Milk production potential and reproductive performance of Egyptian buffalo cows

Machine Learning to Predict Pregnancy in Dairy Cows: An Approach Integrating Automated Activity Monitoring and On-Farm Data

Automated activity monitoring (AAM) systems are critical in the dairy industry for detecting estrus and optimizing the timing of artificial insemination (AI), thus enhancing pregnancy success rates in cows. This study developed a predictive model to improve pregnancy success by integrating AAM data with cow-specific and environmental factors. Utilizing data from 1,054 cows, this study compared the pregnancy outcomes between two AI timings-8 or 10 h post-AAM alarm. Variables such as age, parity, body condition, locomotion, and vaginal discharge scores, peripartum diseases, the breeding program, the bull used for AI, milk production at the time of AI, and environmental conditions (season, relative humidity, and temperature-humidity index) were considered alongside the AAM data on rumination, activity, and estrus intensity. Six predictive models were assessed to determine their efficacy in predicting pregnancy success: logistic regression, Bagged AdaBoost algorithm, linear discriminant, random forest, support vector machine, and Bagged Classification Tree. Integrating the on-farm data with AAM significantly enhanced the pregnancy prediction accuracy at AI compared to using AAM data alone. The random forest models showed a superior performance, with the highest Kappa statistic and lowest false positive rates. The linear discriminant and logistic regression models demonstrated the best accuracy, minimal false negatives, and the highest area under the curve. These findings suggest that combining on-farm and AAM data can significantly improve reproductive management in the dairy industry.

Factors Affecting the Milk Production Traits and Lactation Curve of the Indigenous River Buffalo Populations in Bangladesh

Household buffalo dairy farming is gaining popularity nowadays in Bangladesh because of the outstanding food value of buffalo milk as well as the lower production cost of buffalo compared to cattle. An initiative has recently been taken for the genetic improvement of indigenous dairy buffaloes. The present study was carried out to determine the influence of some environmental factors like age, parity, season of calving, calving interval, dry period on the lactation yield, and lactation curve of indigenous dairy buffaloes of Bangladesh. A total of 384 indigenous dairy buffaloes from the 3rd and 4th parity of seven herds under two different agroecological zones covering four seasons were selected and ear tagged for individual buffalo milk recording. A milk yield of 300 days (MY300d) was calculated following the International Committee for Animal Recording (ICAR) and the data were evaluated using the generalized linear model (GLM). In production traits, the mean of calculated lactation period (CLP), calculated lactation yield (CLY), and milk yield of 300 days (MY300d) of the overall population were 267.28 days, 749.36 kg, and 766.92 kg, respectively, whereas calving interval (CI) and dry period (DP) as reproductive traits were 453.06 days and 185.78 days, respectively. The season of calving, age of buffalo cows, population or herd, agroecological zone, calving interval, and dry period had significant effects on production traits (p < 0.05 to p < 0.001). The season of calving, level of milk production of 300 days, population, and agroecological zone significantly affected the reproduction traits (p < 0.01 to p < 0.001). Parity was found to be non-significant for both types of traits. The average peak yield of test day (TD) milk production was highest at TD4 (4.47 kg, 98th day of lactation). The average MY300d of milk production was the highest in the Lalpur buffalo population (1076.13 kg) and the lowest in the buffalo population of Bhola (592.44 kg). The correlations between milk production traits (CLP, CLY, and MY-300d) and reproduction traits (CI and DP) were highly significant (p < 0.01 to p < 0.001). Positive and high correlation was found within milk traits and reproduction traits, but correlation was negative between milk traits and reproduction traits. Therefore, these non-genetic factors should be considered in the future for any genetic improvement program for indigenous dairy buffaloes in Bangladesh.

