Milk quality assessment in automatic milking systems: accounting for the effects of variable intervals between milkings on milk composition

Randomized-controlled study assessing the effect of milking permission settings and concentrate supplementation on milking frequency and milk yield in a pasture-based automatic milking system

This study aimed to verify the effect of milking permission (MPE) and concentrate supplementation (CS) on milking frequency (milkings per cow per day) and milk yield (kilograms per cow per day) in a farm using a pasture-based automatic milking system (AMS). Sixty-eight cows milked using this AMS unit were randomly assigned to 1 of 4 groups homogeneous for parity, DIM, and milk yield. Treatments used were frequent or restricted MPE, that granted cows permission to milk after 6 to 8 h or 9.6 to 14 h of the previous milking, respectively; and low (LC) or high (HC) CS of 0.5 kg or 3.5 kg/cow per day, respectively. The combination of the 2 levels of MPE and the 2 levels of CS resulted in the 4 treatment combinations (frequent HC [FHC], restricted HC [RHC], frequent LC [FLC], and restricted LC [RLC]). This study was designed as a 2 × 2 factorial arrangement with treatment crossover: each of the 4 cow groups was randomly assigned to 1 of the 4 treatment combinations for a 5-wk experimental period (1 pretreatment week and 4 treatment weeks), and after each 5-wk period groups crossed over to another treatment combination until they experienced all. Statistical analysis assessed the effect of MPE, CS, and their interaction on milk yield, milking frequency, box time, milking time, and average milk-flow rate. This was done using a mixed model analysis with repeated measures to account for repeated observations on the experimental unit (cow). Milk yield per cow per day and milkings per cow per day were significantly higher with the frequent compared with the restricted MPE (1.5 kg and 0.65 milkings, respectively). Milk yield per cow per day and milkings per cow per day were significantly higher with the HC compared with the LC CS (3.1 kg and 0.25 milkings, respectively). Additionally, milk yield per cow per day was affected by the interaction of MPE and CS and it was highest with the FHC (20.1 kg) treatment combination, followed by RHC (18.2 kg) treatment combination. The number of milkings per cow per day were also affected by the interaction of MPE and CS. The highest estimated number of milkings per cow per day was recorded for the FHC (2.12) and the FLC (1.77) treatment combinations, followed by the RHC (1.38) and RLC (1.23) treatment combinations. Similarly, milking interval was 2.5 h longer for the RLC treatment combination compared with RHC. The shortest milking interval was observed for the FHC (11 h) and FLC (12.8 h) treatment combinations. In conclusion, the study showed that allowing access to the robot between 6 to 8 h after the previous milking was sufficient (even with a minimal level of CS) to achieve acceptable milk production and milking performance in a pasture-based AMS.

Evaluation of Heat Stress Effects in Different Geographical Areas on Milk and Rumen Characteristics in Holstein Dairy Cows Using Robot Milking and Rumen Sensors: A Survey in South Korea

This survey investigated, using robotic milking and rumen sensors, the effects of an adjusted temperature−humidity index (THI) in different geographical areas on milk yield, fat and protein, rumen temperature, and activity in lactating Holstein cows. We additionally explored the effect of parity on milk and rumen temperature and activity under different THI levels during the summer. From January to September 2020, four farms (276 dairy cows) were subjected to the use of robot milking machines, and two farms (162 dairy cows) to the use of rumen sensors. For the temperature and humidity data, the THI was calculated on the basis of the data from the Korea Meteorological Administration (KMA). The data were analyzed using the GLM procedure of SAS. Milk yield and milk protein decreased (p < 0.05), and milk fat increased (p < 0.05) at all farms during the summer, from July to August, when the temperature and humidity were high (THI = 72−79). Milk yields were the highest in the fifth, sixth, seventh, and eighth parities, and the lowest in the fourth (p < 0.05). Milk fat concentration was the highest in the fourth parity and the lowest in the first parity (p < 0.05). In the first parity, the highest levels of milk protein and lactose were seen (5.24% and 4.90%, respectively). However, milk protein concentration was the lowest in the third parity, and the lactose concentration was the lowest in the fifth, sixth, seventh, and eighth parities. According to the rumen sensor, the rumen temperature of the dairy cows at the two farms also continued to increase (p < 0.05) from July to August, and then decreased (p < 0.05) in September. However, the activity in the rumen was increased (p < 0.05) from July to September. In the second parity, the highest rumen temperature (39.02 °C) was observed, while the lowest value (38.28 °C) was observed in the third parity. The highest value of rumen activity (12.26 mg) was observed in the second parity and the lowest value (11.31 mg) in the fourth parity. These data, taken together, confirm that a high THI during summer conditions negatively affects milk yield, milk protein content, and rumen temperature and activity in lactating Holstein cows. It is also demonstrated that various parities affect milk characteristics and the rumen environment in the summer season.

