Test-day Milk Yield Research Articles

A comprehensive analysis of the effects of DGAT1 K232A polymorphism on milk production and fertility traits in Holstein Friesian and Jersey cows reared in Türkiye

Abstract. Research on the diacylglycerol acyltransferase 1 (DGAT1) K232A marker in cattle shows inconsistent results across regions, largely due to small sample sizes, limited genetic variation, and data restricted to few lactations, which complicates establishing a reliable genotype–phenotype correlation. This research aimed to determine the effect of the K232A polymorphism of the bovine DGAT1 gene on milk production and quality traits in dairy cattle. We used 1104 cattle, including 828 Holstein Friesian and 276 Jersey cows. The analysis utilized extensive data from six lactations of cows raised on four commercial dairy farms. We genotyped the population using the polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) technique and Sanger sequencing for verification. We then evaluated the 305 d and test-day milk yields as well as fat and protein yields and percentages. The number of inseminations per conception and calving ease were also assessed as reproduction indices. Genotype–phenotype associations were quantified using linear mixed models. The AA genotype was absent in Jersey cows, and the heterozygous genotype was predominant in both breeds. The K232A marker was significantly associated with test-day milk yield, fat, and protein content in Jersey cows. Further, it substantially affected the fat percentage of milk in Holstein Friesian cows (p<0.001). We found that the KK genotype is highly desirable for milk quality and especially fat content. This comprehensive assessment demonstrated that the KK genotype of the DGAT1 K232A polymorphism significantly influenced fat and protein contents in 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.

Genetic parameters, genome-wide association study, and selection perspective on gestation length in 16 French cattle breeds

Lifetime productivity is a trait of great importance to dairy cattle populations as it combines information from production and longevity variables. Therefore, we investigated the genetic background of lifetime productivity in high-producing dairy cattle by integrating genomics and transcriptomics data sets. A total of 3,365,612 test-day milk yield records from 134,029 Chinese Holstein cows were used to define 6 lifetime productivity traits, including lifetime milk yield covering full lifespan and 5 cumulative milk yield traits covering partial lifespan. Genetic parameters were estimated based on univariate and bivariate linear animal models and the Restricted Maximum Likelihood (REML) method. Genome-wide association studies (GWAS) and weighted gene co-expression network analyses (WGCNA) were performed to identify candidate genes associated with lifetime productivity based on genomic data from 3,424 cows and peripheral blood RNA-seq data from 23 cows, respectively. Lifetime milk yield averaged 24,800.8 ± 14,396.6 kg (mean ± SD) across an average of 2.4 parities in Chinese Holstein population. The heritability estimates for lifetime productivity traits ranged from 0.05 (±0.01 for SE) to 0.10 (±0.02 for SE). The estimate of genetic correlation between lifetime milk yield and productive life is 0.88 (±0.3 for SE) while the genetic correlation with 305d milk yield in the first lactation was 0.49 (±0.08 for SE). Absolute values for most genetic correlation estimates between lifetime productivity and type traits were lower than 0.30. Moderate genetic correlations were found between udder related traits and lifetime productivity, such as with udder depth (0.33), rear udder attachment height (0.33), and udder system (0.34). Some single nucleotide polymorphisms and gene co-expression modules significantly associated with lifetime milk yield were identified based on GWAS and WGCNA analyses, respectively. Functional enrichment analyses of the candidate genes identified revealed important pathways related to immune system, longevity, energy utilization and metabolism, and FoxO signaling. The genes NTMT1, FNBP1, and S1PR1 were considered to be the most important candidate genes influencing lifetime productivity in Holstein cows. Overall, our findings indicate that lifetime productivity is heritable in Chinese Holstein cattle and important candidate genes were identified by integrating genomic and transcriptomic data sets.

