生产贮藏条件对原料奶中微生物的影响

以手工挤奶条件下不同来源的原料奶为研究对象,在冷藏(4℃)、室温(19℃)条件下,分别贮藏0,3,6,9 h后检测原料奶中菌落总数(TBC)、芽孢总数(TSC)、耐热芽孢数、嗜冷菌总数。结果表明,随着贮藏温度增加、时间延长,原料奶中微生物增幅加快(P<0.05),冷链条件下(4℃)贮运原料奶对控制微生物生长繁殖具有重要作用。手工挤奶条件下室温(19℃)贮奶3h、冷藏(4℃)贮奶6h后原料奶中菌落总数超过5×105 mL-1的国家标准,原料奶的芽孢总数、耐热芽孢数和嗜冷菌数均超出液态奶的生产要求,特别不利于长效奶(保质期30 d以上)的生产。改善牛奶生产环境对提高原料奶品质具有重要的作用。     

手工刚挤出的新鲜牛奶含菌量变化的研究

本文研究了手工挤奶的原料奶中微生物随挤奶时间的变化。结果表明，随着挤奶时间的延长，原料奶中细菌总数和芽孢总数逐渐下降，因此可对挤奶的头三把进行废弃，是生产优质原料奶的措施之一。     

不同贮运条件及挤奶方式对原料奶风味相关指标的影响

以原料奶为材料,从不同贮藏务件、运输条件及挤奶方式进行研究,根据测定指标的变化,确定保持原料奶风味的最佳条件,为建立原料奶风味的HACCP体系提供参考.研究表明,不同贮藏运输条件下,随温度的升高和时间的延长,原料奶的酸度呈上升趋势,美蓝褪色时间和感官评分呈减少和下降趋势;在不同温度下放置不同时间乳成分变化不大,但其质量和风味随温度升高和时间的延长发生很大的变化;机器挤奶优于手工挤奶.但其酸度、美蓝褪色时间及感官评分均差异不显著(P＞0.05).     

我国部分地区原料奶中蜡样芽孢杆菌污染情况调查

对我国冀宁浙3个省典型乳品企业的原料奶中微生物污染情况进行调查与鉴定,主要检测原料奶中蜡样芽孢杆菌数,以及菌落总数和大肠菌群数,确定原料奶中微生物的污染状况与蜡样芽孢杆菌污染的关系.结果表明,3个地区的蜡样芽孢杆菌数平均值均≤105 mL-1.而细菌总数平均值.冀某乳品企业为7.61×105 mL-1,浙和宁为106 mL-4,属于超标奶.就大肠菌群平均值而言(最近似值),冀某乳品企业为21.17 mL-1,浙某乳品企业为25.15 mL-1,宁某乳品企业为15.98 mL-1.综合分析表明,蜡样芽孢杆菌数与原料奶的微生物污染状况没有相关性.本调查分析也对我国原料奶蜡样芽孢杆菌污染的溯源奠定基础.     

天津市5年内取消生奶收购站

从日前召开的天津市奶业工作会上获悉．从2006年超，天津市将加强牛奶购销环节秩序监管，利用5年时间，逐步取消手工挤奶和乍鲜奶收购站。到2010年．全市90％以上的奶牛都将住进现代化的规模养殖小区．享受机械化挤奶设备，，随着乳品食业的优化升级．对鲜奶质量提出了更高的要求．手工挤奶已小能适应现代乳品企业的市场需要。截至目前．天津市凡通过无公害牛奶生产基地认证的小区均已建成现代化挤奶厅并实现集中机械化挤奶，机械挤奶、管道输送、     

商品奶与原料奶中农药与亚硝酸钠残留分析

采用高效气相色谱(GC)分析法对商品奶和原料奶中农药和亚硝酸钠残留进行分析,并分析不同饲养模式对原料奶中农药残留的影响。结果表明:在所测定的5种农药中,甲胺磷、敌敌畏和马拉硫磷等3种农药残留检出率较高,商品奶为45.0%～67.5%,原料奶为46.8%～71.2%。商品奶和原料奶中甲胺磷、敌敌畏、敌百虫、马拉硫磷和倍硫磷检出率和残留量差异不显著(P﹥0.05);而商品奶和原料奶中亚硝酸钠检出率和残留量差异极显著(P﹤0.01)。原料奶中5种农药加权平均检出率、农药加权平均残留量和加权残留总量均为规模化饲养显著低于农户散养和合作社饲养(P﹤0.05),后两者之间无显著差异(P﹥0.05)。     

