Milk Yield In Dairy Cows Research Articles

Symposium Review: The Impact of Fatty Acids as Bioactive Nutrients on the Development of Offspring

Fatty acids (FA) are normally considered a source of energy; however, some FA are essential nutrients with different biological functions such as ligands to membrane and nuclear receptors, and upon binding they modify cell function and transcript expression. The bioactive effects of the FA depend on the FA type and family (i.e., n-6 vs n-3). The FA effects on developmental programming have been studied in cattle and sheep with some similarities in the outcomes between species. Feeding n-3 FA during late gestation improves offspring production performance (i.e., milk yield in dairy cows and growth in beef cattle and sheep) compared with the offspring of dams supplemented with mono- and unsaturated FA or with offspring of dams with no FA supplementation. Also, there is a sexual dimorphism in the outcomes of n-3 FA supplementation, where the increase in growth due to n-3 FA seems to be more evident in males; but it might decrease growth in females. There are multiple assumptions as to how this physiological process occurs. Based on published literature, the developmental effect does not appear to be due to changes in hypothalamic regulations of dry matter intake and energy expenditure or liver and adipose tissue functions. The changes in offspring growth can be attributed to changes in gastrointestinal tract physiology, changes in immune response, or both, probably due to epigenetic changes in those tissues. Feeding n-3 FA in late gestation to the pregnant dam increases expression of amino acid transporters (mRNA and protein) in the offspring's duodenum, associated with changes in DNA methylation. Regarding immune function, the increase in offspring performance has been associated with decreased haptoglobin after weaning in calves or increases in lipid mediators, such as resolvin-D1 at birth. Omega-3 supplementation during late gestation affects offspring growth; changes in the offspring's gut and immune system biology can explain the sexual dysmorphism observed in changed body weight; however, we are unaware which of these basic mechanisms is responsible for the observed changes in biology.

PROSPECTS FOR THE USE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE DIGITALIZATION OF AGRICULTURE AND LIVESTOCK FARMING (EXAMPLE IN THE REPUBLIC OF KARAKALPAKSTAN)

The article provides detailed reviews and analyzes of the perspective of artificial intelligence in Uzbekistan. The importance of artificial intelligence in the development of firms, companies, as well as in the fields of medicine, agriculture has been need by process of digitalization. Also, the demographic changes of the Republic of Uzbekistan were analyzed and forecast indicators for 2024-2030 were developed. Based on these forecast indicators, it was determined that by 2030, the demand of the population of the Republic for meat and dairy products will increase by 19.2%, and for milk products by 16.3%. To meet this demand, suggestions and recommendations have been developed for the automation of livestock complexes based on digital technologies, the use of robotic CR-1 and MR-S2 remote-controlled digital technologies for increasing the milk yield of dairy cows

EFFECT OF HEAT STRESS ON PRODUCTION PERFORMANCE IN DAIRY ANIMALS

The high-temperature stress and combination of high temperature and humidity during summer months in Punjab is an important factor for dairy cows as it directly affects the daily milk yield of dairy cows. The research was conducted on to effect of high-temperature stress on different parameters associated with daily milk yield. For this purpose, 5 freshly parturated Sahiwal breeds of dairy cows were to evaluate the effect of temperature humidity index on Daily milk yield at different temperature humidity index levels i.e. comfortable (≤ 70), mild stress (71–80), moderate stress (81–90), and stressful (≥90) zone. Blood samples were collected and centrifuged to get serum and ELISA was accomplished by using a spectrometer. Results revealed high significant (P < 0.001) increment in cortisol and triglycerides concentration and high significant reduction in serum progesterone (P < 0.001) and Luteinizing hormone (P < 0.001) concentration in cows during stressful environments. Daily milk yield has significant (P < 0.05) variation and a decreasing trend in cows from comfortable to stressful conditions. Progesterone and LH had a highly significant positive correlation with daily milk yield. Cortisol and triglycerides had a non-significant negative correlation with daily milk yield. It is concluded that heat stress has a negative impact on the daily milk yield as it disturbs the hormonal balance of the animal.

