Ragothaman Venkataramanan

This study presents the growth data of indigenous Bargur cattle calves maintained at the Bargur Cattle Research Station, Tamil Nadu, India. Bargur cattle are an endangered breed known for their adaptability to hilly environments and production potential. The dataset included 1803 weight–age records collected from 174 calves, covering measurements from birth (age code 1) to approximately 16 months of age (age code 17). In the research station database, birth weight was recorded as age code 1, with subsequent age codes representing approximately monthly weight records. To describe the growth pattern, five non-linear models, Brody, Logistic, Von Bertalanffy, Gompertz, and Generalized Weibull, were fitted to the data. Key growth parameters, such as asymptotic weight, initial weight, and growth rate were estimated, along with indicators like age and weight at inflection. Because the available records covered growth from birth (age code 1) to approximately 16 months of age (age code 17), asymptotic weight estimates should be interpreted as model-derived projections rather than observed mature body weight. Among the models evaluated, the Von Bertalanffy model showed the best overall statistical fit based on AIC, BIC, and RMSE criteria, followed by the Gompertz model. The Logistic model, although not the best-fitting model statistically, retained biological interpretability in describing early growth patterns in calves. The dataset, along with graphical outputs of growth curves and residuals, provides useful insights into the early growth trajectory of Bargur cattle and may support conservation, management, and future breeding programs.

Tamil Nadu, a state in southern India, is bereft of any native dairy or dual-purpose cattle breeds, and the state depends chiefly on crossbred cattle for milk production. Jersey crossbred cattle account for 90% of the state’s milk production. This genetic evaluation study aimed to assess milk production traits, including test-day milk yields (TDMYs), total lactation milk yield (TMY), and 305-day milk yield (305MY), in these cattle reared under farmers’ conditions. Data on monthly TDMYs (TDMY1 to TDMY10) of Jersey crossbred cows (n = 75,627) reared by the farmers over 11 years (2012–2022) were collected for evaluation. The influence of non-genetic factors viz., location (agroclimatic zones), period and season of calving, and parity on TDMYs, TMY and 305MY were assessed, and the variance and covariance components for genetic parameters (heritability and genetic correlation) of the traits were estimated by the method of restricted maximum likelihood (REML) under a multivariate animal model and by a random regression model (RRM). The overall means of various TDMYs ranged from 4.98 (TDMY10) to 9.95 kg (TDMY2), and for TMY and 305MY, the means were 2480.33 and 2393.71 kg, respectively. The heritabilities estimated by the multivariate animal model for TDMYs were moderate and ranged from 0.26 ± 0.02 to 0.37 ± 0.02 and the estimates were less variable throughout lactation, while the estimates obtained by RRM were medium to high and ranged between 0.29 ± 0.01 and 0.67 ± 0.02 for milk yield on day 5 to 305 (DIM 5–DIM 305). The heritabilities estimated for TMY and 305MY were 0.43 ± 0.02 and 0.43 ± 0.02, respectively. The estimates of heritability for mid-lactation yields were comparatively less than those at the beginning and the end of lactation. The moderate to high estimates of heritabilities for TDMYs, TMY, and 305MY obtained in the present study offer ample scope for improving milk production through selective breeding. Genetic correlations between TDMYs estimated by the multivariate animal model were positive and high, with a range between 0.75 ± 0.03 and 0.99 ± 0.00. Considerably higher estimates (0.98–0.99) were observed between adjacent TDMYs showing strong genetic associations. By RRM, genetic correlations estimated between DIMs were positive (except for DIM 5 with DIM 125 to DIM 185 and DIM 125 with DIM 305), and the magnitude of genetic correlation decreased with an increase in the interval between the DIMs. The high genetic correlation observed between the TDMYs (in the early stage of lactation) and total lactation milk yield suggested that these test-day yields could be used favorably for the early selection of cows for milk production, which facilitates reduction in the generation interval and consequently increases the annual genetic gain for the milk production traits.

