The blood-milk barrier (BMB) forms at parturition when the gland switches form a non-lactating state to one of copious milk production and becomes leaky again when milk removal ceases and mammary involution is initiated. In this review the importance of the BMB in milk production and, in particular, its hormonal regulation is explored. Tight junctions (TJ) between adjacent mammary epithelial cells form a barrier to the two-directional paracellular movement of small molecules between the blood and milk and are responsible for establishing and maintaining the BMB. They form part of the cell's junctional complex and consist of transmembrane proteins that are linked to the mammary cell's cytoskeleton. This means that when, during lactation, TJ become “leaky” the resulting perturbation of the cytoskeleton interferes with the cell's secretory function. As such, TJ are involved in regulating and maintaining milk production. Mammary TJ are under hormonal control, with progesterone, glucocorticoids, prolactin, parathyroid hormone-related peptide (PTHrP) and serotonin (5-HT) being the key hormones. Progesterone prevents closure of TJ and the immediate prepartum drop in its concentration is a prerequisite for TJ closure. A simultaneous increase in the levels of glucocorticoids and prolactin is necessary for full TJ closure and initiation and maintenance of lactation. Both PTHrP and 5-HT are important hormones in maintaining extracellular calcium concentrations, a requirement for maintaining TJ integrity. Whereas PTHrP reduces TJ permeability, necessary for establishing and maintaining milk production, 5-HT has an opening effect on TJ. The latter may help speed up mammary involution and facilitate the movement of immune factors into the gland, preventing intramammary infections. In summary, mammary TJ make up the BMB and play a role in establishing and maintaining milk production and are under hormonal control, with progesterone, glucocorticoids, PTHrP and 5-HT being key regulatory hormones and prolactin likely playing a supporting role.

Blockchain, an emerging technology exhibiting swift growth, significantly bolsters transparency within a given supply chain, enabling secure traceability, backtracking, and info tracing. Blockchain technology holds substantial promise for the dairy sector, offering enhancements unnecessary market intermediaries, thereby broadening access to credit and insurance for farmers, particularly in developing economies Such advancements could lead to more sustainable, efficient, and resilient livestock practices. However, the technology faces challenges, including the need for sophisticated infrastructure, cross-platform software, and skilled personnel with advanced expertise. Divergence in technological capabilities between developed and developing nations may hinder trade and exacerbate disparities. Regulatory barriers could also restrict blockchain's application. Thus, it is imperative to enhance blockchain knowledge among trade authorities and policymakers to facilitate its broader adoption. The objective of this review is to discuss principles of blockchain and proposed future work pathways for its use in the dairy industry.

This Research Paper addresses the hypothesis that wastewater characteristics in the dairy industry vary with product type and operational procedures, and that current treatment methods face limitations in managing such variability. The study examined raw and clean-in-place (CIP) wastewater from a Serbian dairy plant over three years. Physico-chemical and microbiological analyses included pH, protein, fat, carbohydrates, total solids, total microorganisms, E. coli, Enterobacteriaceae, chemical oxygen demand (COD), total suspended solids (TSS), nitrogen (N), phosphorus (P), and dissolved oxygen (O2).Dairy plants produced 0.2–10 L wastewater per litre of milk. Protein content ranged 0.07–0.31 g/100 ml, fat 0.01–0.19 g/100 ml, and carbohydrates up to 1.37%. Total solids were 0.13–2.95%. pH varied from 4.41 to 12.76, affected by lactic fermentation and cleaning agents. COD values (529–12,476 mg/l) indicated strong organic loads. Microbiological counts were highly variable, with E. coli up to 103 cfu/ml and total microorganisms up to 1 × 108 cfu/ml. Nitrogen ranged 36–104 mg/l and phosphorus reached 10.91 mg/l, sometimes exceeding limits. Principal component analysis (PCA) explained 61.86% of variance, driven by N, pH, P, E. coli, Enterobacteriaceae, and oxygen content. Seasonal patterns were identified: higher TSS during spring and summer, and increased microbial loads, COD, and oxygen fluctuations in autumn and winter. The findings demonstrate that dairy wastewater is complex and variable, requiring adaptive treatment strategies. Optimised management, including pH control, nutrient removal, and combined biological and advanced technologies, can improve treatment efficiency, support reuse, and mitigate environmental impact.