Fermented total mixed ration enhances nutrient digestibility and modulates the milk components and fecal microbial community in lactating Holstein dairy cows.

Fermented total mixed ration (FTMR) is an effective method of preserving high-moisture byproducts with higher aerobic stability after fermentation. FTMR has the potential to fulfill the daily nutritional requirements of cattle and enhance their production performance. The objective of this research was to examine the influence of FTMR on lactation performance, total tract apparent digestibility, fecal microbiota communities, and fermentation profiles in lactating dairy cows. A total of 12 cows were randomly assigned into two groups: the TMR group and the FTMR group. The TMR group was fed a total mixed ration (TMR) diet, and the FTMR group was fed an FTMR diet. The FTMR did not impact milk yield in dairy cows despite a decrease in dry matter intake, which increased the efficiency of the feed. In contrast to that in the TMR group, the milk fat content in the FTMR group was greater. The FTMR group showed greater digestibility of neutral detergent fiber (NDF), organic matter (OM), dry matter (DM), crude protein (CP), and acid detergent fiber (ADF) in the total digestive tract than did the TMR group. The FTMR increased the concentration of butyrate in the fecal matter and reduced the pH of the feces. The Chao1, ACE, and Shannon indices of the archaeal community in dairy cow feces were significantly higher in cow fed the FTMR compared to those fed the TMR. LefSe analysis revealed higher levels of Oscillospira, Lactobacillus, Prevotella, and Dehalobacterium in the feces of dairy cows fed the FTMR than in those fed the TMR. However, the abundances of Roseburia, rc4-4, Bulleidia and Sharpea exhibited the opposite trend. The abundances of Halobacteria, Halobacteriales, and Halobacteriaceae, which are biomarkers for distinguishing fecal archaea in the TMR from the FTMR, were substantially greater in the feces of dairy cows that consumed the TMR than in those that consumed the FTMR. Therefore, FTMR can improve the milk fat content, total tract apparent feed digestibility efficiency, and diversity of archaea in the feces. Additionally, this work provides a theoretical basis for the feasibility of FTMR feeding for dairy cows.

Lactation Performance and Rumen Fermentation in Dairy Cows Fed a Diet Supplemented with Monensin or Gum Arabic-Nano Montmorillonite Compost.

The exploration of natural alternatives to antibiotics for enhancing productivity and performance in dairy cows is a crucial objective in farm animal management. This is the first study aimed at developing and evaluating the physicochemical properties and effects of Arabic gum-nano montmorillonite (AGNM) compost compared to ionophore monensin as feed additives on rumen fermentation, blood metabolites, and milk production of Holstein dairy cows. In a replicated 4 × 4 Latin square design, four multiparous mid-lactation Holstein dairy cows with an average body weight of 520 ± 15 kg were enrolled. The dietary treatments included a control diet (basal diet without feed additives), monensin diet [a basal diet supplemented with 35 mg/kg dry matter (DM) monensin], and AGNM diets comprising basal diet supplemented with two levels: low (L-AGNM) at 1.5 g/kg DM, and high (H-AGNM) at 3 g/kg DM. AGNM as a feed additive demonstrated promising physiochemical parameters, including containing highly bioactive components (α-amyrin and lupeol), functional groups (OH and Si-O), and essential mineral contents (Mg2+). Supplementations with H-AGNM significantly improved ruminal (p = 0.031) concentrations of total volatile fatty acids (VFAs), acetic (p = 0.05) and butyric (p = 0.05), enhanced (p < 0.05) digestibility of fiber and organic matter, while decreased (p = 0.013) estimated methane production. However, an increase (p = 0.04) in blood high-density lipoprotein levels and decrease (p < 0.05) in concentrations of creatinine (CREA), bilirubin (BILT), cholesterol (CHOL), and sodium (Na) were observed with H-AGNM supplementation. Both monensin and H-AGNM improved (p = 0.008) feed efficiency compared to L-AGNM; however, neither AGNM nor monensin affected the milk composition or energy status indicators of the dairy cows. The findings of this study highlight the potential of AGNM as a natural candidate to replace monensin in enhancing ruminal VFA production, nutrient digestibility, feed efficiency, blood metabolites, and milk yield in dairy cows.

