• Asian-Australasian Journal of Animal Sciences
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• v.33 no.3
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• pp.408-415
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• 2020

Objective: The aim of the paper was to compare the fit of data derived from daily automatic milking systems (AMS) and monthly test-day records with the use of lactation curves; data was analysed separately for primiparas and multiparas. Methods: The study was carried out on three Polish Holstein-Friesians (PHF) dairy herds. The farms were equipped with an automatic milking system which provided information on milking performance throughout lactation. Once a month cows were also subjected to test-day milkings (method A4). Most studies described in the literature are based on test-day data; therefore, we aimed to compare models based on both test-day and AMS data to determine which mathematical model (Wood or Wilmink) would be the better fit. Results: Results show that lactation curves constructed from data derived from the AMS were better adjusted to the actual milk yield (MY) data regardless of the lactation number and model. Also, we found that the Wilmink model may be a better fit for modelling the lactation curve of PHF cows milked by an AMS as it had the lowest values of Akaike information criterion, Bayesian information criterion, mean square error, the highest coefficient of determination values, and was more accurate in estimating MY than the Wood model. Although both models underestimated peak MY, mean, and total MY, the Wilmink model was closer to the real values. Conclusion: Models of lactation curves may have an economic impact and may be helpful in terms of herd management and decision-making as they assist in forecasting MY at any moment of lactation. Also, data obtained from modelling can help with monitoring milk performance of each cow, diet planning, as well as monitoring the health of the cow.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.10
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• pp.1347-1352
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• 2000

The objectives of the study were to examine non-genetic factors that influence somatic cell counts in dairy cattle and to estimate the genetic parameters of somatic cell counts. A total of 34, 097-test day somatic cell count records were obtained from the Zimbabwe Dairy Services Association (ZDSA). The data were from 5, 615 Holstein daughters of 390 sires and 2, 541 dams tested between May 1994 and December 1998. First lactation cows contributed 22, 147 records to the data set, while 11, 950 records were from second and later parity cows. The model for analysis included fixed effects of month of calving, year of calving, stage of lactation, calving interval and test date. Milk yield and age on test day were fitted in the model as covariates. The additive genetic effects pertaining to cows, sires and dams and the residual error were the random effects. The Average Information Restricted Maximum Likelihood algorithm was used for analysis. The heritability of somatic cell scores was low at $0.027{\pm}0.013$ for parity one cows and $0.087{\pm}0.031$ for parity two and above. Repeatability estimates were $0.22{\pm}0.01$ and $0.30{\pm}0.01$ for the two lactation groups, respectively. Genetic and phenotypic correlations between the somatic cell scores and test day milk production were small and negative. It seems that there is no genetic link between somatic cell counts and milk yield in Holstein cattle in Zimbabwe. The results also seem to indicate that somatic cell count is a trait that is mainly governed by environmental factors.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.73-80
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• 2023

This study was conducted to analyze changes of irrigation water and soil volumetric water content by irrigation method of field soil in Saemangeum reclaimed tideland. The main test irrigation methods was surface drip irrigation, sprinkler irrigation, and sub drip irrigation. In addition, the correlation between irrigation amounts and crop yield by irrigation method was investigated. For soil volumetric water contents increases by 25%, surface drip irrigation took 1.5 hour, sprinkler irrigation took 2.0 hours, and sub drip irrigation took 3.0 hours. As a result of analyzing the irrigation amounts according to the yield, the surface drip irrigation was 2.66 mm/day in the seedling stages, 3.31 mm/day in the vegetative growth stages, and 5.09 mm/day in the flowering stages. Sprinkler irrigation was 2.90 mm/day in the seedling stages, 3.87 mm/day in the vegetative growth stages, and 7.11 mm/day in the flowering stages. Sub drip irrigation was 2.42 mm/day in the seedling stages, 3.09 mm/day in the vegetative growth stages, and 4.87 mm/day in the flowering stages. It was analyzed that there was a statistically significant difference in irrigation amounts by fresh weight and irrigation method (F=4.002, p=0.022), and irrigation amounts by dry weight and irrigation method (F=3.499 p=0.034). Surface drip irrigation was judged to be more appropriate than sprinkler irrigation or sub drip irrigation for field crops in Saemangeum reclaimed land.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.3
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• pp.334-340
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• 2019

