Management practices associated with conception rate and service rate of lactating Holstein cows in large, commercial dairy herds

Adjusting the timing of inseminations to the time lag on luteolysis alerts results in higher conception in dairy cattle

Dairy cow fertility is a complex trait that depends on the cow's physiological status, the farm's environmental and management conditions, and their interactions. Already the slightest improvement in fertility can positively impact a farm's profitability and sustainability. In research, milk progesterone (P4) has often been used as an accurate and feasible way to identify a dairy cow's reproduction status. Moreover, in Europe and Canada, it has been used to improve fertility management on commercial farms as it allows to accurately identify reproduction issues, pregnancy and the optimal insemination window. An on-farm P4 device (OPD) automatically samples, measures and monitors the milk P4 concentration of individual cows. To this end, the P4 data is smoothed to be robust for measurement errors and outliers, and fixed thresholds are used to estimate the time of luteolysis preceding ovulation, thereby generating a luteolysis alert (LA). By smoothing the P4 data, the OPD introduces a time lag on the LA. Variation in this time lag is not considered in the estimation of the optimal insemination window that is advised to the farmer. Ignoring this variation might decrease the accuracy of the optimal insemination window and, therefore, decreases the likelihood of conception. We hypothesize that considering the length of the time lag and adapting the advice accordingly improves the conception rate. This observational retrospective study uses an extensive data set from 17 commercial dairy farms that are equipped with an OPD. We estimated the time lag on the alerts and evaluated their relationship with the interval from LA to insemination for successful (n = 3721) and unsuccessful inseminations (n = 3896) separately. Results showed that the probability of conception increases when a longer LA time lag is compensated with a shorter interval from LA to insemination and vice versa. In addition, for successful inseminations, we found a clear negative relation between the time lag and the interval from LA to insemination and the interval was significantly shorter when the time lag of the LA was longer. This negative relation between time lag and interval from LA to insemination was less pronounced for unsuccessful inseminations. Additionally, we evaluated the conception rates for inseminations that are performed too early, in time or too late with respect to the optimal insemination window advised by the OPD, in function of their associated time lags. We found that, for inseminations that were preceded by a short time lag (<8 h), the conception rate was 17.5 percentage points higher when cows were inseminated later than advised. Likewise, when inseminations were preceded by a long time lag (≥24 h), we found that the conception rate was 13 percentage points higher when cows were inseminated earlier than advised. Our results suggest that farmers using an OPD could potentially increase their conception success by compensating the variable time lag on the LA by adapting the interval from alert to insemination accordingly. This could be used to develop reproductive management strategies to improve reproductive performance on those farms, which can positively impact their sustainability.

Development and evaluation of predictive models for pregnancy risk in UK dairy cows

One suggested approach to improve the reproductive performance of dairy herds is through the targeted management of subgroups of biologically similar animals, such as those with similar probabilities of becoming pregnant, termed pregnancy risk. We aimed to use readily available farm data to develop predictive models of pregnancy risk in dairy cows. Data from a convenience sample of 108 dairy herds in the UK were collated and each herd was randomly allocated, at a ratio of 80:20, to either training or testing data sets. Following data cleaning, there were a total of 78 herds in the training data set and 20 herds in the testing data set. Data were further split by parity into nulliparous, primiparous, and multiparous subsets. An XGBoost model was trained to predict the insemination outcome in each parity subset, with predictors from farm records of breeding, calving and milk recording. Training data comprised 74,511 inseminations in 45,909 nulliparous animals, 86,420 inseminations in 39,439 primiparous animals, and 158,294 inseminations in 32,520 multiparous animals. The final models were evaluated by predicting with the testing data, comprising 31,740 inseminations in 19,647 nulliparous animals, 38,588 inseminations in 16,215 primiparous animals, and 65,049 inseminations in 12,439 multiparous animals. Model discrimination was assessed by calculating the area under receiver operating characteristic curves (AUC); model calibration was assessed by plotting calibration curves and compared across test herds by calculating the expected calibration error (ECE) in each test herd. The models were unable to discriminate between insemination outcomes with high accuracy, with an AUC of 0.63, 0.59 and 0.62 in the nulliparous, primiparous and multiparous subsets, respectively. The models were generally well-calibrated, meaning the model-predicted pregnancy risks were similar to the observed pregnancy risks. The mean (SD) ECE in the test herds was 0.038 (0.023), 0.028 (0.012) and 0.020 (0.008) in the nulliparous, primiparous and multiparous subsets respectively. The predictive models reported here could theoretically be used to identify subgroups of animals with similar pregnancy risk to facilitate targeted reproductive management; or provide information about cows' relative pregnancy risk compared with the herd average, which may support on-farm decision-making. Further research is needed to evaluate the generalizability of these predictive models and understand the source of variation in ECE between herds; however, this study demonstrates that it is possible to accurately predict pregnancy risk in dairy cows using readily available farm data.