更正

从日前召开的天津市奶业工作会上获悉．从2006年超，天津市将加强牛奶购销环节秩序监管，利用5年时间，逐步取消手工挤奶和乍鲜奶收购站。到2010年．全市90％以上的奶牛都将住进现代化的规模养殖小区．享受机械化挤奶设备，，随着乳品食业的优化升级．对鲜奶质量提出了更高的要求．手工挤奶已小能适应现代乳品企业的市场需要。截至目前．天津市凡通过无公害牛奶生产基地认证的小区均已建成现代化挤奶厅并实现集中机械化挤奶，机械挤奶、管道输送、     

9JH-1型活塞式挤奶小车

1 前言挤奶作业是一项繁重且复杂、单调的体力劳动,占整个奶牛场劳动量的30%～40%。国外一些工业发达的国家早已实现了机械化挤奶。我国一些大中城市的奶牛场,有一部分也实现了机械化挤奶,但全国大多数奶牛基本上都是手工挤奶。随着经济改革的     

无线射频和云计算技术在奶牛养殖场中的应用

研究了无线射频技术在奶牛养殖和挤奶的应用,提出和设计了基于无线射频和云计算技术的奶牛信息管理系统和原料乳品质管理系统。通过无线射频系统、中继服务器和云服务器端对奶牛信息及其所产原料乳的信息进行识别、读取、保存和共享,饲养员可以方便的对奶牛的饲养和挤奶过程进行监控和管理。无线射频和云计算技术在奶牛养殖场和挤奶过程中的运用,将会极大的提高饲养员对奶牛的饲养和管理的效率和效果,同时减少对原料乳品质造成不良影响的因素,保证原料乳的良好品质。     

挤奶阶段对羊乳中微生物的影响

对奶山羊挤奶过程中不同挤奶阶段的乳样中微生物变化规律进行了研究.结果表明,不同挤奶阶段乳中微生物数量有明显变化,挤出的前期乳样中微生物数量最高,中期乳样次之,末期乳样最低.前期乳样的菌落总数、大肠菌群、嗜冷菌、嗜热菌、蛋白分解菌和脂肪分解菌的数量分别高达3×106、1.4×104、3.3×104、1.3 x 103、1.5×105和1.3×105cfu/mL,明显高于中期乳样和末期乳样(P＜0.05),尤其是前期乳样中的菌落总数已高出GB19301-2010中收购生鲜乳的微生物指标(2×106cfu/mL).因此在挤奶过程中,应尽可能的弃去前期乳样,以提高原料羊乳的卫生质量.     

Factors associated with productivity on automatic milking system dairy farms in the Upper Midwest United States

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

Milk quality on Danish farms with automatic milking systems

The bulk-milk quality of 98 Danish farms with automatic milking systems was analyzed from 1 yr before introduction of automatic milking until 1 yr after. Bulk-milk total bacterial count, spores of anaerobes, somatic cell count (SCC), and freezing point increased when automatic milking was introduced and the frequency of milk-quality failures almost doubled. Milk-quality failures were most frequent in the first 3 mo after the start of automatic milking. The increase in spores of anaerobes indicated that the increase in total bacterial count originated partly from contamination of milk from the teat surface and partly from lack of cleaning of the milking equipment or cooling of the milk. The increase in bulk-milk SCC indicated that milk from clinically infected cows and cows with high cell counts was not diverted to the same degree, milking automatically rather than milking conventionally. A self-monitoring program including survey of the bulk-milk quality was established to help farmers in the transition period going from conventional to automatic milking. The program was introduced on 84 farms. Farms on the self-monitoring program reduced bulk-milk cell count. Application of the program did not reduce the frequency of high total bacterial counts and freezing points of the bulk milk to the level of conventional milking. However, the program reduced the overall frequency of milk-quality failures.     

