Chiou, Peter Wen-Shyg;Chuang, Chi-Hao;Yu, Bi;Hwang, Sen-Yuan;Chen, Chao-Ren
Asian-Australasian Journal of Animal Sciences
/
v.19
no.6
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pp.857-864
/
2006
The aim of this study was to apply the Cornell net carbohydrate and protein system (CNCPS) in subtropical Taiwan. This was done by means of 3 trials, viz, in situ, lactation and metabolic trials, the latter using the urinary purine derivatives (UPD) to estimate the ruminal microbial yield. Dietary treatments were formulated according to different nutrient requirement systems including, (1) a control NRC78 group on NRC (1978), (2) a NRC88 group on NRC (1988), and (3) a CNCPS group on Cornell Net carbohydrate and protein system model. Results from the lactation trial showed that DM intake (DMI) was higher (p<0.05) in the NRC78 than the other treatment groups. The treatments did not significantly influence milk yield, but milk yield after covariance adjustment for DMI was higher in the CNCPS group (p<0.05). The FCM, milk fat content and yield were greater in both the NRC78 and the NRC88 group over the CNCPS group (p<0.05). The treatments did not significantly influence the DMI adjusted FCM. The solid-non-fat and milk protein contents were higher in the CNCPS group (p<0.05) with or without DMI covariance adjustment. Lactating efficiency was higher in the CNCPS group (p<0.05) compared to the other groups. The significantly lowest milk urea-N (MUN) with better protein utilization efficiency in the CNCPS group (p<0.05) suggested that less N would be excreted into the environment. Cows in the CNCPS group excreted significantly more and the NRC88 group significantly less urinary purine derivatives (UPD) implying that more ruminal microbial protein was synthesized in the CNCPS over the NRC88 group. The CNCPS could become the most useful tool in predicting the trends in milk yield, microbial yield and MUN.
Background: The modelling of lactation curve provides guidelines in formulating farm managerial practices in dairy cows. The aim of the present study was to determine the suitable non-linear model which most accurately fitted to lactation curves of five lactations in 134 Gir crossbred cows reared in Research-CumDevelopment Project (RCDP) on Cattle farm, MPKV (Maharashtra). Four models viz. gamma-type function, quadratic model, mixed log function and Wilmink model were fitted to each lactation separately and then compared on the basis of goodness of fit measures viz. adjusted $R^2$, root mean square error (RMSE), Akaike's Informaion Criteria (AIC) and Bayesian Information Criteria (BIC). Results: In general, highest milk yield was observed in fourth lactation whereas it was lowest in first lactation. Among the models investigated, mixed log function and gamma-type function provided best fit of the lactation curve of first and remaining lactations, respectively. Quadratic model gave least fit to lactation curve in almost all lactations. Peak yield was observed as highest and lowest in fourth and first lactation, respectively. Further, first lactation showed highest persistency but relatively higher time to achieve peak yield than other lactations. Conclusion: Lactation curve modelling using gamma-type function may be helpful to setting the management strategies at farm level, however, modelling must be optimized regularly before implementing them to enhance productivity in Gir crossbred cows.
Angeles-Hernandez, J.C.;Albarran-Portillo, B.;Gomez Gonzalez, A.V.;Pescador Salas, N.;Gonzalez-Ronquillo, M.
Asian-Australasian Journal of Animal Sciences
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v.26
no.8
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pp.1119-1126
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2013
The objective of this study was to compare the goodness of fit of four lactation curve models: Wood's Gamma model (WD), Wilmink (WL), and Pollott's multiplicative two (POL2) and three parameters (POL3) and to determine the environmental factors affecting the complete lactation curve of F1 dairy sheep under organic management. A total of 5,382 weekly milk yields records from 150 ewes, under organic management were used. Residual mean square (RMS), determination coefficients ($R^2$), and correlation (r) analysis were used as an indicator of goodness of fit for each model. WL model best fitted the lactation curves as indicated by the lower RMS values (0.019), followed by WD (0.023), POL2 (0.025) and POL3 (0.029). The four models provided total milk yield (TMY) estimations that were highly correlated (0.93 to 0.97) with observed TMY (89.9 kg). The four models under estimated peak yield (PY), whereas POL2 and POL3 gave nearer peak time lactation estimations. Ewes lambing in autumn had higher TMY and showed a typical curve shape. Higher TMY were recorded in second and third lambing. Season of lambing, number of lambing and type of lambing had a great influenced over TMY shaping the complete lactation curve of F1 dairy sheep. In general terms WL model showed the best fit to the F1 dairy sheep lactation curve under organic management.
