Automatic Milking System Research Articles

Novel ways to use sensor data to improve mastitis management

Current sensor systems are used to detect cows with clinical mastitis. Although, the systems perform well enough to not negatively affect the adoption of automatic milking systems, the performance is far from perfect. An important advantage of sensor systems is the availability of multiple measurements per day. By clearly defining the need for detection of subclinical mastitis (SCM) and clinical mastitis (CM) from the farmers' management perspective, detection and management of SCM and CM may be improved. Sensor systems may also be used for other aspects of mastitis management. In this paper we have defined 4 mastitis situations that could be managed with the support of sensor systems. Because of differences in the associated management and the epidemiology of these specific mastitis situations, the required demands for performance of the sensor systems do differ. The 4 defined mastitis situations with the requirements of performance are the following: (1) Cows with severe CM needing immediate attention. Sensor systems should have a very high sensitivity (>95% and preferably close to 100%) and specificity (>99%) within a narrow time window (maximum 12 h) to ensure that close to all cows with true cases of severe CM are detected quickly. Although never studied, it is expected that because of the effects of severe CM, such a high detection performance is feasible. (2) Cows with mastitis that do not need immediate attention. Although these cows have a risk of progressing into severe CM or chronic mastitis, they should get the chance to cure spontaneously under close monitoring. Sensor alerts should have a reasonable sensitivity (>80%) and a high specificity (>99.5%). The time window may be around 7 d. (3) Cows needing attention at drying off. For selective dry cow treatment, the absence or presence of an intramammary infection at dry-off needs to be known. To avoid both false-positive and false-negative alerts, sensitivity and specificity can be equally high (>95%). (4) Herd-level udder health. By combining sensor readings from all cows in the herd, novel herd-level key performance indicators can be developed to monitor udder health status and development over time and raise alerts at significant deviances from predefined thresholds; sensitivity should be reasonably high, >80%, and because of the costs for further analysis of false-positive alerts, the specificity should be >99%. The development and validation of sensor-based algorithms specifically for these 4 mastitis situations will encourage situation-specific farmer interventions and operational udder health management.

Regularly fluctuating somatic cell count pattern in dairy herds

Online somatic cell count (SCC) measurement is widely used in dairy herds milked with automatic milking systems (AMS) and gives the opportunity to closely monitor individual cow udder health. Using automated SCC data, we observed cows displaying a remarkably regularly fluctuating SCC (rfSCC) pattern, which is described in this study. We aimed to (1) estimate the prevalence of rfSCC in cows milked by AMS, (2) characterize the rfSCC pattern, and (3) identify factors potentially associated with the rfSCC pattern. We analyzed 30-d episodes of composite SCC recordings of 1,000 cows from 55 dairy herds from 6 countries using an AMS with automated SCC measurement, and we identified the rfSCC pattern in 4.7% (95% CI: 3.5-6.2%) of these episodes. The rfSCC episodes had a median SCC of 701 × 1,000 cells/mL (2.5-97.5% quantile: 539-1,162), a median amplitude of 552 × 1,000 cells/mL (2.5-97.5% quantile: 409-886), and a median cycle length of 4.1 d (2.5-97.5% quantile: 3.7-4.9). Bacteriological culture data from quarter-milk samples collected every 2 wk in 1 Dutch AMS herd were analyzed, yielding no clear association between pathogen species and the rfSCC pattern found in that herd. Altogether, we described an intriguing phenomenon, present in almost 5% of the cows during a 1-mo study period. Further work is needed to quantify its importance in terms of udder health, but also to elucidate the mechanism behind this remarkable SCC pattern.