Efficiency of Crude Protein Utilisation in Grazing Dairy Cows: A Case Study Comparing Two Production Systems Differing in Intensification Level in New Zealand

Simple Summary

Improving the dietary crude protein utilisation in dairy cows is a key aspect of agronomically and environmentally sustainable production systems. The intensification process of grazing dairy systems identified with the increase of milking cows linked with the addition of supplementary feed along with the increasing use, and particularly inefficient use, of nitrogen fertiliser, has led to increasing pressure on the environment. However, feeding solely on pasture could result in an excess of crude protein intake relative to nutritional requirements, and this could reduce the dietary crude protein utilisation. In this study, we modelled the dietary crude protein utilisation, along with nitrogen excreta partitioning of milking cows, of two contrasting spring-calving pasture-based herds differing in intensification level in New Zealand. We found that feeding diets with higher fresh pasture proportions, such as those employed in low-intensification dairy systems, led to an excess of crude protein intake with greater nitrogen partitioned towards urine, which is sensitive in terms of body water eutrophication. In the high-intensity production system, the inclusion of low-crude protein supplements resulted in better dietary crude protein utilisation, along with less urinary nitrogen losses.

Abstract

In this study, we modelled and compared lactation curves of efficiency of crude protein utilisation (ECPU) and the nitrogen (N) excreta partitioning of milking cows of two contrasting spring-calving pasture-based herds to test some aspects of farming intensification practices on cow performance and N partition. In the low-intensity production system (LIPS), 257 cows were milked once-daily and fed diets comprised of pasture with low supplementary feed inclusion during lactation (304 kg pasture silage/cow). In the high-intensity production system (HIPS), 207 cows were milked twice-daily and fed pasture with higher supplementary feed inclusion (429 kg pasture silage and 1695 kg concentrate/cow). The dietary crude protein (CP) utilisation was calculated for each cow at every herd test date as the ECPU as a proportion of protein yield (PY) from the CP intake (CPI) derived from intake assessments based on metabolisable energy requirements, and the CP balance (CPB) calculated as the difference between CPI and PY. Total N excreta partitioned to faeces (FN) and urine (UN) was estimated by back-calculating UN from FN, considering dietary N, and from N retained in body tissues, taking into account live weight change during the lactation. The higher CPI (2.7 vs. 2.5 kg CP/day), along with the reduced milk yield (1100 kg milk/cow less), of the LIPS cows led to a lower ECPU (23% vs. 31%) and to a higher CPB (2.1 vs. 1.8 kg CP/day) when compared to the HIPS cows. Mean N excreta, and particularly UN, was significantly higher in LIPS cows, and this was explained by higher dietary CP and by the reduced PY when compared to the HIPS cows. Reducing the low-CP supplementation in the “de-intensified” herd lessened the ECPU, resulting in higher UN, which is sensitive in terms of body water eutrophication.

Antibiotic dry cow therapy, somatic cell count, and milk production: Retrospective analysis of the associations in dairy herd recording data using multilevel growth models

Antibiotic dry cow therapy (DCT) is an important part of most mastitis control programs. Updating DCT recommendations is an ongoing topic due to the global problem of antimicrobial resistance. Finland, along with other Nordic countries, has implemented selective DCT for decades. Our study analyzed Dairy Herd Improvement (DHI) information from 241 Finnish farmers who participated in a survey about their drying-off practices. The aim was to evaluate herd-level associations between milk somatic cell count (SCC), milk production, and various antimicrobial DCT approaches both cross-sectionally in 2016 and longitudinally in 2012-2016. The three DCT approaches in the study were selective, blanket, and no DCT use. An additional aim was to evaluate whether dynamic changes occurred in herd-average SCC and annual milk production over five years, and whether these potential changes differed between different DCT approaches. The method for the longitudinal analyses was growth modeling with random coefficient models. Differences in SCC and milk production between farms with different DCT approaches were minor. Regardless of the farm's DCT approach, annual milk production increased over the years, while average SCC was reasonably constant. The variability in SCC and milk production across all DCT groups was low between years, and most of the variability was between farms. Compared to other milking systems, farms with automatic milking system (AMS) had higher SCC, and in 2016 higher milk production. The results of this study suggest that it is possible to maintain low herd-average SCC and good milk production when using selective DCT and following the guidelines for prudent antimicrobial use. Average SCC and milk production varied across the herds, suggesting that advice on DCT practices should be herd-specific. The methodology of growth modeling using random coefficient models was applicable in analyzing longitudinal data, in which the time frame was relatively short and the number of herds was limited.

