RF; allowed a maximum of 0.75 kg/d of AMS pellet for the first week (14 to 8 d before dry-off) of treatment, and 0.50 kg/d for the second week (7 to 1 d before dry-off) of treatment], or (2) nonreduced feed (NF; allowed up to 2 kg/d of AMS pellet), and either (1) reduced milking (RM; reduced to 2 milkings/d or as many times as required to yield 17 kg/milking), or (2) nonreduced milking (NM; allowed up to 6 AMS milkings/d) and no maximum production. Feed and milking behavior data, as well as milk yield and SCC were collected from the AMS software. The RF cows had lower AMS feed delivered during the treatment period, as per the experimental design. Across the treatment period, the NF-NM cows had the highest milking frequency (2.7 times/d), followed by the RF-NM cows (2.4 times/d), and then both of the RM groups (1.8 times/d), which did not differ from each other. All cows, except the NF-NM cows, were gradually milked less frequently as dry-off approached. Across the entire 2-wk treatment period before dry-off, cows with RM allowance experienced a higher reduction in milk yield compared with the cows with no milking allowance restrictions (−4.8 vs. −3.6 kg). Similarly, cows with a RF allocation tended to have a higher reduction in milk yield than cows with NF (−4.6 vs. −3.7 kg). As result, those cows with both reduced milking permissions and feed allocation at the AMS experienced the greatest drop in milk production before dry-off. There were no differences between treatments for milking frequency or yield in the next lactation. Somatic cell score (calculated from SCC) was not different between treatments in the 2-wk or day before dry-off, nor in the first month after calving. Overall, these data suggest that reducing both milking frequency and feed quantity in the AMS is the most efficient method to decrease milk yield before dry-off, without negatively influencing milking frequency or yield in the next lactation, as well as without affecting milk quality.

The aim of this study is to determine the effect of first-calving age (FCA) on yield parameters and productive life in dairy farms using a robotic milking system in Turkey. The cows (n = 1579) were divided into five groups (24, 25, 26, 27, 28 months and above FCA. The average milk yield was highest in 24 months of FCA (9140.31 ± 145.55 kg) and was lowest in 27 months of FCA (8534.55 ± 131.00 kg) (p <.05). The average service period length in the first lactation was longer in cows of 28 months old (158.92 ± 7.28 days) than 26 and 27 months (131.96 ± 4.45and 130.51 ± 54.97 days respectively) old groups (p <.05). A number of lactations of cows that were 26 months old (2.52 ± 0.09) at FCA was higher than those FCA was 24 months and 28 months (2.03 ± 0.15 and 2.18 ± 0.09 respectively) (p <.05). Replacement rates were not differing statistically at different lactations. The most frequent reasons for culling were mastitis and reproduction in all groups. As a result, cows in 24 months of FCA had no undesirable results in terms of milk yield, service period, number of insemination per lactation. [ABSTRACT FROM AUTHOR]

ABSTRACT: Provision of a palatable feed in automated milking systems (AMS) is considered an essential motivating factor to encourage voluntary visits to the milking stall. Although the quantity and composition of AMS concentrates have been previously investigated, the form of the concentrate has not been extensively evaluated. The objective of this study was to evaluate the effects of feeding pelleted (PB; 132.9 ± 56 DIM, 47.4 ± 9.51 kg/d milk yield) versus steam-flaked barley (SFB; 133.0 ± 63 DIM, 40.5 ± 8.23 kg/d milk yield) in an AMS on dry matter intake, AMS visits, milk and milk component yield, and partial mixed ration (PMR) feeding behavior. Twenty-nine Holstein cows of varying parities were enrolled in this study. Cows were housed in freestall housing with a feed-first guided-flow barn design; 7 cows were housed in a separate freestall pen to enable individual PMR intake and feeding behavior monitoring. This study was conducted as a 2-way crossover, with two 21-d periods in which each cow received the same basal PMR but was offered 2 kg/d (dry matter basis) of PB or SFB in the AMS. Cows receiving the SFB had fewer voluntary AMS visits (2.71 vs. 2.90 ± 0.051, no./d), tended to have a longer interval between milkings (541.7 vs. 505.8 ± 21.02 min), spent more time in the holding pen before entering the AMS (139.9 vs. 81.2 ± 11.68 min/d), and had lower total box time (19.7 vs. 21.4 ± 0.35 min/d) than cows fed PB. Despite changes in AMS attendance, there were no differences for average milk (44.0 kg/d), fat (1.62 kg/d), and protein (1.47 kg/d) yields or AMS concentrate intake (2.02 kg/d). These behavioral changes indicate that offering SFB as an alternative to PB may reduce motivation for cows to voluntarily enter the AMS.

