Automatic Milking System Herds Research Articles

Farmers' Perceptions on Implementing Automatic Milking Systems in Large USA Dairies: Decision-Making Process, Management Practices, Labor, and Herd Performance.

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.

What have we learned about milk quality and udder health in automatic milking systems?

The adoption of automatic milking systems (AMS) continues to increase across North America. An analysis of milk quality data from more than 3,000 dairy farms in Ontario, Canada sug­gests that while AMS herds have a slightly higher average raw bacteria count and higher risk of incurring a bacterial or freez­ing point penalty, the bulk tank somatic cell count is similar among farms using pipeline, parlor and AMS systems. These results indicate that farms milking with AMS can produce milk of similarly high quality as those milking with conventional milking systems (CMS). A panel of individuals shared their opinions about advantages and disadvantages of AMS and CMS, with a number of AMS attributes appearing in both lists, sug­gesting that the perception will depend on the context and the attributes of the AMS.

Association between change in body weight during early lactation and milk production in automatic milking system herds

The objective of this observational study was to investigate the association between percent body weight (BW) change in early lactation and the 90-d cumulative milk yield of dairy cows in automatic milking system (AMS) herds. Retrospective daily cow data were collected from the Lely T4C (Lely Industries, Maassluis, the Netherlands) software on 34 farms. Cows were categorized by parity into parity 1 (P1), parity 2 (P2), or parity 3 and greater (P3+). The BW change over the first 21 d of lactation was calculated as the percentage difference between the cow's average BW across d 20 through 22 and the average BW across d 2 through 4 (initial BW) postpartum. The 90-d cumulative milk yield was the outcome variable in a mixed linear regression model, with BW change, parity, their interaction, and season of calving as explanatory variables. Farm and cow nested within farm (n = 4,695) were random effects in the model. On average, cows in all 3 parity groups lost BW during the first 21 d in milk. The 21-d BW change had a negative quadratic relationship with 90-d cumulative milk yield for all parity groups; P1, P2, and P3+ cows with a 21-d BW change of −7.42%, −5.02%, and −4.52%, respectively, were more productive over 90 d in milk (P1 = 3,123 ± 52.6 kg, P2 = 4,271 ± 52.8 kg, and P3+ = 4,548 ± 52.2 kg). The findings of this study highlight the benefits of monitoring BW change in early lactation and may contribute to future research aimed to develop or improve predictive models for milk production in herds using AMS.

Disentangling the relationships between lameness, milking frequency and milk production in Dutch dairy herds using an automatic milking system

Lameness in dairy cattle commonly results in milk production losses. Previous studies have quantified this effect in cows milked with conventional milking systems. The effect of lameness on milk production in herds with automatic milking systems (AMS) might differ because lame cows may visit the AMS less frequently, resulting in an additional milk production loss. This observational study aimed to quantify the direct and indirect effect, through milking frequency, of lameness on milk production in AMS herds by means of a multilevel mediation analysis. Lameness scores, the number of visits to the AMS, and daily milk production records of 1608 dairy cows from 87 Dutch AMS herds were available for analysis. Of those cows, 27 % were mildly lame and 11 % were severely lame. Using a multilevel Poisson regression model, both mildly lame and severely lame cows were identified to visit the AMS less frequently. A separate multilevel linear regression model identified that the direct effect of severe lameness on milk production was 1.4 kg per day. The subsequent multilevel mediation analysis showed that the relationship between severe lameness and milk production was mediated through a reduced milking frequency; 63 % of the total milk production losses (3.9 kg per day) were due to a reduced milking frequency (2.5 kg per day). The mediating effect of a reduced milking frequency was most prominent in cows with parity ≥ 4 and in cows that were ≥ 30 days in lactation. This study suggests that milk production losses due to severe lameness of dairy cows might be higher in AMS herds compared with cows in herds with a conventional milking system, a likely result of a reduced milking frequency of those cows. Prevention of severe lameness may be more cost-effective in AMS herds than in herds with a conventional milking system.

Prediction of cow's fertility based on data recorded by automatic milking system during the periparturient period.