We assessed the feeding behavior, intake, apparent total tract digestibility, blood parameters, milk yield, milk composition and milk fatty acid profile in dairy goats fed diets containing increasing levels of whole cottonseed (WCS), specifically a control diet with no WCS and diets containing 120, 150 and 180 g WCS/kg DM. Eight lactating dairy goats were distributed in a replicated 4 × 4 Latin square design with four 21-day periods. Rumination and total chewing time increased linearly with WCS inclusion levels. However, no effects on dry matter intake or digestibility were observed. Milk production was similar among treatments, but milk fat and lactose concentration increased linearly with WCS dietary levels, as did monounsaturated and polyunsaturated FA and conjugated linoleic acid concentration in milk fat. This study demonstrates that increasing the inclusion of WCS in the diets of dairy goats offers potential nutritional benefits without negatively impacting intake, digestibility or ruminal fermentation. Notably, WCS-enhanced diets led to improved milk fat composition, with increased concentrations of beneficial FA. These results support the use of WCS as an effective feed ingredient to enhance milk quality in dairy goats, leveraging both the high-energy content and fiber structure of WCS, which promotes rumen health and function without reducing DMI. Dairy goat producers can incorporate up to 180 g WCS/kg DM in diets to produce milk with a healthier fat profile, potentially enhancing its marketability.

The effect of different levels of residue from the extraction of cassava starch (REAM) in the diet of multiparous lactating Holstein cows was investigated. The experiment was carried out at the Universidade Estadual do Oeste do Paraná. Five multiparous Holstein cows, with mean milk production of 30.65 ± 1.17 kg/day were distributed into five treatments, using Latin squares (5 × 5). Treatments consisted of inclusion (0, 8, 16, 24 and 32% dry matter basis) of REAM in the diet. Intake, digestibility, milk production and composition as well as blood parameters were measured. Except for ether extract and total digestible nutrients, the intake variables did not differ significantly between treatments. Except for the digestibility of crude protein, all digestibility values decreased progressively with inclusion levels of REAM. The inclusion of REAM in feeding of lactating Holstein cows up to 32% of the diet reduced milk production by 15%, however, it did not alter milk production efficiency, fat, total solids and blood parameters. We conclude that up to 16% of REAM can be included in the diet; however, higher levels may lead to a significant fall in productivity.

The productivity of dairy animals is heavily reliant on the timely access to sufficient, high-quality fodder. This study analyzes the ex-post impact of Model Fodder Villages on the livelihood security of dairy farmers in Bundelkhand region of central India. The study used cross-sectional data from 437 farmers and employed the inverse-propensity-weighting regression adjustment (IPWRA) method for impact evaluation and verified the robustness of the results through matching methods. The findings show that dairy farmers in model fodder villages are 14–19% more likely to improve habitat security, 19–21% more likely to enhance economic security, and 13–16% more likely to increase food security. Our findings also indicate that factors such as age, education, household size, dependency ratio, off-farm income, adult cattle units, and access to roads, markets, credit, and training play significant roles in adopting improved fodder practices. The study's methodology provides a reliable approach to evaluating the impact of interventions that improve access to fodder and related resources, aiming to enhance the livelihoods of dairy farmers and agricultural communities.