There is a large variability in profitability and productivity between farms operating with automatic milking systems (AMS). The objectives of this study were to identify the physical factors associated with profitability and productivity of pasture-based AMS and quantify how changes in these factors would affect farm productivity. We utilised two different datasets collected between 2015 and 2019 with information from commercial pasture-based AMS farms. One contained annual physical and economic data from 14 AMS farms located in the main Australian dairy regions; the other contained monthly, detailed robot-system performance data from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile. We used linear mixed models to identify the physical factors associated with different profitability (Model 1) and partial productivity measures (Model 2). Additionally, we conducted a Monte Carlo simulation to evaluate how changes in the physical factors would affect productivity. Our results from Model 1 showed that the two main factors associated with profitability in pasture-based AMS were milk harvested/robot (MH; kg milk/robot per day) and total labour on-farm (full-time equivalent). On average, Model 1 explained 69% of the variance in profitability. In turn, Model 2 showed that the main factors associated with MH were cows/robot, milk flow, milking frequency, milking time, and days in milk. Model 2 explained 90% of the variance in MH. The Monte Carlo simulation showed that if pasture-based AMS farms manage to increase the number of cows/robot from 54 (current average) to ∼ 70 (the average of the 25% highest performing farms), the probability of achieving high MH, and therefore profitability, would increase from 23% to 63%. This could make AMS more attractive for pasture-based systems and increase the rate of adoption of the technology.

The primary objective of this study was to develop an automated infrared thermography platform (Estrus BenchMark) capable of measuring skin temperature and tail movements as a means of identifying cows in estrus. The secondary objective was to evaluate the accuracy of Estrus BenchMark to detect estrus compared to in-line milk progesterone (P4) analysis (Herd Navigator System) in a commercial dairy herd managed under a robotic milking system. Data were collected on forty-six cows from 45 to 120 d after calving. Cows were flagged in estrus when milk P4 fell below 5 ng/mL. The Estrus BenchMark true positive estrus alerts (Sensitivity; Se%) were compared to Herd Navigator System estrus alerts at different time-windows (±12 h, ±24 h, ±48 h, and ±72 h) relative to the Estrus BenchMark estrus alerts for all the estrus alerts (AE) and confidence-quality estrus (CQE; >80% quality) alerts identified by Herd Navigator System. The Estrus BenchMark captured skin temperature and tail movements resulting in vulva exposure (left tail movements, LTail; right tail movements, RTail; and pooled tail movements, PTail) for each milking event. Skin temperature tended to increase when the milk P4 concentration (Least-Squares Means ± SE) dropped for AE (estrus day [d 0]; P4; 3.51 ± 0.05 ng/mL, Skin temperature; 33.31 ± 2.38 °C) compared with d −7 (P4; 20.22 ± 0.73 ng/mL; Skin temperature: 32.05 ± 3.77 °C). The increase in skin temperature, however, was significant in cows with CQE > 80% at d 0 (32.75 ± 0.29 °C) compared to d −7 (31.80 ± 0.28 °C). The prevalence of tail movements to expose vulva was greater (P = 0.01) in AE at d 0 (LTail: 62.50%; PTail; 68.75%; and RTail: 56.25%) compared with d −7 (LTail: 18.75%; PTail: 9.37%: and RTail: 9.37%), and d +4 (LTail: 9.37%; PTail: 9.37%; and RTail: 12.5%). Moreover, the higher prevalence of tail movements at d 0 was observed in cows with CQE > 80% (LTail; 65%, PTail; 80%, and RTail; 70%) compared to those with CQE

