Body condition score (BCS) is a widely used indicator of body energy status and is closely associated with metabolic status, reproductive performance, and health in dairy cattle; however, conventional visual scoring is subjective and labor-intensive. Computer vision approaches have been applied to BCS prediction, with depth images widely used because they capture geometric information independent of coat color and texture. More recently, three-dimensional point cloud data have attracted increasing interest due to their ability to represent richer geometric characteristics of animal morphology, but direct head-to-head comparisons with depth image-based approaches remain limited. In this study, we compared top-view depth image and point cloud data for BCS prediction under four settings: 1) unsegmented raw data, 2) segmented full-body data, 3) segmented hindquarter data, and 4) handcrafted feature data. Prediction models were evaluated using data from 1,020 dairy cows collected on a commercial farm, with cow-level cross-validation to prevent data leakage. Depth image-based models consistently achieved higher accuracy than point cloud-based models when unsegmented raw data and segmented full-body data were used, whereas comparable performance was observed when segmented hindquarter data were used. Both depth image and point cloud approaches showed reduced accuracy when handcrafted feature data were employed compared with the other settings. Overall, point cloud-based predictions were more sensitive to noise and model architecture than depth image-based predictions. Taken together, these results indicate that three-dimensional point clouds do not provide a consistent advantage over depth images for BCS prediction in dairy cattle under the evaluated conditions.

Dairy cow estrus is marked by acute changes in behavior, such as increased activity and decreased rumination. These changes can be measured on the farm using wearable sensors and other monitoring systems. The main goals of this study were to quantify the expression of estrus in lactating dairy cows, evaluate its genetic variability, and assess its relationship with milk production and reproductive performance. Data consisted of 46,048,112 bi-hourly activity and rumination records collected using automated monitoring devices in 10,335 lactating dairy cows during 2 years on a large commercial farm. We developed an open-source, freely available algorithm that quantifies changes in activity and rumination to infer the duration and intensity of estrus. First, we evaluated the association between estrous traits and pregnancy success using alternative logistic regression models, adjusting by lactation, year-season, and sex-sorted or conventional semen. The statistical analysis showed that the addition of a detected estrus contributes significantly to explaining pregnancy success, relative to insemination in the absence of a detected estrus. Similar results were found for estrous duration and intensity. Second, we assessed the heritability of estrous traits using a sliding 21-d window between 21 and 100 DIM. We also evaluated the heritability of number of estrus between 11 and 70 DIM (voluntary waiting period). The statistical models included herd-season and lactation as fixed effects and cow as a random effect. Heritability estimates for presence/absence of estrus were around 0.20 ± 0.02 in the period 21–50 DIM and 0.09 ± 0.03 in the period 50–100 DIM. Similar results were observed for the duration and intensity of estrus. The number of estrus during the voluntary waiting period (11–70 DIM) showed a heritability estimate of 0.23 ± 0.02. Finally, we evaluated the relationship between estrous traits and productive performance, including average milk production in the period 11–70 DIM and also 305-d mature equivalent milk production. All estrous expression traits showed negative genetic correlations with productive traits, ranging from −0.14 ± 0.06 to −0.37 ± 0.05. Overall, the duration and intensity of estrus can be measured in a digital, standardized, and scalable framework, and these traits are significantly associated with pregnancy success. Notably, estrous behavior traits are heritable, which implies that breeding for improved expression of estrus has the potential to improve on-farm detection and increase insemination and pregnancy rates. Additionally, estrous traits are genetically unfavorably correlated with milk production.

Computer vision provides automated, non-invasive, and scalable tools for monitoring dairy cattle, thereby supporting management, health assessment, and phenotypic data collection. Although transfer learning is commonly used for predicting body weight from images, its effectiveness and optimal fine-tuning strategies remain poorly understood in livestock applications, particularly beyond the use of pretrained ImageNet or COCO weights. In addition, while both depth images and three-dimensional point-cloud data have been explored for body weight prediction, direct comparisons of these two modalities in dairy cattle are limited. Therefore, the objectives of this study were to 1) evaluate whether transfer learning from a large farm enhances body weight prediction on a small farm with limited data, and 2) compare the predictive performance of depth-image- and point-cloud-based approaches under three experimental designs. Top-view depth images and point-cloud data were collected from 1,201, 215, and 58 cows at large, medium, and small dairy farms, respectively. Four deep learning models were evaluated: ConvNeXt and MobileViT for depth images, and PointNet and DGCNN for point clouds. Transfer learning markedly improved body weight prediction on the small farm across all four models, outperforming single-source learning and achieving gains comparable to or greater than joint learning. These results indicate that pretrained representations generalize well across farms with differing imaging conditions and dairy cattle populations. No consistent performance difference was observed between depth-image- and point-cloud-based models. Overall, these findings suggest that transfer learning is well suited for small farm prediction scenarios where cross-farm data sharing is limited by privacy, logistical, or policy constraints, as it requires access only to pretrained model weights rather than raw data.