Effects of Dandelion Extract on Promoting Production Performance and Reducing Mammary Oxidative Stress in Dairy Cows Fed High-Concentrate Diet.

This study investigated the effects of rumen bypass dandelion extract on the lactation performance, immune index, and mammary oxidative stress of lactating dairy cows fed a high-concentrate diet. This study used a complete randomized block design, and initial milk production, somatic cell counts, and parities were set as block factors. Sixty Holstein cows with similar health conditions and lactating periods (70 ± 15 d) were divided into three groups with 20 replicates per group. The treatments included the LCD group (low-concentrate diet, concentrate-forage = 4:6), HCD group (high-concentrate group, concentrate-forage = 6:4), and DAE group (dandelion aqueous extract group, HCD group with 0.5% DAE). The experimental period was 35 d, and cows were fed three times in the morning, afternoon, and night with free access to water. The results showed the following: (1) Milk production in the HCD and DAE groups was significantly higher (p < 0.05) than that in the LCD group from WK4, and the milk quality differed during the experimental period. (2) The HCD group's pH values significantly differed (p < 0.01) from those of the LCD and DAE groups. (3) In WK2 and WK4 of the experimental period, the somatic cell counts of dairy cows in the HCD group were significantly higher (p < 0.05) than those in the DAE group. (4) The serum concentrations of 8-hydroxy-2'-deoxyguanosine (8-OHdG) and protein carbonyl (PC) in the HCD group were significantly higher (p < 0.05) than those in the LCD group. The activity of catalase (CAT) in the LCD and DAE groups was stronger (p < 0.01) than that in the HCD group. (5) The correlation analysis revealed significantly positive correlations between the plasma LPS concentration and serum concentrations of 8-OHdG (p < 0.01), PC (p < 0.01), and malondialdehyde (MDA, p < 0.05) and significantly negative correlations (p < 0.01) between the plasma LPS concentration and activities of CAT and superoxide dismutase. (6) Compared with that in the HCD and DAE groups, the mRNA expression of α, β, and κ casein and acetyl CoA carboxylase in bovine mammary epithelial cells was significantly higher (p < 0.05) in the LCD group, and the mRNA expression of fatty acid synthetase and stearoyl CoA desaturase in the LCD group was significantly higher (p < 0.01) than that in the HCD group. (7) Compared with that in the LCD and HCD groups, the mRNA expression of Nrf2 was significantly higher (p < 0.01) in the DAE group, and the mRNA expression of cystine/glutamate transporter and NAD (P) H quinone oxidoreductase 1 in the DAE group was significantly higher (p < 0.05) than that in the HCD group. Overall, feeding a high-concentrate diet could increase the milk yield of dairy cows, but the milk quality, rumen homeostasis, and antioxidative capability were adversely affected. The supplementation of DAE in a high-concentrate diet enhanced antioxidative capability by activating the Nrf2 regulatory factor and improved rumen homeostasis and production performance.

The effect of selective dry cow therapies based on 2 different algorithms on antimicrobial use, udder health, milk production, and culling in the absence of internal teat sealant use at dry-off

