• 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.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.19 no.11
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• pp.1551-1555
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• 2006

This study was carried out to investigate the use of three different mathematical models (Wood, Quadratic and Cubic) for describing the lactation curve of Akkaraman ewes. Data were collected from 42 ewes that were three years of age and from the same flock raised in The State Farm of $G{\ddot{o}}zl{\ddot{u}}$ in Konya Province. All ewes lambed in March. They were hand milked twice daily and the first milk test was performed with in the first month after lambing (mean = 27.8 day, SD = 4.26) in an attempt to describe the peak yield. The differences between estimated total milk yields by the models were not significant. The models were adequate for describing total milk yield. The differences between peak yields were not significant. The Wood model estimated the time of peak yield earlier than the other models and observed values (p<0.01). Especially the Cubic model's peak time was very close to really peak time (34.30 vs. 35.33 days). $R^2$ values of the models ranged from 85.85% to 96.20%. The Cubic model gave the best $R^2$ value (p<0.01). Correlation coefficients between descriptive values of the models changed from 0.32 to 1.00. Total milk yields of the models were highly correlated (above 0.99) with the total milk yield calculated by the Fleischmann method (p<0.01). As a result the Cubic model showed the best fit to the data collected from Akkaraman ewes and allowed a suitable description of the shape of the lactation curve.

• Asian-Australasian Journal of Animal Sciences
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• v.1 no.1
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• pp.47-53
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• 1988

Daily milk yield estimates were obtained on 74 lactations of 35 Japanese Black cows on weeks 1 to 9, 11, 13, 17, 21 and 26 postpartum using the weigh-suckle-weigh method. The data obtained were analyzed by least-squares procedures according to the models including the following effects; dam, lactation number, season of calving, week of experiment and partial regressions on the lactation number. The overall mean and standard error for daily milk yield was $4.55{\pm}0.04\;kg$. The milk yield declined essentially linearly throughout the experimental period. The main effects of lactation number, season of calving and week of experiment were highly significant. Differences in daily milk yield among cows were significant, and the repeatability was estimated as $0.60{\pm}0.06$. The overall means(kg) and repeat-abilities of cumulative milk yield for 1, 4, 9, 13, 17, 21 and 26 weeks postpartum were 41, 0.08; 166, 0.40; 361, 0.61; 503, 0.66; 632, 0.67; 749, 0.65 and 884, 0.58; respectively. The means of calf weight at birth and 26 weeks weight for different lactations ranged from 25.5 to 33.6 kg and 145.6 to 185.4 kg, respectively. Calf daily gains between experiment weeks were low in earlier stages of lactation.

• Journal of Animal Science and Technology
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• v.44 no.6
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• pp.661-668
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• 2002

This study was conducted to investigate the effect of environmental factor on milk yield and to estimate lactation curve in Korean cattle. The data for milk yields were collected from 118 cows from 1997 to 2000 at National Livestock Research Institute in Daekwanryoung, Kangwon-do. Average daily milk yields for 1st, 2nd, 3rd and 4th month after calving were 3.74kg, 3.64kg, 3.26kg and 2.99kg. Average daily milk yield for the four months was 3.52kg. The milk yields for cows calved in spring were larger than those calved in fall. Lactation curve of Korean cattle was $y_n$=$2.4845n^{0.1734}e^{－0.0060n}$. Peak milk yield was 3.75kg on 29.03 day after calving. The peak milk yields for multi-parous cows were larger than those of primiparous cows. The peak milk yields for multi-parous cows reached later than those for primiparous cows. The cows calved in spring had higher and earlier peak milk yields than those calved in fall had.

• Journal of agriculture & life science
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• v.50 no.5
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• pp.173-182
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• 2016