The results of most studies show the beneficial effect of milking automation on production parameters of dairy cows, but its effect on fertility traits is debatable. Therefore, a study was undertaken to predict cow fertility - services per conception (SC) and calving interval (CI) - based on automatic milking system (AMS) data collected in the periparturient period subdivided into the second and first week before calving, 1-4, 5-7, 8-14, 15-21 and 22-28days of lactation. SC and CI were predicted using daily indicators such as concentrate intake, number of milkings, cow box time, milking time, milking speed, colostrum and milk yield, composition, temperature and electrical conductivity. The study material was derived from the AMS management system and from the SYMLEK milk recording system. The analysis covered data for 16,329 milkings of 398 Polish Holstein-Friesian (PHF) cows, which were used in three AMS herds. The collected numerical data were statistically analysed by correlation analysis in parallel with decision tree technique (SAS statistical package). The present study showed that due to the low, mostly non-significant coefficients of correlation between AMS data collected between 2weeks before and 4weeks after calving, it is not possible to predict cow fertility based on single traits. It has been established that the decision tree method may help breeders, already during the postcalving period, to choose the level of factors associated with AMS milking, which will ensure good fertility of cows in a herd. The most favourable number of services per conception is to be expected from cows that were milked <1.6 times per day from 1 to 4days of lactation and electrical conductivity of their colostrum did not exceed 69 mS during that time. In turn, shortest CI (366days) will be characteristic of the cows whose average daily colostrum yield did not exceed 20.2kg and their daily concentrate intake from 8 to 14days of lactation was at least 5.0kg.

Progression of different udder inflammation indicators and their episode length after onset of inflammation using automatic milking system sensor data

In automatic milking systems (AMS), sensors can measure cow behavior and milk composition at every milking. The aim of this observational study of previously collected data was to gain insight into the differences in dynamics of udder inflammation indicators between cows that recover and those that do not recover after detection of an initial inflammation. Milk diversion (milk separated from the bulk tank and thus indicating farmer intervention), conductivity, and somatic cell count (SCC) data from 4 wk before the initial inflammation to 12 wk after the initial inflammation were used to analyze 2,584 cases of udder inflammation. An udder inflammation case was defined as an initial observation of SCC ≥200,000 cells/mL as well as 1 additional SCC measurement >200,000 cells/mL within 10 d after the initial case, among other requirements. The data originated from 15 AMS herds in 6 countries. Four subsets of cows were created based on whether milk was diverted after the initial inflammation and whether the udder inflammation cases recovered, using a 10-d rolling average SCC threshold of 200,000 cells/mL and checking whether this rolling mean was below the threshold within 90 d after the initial inflammation as the indication of recovery. This formed the following subsets of cow lactations: milk diverted-recovered, milk diverted-not recovered, no milk diverted-not recovered, no milk diverted-recovered. Thresholds of 100,000 SCC/mL and 300,000 SCC/mL for the definition of case and recovery were also applied in a sensitivity analysis but with no substantial difference in results. Linear mixed models were used for each subset to study the variation in SCC (natural logarithm of SCC divided by 1,000) and σ-conductivity (natural logarithm of standard deviation of quarter conductivities). When observing the fraction of cows with SCC <200,000 cells/mL in the recovery subsets, most cows recovered within 20 d after the initial inflammation. In the recovery subsets, both σ-conductivity and SCC stabilized, mostly within 3 to 4 wk after the initial inflammation. σ-Conductivity stabilized above the pre-onset level in all subsets and did not show a clear increase in the no-milk-diverted subgroups, whereas SCC stabilized closer to the pre-onset level. Overall, this study indicated a cutoff point between nonchronic and chronic changes in indicators 3 to 4 wk after the initial inflammation for SCC and σ-conductivity.