The objective of this study was to evaluate the effect of selective dry cow therapy (SDCT) strategies based on 2 different algorithms as compared with blanket dry-cow therapy for measures of udder health, milk yield, and culling in herds not using internal teat sealant. Cows from 2 commercial farms in West Texas were randomized into 3 different groups: SDCT Algorithm 1 (ALG1; n = 455) cows treated with an intramammary antimicrobial at dry-off if somatic cell count (SCC) > 200,000 cells/mL at any Dairy Herd Improvement Association (DHIA) test date or if the cow had 2 or more cases of clinical mastitis during the enrollment lactation; SDCT Algorithm 2 (ALG2; n = 458) cows treated with an intramammary antimicrobial at dry-off if SCC >200,000 cell/mL at last test date or any case of clinical mastitis during the enrollment lactation; Control cows (CON = 447) received blanket dry cow therapy. All cows enrolled in the study did not receive an internal or external teat sealant. Data related to milk and somatic cell count linear score (LSCC) was collected monthly. Milk yield and LSCC during the first 6 mo of lactation were analyzed using repeated measures ANOVA models, while Cox's Proportional Hazards models were fitted to culling and clinical mastitis data. The farm was fitted as a random effect in all models. The percentage of cows receiving antimicrobials at dry cow was 51.3, 24.7, and 100% for ALG1, ALG2, and CON, respectively. Treatment did not influence the IMI dynamics during the dry period. Additionally, no statistical differences related to treatment were observed for LSCC and milk yield. The LSCC for ALG1, ALG2, and CON was 2.44, 2.41, and 2.26, respectively. The average milk yield for ALG1, ALG2, and CON cows was 43.2, 43.2, and 44.0 kg/d, respectively. Treatment did not affect clinical mastitis incidence and culling. The cumulative incidence of clinical mastitis was 19.6%, 19.4%, and 21.4% for ALG1, ALG2, and CON cows respectively. Additionally, the cumulative risk of death or culling was 18.5%, 17.1%, and 19.5% for ALG1, ALG2, and CON cows respectively. In conclusion, SDCT strategies led to a decrease in antimicrobial drug use at dry-off, without significantly impacting the incidence of clinical mastitis, the risk of culling, LSCC and milk yield of dairy cows. However, numerical differences in LSCC and milk yield were observed between treatment groups.

Exploring factors influencing machine milk yield of dairy cows in cow-calf contact systems: Cow behavior, animal characteristics, and milking management

The objective of this study was to explore factors contributing to low machine milk yield of cows with cow-calf contact. Danish Holstein dairy cows were assigned to 1 of 3 cow-calf contact treatments: full-time with the calf (n = 24), part-time with the calf between morning (a.m.) and afternoon (p.m.) milking and separated overnight (n = 23), or separated from the calf at birth (n = 23). Behavior of cows (stepping and kicking in the parlor) and milking procedures (teat or udder stimulation frequency, preparation duration) were recorded at a.m. and p.m. milking on 2 d during wk 4 and 6 of the treatment period (8 milkings per cow). Full-time cows had the lowest machine milk yields (mean ± SE; 6.1 and 6.0 ± 0.7 L at a.m. and p.m. milking, respectively), whereas part-time cows had similar yield to no-contact cows at a.m. milking (19.6 ± 0.7 L) and similar yield to full-time cows at p.m. milking (6.4 ± 0.7 L). Lower machine milk yields in cows with calf contact could not be fully attributed to milk consumption by the calf; however, they also could not be explained by differences in cow parity, calf sex, milking procedures (stimulation frequency; preparation duration), or stepping and kicking (restless) behavior at milking. All cows showed more restless behavior at p.m. milking, which weakly negatively affected machine milk yield, but this was unrelated to stimulation frequency in cow-calf contact cows, indicating that there may be other sources of discomfort or stress at the time of milking for these cows. Part-time versus full-time calf contact improved daily machine milk yields, but low machine milk yield issues merit further research so that cow-calf contact systems can be sustainable for producers.

The Application of Ordinal Logistic Regression Model as a Robust Tool for Enhanced Prediction of Milk Yield in Dairy Cows.

The Application of Ordinal Logistic Regression Model as a Robust Tool for Enhanced Prediction of Milk Yield in Dairy Cows.