BACKGROUND:Cows that develop metritis experience dysbiosis of their uterine microbiome, where opportunistic pathogens overtake uterine commensals. An effective immune response is critical for maintaining uterine health. Nonetheless, periparturient cows experience immune dysregulation, which seems to be intensified by prepartum over-condition. Herein, Bayesian networks were applied to investigate the directional correlations between prepartum body weight (BW), BW loss, pre- and postpartum systemic immune profiling and plasma metabolome, and postpartum uterine metabolome and microbiome. RESULTS:The Bayesian network analysis showed a positive directional correlation between prepartum BW, prepartum BW loss, and plasma fatty acids at parturition, suggesting that heavier cows were in lower energy balance than lighter cows. There was a positive directional correlation between prepartum BW, prepartum systemic leukocyte death, immune activation, systemic inflammation, and metabolomic changes associated with oxidative stress prepartum and at parturition. Immune activation and systemic inflammation were characterized by increased proportion of circulating polymorphonuclear cells (PMN) prepartum, B-cell activation at parturition, interleukin-8 prepartum and at parturition, and interleukin-1β at parturition. These immune changes together with plasma fatty acids at parturition had a positive directional correlation with PMN extravasation postpartum, which had a positive directional correlation with uterine metabolites associated with tissue damage. These results suggest that excessive PMN migration to the uterus leads to excessive endometrial damage. The aforementioned changes had a positive directional correlation with Fusobacterium, Porphyromonas, and Bacteroides in cows that developed metritis, suggesting that excessive tissue damage may disrupt physical barriers or increase substrate availability for bacterial growth. CONCLUSIONS:This work provides robust mechanistic hypotheses for how prepartum BW may impact peripartum immune and metabolic profiles, which may lead to uterine opportunistic pathogens overgrowth and metritis development.

Assessing cattle behaviors provides insights into animal health, welfare, and productivity to support on-farm management decisions. Wearable accelerometers offer an alternative approach to traditional human evaluation, providing a more objective and efficient method for predicting cattle behavior. Random cross-validation (CV) is commonly used to evaluate behavior prediction by splitting data into training and testing sets, but it can yield inflated results when records from the same animal are included in both sets. Block CV splits data by block effects, offering a more realistic evaluation but remains underexplored for predicting multi-class imbalanced cattle behavior. Additionally, deep learning (DL) models have not been fully explored for behavior prediction compared to machine learning (ML) models. The objectives of this study were to examine the impact of CV designs on multi-class imbalanced cattle behavior prediction and to compare the performance of ML and DL models. Three ML and two DL models were used to predict the four behaviors of six beef cows from a public tri-axial accelerometer dataset, with model performance evaluated using both hold-out and leave-cow-out CV designs representing random and block CV, respectively. In hold-out CV, ML models achieved accuracies of 0.94 to 0.95 and F1 scores of 0.93 to 0.94, while DL models achieved accuracies of 0.9 to 0.92 and F1 scores of 0.89 to 0.91. In the leave-cow-out CV, ML models obtained accuracies of 0.72 to 0.82 and F1 scores of 0.65 to 0.76, whereas DL models obtained accuracies of 0.76 to 0.82 and F1 scores of 0.64 to 0.76. Generally, ML models outperformed DL models in the hold-out CV, but the multi-layer perceptron DL model demonstrated comparable or superior performance in the leave-cow-out CV. All models performed better with hold-out CV than leave-cow-out CV. Our results suggest that CV designs can affect behavior prediction performance. While a random CV produces seemingly good predictions, these results can be artificially inflated by the data partition. A block CV that strategically partitions data could be a more appropriate design.

Pregnancy after embryo transfer (P/ET) in synchronized beef cows that show estrus is usually less than 50 %. Fertility is associated with the ability of the uterus to sustain embryo development, interferon-tau (IFNT) signaling and placentation. The hypothesis is that expression of functional markers of interferon signaling and placentation increases according to cow fertility. Cyclic, Bos indicus-influenced, primiparous cows (n = 50) were submitted to ET 7 days after estrus (D7). Pregnancy diagnosis was conducted on D20, D32, D39 and D46. Blood was collected on D20 to quantify expression of interferon-stimulated gene 15 (ISG15) from peripheral blood mononuclear cells (PBMC) and on D25, D32, and D39 to quantify the abundance of pregnancy associated glycoproteins (PAGs). Pregnancies were terminated on D46. After a 35-day recovery period, the same animals were enrolled in a new round of ET, for eight subsequent rounds. Estrus expression was 72.3 %. P/ET was 82.3, 64.3, 61,6 and 59.5 % on D20, D32, D39 and D46, respectively. There was no effect of round, season (warm vs. cold) or year (1 vs. 2) on P/ET. Based on D46 P/ET, cows were classified as Fertile (FERT; n = 7), Moderately fertile (MF; n = 24) or Subfertile (SF; n = 17). Expression of ISG15 was 71.4 % greater in Fert vs. SF cows. Serum concentrations of PAGs increased over time regardless of fertility classification. In conclusion, responsiveness to IFNT was reduced in SF cows. Placental function based on PAG concentrations was similar among fertility classifications. The identification of FERT and SF cows can profoundly impact the cow-calf sector of the beef industry.

