Composition and enzymatic activity in bulk milk from dairy farms with conventional or robotic milking systems

Milking system and diet forage type effect on milk quality of Italian Holstein-Friesian

Moving from conventional (CMS) to automatic (AMS) milking systems could affect milk quality. Moreover, the type and preservation methods of the forages used in the TMR, such as alfalfa hay (HTMR) or corn silage (STMR) have been demonstrated to modify milk composition. Thus, this study investigated the effect of implementing AMS and different diet forage types on the quality of Italian Holstein-Friesian bulk milk. Milk samples (n = 168) were collected monthly from 21 commercial farms in northern Italy during a period of 8 mo. Farms were categorized into 4 groups according to their milking system (CMS vs. AMS) and diet forage type (HTMR vs. STMR). Milk quality data were analyzed through the mixed procedure for repeated measurement of SAS with the milking system, diet forage type, and sampling day as fixed effects. Milking through the AMS led to lower milk fat, freezing point, and β-LG A; longer coagulation time; and higher K content, pH, and β-LG B than CMS. Cows fed STMR produced milk with greater fat, protein, casein, Mg content, titratable acidity, and β-LG A, but with reduced curd firming time, freezing point, and β-LG B than those fed HTMR. In conclusion, milk quality is not only altered by the diet's forage type and characteristics but also by the milking system.

Does Keeping Cows for More Lactations Affect the Composition and Technological Properties of the Milk?

This study investigated differences in the raw milk composition and technological properties between cows with different numbers of lactations. In total, 12 commercial herds were visited within a period of 12 weeks. On each farm, milk samples from five young cows (lactations 1-2) and five older cows (lactation ≥ 3) were collected. For each farm, milk samples from the young cows and the older cows, respectively, were pooled. The pooled milk samples were analyzed for gross composition and technological properties. Using principal component analysis (PCA) to assess the overall variation in milk quality attributes and the potential clustering of milk from young cows and older cows, respectively, an effect of breed, but no clear effect of lactation number, was observed. In contrast, one-way ANOVA showed higher plasmin activity (p = 0.002) in pooled milk from the older cows, whereas plasminogen-derived activity (p = 0.001) and total proteolysis (p = 0.029) were higher in milk from the young cows. Likewise, orthogonal projections to latent structure discriminant analysis (OPLS-DA) showed higher plasmin activity in milk from older cows, whereas younger cows had higher plasminogen-related activity and higher total proteolysis. To conclude, except for plasmin and plasminogen-related activities, there were no major differences in the composition and technological properties between milk from older cows and young cows.

Relation between αS1-casein, genotype, and quality traits of milk from Swedish dairy goats

Locally produced food is becoming popular among Swedish consumers. One product that has increased in popularity is artisan-manufactured goat cheese, and although the dairy goat industry in Sweden is small-scale, production is gradually increasing. In goats, the CSN1S1 gene regulates expression of the protein α_S1-casein (α_S1-CN), which has been found to be important for cheese yield. Over the years, breeding animals have been imported to Sweden from Norway. Historically, a high frequency of the Norwegian goat population carried a polymorphism at the CSN1S1 gene. This polymorphism, called the Norwegian null allele (D), leads to zero or significantly reduced expression of α_S1-CN. Using milk samples from 75 goats, this study investigated associations between expression of α_S1-CN and genotype at the CSN1S1 gene on milk quality traits from Swedish Landrace goats. Milk samples were grouped according to relative level of α_S1-CN (low: 0-6.9% of total protein; medium-high: 7-25% of total protein) and genotype (DD, DG, DA/AG/AA). While the D allele leads to extremely low expression of α_S1-CN, the G allele is low expressing and the A allele is highly expressing for this protein. Principal component analysis was used to explore the total variation in milk quality traits. To evaluate the effect of different allele groups on milk quality attributes, 1-way ANOVA and Tukey pairwise comparison tests were used. The majority (72%) of all goat milk samples investigated showed relative α_S1-CN content of 0% to 6.82% of total protein. The frequency of individuals homozygous for the Norwegian null allele (DD) was 59% in the population of sampled goats, and only 15% carried at least one A allele. A low relative concentration of α_S1-CN was associated with lower total protein, higher pH, and higher relative concentration of β-casein and levels of free fatty acids. Milk from goats homozygous for the null allele (DD) showed a similar pattern as milk with low relative concentration of α_S1-CN, but total protein was only numerically lower, and somatic cell count and α_S2-CN were higher than for the other genotypes. The associations between levels of α_S1-CN and the investigated genotype at the CSN1S1 gene indicate a need for a national breeding program for Swedish dairy goats.