Resilience indicator traits in 3 dairy cattle breeds in Baden-Württemberg

In recent years, research in animal breeding has increasingly focused on the topic of resilience, which is expected to continue in the future due to the need for high-yielding, healthy, and robust animals. In this context, an established approach is the calculation of resilience indicator traits with time series analyses. Examples are the variance and autocorrelation of daily milk yield in dairy cows. We applied this methodology to the German dairy cow population. Data from the 3 breeds (German Holstein, German Fleckvieh, and German Brown Swiss) were obtained, which included 13,949 lactations from 36 farms from the state Baden-Württemberg in Germany working with automatic milking systems. Using the milk yield data, the daily absolute milk yields, deviations between observed and expected daily milk yields, and relative proportions of daily milk yields in relation to lactation performance were calculated. We used the variance and autocorrelation of these data as phenotypes in our statistical analyses. We estimated a heritability of 0.047 for autocorrelation and heritabilities between 0.026 and 0.183 for variance-based indicator traits. Furthermore, significant breed differences could be observed, with a tendency of better resilience in Brown Swiss. The breed differences can be due to both genetic and environmental factors. A high value of a variance-based indicator trait indicates a low resilience. Performance traits were positively correlated with variance-based indicator traits calculated from absolute daily milk yields, but they were negatively correlated with variance-based indicators calculated from relative daily milk yields. Thus, they can be considered as different traits. Although variance-based indicators based on absolute daily milk yields were affected by the performance level, variance-based indicators based on relative daily milk yields were corrected for the performance level and also showed higher heritabilities. Thus, they seem to be more suitable for practical use. Further studies need to be conducted to calculate the correlations between resilience indicators, functional traits, and health traits.

Milk Yield Prediction and Economic Analysis of Optimized Rearing Environment in a Cold Region Using Neural Network Model

The milk yield of dairy cows in a non-stressed state in the cold region in China is lower during cold seasons. In this study, the correlations between indoor environmental factors and milk production were analyzed. Temperature, relative humidity, and light intensity were found to be the main factors affecting milk yield. The warning values of these factors for lower milk production were 5 °C, 60%, and 300 lx, respectively. A neural network model predicting milk yield based on environmental factors was established, and the optimal model parameters were determined, resulting in a high accuracy of R2 = 0.802. This model was used to investigate the optimal measure for improving the indoor environment, which helps to increase milk production and economic benefits, including LED lights, heating radiators, and dehumidifiers. In conclusion, each type of device led to the growth of milk yield, reaching 2.341, 1.706, and 1.893 kg cow−1 Day−1. The combination of heating radiator and LED light resulted in the highest increased net benefit of 16.802 CNY cow−1 Day−1. This is the first time that a neural network model was successfully built to predict milk yield based on climatic features which was also applied to economic analysis of indoor environment improvement for dairy barns in extreme cold regions.

Fitting mathematical functions to extended lactation curves and forecasting late-lactation milk yields of dairy cows

A 305-d lactation followed by a 60-d dry period has traditionally been considered economically optimal, yet dairy cows in modern intensive dairy systems are frequently dried off while still producing significant quantities of milk. Managing cows for an extended lactation has reported production, welfare, and economic benefits, but not all cows are suitable for an extended lactation. Implementation of an extended lactation strategy on-farm could benefit from use of a decision support system, based on a mathematical lactation model, that can identify suitable cows during early lactation that have a high likelihood of producing above a target milk yield (MY) at 305 d in milk (DIM). Therefore, our objectives were (1) to compare the suitability of 3 commonly used lactation models for modeling extended lactations (Dijkstra, Wood, and Wilmink) in primiparous and multiparous cows under a variety of lactation lengths, and (2) to determine the amount of early-lactation daily MY data needed to accurately forecast MY at d 305 by using the most suitable model and determine whether this is sufficient for identifying cows suitable for an extended lactation before the end of a typical voluntary waiting period (50-90 d). Daily MY data from 467 individual Holstein-Friesian lactations (DIM >305 d; 379 ± 65-d lactation length [mean ± SD]) were fitted by the 3 lactation models using a nonlinear regression procedure. The parameter estimates of these models, lactation characteristics (peak yield, time to peak yield, and persistency), and goodness-of-fit were compared between parity and different lactation lengths. The models had similar performance, and differences between parity groups were consistent with previous literature. Then, data from only the first i DIM for each individual lactation, where i was incremented by 30 d from 30 to 150 DIM and by 50 d from 150 to 300 DIM, were fitted by each model to forecast MY at d 305. The Dijkstra model was selected for further analysis, as it had superior goodness-of-fit statistics for i= 30 and 60. The data set was fit twice by the Dijkstra model, with parameter bounds either unconstrained or constrained. The quality of predictions of MY at d 305 improved with increasing data availability for both models and assisting the model fitting procedure with more biologically relevant constraints on parameters improved the predictions, but neither was reliable enough for practical use on-farm due to the high uncertainty of forecasted predictions. Using 90 d of data, the constrained model correctly classified 66% of lactations as being above or below a target MY at d 305 of 25 kg/d, with a probability threshold of 0.95. The proportion of correct classifications became smaller at lower targets of MY at d 305 and became greater when using more lactation days. Overall, further work is required to develop a model that can forecast late-lactation MY with sufficient accuracy for practical use. We envisage that a hybridized machine learning and mechanistic model that incorporates additional historical and genetic information with early-lactation MY could produce meaningful lactation curve forecasts.