Implementing accelerometer technologies in beef operations is an alternative to increase precision in estrous detection. We hypothesized that (1) the accelerometer algorithm has similar accuracy in detecting behavioral estrus as does visual observation of pressure-sensitive sensors (estrus patches) in grazing beef cows; (2) variables measured by the accelerometer, such as estrus intensity, are associated with hormonal, ovarian, and uterine variables monitored before, during, and after estrus; and (3) the accelerometer variables are associated with the probability of pregnancy in grazing beef cows submitted to embryo transfer (ET). Fifty cows were fitted with accelerometer and patches to detect estrus after a synchronization protocol in eight subsequent rounds. For each round, only cows that showed estrus (day 0; D0) received ET. Follicular diameter, endometrial thickness, corpus luteum (CL) area, and estradiol (E2) and progesterone (P4) concentrations were measured during proestrus, estrus, and early diestrus. On D7, ET was performed. Pregnancies were diagnosed on D46 and cows recovered for 35D before a new replicate. Patches had a greater accuracy (98% vs. 91%) of detection of behavioral estrus than accelerometer algorithm. Cows with lower estrus intensity in the accelerometer had greater follicular diameter on D0 (P = 0.022), CL area on D4 and D7 (P = 0.05), endometrial thickness on D-1 (P = 0.10), and reduced E2 concentrations on D-1 (P = 0.0032). The accelerometer variables did not predict accurately the probability of pregnancy/ET. In conclusion, visual observation of patches was more accurate in detecting estrus than the accelerometer algorithm and most of the associations between accelerometers and physiological variables were for characteristics measured at proestrus.

The goal of this study was to calculate the cost of purulent vaginal discharge (PVD) in dairy cows. The dataset included 11,051 cows from 16 dairy herds located in 4 regions of the United States. Purulent vaginal discharge was characterized as a mucopurulent, purulent, or reddish-brownish vaginal discharge collected at 28 f 7 DIM. Gross profit was calculated as the difference between incomes and expenses, and the cost of PVD was calculated by subtracting the gross profit of cows with PVD from the gross profit of cows without PVD. Continuous outcomes such as milk production (kg/cow), milk sales ($/cow), cow sales ($/cow), feed costs ($/cow), reproductive management costs ($/cow), replacement costs ($/ cow), and gross profit ($/cow) were analyzed using linear mixed effects models. Pregnancy and culling by 305 DIM were analyzed by generalized linear mixed effects models using logistic regression. Models included the fixed effects of PVD, metritis, parity, region, season of calving, and morbidity in the first 60 DIM, as well as the interactions between PVD and metritis, PVD and parity group, and PVD and morbidity. Farm and the interaction between PVD and farm were considered random effects in all the statistical models. A stochastic analysis was conducted using 10,000 iterations with varying relevant inputs. Cows with PVD produced less milk (9,753.2 f 333.6 vs. 9,994.6 f 330.9 kg/cow), were less likely to be pregnant (70.7 f 1.7% vs. 78.9 f 1.2%), and were more likely to be culled by 305 DIM (34.6 f 1.7% vs. 27.2 f 1.3%) compared with cows without PVD. Consequently, milk sales (4,744.7 f 162.3 vs. 4,862.1 f 161.0 $/cow) and residual cow value (1,079.6 f 23.0 vs. 1,179.3 f 20.3 $/cow) were lesser for cows with PVD. Replacement (639.4 f 26.4 vs. 526.0 f 23.4 $/cow) and reproductive management costs (76.3 f 2.5 vs. 69.0 f 2.4 $/cow) were greater for cows with PVD. The mean cost of PVD was $202. The stochastic analysis also showed a mean cost of $202, ranging from $152 to $265. The robust dataset and the stochastic analysis strengthen both the external and internal validity of our findings, offering a deeper understanding of the economic consequences of PVD. In conclusion, PVD resulted in large economic losses to dairy herds by being associated with decreased milk yield, impaired reproduction, and greater culling.

In vivo microbial challenge models are an important tool to study the mechanistic details of bovine mastitis. A critical element of these studies is the enumeration of the bacterial load in milk to determine if interventions induce physiological changes that affect bacterial clearance. Herein, we use Escherichia coli P4 enumeration from milk in the context of mastitis microbial challenge models to show that use of a microdrip method reduces cost and time without negatively affecting rigor and reproducibility. We used E. coli P4–spiked milk samples as a model to test an alternative to Escherichia and costly standard plating methods. Importantly, linear regression analysis comparing the microdrip and standard plate count shows no difference between the methods or technicians, and Bland–Altman analysis shows enumeration via microdrip method has a slight positive bias, proportional across the tested concentrations compared with the standard method. Additionally, the microdrip method has a sensitivity of 103 cfu/mL compared with 104 cfu/mL for quantitative PCR. Economic analysis of consumable cost shows the microdrip method is nearly half as expensive compared with the standard plate method. Furthermore, these data also show milk samples can be stored at 4°C or −80°C without affecting colony-forming unit count, enabling batched sample processing. Taken together, the data presented here indicate that the microdilution and microdrip method can be used to decrease time and cost of E. coli P4 enumeration from milk without negatively affecting the rigor and reproducibility of the data. Future studies can develop this method for use in broader applications, such as validation of additional challenge pathogens for research purposes, clinical monitoring of mastitis cases, or bulk tank monitoring of pathogens.

