• Korean Chemical Engineering Research
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• v.60 no.3
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• pp.392-397
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• 2022

Methane is one of the major greenhouse gases, recently, the biotechnological conversion from methane to high-value added chemicals have emerged as an effort to reduce methane gas emission. In this study, we optimized ectoine bio-milking conditions in which cells were repeatedly used to improve intracellular and extracellular ectoine yield from methane by using Methylomicrobium alcaliphilum 20ZDP2. First, the cultivation and intracellular ectoine accumulation conditions were optimized with respect to the growth phase and medium salinity to achieve the highest yield of synthesis. Second, ectoine excretion was optimized by determining the ectoine secretion time (15 min) in appropriate medium salinity under hypoosmotic conditions (1% NaCl). Finally, bio-milking of ectoine was successfully repeated more than 10 times using M. alcaliphilum 20ZDP2, and the ectoine yield was improved up to 129.29 mg/ DCW g.

• Journal of Animal Environmental Science
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• v.14 no.3
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• pp.149-158
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• 2008

The purpose of this study was to determine the effect of milking system type on milking center effluent production through the four seasons. Four different types of milking systems (Bucket, Pipeline, Tandem and Herringbone) were estimated, in duplicate, through the different seasons. The following conclusions can be drawn from this study. 1. The quantity of wastewater produced from Tandem and Herringbone milking systems were significantly larger than Bucket milking system (p<0.05). 2. The main wastewater production was from the washing of milking apparatus. Tandem and Herringbone milking systems produced 398.8 and $407.7{\ell}$/day of wastewater, respectively, for apparatus washing. These values were significantly higher than the other milking systems during the summer (p<0.05). 3. The average wastewater production from the various milking systems was $15.4{\ell}$/head/day. The quantity of wastewater production during summer ($16.4{\ell}$/head/day) season was higher than of the other seasons. 4. The highest level of $BOD_5$ ($906.4mg/\ell$) was produced from the washing of the parlor floor and the lowest level of $BOD_5$ ($212.4mg/\ell$) was produced from the washing of the udders of the cows. 5. The pH of dairy wastewater was in the range of $7.3{\sim}8.2$ and the average levels of $BOD_5$, COD, SS, T-N, and T-P were 731.2, 479.0, 751.6, 79.1, $14.7mg/\ell$, respectively. Following conclusions can be drawn from this experiment. The quantities of wastewater production from Bucket, Pipeline, Tandem and Herringbone milking system were 143.9, 487.9, 914.0, and $856.7{\ell}$, respectively. The average wastewater produced from the milking systems was $15.4{\ell}$/head per day. In order to effectively manage on the wastewater from milking systems, dairy farms need to consider the milking system type and farm size when determining the optimum wastewater treatment system.

• Journal of Animal Science and Technology
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• v.65 no.1
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• pp.244-257
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• 2023

The study aimed to investigate the effect of low-frequency oscillations on the cow udder, milk parameters, and animal welfare during the automated milking process. The study's objective was to investigate the impact of low-frequency oscillations on the udder and teats' blood circulation by creating a mathematical model of mammary glands, using milkers and vibrators to analyze the theoretical dynamics of oscillations. The mechanical vibration device developed and tested in the study was mounted on a DeLaval automatic milking machine, which excited the udder with low-frequency oscillations, allowing the analysis of input parameters (temperature, oscillation amplitude) and using feedback data, changing the device parameters such as vibration frequency and duration. The experimental study was performed using an artificial cow's udder model with and without milk and a DeLaval milking machine, exciting the model with low-frequency harmonic oscillations (frequency range 15-60 Hz, vibration amplitude 2-5 mm). The investigation in vitro applying low-frequency of the vibration system's first-order frequencies in lateral (X) direction showed the low-frequency values of 23.5-26.5 Hz (effective frequency of the simulation analysis was 25.0 Hz). The tested values of the first-order frequency of the vibration system in the vertical (Y) direction were 37.5-41.5 Hz (effective frequency of the simulation analysis was 41.0 Hz), with higher amplitude and lower vibration damping. During in vivo experiments, while milking, the vibrator was inducing mechanical milking-similar vibrations in the udder. The vibrations were spreading to the entire udder and caused physiotherapeutic effects such as activated physiological processes and increased udder base temperature by 0.57℃ (p < 0.001), thus increasing blood flow in the udder. Used low-frequency vibrations did not significantly affect milk yield, milk composition, milk quality indicators, and animal welfare. The investigation results showed that applying low-frequency vibration on a cow udder during automatic milking is a non-invasive, efficient method to stimulate blood circulation in the udder and improve teat and udder health without changing milk quality and production. Further studies will be carried out in the following research phase on clinical and subclinical mastitis cows.