Effects of feeding level, milking frequency, and single injection of cabergoline on feed intake, milk yield, milk leakage, and clinical udder characteristics during dry-off in dairy cows

Abrupt and gradual dry-off strategies by reducing feeding level (normal vs. reduced energy density), reducing milking frequency (twice vs. once daily), and administration of a dopamine agonist after last milking (i.m. saline vs. cabergoline injection) were investigated (2 × 2 × 2 factorial arrangement) for their effects on feed intake, milk yield, energy balance, milk leakage, and clinical udder characteristics in 119 Holstein cows. In the last week before dry-off, cows were assigned to 1 of 4 combinations of feeding level and milking frequency. Within 3 h after last milking, cows were injected with either saline or a dopamine agonist (cabergoline; Velactis, Ceva Santé Animale; labeled for use only with abrupt dry-off, i.e., no preceding reduction in feeding level or milking frequency before last milking). After dry-off, all cows were fed the same diet for dry cows, and data collection continued for a week. Dry matter intake (DMI) was recorded in automated feed bins and milk yield in an automatic milking system where additional concentrate was fed. Clinical udder characteristics and milk leakage were scored 10 times during the week before and the week after dry-off. Before dry-off, total DMI decreased with reduced feeding level compared with normal feeding level, but did not differ between milking frequencies. The combined effect of reduced DMI and diet energy concentration resulted in a 47% lower net energy intake with reduced feeding level compared with normal feeding level during the week before dry-off. Milk yield was approximately 30% lower during the week before dry-off when either feeding level or milking frequency was reduced compared with no change in feeding level or milking frequency, whereas milk yield was 45% lower when both feeding level and milking frequency were reduced. The net energy balance during the week before dry-off was negative with reduced feeding level and more negative when combined with twice-daily milking. After dry-off, udder engorgement was reduced in the 3 gradual dry-off treatments compared with abrupt dry-off. Cabergoline injection after last milking resulted in least udder engorgement and signs of milk leakage for 48 h, but also resulted in abrupt reduction of DMI lasting approximately 24 h irrespective of treatment before dry-off. In conclusion, gradual cessation of lactation by reducing milking frequency to once daily without reducing the feeding level decreased milk yield before dry-off in high-yielding dairy cows and reduced udder engorgement after dry-off without inducing negative energy balance during the period of dry-off. In contrast, reduced feeding level induced negative energy balance, which may compromise welfare due to metabolic stress and hunger. No clear differences in risk of milk leakage after dry-off were observed between abrupt and gradual dry-off management strategies. Use of cabergoline led to fewer signs of milk leakage and reduced udder engorgement during the first days after dry-off, which may positively affect welfare at dry-off. However, the mechanism behind and the welfare consequences of the concomitant abrupt decrease in DMI lasting approximately 24 h needs further investigation to complete our understanding of dopamine agonist use for dry-off.

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.

Survival of Polish Holstein-Friesian Cows to Second, Third and Fourth Lactation in Conventional and Automatic Milking Systems

Abstract The main objective of the study was to determine the effect of transition from a conventional milking system (CMS) to an automatic milking system (AMS) on survival of 6361 Polish Holstein-Friesian cows to second (SL2), third (SL3) and fourth (SL4) lactation as well culling reasons. The cows were born between 2002 and 2015 and calved between 2004 and 2018. All data for the survival analysis and culling reasons of cows in 17 herds during operation of CMS and AMS were extracted from the SYMLEK official milk recording system. Cow survival (SL2, SL3 and SL4) was analysed with multiple logistic regression using the following effects in the model: milking system (MS), first calving season (CS), age at first calving (AFC), ease of first calving (CE), birth of a dead calf at first calving (DC), milk yield (MY) for full first lactation (MY – this effect was ignored in SL2 analysis), herd (H), and MS × H interaction. In the next stage of the study, χ2 test was used to analyse culling reasons of cows (udder diseases, low fertility – infertility and reproductive disorders, locomotor diseases, low milk yield, other diseases – metabolic, digestive and respiratory diseases, accidents and chance events) in the first, second and third lactation and collectively in the first three lactations. Logistic regression analysis indicated a significant effect of MS, AFC, DC on SL2 and SL3, and of MY on SL3 and SL4. Moreover, H and MS × H interaction had a highly significant effect on SL2, SL3, and SL4. Cows used in AMS barns were characterized by significantly worse SL2 and SL3 compared to CMS (odds ratio), by 27.8% and 31.0%, respectively. It was also observed that the effect of switching from CMS to AMS on cow survival was determined by herd membership – in most herds this effect was unfavourable. A distinctly positive effect of milking automation on cow survival (SL2, SL3, SL4) was noted in only one barn (herd) – it was a new barn with a considerably expanded number of milked cows, where the lying area was covered with straw. When analysing the reasons for culling in the first three lactations collectively, it was found that after the AMS system was introduced into the herds, there were increases in the rate of culling for locomotor diseases (by 0.85 percentage points (p.p.)), low milk yield (1.36 p.p.) and other diseases (3.01 p.p.). It was also observed that the automation of milking reduced culling due to udder diseases by 0.37 p.p., low fertility by 3.24 p.p., and accidents and chance events by 1.60 p.p.