The creation of compact robotic milking manipulators involves their use not only in single-box robotic milking stations (monobox), but also in milking parlors such as “Parallel” or “Carousel” with a capacity of up to simultaneously milking 120 heads.(Research purpose) To substantiate milking parlors technological schemes and options for their use on livestock farms of various capacities.(Materials and methods) The authors studied the organizing experience of machine and robot milking system for cattle in the Russian Federation and foreign countries, leading in milk production. They analyzed the assortment of machines for dairy farming. Based on the results of simulation in the Any Logic software, they proposed the concepts of robotic milking systems. They compared the throughput when performing milking service operations with robotic types of milking machines and standard systems operated by the manipulator.(Results and discussion) The authors performed calculations and gave a comparative assessment of the duration of the service cycles for animals in automated and robotic milking parlors of various configurations: “Fan”, “Herring-bone”, “Parallel”. To reduce the capital intensity of robotic facilities, to increase the level of production process execution accuracy, to reduce the time required to service animals, a robotic animal sanitation system was proposed at the entrance to the milking parlor. The authors proved that the introduction of the proposed configuration of robotic milking parlors would reduce the capital intensity of equipment in comparison with single-box models of robotic milking stations. They noted the advantages of such systems: the general infrastructure of the component base (milk pipelines, milk receivers, automatic rinsing, etc.) and was not separated by different streams.(Conclusions) The authors revealed the optimal ratio of the number of milking posts and medical stations – 13:1. It was proved that it provided maximum labor productivity for a robotic milking parlor. According to the results of theoretical calculations, it was determined that the proposed robotics schemes for milking parlors exceed the productivity of existing single-box robotic milking stations.

The effect of the transition from a conventional milking (CM) system in a tie-stall barn to an automated milking (AM) system specialized for a tie-stall barn on milk yield, milk composition, teat-end score, body condition score (BCS), and lying time was evaluated. This study was conducted at a commercial dairy farm from (15 to 153 days after the installation of the AM system. Lactating cows milked with the CM system (average 85 heads) were gradually transitioned to the AM system, and finally, 57 heads were milked with the AM system. No obvious effects of the milking system on milk yield, milk components, BCS, and daily lying time were found based on comparisons between CM and AM cows in the same herd under the same situation of twice-daily milking. The linear mixed-effects model for teat-end scores showed higher scores for front than for rear teats, and small but negative estimates for independent variables of the day after AM, indicating worse teat-end condition for front than for rear teats and improvement of the condition with increased time since AM. [ABSTRACT FROM AUTHOR]

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Copyright of Canadian Journal of Animal Science is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Udder health is at risk when herds transition from a conventional milking system (CMS) to an automatic milking system (AMS). Somatic cell counts (SCC) are generally elevated for several months following a transition. However, such observations were made in studies conducted in the early 2000s. Technical improvements to AMS have likely been made since then, and farm management may have improved, learning from past experiences. This longitudinal observational study quantified national trends in SCC deteriorations in dairy herds that transitioned from a CMS to an AMS. Census data from the Dutch test day recording was used to determine these trends. It consisted of all cow-level SCC measurements conducted in the Netherlands from January 1, 2007 to December 31, 2019. Three udder health indicators, including the natural logarithm of the yield-corrected bulk-milk SCC, the proportion of cows having a composite SCC ≥200,000 cells/mL, and the proportion of cows having a new elevated SCC, were evaluated using multivariable population-averaged generalized estimation equations. Predicted values resulting from these statistical models were interpreted to determine trends in SCC deteriorations from 12 mo before and 12 mo after the transition to an AMS. Decreasing SCC deteriorations were observed during the study period for all 3 udder health indicators. Udder health deteriorations around the transition to an AMS were still observed, but they decreased in magnitude over the course of the study period. Bulk-milk SCC deteriorations were, for instance, 2.5 times lower in 2019 compared with those observed in 2007. Therefore, the effect of transitioning to an AMS on udder health became less severe in more recent years. However, deteriorations in the proportion of new SCC elevations were still evident toward the end of the study period. Efforts to lower udder health deteriorations in herds that transition to an AMS should therefore be continued and should intensify on factors lowering the proportion of cows having a new elevated SCC. [ABSTRACT FROM AUTHOR]