Fusion of udder temperature and size features for the automatic detection of dairy cow mastitis using deep learning

Mastitis seriously reduces the welfare and milk yield of dairy cows and affects the economic value of farms. Most dairy cow mastitis detection algorithms based on computer vision used the threshold classification method to detect mastitis only according to the temperature feature of cows’ udders, and the results were easily affected by the farm environment and individual cow characteristics. In this work, an automatic detection method for dairy cow mastitis using the fusion of udder temperature and size features based on deep learning was proposed. First, you only look once version 7 (YOLOv7) network was used to automatically detect the eye and udder regions of dairy cows, extract the corresponding temperatures, and construct an udder temperature feature vector. Then, the CenterNet network was used to automatically detect the keypoints of the udder region, calculate its corresponding size, and construct an udder size feature vector. Finally, both the temperature and size features of udders were fused, and the support vector machine (SVM) algorithm based on the wrapper was used to automatically detect the degree of mastitis. Thermal infrared videos of 196 dairy cows were used as the dataset for evaluating the performance of the proposed method, and the accuracy of the proposed method was 88.61%. The sensitivity and specificity values of the model for clinical mastitis (CM) were 87.50% and 94.03%, respectively, those for subclinical mastitis (SCM) were 81.25% and 91.94%, respectively. The proposed method constitutes a promising approach for automatic diagnosis in the early stages of dairy cow mastitis, and it can be used for objective monitoring and optimal management of commercial farms.

Can meteorological data improve the short-term prediction of individual milk yield in dairy cows?

Many farms document daily milk yields of individual cows because these are a good indicator of cow well-being. It is established that extreme meteorological conditions influence the milk yields by causing heat and cold stress, whereas less is known about the effects of moderate changes in meteorological conditions. Thus, the aim of the present study was to evaluate whether individual daily milk yield predictions can be improved by considering such changes. We evaluated 8 years of milking and meteorological data from Eastern Switzerland with a total of 33,938 daily milkings from 145 Brown Swiss and 64 Swiss Fleckvieh cows. The cows were aged between 1.9 and 13.5 years at parturition. The data set was split into 7 periods according to the days in milk (DIM) and subsequently filtered into subsets by breed and parity. We applied Gaussian process regression to predict individual daily milk yield. We compared different models including DIM, lagged milk yield, and meteorological variables as features and found that models including the lagged milk yield performed best. Within the period of 5 to 90 DIM, we were able to predict individual next-day milk yield from the cow's last milkings with a root mean squared error (RMSE) of 2.1 kg. In contrast, without information on the previous milk yield, accuracy of milk yield prediction was lower, with an RMSE close to 8 kg. The models holding information about previous milk yields showed a substantial increase in performance. Within a more homogeneous data subset filtered by breed or parity or both, predictions were even better, with a relative RMSE of 4.3% for first-parity Fleckvieh cows. However, we found that including meteorological features, such as temperature, rainfall, wind speed, temperature humidity index, cooling degree, and barometric pressure, did not improve the predictions in any of the evaluated periods. This finding indicates that considering meteorological features in daily milk yield prediction models is not useful in moderate climates; considering lagged milk yield is sufficient. We hypothesize that this meteorological information, among other influences, is indirectly contained in the lagged milk yield.