Detecting and predicting changes in milk homogeneity using data from automatic milking systems

To ensure milk quality and detect cows with signs of mastitis, visual inspection of milk by prestripping quarters before milking is recommended in many countries. An objective method to find milk changed in homogeneity (i.e., with clots) is to use commercially available inline filters to inspect the milk. Due to the required manual labor, this method is not applicable in automatic milking systems (AMS). We investigated the possibility of detecting and predicting changes in milk homogeneity using data generated by AMS. In total, 21,335 quarter-level milk inspections were performed on 5,424 milkings of 624 unique cows on 4 farms by applying visual inspection of inline filters that assembled clots from the separate quarters during milking. Images of the filters with clots were scored for density, resulting in 892 observations with signs of clots for analysis (77% traces or mild cases, 15% moderate cases, and 8% heavy cases). The quarter density scores were combined into 1 score indicating the presence of clots during a single cow milking and into 2 scores summarizing the density scores in cow milkings during a 30-h sampling period. Data generated from the AMS, such as milk yield, milk flow, conductivity, and online somatic cell counts, were used as input to 4 multilayer perceptron models to detect or predict single milkings with clots and to detect milking periods with clots. All models resulted in high specificity (98-100%), showing that the models correctly classified cow milkings or cow milking periods with no clots observed. The ability to successfully classify cow milkings or cow periods with observed clots had a low sensitivity. The highest sensitivity (26%) was obtained by the model that detected clots in a single milking. The prevalence of clots in the data was low (2.4%), which was reflected in the results. The positive predictive value depends on the prevalence and was relatively high, with the highest positive predictive value (72%) reached in the model that detected clots during the 30-h sampling periods. The misclassification rate for cow milkings that included higher-density scores was lower, indicating that the models that detected or predicted clots in a single milking could better distinguish the heavier cases of clots. Using data from AMS to detect and predict changes in milk homogeneity seems to be possible, although the prediction performance for the definitions of clots used in this study was poor.

Milk losses linked to mastitis treatments at dairy farms with automatic milking systems

Mastitis-associated milk losses in dairy cows have a massive impact on farm profitability and sustainability. In this study, we analyzed milk losses from 4 553 treated mastitis cases as recorded via treatment registers at 41 AMS dairy farms. Milk losses were estimated based on the difference between the expected and the actual production. To estimate the unperturbed lactation curve, we applied an iterative procedure using the Wood model and a variance-dependent threshold on the milk yield residuals. We calculated milk losses both in a fixed window around the first treatment day of each mastitis case and in the perturbations corresponding to this day, at the cow level as well as at the quarter level. In a fixed time window of day -5 to 30 around the first treatment, the absolute median milk losses per case were 101.5 kg, highly dependent on the parity and the lactation stage with absolute milk losses being highest in multiparous cows and at peak lactation. Relative milk losses expressed in percentage were highest on the first treatment day, and full recovery was often not reached within 30 days from treatment onset. In 62 % of the cases, we found a perturbation in milk yield at the cow level at the time of treatment. On average, perturbations started 8.7 days before the first treatment and median absolute milk losses increased to 128 kg of milk per perturbation. Mastitis is not expected to have equal effects on the four quarters so this study additionally investigated losses in the individual udder quarters. We used a data-based method leveraging milk yield and electrical conductivity to project the presumably inflamed quarter. Next, we compared losses with the average of presumably non-inflamed quarters. Median absolute losses in a fixed 36-day window around treatment varied between 50.2 kg for front and 59.3 kg for hind inflamed quarters compared to respectively 24.7 and 26.3 kg for the median losses in the non-inflamed quarters. Also here, these losses differed between lactation stages and parities. Expressed proportionally to expected yield, the relative median milk losses in inflamed quarters on the treatment day were 20 % higher in inflamed quarters with a higher variability and slower recovery. In 86 % of the treated mastitis cases, at least one perturbation was found at the quarter level. This analysis confirms the high impact of mastitis on milk production, and the large variation between quarter losses illustrates the potential of quarter analysis for on-farm monitoring at farms with an automated milking system.