The Role of Housing Conditions on the Success of Artificial Insemination in Intensively Reared Dairy Ewes in Greece

The objective was to assess the effect of housing conditions during the summer months on the success rates of cervical artificial insemination (AI) with cooled semen, in intensively reared dairy ewes in Greece. The study involved 2083 Lacaune ewes from 23 flocks that were serviced during May to September. An estrous synchronization protocol with the insertion of progestogen sponges for 14 days and eCG administration at sponge removal, was used. All ewes were inseminated 54−57 h after sponge removal with cooled semen (15 °C) from 10 Lacaune rams. Pregnancy diagnosis was performed via trans-dermal ultrasonography at 35−40 days after AI. Data recording started the day after sponge placement (15 days prior to AI), and lasted up to 14 days after AI. Daily records included temperature, relative humidity, and Temperature-Humidity Index (THI) inside the shed. Available space and volume per animal, frequency of bedding renewal, access to a yard, and indoor light were also recorded in each farm. Binary logistic regression of data records showed that temperature and THI increases at days −15 to +4 around AI (day 0) had a negative effect on pregnancy rates (reducing the likelihood of pregnancy by 3−6% and 7%, respectively). The latter also decreased significantly (p < 0.05) in farms with high stocking density, non-frequent bedding renewal, and outdoor access by ewes (by 30%, 34%, and 44%, respectively). Overall, the results indicate that appropriate housing conditions are warranted to increase the success of AI in dairy ewes during the summer months.

Symposium review: The implications of spontaneous versus synchronized ovulations on the reproductive performance of lactating dairy cows

Lactating dairy cows are classified as spontaneous ovulators, in which establishment of pregnancy depends on the accuracy of detection of behavioral estrus for correct timing of artificial insemination (AI). Development of the Ovsynch protocol, a hormonal protocol that synchronizes ovarian function, thereby allowing for timed AI (TAI) without the need to detect estrus, provided a management tool for increasing AI service rates but not pregnancies per AI (P/AI). A review of 7 randomized, controlled experiments that compared P/AI of cows inseminated after a detected estrus to that of cows receiving TAI after submission to Presynch-Ovsynch or Double-Ovsynch protocols supports that the newest programs for TAI yield more P/AI than cows inseminated after a detected estrus. The physiologic and endocrine mechanisms that explain how fertility programs increase P/AI are a culmination of over 20 yr of research aimed at increasing reproductive performance in lactating dairy cows. We illustrate the dramatic change in reproductive performance of US dairy cows over time by comparing the phenotypic trend in days open with the genetic trend in daughter pregnancy rate and the phenotypic trend in cow conception rate. Whereas days open increased from 1955 to 2000, days open from 2000 to 2010 dramatically decreased without a concurrent increase in the genetic trend for daughter pregnancy rate. By contrast, the dramatic decrease in days open over the past 20 yr is associated with a dramatic increase in the phenotypic trend in cow conception rate. Although many management factors affect P/AI, adoption and implementation of TAI programs that directly increase P/AI is an important component of the dramatic increase in reproductive performance in lactating dairy cows in the United States over the past 20 yr.

Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows

As the reproductive efficiency of dairy cattle continues to improve in response to better management and use of technology, novel reproductive management approaches will be required to improve herd performance, profitability, and sustainability. A potential approach currently being explored is targeted reproductive management. This approach consists of identifying cows with different reproductive and performance potential using multiple traditional and novel sources of biological, management, and performance data. Once subgroups of cows that share biological and performance features are identified, reproductive management strategies specifically designed to optimize cow performance, herd profitability, or alternative outcomes of interest are implemented on different subgroups of cows. Tailoring reproductive management to subgroups of cows is expected to generate greater gains in outcomes of interest than if the whole herd is under similar management. Major steps in the development and implementation of targeted reproductive management programs for dairy cattle include identification and validation of robust predictors of reproductive outcomes and cow performance, and the development and on-farm evaluation of reproductive management strategies for optimizing outcomes of interest for subgroups of cows. Predictors of cow performance currently explored for use in targeted management include genomic predictions; behavioral, physiological, and performance parameters monitored by sensor technologies; and individual cow and herd performance records. Once the most valuable predictive sources of variation are identified and their effects quantified, novel analytic methods (e.g., machine learning) for prediction will likely be required. These tools must identify groups of cows for targeted management in real time and with no human input. Despite some encouraging research evidence supporting the development of targeted reproductive management strategies, extensive work is required before widespread implementation by commercial farms.

Effect of intrauterine infusion of two cephalosporins, ceftazidime and cephapirin, on uterine bacterial load and uterine horn diameter in bovine subclinical endometritis

This study aimed to evaluate the effect of intrauterine infusion of ceftazidime and cephapirin on uteri­ne bacterial load and uterine horn diameter in bovine subclinical endometritis. At 7-8 weeks postpartum, a total of 122 cows suffering from subclinical endometritis were divided into three groups. Group I cows were intrauterinely (IU) infused with 2 g ceftazidime diluted with 50 mL saline; group II cows were IU infused with 2 g cephapirin diluted with 50 mL saline; and group III cows were kept as untreated control. Vaginal examination, ultrasonography and bacterial examination were done before treatment programme and later repeated twice at 10-day intervals. Staphylococcus spp., Klebsiella spp., Streptococcus spp., Escherichia coli and Proteus spp. were isolated. After the end of the treatment programme, proportions of cows infected with Staphylococcus spp. and Streptococcus spp. were significantly (P<0.05) decreased in ceftazidime and cephapirin groups. However, proportions of cows infected with Escherichia coli were significantly (P<0.05) decreased in the ceftazidime group only. Uterine bacterial loads in ceftazidime and cephapirin groups were significantly decreased (P<0.05). Mean uterine horn diameters in ceftazidime group (2.44±0.03 cm) became significantly lower (P<0.05) than those in cephapirin (2.70±0.04 cm) and control (3.06±0.06 cm) groups. Conception rate in ceftazidime group (80.95%) was significantly (P<0.05) higher than rates recorded in cephapirin (64.00%) and control (26.67%) groups. In conclusion, ceftazidime and cephapirin decreased uterine bacterial load. Moreover, ceftazidime significantly reduced uterine horn diameter compared to the other groups and was associated with significantly higher conception rate. Thus, ceftazidime is recommended for treatment of subclinical endometritis in dairy cows.

Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study

Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.

Environment, nutrition, and management practices for far-off, close-up, and fresh cows on Canadian dairy farms - A retrospective descriptive study