Changes in the Qualitative Composition of the milk of Holstein Cows During Summer Chronic Heat Stress

Seasonal summer heat stress leads to significant economic losses, resulting in a drop in milk yield in dairy cows and a deterioration in milk quality. The purpose of this study was to determine the changes in some qualitative parameters of milk in Holstein cows during chronic heat stress on one of the largest commercial dairy farms Ukraine (50°49′14″ N, 31°49′23″ E). Five multiparous cows of medium lactation with a milk yield of about 30 kg per day were randomly selected into the reference (in the spring, at the end of May) and the experimental group (in the summer, at the end of August). Milk samples were taken from cows during the morning milking. Qualitative analysis of milk included the identification of milk solids-not-fat, density, mass fraction of lactose, fat, protein and minerals, as well as the freezing point, electrical conductivity and active acidity using ultrasonic method. Animals were kept in naturally ventilated barns. The total mixed single-type balanced diet consisting of corn silage and concentrates that the cows received remained unchanged. The sampling of milk from cows of the experimental group was preceded by a continuous 26-day hot period, during which the maximum daily values of the temperature and humidity index did not fall below 72 units. The results showed that in the milk of the animals of the experimental group there was a significant decrease in the content of milk solids-not-fat, the mass fraction of fat and protein (P0.05). The mass fraction of lactose and minerals tended to increase. It should be noted that the average daily milk yield of dairy cows in the herd in spring and summer was almost at the same level. In conclusion, the results of the study showed that high summer temperatures lead to a deterioration in the quality of milk in Holstein cows. Despite the decrease in milk density and freezing point, these figures met the requirements of the standard. Organoleptic indicators of milk, electrical conductivity and active acidity of milk did not change in hot weather, their value indicated the naturalness of milk. The mass fraction of milk fat, which undergoes the greatest change under the influence of seasonal heat stress, is one of the most valuable components of milk, which has a direct effect on nutritional value and purchase price of raw milk. Therefore, a further deeper study of the fatty acid composition of milk using the method of chromato-mass spectrometry will provide valuable data necessary to search for possible herd management strategies to maintain high milk quality under conditions of seasonal heat stress.

A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems.

Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows' health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows' health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows' behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.

Effects of dairy cows' personality traits on their adaptation to an automated milking system following parturition

The objectives of this study were to determine how dairy cow personality traits affect their adaptation to an automated milking system (AMS) upon parturition, as well as whether these personality traits are consistent across the transition from gestation to lactation. Sixty Holstein dairy cows (19 primiparous and 41 multiparous) were assessed for personality traits using a combined arena test conducted at 24 d before parturition and 24 d after first introduction to an AMS, which occurred ∼3 d after parturition. The combined arena test comprised 3 parts: a novel arena test, a novel object test, and a novel human test. Principal component analysis of the behaviors recorded during the personality assessment revealed 3 factors interpreted as personality traits (75% cumulative variance) in the pre-calving test, interpreted as explore, active, and bold. The post-calving test revealed 2 factors (78% cumulative variance), interpreted as active and explore. Data from d 1 to 7 after introduction to the AMS were summarized by cow and associated with the pre-calving factors, and data from d 21 to 27 after introduction to the AMS were summarized by cow and associated with the post-calving factors. The active trait had a moderate positive correlation between the pre- and post-calving tests, whereas exploration had a weak positive correlation between tests. Cows that scored high for activeness in the pre-calving test tended to have fewer fetching events and a higher coefficient of variation of milk yield in the first 7 d after introduction to the AMS, whereas bolder cows tended to have higher milk yield during that period. In the post-calving test, more active cows tended to have more frequent milkings and voluntary visits per day, as well as a lower cumulative milk yield from d 21 to 27 after introduction to the AMS. Overall, these results indicate that personality traits of dairy cows are associated with adaptation and performance in an AMS, and that personality traits are consistent across the transition period. Specifically, cows that scored high for boldness and activeness adapted better to the AMS immediately after calving, whereas cows that scored low for activeness and high for boldness performed better in terms of milk yield and milking activity in early lactation. This study demonstrates that personality traits affect milking activity and milk yield of dairy cows milked with an AMS and, therefore, may be useful for selection of cows who might best adapt to and use an AMS.

