9 pp. / As today's dairy industry consolidates, cows are being milked more rapidly through larger milking parlors on larger dairies than ever before. Because milk is the primary commodity and source of income for producers, the harvesting of milk is the single most important job on any dairy. Producing high-quality milk to maximize yields and economic value requires effective parlor management, an enormous challenge for producers. Managing large parlors includes managing labor, milking equipment, as well as monitoring and evaluating parlor performance. Decisions concerning the milking center are some of the most complicated decisions a dairy producer has to make. Milking procedures, herd size, milking interval, the milk market, and the equity position of a producer influence these decisions. Producers will have to make the following decisions before they can select or develop management protocols for a milking parlor: 1. How many cows will be milked through the parlor? 2. What milking procedure will be used (minimal or full)? 3. If a full milking routine; how much contact time do you want (strips per teat)? 4. Which milking routine will be used (sequential, grouping, or territorial)? 5. Are you willing to train teams of milkers to operate large parlors?

Tyrimų metu nustatyta, kad iš visų tešmens ketvirčių daugiau primelžta pieno rytinio nei vakarinio melžimo metu. Rytinio melžimo metu nustatytas 1,39 proc. didesnis tešmens indeksas palyginus su vakariniu melžimu. Palyginus primelžto pieno kiekį iš priekinių ir užpakalinių tešmens ketvirčių nustatyta, kad daugiau pieno primelžta iš užpakalinių tešmens tiek rytinio, tiek vakarinio melžimo metu. Rytinio melžimo metu iš užpakalinių tešmens ketvirčių primelžta vidutiniškai 3,05 kg daugiau, o vakarinio melžimo metu 2,76 kg daugiau negu iš priekinių ketvirčių (p<0,01). Išanalizavus priekinių ir užpakalinių tešmens ketvirčių melžimo trukmę rytinio ir vakarinio melžimo metu, nustatyta ilgesnė melžimo trukmė užpakaliniuose tešmens ketvirčiuose. Rytinio melžimo metu užpakalinių tešmens ketvirčių melžimo trukmė buvo 1,07 min. ilgesnė nei priekinių (p<0,01). Vakarinio melžimo metu šis skirtumas buvo 0,95 min. (p<0,01). Priekiniuose tešmens ketvirčiuose melžimo trukmė buvo 0,53 min. (p<0,01) ilgesnė, o užpakaliniuose 0,65 min.(p<0,01) rytinio melžimo metu palyginus su vakariniu. Tiek priekinių, tiek užpakalinių tešmens ketvirčių melžimo greitis didesnis nustatytas rytinio melžimo metu palyginus su vakariniu. Priekiniuose tešmens ketvirčiuose nustatyta 0,09 kg/min., o užpakaliniuose 0,07 kg/min. didesnis melžimo greitis rytinio melžimo metu palyginus su vakariniu. Suskirsčius karves į grupes pagal SLS klases palygintas atskirų tešmens ketvirčių primelžto pieno kiekis rytinio ir... [toliau žr. visą tekstą] / The analyses showed that all quarters of the udder produced more milk during the morning milking compared to the evening milking. During the morning milking the udder index was by 1.39 % higher than during the evening milking. The comparison of the quantity of milk produced by the front and rear quarters of the udder showed that the rear quarters of the udder produced more milk both during the morning and evening milking. During the morning milking the rear quarters of the udder produced 3.05 kg more milk on average compared to the front quarters, whereas during the evening milking they produced 2.76 kg more milk on average compared to the front ones (p<0.01). The analysis of the duration of milking the front and rear quarters of the udder during the morning and evening milking showed that the milking duration was longer in the case of the rear quarters of the udder. During the morning milking the duration of milking the rear quarters of the udder was by 1.07 min longer compared to the front quarters (p<0.01). During the evening milking the difference was 0.95 min (p<0.01). During the morning milking the milking duration was by 0.53 min (p<0.01) longer in the case of the front quarters and by 0.65 min (p<0.01) longer in the case of the rear quarters compared to the evening milking. After dividing the cows into groups according to the classes determined by the number of somatic cells, the quantities of milk produced by the separate quarters of the udder during the morning and... [to full text]