Genetic Parameters Estimation of Milking Traits in Polish Holstein-Friesians Based on Automatic Milking System Data.

Simple SummaryThe automatic milking system provides a large amount of information characterizing the course of each cow milking, which is not available in the conventional system. From a breeding point of view, it is interesting to establish the genetic variability of these traits, as well as to establish the relationship between them. The aim of our study was to estimate heritability and genetic correlations for milk yield (MY), milking frequency (MF), and speed (MS) for 1713 Polish Holstein-Friesian primiparous cows milked in barns with an automatic milking system. Our study conclusively indicated that it is possible to carry out effective selection for milking speed, which provides an opportunity to increase the number of cows per milking robot, and thus increase the profitability of production in the herd. We proved that selection for milk yield and daily milking frequency is also feasible. Our research showed a high, positive genetic correlation between milking frequency and milk yield, which allows us to conclude that preferring breeding cows with a natural tendency to frequent visits to the milking robot should indirectly improve the genetic basis of milking.The automatic milking system (AMS) provides a large amount of information characterizing the course of each milking cow, which is not available in the conventional system. The aim of our study was to estimate heritability and genetic correlations for milk yield (MY), milking frequency (MF), and speed (MS) for 1713 Polish Holstein-Friesian primiparous cows milked in barns with an AMS. Daily heritability indicators estimated using second-order Legendre polynomials and Random Regression Models showed high variation during lactation, ranging 0.131–0.345 for MY, 0.153–0.322 for MF, and 0.336–0.493 for MS. The rates of genetic correlation between traits ranged: 0.561–0.929 for MY-MF, (−0.255)−0.090 for MF-MS, (−0.174)−0.020 for MY-MS. It is possible to carry out effective selection for milking speed, which provides an opportunity to increase the number of cows per milking robot, and thus increase the profitability of production in the herd. The results proved that selection for milk yield and daily milking frequency is also feasible. The research showed a high, positive genetic correlation between milking frequency and milk yield, which allows us to conclude that preferring breeding cows with a natural tendency to frequent visits to the milking robot should indirectly improve the genetic basis of milking.

Performance perception of Canadian dairy producers when transitioning to an automatic milking system

Adoption of automated milking systems (AMS) has increased exponentially around the world in recent years. The objective of this observational study was to evaluate how producer perception of changes in cow-average milk yield and somatic cell count (SCC) compared with the actual changes in their herds after the introduction of AMS in Canadian commercial dairy herds. Data were collected (in 2014 and 2015) through a survey of 97 Canadian dairy herds that shifted to AMS from 2000 to 2014. Producers were asked their perception about milk yield and SCC changes (increase, decrease, or no change) after AMS introduction. Actual herd performance data were obtained from dairy herd improvement organizations. Differences between the 12-mo rolling herd-average milk yield (kg/cow per year) and SCC (cells/mL) at the closest test 2 yr after transitioning to AMS and at the last test before the transition were calculated and compared with the producer perception answers. After AMS adoption, milking herd size, milk yield, SCC, and number of AMS units per herd averaged (± standard deviation) 99.8 ± 54.4 cows, 9,619 ± 1,354 kg/cow per year, 248,825 ± 97,286 cells/mL, and 1.9 ± 1.1 units, respectively. On average, after AMS introduction, herd size, milk yield, and culling rate increased by 11.3 cows, 441 kg/cow per year, and 1.3%, respectively, and calving interval decreased by 7 d. For producers who perceived an increase, actual milk yield and SCC increases averaged (mean ± standard deviation) +534 ± 1,003 kg/cow per year and +56,679 ± 66,662 cells/mL, respectively. Alternatively, for producers who perceived a decrease, actual milk yield and SCC decreases averaged −984 ± 658 kg/cow per year and −26,976 ± 94,099 cells/mL, respectively. An actual milk yield change of +83.1 ± 1,113.3 kg/cow per year and an SCC change of +6,135 ± 72,609 cells/mL were observed in the herds in which the dairy producers perceived no change with the AMS introduction. Hence, dairy producers were, on average, able to discern their actual milk yield and SCC changes after AMS adoption. However, the proportions of dairy producers who accurately perceived their actual milk yield and SCC changes after AMS introduction were 39.4% for milk yield (increase: 36.3%; decrease: 100.0%; and no change: 45.5%) and 46.7% for SCC (increase: 50.0%; decrease: 39.0%; and no change: 54.1%). From these results, we concluded that several dairy producers distorted their actual milk yield and SCC changes or were not fully aware of those changes.