Udder health of early-lactation primiparous dairy cows based on somatic cell count categories

The aims were to investigate the prevalence of intramammary infection in early-lactation primiparous dairy cows (PC) in Sweden using milk recording cow composite somatic cell count (CSCC) categories based on classification of CSCC at the first 2 milk recordings after calving as low or high using cut-offs indicating intramammary infection. We also wanted to evaluate if herd-specific patterns in CSCC categories among PC can be identified to indicate success and problem herds as well as cow-level associations between CSCC categories and breed and sire, and herd-level associations between CSCC categories and herd size, milk production, production system, milking system, and year. A total of 1,597 dairy herds were included. Inclusion criteria were enrolment in the Swedish official milk recording scheme and having production data from at least 10 PC per year during 2014, 2015, and 2016. Herd (size, SCC, milk production, production system, milking system) and cow (breed, lactation number, calving date, CSCC, milk production) data were collected from the Swedish official milk recording scheme. Each PC was assigned a CSCC category (low-low, low-high, high-high, high-low, or inconclusive) based on the CSCC at the first 2 milk recordings using the following cut-offs. At each milk recording a CSCC ≤75,000 cells/mL was considered low and a CSCC >100,000 cells/mL was considered high, whereas a CSCC in between those values was considered inconclusive. Associations between CSCC categories and breed or sire of the PC were analyzed using multivariable multinomial logistic regression models. Associations at herd level between number of PC in a CSCC category and herd variables were analyzed using multivariable Poisson or negative binomial regression models. The annual proportions of CSCC categories for all PC were 51.3%, 5.5%, 15.5%, 13.7%, and 14.0% for the low-low, low-high, high-low, high-high, and inconclusive categories, respectively. The distribution of CSCC categories varied markedly between herds. Overall, the median herd prevalence was 50.2 to 54.2% and 11.7 to 13.2% per year for the low-low and high-high categories, respectively. At cow level, both breed and sire were significantly associated with CSCC categories, showing for example that a higher proportion of Jersey PC were categorized as high-high compared with Swedish Holstein (SH) and Swedish Red (SR) PC, and that PC of certain SH and SR sires more often were categorized as low-low or high-high cows than PC of other sires within each breed. All herd factors, except conventional and organic production, were significantly associated with the proportion of PC in a CSCC category at herd level. For example, the proportion of PC in the low-low category was significantly lower in larger herds (≥80 cows) compared with smaller herds (<80 cows) and significantly lower in automatic milking system herds than in herds with other milking systems, but significantly higher in herds with higher milk production. Overall, the results indicate a substantial need for prevention of subclinical mastitis in early-lactation PC as only 50% of these cows had low CSCC at both milk recordings after calving. Moreover, we conclude that CSCC categories may be a useful tool for identifying success and problem herds.

Are farmer assessed temperament, milking speed, and leakage genetically the same traits in automatic milking systems and traditional milking systems?

The aim was to investigate whether subjectively scored milking speed, temperament, and leakage are genetically the same trait when measured in different milking systems. Data were provided by the Norwegian Dairy Herd Recording System and included a total of 260,731 first-parity Norwegian Red cows calving between January 2009 and February 2019 and milked either in a traditional milking system (milking parlor or pipeline) or by an automatic milking system (AMS). Genetic parameters were estimated and lower heritabilities and less genetic variation were found for the 3 traits when measured in AMS herds. The heritability of temperament, leakage, and milking speed were 0.05, 0.04, and 0.22, respectively, with data from AMS herds; and 0.09, 0.14, and 0.27, respectively, with data from cows milked in traditional milking systems. The genetic correlations between temperament and leakage (-0.19), between milking speed and leakage (-0.88), and between milking speed and temperament (0.30) in AMS were slightly stronger than between the corresponding traits assessed in other milking systems (-0.15, -0.82, and 0.16, respectively). The genetic correlations between traits across milking systems were strong: 0.98, 0.96, and 0.86 for milking speed, leakage, and temperament, respectively. Strong correlations indicate that the traits were almost genetically similar despite being scored in different milking systems. The rank correlations among estimated sire breeding values were strong: 0.98 and 0.99 for milking speed and leakage, with little or no reranking of bull performance across milking systems. Temperament had the lowest genetic correlation (0.86) and rank correlation (0.91) across milking systems. These data suggest that AMS farmers evaluate temperament slightly differently from farmers using other milking systems or that different aspects of temperament are important for farmers with AMS.