Housing strategies to alleviate the negative effects of heat stress on the performance of pre-weaned dairy calves have become a focus of research in recent years. Experiments evaluating such strategies have focused on thermoregulatory responses, behavior, and performance. To date, no experiments have evaluated their effects on the microbiota of the upper respiratory tract. Understanding this relationship is crucial for assessing its impact on respiratory health, disease susceptibility, and calf well-being. We conducted an experiment to characterize nasal microbiota from calves housed outdoors, under a naturally ventilated barn, with and without the provision of fans. The experiment was conducted in a commercial dairy in southern GA. Male Holstein calves (n = 60) were assigned randomly at birth (day 0) to 1 of 3 treatments: hutch outdoors with 50% of its area covered with plywood (control = 20), hutch in an open-sided barn with no supplemental cooling (SH = 21), and hutch in an open-sided barn with ceiling fans (SHF = 19), and followed until 68 d of age. Following the removal of obvious debris from the nostrils, nasal swabs were collected from all calves on week 5 (35 ± 8.9 d) and 9 (63 ± 3.2 d) of life and qPCR and 16S rRNA sequencing was performed. Treatment did not affect total bacterial 16S gene copy numbers or alpha diversity (i.e., Shannon and Simpson indexes) at 5 or 9 wk of age. We observed differences, however, in the nasal microbiome structure at weeks 5 and 9 among treatments, with variations in the mean relative abundance (MRA) of certain bacterial genera. On week 5, SHF treatment had reduced MRA of Mycoplasma compared with control and SH treatments and greater MRA of Acinetobacter than calves in the SH treatment. On week 9, control calves had reduced MRA of Escherichia compared with SHF calves and greater Moraxella MRA compared with those in the SH and SHF treatments. We observed differences in nasal microbiome structure of pre-weaned dairy calves as a result of housing strategy. While the results presented herein suggest a potential link between housing conditions and the risk of respiratory disease, further research is necessary to investigate this hypothesis. Improved understanding of the impact of housing environment on respiratory health as well as on heat stress could help producers make informed management decisions to improve calf health and well-being.

This study aimed to investigate the associations among early postpartum estrous characteristics (EPEC) and reproductive outcomes in dairy cows fitted with automated monitoring devices (AMD). We hypothesized that EPEC within 41 DIM reflects cows' physiological resilience and serve as predictors of subsequent fertility. Furthermore, we trained and tested algorithms predicting the fertility potential of cows and compared the reproductive performance of cows classified as low, moderate, and high fertility. The study was conducted using data from 4,578 Holstein cows across 3 commercial dairy herds. Data regarding calving characteristics, postpartum health, milk yield, EPEC (estrus events, duration, rumination nadir, activity peak, and heat index), and the environment were collected. Reproductive outcomes of interest included pregnancy 77 ± 14 d after the first service, fertility class (high-fertility [HFERT] = pregnant to first service; low-fertility [LFERT] = nonpregnant after the first 3 services), and hazard of pregnancy up to 250 DIM. Using a training dataset, we developed an algorithm that was applied to the training and testing datasets to classify cows into bottom, moderate, and top fertility; the reproductive performance of these cows was then compared. Statistical analyses included logistic and Cox proportional hazard regressions. Overall, 38% of cows were pregnant 77 ± 14 d after first service, with a cumulative pregnancy of 67% by the third service. Occurrence of at least 1 estrus event within 41 DIM increased the odds of pregnancy to the first service (0 = referent, 1 = 1.20 [95% CI = 1.01-1.44], ≥2 = 1.16 [95% CI = 0.89-1.52]) and of a cow being HFERT (0 = referent, 1 = 1.26 [95% CI = 1.00-1.59], ≥2 = 1.09 [95% CI = 0.78-1.54]). However, more than 1 estrus event did not further improve fertility outcomes. The positive association between estrus within 41 DIM and hazard of pregnancy was reflected in shorter median days open (0 = 115.0 d, 1 = 94.0 d, ≥2 = 89.0 d). Lower rumination nadir was associated with increased odds of pregnancy to the first service and of a cow being HFERT. Similarly, cows in the lowest tertile of rumination nadir had the greatest hazard of pregnancy by 250 DIM. Cows in the testing dataset classified as bottom and top 25% percentiles of fertility had a 23-d difference in median days open (127 d vs. 104 d) and a 5.3-percentage point difference in censoring by 250 DIM (28.6% vs. 23.3%). This study demonstrates that EPEC are valuable indicators of reproductive potential in dairy cows. Integrating health, genetic, and environmental data with EPEC improves the prediction of fertility outcomes, providing opportunities to optimize reproductive management and efficiency in dairy herds. Finally, the results of our algorithms demonstrate the potential of EPEC to predict long-term reproductive performance of lactating Holstein cows.