Evaluation of biomarkers of heat stress using automatic health monitoring system in dairy cows

The aim of this study was to evaluate biomarkers of heat stress (HS) from an automatic milking system (AMS), the relationships between measurements of the temperature-humidity index (THI), reticulorumen pH and temperature, and some automatic milking systems parameters in dairy cows (rumination time (RT), milk traits, body weight (BW) and consumption of concentrate (CC)) during the summer period. Lithuanian Black and White dairy cows (n=365) were selected. The cows were milked with Lely Astronaut® A3 milking robots with free traffic. Biomarkers were collected from the Lely T4C management program for analysis. The pH and temperature of the contents of the cow reticulorumen were measured using specific Smax-tec boluses. The farm zone's daily humidity and air temperature were obtained from the adjacent weather station (2 km away). According to this study, during HS, the higher THI positively cor- relates with milk lactose (ML), which increases the risk of mastitis and decreases CC, RT, BW, MY, reticulorumen pH, and F/P. Some biomarkers of HS can be milk yield, milk lactose, somatic cell count, concentrate intake, rumination time, body weight, reticulorumen pH, and milk fat - protein ratio. We can recommend monitoring these parameters in the herd management program to identify the possibility of heat stress.

Homogeneity density scores of quarter milk in automatic milking systems

Milk quality and clinical mastitis in dairy cows are monitored by detecting visually abnormal milk. A standardized method to evaluate clots in milk and studies of the incidence and dynamics of clots in milk at the quarter level are lacking. We validated a method to score clot density in quarter milk samples and describe the prevalence and dynamics of the density scores between consecutive samplings and periods in 4 farms with automatic milking systems. Using in-line filters, we collected quarter milk samples at each milking during 3 periods of 30 h each in each farm. Clot density was scored based on coverage of the filter area as 0 (negative), 1 (trace), 2 (mild), 3 (moderate), 4 (heavy), and 5 (very heavy). The score for a specific quarter and milking is referred to as the quarter milking score (QMS). Three assessors independently scored 902 images of filter samples with a Fleiss kappa value of 0.72. In total, 21,202 quarter milk samples from 5,398 milkings of 621 cows were collected. Of the quarter filter samples, 2.4% had visible clots, distributed as mild (1.4%), moderate (0.6%), heavy (0.3%), and very heavy (<0.1%, n = 8). Cases with a cow period sum of QMS ≥ 4, corresponding to 9.4% of all periods, harbored 86% and 94% of all QMS of 2 to 5 and 3 to 5, respectively. Of these cases, cows sampled in all 3 periods and clots in only 1 period had a quarter period sum score ≥ 1 in 1.8 different quarters in average. Corresponding numbers for the cows with clots or traces in 2 or 3 periods were 2.2 and 2.5 different quarters, respectively. A QMS of 2 to 5 in the preceding milking increased the chance of a QMS >1 in the following milking, with an average chance of 38%. The probability of a QMS > 1 increased with increasing previous QMS, a higher sum of QMS during the milking period, longer milking interval, and higher lactation number, but decreased with increasing days in milk. Our study showed that the method of clot-density scoring is feasible to perform and reproducible for investigating the occurrence and dynamics of clots in milk. Elevated clot-density scores clustered within certain cows and cow periods and appeared in new quarters of the cows over time. The low recurrence of QMS of 1 and 2 within quarters indicated that QMS 3 could be a reasonable threshold for detecting quarters with abnormal milk that require further attention.