Automatic milking systems (AMS) have the potential to increase dairy farm productivity and profitability; however, adoption rates, particularly in pasture-based systems, have been lower than expected. The objectives of this study were to compare the physical and economic performance of pasture-based AMS with conventional milking systems (CMS) and to identify gaps for improving AMS productivity and profitability. We used data from 14 AMS and 100 CMS located in the main Australian dairy regions and collected over 3 yr (2015–2016, 2016–2017, 2017–2018). Farms within similar regions and herd sizes were compared. Results showed that all the main physical performance indicators evaluated such as milk production per cow, milk production per hectare, pasture grazed per hectare, or milk solids per full-time equivalent were similar between systems. The AMS farms had higher overhead costs such as depreciation and repairs and maintenance; however, no differences in total labor costs were observed between systems. Profitability, measured as earnings before interest and tax, operating profit margin, and return on total assets, was not significantly different between AMS and CMS. Opportunities for improving pasture utilization, labor efficiency, and robot utilization in AMS farms were identified. Improving efficiency in these areas could improve productivity and profitability of these systems, and therefore increase the interest of this technology. [ABSTRACT FROM AUTHOR]

When a farm that was using a conventional milking system introduces an automatic milking system (AMS) possible risk factors can affect milk quality. The aim of the study was to investigate the influence of milking with automatic milking systems on milk quality variables over a long time-period post-installation. Bulk milk total bacterial count (BMTBC) and somatic cell count (BMSCC) were analysed and compared from 2 years before introduction of automatic milking until 4 years after. Differences regarding these quality parameters were contrasted using t-test and one-way analysis of variance (ANOVA) and post hoc comparisons were performed. A significant increase in BMTBC was observed during the first three months after introduction of AMS, counts then declined to equivalent levels pre-AMS installation, from 25,000 to 50,000 cfu mL−1. Although differences were significant for the first two years post-installation, they became non-significant during the following two years. The difference in BMSCC was not statistically significant between pre and post-AMS installation time periods, but by grouping data into annual periods, significantly higher values of BMSCC were found during the first year after introduction. Nevertheless, these values decreased over time and even showed a significant improvement in the third year with respect to pre-introduction. The data show that the installation of AMS had a marked impact on milk quality. However, as soon as farmers become accustomed to managing the new equipment and the adaption of cows is real, a level of milk quality which can be maintained over time is achievable.

Automatic milking systems (AMS), or milking robots, are becoming widely accepted as a milking technology that reduces labour and increases milk yield. However, reported amount of labour saved, changes in milk yield, and milk quality when transitioning to AMS vary widely. The purpose of this study was to document the impact of adopting AMS on farms with regards to reported changes in milking labour management, milk production, milk quality, and participation in dairy herd improvement (DHI) programmes. A survey was conducted across Canada over the phone, online, and in-person. In total, 530 AMS farms were contacted between May 2014 and the end of June 2015. A total of 217 AMS producers participated in the General Survey (Part 1), resulting in a 41% response rate, and 69 of the respondents completed the more detailed follow-up questions (Part 2). On average, after adopting AMS, the number of employees (full- and part-time non-family labour combined) decreased from 2.5 to 2.0, whereas time devoted to milking-related activities decreased by 62% (from 5.2 to 2.0 h/day). Median milking frequency was 3.0 milkings/day and robots were occupied on average 77% of the day. Producers went to fetch cows a median of 2 times/day, with a median of 3 fetch cows or 4% of the herd per robot/day. Farms had a median of 2.5 failed or incomplete milkings/robot per day. Producers reported an increase in milk yield, but little effect on milk quality. Mean milk yield on AMS farms was 32.6 kg/cow day. Median bulk tank somatic cell count was 180 000 cells/ml. Median milk fat on AMS farms was 4.0% and median milk protein was 3.3%. At the time of the survey, 67% of producers were current participants of a DHI programme. Half of the producers who were not DHI participants had stopped participation after adopting AMS. Overall, this study characterized impacts of adopting AMS and may be a useful guide for making this transition.