Large-scale cross-sectional study of relationships between somatic cell count and milking-time test results in different milking systems

Milking-time testing (MTT) is a method for evaluating the vacuum conditions in the teatcup during milking. The purpose is to evaluate the possible impact of the milking and milking equipment on udder health and milk quality. The method is commonly implemented by herd health advisory services, but results are interpreted empirically due to lack of scientific documentation on relationships between MTT result variables and objective measures of udder health.The current study was conducted to increase our understanding of associations between cow-level differences in composite milk somatic cell count (CMSCC) and MTT results in dairy cows milked in 3 different milking systems; automatic milking systems (AMS), milking parlors, and pipeline milking systems. Data from 7069 cows (predominantly Norwegian Red breed) in 1009 herds were used in a cross-sectional study. Multilevel linear regression models with a random intercept at herd level were used to describe relationships between CMSCC (on logarithmic scale) and the following MTT explanatory variables: average vacuum level in the short milk tube and mouthpiece chamber in the main milking and overmilking periods, the duration of these two periods, and vacuum stability, measured by sudden vacuum drops in the short milk tube. The models were corrected for the herd effect, mastitis history and differences in milk yield, lactation stage and parity between cows. Separate models were run for AMS, milking parlors, and pipeline milking systems, because this approach allowed for comparison between systems and for evaluation of the herd effect independently of milking system.The models described 8–10 % of the variation in CMSCC, indicating that MTT could only explain a relatively small proportion of a large total variation in CMSCC. In most observations, vacuum levels in the short milk tube during main milking were within the range recommended by the International Organization for Standardization. The results from our multivariable models showed decreasing CMSCC with increasing vacuum level in the short milk tube during the main milking period in AMS and milking parlors. Similarly, decreasing CMSCC was also associated with increasing duration of the main milking period in all 3 systems. These relationships are important for the interpretation of MTT results under practical conditions; finding high vacuum levels and long milking durations in a MTT is not associated with elevated CMSCC. In AMS herds, we also found indications that the relationships were different for cows where a case of mastitis had been treated before the MTT.

Housing, management characteristics, and factors associated with lameness, hock lesion, and hygiene of lactating dairy cattle on Upper Midwest United States dairy farms using automatic milking systems

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.

Investigating the within-herd prevalence and risk factors for ketosis in dairy cattle in Ontario as diagnosed by the test-day concentration of β-hydroxybutyrate in milk

An observational study of 790 to over 3,000 herds was conducted to estimate the within-herd prevalence and cow-level risk factors for ketosis in dairy cattle in herds that participate in a Dairy Herd Improvement Association (DHIA) program. Ketosis or hyperketolactia (KET) was diagnosed as milk β-hydroxybutyrate ≥0.15 mmol/L at first DHIA test when tested within the first 30 d in milk. Seven hundred ninety-five herds providing at least 61 first milk tests from June 2014 to December 2015 were used to estimate the provincial within-herd prevalence of KET. All herds on DHIA in Ontario (n = 3,042) were used to construct cow-level multilevel logistic regression models to investigate the association of DHIA collected variables with the odds of KET at first DHIA milk test. Primiparous and multiparous animals were modeled independently. The cow-level KET prevalence in Ontario was 21%, with an average within-herd prevalence of 21% (standard deviation = 10.6) for dairy herds enrolled in a DHIA program. The prevalence of KET had a distinct seasonality with the lowest prevalence occurring from July to November. Automatic milking systems (AMS) were associated with increased within-herd prevalence, as well as increased odds of KET in multiparous animals at first test (odds ratio: 1.45; 95% confidence interval: 1.30 to 1.63). Jersey cattle had over 1.46 times higher odds of KET than Holstein cattle. Milk fat yield ≥1.12 kg/d at the last test of the previous lactation was associated with decreased odds of KET in the current lactation (odds ratio: 0.56; 95% confidence interval: 0.53 to 0.59). Increased days dry and longer calving intervals, for multiparous animals, and older age at first calving for primiparous animals increased the odds of KET at first test. This study confirms previous findings that increased days dry, longer calving intervals, and increased age at first calving are associated with increased odds of KET and is the first report of increased KET in herds with AMS and in relation to milk fat yield at the final test of the previous lactation. Feeding management on AMS herds likely contributes to the increased prevalence of KET and further work is required to investigate modifications to current management to minimize risk. Milk fat yield during the previous lactation may be representative of energy partitioning.

