Lactation 305-day Milk Yield Research Articles

Comparative Efficiency of Conventional and Connectionist Models in Prediction of First Lactation 305-day Milk Yield in Murrah Buffaloes

Comparative Efficiency of Conventional and Connectionist Models in Prediction of First Lactation 305-day Milk Yield in Murrah Buffaloes

Strong associations between the FGF-2 gene and productivity traits of Holstein-Friesian dairy cattle

Sustainability in dairy cattle farms depends on the efficiency of milk yield and reproductive traits. Thus, this study aimed to investigate the effect of the FGF-2/Csp6I gene and major environmental factors on these traits in Holstein-Friesian cattle. A total of 212 whole blood samples were collected from the Vena coccygea of cattle and the data obtained from these samples were used in all statistical analyses. Then, the restriction fragment length polymorphism (RFLP) method (determination of genotypes) was conducted and programs including PopGene (allele and genotype frequencies), Minitab (association analyses) and MTDFREML (variance components and genetic parameters) were used. Alleles A (0.4269) and G (0.5731) as well as genotypes AA (0.174), AG (0.505) and GG (0.321) were found, indicating that the population is polymorphic and in Hardy-Weinberg equilibrium (P > 0.05). The effect of the Csp6I polymorphism of FGF-2 gene on peak milk yield (PMY) (P < 0.01); lactation milk yield (LMY), milking time (MT), 305-day and 200-day lactation milk yield (LMY305 and LMY200), average daily milk yield (ADMY) (P < 0.05); 100-day lactation milk yield (LMY100), age of using in first breeding (AUFB) and number of inseminations per conception (NIPC) (P < 0.10) were significant. The heritability of milk yield traits and the correlation between direct and maternal heritability for reproductive traits were high. Furthermore, the breeding value of PMY was higher for the AA genotype (0.745 ± 0.292) than for the AG genotype (−0.268 ± 0.171) (P < 0.05). As a result, the A allele and AA genotype for the FGF-2/Csp6I gene had an increasing effect on milk yield without compromising reproductive performance in Holstein-Friesian dairy cattle.

Evaluating Peak Milk Yield as a Predictor for First Lactation 305-Day Milk Yield in Murrah Buffaloes

Background: Peak milk yield is an important early observable trait that holds immense practical significance in dairy farming. It is often utilized to assess the production potential of dairy animals under field conditions. Therefore, the present study aimed to predict the first lactation 305-day milk yield (FL305DMY) based on peak milk yield (PY) and subsequently, compare its efficiency with monthly test-day milk yield (TD) records alone or in combinations. Methods: Data on 350 PY and 3,850 TD records pertaining to first lactation of 350 Murrah buffaloes that calved in between 1993 and 2017 at ICAR-National Dairy Research Institute, Karnal, India were utilized for the investigation. A total of 11 TD records were taken from each animal at an interval of 30 days, starting from 6th day onwards until 305th day of lactation. The prediction of FL305DMY was performed by univariate and stepwise backward multiple linear regression analysis. The efficiency of the prediction equations was evaluated by Coefficient of determination (R2), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Root Mean Square Error (RMSE). Result: It was observed that the peak milk yield alone could predict the FL305DMY with 64.15% accuracy, which was the highest in univariate linear regression analysis. Furthermore, the regression coefficients and R2 values indicated that mid-lactation monthly test-day milk yield records up to TD-7 could be utilized in conjunction with peak milk yield for early prediction of FL305DMY. The stepwise backward multiple linear regression analysis revealed that the most optimal prediction equation, including peak milk yield as one of the variables, was found to be composed of three variables (PY, TD-4 and TD-7) showed 84.79% R2. The results could be utilized for early selection of genetically superior animals in breeding strategies.

Bayesian approach for evaluation of lactation curve in cross bred cattle based on monthly and bimonthly test day milk yield.