The aim of the study was to determine the effect of the free approach to automatic milking system of primiparous (PP) Holstein-Friesian cows with varying locomotion score on the milking frequency, yield per milking and daily milk yield in the subsequent phases of lactation, and the consequent production and economical losses. The evaluation of cows’ locomotion was successively repeated in seven consecutive months, using a five-point scale of posture and movement assessment. It has been found that in the case of analysed PP cows with the locomotion score 4 led to reduction in the milk production by 11% in third stage of lactation compared to the healthy PP. PP cows with locomotion score 4 less frequent went to milking robot and was characterized by the lowest daily milk production.

In recent decades, the costs of energy in dairy farming increased mainly due to rising energy prices but also due to increased mechanisation and automatisation. Electric energy in dairy farming is essentially used for milking and milk cooling. However, the energy consumption of automatic milking systems (AMS) depend on many factors (e.g., machine generations, machine configurations and settings, and operative conditions). To evaluate the differences in performance and energy efficiency of AMS, the impact of different technologies within the attachment arm in practical conditions, a detailed quantification of energy consumption was carried out on two consecutive single box automatic milking systems (AMS) of a dairy farm in southern Bavaria (Germany). The AMS equipped with an electrical drive of the attachment arm was more efficient and showed a higher capacity regarding cows in the herd. The replacement of the pneumatic drive with electrical drives leads to higher energy consumptions of the milking robot but reduces the energy consumption of the air compressor. Hence, the energy efficiency of the electric attachment arm showed strong advantages in the energetic efficiency of the whole milking process. Advances of sustainability due to the increased performance are and should be investigated in further research.

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 R2 were low: same day (slope = 0.95, R2 = 0.07), CMA (slope = 1.05, R2 = 0.07), BMA (slope = 0.65, R2 = 0.04), and FMA (slope = 1.23, R2 = 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. [ABSTRACT FROM AUTHOR]

• Human-cow-robot relations around Automated Milking Systems can be dissonant. • These relations can be analysed through the concept of divergent conduct. • Divergent conduct highlights that actors' conduct can follow multiple trajectories. • These trajectories co-constitute topological and topographical spatialities. This paper begins to develop a terminology for discussing less-than-convivial more-than-human entanglements. The paper reviews existing work on such relations, showing how they tend to have been conceptualised in terms of animal transgression and resistance. It then develops critiques of these terms, focusing on their problematic representations of animals' actions and subjectivities, and engaging with arguments that non-living nonhumans also need to be considered in conceptualisations of problematic more-than-human situations. Drawing on empirical material from research into automated (or robotic) milking systems (AMS), and the associated relations between machines, humans and cows in specific places, the paper proposes and outlines the concept of divergent conduct. It argues that this is a way of exploring how heterogeneous entities co-produce activity which is likely to differ from accounts of trouble-free introductions of technologies and practices. The concept draws together an emphasis on the 'lively' nature of machines with a focus on the agency of nonhuman animals and the topological relationships involved in attempts to establish AMS in UK dairy farming. It suggests that the characteristics and capacities of heterogeneous entities make multiple and relational differences to situations. As such, the concept emphasises the constitution of AMS in relation to multiple human and nonhuman requirements, and their related conducts, which may pull in different directions. The paper argues that divergent conduct provides a way of exploring problematic and politicised entanglements in which inequalities of power can be many-layered and intersectional. [ABSTRACT FROM AUTHOR]

Over the last two decades, the dairy industry has adopted the use of Automatic Milking Systems (AMS). AMS have the potential to increase the effectiveness of the milking process and sustain animal welfare. This study assessed the state of the art of research activities on AMS through a systematic review of scientific and industrial research. The papers and patents of the last 20 years (2000–2019) were analysed to assess the research tendencies. The words appearing in title, abstract and keywords of a total of 802 documents were processed with the text mining tool. Four clusters were identified (Components, Technology, Process and Animal). For each cluster, the words frequency analysis enabled us to identify the research tendencies and gaps. The results showed that focuses of the scientific and industrial research areas complementary, with scientific papers mainly dealing with topics related to animal and process, and patents giving priority to technology and components. Both scientific and industrial research converged on some crucial objectives, such as animal welfare, process sustainability and technological development. Despite the increasing interest in animal welfare, this review highlighted that further progress is needed to meet the consumers’ demand. Moreover, milk yield is still regarded as more valuable compared to milk quality. Therefore, additional effort is necessary on the latter. At the process level, some gaps have been found related to cleaning operations, necessary to improve milk quality and animal health. The use of farm data and their incorporation on herd decision support systems (DSS) appeared optimal. The results presented in this review may be used as an overall assessment useful to address future research.