The complex and interrelated management components of dairy farming are associated with health, production, and profitability of the herd, yet there is limited objective data on current management practices of the far-off, close-up, and fresh periods across Canadian dairy farms. We aimed to describe management practices of Canadian dairy farms by using a pre-existing risk assessment tool and outline potential management opportunities. Upon veterinarians' or producers' request, a transition management risk assessment (The Vital 90, Elanco) was performed by trained observers (n = 10) during farm visits (n = 78) between August 2014 and March 2018. Most farms were in Ontario (n = 64), whereas the remaining were in Alberta (n = 5), British Columbia (n = 4), Manitoba (n = 1), Prince Edward Island (n = 2), Newfoundland (n = 1), and Saskatchewan (n = 1). The study included 79 questions about nutrition, pen management, and cow comfort of the dry (approximate ranges: far-off, -60 to -20 d in milk; close-up, -20 to 0 d in milk) and fresh (0-30 d in milk) periods. The herds averaged 125 milking cows, and most had 2 defined dry groups (81%). Freestall (FS; 54%) and straw-bedded loose pack (BP; 81%) were the most common housing systems observed in the far-off and close-up periods, respectively. Heifers and cows were housed together in 56, 80, and 59% of the far-off, close-up, and fresh pens, respectively. A large proportion of the far-off (FS: >100% stocking density; BP: <9.3 m²/cow; 41%), close-up, and fresh pens (FS: >80% stocking density; BP: <13.9 m²/cow; 52 and 49%, respectively) were overstocked. Poor water access was observed across all periods (65, 58, and 24% of the far-off, close-up, and fresh, respectively). Only a few farms had proper heat abatement systems in place (absence of properly functioning soakers or fans; <10% in the dry and 15% in the fresh periods). Cows were able to sort their ration in 60% of the dry period pens and 31% of the fresh pens. In 73% of the farms, fresh cow health monitoring protocols were not in place. Colostrum cows and sick cows were housed together in 40% of the farms; 59% separated the newborn from the dam within 2 to 12 h of birth with colostrum harvested immediately thereafter. This work describes prevalent management practices in the dry and fresh periods and highlights areas for potential improvement. Future research should focus on the associations between management choices and health performance of dairy farms.

Orchestrating the expression levels of sperm mRNAs reveals CCDC174 as an important determinant of semen quality and bull fertility

Bulls with acceptable semen quality vary in actual field fertility and this can be elucidated by studying the expression levels of mRNAs in the sperm. The present study aimed at assessing the variations in the sperm gene expression levels of PRM1, CCDC174, RPL36A, TMCO2, SWI5 and OIT3 in bulls differing in fertility status. Frozen semen samples from Holstein-Friesian bulls were classified into high-fertile (n = 8, average field conception rate = 46.1 ± 0.51, p < 0.001) and sub-fertile (n = 7, average field conception rate = 39.4 ± 0.69) groups. In the post-thaw semen samples, sperm kinematics, structural and functional membrane integrities, mitochondrial membrane potential and chromatin distribution were analyzed. The sperm total RNA was subjected to gene expression studies by Real-Time PCR. Multivariate regression analysis was performed using gene expression levels and conception rates. The sperm functional attributes did not differ significantly between the groups. The relative mRNA levels (fold change) of CCDC174 (6.20), RPL36A (4.66), SWI5 (1.86) and OIT3 (1.53) were higher in high-fertile bulls. Further, the expression level of the CCDC174 gene was significantly (p = 0.02) up-regulated in high-fertile bulls. The fertility prediction multivariate model with genes, CCDC174, RPL36A, TMCO2 and OIT3 had the maximum coefficient of determination (R² = 0.68) with the field conception rate. This model had 93.3% bull fertility prediction accuracy with 100% sensitivity and 87.5% specificity. The study suggests that the expression level of CCDC174 can be used as a potential marker for assessing bull fertility.

Economic Viability of Adoption of Automated Oestrus Detection Technologies on Dairy Farms: A Review

Simple Summary

The accurate and timely detection of oestrus is a central element of good dairy herd management as it ultimately determines the level of milk production and is core to the economic viability of the farm business. However, the traditional method of oestrus detection, which occurs by observing the dairy cows standing immobile while being mounted, is usually time-consuming, repetitive and requires considerable skill and experience on the part of the farmer to attain a reasonable level of efficiency. Given the limitation of the traditional method of oestrus detection, a number of automated oestrus detection (AOD) technologies have been developed. However, the rate of adoption of these technologies remains low. One reason that has been proposed for farmers’ low adoption of such technologies has been their lack of knowledge around the potential economic returns from investing in AOD technologies. In this paper, we review the empirical literature on the viability of investment in AOD technologies from an economic perspective. The conclusion of this study provides evidence from which farmers can make more informed decisions in relation to investing in AOD technologies. The review and analysis is also of importance for informing policy, as it provides an examination of the incentives and levers that could improve productivity on dairy farms.