In field progeny testing program milk recording at monthly or bimonthly intervals and prediction of first lactation 305-day milk yield (FL305DMY) from these test day yields have been adapted as an alternative to daily milk recording. Wood's incomplete gamma function is the one of the commonly used nonlinear lactation curve model. In recent years Bayesian approach of fitting nonlinear biological models is gaining attention among researchers. In this study Wood's incomplete gamma function was fitted using Bayesian approach using monthly (MTDY) and bimonthly test day (BTDY) yields. The lactation curve parameters thus obtained were used for prediction of FL305DMY. Efficiency of prediction based on monthly and bimonthly test day milk yield were compared using error of prediction. It was found to be 5.78% and 7.59% as root mean square error (RMSE) based on MTDY and BTDY respectively.The Breeding values of 97 Karan Fries sires were estimated using BLUP-AM based on actual and predicted FL305DMY thus obtained. The RMSE was calculated as the difference between estimated breeding values based on actual and predicted yield. It was found that RMSE calculated based on MTDY showed only a marginal superiority of 0.79% over BTDY and showed high degree of correlation with actual yield. Therefore, recording at bimonthly intervals could be an economical alternative without compromising the efficiency.

Modelling Lactation Curve for Genetic Evaluation of Crossbred Cattle

The present study was conducted on first lactation 4415 monthly test-day milk yield records of 466 crossbred (CB) cattle sired by 89 bulls during 2000–2018 (19 years) maintained at Directorate of Livestock Farms, GADVASU, Ludhiana, Punjab, India. The crossbred cattle with lactation length of minimum 100 days were considered for the study. The aim was to develop the best lactation curve model and to compare the breeding values of sires based on actual and predicted first lactation 305-day milk yield (FL305DMY). The data were classified and coded according to different season and age at first calving groups for first lactation 305-day milk yield. Monthly test-day milk yields of first lactation were used to develop the best lactation curve model. The Polynomial Regression Function (PRF) model was the best model amongst all the models based on both R2 and RMSE values. The breeding values of 60 HF crossbred sires with two or more daughters were estimated from the actual and predicted FL305DMY using Polynomial Regression Function by applying two sire evaluation methods viz. least squares method (LSQ) and restricted maximum likelihood method (REML). The effectiveness of LSQ and REML methods of sire evaluation was compared on the basis of Spearman’s rank correlations, coefficients of determination (R2) and coefficients of variation (CV). LSQ was found most efficient and accurate method for sire evaluation using actual and predicted first lactation 305-day milk yield in HF crossbred cattle.

Genetic variants in 5'UTR and exonic region of NPY gene alter the reproduction performance in Indian cattle breeds.

Neuropeptide Y (NPY) is one of the most potent orexigenic factors which can produce diverse effects on behaviour and other physiological functions and is highly conserved in evolution. The present study was aimed to identify and associate SNPs in the 5' UTR and exon2 region of the NPY gene with reproduction and production traits in Kankrej cattle of Indian origin. Three mutations in the 5'-UTR region and one mutation in the exon2 region of the NPY gene were identified by PCR-SSCP and PCR-RFLP, respectively, followed by sequencing. Further, association studies were conducted with reproduction and production traits in Kankrej cattle. The GACCGA genotyped animals based on the 5'UTR variants indicated better dry period and calving interval, whereas with GGCCGG genotypes showed higher total lactation milk yield and 305-day milk yield in comparison to other genotypes. Also, service period and inter calving period varied significantly among the genotypes of exon2, as the GG genotyped animals had significantly longer calving interval. Other traits like age at first heat, age at first service and age at first calving were not affected by the mutations. So, the present study outlined that the bovine NPY gene may be considered to be one of the candidate gene for improvement of reproductive performance of cattle, after validation on large sample size.

Prediction of first lactation 305 days milk yield using artificial neural network in Murrah buffalo

In the present study, first lactation test day and monthly milk records of 301 Murrah buffaloes were used for prediction of first lactation 305-day milk yield (FL305DMY) using artificial neural network (ANN) and was compared with multiple linear regression (MLR). Models were evaluated on the basis of coefficient of determination and root mean square error (RMSE). Two different input sets (Input set-1 and Input set-2) were used in the study. In input set-1, four test day milk yields (6th, 36th, 66th and 96th day of lactation) along with age at first calving (AFC) and peak yield (PY) were taken together and in input set-2, four monthly milk yields record (1st, 2nd, 3rd and 4th month yield) along with AFC and PY were taken together. The ANN was trained using back propagation (BP) algorithm which is also known as Bayesian regularization (BR). ANN achieved highest accuracy of 82% with lowest RMSE value of 16.46% for input set-1 while MLRs accuracy was 80.53% with RMSE value of 17.48%. Higher accuracy and lower RMSE value for ANN clearly showed its better performance than MLR model. Hence, ANN could be alternatively used as a tool for prediction of FL305DMY in Murrah buffaloes using input set-1 with more than 80% accuracy. So, 96th day test day yield (TD4) can be used for prediction of FL305DMY and as a trait for early genetic evaluation of sires.

