Biocrystallisations: Milk treatments

Following two milk studies performed by the Louis Bolk Instituut, the hypothesis that processing of milk has an important effect on bio crystallisation pictures was investigated. Two raw whole milk tank samples, coded A and B, and 5 treatments performed on these samples (in total A/B 1-6) were offered for analysis. Evaluation was performed Visually and by means of computerized Texture analysis. Conclusions: Processing of milk has a strong effect on the crystallisation pictures. Especially homogenisation of milk had a large impact on the crystallisation picture. Surprisingly, this influence is higher than the treatment with ultra high temperatures at 140C

Haptoglobin and serum amyloid A in relation to the somatic cell count in quarter, cow composite and bulk tank milk samples

Milk somatic cell count (SCC) is the gold standard in diagnosis of subclinical mastitis, and is also an important parameter in quality programmes of dairy cooperatives. As routine SCC analysis is usually restricted to central laboratories, much effort has been invested in the search for alternative biomarkers of mastitis and milk quality, including the presence in the milk of the acute phase proteins (APP), haptoglobin (Hp) and serum amyloid A (SAA). The aim of this study was to investigate relationships between Hp, SAA and SCC in quarter, cow composite, and bulk tank milk samples. Cows (n=165), without any clinical signs of disease or abnormalities in the milk or udder, from three different dairy farms, were used. Cow composite milk samples from all cows delivering milk at the sampling occasion were taken once in each herd. In one of the farms, representative quarter milk samples (n=103) from 26 cows were also collected. In addition, bulk tank milk samples from 96 dairy farms were included in the study. Samples were analysed for Hp, SAA and SCC, and relationships between the parameters were evaluated at quarter, cow and tank milk levels using Chi-square analysis. Milk samples were categorized according to their SCC, and the presence, or no presence, of SAA and Hp, based on the detection limits of the screening methods (0.3 mg/l and 1.0 mg/l for SAA and Hp, respectively). Hp and SAA were found in milk at quarter, cow composite and bulk tank levels. A large proportion (53%) of the animals had detectable milk concentrations of APP, and SAA was detected more frequently, and at higher concentrations than Hp, regardless of sample type. SAA was detected in as many as 82% of the bulk tank milk samples. Significant relationships were found between Hp, SAA and SCC at quarter and cow composite milk levels, but only between SAA and SCC at bulk tank milk level. Detectable levels of APP were more common at high SCC

A nonparametric characteristics model of the demand for milk

Characteristics models in demand analysis capture the idea that people value goods not For the commodity itself but for the characteristics (or attributes) or embodied in the good. For example, agents may care about the fat content and the taste of different sorts of milk but not the actual type of milk. When we have fewer characteristics than types of good the theory imposes restrictions on observables. We present a revealed preference characteristics model analysis of the demand for milk in Denmark

Scheduling and Routing Milk from Farm to Processors by a Cooperative

A milk marketing cooperative (MMC) was created by Florida dairy farmers to link the primary supply of fluid milk with the derived demand of processors in the vertical market. For any given milk supply, the revenue or return to farmers per unit of milk is the average milk price received by the MMC minus the MMC’s transfer cost. An important task for the MMC is to operate the fluid milk hauling system that optimizes the MMC’s milk transfer cost (routing and scheduling cost) subject to farm and plant schedules. The objective of this study is to determine if it is economically feasible to implement a more efficient routing and scheduling of farm-to-plant milk collection by the MMC.cooperatives, margins, milk, routing, scheduling, Demand and Price Analysis, Productivity Analysis,

Impact of market deregulation on the competitiveness of commercial milk producers in East Griqualand: a unit cost ratio (UCR) analysis: 1983-2006