Abstract

The decision for dairy farmers to invest in automated oestrus detection (AOD) technologies involves the weighing up of the costs and benefits of implementation. In this paper, through a review of the existing literature, we examine the impacts of investment in AOD technologies in relation to the profitability and technical performance of dairy farms. Peer-reviewed articles published between 1970 and 2019 on the investment viability of AOD technologies were collated and analysed. We capture the different measures used in assessing the economic performance of investment in AOD technologies over time which include net present value (NPV), milk production, Benefit-Cost Ratio (BCR), internal rate of return (IRR) and payback period (PBP). The study concludes that investment in AOD technologies is not only worthwhile but also contributes to farm profitability.

Effects of Chickpea in Substitution of Soybean Meal on Milk Production, Blood Profile and Reproductive Response of Primiparous Buffaloes in Early Lactation

Simple Summary

Currently, the protein needs of lactating buffaloes are mainly covered by soybean derivatives produced predominantly overseas. In order to promote the use of locally produced protein sources, in this study we tested the effects of total replacement of soybean meal by using chickpea meal, a protein-rich legume well adapted to and traditionally grown in the Mediterranean area. We evaluated the effects of these two alternative protein sources on blood profile, reproductive response and milk traits in primiparous buffaloes in early lactation. Based on our findings, chickpea meal does not impair the productive and reproductive performances of primiparous dairy buffaloes. In addition, chickpeas may represent a good alternative protein source for organic farms as it is not at risk of contamination by genetically modified cultivars.

Abstract

This study aimed to evaluate the effects of the use of chickpea meal in substitution of soybean meal on plasma metabolites, reproductive response, milk yield and composition and milk coagulation traits of primiparous buffaloes in early lactation. Eighteen primiparous buffaloes were blocked by age, body weight and days in milk and equally allotted to two experimental groups from 10 to 100 days of lactation. The experimental diets consisted of the same forage integrated with two different isonitrogenous and isoenergetic concentrates containing either 210 g/kg of soybean meal or 371 g/kg chickpea. The use of chickpea meal had no negative effects on dry matter intake (p = 0.69), body condition score (p = 0.33) and milk yield (p = 0.15). Neither milk composition nor blood metabolites were influenced by dietary treatments (p > 0.05), but an increment of urea concentrations in milk (p < 0.05) and blood plasma (p < 0.001) were observed in buffaloes fed chickpeas. Moreover, no effect (p > 0.05) of the dietary treatment was highlighted on milk coagulation traits as well as buffalo reproductive responses. We concluded that soybean meal can be replaced by chickpea meal in the diet for primiparous dairy buffaloes in the early lactation period without impairing their productive and reproductive performance.

Fertility response of lactating dairy cows subjected to three different breeding programs under subtropical conditions

Background

It is comprehensively recognized that reduced reproductive efficiency represents a great economic loss to dairy producers. Ovarian cysts and anestrus syndromes are considered the greatest significant causes of low reproductive efficiency in dairy herds worldwide as they detrimentally affect the longevity and profitability of dairy herd. Pregnancy rate is the best available single deciding parameter used for assessment of the reproductive efficiency at the herd level which measures the probability that open cows become pregnant per unit of time. So, the current study was planned to evaluate the suitability of using Ovsynch plus CIDR and G6G resynchronization protocols as an efficient treatment regimen for cystic ovarian diseased cows and anestrus cows, respectively, through comparing pregnancy rates of cystic ovarian diseased cows that subjected to Ovsynch supplemented with controlled internal drug release device with the pregnancy rate of healthy cows that subjected to a Presynch-Ovsynch synchronization protocol, as well as through comparing pregnancy rates of anestrus cows that subjected to G6G treatment protocol with the pregnancy rate of healthy cows. Moreover, possible factors such as breed, parity, and season which may affect the treatment success were also evaluated.