Changes in maternal nutrition during the periconceptional period can influence postnatal growth in cattle. This study aimed to identify the impact of supplementing beef cows with rumen-protected methionine (RP-Met) during the periconceptional period on their female progeny. In experiment 1, plasma methionine (Met) levels were analyzed in samples from 10 Angus crossbred, non-lactating beef cows. Cows were randomly assigned to receive 454 g of cottonseed meal with 15 g/d of RP-Met (RPM; Smartamine M, Adisseo) or not (CON) for 5 d and data were analyzed as a completely randomized design with repeated measures. A treatment-by-day interaction was observed (P < 0.001), where plasma Met concentrations increased in the RPM treatment yet remained basal in CON. In experiment 2, 114 cows were fed a roughage-based diet and randomized to receive 454 g/d of corn gluten supplemented with 15 g/d of RP-Met (RPM n = 56) or not (CON n = 58) from days -7 to 7 relative to timed-artificial insemination using sexed semen to obtain females. Amino acids were measured in plasma samples from days -8, 0, and 7 in cows. In the female progeny, body weight, withers height, body length, and heart girth were measured every 60 d from birth through weaning at an average age of 242 ± 5.8 d. Liver, adipose tissue, and longissimus dorsi muscle biopsies were collected at 187.88 ± 5.5 d of age and a subset of 20 random samples (CON = 10; RPM = 10) were selected for RNA-seq on each tissue. Data were analyzed using a generalized randomized block design with repeated measures. Methionine was increased (P < 0.01) in plasma from cows in the RPM treatment on days 0 and 7. After calving, 34 female calves (CON = 16; RPM = 18) remained in the study and no difference was observed in birth weights between treatments. Calves were taller at the withers for RPM than CON (P = 0.03; CON = 92 ± 1.0 cm; RPM = 95 ± 1 cm) but there were no effects of treatment on other measures of body size. A total of 30, 24, and 2 differentially expressed genes (DEGs; P < 0.01) were observed in liver, longissimus dorsi muscle, and adipose tissue respectively. In summary, feeding RP-Met to cows in the periconceptional period resulted in female calves that were taller than CON before weaning. There were DEGs in the tissue samples but no other changes in measurements associated with body size. In conclusion, supplementation of RP-Met to beef cows during the periconceptional period caused minor changes in the female offspring before weaning.

Accessibility to automated monitoring devices (AMD) has led to exploration of alternative reproductive management to ovulation synchronization protocols (OvSP) for first postpartum artificial insemination (AI) according to the cow's early postpartum estrus characteristics (EPEC). We hypothesized that pregnancy and economic outcomes of cows subjected to a targeted reproductive management (TRM) are not inferior to those of cows subjected to an OvSP for the first AI. This was a noninferiority, randomized clinical trial. Cows (n = 2,635) from one dairy were fitted with AMD and classified according to EPEC at 45 ± 3 DIM as estrual (high intensity AMD-detected estrus [primiparous: heat index ≥90, multiparous: heat index ≥70; 0 = minimum, 100 = maximum]) and anestrus (no estrus or low intensity estrus). Cows in the control treatment were enrolled in the Double-Ovsynch (GnRH on d -27, PGF2α on d -20, GnRH on d -17 and -10, PGF2α on d -3 and -2, GnRH on d -1, and timed AI [TAI] on d 0 at 73 ± 3 DIM). Anestrus cows enrolled in the TRM treatment were assigned to the hCG-Ovsynch (TRM1; hCG on d -17, GnRH on d -10, PGF2α on d -3 and -2, GnRH on d -1, and TAI on d 0 at 73 ± 3 DIM). Estrual cows received PGF2α at 60 to 73 DIM, when they were 6 to 22 d after a previous estrus, and if not AI in estrus within 7 d, were enrolled in the hCG-Ovsynch at 70 to 77 DIM (TRM2). Estrual cows in the TRM treatment that were ≥23 d from a previous estrus at 63 ± 3 DIM were enrolled in the hCG-Ovsynch at 63 ± 3 DIM and received TAI at 80 ± 3 DIM (TRM3). Pregnancy was diagnosed 32 ± 3 and 67 ± 3 d after AI. Cows were re-inseminated at AMD-detected estrus or at fixed time within 10 d after nonpregnancy diagnosis. The lactation gross profit was calculated as follows: (milk income + sale value + subsequent lactation calf value) - (feed cost + replacement cost + fixed cost + depreciation + reproductive management cost). Cows in the control treatment were more likely to be diagnosed pregnant 67 d after AI (control = 53.9% [95% CI = 51.1%, 56.6%]; TRM = 50.1% [95% CI = 47.2%, 53.0%]), independent of EPEC. The interaction between treatment and EPEC tended to affect the hazard of pregnancy throughout the lactation (control = referent; anestrus-TRM: adjusted hazard ratio = 1.01, 95% CI = 0.91, 1.13; estrual-TRM: adjusted hazard ratio = 0.83, 95% CI = 0.74, 0.94). Treatment did not affect gross profit, independent of EPEC (control = US$2,196.9 ± 25.6; TRM = US$2,221.9 ± 26.5). Alternative strategies for first postpartum AI according to a cow's EPEC may be possible with AMD, without affecting gross profit. The use of a single hCG treatment to presynchronize the estrous cycle of anestrus cows may be an alternative to the presynchronization with the Ovsynch protocol because despite slightly decreasing P/AI, it did not affect gross profit.