The Murciano-Granadina goat breed was imported from Spain to the southern region of Iran to enhance production efficiency in the native and nomadic goat flocks of the region, primarily maintained under a low-input, low-output production system. In this study, the genetic and phenotypic aspects of milk production traits in the Murciano-Granadina goat breed were investigated using data collected from a private dairy farm in Ghale-Ganj city, located in southern Kerman province, Iran, between 2017 and 2024. Data on 76,874 test-day lactation records of milk yield (MY), fat yield (FY), protein yield (PY) and somatic cell count from 7,196 first-parity Murciano-Granadina does were used. The investigated traits were total MY, total FY, total PY, average fat-to-protein ratio (FPR) and average somatic cell score (SCS), all calculated based on a 275-day lactation period. A multivariate animal model was used to estimate the genetic and phenotypic parameters of the investigated traits. The heritability estimates for MY, FY, PY, FPR and SCS were 0.15, 0.06, 0.09, 0.10 and 0.26, respectively. The estimates of genetic correlations among the studied traits ranged from –0.38 for FY-FPR to 0.91 for FY-PY, while the phenotypic correlations ranged from –0.05 for MY-FPR to 0.90 for PY-FY. Genetic correlations between SCS and MY, FY and PY were low estimates of 0.16, 0.19 and 0.08, respectively. The corresponding phenotypic correlations were low estimates of 0.03 (MY-SCS), 0.05 (FY-SCS) and 0.06 (PY-SCS). Genetic and phenotypic correlation estimates among MY, FY and PY were positive and medium to high, with the corresponding genetic correlations generally higher than the phenotypic ones. These low heritability estimates for all the studied traits, except for SCS, suggest that non-additive genetic and environmental effects play a more significant role in the performance of the Murciano-Granadina goat. The high positive genetic correlation estimates between MY, FY and PY suggest that selection for higher MY should also increase in both FY and PY.

This experiment assessed the effects of adding hemp (seeds or hay) and stevia by-products to dairy cow diets on milk yield and on the fatty acid profile and oxidative stability of the milk. Additional analyses included composition, total phenolic content and total antioxidant capacity of milk samples as well as blood serum parameters. Thirty-five Holstein dairy cows were involved for 60 days, divided into five groups: control, hemp seed, hemp hay, stevia and a combination of hemp seeds and stevia leaves. No significant changes were observed in milk yield or composition. While monounsaturated fatty acid (MUFA) content did not differ significantly between control and experimental diets, milk from cows fed hemp seeds had higher MUFA compared to those fed hemp hay. Further research is recommended to determine the optimal proportion of these by-products in cow diets.

We investigated the hypothesis that supplementing milk replacer (MR) with exogenous lactoferrin (LF) would improve average daily gain (ADG) and feed efficiency and decrease scouring incidence in dairy calves. Lactoferrin is an antimicrobial and anti-inflammatory glycoprotein naturally found in bovine colostrum and milk that is low in MR. Previous studies suggest that supplementing LF to MR enhances ADG and feed efficiency while reducing disease occurrence in pre-weaning dairy calves. In our experiment, 103 Holstein heifer calves were randomly assigned to 1 of 4 treatments in a randomized complete block design from birth to d56 of age. Each calf received 340.1 g/d of 24% protein, 20% fat basal MR fed twice daily from d1 to 42 and once daily from d43 to 49, supplemented with 0 (L0), 1 (L1), 2 (L2) or 4 (L4) g/hd/d of LF treatment (45% purity). Calves were weaned at 49d of age. Body weight was measured at d1, 14, 28, 42, 49 and 56 of age. Faecal scores were measured weekly. Milk replacer and calf starter intake was measured daily and calculated biweekly. Data were analyzed using a linear mixed model with fixed effects of LF inclusion, and random effects of source herd and nursery room. In the first two weeks of life, ADG and gain-to-feed ratio (G:F) were numerically (non-statistically) increased in L4 tended compared with L1 and L2, but this effect was not maintained throughout the rest of the pre-weaning period or entire experiment. Average faecal score during the entire 56d experiment was greater in L2 compared with L0, L1 and L4, although faecal scores of all treatment groups were generally low. Under the conditions of the present study, LF supplementation at the inclusion levels provided showed minimal effects on feed intake, growth rate or calf health.