Transmission dynamics of Staphylococcus aureus and Streptococcus agalactiae in a Dutch dairy herd using an automatic milking system

Staphylococcus aureus and Streptococcus agalactiae are important contagious mastitis pathogens and are considered to mainly transmit between cows through the milking machine. Controlling contagious mastitis on dairy farms requires a reduction of the transmission rate or the duration of intramammary infections (IMI), or both. These parameters may differ in dairy herds milked with an automatic milking system (AMS) as compared to those milked with a conventional milking system (CMS). The aims of this prospective longitudinal study were to estimate the transmission rate, the median duration of IMI and the basic reproduction number (R0) of Staph. aureus and Strep. agalactiae in a Dutch AMS herd. Bacteriological cultures of quarter milk samples were collected every 2 wks. Using 3 different definitions of IMI, we estimated the transmission rate for Staph. aureus to be within the range of 0.002 (95 % CI: 0−0.005) quarter-day−1 to 0.019 (95 % CI: 0.010−0.032) quarter-day−1, and for Strep. agalactiae of 0.007 (95 % CI: 0.005−0.010) quarter-day−1 to 0.019 (95 % CI: 0.011−0.032) quarter-day−1, the median duration of chronic IMI at 95 (95 % CI: 72−125) days for Staph. aureus and at 86 (95 % CI: 67−111) days for Strep. agalactiae, and the R0 between 0.16 (95 % CI: 0.05−0.27) and 0.34 (95 % CI: 0.20−0.48) for Staph. aureus, and between 0.64 (95 % CI: 0.41−0.87) and 0.68 (95 % CI: 0.48−0.88) for Strep. agalactiae.Transmission of these two contagious pathogens in this herd was limited and theoretically the IMI would not sustain, given that R0 of both pathogens was lower than 1. The estimated transmission rate of Staph. aureus in this AMS herd was found to be comparable to those described for CMS herds, while for Strep. agalactiae, it was slightly higher than in CMS herds. The duration of Staph. aureus IMI was in line with results from CMS farms, while the duration of Strep. agalactiae was lower than what has been described in CMS herds. The R0 of these contagious pathogens was found to be lower than the estimates in CMS herds. Our study suggests that the transmission rate of these two contagious pathogens in this AMS herd were comparable to what has been reported about well-performing CMS herds that have a low rate of transmission.

Multipurpose simulation model for pasture-based mobile Automated Milking and Marketing System, Part-I: Pasture, milk yield, and milk marketing characteristics

It is essential to promote sustainable dairy farming which could lead to improved animal welfare, economic benefits, biodiversity and environmental benefits, milk quality, and customer satisfaction. In this regard, a mobile automated milking system (AMS) could contribute a lot. However, mobile AMS is a new innovative system which is not investigated well. Therefore, a simplified and integrated management approach should be introduced. The main objective of this study was to develop a multipurpose simulation model (DigiMilk model) specific to pasture-based mobile AMS. The model comprises five major subsystems: Pasture yield as dry matter (DM) and grazing characteristics; AMS Milking and milk yield characteristics; Milk handling and marketing; Resource consumption; and Economic assessment. This paper (Part-I) focuses on the first three components while the remaining two subsystems would be addressed in Part-II of this paper. DigiMilk model was built in MATLAB-Simulink environment. It was tested and evaluated using mainly secondary data and limited primary information acquired from a dairy farm in central Sweden. In this initial analysis, a continuous stocking system on pasture was assumed to be implemented from May 15 till September 15. Multiple sensitivity analyses were successfully conducted to get more insights. The results indicated that, considering maximum pasture growth rate of 77 kgDM day−1ha−1, the accumulated average pasture yield, over the grazing season, was estimated to be 6928 kgDM ha−1. For cows with average grazing rate of 16–18 kgDM day−1cow−1, the stocking rate of 3 cow ha−1 could lead to good performance of grazing management. When stocking rate and grazing rate of 3 cow ha−1 and 16 kgDM day−1cow−1 were considered respectively, the cumulative milk yield values (excluding amount consumed by calves) over the grazing season were estimated to be 2101 L cow−1 and 6303 L ha−1. Out of this 6303 L ha−1, 2952 L ha−1was estimated to be sold on-site, using milk vending machine (MVM), while 3351 L ha−1 was to be delivered to super market. The accuracy of results from the the simulation model could be improved with future work with more real data from actual demonstration of mobile AMS over the entire grazing season. In addition to its capacity to serve as an integrated decision making tool, DigiMilk model enables to have organized digital data that could be useful for future researches to evaluate the environmental and/or economic performances of pasture-based dairy systems with mobile AMS.