The milking frequency of primiparous cows in their early stage of lactation and its impact on milking performance

An automatic milking system allows cows to present their full production capability by not limiting them to a specific time when the milking occurs or a fix number of milkings per day. The beginning of the first lactation is a key point in terms of subsequent milk production. The aim of the present study was to indicate the relationship between the milking frequency of primiparous cows during the first month of lactation and their subsequent milk performance. Material of the study consisted of 25 Polish herds of Holstein–Friesian dairy cattle. All cows were milked with the use of an automatic milking system. Animals were divided into five groups, depending on the milking frequency in the first month after calving (MFF). The collected data were statistically processed using the multifactorial ANOVA. The best milk and milking parameters characterised primiparous cows, for which the average number of milkings per day was at the level of 3–3.5 or above, this group did not have a preferred time for their milking. This group of cows milked more frequently during the first month of lactation (MFF5) and had the highest milk yield (MY) and milking duration. The highest culling percentage (57.77%) was noted within the group of primiparous cows with the lowest milking frequency during the first month of lactation (MFF1). MFF5 animals maintained better milk and milking parameters in all months of lactation than did those in the other groups. Older animals, that calved after the 28th month of life, and those that calved during warmer seasons, showed the tendency to have a lower milking frequency and poorer milk and milking parameters. The findings obtained in the present study are interesting in terms of their potential use, because they show that frequent milking during the first month after calving corresponds to a better overall MY during that lactation. Hopefully, by promoting frequent milkings at the beginning of lactation, farmer may increase the overall lactation MY.

A new somatic cell count index to more accurately predict milk yield losses

Abstract. Intramammary infection and clinical mastitis in dairy cows leads to considerable economic losses for farmers. The somatic cell concentration in cow's milk has been shown to be an excellent indicator for the prevalence of subclinical mastitis. In this study, a new somatic cell count index (SCCI) was proposed for the accurate prediction of milk yield losses caused by elevated somatic cell count (SCC). In all, 97 238 lactations (55 207 Holstein cows) from 2328 herds were recorded between 2010 and 2014 under different scenarios (high and low levels of SCC, four lactation stages, different milk yield intensities, and parities (1, 2, and  ≥  3). The standard shape of the curve for SCC was determined using completed standard lactations of healthy cows. The SCCI was defined as the sum of the differences between the measured interpolated values of the natural logarithm of SCC (ln(SCC)) and the values for the standard shape of the curve for SCC for a particular period, divided by the total area enclosed by the standard curve and upper limit of ln(SCC)  =  10 for SCC. The phenotypic potential of milk yield (305-day milk yield – MY305) was calculated using regression coefficients estimated from the linear regression model for parity and breeding values of cows for milk yield. The extent of daily milk yield loss caused by increased SCC was found to be mainly related to the early stage of lactation. Depending on the possible scenarios, the estimated milk yield loss from MY305 for primiparous cows was at least 0.8 to 0.9 kg day−1 and for multiparous cows it ranged from 1.3 to 4.3 kg day−1. Thus, the SCCI was a suitable indicator for estimating daily milk yield losses associated with increased SCC and might provide farmers reliable information to take appropriate measures for ensuring good health of cows and reducing milk yield losses at the herd level.

DNA carryover in milk samples from routine milk recording used for PCR-based diagnosis of bovine Staphylococcus aureus mastitis