TWAS revealed significant causal loci for milk production and its composition in Murrah buffaloes

Milk yield is the most complex trait in dairy animals, and mapping all causal variants even with smallest effect sizes has been difficult with the genome-wide association study (GWAS) sample sizes available in geographical regions with small livestock holdings such as Indian sub-continent. However, Transcriptome-wide association studies (TWAS) could serve as an alternate for fine mapping of expression quantitative trait loci (eQTLs). This is a maiden attempt to identify milk production and its composition related genes using TWAS in Murrah buffaloes (Bubalus bubalis). TWAS was conducted on a test (N = 136) set of Murrah buffaloes genotyped through ddRAD sequencing. Their gene expression level was predicted using reference (N = 8) animals having both genotype and mammary epithelial cell (MEC) transcriptome information. Gene expression prediction was performed using Elastic-Net and Dirichlet Process Regression (DPR) model with fivefold cross-validation and without any cross-validation. DPR model without cross-validation predicted 80.92% of the total genes in the test group of Murrah buffaloes which was highest compared to other methods. TWAS in test individuals based on predicted gene expression, identified a significant association of one unique gene for Fat%, and two for SNF% at Bonferroni corrected threshold. The false discovery rates (FDR) corrected P-values of the top ten SNPs identified through GWAS were comparatively higher than TWAS. Gene ontology of TWAS-identified genes was performed to understand the function of these genes, it was revealed that milk production and composition genes were mainly involved in Relaxin, AMPK, and JAK-STAT signaling pathway, along with CCRI, and several key metabolic processes. The present study indicates that TWAS offers a lower false discovery rate and higher significant hits than GWAS for milk production and its composition traits. Hence, it is concluded that TWAS can be effectively used to identify genes and cis-SNPs in a population, which can be used for fabricating a low-density genomic chip for predicting milk production in Murrah buffaloes.

Relationships between body- and udder-related type traits with somatic cell counts and potential use for an early selection method for water buffaloes (Bubalus bubalis)

Water buffalo milk is a reliable source of high-quality nutrients; however, the susceptibility of mastitis in buffaloes must be taken into consideration. An animal with somatic cell count (SCC) of greater than 250,000 cells/mL is reported to be likely to have mastitis which has serious adverse effects on animal health, reproduction, milk yield, and milk quality. Type traits (TTs) of water buffalo can affect SCC in animal milk to some extent, but few reports on the correlation between SCC and TTs are available. In this study, a total of 1908 records collected from 678 water buffaloes were investigated. The general linear model was used to identify factors associated with phenotypic variation of the somatic cell score (SCS) trait, including parity, lactation length, calving year, and calving season as fixed effects. Using PROC CORR analysis method, taking calving year and lactation length as covariates, the correlation co-efficient between TT and SCS was obtained. Our results showed that correlation co-efficients between the 45 TTs with SCS ranged from 0.003 to 0.443 (degree of correlation). The correlation between udder traits and SCS was greater than that between body structure traits and SCS. Among udder traits, distance between teats (including front and rear teat distance [r = 0.308], front teat distance [r = 0.211], and teat crossing distance [r = 0.412]) and teat circumference (r = 0.443) had the highest correlation with SCS, followed by the leg traits including rear leg height (r = -0.354) and hock bend angle (r = -0.170). Animal with high rear legs (>48 cm) and short teat crossing distance (<17 cm), and narrow teat circumference (<11 cm) exhibited low SCS. Using four nonlinear models (Von Bertalanffy, Brody, Logistic, and Gompertz), the optimal growth curves of the TTs highly correlated with the SCS (rear leg height and teat crossing distance) were fitted, and the correction co-efficients of these two TTs rear leg height and teat crossing distance of animal from young age (2 mo old) to first lactation (35 mo old) were attained for establishment of early selection method for water buffaloes with low SCS. This study provides theoretical support for early selection of low-SCS water buffaloes and lays a foundation for improving milk quality and promoting healthy development of water buffalo's dairy industry.