The weaning process negatively affects the haematological parameters and innate immune response of dairy calves, even when managed under an intensive milk program. Here we describe haematological and innate immunity changes in 47 Holstein calves aged 69-85 days subjected to a gradual weaning process. Blood samples were collected at six (D-6), four (D-4), and two (D-2) days before and on the weaning day (D0) for the phagocytosis assay and to measure the production of reactive oxygen species (ROS) after stimulation with Staphylococcus aureus, Escherichia coli, and Mannhemia haemolytica, in addition to total protein (TP), haptoglobin (Hp), and iron concentration. The highest mean neutrophil number was recorded at D-2. The absolute number of monocytes was initially high on D-6 and D-4 but declined as the calf progressed to weaning. The number of basophils decreased rapidly, reaching a low value on D-4, and remained low for the remainder of the study period. The TP, Hp, and Fe concentrations decreased. Overall, polymorphonuclear leukocyte phagocytosis activity induced by S. aureus and E. coli decreased from D-6 to D-2, indicating persistence of the low phagocytosis rate for S. aureus. ROS production was constant for all bacterial stimulations from D-6 to D-2, followed by an increase on D0. Phagocytosis and ROS production indicate that the weaning process dampens the innate immune response relative to exposure to these common pathogenic bacteria in dairy calves. Phagocytosis and the corresponding indicators of intracellular killing activities (ROS production and myeloperoxidase index) represent the most accepted core mechanisms for the early elimination of pathogenic microorganisms in calves. Despite a slow gradual weaning management system, the study concluded that intensive milk production programs contribute to innate immune response suppression during weaning.

Biometric identification represents a transformative, advanced technology with significant implications for herd management. Its adoption addresses the critical requirement of accurate identification methods along with upgraded approaches on higher traceability, disease control, genetic management, and economic returns. In this work, a database of muzzle images was collected from 264 Vrindavani cattle, with ages ranging from 6 months to 10 years. To assess the accuracy of muzzle print as a biometric means of identification, this study investigated the efficiency of a 68-layer convolutional neural network called SqueezeNet for the identification of Vrindavani cattle (a crossbred developed in India) using 2,640 muzzle images. It was observed that SqueezeNet gives a harmonious blend of superior accuracy and minimal complexity, rendering it an optimal option for devices with constrained specifications and computing power. Further, the results of this study showed an identification accuracy of 97.22% with a remarkably small model size (<4 MB). This compact size makes it significantly advantageous compared to other models.

The present study aimed to conduct a meta-analysis using the random-effects model to merge published genetic parameter estimates for metabolic disorders [mastitis (MA), metritis (ME), ketosis (KE), milk fever (MF), retained placenta (RP), displaced abomasum (DA), and lameness (LA)] in dairy cows. A total of 239 heritability and 355 genetic correlation estimates were extracted from 61 peer-reviewed articles published between 1991 and 2024. Heritability estimates for metabolic disorders were generally low, ranging from 0.034 for ME to 0.067 for DA and MF. These estimates were associated with small standard errors and narrow 95% confidence intervals, with all estimates being statistically significant (P < 0.05). The estimates for genetic correlations between metabolic disorders were positive, ranging from 0.038 (MA-DA) to 0.522 (KE-DA). Furthermore, only the genetic correlations for DA-LA (0.147), RP-ME (0.472), MA-LA (0.239), MA-MF (0.365), KE-DA (0.522), KE-RP (0.124), and KE-ME (0.157) were significant (P < 0.05). Positive and low genetic correlations were observed between MA-milk yield (MY), MF-MY, and LA-MY (0.291, 0.135, and 0.263, respectively; P < 0.05). Similarly, positive correlations were found between DA-fat yield (FY), MA-FY, MA-protein yield (PY), LA-FY, and LA-PY (0.121, 0.170, 0.303, 0.176, and 0.236, respectively; P < 0.05). The genetic correlations between KE-fat to protein ratio (FPR) and DA-FPR were positive and ranged from low to moderate (0.520 and 0.196, respectively; P < 0.05). Significant genetic correlations were also noted between MA-days open (DO) (0.416), MA-days from calving to first service (DCFS) (0.207), and LA-calving interval (CI) (0.210; P < 0.05). This meta-analysis provides valuable insights into the genetic contribution to metabolic disorders in dairy cows and highlights the generally low heritability and the variable genetic correlations between these disorders and performance traits. These findings may contribute to more effective breeding strategies aimed at reducing the occurrence of metabolic disorders in dairy herds.