Targeted reproductive management (TRM), employing automated monitoring devices (AMD), is as an alternative to the blanket adoption of ovulation synchronization protocols (OvSP) for first postpartum AI and a means of reducing the use of OvSP for re-insemination of nonpregnant cows. We hypothesized that a TRM that relies heavily on AI of cows on AMD-detected estrus improves reproductive performance and economic return. Early- postpartum estrus characteristics (EPEC) of multiparous (n = 941) cows were evaluated at 40 and 41 DIM (herds 1 and 2, respectively) and EPEC of primiparous (n = 539) cows were evaluated at 54 and 55 DIM (herds 1 and 2, respectively). Cows in the control treatment were enrolled in the Double-Ovsynch protocol and AI at a fixed time (TAI) at 82 and 83 DIM (primiparous cows in herds 1 and 2, respectively) and 68 and 69 DIM (multiparous in herds 1 and 2, respectively). Cows enrolled in the TRM treatment were managed according to EPEC as follows: (1) cows with >= 1 intense estrus (heat index >= 70; 0 = minimum, 100 = maximum) were AI upon AMD-detected estrus starting at 64 (primiparous) and 50 (multiparous) DIM and, if not AI, were enrolled in the Double-Ovsynch, (2) cows without an intense estrus were enrolled in the Double-Ovsynch at the same time as cows in the control treatment. Control cows were re-inseminated based on visual or patch-aided detection of estrus, whereas TRM cows were re-inseminated as described for control cows with the aid of the AMD. All cows received a GnRH injection 27 +/- 3 d after AI and, if diagnosed as nonpregnant, completed the 5-d CoSynch protocol and received TAI 35 +/- 3 d after insemination. The hazard of pregnancy was greater for cows in the TRM treatment (adjusted hazard ratio = 1.17, 95% CI = 1.05, 1.32), resulting in more cows from the TRM treatment starting a new lactation (82.6% vs. 77.2%) and fewer of them sold (15.5% vs. 20.8%). Treatments did not differ regarding total milk yield (control = 12,782.1 +/- 130.6 kg, TRM = 13,054.7 +/- 136.1 kg). The gross profit [(milk income + sale value + subsequent lactation calf value) - (feed cost + replacement cost + fixed cost + reproductive management cost)] of cows in the TRM treatment was $108 greater than the control treatment ($3,061.6 +/- $45.9 vs. $2,953.8 +/- $45.2). According to a Monte Carlo stochastic simulation, the mean (+/- SD) difference in gross profit was $87.8 +/- 12.6/cow in favor of the TRM treatment, and 95% of the scenarios ranged from $67.2/cow to $108.5/ cow (minimum = $30.2/cow, maximum = $141.1/cow). Under the conditions of the current experiment, the TRM treatment improved the gross profit of Holstein cows because the increased hazard of pregnancy changed culling dynamics, reducing replacement cost and cow sales and increasing calf value. The findings of the current experiment emphasize the importance of efficient reproductive management and its substantial economic implications, particularly in the context of high-producing Holstein cows.

This study explored a novel recombinant hormone to produce embryos in Holstein heifers, using a single injection of a long-acting human follicle-stimulating hormone (rFSH) compared with the traditional method of multiple injections of pituitary-derived follicle-stimulating hormone (FSH). We compared both hormones multiple times to evaluate the number of embryos produced. Our results showed that a single injection of rFSH was as effective as multiple injections of FSH in terms of recovered ova/embryos. However, when the single injection of rFSH was used repeatedly in the second part of the study, it resulted in a smaller total number of ova/embryos and freezable embryos. In conclusion, although a single injection of rFSH can effectively stimulate the ovaries, its repeated use may reduce the number of embryos produced. This finding is crucial for advancing embryo production techniques in dairy cattle.

The objective was to evaluate the performance of exploratory models containing routinely available on-farm data, behavior data, and the combination of both to predict metritis self-cure (SC) and treatment failure (TF). Holstein cows (n = 1,061) were fitted with a collar-mounted automated- health monitoring device (AHMD) from −21 ± 3 to 60 ± 3 d relative to calving to monitor rumination and activity. Cows were examined for diagnosis of metritis at 4 ± 1, 7 ± 1, and 9 ± 1 DIM. Cows diagnosed with metritis (n = 132), characterized by watery, fetid, reddish/brownish vaginal discharge (VD) were randomly allocated to one of 2 treatments: Control (CON; n = 62) - no treatment at the time of metritis diagnosis (d 0); Ceftiofur (CEF; n = 70) - subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid on d 0 and 3 relative to diagnosis. Cure was determined 12 d after diagnosis and was considered when VD became mucoid and not fetid. Cows in CON were used to determine SC and cows in CEF were used to determine TF. Univariable analyses were performed using farm-collected data (parity, calving season, calving-related disorders, body condition score, rectal temperature, and days in milk at metritis diagnosis) and behavior data (i.e., daily averages of rumination, activity generated by AHMD, and derived variables) to assess their association with metritis SC or TF. Variables with a P ≤ 0.20 were included in the multivariable logistic regression exploratory models. To predict SC, the area under the curve (AUC) for the exploratory model containing only data routinely available on-farm was 0.75. The final exploratory model to predict SC combining routinely available on-farm data and behavior data increased the AUC to 0.87, sensitivity (Se) 87% and specificity (Sp) 71%. To predict TF, the AUC for the exploratory model containing only data routinely available on-farm was 0.90. The final exploratory model combining routinely available on-farm data and behavior data increased the AUC to 0.93, Se of 93% and Sp of 82%. Cross-validation analysis revealed that generalizability of the exploratory models was poor, which indicates that the findings are applicable to the conditions of the present exploratory study. In summary, the addition of behavior data contributed to increasing the prediction of SC and TF. Developing and validating accurate prediction models for SC could lead to a reduction in antimicrobial use, whereas accurate prediction of cows that would have TF may allow for better management decisions.