Objective: The objective of this study was to investigate the genetic components of daily milk yield and to re-rank bulls in South Korea by estimated breeding value (EBV) under heat stress using the temperature-humidity index (THI). Methods: This study was conducted using 125,312 monthly test-day records, collected from January 2000 to February 2017 for 19,889 Holstein cows from 647 farms in South Korea. Milk production data were collected from two agencies, the Dairy Cattle Genetic Improvement Center and the Korea Animal Improvement Association, and meteorological data were obtained from 41 regional weather stations using the Automated Surface Observing System (ASOS) installed throughout South Korea. A random regression model using the THI was applied to estimate genetic parameters of heat tolerance based on the test-day records. The model included herd-year-season, calving age, and days-in-milk as fixed effects, as well as heat tolerance as an additive genetic effect, permanent environmental effect, and direct additive and permanent environmental effect. Results: Below the THI threshold (${\leq}72$; no heat stress), the variance in heat tolerance was zero. However, the heat tolerance variance began to increase as THI exceeded the threshold. The covariance between the genetic additive effect and the heat tolerance effect was -0.33. Heritability estimates of milk yield ranged from 0.111 to 0.176 (average: 0.128). Heritability decreased slightly as THI increased, and began to increase at a THI of 79. The predicted bull EBV ranking varied with THI. Conclusion: We conclude that genetic evaluation using the THI function could be useful for selecting bulls for heat tolerance in South Korea.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.278-278
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• 2022

Recently, the yield potential of high yield Tongil-type rice varieties has greatly increased, reaching 817kg/10a(Geumgang No.1). Moreover, in order to promote rice consumption and strengthen the competitiveness of the rice processing industry, the R&D of high yield Tongil-type rice varieties for each processing purpose, such as rice noodles, grain feed and industrial materials, has been continuously carried out. However, because Tongil-type rice varieties or lines are generally very vulnerable to cold damage, cold tolerance test can be said to be absolutely necessary to improve the cultivation safety. This study is the result of the seedling and field cold tolerance characteristic tests carried out in 2021 of high yield Tongil-type rice breeding lines. For the cold tolerance characteristic test of seedlings, total 303 high yield rice breeding lines for processing were treated in cold water of 13℃ and irrigation depth of 4cm for 10 days from the third-leaf age, then it was evaluated by comparing the degree of discoloration and withering with the checked varieties(Boramchan, Hanahreum No.2). Also, for the test of field, total 186 high yield rice breeding lines for processing were treated in cold water of 17℃ by keeping pouring day and night from 30 days after transplanting to ripening stage, then it was evaluated by comparing the degree of discoloration, delay of heading, shortening rate of stem length and percent of fertile grain etc. with the checked varieties. And the cold tolerance evaluative criteria were classified as strong(1~3), medium(4~6) and weak(7~9) in overall cold tolerance. (Seedling test) As for the degree of cold tolerance of the check variety, 'Boramchan' and 'Hanahreum No.2' showed a response of 'medium' and 'weak', respectively. However, there was no 'strong' line in the high yield rice breeding lines, 2 lines showed a 'medium' response, and 301 lines showed a 'weak' response. Therefore, except for a few lines(0.7%), most lines(99.3%) showed a 'weak' response. (Field test) In terms of the overall cold tolerance of the check variety, both 'Boramchan' and 'Hanahreum No.2' showed a 'medium' response. Similarly, there was no 'strong' line in the case of high yield rice breeding lines, 20 lines showed a 'medium' response, and 166 lines showed a 'weak' response. Therefore, except for some(10.8%) lines, most(89.2%) lines showed a 'weak' response. From the above results, we selected about 100 individuals with less seed shattering and degeneration of the ear tip, and with a relatively high percent of fertile grain, and are continuing to select lines with improved cold tolerance in the F4~F5 group in this year. As such, most of the Tongil-type rice varieties have poor cold tolerance and thus have low cultivation safety at low temperatures. However, it is important to select improved lines through generational progress because there are some lines that still have a certain level of cold tolerance among them.