This study investigates the impact of dairy market deregulation on the competitiveness of milk producers who comprise the East Griqualand (EG) study group in KwaZulu-Natal and the Eastern Cape Province of South Africa. The study uses a microeconomic approach, the unit cost ratio (UCR) method of competitiveness analysis, to assess changes in the relative competitiveness of EG milk producers from 1983 – 2006. Findings of previous research indicate that dairy market deregulation in the 1980s and 1990s caused lower real milk producer prices, increased uncertainty and higher exit rates in the South African dairy industry. Results of the UCR analysis suggest that EG milk producers were not competitive based on the net local price received for milk but were competitive when dairy cattle trading income was included. This suggests that dairy cattle trading income played an important role in enhancing the profitability of EG dairy enterprises in the study period. Further UCR analysis revealed that the top one-third of EG milk producers were relatively competitive from 1983 – 2006 due to higher real milk prices and lower unit costs. A panel data study of individual EG milk producers could be used to identify other important factors affecting milk producer competitiveness over time.dairy market deregulation, East Griqualand milk producers, competitiveness, unit cost ratio analysis, Livestock Production/Industries,

Sensory quality of organic milk based on grazing and high ratio of legumes in the feeding ration

Organic milk forms an important segment of the fresh milk production in Denmark. However, studies are needed to substantiate the high quality and future development of new variations of organic milk for different consumers. Differences in the composition of organically and conventionally produced milk (free fatty acids and a higher content of antioxidants in organic milk) are suggested to be a result of differences in feeding regimes (maize components in conventional production vs. grass and legumes in organic production). Also, milk from dairy cows fed grass silage has a different flavour compared to milk from dairy cows fed maize silage. This study evaluated the sensory properties of organic milk from dairy cows from different feeding trials. The effect of four different legumes and herbs, lucerne (Medicargo sativa), red clover (Trifolium pratense), white clover (Trifolium repens) and chicory (Cichorium intybus), was studied following a schedule including 4*12 Holstein Frisian cows. Descriptive sensory analysis was performed on the fresh pasteurized unhomogenized full-fat milk (6 replicates in 2 sessions) with a trained panel of 10 assessors. The preliminary results from the descriptive analysis of summer feeding (grazing) and winter feeding (silage) show that feeding with legumes and grass affects the sensory quality of full-fat unhomogenized organic milk. The most distinct milk was obtained from feeding ration high in chicory. This milk was characterized by a bitter and metallic taste and an astringent aftertaste both from the summer grazing and winter silage feeding trials. Red clover was characterized by a boiled milk flavour (summer), lucerne by a fatty aftertaste (winter) and white clover by a sweet and creamy flavour (winter). The results of the first season, which will also include relations between the sensory quality and the milk composition, serve as important inputs for the extensive studies to be conducted during the next three seasons. These studies include farm studies and consumer studies (product information, preference and purchase motives)

Biosensor assay for determination of haptoglobin in bovine milk

Despite more than 30 years of research into mastitis diagnostics, there are few alternatives to the somatic cell count (SCC) in practical use for identification of cows with subclinical mastitis. Mastitis is not only an animal welfare problem, but also affects the yield, composition and technological properties of milk. Hence, dairy cooperatives give farmers a premium quality payment to encourage low SCC although there is no clear scientific data defining the level of SCC in bulk tank milk that is associated with additional benefits in terms of milk quality. Recent research on alternative markers for inflammatory reactions in the lactating cow, e.g. in mastitis, includes investigations of the acute phase protein, haptoglobin (Hp). So far, the content of Hp in milk has mainly been studied in relation to mastitis diagnostics, with little attention given to its importance for milk composition and technological properties. At present, Hp in milk is measured using ELISA, but this technique is not suitable for routine large-scale analysis. In recent years, optical biosensor technology has been used for automated and rapid quantitative analysis of different components in milk, but so far not for analysis of acute phase proteins. The aim of the present study was to develop a rapid and sensitive biosensor method to determine Hp in milk. An affinity sensor assay based on the interaction between Hp and haemoglobin was developed using surface plasmon resonance (SPR) biosensor technology. The assay was used to analyse Hp in composite milk samples from cows without any clinical signs of mastitis and quarter milk samples with a weak to strong reaction in the California Mastitis Test (CMT). A commercial ELISA for determination of Hp in milk was used for comparison. The limit of detection (LOD) of the biosensor assay was determined as 1.1 mg/l. Within-assay and betweenday variations were determined both with bulk tank milk spiked with human Hp and with composite milk samples containing bovine Hp. Coefficients of variation varied between 3.6 and 8.6% at concentrations between 4.0 and 12 mg/l, respectively. Agreement between the results obtained by the biosensor assay and the ELISA was satisfactory ; however, the results obtained by the biosensor were generally lower than the results obtained by the ELISA. Possible explanations for this observation are discussed