Results

The results of the current study revealed an overall mean pregnancy rate of 36.64%. Moreover, Simmental cows recorded a greater (p < 0.01) pregnancy rate (45.16%) than that recorded for Holstein cows (34.98%). A highly significant seasonal effect was observed, as a higher (p < 0.01) pregnancy rate was recorded for cows inseminated during cold months (39.54%) compared with that recorded for cows inseminated during hot months (29.18%).

Conclusions

No significant differences were detected in the pregnancy rates among the three breeding programs; thence, the application of the G6G synchronization protocol for anestrus cows and Ovsynch-CIDR synchronization protocol for cows with ovarian cysts could be used as effective treatment regimens as they resulted in nearly the same pregnancy rates that recorded for healthy cows. In addition, the treatment response was highly influenced by cow’s breed, parity, and season of breeding.

Extended lactation in high-yielding dairy cows. I. Effects on reproductive measurements

The objective of this prospective field study was to evaluate the effects of extending the lactation period on various reproductive measurements of high-yielding Holstein cows. On 40 d in milk (DIM), cows were gynecologically examined (transrectal palpation, sonography, vaginoscopy). Cows without signs of clinical endometritis were blocked by parity and were randomly allocated to 1 of 3 experimental groups with a voluntary waiting period (VWP) of 40, 120, and 180 d, respectively (G40, n = 135; G120, n = 141; G180, n = 139). Cows of G120 and G180 were reexamined at the end of the VWP. If natural estrus was detected within 46 d after the end of the VWP, an artificial insemination was performed. If no estrus was detected, the respective cows were synchronized by applying the classical Ovsynch protocol. We found no difference in the proportion of cows in which estrus was detected between 40 to 86 DIM or in the days to first estrus between the 3 groups. Estrus detection in this period was lower in cows with body condition score <3 on 90 DIM compared with body condition score ≥3 (61.5 vs. 76.0%) and in cows with high energy-corrected milk production (ECM) on 92 DIM [58.6 vs. 70.1%, for cows with higher and lower than the median (39.9 kg) ECM, respectively]. The proportion of cows that estrus was detected within 46 d after the VWP was greater in G120 (88.9%) and G180 (90.8%) compared with G40 (70.4%). These effects were more apparent in cows with high ECM. The rate of estrus detection and of becoming pregnant in this period was greater for G120 (hazard ratio = 2.2 and 1.6, respectively) and for G180 (hazard ratio = 2.4 and 1.8) compared with G40. Cows in both groups with extended lactation had greater overall first service conception rates (G120 = 48.9%; G180 = 49.6%) and a lower number of services per pregnant cow (G120 = 1.56 ± 0.1; G180 = 1.51 ± 0.1) compared with G40 (36.6%; 1.77 ± 0.1). We observed no difference in pregnancy loss or in the proportion of cows culled up to 305 d of lactation between the 3 groups. The number of Ovsynch protocols per 1,000,000 kg of ECM was reduced by 75% in G180 and by 74% in G120 compared with G40 (5.9 vs. 7.1 vs. 25.1). In conclusion, extending the lactation of dairy cows can improve main reproductive measurements in high-yielding cows.