Short communication: Genetic variation in choice consistency for cows accessing automatic milking units.

Dairy cows milked in automatic milking systems (AMS) with more than 1 milking box may, as individuals, have a preference for specific milking boxes if allowed free choice. Estimates of quantitative genetic variation in behavioral traits of farmed animals have previously been reported, with estimates of heritability ranging widely. However, for the consistency of choice in dairy cows, almost no published estimates of heritability exist. The hypothesis for this study was that choice consistency is partly under additive genetic control and partly controlled by permanent environmental (animal) effects. The aims of this study were to obtain estimates of genetic and phenotypic parameters for choice consistency in dairy cows milked in AMS herds. Data were obtained from 5 commercial Danish herds (I-V) with 2 AMS milking boxes (A, B). Milking data were only from milkings where both the present and the previous milkings were coded as completed. This filter was used to fulfill a criterion of free-choice situation (713,772 milkings, 1,231 cows). The lactation was divided into 20 segments covering 15d each, from 5 to 305d in milk. Choice consistency scores were obtained as the fraction of milkings without change of box [i.e., 1.0 - µ(box change)] for each segment. Data were analyzed for one part of lactation at a time using a linear mixed model for first-parity cows alone and for all parities jointly. Choice consistency was found to be only weakly heritable (heritability=0.02 to 0.14) in first as well as in later parities, and having intermediate repeatability (repeatability coefficients=0.27 to 0.56). Heritability was especially low at early and late lactation states. These results indicate that consistency, which is itself an indication of repeated similar choices, is also repeatable as a trait observed over longer time periods. However, the genetic background seems to play a smaller role compared with that of the permanent animal effects, indicating that consistency could also be a learned behavior. We concluded that consistency in choices are quantifiable, but only under weak genetic control.

Modelling Pasture-based Automatic Milking System Herds: System Fitness of Grazeable Home-grown Forages, Land Areas and Walking Distances

To maintain a predominantly pasture-based system, the large herd milked by automatic milking rotary would be required to walk significant distances. Walking distances of greater than 1-km are associated with an increased incidence of undesirably long milking intervals and reduced milk yield. Complementary forages can be incorporated into pasture-based systems to lift total home grown feed in a given area, thus potentially ‘concentrating’ feed closer to the dairy. The aim of this modelling study was to investigate the total land area required and associated walking distance for large automatic milking system (AMS) herds when incorporating complementary forage rotations (CFR) into the system. Thirty-six scenarios consisting of 3 AMS herds (400, 600, 800 cows), 2 levels of pasture utilisation (current AMS utilisation of 15.0 t dry matter [DM]/ha, termed as moderate; optimum pasture utilisation of 19.7 t DM/ha, termed as high) and 6 rates of replacement of each of these pastures by grazeable CFR (0%, 10%, 20%, 30%, 40%, 50%) were investigated. Results showed that AMS cows were required to walk greater than 1-km when the farm area was greater than 86 ha. Insufficient pasture could be produced within a 1 km distance (i.e. 86 ha land) with home-grown feed (HGF) providing 43%, 29%, and 22% of the metabolisable energy (ME) required by 400, 600, and 800 cows, respectively from pastures. Introduction of pasture (moderate): CFR in AMS at a ratio of 80:20 can feed a 400 cow AMS herd, and can supply 42% and 31% of the ME requirements for 600 and 800 cows, respectively with pasture (moderate): CFR at 50:50 levels. In contrast to moderate pasture, 400 cows can be managed on high pasture utilisation (provided 57% of the total ME requirements). However, similar to the scenarios conducted with moderate pasture, there was insufficient feed produced within 1-km distance of the dairy for 600 or 800 cows. An 800 cow herd required 140 and 130 ha on moderate and high pasture-based AMS system, respectively with the introduction of pasture: CFR at a ratio of 50:50. Given the impact of increasing land area past 86 ha on walking distance, cow numbers could be increased by purchasing feed from off the milking platform and/or using the land outside 1-km distance for conserved feed. However, this warrants further investigations into risk analyses of different management options including development of an innovative system to manage large herds in an AMS farming system.