Estimation of the threshold for heat stress and genetic features for milk yield in Mehsana buffaloes in India

Heat stress is one of the primary environmental factors that harm both the productivity and health of buffaloes. The current study was conducted to estimate the threshold of temperature humidity index (THI)1 and genetic features for milk yield of first-lactation Mehsana buffaloes using an univariate repeatability test-day model. The data included 130,475 first lactation test-day milk yield (FLTDMY) records of 13,887 Mehsana buffaloes and the daily temperature and humidity. The statistical model included herd test day as fixed effects, days-in-milk (DIM) classes, age of the animal, as well as random factors such as the additive genetic effect (AGE) of animal in general conditions (intercept), AGE of the buffaloes subjected to heat stress (slope), permanent environmental effect of animal in general conditions (intercept), permanent environmental effect of animal under heat stress conditions (slope) and random residual effect. It was expected that the general effects and the heat-tolerance effects would be correlated, represented by the present investigation's repeatability models. The variance components of FLTDMY in the present study were computed using the REML method. The threshold for THI was 78. At the THI below the threshold, the heritability estimated for the FLTDMY trait was 0.29, and the additive genetic variance (AGV) for heat stress conditions was 0. At THI of 83, AGV for heat stress conditions was highest for FLTDMY. The genetic correlation of general AGE to heat-tolerant AGE was −0.40. The results indicated that a consistent selection for milk production, avoiding the thermal tolerance, may diminish the thermal tolerance capacity of Mehsana buffaloes.

Genetic analysis of test-day milk yield in sheep recorded at two governmental breeding and multiplication centers in Ethiopia

ABSTRACT One aim was to compare four classes of ewes for test-day milk yield (TDMY). The classes are the local Menz breed, the Awassi breed, and their main crossbreds (50 and 75%). Another aim was to estimate variance components to facilitate the prediction of breeding values and their accuracy. A total of 1040 TDMY records of 211 ewes were used. A univariate repeatability model with Legendre polynomial coefficients were used to model lactation curves. The 100% Awassi ewes produced significantly (p < 0.05) more milk than the other studied ewe groups within 120 Days in milk (DIM). The Local ewes produced significantly less than the other groups. Estimates of heritability (h 2) and repeatability (r) of TDMY were 0.10 ± 0.08 and 0.15 ± 0.03, respectively. The genetic variance indicated for TDMY could give a genetic gain in the dissemination program if recorded for all ewes at the two centres.

Milking Temperament and Its Association with Test-Day Milk Yield in Bulgarian Murrah Buffaloes.

The goal of this research was to evaluate milking temperament and its relationship with test-day milk (TDMY0) yield in Bulgarian Murrah buffaloes. This study involved 90 buffalo cows reared under a tie-stall production system which were milked twice a day with a milking pipeline. The behavioral responses of the buffaloes were reported during preparation for milking and during actual milking. The average temperament score during preparation for milking was 1.83, and 1.93 during milking itself. The most common reaction was leg lifting (18.9%), followed by cows moving on the stall bed (10%), definite kicking (9.9%), and 13.3% managing to remove the milking cluster during milking. The frequency of buffaloes showing adverse reactions (scores 4 and 5) increased considerably during milking compared to preparation for milking. Repeated scoring of temperament during the same lactation did not show a significant difference in the frequency of temperament assessments both in preparation for milking and during milking. The minimal difference may be due to the accuracy of the assessment or a momentary change in the condition of the animals during the two scorings. Cows with the most unwanted milking behavior (scores 5 and 4) had the highest LS means for TDMY, 8.18 kg and 7.65 kg, respectively. The reasons for these buffaloes remaining until later lactations was their high milk yield and the injection of oxytocin before milking, which helps them to be fully milked.