Sub-clinical ketosis (SCK) significantly affects post-partum dairy cow performance and welfare. A total of 11,327,959 test-day (TD) records over two years on 1.76 million Holstein cow lactations and 2840 farms were processed to ascertain thresholds for milk acetone (mACE) and β-hydroxybutyrate (mBHB) as indicators of SCK on the basis of a significant milk yield loss at the TD. The set thresholds for mACE and mBHB were 0.10 mmol/L and 0.14 mmol/L, respectively. The prevalence of SCK in the population during the first 60 days in milk (DIM) was estimated based on herd size and milk yields, utilizing one or both of these metabolites surpassing their respective thresholds. Analyzing both mACE and mBHB together revealed a higher occurrence of SCK in small herds (fewer than 100 cows) and a lower occurrence in the two most productive milk categories. The prevalence had an inverse relationship with the daily milk yield at 60 DIM, indicating a surprisingly high frequency of low-productivity herds in the risk classes exceeding 30%. These results suggest that assessing SCK prevalence through the combined evaluation of mACE and mBHB is a more effective approach than using the milk fat to protein ratio, especially when considering different herd sizes and daily milk yield at 60 DIM.

In recent years, measuring hair cortisol concentration in dairy cows has gained popularity as a welfare indicator. The aim of this study was to evaluate the usefulness of measuring hair cortisol concentration (HCC) in dairy cows as an indicator of dairy cow welfare. A total of 290 cows from six commercial herds located in Spain, Italy and Finland (two herds each) were included in the study. A hair sample was taken from each cow and cut into two parts (study period T1 and T2). HCC was measured using an automated assay based on a competitive solid phase chemiluminescent enzyme immunoassay. Data relevant to each animal and its welfare (animal-based assessment according to the Welfare Quality protocol, veterinary treatments, meteorological data, cows’ parity and stage of lactation) were collected during the study. The welfare data were used to categorize cows into three welfare classes at each study period (T1 and T2), representing animals with good, medium and poor welfare. Analytical validation of the HCC automated assay was performed on a sub-sample of collected hair and included determination of accuracy, precision, sensitivity and stability of the method. A linear mixed model was fitted to explore the associations between log transferred HCC and welfare class, herd, stage of lactation, parity and season. The results of analytical validation showed that the HCC automated assay method was accurate and able to measure HCC in a linear manner with inter- and intra-assay precision with CVs less than 15%. HCC was explained by the variables herd, cow parity and study period (T1 or T2). We found no evidence that lactation stage and welfare class explained HCC at the individual animal level. This study highlights the challenges of using HCC as an indicator of animal welfare on commercial farms.