Effects of season, milking routine and cow cleanliness on bacterial and somatic cell counts of bulk tank milk

The aim of the study was to investigate the effects of season, cow cleanliness and milking routine on bacterial and somatic cell counts of bulk tank milk. A total of 22 dairy farms in Lombardy (Italy) were visited three times in a year in different seasons. During each visit, samples of bulk tank milk were taken for bacterial and somatic cell counts; swabs from the teat surface of a group of cows were collected after teat cleaning and before milking. Cow cleanliness was assessed by scoring udder, flanks and legs of all milking cows using a 4-point scale system. Season affected cow cleanliness with a significantly higher percentage of non-clean (NC) cows during Cold compared with Mild season. Standard plate count (SPC), laboratory pasteurization count (LPC), coliform count (CC) and somatic cell count, expressed as linear score (LS), in milk significantly increased in Hot compared with Cold season. Coagulase-positive staphylococci on teat swabs showed higher counts in Cold season in comparison with the other ones. The effect of cow cleanliness was significant for SPC, psychrotrophic bacterial count (PBC), CC and Escherichia coli in bulk tank milk. Somatic cell count showed a relationship with udder hygiene score. Milking operation routine strongly affected bacterial counts and LS of bulk tank milk: farms that accomplished a comprehensive milking scheme including two or more operations among forestripping, pre-dipping and post-dipping had lower teat contamination and lower milk SPC, PBC, LPC, CC and LS than farms that did not carry out any operation.     

Milk yield and milking station visits of primiparous versus multiparous cows on automatic milking system farms in the Upper Midwest United States

The objective of this study was to investigate milk yield and frequency of visits to the milking station of primiparous versus multiparous cows at different stages of lactation on farms with automatic milking systems (AMS) in the Upper Midwest United States. Forty farms were included in the study, and daily AMS software data were collected for 18 mo. For the investigation of milk yield and milking visits, stage of lactation was categorized into 14 periods, 7 d in length for the first 28 d in milk (DIM) and 30 d in length thereafter until 328 DIM. Cow traffic flow to the AMS (free or guided) was included in the model. For the evaluation of failures and refusals, stage of lactation was categorized into 6 periods, 7 d in length each for the first 28 DIM, and 2 periods of 150 d in length each thereafter until 328 DIM. Failures are milking station visits where a cow fails to be milked due to lack of machine attachment although it is time for the cow to be milked. Refusals are milking station visits before adequate time has passed since previous milking, thus the cow leaves the milking station without being milked. Data from lactation days beyond 328 DIM were excluded from the study. Primiparous cows in free-flow systems produced less milk than multiparous cows until the 11th stage of lactation and produced more milk from the 12th stage until the end of the study period. Primiparous cows in guided-flow systems produced less milk than multiparous cows all 14 stages of lactation, but were approaching the milk yield of multiparous cows at the end of the study period. This was a biologically normal lactation curve for primiparous cows. However, estimated peak ratio (primiparous vs. multiparous cows' peak milk yield) was lower than industry standards. Both traffic flow systems had fewer milking visits for primiparous cows compared with multiparous cows in early lactation. This lower milking frequency persisted until the 11th stage of lactation in free-flow systems. In guided-flow systems, primiparous cows were milked less frequently until the 5th stage of lactation, had similar milking frequency in the 6th stage of lactation, and were milked more frequently thereafter. Failures were greater for primiparous cows during all stages of lactation. However, the greatest differences were detected in the early stages of lactation. Primiparous cows had 0.067 more failures/cow per day on average than multiparous cows during wk 1 of lactation. For the remaining lactation stages, differences in failures ranged from 0.003 to 0.039. Refusals were less frequent (0.4 to 0.6/d) for primiparous cows during the first 2 wk of lactation, similar for wk 3 of lactation, and more frequent for the remaining lactation stages (0.10 to 0.14/d). Failures and refusals were only evaluated in free-flow systems. These findings appear to indicate a potential lagging performance for primiparous cows in early lactation as compared with multiparous cows. Additional investigation into improving the adaptation of primiparous cows to AMS in early lactation may be warranted.     

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.     

Water use on nonirrigated pasture-based dairy farms: Combining detailed monitoring and modeling to set benchmarks