Genetic analysis of phenotypic indicators for heat tolerance in crossbred dairy cattle

Climate change-induced rise in global temperatures has intensified heat stress on dairy cattle and is contributing to the generally observed low milk productivity. Selective breeding aimed at enhancing animals’ ability to withstand rising temperatures while maintaining optimal performance is crucial for ensuring future access to dairy products. However, phenotypic indicators of heat tolerance are yet to be effectively factored into the objectives of most selective breeding programs. This study investigated the response of milk production to changing heat load as an indication of heat tolerance and the influence of calving season on this response in multibreed dairy cattle performing in three agroecological zones Kenya. First-parity 7-day average milk yield (65 261 milk records) of 1 739 cows were analyzed. Based on routinely recorded weather data that were accessible online, the Temperature-Humidity Index (THI) was calculated and used as a measure of heat load. THI measurements used represented averages of the same 7-day periods corresponding to each 7-day average milk record. Random regression models, including reaction norm functions, were fitted to derive two resilience indicators: slope of the reaction norm (Slope) and its absolute value (Absolute), reflecting changes in milk yield in response to the varying heat loads (THI 50 and THI 80). The genetic parameters of these indicators were estimated, and their associations with average test-day milk yield were examined. There were no substantial differences in the pattern of milk yield response to heat load between cows calving in dry and wet seasons. Animals with ≤50% Bos taurus genes were the most thermotolerant at extremely high heat load levels. Animals performing in semi-arid environments exhibited the highest heat tolerance capacity. Heritability estimates for these indicators ranged from 0.06 to 0.33 and were mostly significantly different from zero (P < 0.05). Slope at THI 80 had high (0.64–0.71) negative correlations with average daily milk yield, revealing that high-producing cows are more vulnerable to heat stress and vice versa. A high (0.63–0.74) positive correlation was observed between Absolute and average milk yield at THI 80. This implied that low milk-producing cows have a more stable milk production under heat-stress conditions and vice versa. The study demonstrated that the slope of the reaction norms and its absolute value can effectively measure the resilience of crossbred dairy cattle to varying heat load conditions. The implications of these findings are valuable in improving the heat tolerance of livestock species through genetic selection.

Comparison of nonlinear functions to describe lactation curves for cumulative milk production in buffalo.

The aim of this study was to examine the suitability of different growth functions (linear, sinusoidal, Gompertz, Schumacher and Richards) to fit cumulative milk production data from buffalo cows. Cumulative milk production at each day in milk was calculated from two published datasets reporting (i) fortnightly test-day milk yield records of the first lactation of Murrah buffalo that had calved during 1977-2012 and (ii) the first lactation records of Jaffarabadi buffalo collected from history-cum-pedigree registers for each quinquennium between 1991 and 2010. Each function was fitted to the lactation curves using nonlinear regression procedures. The Richards and sinusoidal equations provided the smallest root mean square error values, Akaike's and Bayesian information criteria and, therefore, the best fit for the cumulative lactation curves for milk yield. The Richards equation appeared to provide the most accurate estimate of the cumulative milk production at peak milk yield. Sinusoidal and flexible classical growth functions are appropriate to describe cumulative milk production curves and estimate lactation traits in buffalo.

Modelling the lactation curve in Alpine × Beetal crossbred dairy goats using random regression models fitted with Legendre polynomial and B-spline functions.

The current study sought to genetically assess the lactation curve of Alpine × Beetal crossbred goats through the application of random regression models (RRM). The objective was to estimate genetic parameters of the first lactation test-day milk yield (TDMY) for devising a practical breeding strategy within the nucleus breeding programme. In order to model variations in lactation curves, 25,998 TDMY records were used in this study. For the purpose of estimating genetic parameters, orthogonal Legendre polynomials (LEG) and B-splines (BS) were examined in order to generate suitable and parsimonious models. A single-trait RRM technique was used for the analysis. The average first lactation TDMY was 1.22 ± 0.03 kg and peak yield (1.35 ± 0.02 kg) was achieved around the 7th test day (TD). The present investigation has demonstrated the superiority of the B-spline model for the genetic evaluation of Alpine × Beetal dairy goats. The optimal random regression model was identified as a quadratic B-spline function, characterized by six knots to represent the central trend. This model effectively captured the patterns of additive genetic influences, animal-specific permanent environmental effects (c2 ) and 22 distinct classes of (heterogeneous) residual variance. Additive variances and heritability (h2 ) estimates were lower in the early lactation, however, moderate across most parts of the lactation studied, ranging from 0.09 ± 0.04 to 0.33 ± 0.06. The moderate heritability estimates indicate the potential for selection using favourable combinations of test days throughout the lactation period. It was also observed that a high proportion of total variance was attributed to the animal's permanent environment. Positive genetic correlations were observed for adjacent TDMY values, while the correlations became less pronounced for more distant TDMY values. Considering better fitting of the lactation curve, the use of B-spline functions for genetic evaluation of Alpine × Beetal goats using RRM is recommended.