Real-time PCR techniques are increasingly used to detect udder pathogens from milk samples collected non-aseptically at routine milk recording. The objectives of this study were (1) to estimate the statistical associations between cycle threshold (Ct) values for Staphylococcus aureus in non-aseptically collected composite samples taken at routine milk recording from cows milked consecutively with the same milking unit and milk meter; and (2) to formulate practical and plausible guidelines for understanding the diagnostic implications of PCR testing for Staph. aureus intramammary infection at routine milk recording. The study included 4 herds with conventional milking parlors and repeatedly low Ct-values for Staph. aureus (representing a high DNA load) in bulk tank milk. Composite milk samples were collected from all cows at all milking units during routine milk recording using the Tru-Test electronic milk meter (Tru-Test Group, Auckland, New Zealand) and analyzed using the PathoProof PCR (Thermo Fisher Scientific, Vantaa, Finland) assay. Milking clock times were retrieved at each milk meter to establish the milking order of the cows at each unit. A multinomial logistic regression was applied to estimate the association between Ct-values from cows milked consecutively with the same milking unit and milk meter. The following groups were selected based on Ct-values: (1) 0-31.3, (2) 31.4-33.9, (3) 34.0-37, (4) 37.1-39.9, and (5) 40 (negative result). The association between groups from cows milked consecutively with the same milking unit and milk meter was statistically significant. Approximately 60% of cows were in Ct group 5 if the antecedent cow was also in Ct group 5, but only 20% of cows were in Ct group 5 if the antecedent cow was in Ct group 1. The probability of cows being in Ct group 1 was not markedly influenced by the group of the antecedent cow. Statistical relationships in the intermediate range gave a plausible indication of a dose-response relationship. Carryover of bacterial DNA via the milking unit and milk meter is very likely to affect PCR results for Staph. aureus. Therefore, information about milking order must be considered in mastitis control efforts. We suggest a practical interpretation of PCR results: cows with a Ct-value <32 can be labeled "very likely to be infected with Staph. aureus," but cows with Ct-values of >37 and 32-37 can be labeled "very likely to be negative for Staph. aureus" and "uncertain Staph. aureus status," respectively.

Carryover of bovine leukemia virus antibodies in samples from shared milk meters

Screening for infectious diseases of cattle using milk from the dairy herd improvement (DHI) sampling process is very convenient. However, when samples from shared milk meters are used, carryover of antibodies or other diagnostic targets can complicate the interpretation of the diagnostic test results for diseases, including bovine leukosis. The objectives of this study were (1) to assess the potential for carryover of antibodies against bovine leukemia virus (BLV) in milk samples obtained from shared meters, and (2) to determine if adjustment of the diagnostic test cut-off value would improve the test characteristics for meter-collected milk ELISA results. Eight dairy farms were randomly selected from herds with a wide range of BLV prevalence levels in Prince Edward Island, Canada. Within each chosen farm, 2 to 4milk meters were randomly selected. During the routine procedures of DHI sampling, 2 simultaneous milk samples, 1 hand-collected at the beginning of milking (after udder preparation) and the other from the corresponding milk meter, were taken from all lactating cows (n=236) that were milked at the selected meters (n=26). The sequence of cows using each meter was recorded. All samples were tested for BLV antibodies using a commercial indirect ELISA. Antibody carryover potential was assessed in meter-collected samples which were preceded by other cows using the same meters. Applying the hand-collected sample results as our reference standard, a new cut-off was defined for meter-collected samples to optimize the test characteristics. At the standard cut-off value of the diagnostic test, 110 (46.6%) of the hand-collected and 136 (57.6%) of the meter-collected samples were positive. For low-titer cows (e.g., true negatives), the likelihood of antibody carryover significantly increased as the titer of preceding cows increased, whereas this change was not substantial for high-titer cows. The odds of obtaining false diagnoses in meter-positive samples became larger with increase in the titer of preceding cows. A suspicious category for meter ELISA results was defined, and a retest was recommended for the cows falling into this category. This strategy effectively assisted in reducing the number of consequent false-positive results. When DHI-collected samples are used, carryover can affect the interpretation of dichotomous test results and may require adjustment of assay cut-off values.

Changes in milking parameters with robotic milking

Abstract. The aim of this present study is to describe changes occurring during the milking of cows in various periods following the introduction of an AMS (automatic milking system). The following cow milking parameters were analysed: milkings per cow per day, milking yield, milking speed and milking duration. An increase in milk yield in AMS barns has been found to be possible, but it is affected by a number of factors related to cow milking performance. Milk yield was observed to gradually grow with time after the installation of the robots. Older cows in their third and fourth lactations achieved higher milking parameter values as compared to cows in their first and second lactations. The average milk yield for the whole period was on a similar level, but, due to the fact that the duration of lactation in herd B was more than 100 days longer, that herd achieved a higher milk yield. The use of AMSs in barns enables farmers to monitor cow performance traits and study the relationships between them; farmers should try to select for traits ensuring high performance and directly related to milk yield. This study found a positive relationship between milking duration and milk yield. On the other hand, a highly negative relationship was found between milking duration and milking speed, which means that these parameters should be closely monitored. This study found that the optimal number of milkings per cow per day was in the range of 2.6 to 2.8 milkings a day with a 2.6 kg min−1 milking speed.