Birth of dairy 4.0: Opportunities and challenges in adoption of fourth industrial revolution technologies in the production of milk and its derivatives

Embracing innovation and emerging technologies is becoming increasingly important to address the current global challenges facing many food industry sectors, including the dairy industry. Growing literature shows that the adoption of technologies of the fourth industrial revolution (named Industry 4.0) has promising potential to bring about breakthroughs and new insights and unlock advancement opportunities in many areas of the food manufacturing sector. This article discusses the current knowledge and recent trends and progress on the application of Industry 4.0 innovations in the dairy industry. First, the "Dairy 4.0" concept, inspired by Industry 4.0, is introduced and its enabling technologies are determined. Second, relevant examples of the use of Dairy 4.0 technologies in milk and its derived products are presented. Finally, conclusions and future perspectives are given. The results revealed that robotics, 3D printing, Artificial Intelligence, the Internet of Things, Big Data, and blockchain are the main enabling technologies of Dairy 4.0. These advanced technologies are being progressively adopted in the dairy sector, from farm to table, making significant and profound changes in the production of milk, cheese, and other dairy products. It is expected that, in the near future, new digital innovations will emerge, and greater implementations of Dairy 4.0 technologies is likely to be achieved, leading to more automation and optimization of this dynamic food sector.

Graduate Student Literature Review: Farm management practices: Potential microbial sources that determine the microbiota of raw bovine milk

Environmental and herd-associated factors such as geographical location, climatic conditions, forage types, bedding, soil, animal genetics, herd size, housing, lactation stage, and udder health are exploited by farmers to dictate specific management strategies that ensure dairy operation profitability and enhance the sustainability of milk production. Along with milking routines, milking systems, and storage conditions, these farming practices greatly influence the microbiota of raw milk, as evidenced by several recent studies. During the past few years, the increased interest in high-throughput sequencing technologies combined with culture-dependent methods to investigate dairy microbial ecology has improved our understanding of raw milk community dynamics throughout storage and processing. However, knowledge is still lacking on the niche-specific communities in the farm environment, and on the factors that determine bacteria transfer to the raw milk. This review summarizes findings from the past 2 decades regarding the effects of farm management practices on the diversity of bacterial species that determine the microbiological quality of raw cow milk.

Graduate Student Literature Review: Current understanding of the influence of on-farm factors on bovine raw milk and its suitability for cheesemaking

Relationships between dairy farm practices, the composition and properties of raw milk, and the quality of the resulting cheese are complex. In this review, we assess the effect of farm factors on the quality of bovine raw milk intended for cheesemaking. The literature reports several prominent farm-related factors that are closely associated with milk quality characteristics. We describe their effects on the composition and technological properties of raw milk and on the quality of the resulting cheese. Cow breed, composite genotype, and protein polymorphism all have noticeable effects on milk coagulation, cheese yield, and cheese composition. Feed and feeding strategy, dietary supplementation, housing and milking system, and seasonality of milk production also influence the composition and properties of raw milk, and the resulting cheese. The microbiota in raw milk is influenced by on-farm factors and by the production environment, and may influence the technological properties of the milk and the sensory profile of certain cheese types. Advances in research dealing with the technological properties of raw milk have undoubtedly improved understanding of how on-farm factors affect milk quality attributes, and have refuted the concept of one milk for all purposes. The specific conditions for milk production should be considered when the milk is intended for the production of cheese with unique characteristics. The scientific identification of these conditions would improve the current understanding of the complex associations between raw milk quality and farm and management factors. Future research that considers dairy landscapes within broader perspectives and develops multidimensional approaches to control the quality of raw milk intended for long-ripening cheese production is recommended.

Variation in Dairy Milk Composition and Properties Has Little Impact on Cheese Ripening: Insights from a Traditional Swedish Long-Ripening Cheese

The monthly variation in raw dairy silo milk was investigated and related to the ripening time of the resulting cheese during an industrial cheese-making trial. Milk composition varied with month, fat and protein content being lowest in August (4.19 and 3.44 g/100 g, respectively). Casein micelle size was largest (192–200 nm) in December–February and smallest (80 nm) in August. In addition, SCC, total bacteria count, proteolytic activities, gel strength, and milk fatty acid composition were significantly varied with month. Overall sensory and texture scores of resulting cheese were mainly influenced by plasmin and plasminogen activity, indicating the importance of native proteolytic systems. Recently, concepts based on the differentiated use of milk in dairy products have been suggested. For the investigated cheese type, there might be little to gain from such an approach. The variation in the investigated quality characteristics of the dairy milk used for cheese making had little effect on cheese ripening in our study. In contrast to our hypothesis, we conclude that as long as the quality of the milk meets certain minimum criteria, there are only weak associations between cheese milk characteristics and the time required for the development of aroma and texture in the cheese. To find answers behind the observed variation in cheese ripening time, studies on the effects of process parameters are needed.

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.