Evaluation of heat stress abatement for pre-weaned dairy calves is a rare endeavor. We aimed to assess the impacts of cooling the environment of pre-weaned calves through ceiling fans on their performance after weaning and during their first lactation. We randomly assigned female Holstein calves to one of two treatment at birth (day 0): individual frame-wire hutches in a non-cooled barn ("SH", n = 125) and individual frame-wire hutches in a barn equipped with ceiling fans ("SHF", n = 101). Calves were housed under the same barn, with treatments applied in three alternating sections. Ceiling fans (2.1 m in diameter) were positioned 4.1 m from the ground and 7.6 m apart (center-to-center). Shade cloths were used to separate the sections designated for the SH and SHF treatments. Post-weaning, heifers were commingled. We recorded body weight (BW) and average daily gain (ADG) at weaning, 5, 7, and 10 mo of age. Pregnancy to first artificial insemination (P/1AI), hazard of pregnancy, and the hazard of commencing the first lactation are reported. Body weight at first calving, P/1AI, hazard of pregnancy, and milk yield in the first lactation are reported. No differences in BW (5 mo: SH = 162.9 +/- 1.6 kg vs. SHF = 162.3 +/- 1.6 kg; 7 mo: SH = 200.8 +/- 2.2 kg vs. SHF = 201.1 +/- 2.3 kg; 10 mo: SH = 300.5 +/- 2.6 kg vs. SHF = 300.0 +/- 2.8 kg) and ADG (SH = 0.94 +/- 0.02 kg/d, SHF = 0.94 +/- 0.02 kg/d) from 5 to 10 mo of age were detected. Treatment did not affect P/1AI (SH = 53.5 %, SHF = 45.9 %) and hazard of pregnancy [SH = referent, SHF - adjusted hazard ratio (AHR) = 0.87 (95 % CI = 0.65, 1.18)], but heifers in the SHF treatment were less likely to initiate the first lactation (76.2 % vs. 86.4 %). Body weight at calving (SH = 612.4 +/- 5.3 kg, SHF = 618.2 +/- 5.9 kg) and milk yield (SH = 39.0 +/- 0.48 kg/d, SHF = 38.3 +/- 0.57 kg/d) were not different, but the SHF treatment resulted in lower P/1AI (38.4 % vs. 51.4 %) and hazard of pregnancy (AHR = 0.68, 95 % CI = 0.49, 0.93) and fewer cows starting their second lactation (57.4 % vs. 72.8 %). In our experiment, providing cooling through ceiling fans during the pre-weaning phase had a negative impact on the reproductive performance of Holstein cows during their first lactation.

The objectives of this retrospective observational study were to investigate the association between body condition score (BCS) at 21 d before calving with prepartum and postpartum dry matter intake (DMI), energy balance (EB), and milk yield. Data from 427 multigravid cows from 11 different experiments conducted at the University of Florida were used. Cows were classified according to their BCS at 21 d before calving as FAT (BCS ≥4.00; n = 83), MOD (BCS 3.25 to 3.75; n = 287), and THIN (BCS ≤3.00; n = 57). Daily DMI from −21 to −1 and from +1 to +28 DIM was individually recorded. Energy balance was calculated as the difference between net energy for lactation consumed and required. Dry matter intake in FAT cows was lesser than in MOD and THIN cows both prepartum (FAT = 9.97 ± 0.21, MOD = 11.15 ± 0.14, THIN = 11.92 ± 0.22 kg/d) and postpartum (FAT = 14.35 ± 0.49, MOD = 15.47 ± 0.38, THIN = 16.09 ± 0.47 kg/d). Dry matter intake was also lesser for MOD cows compared with THIN cows prepartum, but not postpartum. Energy balance in FAT cows was lesser than in MOD and THIN cows both prepartum (FAT = −4.16 ± 0.61, MOD = −1.20 ± 0.56, THIN = 0.88 ± 0.62 Mcal/d) and postpartum (FAT = −12.77 ± 0.50, MOD = −10.13 ± 0.29, THIN = −6.14 ± 0.51 Mcal/d). Energy balance was also lesser for MOD cows compared with THIN cows both prepartum and postpartum. There was a quadratic association between BCS at 21 d before calving and milk yield. Increasing BCS from 2.5 to 3.5 was associated with an increase in daily milk yield of 6.0 kg and 28 d cumulative milk of 147 kg. Increasing BCS from 3.5 to 4.5 was associated with a decrease in daily milk yield of 4.4 kg and 28 d cumulative milk of 116 kg. In summary, a moderated BCS at 21 d before calving was associated with intermediate DMI and EB pre- and postpartum but greater milk yield compared with thinner and fatter cows. Our findings indicate that a moderated BCS is ideal for ensuring a successful lactation.