Modelling Pasture-based Automatic Milking System Herds: The Impact of Large Herd on Milk Yield and Economics.

The aim of this modelling study was to investigate the effect of large herd size (and land areas) on walking distances and milking interval (MI), and their impact on milk yield and economic penalties when 50% of the total diets were provided from home grown feed either as pasture or grazeable complementary forage rotation (CFR) in an automatic milking system (AMS). Twelve scenarios consisting of 3 AMS herds (400, 600, 800 cows), 2 levels of pasture utilisation (current AMS utilisation of 15.0 t dry matter [DM]/ha, termed as ‘moderate’; optimum pasture utilisation of 19.7 t DM/ha, termed as ‘high’) and 2 rates of incorporation of grazeable complementary forage system (CFS: 0, 30%; CFS = 65% farm is CFR and 35% of farm is pasture) were investigated. Walking distances, energy loss due to walking, MI, reduction in milk yield and income loss were calculated for each treatment based on information available in the literature. With moderate pasture utilisation and 0% CFR, increasing the herd size from 400 to 800 cows resulted in an increase in total walking distances between the parlour and the paddock from 3.5 to 6.3 km. Consequently, MI increased from 15.2 to 16.4 h with increased herd size from 400 to 800 cows. High pasture utilisation (allowing for an increased stocking density) reduced the total walking distances up to 1 km, thus reduced the MI by up to 0.5 h compared to the moderate pasture, 800 cow herd combination. The high pasture utilisation combined with 30% of the farm in CFR in the farm reduced the total walking distances by up to 1.7 km and MI by up to 0.8 h compared to the moderate pasture and 800 cow herd combination. For moderate pasture utilisation, increasing the herd size from 400 to 800 cows resulted in more dramatic milk yield penalty as yield increasing from c.f. 2.6 and 5.1 kg/cow/d respectively, which incurred a loss of up to $AU 1.9/cow/d. Milk yield losses of 0.61 kg and 0.25 kg for every km increase in total walking distance (voluntary return trip from parlour to paddock) and every one hour increase in MI, respectively. The high pasture utilisation combined with 30% of the farm in CFR in the farm increased milk yield by up to 1.5 kg/cow/d, thereby reducing loss by up to $0.5/cow/d (c.f. the moderate pasture and 800 cow herd scenario). Thus, it was concluded that the successful integration of grazeable CFS with pasture has the potential to improve financial performance compared to the pasture only, large herd, AMS.

Modelling Pasture-based Automatic Milking System Herds: Grazeable Forage Options.

One of the challenges to increase milk production in a large pasture-based herd with an automatic milking system (AMS) is to grow forages within a 1-km radius, as increases in walking distance increases milking interval and reduces yield. The main objective of this study was to explore sustainable forage option technologies that can supply high amount of grazeable forages for AMS herds using the Agricultural Production Systems Simulator (APSIM) model. Three different basic simulation scenarios (with irrigation) were carried out using forage crops (namely maize, soybean and sorghum) for the spring-summer period. Subsequent crops in the three scenarios were forage rape over-sown with ryegrass. Each individual simulation was run using actual climatic records for the period from 1900 to 2010. Simulated highest forage yields in maize, soybean and sorghum- (each followed by forage rape-ryegrass) based rotations were 28.2, 22.9, and 19.3 t dry matter/ha, respectively. The simulations suggested that the irrigation requirement could increase by up to 18%, 16%, and 17% respectively in those rotations in El-Niño years compared to neutral years. On the other hand, irrigation requirement could increase by up to 25%, 23%, and 32% in maize, soybean and sorghum based rotations in El-Nino years compared to La-Nina years. However, irrigation requirement could decrease by up to 8%, 7%, and 13% in maize, soybean and sorghum based rotations in La-Nina years compared to neutral years. The major implication of this study is that APSIM models have potentials in devising preferred forage options to maximise grazeable forage yield which may create the opportunity to grow more forage in small areas around the AMS which in turn will minimise walking distance and milking interval and thus increase milk production. Our analyses also suggest that simulation analysis may provide decision support during climatic uncertainty.