The aim of the study was to determine the effect of the free approach to automatic milking system of primiparous (PP) Holstein-Friesian cows with varying locomotion score on the milking frequency, yield per milking and daily milk yield in the subsequent phases of lactation, and the consequent production and economical losses. The evaluation of cows' locomotion was successively repeated in seven consecutive months, using a five-point scale of posture and movement assessment. It has been found that in the case of analysed PP cows with the locomotion score 4 led to reduction in the milk production by 11% in third stage of lactation compared to the healthy PP. PP cows with locomotion score 4 less frequent went to milking robot and was characterized by the lowest daily milk production. [ABSTRACT FROM AUTHOR]

Automatic milking systems (AMS), or milking robots, are becoming widely accepted as a milking technology that reduces labour and increases milk yield. However, reported amount of labour saved, changes in milk yield, and milk quality when transitioning to AMS vary widely. The purpose of this study was to document the impact of adopting AMS on farms with regards to reported changes in milking labour management, milk production, milk quality, and participation in dairy herd improvement (DHI) programmes. A survey was conducted across Canada over the phone, online, and in-person. In total, 530 AMS farms were contacted between May 2014 and the end of June 2015. A total of 217 AMS producers participated in the General Survey (Part 1), resulting in a 41% response rate, and 69 of the respondents completed the more detailed follow-up questions (Part 2). On average, after adopting AMS, the number of employees (full- and part-time non-family labour combined) decreased from 2.5 to 2.0, whereas time devoted to milking-related activities decreased by 62% (from 5.2 to 2.0 h/day). Median milking frequency was 3.0 milkings/day and robots were occupied on average 77% of the day. Producers went to fetch cows a median of 2 times/day, with a median of 3 fetch cows or 4% of the herd per robot/day. Farms had a median of 2.5 failed or incomplete milkings/robot per day. Producers reported an increase in milk yield, but little effect on milk quality. Mean milk yield on AMS farms was 32.6 kg/cow day. Median bulk tank somatic cell count was 180 000 cells/ml. Median milk fat on AMS farms was 4.0% and median milk protein was 3.3%. At the time of the survey, 67% of producers were current participants of a DHI programme. Half of the producers who were not DHI participants had stopped participation after adopting AMS. Overall, this study characterized impacts of adopting AMS and may be a useful guide for making this transition. [ABSTRACT FROM AUTHOR]

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 ß-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. [ABSTRACT FROM AUTHOR]

Despite the growing popularity of automatic milking systems (AMS), or milking robots, in Canada, little documentation is available on how Canadian dairy producers experience the transition to this milking technology. The objective of this national study was to document the experiences of Canadian dairy producers during the transition to, and use of, AMS. This paper reports on producers' experiences with cow training, challenges during the transition and their solutions, and effect of the AMS on quality of life. The AMS producers (n = 217) were surveyed from 8 Canadian provinces. Overall, producers experienced a positive transition to AMS. Producers perceived that AMS improved profitability, quality of their lives and their cows' lives, and had met expectations, despite experiencing some challenges during transition such as learning to use the technology and data, cow training, demanding first few days, and changing health management. Less than half of the AMS producers (42%) trained cows or heifers to use the AMS before the first milking with the robot. Producers who implemented training before first milking reported that it took an average of 1 wk to train a cow or heifer to use the AMS. Producers reported it took a median of 30 d for an entire herd to adapt to the AMS, whether or not cow training took place. On average, 2% of a herd was culled for not adapting, or not voluntarily milking, when otherwise physically and behaviorally normal. With AMS, producers suggested they gained more time flexibility, found work to be less stressful and physically demanding, found employee management easier, and had improved herd health and management. The vast majority (86%) of producers would recommend others to transition to AMS. [ABSTRACT FROM AUTHOR]