Tamil Nadu, a state in southern India, is bereft of any native milch or dual-purpose cattle breed and the state depends chiefly on crossbred cattle for milk production. As most (90 percent) of the milk production in the state was contributed by Jersey crossbred cattle, the genetic evaluation study was intended to assess the milk production traits viz., test-day milk yields (TDMYs), total milk yield (TMY) and 305-day milk yield (305MY) in these crossbred cattle reared under farmers’ rearing environment. Data on monthly TDMYs (TDMY1 to TDMY10) of Jersey crossbred cows (n=81,653) reared by the farmers over 24 years (1999-2022) were collected for evaluation. The influence of non-genetic factors viz., location (agroclimatic zones), period and season of calving, and parity on TDMYs, TMY, and 305MY were assessed, and the variance and covariance components for genetic parameters (heritability and genetic correlation) of the traits were estimated by the method of restricted maximum likelihood (REML) under multivariate animal model and by random regression model (RRM). The overall means of various TDMYs ranged from 4.98 (TDMY10) to 9.95 kg (TDMY2) and, for TMY and 305MY the means were 2480.33 and 2393.71 kg respectively. The heritabilities estimated by animal model for TDMYs were high and ranged from 0.39 ± 0.01 to 0.47 ± 0.01 and were fairly less variable throughout the lactation while the estimates obtained by RRM were medium to high and ranged between 0.28 ± 0.01 and 0.65 ± 0.02 for different days in milk (DIMs). The heritability estimated for TMY and 305MY were 0.48 ± 0.01 and 0.49 ± 0.01 respectively. The estimates of heritability in mid-lactation yields were comparatively little less than those at the beginning and the end of lactation. The high estimates of heritabilities for TDMYs, TMY, and 305MY obtained in the present study offered an ample scope for improving milk production through selective breeding. Genetic correlations between TDMYs estimated by animal model were positive and moderate to high, with a range between 0.47 ± 0.01 and 0.99 ± 0.00. Considerably higher estimates (0.97-0.99) were observed between adjacent TDMYs showing strong genetic associations. By RRM, genetic correlations estimated between DIMs were positive, except for a few estimates and the magnitude of genetic correlation decreased with an increase in the interval between the DIMs. The high genetic correlation observed between the TDMYs in early and mid stages of lactation with TMY and 305MY suggested that these test day yields could be used favorably for the early selection of cows for milk production which facilitates reduction in the generation interval and consequently increase the annual genetic gain for the milk production traits.

Background: Understanding genetic and non-genetic factors that influence the quality and quantity of semen production plays a crucial role in determining the reproductive efficiency and genetic progress within the Sahiwal breed. The objective of this study was to assess the impact of genetic and non-genetic factors on the semen production potential of Sahiwal bulls. Methods: Semen ejaculate data, including volume (VOL), sperm concentration per milliliter (CONC), total sperm count per ejaculate (TS), initial motility (INIT) and post-thaw motility (PT), as well as semen quality traits such as hypo-osmotic swelling test (HOST) and acrosome integrity of frozen semen (AIFS), were obtained from 52 Sahiwal bulls. The data spanned from January 2011 to December 2018 and were collected at BAIF’s frozen semen station in Jind, Haryana, India. To ensure data reliability, observations beyond the mean±4 standard deviations (S.D.) were identified as outliers and subsequently excluded from the study. Two key factors, namely the season of semen collection (winter, summer and monsoon) and the age of bulls at collection (categorized into 12-month intervals ranging from less than 36 months to 156 months), were considered influential factors affecting semen characteristics. To investigate the correlation between bulls (genetic + permanent environmental correlation) and semen parameters, a repeatability animal model was employed, treating the bull as a random effect. The statistical analysis was conducted using WOMBAT software. Result: The findings revealed that the season of semen collection had a highly significant effect (P less than 0.01) on VOL, CONC, TS and PT. However, INIT, HOST and AIFS were not influenced (P greater than 0.05) by the season of collection. Furthermore, age at semen collection significantly affected all semen characteristics. VOL and TS exhibited an increasing trend until 72 to 84 months, followed by a decline until 96 to 108 months.