Reliability of Milk Recording Data Under Conditions of Automatic Milking System

Automatic milking system (AMS) brings a change in approach to ensure the data reliability in the official milk recording (MR). The AMS is equipped with flowmeter. AMS software provides the daily milk yield (DMY) and average of the last 7 daily milk yields (AVG7) for MR. Classic MR uses DMY. AVG7 could be more reliable value. Origin of both records (DMY and AVG7) is from AMS flowmeter. The aim of paper was to compare the values of milk yield of cows from daily (DMY) and the extended records (AVG7) from AMS for objective assess of lactations to be used in cattle breeding. Study (2013) with 2 AMS herds (DeLaval and Lely Astronaut): herd 1 – Holstein (H) dairy cows; herd 2 – Czech Fleckvieh (CF) dairy cows. There were following milk records: n = 521 DeLaval (H); n = 567 Lely Astronaut (CF); 70 (H) and 68 (CF) dairy cows. MR samples were analyzed on: fat content; crude protein; somatic cell count. Correlations between AVG7 and DMY were: 0.888 (H); 0.898 (CF, both P ≤ 0.001). There were insignificant differences (P &gt; 0.05; −0.07 ± 3.29 kg for H and 0.28 ± 3.3 kg for CF) between AVG7 and DMY for both robots. The same is valid for differences in the production of milk components. According to this comparison experiment the AVG7 of AMS is a suitable equivalent for the DMY regarding official MR for assessment of lactations.

Genetic evaluation of in-line recorded milkability from milking parlors and automatic milking systems

The overall objective of this study was to assess the use of in-line recorded milkability information from dairy herds with conventional milking parlors (CMP) and from herds with automatic milking systems (AMS) for genetic evaluation. Some genetic parameters were previously studied on AMS data for 2,053 Swedish Holstein (SH) and 1,749 Swedish Red (SR) cows in 19 herds. These data were combined in the present paper with milkability information from 74 herds with CMP, including 11,123 SH cows and 7,554 SR cows. Genetic parameters were estimated for the CMP data and genetic correlations were estimated between milkability traits measured in the 2 systems. Average flow rate and milking time were derived and used as similar milkability traits for both systems, whereas box time was used only for AMS herds. Estimated heritabilities were in the range from 0.24 to 0.49. Even though the traits were differently defined in the 2 milking systems, the corresponding traits recorded in AMS and CMP were genetically closely related (0.93–1.00). Similarly, close genetic relationships were shown between milkability traits in different lactations in both breeds (0.93–0.99). Thus, it should be possible to treat milkability traits in different lactations and from different milking systems as the same traits in genetic evaluations. The various milkability traits were also highly genetically correlated, indicating that the inclusion of just one trait in the genetic selection program would efficiently select for milkability without the need to consider all measures. Comparisons of repeatability and random regression models, combining all information from the 2 systems for genetic evaluation, were done to find the most suitable model for genetic evaluation purposes. Even though the random regression models were favored in the formal model tests to evaluate suitability, correlation coefficients between test-days within lactation were high (0.7–0.8) and small differences in breeding values resulted among different models. That would indicate that a few test-days per cow would produce accurate breeding values for milkability.

Associations of housing, management, milking activity, and standing and lying behavior of dairy cows milked in automatic systems