Changes in the milk metabolome of the Giant Panda (Ailuropoda melanoleuca) with time after birth: three phases in early lactation and progressive individual differences

Ursids (bears) in general, and giant pandas in particular, are highly altricial at birth. The components of bear milks and their changes with time may be uniquely adapted to nourish relatively immature neonates, protect them from pathogens, and support the maturation of neonatal digestive physiology. Serial milk samples collected from three giant pandas in early lactation were subjected to untargeted metabolite profiling and multivariate analysis. Changes in milk metabolites with time after birth were analysed by Principal Component Analysis, Hierarchical Cluster Analysis and further supported by Orthogonal Partial Least Square-Discriminant Analysis, revealing three phases of milk maturation: days 1–6 (Phase 1), days 7–20 (Phase 2), and beyond day 20 (Phase 3). While the compositions of Phase 1 milks were essentially indistinguishable among individuals, divergences emerged during the second week of lactation. OPLS regression analysis positioned against the growth rate of one cub tentatively inferred a correlation with changes in the abundance of a trisaccharide, isoglobotriose, previously observed to be a major oligosaccharide in ursid milks. Three artificial milk formulae used to feed giant panda cubs were also analysed, and were found to differ markedly in component content from natural panda milk. These findings have implications for the dependence of the ontogeny of all species of bears, and potentially other members of the Carnivora and beyond, on the complexity and sequential changes in maternal provision of micrometabolites in the immediate period after birth

Variation in carbon footprint of milk due to management differences between Swedish dairy farms

To identify mitigation options to reduce greenhouse gas (GHG) emissions from milk production (i.e. the carbon footprint (CF) of milk), this study examined the variation in GHG emissions among dairy farms using data from previous CF studies on Swedish milk. Variation between farms in these production data, which were found to have a strong influence on milk CF were obtained from existing databases of e.g. 1051 dairy farms in Sweden in 2005. Monte Carlo analysis was used to analyse the impact of variations in seven important parameters on milk CF concerning milk yield (energy corrected milk (ECM) produced and delivered), feed dry matter intake (DMI), enteric methane emissions, N content in feed DMI, N-fertiliser rate and diesel used on farm. The largest between farm variation among the analysed production data were N-fertiliser rate (kg/ha) and diesel used (l/ha) on farm (coefficient of variation (CV) 31-38%). For the parameters concerning milk yield and feed DMI the CV was approx. 11 and 8%, respectively. The smallest variation in production data was found for N content in feed DMI. According to the Monte Carlo analysis, these variations in production data led to a variation in milk CF of between 0.94 and 1.33 kg CO2 equivalents (CO2e) per kg ECM, with an average value of 1.13 kg/CO2e kg ECM. We consider that this variation of ±17% that was found based on the used farm data would be even greater if all Swedish dairy farms were included, as the sample of farms in this study was not totally unbiased. The variation identified in milk CF indicates that a potential exists to reduce GHG emissions from milk production on both national and farm level through changes in management. As milk yield and feed DMI are two of the most influential parameters for milk CF, feed conversion efficiency (i.e. units ECM produced per unit DMI) can be used as a rough key performance indicator for predicting CF reductions. However, it must be borne in mind that feeds have different CF due to where and how they are produced