Sire evaluation using conventional methods and animal models in Sahiwal cattle

Sire evaluation is one of the most important aspects of dairy cattle genetic improvement programme which involves the estimation of breeding value of the bulls on the basis of first lactation 305-day milk yield. The present study was undertaken to evaluate the Sahiwal sires on the basis of observed and predicted first lactation 305-days milk yield. The study was conducted utilizing 19 Sahiwal sires with three or more daughters per sire which were evaluated and ranked on the basis of first lactation 305-day milk yield. Three different sire evaluation methods, viz. Simple daughter average, Least squares method (LSM), Best linear unbiased prediction method(BLUP) were compared. The BLUP method was found to be superior followed by LSM and Simple Daughter Average Method for evaluating the sires for first lactation 305-day milk yield

Sire Evaluation Based on First Lactation 305 Day Milk Yield and Individual Part Lactation Records in Frieswal Cattle

Background: Frieswal, the crossbred cattle, between sahiwal and Holstein Friesian breed with 62.50 per cent of exotic (inheritance) with total lactation milk yield of (4000 Kg) with average lactation length 300 days (PDC Annual Report, 2015-16). Presently, the Frieswal Animal is mainly maintained at 36 Military Farms located in various agro-climatic regions in the country.Methods: The present study was undertaken on first lactation 305 days and part lactation records of 1470 Frieswal cows maintained over a period of 10 years (2003-2012) from military farm at CIRC Meerut Uttar Pradesh as well as Ambala. The success of any breed improvement programme depends mainly on the Sire evaluation which involves the estimation of expected breeding value of the bulls on the basis of average first lactation 305-day or less milk yield and part lactation records of their daughters (FL305DMY).Result: The estimate breeding value of 55 Frieswal sires on the basis of first lactation 305-day or less milk yield and individually part lactation records, by LSM, BLUP and DFREML methods were 2701.48 kg and 2660.70 kg, followed by 333.40, kg and 329.17 kg, respectively. A number of 55 Frieswal sires with three or more daughters were evaluated and ranked on the basis of first lactation 305-day milk yield and individual third month part lactation was having better heritability and comparatively higher genetic correlations with the first lactation 305-days or less milk yield.

Multiple linear regression analysis using monthly test day milk yield predicting the first lactation production performance for sire evaluation in Murrah buffaloes

A total of 9071 first lactation monthly test-day milk yield records of Murrah buffaloes were used to predict the first lactation 305-day milk yield (FL305DMY) by using stepwise backward regression analysis. For the prediction of FL305DMY best combination of monthly test-day milk yields were selected based on adjusted R 2 and RMSE values. The objective of the study was to compare various methods of sire evaluation viz., least squares (LSQ), simple regressed least squares (SRLS), best linear unbiased prediction sire model (BLUP-SM) and best linear unbiased prediction animal model (BLUP-AM) in terms of accuracy and efficiency. The methods were compared on the basis of error variance, coefficient of determination, coefficient of variation and rank correlations among the methods. The accuracy of prediction of FL305DMY from monthly test-day milk yields were observed to be best for TD-2 (45 th day), TD-4 (105 th day) and TD-6 (165 th day) combination with BLUP-AM as the most efficient method for sire evaluation. Individual, key monthly TD-6 (165 th day) milk yield has high rank correlation with EBVs obtained from actual 305-day milk yield. It was concluded that the optimum combination of TD-2 (45 th day), TD-4 (105 th day) and TD-6 (165 th day) or individual TD-6 (165 th day) can be used for genetic evaluation of Murrah sires.