The domestication triangle: How humans, animals and technology shape each other – The case of automated milking systems

This article investigates the domestication of milking robots, most often labelled automatic milking systems (AMS) into dairy farms in Norway. It shows that producers of AMS tend to represent the integration of the technology as a process where their expert systems aid and guide the farmer so that she learns how to be an ‘AMS farmer’. However, farmers' AMS-stories shows us that learning to live with AMS is a process that continues even after the AMS technology seems to have been fully integrated. Furthermore, cows and fellow farmers are central actors, but machines also learn in the process. Hence, we find a extended domestication process where farmers and cows not only adapt to the machine, but indeed that the machine also need to ‘learn’ how to function in particular farms. As such, we target a domestication triangle of machines, animals and farmers where all elements co-evolve. From this we argue that the concept of domestication in studies of agricultural technology needs to take a posthuman turn and focus on a domestication triangle of humans, animals and machines.

Effects of photoperiod and light intensity on milk production and milk composition of dairy cows in automatic milking system.

The purpose of this study was to determine the effects of photoperiod and light intensity on milk production, milk composition, hormones levels and blood metabolites indices of Korean Holstein dairy cows in automatic milking system (AMS). A total of 24 Holstein dairy cows were selected and used to four subsequent treatments for the experimental periods of 60 days. The light programs consisted of (1) Control: the natural photoperiod with 14.2 h of the light period and 9.4 h of the dark period (below 10 Lux); (2) T1: 16 h of the long day photoperiod (LDPP) with 50 Lux of light; (3) T2: 16 h of LDPP with 100 Lux of light; and (4) T3: 16 h of LDPP with 200 Lux of light, respectively. Importantly, there was a significant difference in the thurl activity of dairy cows between the different light intensity programs (p < 0.05). Milk yield was higher in T1 and T2 (40.80 ± 1.71 and 39.90 ± 2.02 kg/d, respectively) than those of Control and T3 (32.18 ± 1.51 and 35.76 ± 2.80 kg/d, respectively) (p < 0.05), but DMI was lower in T1, T2, and T3 compared to Control (p < 0.05). Also, milk fat percentage, the contents of milk fat and total solids were higher in T2 than those in the others (p < 0.05). The average daily melatonin level in milk was high to T3 (28.20 ± 0.43 pg/mL), T2 (24.62 ± 0.32 pg/mL), T1 (19.78 ± 0.35 pg/mL), and Control (19.36 ± 0.45 pg/mL) in order (p < 0.05). Also, the cortisol levels in milk and blood were lower in treatment groups than in Control (p < 0.05). The results of this study showed that it will be effective to improve the milk yield and milk composition, and to reduce the stress of dairy cows when the light conditions regulate to extend the photoperiod to 16 h at a light emitting diode (LED) intensity of 100 Lux under the AMS in dairy farm.

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions.

Simple SummarySustainability is a necessary goal for animal-derived products due to the mounting pressure on the livestock sector to meet the growing demand of an increasing population with rising incomes and the need to reduce the exploitation of resources and environmental impact, while safeguarding animal welfare. We found that by considering a precision livestock farming approach to feeding, advanced numerical methods could represent a reliable and viable tool for the evaluation of future productive scenarios of dairy cows in the presence of changing climate conditions. We believe that the model proposed here could help to develop and improve decision support for farmers to increase both milk yield and animal welfare and, on the other hand, to reduce the resources needed, hence increasing sustainability of the dairy sector.Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016–2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production.

A Preliminary Investigation of Social Network Analysis Applied to Dairy Cow Behavior in Automatic Milking System Environments.