The effect of k-casein (k-CN) variant on milk production traits (milk yield, fat yield, protein yield, fat percentage and protein percentage) was estimated for 568 Holstein cows in the first lactation. The k-CN valiant were determined by PCR-RFLP (restriction fragment length polymorphism) technique at the DNA level. Single trait linear model was used for the statistical analysis of the data. Result of this study indicated that k-CN variant affected significantly milk yield (P<0.05) and protein yield (P<0.01). Animals with the BB variant produced 622kg milk more and had protein yield higher by 32kg compared with animals with the AA variant No associations between the k-CN variants and other milk production trait were found. Therefore, milk and protein yield may be improved through milk protein typing by increasing the frequencies of k-CN B variant in dairy cattle population. In cheese making, it will be also preferable to have milk with the B variant of k-CN, which gives higher yield having a better quality than the A variant milk.
An experiment was carried out to study the effect of monensin administration on mammary functions in crossbred Holstein cattle. Fourteen non-pregnant late lactating crossbred Holstein cattle, approximately 270 days postpartum, were selected for the experiment. They were divided into two groups of 7 animals each. Seven animals in the treated group were given sodium monensin orally in a slow-release capsule. Animals in both control and treated groups were fed the similar diet to maintain milk production and body score at 2.5. Rice straw was fed as a source of dietary fiber throughout the experimental period. After monensin administration, a significant increase in the molar percent of ruminal propionate (p<0.05) and a significant decrease in the molar percent of ruminal acetate (p<0.05) were apparent in comparison to the pretreated period. The ratio of acetate to propionate concentration decreased significantly after monensin administration (p<0.05), while it was maintained at the similar level throughout the period of experiment in the control group. Monensin did not affect the molar percent of ruminal butyrate and valerate. The concentration of milk allantoin between the control group and monensin treated group was not different. An excretion rate of allantoin in milk decreased in animals treated with monensin (p<0.05). Mammary blood flow did not show significant difference between control and monensin treated groups. The plasma glucose concentration, arteriovenous concentration difference and mammary gland uptake of glucose remained constant in both groups. Milk yield of the later stage of lactation in the control group declined during lactation advance while a tendency to increase in the milk yield was apparent after 21 days monensin administration. Milk compositions for concentration of lactose, fat and protein in both control group and monensin treated group did not change throughout the experimental periods. From these results, it can be concluded that the action of monensin could affect the ruminal fermentation pattern. Monensin could not increase milk yield in the late lactating period.
Data on 1,037 Nili-Ravi buffaloes from four institutional herds were used to study lifetime milk yield, herd life, productive life and breeding efficiency. A general linear model was used to study the environmental effects while an animal model having herd, year of birth and age at first calving (as covariate) along with random animal effect was used to estimate breeding values. The lifetime milk yield, herd life, productive life and breeding efficiency averaged $7,723{\pm}164$ kg, $3,990{\pm}41$ days, $1,061{\pm}19$ days and 64 percent, respectively. All the traits were significantly (p<0.01) affected by the year of birth and herd of calving, while the herd life was also affected (p<0.01) by the age at first calving. The heritabilities for lifetime milk yield, herd life, productive life and breeding efficiency were $0.093{\pm}0.056$, $0.001{\pm}0.055$, $0.144{\pm}0.079$ and 0.001, respectively. The definition for productive life, where each lactation gets credit upto 10 months had slightly better heritability and may be preferred over the definition where no limit is placed on lactation length. The genetic correlation between productive life and lifetime milk yield was low but high between productive life and herd life. The selection for productive life will increase herd life while lifetime milk yield will also improve. The overall phenotypic trend during the period under the study was negative for lifetime milk yield (-280 kg/year), herd life (-93 days), productive life (-42 days/year) and breeding efficiency (-0.36 percent/year), whereas the genetic trend was positive for lifetime milk yield (+15 kg/year) and productive life (+4 days/year).
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.
To study the relationship between certain hormones and metabolites and between hormones and milk yield during different stage of lactation, six lactating Karan Swiss cows and six Murrah buffaloes were maintained. Growth hormone, insulin, $T_3$, $T_4$, glucose, BHBA, NEFA and milk yield were studied. Highly negative relationship of growth hormone with insulin and triiodothyronine in cows and marginally negative in buffaloes suggest that insulin and triiodothyronine aid in the process of partitioning of nutrients towards milk production through reducing the demands of nutrients by peripheral tissue. The significant and negative correlation of growth hormone with dry matter intake in both the species suggest that the availability of nutrients from the digestive tract play a role in the regulation of growth hormone secretion. Positive relationship of growth hormone with non esterified fatty acids in both the species suggest that high growth hormone levels may result in fat mobilization and thereby increase the availability of energy precursors for milk synthesis. Insulin was negatively correlated with milk yield and lactose content and positively with milk fat and protein but the degree of relationship varied. In both the species the relationship between triiodothyronine and milk yield was negative and between thyroxine and milk yield was positive. However, it was significant only in cows and not in buffaloes. Thyroxine was positively correlated with beta-hydroxybutyrate and non-esterified fatty acids with milk yield in both the species.
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.
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.
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