Water use in intensively managed, confinement dairy systems has been widely studied, but few reports exist regarding water use on pasture-based dairy farms. The objective of this study was to quantify the seasonal pattern of water use to develop a prediction model of water use for pasture-based dairy farms. Stock drinking, milking parlor, and total water use was measured on 35 pasture-based, seasonal calving dairy farms in New Zealand over 2 yr. Average stock drinking water was 60 L/cow per day, with peak use in summer. We estimated that, on average, 26% of stock drinking water was lost through leakage from water-distribution systems. Average corrected stock drinking water (equivalent to voluntary water intake) was 36 L/cow per day, and peak water consumption was 72 L/cow per day in summer. Milking parlor water use increased sharply at the start of lactation (July) and plateaued (August) until summer (February), after which it decreased with decreasing milk production. Average milking parlor water use was 58 L/cow per day (between September and February). Water requirements were affected by parlor type, with rotary milking parlor water use greater than herringbone parlor water use. Regression models were developed to predict stock drinking and milking parlor water use. The models included a range of climate, farm, and milk production variables. The main drivers of stock drinking water use were maximum daily temperature, potential evapotranspiration, radiation, and yield of milk and milk components. The main drivers for milking parlor water use were average per cow milk production and milking frequency. These models of water use are similar to those used in confinement dairy systems, where milk yield is commonly used as a variable. The models presented fit the measured data more accurately than other published models and are easier to use on pasture-based dairy farms, as they do not include feed and variables that are difficult to measure on pasture-based farms.     

Farm-level factors associated with lameness prevalence,productivity, and milk quality in farms with automated milking systems

Impaired locomotion (lameness) may negatively affect the ability and desire of cows to milk voluntarily, which is a key factor in success of automated milking systems (AMS). The objective of this study was to identify factors associated with herd-level lameness prevalence and associations of lameness and other farm-level factors with milking activity, milk yield, and milk quality in herds with AMS. From April to September 2019, 75 herds with AMS in Ontario, Canada, were visited, and data on barn design and farm management practices were collected. Data from AMS were collected, along with milk recording data, for the 6-mo period before farm visits. Farms averaged 98 ± 71 lactating cows, 2.3 ± 1.5 robot units/farm, 43.6 ± 9.4 cows/robot, 36.4 ± 4.9 kg/d of milk, a milking frequency of 3.01 ± 0.33 milkings/d, and a herd average geometric mean SCC of 179.3 ± 74.6 (× 1,000) cells/mL. Thirty percent of cows/farm (minimum of 30 cows/farm) were scored for body condition (1 = underconditioned to 5 = over conditioned) and locomotion (1 = sound to 5 = lame; clinically lame ≥3 out of 5 = 28.3 ± 11.7%, and severely lame ≥4 out of 5 = 3.0 ± 3.2%). Clinical lameness (locomotion score ≥3) was less prevalent on farms with sand bedding, with increased feed bunk space per cow, and on farms with non-Holstein breeds versus Holsteins, and tended to be less prevalent with lesser proportion of underconditioned cows (with body condition score ≤2.5). Severe lameness occurrence (farms with any cows with locomotion score ≥4) was associated with a greater proportion of underconditioned cows and in farms with stalls with greater curb heights. Herd average milk yield/cow per day increased with lesser prevalence of clinical lameness (each 10-percentage-point decrease in clinical lameness prevalence was associated with 2.0 kg/cow per day greater milk yield) and greater milking visit frequency per day, and tended to be greater with increased feed push-up frequency. Lesser herd average somatic cell count was associated with lesser clinical lameness prevalence, herd average days in milk, and proportion of overconditioned cows, and somatic cell count tended to be lesser for farms with sand bedding versus those with organic bedding substrates. The results highlight the importance of minimizing lameness prevalence, using of sand bedding, ensuring adequate feed access and feed bunk space, and maintaining proper cow body condition to optimize herd-level productivity and milk quality in AMS herds.     

Milking system and premilking routines have a strong effect on the microbial community in bulk tank milk

