• Asian-Australasian Journal of Animal Sciences
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• 제19권11호
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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.

• Journal of Animal Science and Technology
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• 제58권10호
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• pp.38.1-38.6
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• 2016

Background: Despite the importance of relationships between somatic cell score (SCS) and currently selected traits (milk, fat and protein yield) of Holstein cows, there was a lack of comprehensive literature for it in Iran. Therefore we tried to examine heritabilities and relationships between these traits using a fixed-regression animal model and Bayesian inference. The data set consisted of 1,078,966 test-day observations from 146,765 primiparous daughters of 1930 sires, with calvings from 2002 to 2013. Results: Marginal posterior means of heritability estimates for SCS ($0.03{\pm}0.002$) were distinctly lower than those for milk ($0.204{\pm}0.006$), fat ($0.096{\pm}0.004$) and protein ($0.147{\pm}0.005$) yields. In the case of phenotypic correlations, the relationships between production and SCS were near zero at the beginning of lactation but become increasingly negative as days in milk increased. Although all environmental correlations between production and SCS were negative ($-0.177{\pm}0.007$, $-0.165{\pm}0.008$ and $-0.152{\pm}0.007$ between SCS and milk, fat, and protein yield, respectively), slightly antagonistic genetic correlations were found; with posterior mean of relationships ranging from $0.01{\pm}0.039$ to $0.11{\pm}0.036$. This genetic opposition was distinctly higher for protein than for fat. Conclusion: Although small, the positive genetic correlations suggest some genetic antagonism between desired increased milk production and reduced SCS (i.e., single-trait selection for increased milk production will also increase SCS).

• Animal Bioscience
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• 제34권4호
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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.

• Asian-Australasian Journal of Animal Sciences
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• 제33권3호
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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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• 제31권10호
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• Asian-Australasian Journal of Animal Sciences
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• 제9권4호
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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.

• Animal Bioscience
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• 제34권2호
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• pp.163-171
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• 2021

Objective: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. Methods: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. Results: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. Conclusion: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.

• Asian-Australasian Journal of Animal Sciences
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• 제14권2호
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• pp.189-192
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• 2001

This study was initiated in an effort to determine the normal mean and variations of the somatic cell count (SCC) in milk of buffaloes as influenced by the milking time, stage of lactation, parity and season. The buffaloes were hand milked at 13 and 11 h. interval during evening and morning respectively. On the day of milk sampling the udders were tested for mastitis by California Mastitis Test (CMT). Only those buffaloes, which were found negative in the CMT, were included in the sampling plan. The mean values for morning and evening were 1.09 (range 0.39-1.76) and $0.97(range\;0.57-2.46){\times}10^5cells/ml$, respectively which did not differ significantly. When data of the morning and evening values was compared on the basis of total cell secretion in milk, even then there was no statistical difference between the morning and the evening values, thereby suggesting that no diurnal variation existed in SCC of milk. Paritywise differences were not significant between the 1st to 5th lactation and above. Similarly stage of lactation effect, when tested at 30 day intervals, did not differ significantly. Significant (p<0.05) correlation coefficients (r) between SCC and milk yield during different stages of lactation and parity suggested that SCC per ml of milk was higher during the later stages of lactation. SCC was higher in primiparous than in multiparous buffaloes. On an average the SCC recorded was $1.0{\times}10^5cells/ml$ of milk irrespective of time of milking, parity and stages of lactation. The SCC was low during cold and hot-dry season but were high during the hot-humid season (p<0.05), the respective values being 0.76, 1.08 and $1.35{\times}10^5cells/ml$. These values were lower than the SCC already reported in cows suggesting less stressful condition of the udder of buffaloes in this study.

• Animal Bioscience
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• 제34권8호
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• pp.1283-1289
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• 2021

Objective: The progestogen-associated endometrial protein (PAEP) gene encodes the main whey protein in milk, β-lactoglobulin. The aim of the study was to investigate polymorphism in the PAEP gene and its association with milk yield, composition, and quality. Methods: Test-day records for 782 dairy cows were analysed. A total of 10 single nucleotide polymorphisms (SNP) within the PAEP gene were investigated. The following parameters were recorded: milk yield (MY, kg/d), percent milk fat (%), protein (PP, %), dry matter (DMP, %) and lactose (LP, %), urea content (UC, mg/L) as well as natural logarithm for somatic cell count (LnSCC, ln). Effect on genomic estimated breeding values accuracy was evaluated with pedigree and single step model. Results: Results show that only three SNPs were polymorphic, creating 5 composite genotypes: P1 to P5. Differences in MY between composite genotypes were noted in the two tested herds. Cows with P5 composite genotypes were characterised by the highest PP and LnSCC and the lowest LP and UC (p<0.05). P4 was linked to an increased DMP and UC, while P3 to an increase in LP and decrease in PP and LnSCC. Both factors are important markers in herd management and have high influences on the herds economics. For 5 out of 7 traits the accuracy of prediction was improved by including the haplotype as a fixed effect. Conclusion: Presented results may suggest a new way to optimise breeding programmes and demonstrate the impact of using genomic data during that process.

• Asian-Australasian Journal of Animal Sciences
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• 제14권12호
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• pp.1775-1780
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• 2001

The study was undertaken to find out the normal mean and variations in somatic cell count (SCC) of milk in crossbred and indigenous cows as influenced by stage of lactation, parity and season. On day of milk sampling the udders were tested for mastitis by California Mastitis Test (CMT). Only those cows, which were found negative in the CMT, were taken in the study. Paritywise differences in SCC were not significant between the 1st to 6th lactation and above. Similarly, stage of lactation effect, when tested at 30 day intervals, did not differ significantly. However, the seasons significantly (p<0.05) affected SCC count of milk. The SCC was lower during cold ($1.10{\times}10^5cells/ml$) and hot-dry ($1.11{\times}10^5cells/ml$) season then during hot-humid season ($2.14{\times}10^5cells/ml$). On an average SCC recorded were 1.26, 1.31, 1.54 and $1.61{\times}10^5$ cells per ml respectively in Tharparkar, Sahiwal, Karan Swiss and Karan Fries cows irrespective of stage of lactation, parity and season. Further, crossbred Karan Swiss and Karan Fries cows behave similar to the indigenous Tharparkar and Sahiwal cows but are more vulnerable to hot-humid climate then indigenous ones. Significant correlation between the SCC and milk yield during different stages of lactation (1.38 to $1.74{\times}10^5cells/ml$) and parity (1.47 to $1.63{\times}10^5cells/ml$) suggested that the SCC/ml of milk was higher during the later stages of lactation.