Potential for quantifying general environmental resilience of dairy cattle in sub-Saharan Africa using deviations in milk yield.

Introduction: Genetic improvement of general resilience of dairy cattle is deemed as a part of the solution to low dairy productivity and poor cattle adaptability in sub-Saharan Africa (SSA). While indicators of general resilience have been proposed and evaluated in other regions, their applicability in SSA remains unexplored. This study sought to test the viability of utilizing log-transformed variance (LnVar), autocorrelation (rauto), and skewness (Skew) of deviations in milk yield as indicators of general resilience of dairy cows performing in the tropical environment of Kenya. Methods: Test-day milk yield records of 2,670 first-parity cows performing in three distinct agroecological zones of Kenya were used. To predict expected milk yield, quantile regression was used to model lactation curve for each cow. Subsequently, resilience indicators were defined based on actual and standardized deviations of observed milk yield from the expected milk yield. The genetic parameters of these indicators were estimated, and their associations with longevity and average test-day milk yield were examined. Results: All indicators were heritable except skewness of actual and standardized deviation. The log-transformed variance of actual (LnVar1) and standardized (LnVar2) deviations had the highest heritabilities of 0.19 ± 0.04 and 0.17 ± 0.04, respectively. Auto-correlation of actual (rauto1) and standardized (rauto2) deviations had heritabilities of 0.05 ± 0.03 and 0.07 ± 0.03, respectively. Weak to moderate genetic correlations were observed among resilience indicators. Both rauto and Skew indicators had negligible genetic correlations with both longevity and average test-day milk yield. LnVar1 and LnVar2 were genetically associated with better longevity (rg = -0.47 ± 0.26 and -0.49 ± 0.26, respectively). Whereas LnVar1 suggested that resilient animals produce lower average test-day milk yield, LnVar2 revealed a genetic association between resilience and higher average test-day milk yield. Discussion: Log transformed variance of deviations in milk yield holds a significant potential as a robust resilience indicator for dairy animals performing in SSA. Moreover, standardized as opposed to actual deviations should be employed in defining resilience indicators because the resultant indicator does not inaccurately infer that low-producing animals are inherently resilient. This study offers an opportunity for enhancing the productivity of dairy cattle performing in SSA through selective breeding for resilience to environmental stressors.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review.

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

Unraveling the Genomic Association for Milk Production Traits and Signatures of Selection of Cattle in a Harsh Tropical Environment.

A study was designed to identify the genomic regions associated with milk production traits in a dairy cattle population reared by smallholder farmers in the harsh and challenging tropical savanna climate of Bengaluru, India. This study is a first-of-its-kind attempt to identify the selection sweeps for the dairy cattle breeds reared in such an environment. Two hundred forty lactating dairy cows reared by 68 farmers across the rural-urban transiting regions of Bengaluru were selected for this study. A genome-wide association study (GWAS) was performed to identify candidate genes for test-day milk yield, solids-not-fat (SNF), milk lactose, milk density and clinical mastitis. Furthermore, the cross-population extended haplotype homozygosity (XP-EHH) methodology was adopted to scan the dairy cattle breeds (Holstein Friesian, Jersey and Crossbred) in Bengaluru. Two SNPs, rs109340659 and rs41571523, were observed to be significantly associated with test-day milk yield. No significant SNPs were observed for the remaining production traits. The GWAS for milk lactose revealed one SNP (rs41634101) that was very close to the threshold limit, though not significant. The potential candidate genes fibrosin-like 1 (FBRSL) and calcium voltage-gated channel auxiliary subunit gamma 3 (CACN) were identified to be in close proximity to the SNP identified for test-day milk yield. These genes were observed to be associated with milk production traits based on previous reports. Furthermore, the selection signature analysis revealed a number of regions under selection for the breed-group comparisons (Crossbred-HF, Crossbred-J and HF-J). Functional analysis of these annotated genes under selection indicated pathways and mechanisms involving ubiquitination, cell signaling and immune response. These findings point towards the probable selection of dairy cows in Bengaluru for thermotolerance.