The objective was to assess differences in productive and reproductive performance, and survival associated with vaginal discharge characteristics and fever in postpartum dairy cows located in Western and Southern states of the U.S.A. This retrospective cohort study included data from 3 experiments conducted in 9 dairies. Vaginal discharge was evaluated twice within 12 DIM and scored on a 5-point scale. The highest score observed for each cow was used for group assignment (VD group) as follows: VD 1 and 2 (VD 1/2; n = 1,174) = clear mucus/lochia with or without flecks of pus; VD 3 (n = 1,802) = mucopurulent with < 50% pus; VD 4 (n = 1,643) = mucopurulent with ≥50% of pus or non-fetid reddish/brownish mucous, n = 1,643; VD 5 = fetid, watery, and reddish/brownish, n = 1,800. All VD 5 cows received treatment according to each herd's protocol. Rectal temperature was assessed in a subset of VD 5 cows, and subsequently divided into Fever (rectal temperature ≥39.5°C; n = 334) and NoFever (n = 558) groups. A smaller proportion of cows with VD 5 (67.6%) resumed ovarian cyclicity compared with VD 1/2 (76.2%) and VD 4 (72.9%) cows; however, a similar proportion of VD5 and VD 3 (72.6%) cows resumed ovarian cyclicity. A smaller proportion of VD 5 (85.8%) cows received at least one artificial insemination (AI) compared with VD 1/2 (91.5%), VD 3 (91.0%), or VD 4 (91.6%) cows. Although we did not detect differences in pregnancy at first AI according to VD, fewer cows with VD 5 (64.4%) were pregnant at 300 DIM than cows with VD 1/2 (76.5%), VD 3 (76.2%), or VD 4 (74.7%). Hazard of pregnancy by 300 DIM was smaller for VD 5 compared with VD 1/2, VD 3, or VD 4 cows. A greater proportion of VD 5 cows were removed from the herd within 300 DIM compared with other VD groups. There was 760 kg lesser milk production within 300 DIM for VD 5 compared with VD 2, VD 3, and VD 4, whereas VD 2, VD 3, and VD 4 had similar milk production. We did not detect an association between fever at diagnosis of VD 5 and reproductive performance or milk production. A greater proportion of VD 5 cows without fever were removed from the herd by 300 DIM compared with VD 5 cows with fever. Differences in productive and reproductive performance, and removal of the herd were restricted to fetid, watery, and reddish/brownish vaginal discharge, which was independent of fever.

Much progress has been made in the reproductive efficiency of lactating dairy cows across the USA in the past 20years. The standardisation of evaluation of reproductive efficiency, particularly with greater focus on metrics with lesser momentum and less lag-time such as 21-day pregnancy rates (21-day PR), and the recognition that subpar reproductive efficiency negatively impacted profitability were major drivers for the changes that resulted in such progress. Once it became evident that the genetic selection of cattle for milk yield regardless of fertility traits was associated with reduced fertility, geneticists raced to identify fertility traits that could be incorporated in genetic selection programs with the hopes of improving fertility of lactating cows. Concurrently, reproductive physiologists developed ovulation synchronisation protocols such that after sequential treatment with exogenous hormones, cows could be inseminated at fixed time and without detection of oestrus and still achieve acceptable pregnancy per service. These genetic and reproductive management innovations, concurrently with improved husbandry and nutrition of periparturient cows, quickly started to move reproductive efficiency of lactating dairy cows to an upward trend that continues today. Automation has been adopted in Israel and European countries for decades, but only recently have these automated systems been more widely adopted in the USA. The selection of dairy cattle based on genetic indexes that result in positive fertility traits (e.g. daughter pregnancy rate) is positively associated with follicular growth, resumption of ovarian cycles postpartum, body condition score and insulin-like growth factor 1 concentration postpartum, and intensity of oestrus. Collectively, these positive physiological characteristics result in improved reproductive performance. Through the use of automated monitoring devices (AMD), it is possible to identify cows that resume cyclicity sooner after calving and have more intense oestrus postpartum, which are generally cows that have a more successful periparturient period. Recent experiments have demonstrated that it may be possible to adopt targeted reproductive management, utilising ovulation synchronisation protocols for cows that do not have intense oestrus postpartum and relying more heavily on insemination at AMD-detected oestrus for cows that display an intense oestrus postpartum. This strategy is likely to result in tailored hormonal therapy that will be better accepted by the public, will increase the reliance on oestrus for insemination, will improve comfort and reduce labour by reducing the number of injections cows receive in a lactation, and will allow for faster decisions about cows that should not be eligible for insemination.