Dietary Supplementation of Capsaicin Enhances Productive and Reproductive Efficiency of Chinese Crossbred Buffaloes in Low Breeding Season

The present study investigated the role of dietary capsaicin (CPS) supplementation on milk yield (liters/head) and milk composition (total solids, lactose, albumin, protein, fat, milk urea nitrogen (MUN), somatic cell count (SCC) and somatic cell score (SCS), serum metabolites (lipoprotein esterase (LPL) and aspartate aminotransferase (AST)), and reproductive physiology (follicular development, estrus response, ovulation and pregnancy) following synchronization during the low breeding season. One hundred (n = 100) crossbred buffaloes were randomly assigned to four dietary groups consisting of CPS supplementation dosages (0, 2, 4 or 6 mg/kg of total mixed ration; TMR) as CPS-0 (n = 26), CPS-2 (n = 22), CPS-4 (n = 25) and CPS-6 (n = 27), respectively, in a 30-day feed trial. The results revealed that the CPS-4 group of buffaloes had a better estrus rate (72%) along with improved (p < 0.05) ovulatory follicle diameter (13.8 mm), ovulation rate (68%) and pregnancy rate (48%) compared to other treatment groups. Milk yield improved (p < 0.05) in CPS-4 supplemented buffaloes after day 20 of the trial, comparatively. There was a significant effect (p < 0.05) of milk sampling day (day 30) on total milk solids, lactose, milk protein and MUN levels, whereas lactose, MUN, SCC and SCS were influenced by supplementation dosage (CPS-4). Glucose levels were affected in buffaloes by sampling time (artificial insemination (AI) and 50-day post-AI) and CPS-dose (CPS-4 and CPS-6), respectively. LPL level changed in CPS-2 and CPS-4 groups at AI time and 50 days after AI. In addition, the AST level was different in CPS-4 at AI time and 50 days after AI. Therefore, our data suggest that a medium dose (~4 mg/kg of TMR) of CPS provided a better response in the form of milk yield, milk composition, serum metabolites and reproductive performance in crossbred buffaloes during the low breeding season.

Immunometabolic response in Egyptian water buffalo cows during the transition period

Background and Aim:

The transition period is extremely critical for pregnant producing animals. However, there is very limited research on the metabolic and immunological changes in Egyptian water buffalo cows during the transition period. Therefore, this study was conducted to investigate the immunometabolic changes occurring during the transition period in Egyptian water buffalo cows.

Materials and Methods:

A total of 50 multiparous pregnant Egyptian water buffalo cows were subjected to weekly blood sampling 3 weeks before calving and 3 weeks after calving and on the day of parturition to determine the complete blood count, including red blood cell count, total leukocyte count (TLC), differential leukocyte count, hemoglobin level, and packed cell volume (PCV). Some selected serum biochemical and immunological parameters were analyzed, including serum glucose, beta-hydroxybutyric acid (BHBA), non-esterified fatty acids, triglycerides, high-density lipoprotein, low-density lipoprotein (LDL), very LDL (VLDL), cholesterol, total protein, albumin, globulin, creatinine, blood urea nitrogen (BUN), aspartate aminotransferase, alkaline phosphatase, alanine transaminase, gamma-glutamyl transferase, Haptoglobin, and C-reactive protein and the pro-inflammatory cytokines interleukin β1, interleukin 6 (IL-6), and tumor necrosis factor-alpha. All data were statistically analyzed using the IBM Statistical Package for the Social Sciences statistics software.

Results:

The neutrophil count showed a statistically significant increase at 2 weeks preparturition. There was also a significant increase in PCV, TLC, neutrophil count, and IL-6 and TNF-a level at the time of parturition and even at 2 weeks post parturition, except PCV that returned to normal levels in the 1^st week post parturition. BHBA and BUN levels were increased significantly in the 2^nd and 3^rd weeks postcalving. Serum creatinine and VLDL levels were decreased significantly at the time of parturition, and VLDL levels showed a significant decrease even till the 3^rd week postcalving, whereas creatinine levels gradually returned to the pre-calving levels in the 3^rd week postcalving. Other parameters showed no significant changes.

Conclusion:

The most important immunometabolic changes occur in the first 2 weeks post parturition in Egyptian water buffalo cows, which exhibit a potent, remarkable physiological adaptation achieved by their functional liver, which can help the animal overcome the stressful conditions during the transition period.