The objectives of this cross-sectional study were to describe housing and management practices on farms using automatic milking systems (AMS) in 2 states of the upper Midwest and to evaluate the association of various housing and management factors with 3 measures of animal welfare: prevalence of lameness, severe hock lesions, and dirty cows. Fifty-four farms were visited once to collect facility measurements and observations, interview the dairy producer, and score cows for locomotion, hock lesions, and hygiene. Median number of AMS units/farm was 2 (interquartile range = 1; range = 1 to 8). Factors concerning labor were the most commonly cited reason by dairy producers for making the transition to the AMS; additional commonly cited factors were an improvement in lifestyle and human health. Number of cows fetched per AMS, or manually brought to the AMS if not milked voluntarily, was 4.7 ± 2.3 cows/AMS per day (8% of cows) for free traffic flow farms and 3.3 ± 1.8 cows/AMS per day (5% of cows) for guided traffic flow farms. Cow resting surface was significantly associated with prevalence of lameness and severe lameness. Farms with sand-bedded freestalls (17.2%) and bedded packs (17.4%) had significantly lower lameness prevalence (score ≥3 on a 1 to 5 scale, with 1 = normal locomotion) than farms with mattress freestalls (30.5%), waterbeds (25.0%), and mattresses with access to pasture (22.6%). Farms with mattresses and access to pasture had similar lameness prevalence to farms with waterbeds, but were lower than farms with mattresses only. A somewhat similar result was found for severe lameness prevalence (score ≥4 on a 1 to 5 scale, with 1 = normal locomotion); farms with sand-bedded freestalls (2.8%), bedded packs (0.0%), and mattress freestalls with access to pasture (1.5%) had significantly lower prevalence than farms with mattresses (7.1%) or waterbeds (10.8%). Severe hock lesion prevalence (score = 3 on a 1 to 3 scale, with 1 = normal, 3 = swelling) in herds with sand-bedded freestalls, waterbeds, and bedded packs were similar and significantly lower than the prevalence in mattress-based freestalls. Cows housed in sand-bedded freestalls had significantly lower prevalence of dirty cows (score = 3 or 3.5 on a 1 to 5 scale, with 1 = clean) than those housed on mattresses and waterbeds, and had significantly lower prevalence of severely dirty cows (score = 4, 4.5 or 5 on a 1 to 5 scale, with 1 = clean) than all other housing systems except waterbeds, which was similar. Manure removal system (manual, automatic, or slatted floor) was significantly associated with prevalence of severely dirty cows; farms with manual scraping had lower prevalence of severely dirty cows than farms where alley scraping was practiced automatically or slatted floors were used. Dairy producers using AMS appeared to be successful with a variety of facility designs and management practices. Cow resting surface in AMS herds was associated with some animal health and welfare measurements. [ABSTRACT FROM AUTHOR]