Evaluation of different milking practices for optimum production performance in Sahiwal cows

The production performance of multiparous lactating Sahiwal cows (n = 24) was evaluated according to both milking frequency and method. Selected animals were randomly divided into four groups containing six animals each under a completely randomized design. Cows in groups A & B were milked by the hand milking method three times per day, respectively. Similarly, cows in groups C & D were milked by the machine milking method two and three times per day, respectively. All animals were maintained under uniform feeding and management conditions. Dry matter intake was high in animal groups milked three times per day, and it remained unchanged between the hand and machine milking methods. Milk yield was higher (P < 0.05) in cows milked three times compared to those milked twice per day, and it did not differ between hand and machine milking methods. Milk fat percentage was higher (P < 0.05) in cows milked twice per day compared to those milked three times using both machine and hand milking methods. The percentage of total solids showed a similar pattern as the fat percentage. However, percentages of protein, lactose, and non-fat solids in milk were not significantly different (P > 0.05) among the treatment groups. Collectively, the results show that milking three times per day instead of twice at 8-hour intervals can enhance milk yield in Sahiwal cows using both hand and machine milking methods.

Improved detection of reproductive status in dairy cows using milk progesterone measurements

This study tested a model for predicting reproductive status from in-line milk progesterone ;measurements. The model is that of Friggens and Chagunda [Theriogenology 64 (2005) 155]. Milk progesterone measurements (n = 55 036) representing 578 lactations from 380 cows were used to test the model. Two types of known oestrus were identified: (1) confirmed oestrus (at which insemination resulted in a confirmed pregnancy, n = 121) and (2) ratified oestrus (where the shape of the progesterone profile matched that of the average progesterone profile of a confirmed oestrus, n = 679). The model detected 99.2% of the confirmed oestruses. This included a number of cases (n = 16) where the smoothed progesterone did not decrease below 4 ng/ml. These cows had significantly greater concentrations of progesterone, both minimum and average, suggesting that between cow variation exists in the absolute level of the progesterone profile. Using ratified oestruses, model sensitivity was 93.3% and specificity was 93.7% for detection of oestrus. Examination of false positives showed that they were largely associated with low concentrations of progesterone, fluctuating around the 4 ng/ml threshold. The distribution of time from insemination until the model detected pregnancy failure had a median of 22 days post-insemination. In this test, the model was run using limited inputs, the potential benefits of including additional non-progesterone information were not evaluated. Despite this, the model performed at least as well as other oestrus detection systems.

Detection of Carryover in Automated Milk Sampling Equipment

Equipment for sampling milk in automated milking systems may cause carryover problems if residues from one sample remain and are mixed with the subsequent sample. The degree of carryover can be estimated statistically by linear regression models. This study applied various regression analyses to several real and simulated data sets. The statistical power for detecting carryover milk improved considerably when information about cow identity was included and a mixed model was applied. Carryover may affect variation between animals, including genetic variation, and thereby have an impact on management decisions and diagnostic tools based on the milk content of somatic cells. An extended procedure is needed for approval of sampling equipment for automated milking with acceptable latitudes of carryover, and this could include the regression approach taken in this study.

Quarter health, milking interval, and sampling time during milking affect the concentration of milk constituents

Eleven Danish Holstein cows were used to examine the effects of quarter health (healthy vs. unhealthy), milking interval (12 vs. 6 h), and sampling time during milking on the concentration of 8 milk constituents [acetone, beta-hydroxybutyrate (BHBA), N-acetyl-beta-D-glucosaminidase (NAGase), somatic cell count (SCC), urea, fat, protein, and lactose]. The selection criterion was that each cow should have 2 or 3 healthy and 1 or 2 unhealthy quarters. Foremilk was collected before attaching the teat cups of the milking machinery, and thereafter, milk samples were collected automatically from each quarter every 45 s during milking. Compared with milk from healthy quarters, milk from unhealthy quarters had a higher concentration of BHBA, NAGase, SCC, and protein during the entire milking, whereas urea was higher in the last part of the milking process. Healthy quarters had a higher content of acetone and lactose during the whole milking, whereas fat was higher in the first part of the milking process. When the cows were milked at the 6-h interval, all milk constituents except lactose and protein were higher during the whole (NAGase, SCC, and urea) or part of the milking (acetone, BHBA, and fat) compared with when cows were milked at the 12-h interval. Lactose was higher in the first part of the milking at the 12-h compared with the 6-h interval, whereas protein was not affected by milking interval. beta-Hydroxybutyrate, NAGase, SCC, and fat increased during the milking process, whereas acetone, urea, protein, and lactose decreased. Foremilk was remarkably different for all constituents, except acetone, and should not be used as a representative milk sample to achieve the true level of a milk constituent. If these milk constituents are to be used in an inline management system, these effects should be taken into account.