Genetics and Phenotypic Analysis of First Lactation Production, Reproduction and Part Lactation Traits in Frieswal Cattle

Background: The Frieswal cattle a crossbred cattle breed having 62.50 per cent of exotic (inheritance) having total lactation milk yield of 4000 Kg with average lactation length 300 days (PDC Annual Report, 2015-16). Presently, the Frieswal Animal is mainly maintained at 36 Military Farms located in various agro-climatic regions in the country. Methods: The present study was undertaken on first Part lactation production, reproduction and part lactation records of 1470 Frieswal cows maintained over a period of 10 years (2003-2012) two military farms viz Meerut and Ambala at CIRC, Meerut. Result: The estimate the heritability of production and reproduction traits were 0.29 ±0 .098,0.142±.081, 0.130±0.079, 0.087 ±.074, 0.631 ± 0.255, 0.345 ± 0.247 and 0.100 ± 0.153 for standard 305 days milk yield, age of first calving (AFC), First lactation total milk yield (FLMY), First lactation 305-day or less milk yield (FL305DMY), First lactation length (FLL), Inter calving interval (ICI), First dry period (FDP), First Service Period (FSP), respectively. All the genetic and phenotypic correlation among different production and reproduction traits were high and positive. The estimates of heritability of first lactation individual part monthly yields in Frieswal cattle were found to be 0.100 ± 0.069, 0.105 ± .070, 0.100 ± 0.069, 0.112 ± 0.071, 0.204 ± 0.086, 0.160±0.079, 0.176 ± 0.081, 0.221 ± 0.088, 0.213 ± 0.087 and 0.192 ± 0.09, respectively.

Prediction of First Lactation 305-Day Milk Yield by Test Day Simple and Multiple Regression Models in Holstein Friesian Crossbred Cattle

Prediction of First Lactation 305-Day Milk Yield by Test Day Simple and Multiple Regression Models in Holstein Friesian Crossbred Cattle

Age at first calving in UK Jersey cattle and its associations with first lactation 305-day milk yield, lifetime daily milk yield, calving interval and survival to second lactation

The association between age at fist calving (AFC) and production, fertility and survivability in Jersey heifers requires evaluation and comparison with published literature on Holstein heifers. Lactation records from 7256 pedigree Jersey heifers were grouped by AFC. Analysis comprised mixed effects multivariable regression modelling and binary logistic regression modelling. Increased lifetime daily yield (LDY) was significantly associated with a reduced AFC; calving UK Jersey heifers at 24 months or below is optimal. Heifers calving at 25-26 months had a significantly lower mean calving interval (391 days; 95 per cent CI 386 to 397) than those calving over 30 months (399; 95 per cent CI 394 to 405) (P=0.03). Heifers with an AFC over 30 months were less likely to survive to second lactation than those with an AFC 18-24 months. Spring calving heifers produced significantly less milk per day (9.87 kg; 95 per cent CI 9.64 to 10.1) than autumn (10.18; 95 per cent CI 9.94 to 10.41) or winter calvers (10.07; 95 per cent CI 9.84 to 10.3) (P≤0.01). Heifers that calved in the winter had a significantly shorter calving interval (389 days; 95 per cent CI 394 to 383) (P≤0.01) than heifers calved in other seasons. The impact of AFC on Jersey production, fertility and longevity traits shows a similar trend to that seen in the Holstein, potentially on a lesser scale.

Prediction of First Lactation 305-day Milk Yield Based on Bimonthly Test Day Milk Yield Records in Murrah Buffaloes

The present study was conducted on 2100 first lactation bimonthly test day milk yield (BTDY) records of 350 Murrah buffaloes calved in between 1993 and 2017 at ICAR-NDRI, Karnal. A total of 6 BTDY records were taken from each animal at an interval of 60 days, from 6th day to 305th day of lactation. The prediction of First Lactation 305-Day Milk Yield (FL305DMY) was done by utilizing five conventional and machine learning methods viz., Centering Date Method (CDM), Test Interval Method (TIM), Ratio Method (RM), Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). Error in prediction was estimated by absolute error, percentage absolute error, average error, percentage average error, Root Mean Square Error (RMSE) and percentage RMSE. MLR was found to be the best method with the least error in prediction (5.71% RMSE), followed by ANN (5.77% RMSE). The accuracy (R2) of MLR equation including all 6 BTDY records was 91.86%. The best MLR equation for an early prediction of FL305DMY included 3 BTDY records viz., BTDY-2 (65th day), BTDY-3 (125th day) and BTDY-4 (185th day) with 85.29% R2. The study compared the conventional and computational methods for prediction of first lactation milk yield which could be used for early selection of the animals.