Genomic-enhanced breeding values for heat stress tolerance in Girolando cattle in Brazil

Heat stress is a major challenge for dairy herds and can lead to significant production reduction, mainly in tropical climate. Differences in decreased milk production in animals is associated with genetic variation. The overall goal was to investigate the effect of heat stress in milk yield of Girolando cattle in tropical climate and to identify the most appropriate statistical approach for evaluation and selection for heat tolerance in different breed compositions of Girolando. Therefore, to quantify heat stress effects on milk yield loss in Girolando cattle, random regression models were applied on test-day milk yield records (TDMY) and weather data. In addition, different statistical models were evaluated for estimating variance components and predicting breeding values for heat stress (HS). The data comprised 648,072 TDMY of 69,431 first-lactation Girolando cows of different breed compositions, and 21,147 genotyped animals, with daily temperature and humidity measurements from weather stations closest to the tested herds, between 2000 and 2020. We used daily mean values of temperature-humidity index (THI) for the day of test and the 3 previous days as the measure of HS. Heat stress thresholds estimated for the different breed compositions of Girolando in this study were THI 80 for 1/4H, 1/2H and 5/8H; THI 78 for 3/4H animals and THI 77 for 7/8H animals. Average milk production can decrease by up to 34% due to heat stress. Estimated average heritability for TDMY regressed to the THI was 0.21, while the heritability estimated for TDMY regressed to the DIM was 0.29. A correlation of 0.55 was observed between the GEBVs of HS tolerance and GEBV milk yield. The parameters estimated in this study indicate that genomic selection for heat tolerance in dairy cattle is a step towards ensuring valuable tropical production efficiency and improving animal welfare in the face of predicted increases in heat stress events and increase tropical production efficiency.

The variation in the beta-casein genotypes and its effect on milk yield and genomic values in Holstein-Friesian cows

The A2 milk marker is gaining popularity worldwide; thus, many farms plan to convert their dairy cattle herds to the A2A2 genotype. Variation in beta-casein genotypes needs to be monitored in large dairy cattle populations. Therefore we aimed to evaluate the genotypic distributions, population genetics, and diversity parameters in Holstein-Friesian cows. A total of 1200 cattle were genotyped using the Affymetrix® Axiom® array system. We performed an association analysis regarding the CSN2 genotypes and phenotypic traits, including lactation and test-day milk yield. We next evaluated the effects of the genotypes considering the genetic merit of the animals. Animals were grouped based on their PTAs for milk production, fat, protein, and daughter pregnancy rate. Thus, we tested the genotype × genetic merit interaction for significance. The A2 allele frequency is remarkably high (0.68), and the heterozygous genotype is predominant (46.25%). The marker showed intermediate variability and diversity levels, indicating a considerable frequency of the A1A1 genotype (9.33%) remains in the population. ANOVA results showed no significant association between the CSN2 genotypes and milk yield traits. A similar finding is valid for the genotype × genetic merit regarding the genomic test results. The data presented here may be helpful for further investigations and applications on A2 milk production.