The objective of this cross-sectional study was to describe the housing, feeding management, and characteristics (parity and stage of lactation) of cows on commercial automatic milking system (AMS) dairies and their associations with the standing and lying behavior patterns and milking activity (frequency and yield) of lactating dairy cows. Thirteen AMS herds were enrolled in the study, with an average herd size of 71±30 (mean ± SD; range: 34 to 131) lactating cows. All of the herds used freestall barns, each set up for free cow traffic to the AMS. On-farm measurements were taken to determine stocking density at the freestalls (0.9±0.1 cows/stall; mean ± SD), feed bunk (0.66±0.17m of feed bunk space/cow; mean ± SD), and AMS units (55±11 cows/AMS; mean ± SD). A random sample of 30 cows/herd was selected to monitor standing and lying behavior for 4d using electronic data loggers. Times of feed delivery and feed push-up were recorded daily by the herd managers. Milking times, frequency, and yield were automatically recorded by the AMS units. Data were analyzed in a multivariable mixed regression model to determine which herd-level (housing and feeding management) and cow-level (parity, DIM, and milk yield) factors were associated with behavior and milking activity measures. Lying bout lengths were found to be negatively associated with milk yield and tended to be positively associated with more space at the feed bunk. Increased lying duration was associated with cows of lower milk production, increased space at the feed bunk, and increased frequency of feed push-up. Longer postmilking standing durations were associated with cows of higher parity. An association existed between cows milking less frequently when they were further in lactation, were of higher parity, and as stocking density at the AMS (cows/AMS) increased. Milk yield was positively associated with increased space at the feed bunk and higher parity and negatively associated with DIM. From this study, it can be concluded that increased milking frequency may be achieved in AMS herds by reducing stocking density at the AMS unit. Further, in AMS systems, greater milk yield and lying duration may be achieved by ensuring that cows have ample feed bunk space and have their feed readily available to them in the bunk.

Evaluation of a welfare indicator protocol for assessing animal welfare in AMS herds: researcher, production advisor and veterinary practitioner opinion

AbstractA welfare indicator protocol integrating a total of 38 measures from 4 information sources: housing system, management, animal behaviour and clinical health has been developed for decision support in Automatic Milking System (AMS) herds. Two expert opinion studies focusing on the relevance of the welfare indicator protocol as basis for on-farm welfare assessment have been carried out; one study focused on the opinion of 21 AMS researchers; the other on that of 14 AMS production advisors and 15 veterinary practitioners. The researchers were asked to score the individual welfare relevance of the 38 measures. Furthermore, both the panels were asked to prioritise the 10 most important measures. The 21 researchers generally appreciated the listed protocol measures as highly relevant. The researcher and advisor panels agreed on prioritised measures from all 4 information sources among the 10 most important welfare indicators. In summary, researchers as well as production advisors and veterinarians supported the suggested measures for inclusion in a welfare assessment system. This has demonstrated that integration of different information is considered decisive for operational welfare assessment at herd level.

Influence of Air Intake on the Concentration of Free Fatty Acids and Vacuum Fluctuations During Automatic Milking

The main objective of the study was to determine whether the amount of air intake during quarter milking influences the concentration of free fatty acids (FFA) and vacuum fluctuations at the teat end when milking automatically. Air intake in the teat cup was restricted from the normal inlet of 4.5 to 7 L/min to 1.7 and 0 L/min on 2 farms and experiments were carried out as half-udder studies with 40 cows. Blockage of the air inlet reduced FFA from 1.02 to 0.77 mEq/100g of fat in one herd and from 1.50 to 1.17 mEq/100g of fat in the other herd. Milk yield per milking was the most significant factor influencing FFA. Air intake accounted for <20% of the variation in FFA concentration. Characteristics of the cow explained the most variation, which could mainly be assigned to the effects of milk yield, fat percentage, fat globule size, and fat globule size distribution. The interval between milkings was not significant when adjusting for milk yields. Blockage of the air inlet caused vacuum fluctuations at the teat end to increase from 15.4 to 21.5 kPa for one model of an automatic milking system (AMS), but from 12.8 to 53.6 kPa for another model. Measurements made with a flow simulator and water revealed that the AMS model and water flow were the most important factors influencing vacuum fluctuations, and that interactions existed between the diameter of the short milk tube and air intake. Free fatty acids in bulk milk from 5,980 herds averaged 0.75 mEq/L of milk for conventional herds and varied from 0.77 to 0.94 mEq/L of milk for the 5 AMS models on the Danish market. Fault detection in 55 herds pointed out that the most frequent faults in conventional herds were air leakages and intake of too much air in the cluster, whereas AMS herds had problems with the cooling and stirring of milk. Correction of the cooling faults caused FFA to decrease by 0.52 mEq/L in the AMS herds. We concluded that air intake during automatic milking is not the most important factor in reducing FFA, whereas milk yield per milking matters the most. More attention should be paid to the cooling and stirring of milk. Reducing the air intake causes vacuum fluctuations during milking to increase significantly.