Survey of reproduction management on Canadian dairy farms

The objectives of this study were to (1) quantify current reproduction management practices, and (2) assess the association between these practices and herd reproductive performance on dairy farms in Canada. A bilingual survey was developed, validated, and administered from March to May 2014 to collect general and reproduction management and performance measures [annual 21-d pregnancy rate (PR), 21-d insemination rate (IR), and conception risk (CR)]. Associations between management practices and reproductive performance measures were tested using linear regression models. A total of 832 questionnaires were completed online and by mail, representing a response rate of 9%. On average, farms had 77 lactating cows (median=50) and 13 dry cows (median=10), and Holstein was the most common breed (92% of herds). Lactating cow housing was tiestall on 61% of the farms, freestall on 37%, and bedded pack on 2%. The average voluntary waiting period was 58 d in milk (DIM). The main reproduction management practice per farm was defined as the means employed for >50% of inseminations. Farms reported their main reproduction management practice for first and subsequent inseminations, respectively, as visual estrus detection (51 and 44% of herds), timed AI (21 and 23% of herds), automated activity monitoring (AAM; 10 and 10% of herds), other management practice (bulls; 2 and 2% of herds), and a combination of management practices (16 and 21% of herds). On farms using visual estrus detection, cows were observed for signs of estrus on average 3.5 times per day, for an average total of 36 min/d. The most common use of reproductive hormones was to synchronize ovulation using Ovsynch (58% of the farms). Average PR, IR, and CR were 17.6, 44.1, and 40.5%, respectively. In linear regression analyses adjusted for confounders, pregnancy rate was significantly associated with geographic region, housing (tiestall: PR=15.4%, freestall: PR=17.6%), herd size (<50 lactating cows: PR=16.2%, 50-100 cows: PR=16.5%, >100 cows: PR=17.8%), voluntary waiting period (≤60 DIM: PR=17.6%, >60 DIM: PR=15.9%), and frequency of insemination per day (once daily: PR=16.6%, twice or more daily: PR=18.1%). The main reproduction management practice at first and subsequent inseminations was divergently associated with IR and CR, but not with PR (visual heat detection: PR=17.4%, timed AI: PR=18.4%, AAM: PR=17.1%, combined practices: PR=18.2%).

The effects of building design on hazard of first service in Norwegian dairy cows

Reproductive inefficiency is one of the major production and economic constraints on modern dairy farms. The environment affects onset of ovarian activity in a cow postcalving and influences estrus behavior, which in turn affects a stockperson's ability to inseminate her at the correct time. This study used survival analysis to investigate effects of building design and animal factors on the postpartum hazard of first service (HFS) in freestall-housed Norwegian Red cows. The study was performed on 232 Norwegian dairy farms between 2004 and 2007. Data were obtained through on farm measurements and by accessing the Norwegian Dairy Herd Recording System. The final data set contained data on 38,436 calvings and 27,127 services. Univariate Cox proportional hazard analyses showed that herd size and milk yield were positively associated with HFS. Total free accessible area and free accessible area available per cow year were positively associated with the HFS, as was the number of freestalls available per cow. Cows housed on slatted floors had a lower HFS than those housed on solid floors. Conversely, cows housed on rubber floors had a higher HFS than cows on concrete floors. Dead-ending alleyways reduced the hazard of AI after calving. A multivariable Cox proportional hazards model, accounting for herd management by including a frailty term for herd, showed relationships between hazard of postpartum service and explanatory variables. Animals in herds with more than 50 cows had a higher HFS [hazard ratio (HR)=3.0] compared with those in smaller herds. The HFS was also higher (HR=4.3) if more than 8.8 m(2) of space was available per cow year compared with herds in which animals had less space. The HFS after calving increased with parity (parity 2 HR=0.5, parity ≥3 HR=1.7), and was reduced if a lactation began with dystocia (HR=0.82) or was a breed other than Norwegian Red (HR=0.2). The frailty term, herd, was large and highly significant indicating a significant proportion of the variation resides at herd level. The hazard of first insemination decreased with time for all predictive variables, except dystocia. This study shows that providing adequate environmental conditions for estrus behavior is imperative for reproductive efficiency and after herd management factors and time from calving have been accounted for. Thus, optimizing building design for reproductive efficiency is of significant importance when constructing new cattle housing.