Composition and properties of bovine milk: A study from dairy farms in northern Sweden; Part II. Effect of monthly variation

This study investigated the influence of monthly variation on the composition and properties of raw farm milk collected as part of a full-scale cheese-making trial in a region in northern Sweden. In our companion paper, the contribution of on-farm factors to the variation in milk quality attributes is described. In total, 42 dairy farms were recruited for the study, and farm milk samples were collected monthly over 1 yr and characterized for quality attributes of importance for cheese making. Principal component analysis suggested that milk samples collected during the outdoor period (June-September) were different from milk samples collected during the indoor period. Despite the interaction with the milking system, the results showed that fat and protein concentrations were lower in milk collected during May through August, and lactose concentration was higher in milk collected during April through July than for the other months. Concentrations of free fatty acids were generally low, with the highest value (0.86 mmol/100 g of fat) observed in February and the lowest (0.70 mmol/100 g of fat) observed in June. Plasmin and plasminogen-derived activities varied with sampling month without a clear seasonal pattern. The pH of farm tank milk ranged from 6.60 to 6.82, with the lowest and highest values in September and February, respectively. The highest somatic cell count was observed in August (201 × 10³ cells/mL) and the lowest in April (143 × 10³ cells/mL). The highest value of gel strength, was recorded in December (88 Pa) and the lowest in July (64 Pa). Rennet coagulation time and gel strength were inversely correlated, with the lowest rennet coagulation time value observed in December. Orthogonal projections to latent structures (OPLS) and discriminant analysis adaptation of OPLS identified casein micelle size and total proteolysis as the milk quality attributes with major responses to sampling month, with smaller casein micelle size and higher total proteolysis associated with the outdoor months. Using discriminant analysis adaptation of OPLS to further investigate causes behind the variation in milk traits revealed that there were factors in addition to feeding on pasture that differed between outdoor and indoor months. Because fresh grass was seldom the primary feed in the region during the outdoor period, grazing was not considered the sole reason for the observed difference between outdoor and indoor periods in raw milk quality attributes.

Effect of enhanced hygiene on transmission of Staphylococcus aureus, Streptococcus agalactiae, and Streptococcus dysgalactiae in dairy herds with automatic milking systems

The objective of the study was to evaluate the effect of hygiene measures in automatic milking units on the transmission of 3 mastitis pathogens considered to be mainly or partly transmitted from cow to cow during milking events. Two studies were conducted as within-herd experimental trials in 2 Danish commercial dairy herds (A and B) with automatic milking systems. Interventions to enhance hygiene were implemented on the automatic milking units. The 2 studies evaluated separate interventions. In herd A, the hygiene interventions were manual wash with the Lely foam unit and adjustments on the brush-mediated teat cleaning procedure. In herd B, the hygiene intervention included automatic disinfection spray on the upper surface of the brush motor and daily change of brushes. Composite milk samples were collected longitudinally at 3- or 4-wk intervals from all lactating cows. Additional milk samples were taken from cows entering or leaving the study groups. Milk samples were analyzed with quantitative PCR. A hidden Markov model implemented within a Bayesian framework was used to estimate the transmission probability. For analysis, 701 samples from 156 cows were used for herd A, and 1,349 samples from 390 cows were used for herd B. In the intervention group in herd B, transmission of Streptococcus agalactiae was reduced to 19% (95% posterior credibility interval: 0.00-64%) of the transmission in the control group, whereas transmission of Streptococcus dysgalactiae was reduced to 17% (95% posterior credibility interval: 0.00-85%) of transmission in the control group. This suggests that automatic spray on the upper surface of the brush motor with disinfectant along with daily change of brushes collectively reduced transmission of Strep. agalactiae and Strep. dysgalactiae. Results on Staphylococcus aureus in herd B and results on manual foam cleaning and brush-mediated teat cleaning adjustments in herd A were inconclusive.

Challenges and Tendencies of Automatic Milking Systems (AMS): A 20-Years Systematic Review of Literature and Patents

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

Short communication: Caseins and α-lactalbumin content of camel milk (Camelus dromedarius) determined by capillary electrophoresis

Camel milk has unique physical, nutritional, and technological properties when compared with other milks, especially bovine. Because proteins confer many of the properties of milk and its products, this study aimed to determine the proteins of camel milk, their correlations, and relative distribution. Raw milk samples were collected from 103 dromedary camels in the morning and evening. Capillary electrophoresis results showed wide variation in the concentrations (g/L) of proteins between samples as follows: α-lactalbumin, 0.3 to 2.9; α_S1-casein, 2.4 to 10.3; α_S2-casein, 0.3 to 3.9; β-casein, 5.5 to 29.0; κ-casein, 0.1 to 2.4; unknown casein protein 1, 0.0 to 3.4; and unknown casein protein 2, 0.0 to 4.6. The range in percent composition of the 4 caseins were as follows: α_S1, 12.7 to 35.3; α_S2, 1.8 to 20.8; β, 42.3 to 77.4; and κ, 0.6 to 17.4. The relative proportion of α_S1-, α_S2-, β-, and κ-caseins in camel milk (26:4:67:3, wt/wt) differed from that of bovine milk (38:10:36:12, wt/wt). This difference might explain the dissimilarity between the 2 milks with respect to technical and nutritional properties.

Impact of automatic milking systems on dairy cattle producers' reports of milking labour management, milk production and milk quality

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