Darbo tikslas: Išanalizuoti skirtingų veislių pirmaveršių karvių melžimosi rodiklius ir nustatyti jų įtaką produktyvumui. Darbo uždaviniai: 1. Nustatyti skirtingų veislių pirmaveršių karvių melžimosi rodiklių įtaką pieno kiekiui. 2. Išanalizuoti skirtingų veislių pirmaveršių karvių melžimosi savybes. 3. Nustatyti pirmaveršių karvių melžimosi rodiklių kaitą laktacijos eigoje. Darbas buvo atliktas Lietuvos sveikatos mokslų universitete, Veterinarijos akademijoje, Gyvulininkystės fakultete, Gyvulininkystės katedroje ir X žemės ūkio bendrovėje 2012 – 2014 studijų metais. Pirmame etape buvo atlikta specialiosios literatūros paieška, surinktos informacijos sisteminimas ir analizė. Antrame etape X žemės ūkio bendrovėje buvo atrinktos 2013 metų sausio mėnesį pirmą kartą apsiveršiavusios karvės. Visos karvės yra gimusios 2010 – 2011. Tyrimui buvo atrinktos 72 pirmos laktacijos pirmaveršės karvės ir pagal veisles suskirstytos į 3 skirtingas grupes, kurias sudarė: Holšteinai (H) – n=20, Lietuvos juodmargės (LJ) – n=38, Lietuvos žalosios (LŽ) – n=14. Taip pat viso antro etapo metu buvo renkami ir sisteminami pirmaveršių karvių pieno kiekio ir melžimosi rodikliai atskirais laktacijos mėnesiais. X žemės ūkio bendrovėje melžimosi savybių rodikliai yra matuojami DeLaval firmos elektroniniais pieno matuokliais, kurie yra įdiegti karvių melžimo aikštelėje. Gaunami duomenys apdorojami DeLaval „ALPRO™ Windows” programa. Tiriamojo darbo metu buvo nustatyta ir apskaičiuota pieno kiekis (kg)... [toliau žr. visą tekstą] / The aim of the paper is the following: To analyze different varieties of fresh cows milking characteristics and to determine their impact on productivity. Objectives of the paper: 1. Identify the different varieties of fresh cows milking factors influence the amount of milk. 2. To analyze the different varieties of fresh cows milking qualities. 3. Set fresh cows milking parameters changes during the course of lactation. The research was carried out in the Lithuanian University of Health Sciences, Veterinary Academy, Faculty of Livestock Technology, Livestock Department. During 2012 – 2014 Agricultural company X was the research object. During the first stage, systematization and analysis of literature was carried out. The second stage consisted of selecting first-parity cows in agricultural company X this took place in January of 2013. All of the cows were born in 2010 – 2011. 72 first-parity, stage 1 lactation cows were selected for the research and they were divided into 3 groups according to their breed which consisted: Holstein (H) – n=20, Lithuanian black-and-white cows (LJ) – n=38, Lithuanian Green cows (LZ) – n=14. Furthermore during the second phase of the research data was collected and systemized by collecting first-parity cow’s milk and the milking rates in individual months of lactation. X agricultural company milking data is measured with DeLaval brand electronic milk meters, which are installed in the cow milking areas. Incoming data is processed by DeLaval... [to full text]

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2004. / Härtill 5 uppsatser.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2005. / Härtill 4 uppsatser.

The aim of this work was to gather and evaluate the evidence about the behaviour of dairy cows during the milking by a robot (including physiological reactions running after the milking). The following parameters were observed: number of milking per 24 hours, the time from onset to the robot and the teat cup deployment, the time between milking, the total time of milking, milk yield with each milking, average and total milk yield per day. The results were measured separately in heifers and in cows on the second and subsequent lactations. In the same time, there was also observed the behaviour of cows after leaving the milking robot - feed and water intake, physical activity and the rest for the time of 30 minutes after the milking. Simultaneously, there was also observed the effect of milking technology for the health of the cows, focused on diseases of the mammary gland. There were three ethologic observations which always lasted 24 hours. In addition to the observed data, there were evaluated the data records obtained from the milking machine (e. g. milk yield per one milking, the interval between single milkings, etc.). There were observed an average of 60 pieces of cows. Throughout the watching the cows in the barn behaved calmly and contentedly. They came to the milking machine spontaneously, only a few cases had to be herded for milking by the caregiver. However, they were all the heifers which did not have the sufficient experience with the milking. The counting of teat cup deployment attempts had to be abandoned because of the close proximity of the observer at the milking robot. In this case, the cows were not quiet and did not want to go in the robot spontaneously. The mammary gland disease was diagnosed at an average of 6.9 pieces of milked cows per one month. This high number of cases is ascribed to a poor hygiene of the stables and a poorer quality of feeding. This work was created in cooperation with the grants NAZV QJ1210144 and NAZV QJ1530058.

Milk production is one of the most important parts of agriculture. Task of this paper was to examinate relation between milking frequency and total dairy production in Automatic Milking Systems. The thesis evaluates impact of selected factors, such as lactation stage, parity and season of calving, on dairy production and milking frequency. Total of 44,000 records of milk yield from 3 farms was analysed. Data was collected from 2009 to 2011. Relation between milk yield and milking frequency was prooved. The milk production from a cow rises with parity and is highest in second stage of lactation (cca 60th day after calving). Environment is one of very important factors affecting milk yield. Automatic milking system does not deteriorate milking parameters comparing to traditional way of milking.