Simple SummaryCows are social animals, therefore understanding the ways that they interact can help improve their management and welfare. We used social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). Results from this exploratory study showed that when cows were separated from their affinity-pair cow the day-day variability in milk production increased by a factor of 3, a possible indicator of stress. The results of this exploratory study suggest that SNA could be used as a tool to better understand the social dynamics of dairy cows and inform group and regrouping process to produce positive outcomes.We have applied social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). The SNA was applied to social groups defined by four pens on a dairy farm, each served by an automatic milking system (AMS). Each pen was equipped with an automatic sorting gate that identified when cows voluntarily moved from the resting area to either milking or feeding areas. The aim of this study was two-fold: to determine if SNA could identify affinity pairs and to determine if milk production was affected when affinity pairs where broken. Cow traffic and milking performance data from a commercial guided-flow AMS dairy farm were used. Average number of milked cows was 214 ± 34, distributed in four AMS over 1 year. The SNA was able to identify clear affinity pairs and showed when these pairings were formed and broken as cows entered and left the social group (pen). The trend in all four pens was toward higher-than-expected milk production during periods of affinity. Moreover, we found that when affinities were broken (separation of cow pairs) the day-to-day variability in milk production was three times higher than for cows in an affinity pair. The results of this exploratory study suggest that SNA could be potentially used as a tool to reduce milk yield variation and better understand the social dynamics of dairy cows supporting management and welfare decisions.

Composition and properties of bovine milk: A study from dairy farms in northern Sweden; Part I. Effect of dairy farming system

This study was part of a larger project that aimed to understand the causes for increasing variation in cheese ripening in a cheese-producing region in northern Sweden. The influence of different on-farm factors on raw milk composition and properties was investigated and is described in this paper, whereas the monthly variation in the milk quality traits during 1 yr is described in our companion paper. The dairy farming systems on a total of 42 dairy farms were characterized through a questionnaire and farm visits. Milk from farm tanks was sampled monthly over 1 yr and analyzed for quality attributes important for cheese making. On applying principal component analyses to evaluate the variation in on-farm factors, different types of farms were distinguished. Farms with loose housing and automatic milking system (AMS) or milking parlor had a higher number of lactating cows, and predominantly Swedish Holstein (SH) breed. Farms associated with tiestalls had a lower number of lactating cows and breeds other than SH. Applying principal component analyses to study the variation in composition and properties of tank milk samples from farms revealed a tendency for the formation of 2 clusters: milk from farms with AMS or a milking parlor, and milk from farms with tiestall milking. The interaction between the milking system, housing system, and breed probably contributed to this grouping. Other factors that were used in the characterization of the farming systems only showed a minor influence on raw milk quality. Despite the interaction, milk from tiestall farms with various cow breeds had higher concentrations (g/100 g of milk) of fat (4.74) and protein (3.63), and lower lactose concentrations (4.67) than milk from farms with predominantly SH cows and AMS (4.32, 3.47, and 4.74 g/100 g of milk, respectively) or a milking parlor (4.47, 3.54, and 4.79 g/100 g of milk, respectively). Higher somatic cell count (195 × 103/mL) and lower free fatty acid concentration (0.75 mmol/100 g of fat) were observed in milk from farms with AMS than in milk from tiestall systems (150 × 103/mL and 0.83 mmol/100 g of fat, respectively). Type of farm influenced milk gel strength, with milk from farms with predominantly SH cows showing the lowest gel strength (65.0 Pa), but not a longer rennet coagulation time. Effects of dairy farming system (e.g., dominant breed, milking system, housing, and herd size) on milk quality attributes indicate a need for further studies to evaluate the in-depth effects of farm-related factors on milk quality attributes.

Benchmarking of farms with automated milking systems in Canada and associations with milk production and quality

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

Erratum: Heart rate, heart rate variability, faecal glucocorticoid metabolites and avoidance response of dairy cows before and after changeover to an automatic milking system.

"Erratum: Heart rate, heart rate variability, faecal glucocorticoid metabolites and avoidance response of dairy cows before and after changeover to an automatic milking system" published on 06 Feb 2021 by Akadémiai Kiadó.