Genetic parameters of sperm abnormality traits were evaluated in HF crossbred bulls using Bayesian univariate, bivariate and random regression models. Data from 161 bulls on 1,527 ejaculates collected from 2010 to 2020 at BAIF (Bharatiya Agro Industries Foundation) was utilized for this study. Four sperm abnormality traits viz. total abnormalities (A), head abnormality (HA), mid-piece abnormality (MA) and tail abnormality (TA) were included in the analysis. Gibbs sampling was done to implement a Bayesian framework. Means for A, HA, MA and TA were 10.77%, 2.99%. 2.61% and 5.10% respectively. Heritability and repeatability estimates obtained from univariateanalysis were below 0.1. Genetic correlations obtained for the total abnormalities with semen production traits like ejaculate volume, sperm concentration, initial sperm motility, post-thaw motility and the total number of spermatozoa were negative. Heritability and repeatability estimates were higher in the case of random regression models where the values tend to increase with the age of the bulls. Heritability ranged from 0.025 (3 years) to 0.139 (10 years) for total abnormality estimates. In general, the estimates of genetic parameters for abnormality were low and found to increase with age.

Genetic potential of indigenous bulls with respect to semen production traits over the age of the bulls at semen collection was analyzed using random regression models (RRMs). Data pertaining to 59,641 ejaculates from 189 bulls of 18 indigenous breeds collected from BAIF (Bharatiya Agro Industries Foundation) was utilized for this study. Six semen production traits, viz., ejaculate volume (EV, ml), sperm concentration (SC, 10⁹/ml), initial sperm motility (ISM, %), post-thaw motility (PTM, %), the total number of spermatozoa per ejaculate (TNS, 10⁹/ejaculate), and the theoretical number of semen doses (TNSD) were studied. Univariate and RRM were used to obtain variance components and genetic parameter estimates. Two hundred thousand Gibbs samples were generated for each trait with a burn-in of 20,000 and thinning interval of 50 in a Bayesian framework. Legendre polynomials with orders of fit up to 5 for additive and permanent environmental effects were used. RRM modeled the heritability and repeatability for all ages between 3 and 10 years (productive lifespan). Heritability estimates ranged from 0.18 to 0.36, 0.18 to 0.45, 0.02 to 0.06, 0 to 0.001, 0.09 to 0.32, and 0.14 to 0.42 while the repeatability estimates ranged from 0.41 to 0.72, 0.36 to 0.79, 0.04 to 0.10, 0 to 0.001, 0.37 to 0.56, and 0.32 to 0.57 for EV, SC, ISM, PTM, TNS, and TNSD, respectively. Variability of estimates over the age of the bulls obtained through RRM could be useful to further refine the breeding program for age at selection, deciding the production period and age at culling.

Milk yield and composition traits (fat (%) and SNF (%)) from 1229 test day records of 205 Bargur cattle maintained under a farmers’ production system were studied. This breed is known for its adaptability to the hilly tracts of Erode District in Tamil Nadu. Performance recording was done in the natural habitat through the test-day milk yield from lactating cows in the farmers’ herds. Effects of non-genetic factors like season, year, parity, stage of lactation, and days from calving were studied using mixed models with animal taken as a random effect. Estimated marginal means for daily milk yield (DMY, L/day), fat (%), and SNF (%) were 2.05 ± 0.03, 4.08 ± 0.03%, and 8.19 ± 0.01%, respectively. Stage of lactation was highly significant (P < 0.01) for DMY, fat, and SNF. Season and year were significant for DMY and SNF, where higher productivity was obtained in the monsoon season, but fat remained constant in all the seasons. Parity was significant (P < 0.05) only for SNF, and year was significant for DMY (P < 0.01) and SNF (P < 0.05). Persistency was 5.9% calculated using Wood’s gamma function and 63.0% calculated using the ratio of predicted yields. This was slightly lower compared to dairy breeds of cattle. Thirteen different lactation curve models were used for fitting of Bargur cattle lactation curve, and the Parabolic exponential model was the best fitting model based on model diagnostic criteria.