The aim of the thesis was to assess objectively the exploitation of the functional capacity of milking machines in the system of free moving animals. There were data coming from seven agricultural companies analysed in the thesis. The data collection took place from January to November 2009. We were provided with the preliminary data by the individual farms taking part in a programme called T4C. The information on problematic dairy cows comes directly from their breeders. The average production of the dairy cows was the first assessed criterion. The highest production (28.79kg) was achieved by little private agricultural companies. The lowest production (25.22kg) was ascertained in middle-sized companies. As far as the breed of dairy cows is concerned, the Holstein dairy cows gave the highest possible amount of milk (40.43; 30.16 a 27.01 kg). The CRV Fleckvieh cattle dairy cows gave the lowest possible amount of milk (24.83; 21.04 a 22.74 kg). The assessment of the number of milking by means of the robotic milking system represents the next criterion. Little private agricultural companies showed the highest frequency of milking (140.88). Whereas the big agricultural companies showed the lowest frequency of milking (119.28). Mostly the Holstein dairy cows were milked by the robotic milking machines (130.34). On the other hand, the CRV Fleckvieh cattle dairy cows were milked least by the robotic milking machines by contrast (107.94). The middle-sized agricultural companies achieved the highest number of milking per dairy cow, per day (2.47). The little private agricultural companies proved the lowest number of milking per dairy cow (2.34). The mixed herds of the Holsteins and CRV Fleckviehs proved the rate of 2.47. The Holstein dairy cows showed the milking frequency 2.45 per day while the CRV Fleckvieh dairy cows 2.32. The dairy cows from the big agricultural companies went to be milked by the robotic milking machine most frequently from the point of view of willingness, what follows is that these dairy cows showed the highest number of refusals per one milking (2.19). The lowest values of this criterion were shown in little private agricultural companies (1.10). With reference to the breed, the values of this indicator proved the lowest number of refusals (1.85) whereas the CRV Fleckviehs showed the highest number of refusals (2.25). The exploitation of the time capacity has been proven as the most effective one - 78.61% in little private agricultural companies. Whereas the lowest time exploitation has been proven in big agricultural companies - 68.11%. As far as the cow breed is concerned, it was ascertained that the Holstein dairy cows were milked longest -73.21%. On the other hand the CRV Fleckviehs were milked shortest - 63.17%. The highest amount of the dairy cows that needed to by accompanied to the robotic milking machine was recorded in big agricultural companies - 20.1%. Remarkably lower number of problematic cows was ascertained in middle-sized agricultural companies - 9.7%. The number of problematic cows in little private agricultural companies proved to be similar to the number in middle-sized agricultural companies 9.3%. It was ascertained that the most problematic dairy cows came from the mixed herds - 18.7%. The CRV Fleckviehs were the least problematic - 8.8%.

The aim of thesis was to analyze quality indicators of raw bulk milk close to change of milking technology. Data were derived from pool samples. The quality indicators are: fat content, protein content, total bacterial count (TBC), somatic cell count (SCC), freezing point, solids-not-fat content, urea and casein content.

Made available in DSpace on 2017-07-10T15:14:32Z (GMT). No. of bitstreams: 1 DissertacaoGilbertoArnauts.pdf: 2349757 bytes, checksum: c988d628a97e246d9cbc1a8a670a9410 (MD5) Previous issue date: 2015-03-03 / The milking hygiene is necessary after use, usually in separate process steps which each require water at different temperatures for proper cleaning. But getting the correct temperature for each step is not so simple to be performed manually on farms. From a hot flow and other cold water, the farmer will need to properly mix the two water flows in order to reach a desired temperature for the cleaning step, and due to temperature fluctuations and flow, the required constant intervention of the same to maintain optimum temperature of the mixture. This work developed a mixer with automatic control of cold water flow, coupled to an electric heating system, with the goal of reaching a final ideal temperature required for the cleaning of milking machines, regardless of temperature fluctuations and flow at the entrance. We compared two control boards, Arduino and the IOIO During the first seconds of system operation, the opening angle of the cold water tap is calculated based on the hot and cold water temperatures. After this is accomplished by gradually adjusting the angle. The temperature was considered to be achieved when reached a maximum desviation of 1 ° C compared to the desired temperature. The system was more satisfactory with the IOIO, since the desired temperature was stabilized at lower average time 24 seconds after system startup / A higienização de ordenhadeiras é necessária após sua utilização, processo geralmente separado em etapas onde cada uma delas necessita de água em diferentes temperaturas, para a correta limpeza. Porém, obter a temperatura correta para cada etapa não é tão simples de ser realizado manualmente em propriedades rurais. A partir de uma vazão de água quente e outro de água fria, o agricultor precisará misturar corretamente as duas vazões de água a fim de chegar a uma temperatura desejada para a etapa de higienização, e devido às oscilações de temperatura e vazão, é necessária a constante intervenção do mesmo para manter a temperatura ideal da mistura. Neste trabalho foi desenvolvido um misturador com controle automático de vazão de água fria, acoplado a um sistema de aquecimento elétrico, com o objetivo de se chegar a uma temperatura ideal final necessária à higienização das ordenhadeiras, independentemente das oscilações de temperatura e vazão na entrada. Foram comparadas 2 placas de controle, o Arduino e o IOIO Durante os primeiros segundos de funcionamento do sistema, o ângulo de abertura do registro de água fria é calculado com base nas temperaturas de água quente e fria. Após isto é realizado ajuste gradativo no ângulo. A temperatura foi considerada como alcançada quando atingia um desvio máximo de 1 ºC em relação à temperatura desejada. O sistema se mostrou mais satisfatório com o IOIO, já que a temperatura desejada foi estabilizada em menor tempo médio, 24 segundos após a inicialização do sistema.