Among body measurements, body weight (BW) is one of the most important within the buffalo production system, due to its association with economic characteristics. In previous research, we have shown that body volume (BV) is an effective predictor of BW in lactating adult water buffalo. As there are no equations to predict BW through BV for growing dairy buffaloes (young animals), we hypothesized that equations should be developed to meet this need. BW, body length (BL) and heart girth (HG) data were collected in 160 growing dairy buffaloes raised in commercial farms in southern Mexico, with body volume (BV) then estimated from BL and HG. The ratio between BV and BW was determined by linear, quadratic and allometric equations. The goodness-of-fit of the regression models was evaluated using the Akaike information criterion (AIC), the Bayesian information criterion (BIC), the coefficient of determination (R2), the mean square error (MSE) and the root MSE (RMSE). After this, the k-folds cross-validation was performed to indicate a better fit. Our results showed that the growing dairy buffaloes presented a BW of 256.6 ± 96.82 kg and a BV of 155.3 ± 74.87 dm3. High and positive correlation were observed among all variables studied. All parameters (R2, MSE, RMSE, AIC and BIC) used to evaluate the regression equations showed that the quadratic regression model was more effective than the linear and allometric models for estimating BW using BV. The criteria for evaluating and validating models showed that the quadratic model presented a better predictive performance. Based on these findings, we conclude that body volume data to estimate body weight of growing dairy buffaloes were best fitted using the quadratic regression model.

To achieve more targeted antibiotic use, this research paper addresses the investigation of a quarter-selective dry cow therapy (QSDCT) on 16 commercial dairy farms based solely on the pathogen species detected. Cytobacteriological analysis was performed on quarter milk samples collected 2 weeks prior to drying off and 3 to 5 days after calving. Treatment decisions were based on results before dry-off: Only quarters infected with major bacterial pathogens were treated with antibiotics. To prevent new intramammary infections (IMI), all quarters received an internal teat sealant. A total of 1,155 dry periods were evaluated. Only 8.1% of all quarters (range per farm 2.6% – 28.8%) were treated with antibiotics at dry-off and a high bacteriological cure risk of 97.1% was determined for IMI in these antibiotic-treated quarters. For IMI caused by minor pathogens a self-cure risk of 82.1% was observed. The risk of new IMI after calving was 14.6%. Results of binomial logistic regression models indicated that self-cure of IMI by minor pathogens was not related to the pathogen group, the level of quarter somatic cell count at dry-off, or the presence of at least one other quarter infected with minor pathogens in a cow. Furthermore, the risk for new IMI in uninfected quarters was not increased by the presence of at least one quarter infected with major pathogens within cow. However, 95.4% of all IMI by major pathogens after calving were due to new IMI. In conclusion, a pathogen-based QSDCT can be successfully applied on commercial dairy farms to reduce the antibiotic use, but more attention should be paid to prevent new IMI.

To understand the requirements of animals their calls can be analysed. This potentially enables specific and more precise individual care under different emotional and physiological conditions. This study was conducted to identify three different conditions (oestrus, delayed milking and isolation) of buffaloes based on vocalization patterns. A total of 600 acoustic samples of buffaloes for each condition were collected under different conditions consisting of 300 records for confirming and 300 for non-confirming of a particular condition. Important acoustic features like amplitude (P), total energy (P2s), pitch (Hz), intensity (dB), formants (Hz), number of pulses, number of periods, mean period (sec) and unvoiced frames (%) were extracted using the MFCC (mel frequency cepstrum coefficients) technique. Algorithms (model) were trained by partitioning the acoustic data into training and validation sets to develop predictive models. Three different ratios were assessed: 60%-40%, 70%-30% and 80%-20%. Decision tree models were optimized based on decision and average square error (probability) options and other parameters were set to default values of the software package to deveop the best model. The performance of algorithms was evaluated on the parameter accuracy rate. Decision tree models predicted the physiological conditions oestrus, isolation and delayed milking with an accuracy of 66.1, 84.3 and 71.3%, respectively, while the logistic regression model predicted with an accuracy rate of 59.5, 71.1 and 65.7%, respectively, and the artificial neural network (ANN) model predicted these three conditions with 77.7, 85.2 and 79.4% accuracy, respectively. The ANN model was found to be best on the basis of minimum misclassification rate (on 80%-20% portioning). However, decision tree algorithms also provided the additional information that intensity (maximum), amplitude (minimum) and formant (F1) are the most important features of vocal signals to identify physiological conditions like oestrus, isolation and delayed milking respectively in dairy buffalo.