Effects of hoof trimming on feed consumption, milk yield, oxidant and antioxidant system in dairy cows with hoof deformities

In this study is objectived to reveal the importance of hoof trimming (HT) in cows by determining the changes in feed consumption, milk yield, oxidant, and antioxidant parameters in the days before and after HT in cows with hoof deformities. This research was conducted on 12 female Brown Swiss dairy cows late lactation period that had healthy hooves showing symptoms lameness due to hoof deformities. Daily feed consumption and milk yield findings before and after HT were recorded. Total oxidant capacity (TOC) and total antioxidant capacity (TAC) tests for determination of oxidative stress index in serum in blood samples, for the evaluation of antioxidant potential; glutathione peroxidase (GSH-Px), glutathione (GSH), superoxide dismutase (SOD) enzyme, Vitamin E, A, and C levels were measured. According to the findings of this study, after HT increased feed consumption and milk yield in dairy cows (P

Effects of lipoperoxidation and mitochondrial state on milk yield of dairy cows under technological stress

Evaluation of the physiological state of cattle is crucial in creating healthy, high-performing dairy cattle herds. Technological stress is one of the most critical factors determining the biological potential of higher-yielding cows. This work aimed to assess the effect of technological stress on various oxidative parameters and mitochondrial states in dairy cows' blood, milk yield and milk composition. The study was conducted on the black-and-white breed of healthy herds. Regrouping, changing service personnel, and carrying out veterinary and sanitary manipulations were considered technological stress factors. The concentration of cortisol in the blood serum was studied by the immunological method. The concentrations of malonic dialdehyde (MDA), diene conjugates (D.C.), Schiff bases (S.B.), reduced glutathione and catalase activity were measured spectrophotometrically. The mitochondrial state was estimated by laser interference microscopy. While the milk yield, protein and lipid composition of cow milk were studied using an ultrasound analyzer. The researched indicators were analyzed before and for 30 days after the effect of technological stress. Results of the study suggested that technological stress caused an increase in oxidative processes, along with a reduction of antioxidant activity of blood and milk at the initial stages of registration (1-7 days). The concentration of glutathione remained reduced for 30 days after technological stress. A decrease in mitochondrial refractoriness and disintegration accompanied these processes. The milk yield indicator decreased was not restored to the values of intact animals by 30 days after technological stress. Further, the protein and lipid composition also reduced. Thus, a decrease in the quantity and quality of milk under technological stress may be mediated by the development of oxidative stress, which the refractoriness and disintegration of mitochondria might trigger.

Association between change in body weight during early lactation and milk production in automatic milking system herds

The objective of this observational study was to investigate the association between percent body weight (BW) change in early lactation and the 90-d cumulative milk yield of dairy cows in automatic milking system (AMS) herds. Retrospective daily cow data were collected from the Lely T4C (Lely Industries, Maassluis, the Netherlands) software on 34 farms. Cows were categorized by parity into parity 1 (P1), parity 2 (P2), or parity 3 and greater (P3+). The BW change over the first 21 d of lactation was calculated as the percentage difference between the cow's average BW across d 20 through 22 and the average BW across d 2 through 4 (initial BW) postpartum. The 90-d cumulative milk yield was the outcome variable in a mixed linear regression model, with BW change, parity, their interaction, and season of calving as explanatory variables. Farm and cow nested within farm (n = 4,695) were random effects in the model. On average, cows in all 3 parity groups lost BW during the first 21 d in milk. The 21-d BW change had a negative quadratic relationship with 90-d cumulative milk yield for all parity groups; P1, P2, and P3+ cows with a 21-d BW change of −7.42%, −5.02%, and −4.52%, respectively, were more productive over 90 d in milk (P1 = 3,123 ± 52.6 kg, P2 = 4,271 ± 52.8 kg, and P3+ = 4,548 ± 52.2 kg). The findings of this study highlight the benefits of monitoring BW change in early lactation and may contribute to future research aimed to develop or improve predictive models for milk production in herds using AMS.