• Journal of Animal Science and Technology
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• v.56 no.4
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• pp.13.1-13.5
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• 2014

The production performance of multiparous lactating Sahiwal cows (n = 24) was evaluated according to both milking frequency and method. Selected animals were randomly divided into four groups containing six animals each under a completely randomized design. Cows in groups A & B were milked by the hand milking method three times per day, respectively. Similarly, cows in groups C & D were milked by the machine milking method two and three times per day, respectively. All animals were maintained under uniform feeding and management conditions. Dry matter intake was high in animal groups milked three times per day, and it remained unchanged between the hand and machine milking methods. Milk yield was higher (P < 0.05) in cows milked three times compared to those milked twice per day, and it did not differ between hand and machine milking methods. Milk fat percentage was higher (P < 0.05) in cows milked twice per day compared to those milked three times using both machine and hand milking methods. The percentage of total solids showed a similar pattern as the fat percentage. However, percentages of protein, lactose, and non-fat solids in milk were not significantly different (P > 0.05) among the treatment groups. Collectively, the results show that milking three times per day instead of twice at 8-hour intervals can enhance milk yield in Sahiwal cows using both hand and machine milking methods.

• Journal of Animal Science and Technology
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• v.55 no.6
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• pp.489-500
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• 2013

Daily milk yields on test days were estimated using morning or afternoon partial milk yields collected by official agencies and the accuracy of the estimates was determined. Test-day data for milk yields consisted of 3,156,734 records of AM/PM partial milking measurements of 255,437 milking Holstein cows from 3,708 farms collected from December 2008 to April 2013. A linear regression model (LRM) was applied to estimate daily milk yields using alternate AM/PM milk yield records within lactation stages, milking intervals, and parities on every daily milk yield. The alternate statistical approach was a non-linear hierarchical model (NHM) in which Brody's growth function was implemented by reflecting an animal's physiological milk production cycle. When compared with LRM, daily milk yields predicted by the NHM were assumed to be functionally related to day in milk (or lactation) stage, milking intervals, and partial milk yields. Since the results were in terms of accuracies based on comparisons of different statistical models, accuracies of estimates of daily milk yields by NHM were close to those determined by the LRM. The average of these accuracies was 0.94 for AM partial milk yields and 0.93 for PM partial milk yields for first calving cows. However, the accuracies of AM/PM milk yield estimations from cows under a calving stage higher than the first parity were 0.96 and 0.95, respectively. Correlations between the estimated daily milk yields and the actual daily milk yields ranged from 0.96~0.98. These accuracies were lower for unbalanced AM/PM milking intervals and the first calving cows. Overall, prediction of daily milk yields by NHM would be more appropriate than by LRM due to its flexibility under different milk yield-related circumstances, which provides an idea of the functional relationship between milking intervals and days in milk with daily milk yields from statistical viewpoints.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.487-493
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• 2013

This experiment was conducted to investigate the possibility that milking speed traits can be improved by estimating their genetic parameters and to provide basic information when the goals for dairy cattle improvement are established. The amount of milk within the first three minutes (3MG) was 8.97 Kg and 57% of total milk was produced within 3 minutes, but it was lower than that of the recommended level (70%). The highest milk flow (HMF) and average milk flow (DMHG) in the main milking phase were 3.66kg/min and 2.43kg/min, respectively, which were lower than those of the recommended levels (4.0 5.0kg/min and 3.0 4.0kg/min), suggesting slower milking speed of domestic dairy cattle compared to that of foreign dairy cattle. The heritability estimates on the highest milk flow (HMF), maximum milk flow (HMG) in one minute and average milk flow (DMHG) in the main milking phase were 0.35, 0.31 and 0.29, respectively, which are suitable for the improvement of traits with medium heritability. The genetic correlation between total milk yields (MGG) and average milk flow (DMHG) in the main milking phase was 0.591, while the genetic correlations among milking speed traits including the highest milk flow (HMF), maximum milk flow (HMG) in one minute and average milk flow (DMHG) in the main milking phase were in the range of 0.889 0.997.

• Journal of Animal Environmental Science
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• v.8 no.3
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• pp.165-170
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• 2002

This study was carried out to measure the 3D-distance of a cow model teat for an application possibility on Robotic Milking System(RMS). A teat recognition algorithm was made to find 3D-distance of the model by using Gonzalrez's theory. Some of the results are as follows. 1 . In the distance measurement experiment on the test board, as the measured length, and the length between the center of image surface and the measured image point became longer, their error values increased. 2. The model teat was installed and measured the error value at the random position. The error value of X and Y coordinates was less than 5㎜, and that of Z coordinates was less than 20㎜. The error value increased as the distance of camera's increased. 3. The equation for distance information acquirement was satisfied with obtaining accurate distance that was necessary for a milking robot to trace teats, A teat recognition algorithm was recognized well four model cow teats. It's processing time was about 1 second. It appeared that a teat recognition algorithm could be used to determine the 3D-distance of the cow teat to develop a RMS.