Test-day and other milk recording options for prediction of lactation milk yield in Jaffarabadi (Bubalus bubalis) buffaloes

Generally, standard lactation milk yield is predicted based on test-day records collected at monthly intervals. Test-day milk production at different time intervals other than monthly intervals can be used to predict lactation milk yield of field bovines in field conditions. With the same possibility, this study was carried out to predict lactation milk yield in Jaffarabadi buffaloes from various test-day milk yield data retrieved for different time intervals. A total of 1,15,339 daily milk yield records in 176 lactations of 1st to 6th parity of 30 Jaffarabadi buffaloes lactating at the Cattle Breeding Farm, Kamdhenu University, Junagadh, Gujarat over a period of 28 years (1991 to 2018) were used for the study. Single monthly test-day milk yield recorded on 125th, 155th or 185th day i.e., 5th, 6th and 7th monthly test day yield alone provided only 50% reliability in determining the standard lactation milk yield. Daily peak yield alone was also found to be a poor predictor for lactation yield. Prediction equations using combination of consecutive two monthly test day yields from 4th to 10th monthly test day were found reliable source for prediction of lactation milk yield providing accuracy up to 82.19% whereas, daily peak yield in combination with single monthly test day yield at mid and late lactation was also predicted lactation milk yield with accuracies up to 72.23%. Milk production recorded at weekly interval could also be used to approximate milk production using the equation 15.35+6.91 × Sum of all weekly test-day yields, with precision of 98.93% or milk production recorded at fortnightly interval by the equation 18.04+14.65 × Sum of fortnightly test-day yields, with precision of 97.14%.&#x0D;

Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models.

The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability () calculated by the three REP models: REP1 0.355 0.060, REP2 0.344 0.047 and REP3 0.347 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 0.065, 0.376 0.051 and 0.378 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.

Estimating test-day milk yields by modeling proportional daily yields: Going beyond linearity

Lactation milk yields are not measured directly but are calculated from the test-day milk yields. Still, test-day milk yields are estimated from partial yields obtained from single milkings. Various methods have been proposed to estimate test-day milk yields, primarily to deal with unequal milking intervals dating back to the 1970s and 1980s. The Wiggans (1986) model is a de facto method for estimating test-day milk yields in the USA, which was initially proposed for cows milked 3 times daily, assuming a linear relationship between a proportional test-day milk yield and milking interval. However, the linearity assumption did not hold precisely in Holstein cows milked twice daily because of prolonged and uneven milking intervals. The present study reviewed and evaluated the nonlinear models that extended the Wiggans (1986) model for estimating daily or test-day milk yields. These nonlinear models, except step functions, demonstrated smaller errors and greater accuracies for estimated test-day milk yields compared with the conventional methods. The nonlinear models offered additional benefits. For example, the locally weighted regression model (e.g., LOESS) could utilize data information in scalable neighborhoods and weigh observations according to their distance in milking interval time. General additive models provide a flexible, unified framework to model nonlinear predictor variables additively. Another drawback of the conventional methods is a loss of accuracy caused by discretizing milking interval time into large bins while deriving multiplicative correction factors for estimating test-day milk yields. To overcome this problem, we proposed a general approach that allows milk yield correction factors to be derived for every possible milking interval time, resulting in more accurately estimated test-day milk yields. This approach can be applied to any model, including non-parametric models.

Genetic parameters and evaluation of mortality and slaughter rate in Holstein and Jersey cows