This study was aimed to identify the phenotypic relationships between calving interval and lactation curve parameters in Korean Holstein cow. The data of 36,505 lactation records was obtained from the Dairy Herd Improvement program run by Dairy Cattle Improvemnet Center of National Agricultural Federation of Korea. All lactation records were collectied from the multiparous cows calving between 2011 to 2013. The estimated lactation curves were drawn using Wood model based on actual milk yield records, and NLIN Procedure of SAS program (ver. 9.2). General linear multivariate models for calving interval, 305-d milk yield, lactation parameters(A, b, c), persistency, peak day, and peak yield included fixed effects of calving year-season (spring, summer, fall and winter) and parity(2, 3 and 4). For calving interval, 305-d milk yield, lactation parameters(A, b, c), persistency, peak day and peak yield, all two fixed effect(calving year-season, parity) were significant(p<0.05). The estimated lactation functions using Wood model for 2, 3, and 4 parity were yt=24.66t0.175e-0.00302t, yt=24.69t0.192e-0.00334t, and yt=24.22t0.200e-0.00341t, respectively. Phenotypic correlation (partial residual correlation) between calving interval and 305-d milk yield, A, b, c, persistency, peak day, and peak yield were 0.093, -0.014, 0.028, -0.046, 0.099, 0.085, and 0.052, respectively. To conclude, if calving interval increase then ascent to peak, persistency, peak day and peak yield are increase, and descent after peak is decrease. So, total 305-d milk yield is increase.

• Animal Bioscience
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• v.34 no.4
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• pp.770-782
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• 2021

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood's model) to the prediction of milk yield during lactation. Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance. Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood's models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood's models in the later ones. Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood's model. The application of the SARIMA, NARX and Wood's models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

• Nutrition Research and Practice
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• v.15 no.2
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• pp.160-172
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• 2021

BACKGROUND/OBJECTIVES: Nutritional status and food intake during pregnancy and lactation can affect fetal programming. In the current metabolic syndrome epidemic, high-fructose diets have been strongly implicated. This study investigated the effect of maternal high-fructose intake during pregnancy and lactation on the development of metabolic syndrome in adult offspring. SUBJECTS/METHODS: Drinking water with or without 20% fructose was administered to female C57BL/6J mice over the course of their pregnancy and lactation periods. After weaning, pups ate regular chow. Accu-Chek Performa was used to measure glucose levels, and a tail-cuff method was used to examine systolic blood pressure. Animals were sacrificed at 7 months, their livers were excised, and sections were stained with Oil Red O and hematoxylin and eosin (H&E) staining. Kidneys were collected for gene expression analysis using quantitative real-time Polymerase chain reaction. RESULTS: Adult offspring exposed to maternal high-fructose intake during pregnancy and lactation presented with heavier body weights, fattier livers, and broader areas under the curve in glucose tolerance test values than control offspring. Serum levels of alanine aminotransferase, aspartate aminotransferase, glucose, triglycerides, and total cholesterol and systolic blood pressure in the maternal high-fructose group were higher than that in controls. However, there were no significant differences in mRNA expressions of renin-angiotensin-aldosterone system genes and sodium transporter genes. CONCLUSIONS: These results suggest that maternal high-fructose intake during pregnancy and lactation induces metabolic syndrome with hyperglycemia, hypertension, and dyslipidemia in adult offspring.

• Journal of Animal Science and Technology
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• v.48 no.6
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• pp.767-776
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• 2006

Corresponding author:Deukhwan Lee, Department of Animal Life Resources, Hankyong National Univ. Seokjeong-dong Anseong-si Gyeonggi-do, Rep. of Korea 456-749Tel: 031-670-5091, Fax: 031-676-5091, E-mail : dhlee@hknu.ac.krGeneral performance with including environmental and management effects on linear type traits in Holstein cows were investigated. 115,646 valid records measured from cows over 1 yrs of age by Korean Animal Improvement Association from 2000 to 2004 were used for this study. Farm, appraisal year-month, appraisal person should affect linear type and scoring traits. Most of type traits and scoring traits would be significantly affected by parity and lactation stage after absorbing farm-appraisal year-month-person effects. Otherwise, some traits such as traits related to udder would be affected by registration criteria. However, interval of appraisal time and milking time would not affect these traits. The scores related to udder, teat placement and foot angle would be positively related to parity. Final score would be optimized at cows of 2nd and 3rd parities. Dairy form, front teat placement, rear udder height, rear udder width and final score would show similar to pattern of lactation curve at lactation stage. Dairy capacity composite index would also show similar to lactation curve. This result would be indicated that more concise standardizing system for linear type scoring rules would be needed. Furthermore, correcting system for parity and lactation stage would be needed because this factor should affect physiological status, specially, udder status.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.12
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• pp.1682-1687
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• 2016

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage ($0.213{\pm}0.007$). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.