Exploring the characteristics and dynamics of Ontario dairy herds experiencing increases in bulk milk somatic cell count during the summer

Regionally aggregated bulk milk somatic cell count (BMSCC) data from around the world shows a repeatable cyclicity, with the highest levels experienced during warm, humid seasons. No studies have evaluated this seasonal phenomenon at the herd level. The objectives of this study were to define summer seasonality in BMSCC on an individual herd basis, and subsequently to describe the characteristics and dynamics of herds with increased BMSCC in the summer. The data used for this analysis were from all dairy farms in Ontario, Canada, between January 2000 and December 2011 (n≈4,000 to 6,000 herds/yr). Bulk milk data were obtained from the milk marketing board and consisted of bulk milk production, components (fat, protein, lactose, other solids), and quality (BMSCC, bacterial count, inhibitor presence, freezing point), total milk quota of the farm, and milk quota and incentive fill percentage. A time-series linear mixed model, with random slopes and intercepts, was constructed using sine and cosine terms as predictors to describe seasonality, with herd as a random effect. For each herd, seasonality was described with reference to 1 cosine function of variable amplitude and phase shift. The predicted months of maximal and minimal BMSCC were then calculated. Herds were assigned as low, medium, and high summer increase (LSI, MSI, and HSI, respectively) based on percentiles of amplitude in BMSCC change for each of the 4 seasons. Using these seasonality classifications, 2 transitional repeated measures logistic regression models were built to assess the characteristics of MSI and HSI herds, using LSI herds as controls. Based on the analyses performed, a history of summer BMSCC increases increased the odds of experiencing a subsequent increase. As herd size decreased, the odds of experiencing HSI to MSI in BMSCC increased. Herds with more variability in daily BMSCC were at higher odds of experiencing MSI and HSI in BMSCC, as were herds with lower annual mean BMSCC. Finally, a negative association was noted between filling herd production targets and experiencing MSI to HSI in BMSCC. These findings provide farm advisors direction for predicting herds likely to experience increases in SCC over the summer, allowing them to proactively focus udder health prevention strategies before the high-risk summer period.

On-farm evaluation of the effect of metabolic diseases on the shape of the lactation curve in dairy cows through the MilkBot lactation model

The effects of metabolic diseases (MD) occurring during the transition period on milk production of dairy cows have been evaluated in many different ways, often with conflicting conclusions. The present study used a fitted lactation model to analyze specific aspects of lactation curve shape and magnitude in cows that avoided culling or death in the first 120 d in milk (DIM). Production and health records of 1,946 lactations in a 1-yr follow-up study design were collected from a transition management facility in Germany to evaluate both short- and long-term effects of MD on milk production. Milk production data were fitted with the nonlinear MilkBot lactation model, and health records were used to classify cows as healthy (H), affected by one MD (MD), or by multiple MD (MD+). The final data set contained 1,071 H, 348 MD, and 136 MD+ cows, with distinct incidences of 3.7% twinning, 4.8% milk fever, 3.6% retained placenta, 15.4% metritis, 8.3% ketosis, 2.0% displaced abomasum, and 3.7% mastitis in the first 30 DIM. The model containing all healthy and diseased cows showed that lactations classified as H had milk production that increased faster (lower ramp) and also declined faster (lower persistence) compared with cows that encountered one or more metabolic problems. The level of production (scale) was only lowered in MD+ cows compared with H and MD cows. Although the shape of the lactation curve changed when cows encounter uncomplicated (single) MD or complicated MD (more than one MD), the slower increase to a lower peak seemed to be compensated for by greater persistency, resulting in the overall 305-d milk production only being lowered in MD+ cows. In the individual disease models, specific changes in the shape of the lactation curve were found for all MD except twinning. Milk fever, retained placenta, ketosis, and mastitis mainly affected the lactation curve when accompanied by another MD, whereas metritis and displaced abomasum affected the lactation curve equally with or without another MD. Overall, 305-d milk production was decreased in complicated metritis (10,603 ± 50 kg vs. 10,114 ± 172 kg). Although care should be taken in generalizing conclusions from a highly specialized transition management facility, the current study demonstrated that lactation curve analysis may contribute substantially to the evaluation of both short- and long-term effects of metabolic diseases on milk production by detecting changes in the distribution of production that are not apparent when only totals are analyzed.