The longevity of dairy cattle has economic, animal welfare, and health implications and is influenced by the frequency of mortality on the farm and sale for slaughter. In this study cows removed from the herd due to death or slaughter during the lactation were coded 1 and cows that were not terminated were coded 0. Genetic parameters for mortality rates (MR) and slaughter rates (SR) were estimated for Holstein (H) and Jersey (J) breeds by applying both linear (LM) and threshold (TM) sire models using about 1.2 million H and 2,860,000 J cows. Estimated breeding values (EBV) for MR and SR were predicted using animal models to assess the opportunity for selection and genetic trends. Cow termination data, recorded between 1990 and 2020 on a voluntary basis by Australian dairy farmers, were analyzed. Cow MR has increased from below 1% in the 1990s to 4.1% and 3.6% in H and J cows, respectively. Most dead cows (~36%) left the herd before 120 d of lactation, while cows that were slaughtered left the herd toward the end of the lactation. Using the LM, heritability (h2) estimates for MR were lower (1%) than those for SR (2%-3.5%). When h2 were estimated using a TM, the estimates for both traits varied between 4% and 20%, suggesting that the difference in incidence level is one of the reasons for the difference in the h2 values between MR and SR. Early test-day milk yield (MY) and 305-d MY (305-d MY) have unfavorable genetic correlations (0.32-0.41) with MR in both breeds. The genetic correlations of calving interval with MR were stronger (0.54 to 0.68) than with SR (0.28-0.45) suggesting that poor fertility can serve as an early indicator of poor cow health that may lead to increased risk of death. High early test-day somatic cell count is genetically associated with increased likelihood of slaughter (0.24-0.46), but not with increased likelihood of death. In H, 305-d protein yield (PY) had the strongest genetic correlation (-0.34--0.40) with SR whereas in J, both 305-d PY and Fat yield showed high genetic (-0.64--0.70) and moderate environmental (-0.35--0.37) correlations with SR. The genetic correlation of removal from the herd due to death and slaughter was negative (-0.3) in J and zero in H. Strong selection for improved fertility and survival and less selection emphasis for MY, has led to an improvement in the genetic trend for cow MR in H and the trend in J has stabilized. Although genetic evaluations for cow MR are feasible, the reliabilities of the EBV are low and the level of cow MR in Australia are relatively low compared with similar countries. Therefore, genetic evaluation for survival based on mortality and slaughter data could be sufficient in the current selection circumstances where breeding objectives are broadly defined. Nevertheless, all Australian farmers should be encouraged to continue recording mortality and slaughter data for monitoring of the trends and for future development of genetic evaluations.

Does modeling causal relationships improve the accuracy of predicting lactation milk yields?

This study compared 3 correlational (best prediction, linear regression, and feed-forward neural networks) and 2 causal models (recursive structural equation model and recurrent neural networks) for estimating lactation milk yields. The correlational models assumed associations between test-day milk yields (health conditions), while the casual models postulated unidirectional recursive effects between these test-day variables. Wood lactation curves were used to simulate the data and served as a benchmark model. Individual Wood lactation curves provided an excellent parametric interpretation of lactation dynamics, with their prediction accuracies depending on the coverage of the lactation curve dynamics. Best prediction outperformed other models in the absence of mastitis but was suboptimal when mastitis was present and unaccounted for. Recurrent neural networks yielded the highest accuracy when mastitis was present. Although causal models facilitated the inference about the causality underlying lactation, precisely capturing the causal relationships was challenging because the underlying biology was complex. Misspecification of recursive effects in the recursive structural equation model resulted in a loss of accuracy. Hence, modeling causal relationships does not necessarily guarantee improved accuracies. In practice, a parsimonious model is preferred, balancing model complexity and accuracy. In addition to the choice of statistical models, the proper accounting for factors and covariates affecting milk yields is equally crucial.

Relationship between Milk Yield and Udder Morphology Traits in White Fulani Cows

The study examined the relationship between milk yield and udder morphology traits in White Fulani cows. Fifty-eight apparently healthy cows in early lactation at 2nd, 3rd, and 4th parity were used in the study. The data obtained from the cows were test day milk yield (TDMY) from single milking and udder morphology traits comprising udder length (UL), udder width (UW), udder depth (UD), fore teat length (FTL), rear teat length (RTL), fore teat diameter (FTD), and rear teat diameter (RTD). There was no significant effect of parity on TDMY or the udder morphology traits. Phenotypic correlations between TDMY, UL, UW, and UD were positive and significant. Notably, phenotypic correlations between UL and TDMY at different parities were the strongest. Teat measurements had no significant correlation with TDMY. Stepwise and principal component regressions were implemented to assess the relationship between milk yield and udder morphology traits. Interestingly, UL was the only trait that entered the reduced models. The results suggest a probable genetic correlation between milk yield and udder length. Therefore, since udder conformation traits are heritable, when selecting for udder length in White Fulani cows, a correlated response in milk yield is expected.