Estimation of genetic parameters for milk and fertility traits within and between low, medium and high dairy production systems in Kenya to account for genotype-by-environment interaction.

Dairy records from the Dairy Recording Service of Kenya were classified into low, medium and high production systems based on mean 305-day milk yield using the K-means clustering method. Milk and fertility records were then analysed to develop genetic evaluation systems accounting for genotype-by-environment interaction between the production systems. Data comprised 26,638 lactation yield, 3,505 fat yield, 9,235 age at first calving and 17,870 calving interval records from 12,631 cows which were descendants of 2,554 sires and 8,433 dams. An animal model was used to estimate variance components, genetic correlations and breeding values for the production systems. Variance components increased with production means, apart from genetic group variances, which decreased from the low to the high production system. Moderate heritabilities were estimated for milk traits (0.21-0.27) and fat traits (0.11-0.38). Low heritabilities were estimated for lactation length (0.04-0.10) and calving interval (0.03-0.06). Moderate heritabilities (0.25-0.26) were estimated for age at first calving, except under the high production system (0.05). Within production systems, lactation milk yield, 305-day milk yield and lactation length had high positive genetic correlations (0.52-0.96), while lactation milk yield and lactation length with age at first calving had negative genetic correlations. Milk yield and calving interval were positively correlated except under the low production system. The genetic correlations for lactation milk yield and 305-day milk yield between low and medium (0.48±0.20 and 0.46±0.21) and low and high production systems' (0.74±0.15 and 0.62±0.17) were significantly lower than one. Milk yield in the low production system is, therefore, a genetically different trait. The low genetic correlations between the three production systems for most milk production and fertility traits suggested that sires should be selected based on progeny performance in the targeted production system.

Comparative study of ANN and conventional methods in forecasting first lactation milk yield in Murrah buffalo

Present investigation was undertaken to predict first lactation 305-day milk yield (FL305DMY) using monthly test day milk records. Under this study, multiple linear regression (MLR) and artificial neural network (ANN) approach were used. Effectiveness of both methods was also compared for prediction of FL305DMY in Murrah buffalo. The data on 3336 monthly test day milk yields records of first lactation pertaining to 556 Murrah buffaloes maintained at National Dairy Research Institute, Karnal; Central Institute for research on buffalo; Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana and Choudhary Charan Singh Haryana Agricultural University (CCSHAU), Hisar were used in this study. In MLR study, it was observed that model 14 having four independent variable, i.e. FSP, TD2, TD4 and TD6 fulfilled most criteria such as highest R2, lowest MSE, lowest RMSE, lowest CP, lowest MAE, lowest MAPE, and lowest U value. In the present investigation, the accuracy of prediction obtained from ANN was almost similar to MLR for prediction of FL305DMY using monthly test day milk records in Murrah buffalo. The best ANN algorithm achieved 76.8% accuracy of prediction for optimum model, whereas the MLR explained 76.9% of accuracy of prediction of FL305DMY in Murrah buffalo. MLR method is simple as compared to ANN, hence MLR method could be preferred.

Comparative efficiency of different multiple linear regression prediction equations of first lactation 305-day milk yield for sire evaluation in Murrah buffaloes

In this study, first lactation 39059 weekly test-day milk yield records of 961 Murrah buffaloes were used to predict first lactation 305-day milk yield (FL305DMY) by stepwise backward regression method. The best single, two, three and four test day combinations were selected for prediction of FL305DMY based on adjusted R2 and RMSE values. The sires were evaluated for 305-day actual and predicted first lactation milk yield based on derived multiple regression equations using four methods viz. least squares (LSQ), simple regressed least squares (SRLS), best linear unbiased prediction sire model (BLUP-SM) and best linear unbiased prediction animal model (BLUP-AM) methods. The effectiveness of different sire evaluation methods were judged by error variance, coefficient of determination, coefficient of variation and spearman’s rank correlation. The accuracy of prediction of FL305DMY from weekly test day milk yields were observed to be best for TD-7 (48th day) and TD-22 (153rd day) combination with BLUP-AM as the most efficient method for sire evaluation. It was concluded that the FL305DMY can be predicted as early as 153rd day of lactation and further can be used for early genetic evaluation of Murrah sires.