• Asian-Australasian Journal of Animal Sciences
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• v.9 no.4
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• pp.439-447
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• 1996

Monthly test day production for 12,020 records, were collected from six of the largest specialized dairy farms located in central region of the Kingdom of Saudi Arabia. The records described lactating cows in four parities and two seasons of calving. Monthly test day records were fitted using Wood's model $At{{^b}{_e}}^{-ct}$ with multiple and additive error term. Linear and non-linear regression models were used to find the estimates of the parameters necessary to draw the lactation curves. The shape of the lactation curves of different parities showed that third lactation has the heighest peak (43.08 kg) for linear regression model and (42.08 kg) for non-linear regression model. Fourth lactation has the lowest peak (24.00kg) for linear regression model and (25.64 kg) for non-linear regression models. Cows of second and third lactations reached the peak at 58 day for both linear and non-linear regression models. Cows of first lactation were more persistent and had late peak at 68 and 67 days for both models respectively. While, third lactation cows were lower persistent and had early peak at 58 day for both models. Cows calved at winter months have higher starting values (A), higher ascending slope (b) and higher decending slope (c). Least square means of milk yield of the first four parities and for overall data were 6,653, 7,659, 7,482, 6,988 and 7,614 kg respectively. The corresponding lactation period were 358, 367, 350, 363 and 364 days respectively.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1387-1399
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• 2020

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,-3-lactation random regression test-day model. Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

• Journal of Animal Science and Technology
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• v.48 no.2
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• pp.143-150
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• 2006

The data were collected in the dairy herd improvement program from January 2000 to July 2005. Test data included 825,157 records of first parity and animals with both parents known were included. This study aimed to describe the effect of incomplete lactation records of latest generation to the change in sire's breeding value using Random Regression model (RRM) in genetic evaluation. Estimation of genetic parameter and breeding value for sire used REMLF90 and BLUPF90 program. The phenotypic value on the number of test day records between group TD11, TD8, TD5, TD2 showed no large differences. For all the group heritability of test day milk yield range from 0.30 to 0.36. However TD2 group showed low heritability the least test day recode on the latest generation. The correlation of above 50% between test day and TD11(0.610), TD8(0.616), TD5(0.661) and TD2(0.682) with different records in latest generation. Sire's rank of breeding value varied widely depending on the records on the number of lactation from start to the latest generation. Study showed that change in breeding value ranked if daughter's test recode more so it should have at least 5 test day records. The use of RRM in dairy cattle genetic evaluation would be desirable if complete lactation records for latest generation daughters of young bulls when selection for proven bulls. Random Regression model (RRM) require at least 5 test-day lactation recode.

• Journal of Nutrition and Health
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• v.27 no.8
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• pp.794-804
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• 1994

This study was conducted to investigate the relationship between lactational capacity and intakes of energy and energy-yielding nutrients. Food consumption, intakes of carbohydrate, protein, fat and energy and quantity and proximate composition of milk of 11 lactating Korean mothers were determined at 1, 2 and 3 months postpartum longitudinally. Food consumption was estimated using a 24-hour recall method ; intakes of energy and energy-yielding nutrients were calculated according to the Food Composition Table. Daily milk production was estimated using a 72-hour test-weighing method ; protein, fat, lactose and energy concentrations were analyzed. Average intakes of energy and protein were 1974$\pm$386㎉/day and 67.0$\pm$12.3㎉/day, these were 73% and 74% of the Korean Recommended Allowances, respectively. Average milk yield was 720.1$\pm$123.3ml/day containing energy 59.6$\pm$9.5㎉/dl, protein 1.1$\pm$0.1g/dl, fat 2.7$\pm$0.8g/dl, and lactose 6.3$\pm$0.3g/dl. No relationship existed between the intakes of carbohydrate, protein, fat and energy and the quantity and proximate composition of the milk. However, the intakes of energy, carbohydrate and vegetable protein were inversely related to the concentrations of energy and lactose in the milk. This result indicates that lactational capacity may be affected by the other factors excluding intakes of energy and energy-yielding nutrients and a high intake of energy may not guarantee optimal lactational capacity.