• Journal of Animal Science and Technology
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• v.51 no.4
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• pp.265-272
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• 2009

A total of 486 milk records were collected from 16 diary farms in Imsil-gun, Jeollabuk-do. Results obtained were as follows: The average 3MG (amount of milk within the first three minute) was 7.44 kg and 55% of total milk yield was produced within 3 min. The average of SPL (% of foam in milk) was 33.93% and the average of MNG (strip yield) was 0.14 kg, which was less than 1% of total milk yield. The averages of HMF (highest milk flow), HMG (maximum milk flow rate in one minute) and DMHG (average milk flow in the main milking phase) were 3.03 kg/min, 2.94 kg/min and 2.05 kg/min, respectively and the average milking speed in Imsil-gun was slower than other regions. The average of tS500(time to reach 0.5 kg/min at beginning) was 0.23min (about 14 seconds) and that of tMGG (duration of the total milking) was 7.75min. The average tMBG (duration of the dry milking phase) was 0.58 min (35 seconds) and that of tMNG (duration of the stripping phase) was 0.42min (14 seconds). The averages of ELHMF (electrical conductivity at highest milk flow) and ELAP (beginning peak level of the electrical conductivity) were 6.81 mS/cm and 7.58 mS/cm, respectively. The average of ELMAX (maximum electrical conductivity) was 7.48 mS/cm and that of ELAD (beginning peak difference of the electrical conductivity) was 0.61 mS/cm. While the total milk yields for DMHG, tMHG (duration of the main milking phase), tPL (duration of the plateau phase), tAB (duration of the descending phase) and tMGG were positively correlated (0.35~0.54), those for tMBG and SPL were negatively correlated (-0.11 and -0.27). As the DMHG increased, tMHG, tPL, tAB, tMGG and SPL decreased. While the cows with higher electrical conductivity at the beginning of milking had less somatic cell counts, cows with higher electrical conductivity after the peak of milk yield had more somatic cell counts. The results of this experiment indicated that through milking based on milking and lactating standards and the regular checking of milking status, the qualities of milk and milk yields could be improved.

• Journal of Preventive Medicine and Public Health
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• v.43 no.3
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• pp.205-212
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• 2010

Objectives: The prevalence of work-related musculoskeletal symptoms (WMS) among Korean dairy farmers has not been investigated. The purpose of this study was to assess the prevalence of WMS and to evaluate the relationship between WMS and risk factors. Methods: Self-developed questionnaires including the questionnaire developed by the Korean Occupational Safety and Health agency (KOSHA) were used to investigate WMS among dairy farmers in Gyeonggi Province, Korea. We informed selected dairy farmers about the study and sent the questionnaires by registered mail. They visited a public health center nearby or a branch of public health center on the appointed date and skillful researchers identified or conducted the questionnaires by interview. We analyzed 598 (32.8%) of the 1824 dairy farmers. Multiple logistic regression was implemented to estimate the odds ratios of risk factors. Results: The mean age of the respondents was $50.4{\pm}8.7$ years and the proportion of males was 63.0%. The prevalence of WMS at any site was 33.3%. The prevalence of neck WMS was 2.2%, shoulders 10.0%, arms/elbows 5.0%, hands/wrists/fingers 4.2%, low back 11.5%, and legs/feet 11.7%. The adjusted odds ratio of low back WMS for milking 4 or more hours per day was 4.231 (95% Cl = 1.124 - 15.932) and statistically significant. Low back WMS (2.827, 95% Cl = 1.545 - 5.174) was significantly decreased by education. Conclusions: Low back WMS increased with milking hours and milking 4 or more hours per day was significantly associated with low back WMS. Low back WMS was significantly reduced with education. We hope that there will be increased attention about WMS in dairy farmers and the subject of future investigations.

• Animal Bioscience
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• v.37 no.2
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• pp.173-183
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• 2024

Objective: This study aimed to evaluate whether the methane (CH₄) to carbon dioxide (CO₂) ratio (CH₄/CO₂) and methane-related traits obtained by the sniffer method can be used as indicators for genetic selection of Holstein cows with lower CH₄ emissions. Methods: The sniffer method was used to simultaneously measure the concentrations of CH₄ and CO₂ during milking in each milking box of the automatic milking system to obtain CH₄/CO₂. Methane-related traits, which included CH₄ emissions, CH₄ per energy-corrected milk, methane conversion factor (MCF), and residual CH₄, were calculated. First, we investigated the impact of the model with and without body weight (BW) on the lactation stage and parity for predicting methane-related traits using a first on-farm dataset (Farm 1; 400 records for 74 Holstein cows). Second, we estimated the genetic parameters for CH₄/CO₂ and methane-related traits using a second on-farm dataset (Farm 2; 520 records for 182 Holstein cows). Third, we compared the repeatability and environmental effects on these traits in both farm datasets. Results: The data from Farm 1 revealed that MCF can be reliably evaluated during the lactation stage and parity, even when BW is excluded from the model. Farm 2 data revealed low heritability and moderate repeatability for CH₄/CO₂ (0.12 and 0.46, respectively) and MCF (0.13 and 0.38, respectively). In addition, the estimated genetic correlation of milk yield with CH₄/CO₂ was low (0.07) and that with MCF was moderate (-0.53). The on-farm data indicated that CH₄/CO₂ and MCF could be evaluated consistently during the lactation stage and parity with moderate repeatability on both farms. Conclusion: This study demonstrated the on-farm applicability of the sniffer method for selecting cows with low CH₄ emissions.