Genetic analysis of productive and reproductive traits in multiple-breed dairy cattle populations

Abstract: The objective of this work was to determine whether the random regression model using linear splines (RRMLS) is suitable to estimate the genetic parameters for productive and reproductive traits of a multiple-breed dairy cattle population, as well as to investigate the effect of the genetic group of the progeny on the genetic merit of the sire. The multiple-trait model (MTM) and the RRMLS with one knot fitted for every genetic group were used to obtain the genetic parameters. Records of 1/2 Holstein + 1/2 Gyr (1/2HG), 5/8 Holstein + 3/8 Gyr (5/8HG), and 3/4 Holstein + 1/4 Gyr (3/4HG) crossbreed dams were considered. The RRMLS showed better fitting. The additive and residual variances estimated by the MTM and the RRMLS were similar. Heritability varied from 0.20 to 0.33 for age at first calving, from 0.09 to 0.22 for first lactation length, and from 0.15 to 0.35 for first lactation 305-day milk yield, according to the genetic composition of the dams. The RRMLS is suitable to estimate the genetic parameters for productive and reproductive traits of multiple-breed dairy cattle populations. The genetic merit of the sires is affected by the genetic group of the progeny by which they are evaluated.

Modeling lactation curve for genetic evaluation of Murrah buffaloes

In this study, four lactation curve models viz. Gamma-type function (GF), Mixed log function (MLF), Exponential function (EF) and Polynomial regression function (PRF) were fitted to predict the weekly test-day milk yields pertaining to 961 Murrah buffaloes progeny of 101 sires spread over a period of 36 years (1977-2012) maintained at ICAR-NDRI, Karnal. The PRF was the best fit with highest R2 (99.3%) and lowest RMSE (0.3%) which was used subsequently to estimate the first lactation 305-day or less milk yield (FL305DMY) from weekly test-day milk yields. The sires were evaluated using four linear models viz. Least Squares(LSQ), Simple Regressed Least Squares (SRLS), Sire (BLUP-SM) and animal (BLUP-AM) models from actual as well as PRF predicted FL305DMYs. BLUP-AM was the most efficient, among the sire evaluation models, in predicting the breeding values. Sire evaluation on the basis of predicted data (using PRF) was similar to that on the basis of actual data indicating that the best fitted lactation curve function can be used as an alternative for early animal evaluation and reduction of generation interval.

Random regression test-day milk yield models as a suitable alternative to the traditional 305-day lactation model for genetic evaluation of Sahiwal cattle

In the present study, first three lactation 305-day milk yield variance components, genetic parameters and breeding value were estimated using test-day milk yield and 305-day actual milk yield data of Sahiwal cattle. The estimates were obtained using three methods viz. random regression model (RRM) with homogeneous residual variance (RRM-HOM), RRM with heterogeneous residual variance (RRM-HET) and univariate animal model. The additive genetic variance of 305-day milk yield estimated from RRM was higher compared to univariate animal model for all lactation. From RRM, it was possible to account for permanent environmental effects due to individual milk yield variations during lactation. The heritability estimates were low for first (0.072 to 0.079) and third lactation (0.087 to 0.112) 305-day milk yield from all three methods. For second lactation, low heritability estimate from univariate animal model (0.144) and moderate estimate from different RRM (0.206 to 0.219) were obtained. For all lactation, breeding value rank correlation was more than 0.78 between lactation model and random regression testday model. The same bull was identified as top ranking bull from all three methods. It can be concluded that random regression test-day models can replace conventional 305-day lactation model for genetic evaluation as it resulted in higher additive genetic variance estimates, gave similar/higher heritability value and moderate to high rank correlation estimates for breeding values.