In this study, we investigated the variation in the microbial community present in bulk tank milk samples and the potential effect of different farm management factors. Bulk tank milk samples were collected repeatedly over one year from 42 farms located in northern Sweden. Total and thermoresistant bacteria counts and 16S rRNA gene-based amplicon sequencing were used to characterize microbial community composition. The microbial community was in general heterogeneous both within and between different farms and the community composition in the bulk tank milk was commonly dominated by Pseudomonas, Acinetobacter, Streptococcus, unclassified Peptostreptococcaceae, and Staphylococcus. Principal component analysis including farm factor variables and microbial taxa data revealed that the microbial community in milk was affected by type of milking system. Milk from farms using an automatic (robot) milking system (AMS) and loose housing showed different microbial community composition compared with milk from tiestall farms. A discriminant analysis model revealed that this difference was dependent on several microbial taxa. Among farms using an automatic milking system, there were further differences in the microbial community composition depending on the brand of the milking robot used. On tiestall farms, routines for teat preparation and cleaning of the milking equipment affected the microbial community composition in milk. Total bacteria count (TBC) in milk differed between the farm types, and TBC were higher on AMS than tiestall farms (log 4.05 vs. log 3.79 TBC/mL for AMS and tiestalls, respectively). Among tiestall farms, milk from farms using a chemical agent in connection to teat preparation and a more frequent use of acid to clean the milking equipment had lower TBC in milk, than milk from farms using water for teat preparation and a less frequent use of acid to clean the milking equipment (log 3.68 vs. 4.02 TBC/mL). There were no significant differences in the number of thermoresistant bacteria between farm types. The evaluated factors explained only a small proportion of total variation in the microbiota data, however, despite this, the study highlights the effect of routines associated with teat preparation and cleaning of the milking equipment on raw milk microbiota, irrespective of type of milking system used.     

The relationship between milking interval and somatic cell count in automatic milking systems

The aim of this study was to explore whether, during automatic milking, milking interval or its variation is related to somatic cell count (SCC), even when corrected for effects of production, lactation stage, and parity. Data on milking interval and production level were available from the automatic milking systems of 151 farms. Data on SCC, parity, and lactation stage were derived from dairy herd improvement records of the same farms. Mainly due to incomplete records, data of 100 farms were used in the final analysis. For every cow, only 1 test day was used in the final analysis. Milking interval, the coefficient of variation of milking interval, production rate, the difference in production rate between short- and long-term, parity, days in milk, and some biologically relevant interactions were used in a linear mixed model with farm as random variable to assess their association with log10-transformed SCC. None of the interactions was significantly related to SCC, whereas all main effects were, and thus, stayed in the final model. The effect of milking interval was, although significant, not very strong, which shows that the effect of milking interval on SCC is marginal when corrected for the other variables. The variation in milking intervals was positively related with SCC, showing that the variation in milking interval is even more important than the milking interval itself. In the end, this study showed only a limited association between milking interval and SCC when milking with an automatic milking system.     

Effect of milk flow rate switch-point settings on cow comfort and milking duration

Automatic cluster removers (ACR) operate by ceasing vacuum to the cluster and detaching the milking unit from the udder by means of a retracting cord once the milk flow has decreased to a predefined level (i.e., the milk flow rate switch-point). There is a large body of literature on this topic indicating that increasing the flow rate switch-point (e.g., from 0.2 kg/min to 0.8 kg/min at the udder level) is effective in reducing milking duration while having little effect on milk yield or milk somatic cell count (SCC). However, despite these findings many farms still use a switch-point of 0.2 kg/min because it is believed that emptying the udder completely at each milking is a prerequisite for good dairy cow management, especially in relation to maintaining a low milk SCC. However, there may be additional undocumented benefits in terms of cow comfort to increasing the milk flow rate switch-point, because the low milk flow period at the end of milking is a high-risk time for inducing teat-barrel congestion. The objective of this study was to quantify the effect of 4 milk flow rate switch-point settings on cow comfort, milking duration, and milk yield. In this study, we applied 4 treatments consisting of different milk flow rate switch-points to cows in a crossover design in a spring calving grass based dairy herd in Ireland. The treatments were (1) MFR0.2, where the cluster was removed at a milk flow rate of 0.2 kg/min; (2) MFR0.4, where the cluster was removed at 0.4 kg/min; (3) MFR0.6, where the cluster was removed at 0.6 kg/min, and (4) MFR0.8, where the cluster was removed at 0.8 kg/min. Milking parameters were recorded by the parlor software and leg movements (i.e., kicks or steps) during milking were recorded with an accelerometer. These data were used as a proxy for cow comfort during milking. The results of this study showed significant differences in cow comfort across treatments, as indicated by cow stepping during milking, for a.m. milkings, but these differences were not detected for p.m. milkings, possibly because a.m. milkings were longer than p.m. milkings due to a 16:8 h milking interval on the research farm. Differences tended to distinguish the 2 lower-flow switch-point settings with greater leg movement against the 2 higher-flow switch-point settings with less leg movement during milking. The effect of treatment (milk flow rate switch-point) on daily milking duration was significant. The milk duration for MFR0.8 was 89 s (14%) shorter than MFR0.2. There was no significant effect of treatment on SCC in this study.