The objective of this study was to identify housing and management factors associated with productivity on automatic milking system (AMS) dairy farms measured as daily milk yield/AMS and daily milk yield/ cow. Management, housing, and lameness prevalence data were collected from 33 AMS farms in Minnesota and Wisconsin during a farm visit. All farms in the study used free-flow cow traffic. Mixed model analysis of cross-sectional data showed that farms with automatic feed push-up via a robot produced more milk per AMS/day and per cow/day than farms where feed was pushed up manually. New versus retrofitted facility, freestall surface, manure removal system, and the number of AMS units/pen were not associated with daily milk yield per AMS or per cow. Cow comfort index (calculated as number of cows lying down in stalls divided by total number of cows touching a stall) was positively associated with daily milk yield/cow. Prevalence of lameness and severe lameness, number of cows per full-time employee, depth of the area in front of the AMS milking station, and length of the exit lane from the AMS milking station were not associated with daily milk yield per AMS or per cow. Multivariable mixed model analysis of longitudinal AMS software data collected daily over approximately an 18-mo period from 32 of the farms found a positive association between daily milk yield/AMS and average age of the cows, cow milking frequency, cow milking speed, number of cows/ AMS, and daily amount of concentrate feed offered/ cow in the AMS. Factors negatively associated with daily milk yield/AMS were number of failed and refused cow visits to the AMS, treatment time (the time spent preparing the udder before milking and applying a teat disinfectant after milking), and amount of residual concentrate feed/cow. Similar results were also found for daily milk yield on a per cow basis; however, as it would be expected, average days in milk of the herd were also negatively associated with daily milk yield/ cow. These findings indicate that several management and cow factors must be managed well to optimize AMS productivity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In robotic milking dairy systems lack of control over intakes can be problematic for balancing the forage and concentrate portions of diets. This can lead to problems associated with high concentrate intakes and concomitant low forage intakes. To check this as a problem, the feeding behaviour of cows was observed: the number of daily visits to the feed barrier, the duration of these visits and actual feeding, of high and low yielding cows. The cows were robotmilked and fed a ration comprising, separately, concentrate feed from a robot and a feeder, and a grass/clover silage mix forage at the feed barrier. Individual variation in visiting times and times spent at the feed barrier were greater than the effect of level of production. There was no evidence that cows with higher milk yields are differentially motivated to feed from forage. But more dominant cows spent more time feeding than submissive cows. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High stall stocking rates have been shown to alter behavior of cows in parlor-milked systems; however, no studies have examined stocking rates for cows milked with automatic milking systems (AMSs). Therefore, we examined the influence of stall and feed bunk overstocking on cows' behavior, displacements, cows' activity, and rumination time in an AMS dairy farm. Each of 2 pens contained 60 cows/pen, 58 free stalls, and 60 headlocks. The effect of stocking rate was examined using 3 treatments applied separately to stalls and feed bunks (100% = 58 stalls or 60 headlocks available for 60 cows; 120% = 50 stalls or 50 headlocks available for 60 cows; and 150% = 40 stalls or 40 headlocks available for 60 cows). Each stall or feed bunk treatment was applied separately for 1 week in a randomized order that was different for each pen, with a 1-week washout period between treatments. Data were collected during the last 2 days of each treatment week. Activity level, time spent ruminating, and the number of displacements were recorded for both feed bunk and lying stall treatments. The number of cows lying, perching, or standing in lying stalls was recorded in response to stall stocking rate treatments, and the number of cows eating or standing at the feed bunk was recorded for feed bunk stocking rate treatments. Statistical analyses were performed using R software (version 3.3.1). Overstocking stalls to 150% reduced the number of cows lying and standing in stalls but increased the number of cows perching in stalls compared with 100% or 120% stocking rates (all P ≤ 0.05). Moreover, overstocking feed bunks to 150% increased the number of cows eating and standing at the feed bunk after delivery of fresh feed compared to 100% or 120% stocking rates (all P ≤ 0.05). Finally, overstocking of either lying stalls or the feed bunk to 150% reduced time spent ruminating and increased cow activity and number of cows displaced from stalls or the feed bunk in comparison with lower stocking rates (all P ≤ 0.05). Thus, changes to cows' behavior when overstocked above 120% in AMS farms may detrimentally affect welfare and health. Producers can use these results to determine optimal stocking rates in AMS dairies, allowing them to optimize facility design and management to maintain good welfare and production without wasting space or resources. [ABSTRACT FROM AUTHOR]

When a farm that was using a conventional milking system introduces an automatic milking system (AMS) possible risk factors can affect milk quality. The aim of the study was to investigate the influence of milking with automatic milking systems on milk quality variables over a long time-period post-installation. Bulk milk total bacterial count (BMTBC) and somatic cell count (BMSCC) were analysed and compared from 2 years before introduction of automatic milking until 4 years after. Differences regarding these quality parameters were contrasted usingt-test and one-way analysis of variance (ANOVA) and post hoc comparisons were performed. A significant increase in BMTBC was observed during the first three months after introduction of AMS, counts then declined to equivalent levels pre-AMS installation, from 25,000 to 50,000 cfu mL−1. Although differences were significant for the first two years post-installation, they became non-significant during the following two years. The difference in BMSCC was not statistically significant between pre and post-AMS installation time periods, but by grouping data into annual periods, significantly higher values of BMSCC were found during the first year after introduction. Nevertheless, these values decreased over time and even showed a significant improvement in the third year with respect to pre-introduction. The data show that the installation of AMS had a marked impact on milk quality. However, as soon as farmers become accustomed to managing the new equipment and the adaption of cows is real, a level of milk quality which can be maintained over time is achievable. [ABSTRACT FROM PUBLISHER]