Evaluation of rapid culture, a predictive algorithm, esterase somatic cell count and lactate dehydrogenase to detect intramammary infection in quarters of dairy cows at dry-off

The effect of selective dry cow therapies based on two 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.

Multi-criteria decision analysis for supporting the selection of subclinical mastitis screening tests to use in large- and small-scale dairy farms in Türkiye

The production of high-quality and safe milk is closely associated with the udder health of dairy cows. While there are many mastitis diagnostic tests/methods available, choosing the most appropriate diagnostic test for a sustainable udder health control program could be a challenge. This study was aimed at selecting tests for the screening of subclinical mastitis on small- and large-scale dairy farms in Türkiye, using multi-criteria decision-making methods. An integrated approach employing the analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) together was used to select subclinical mastitis screening tests for on-farm use. While the AHP determines the weights of the evaluation criteria, the TOPSIS provides a final ranking. Nine different subclinical mastitis screening (SCM) methods (DeLaval somatic cell counter, PortaSCC test, California mastitis test (CMT), rapid culture, portable/hand-held electrical conductivity meter, infrared thermography, leukocyte esterase strip test, milk pH, UdderCheck test) were analyzed on the basis of five selection criteria (the market availability of the test, the diagnostic accuracy of the test, the cost of the test, the cow-side use of the test, and the practicality of the test). The selection criteria were determined based on literature review and stakeholder input. The weighting of the criteria with the AHP was based on the pairwise comparison of the criteria by stakeholders. The criteria were weighted from 1 to 9 according to their relative importance as follows: "1: equally important," "3: moderately important," "5: strongly important," "7: very strongly important," "9: extremely important," and "2, 4, 6, 8: intermediate values." Final ranking of SCM tests with the TOPSIS was based on the stakeholder evaluations of fulfillment of the criteria by the alternatives. The most appropriate screening test for both large- and small-scale dairy farms was determined to be the CMT. The CMT is a very useful, easy to perform, and low-cost tool for detecting subclinical mastitis. Being a major element of udder health control programs, the CMT, if regularly used on dairy farms in Türkiye, would enable the culling of chronically infected animals and the reduction of mastitis-associated economic losses. Furthermore, regular CMTs would contribute to reducing milk SCC and improving milk quality. In conclusion, multi-criteria decision-making methods not only provide a systematic approach that may assist both veterinarians and farmers in deciding on the best choice among the different tests available for the screening of subclinical mastitis but also offer potential benefits to policymakers, researchers, and other industry stakeholders.

Risk factors of high somatic cell count and differential somatic cells in early lactation associated with selective dry cow therapy

The routine use of intramammary antimicrobial products in all dairy cows at the beginning of the dry period is no longer allowed in European Union (EU) countries due to the new Regulation (EU) 2019/6 to reduce antimicrobial resistance. This study investigated the application of a selective dry cow therapy scheme and the risk factors of high individual milk somatic cell count (SCC) and individual neutrophil count in early lactation, as a response to the application of a selective dry cow therapy (SDCT) protocol. The study was carried out on three commercial farms, and a total of 243 lactating cows were monitored at the end of lactation and at the beginning of the next one, 91 of which were dried off without the use of antimicrobials (NoT) based on milk SCC, differential somatic cell count (DSCC), and the response of Vetscan DC-Q milk analyser, using a secret algorithm. The remaining 152 cows received antimicrobials (T). After calving, similar means were observed between the two treatment groups for SCC (4.8 vs 4.9 log10 cells/ml for T and NoT, respectively, P = 0.5) and total milk leucocyte count (TLC) (5 vs 5.1 log10 cells/ml for T and NoT, respectively, P = 0.7) in milk. However, the use of antimicrobials led to a lower DSCC (58 vs 64% for T and NoT, respectively, P = 0.01) and lower percentage of neutrophils (59 vs 64% for T and NoT, respectively, P = 0.05), although the levels of DSCC and percentage of neutrophils in cows dried off without antimicrobials remained lower than the risk threshold suggested by the international literature. A logistic regression was computed after the application of selective dry cow therapy to identify risk factors of high milk SCC (≥100 000 cells/mL) at the beginning of lactation. Increased milk SCC after calving was related to high SCC at the end of lactation and abandonment of antimicrobial therapy at dry-off. Moreover, the length of the dry period, milk protein content, and flank cleanliness in the last test day before dry-off were other factors in the logistic regression. Neutrophil counts at the beginning of the next lactation were affected by the same factors that influenced SCC, together with milk production, TLC, and macrophages on the last test day. The results obtained in these studied farms showed that selective dry cow therapy may be applied without adversely affecting the next lactation.

A framework for evaluation of on-farm mastitis diagnostics in Australia

Numerous culture-based diagnostics are available on the Australian and international markets for on-farm detection of bacterial pathogens in milk. Use of such diagnostics may provide an opportunity to improve the prudent use of antimicrobials in udder health management. Farms are low-resource settings in terms of diagnostic microbiology capacity. The World Health Organisation has identified criteria for the evaluation of diagnostic tests in low resource settings based on Accuracy, Sensitivity, Specificity, User-friendliness, being Rapid or Robust, Equipment-free and being Deliverable (ASSURED). Here, we review how those criteria can be interpreted in the context of microbiological diagnosis of mastitis pathogens, and how on-farm diagnostics that are currently available in Australia perform relative to ASSURED criteria. This evaluation identifies multiple trade-offs, both with regard to scientific criteria and with regards to convenience criteria. More importantly, the purpose of testing may differ between farms, and test performance should be evaluated relative to its intended use. The ability of on-farm mastitis diagnostics to inform mastitis treatment decision-making in a timely and cost-effective manner depends not just on test characteristics but also on farm-specific pathogen prevalence, and on the farm enterprise's priorities and the farm manager's potential courses of action. With most assay evaluations to date conducted in professional laboratories, there is a surprising dearth of information on how well any of the diagnostic tests perform on-farm and, indeed, of the on-farm decision-making processes that they aim to inform.

Udder Health Monitoring for Prevention of Bovine Mastitis and Improvement of Milk Quality

To maximize milk production, efficiency, and profits, modern dairy cows are genetically selected and bred to produce more and more milk and are fed copious quantities of high-energy feed to support ever-increasing milk volumes. As demands for increased milk yield and milking efficiency continue to rise to provide for the growing world population, more significant stress is placed on the dairy cow's productive capacity. In this climate, which is becoming increasingly hotter, millions of people depend on the capacity of cattle to respond to new environments and to cope with temperature shocks as well as additional stress factors such as solar radiation, animal crowding, insect pests, and poor ventilation, which are often associated with an increased risk of mastitis, resulting in lower milk quality and reduced production. This article reviews the impact of heat stress on milk production and quality and emphasizes the importance of udder health monitoring, with a focus on the use of emergent methods for monitoring udder health, such as infrared thermography, biosensors, and lab-on-chip devices, which may promote animal health and welfare, as well as the quality and safety of dairy products, without hindering the technological flow, while providing significant benefits to farmers, manufacturers, and consumers.

Invited review: Selective use of antimicrobials in dairy cattle at drying-off

Administering intramammary antimicrobials to all mammary quarters of dairy cows at drying-off [i.e., blanket dry cow therapy (BDCT)] has been a mainstay of mastitis prevention and control. However, as udder health has considerably improved over recent decades with reductions in intramammary infection prevalence at drying-off and the introduction of teat sealants, BDCT may no longer be necessary on all dairy farms, thereby supporting antimicrobial stewardship efforts. This narrative review summarizes available literature regarding current dry cow therapy practices and associated impacts of selective dry cow therapy (SDCT) on udder health, milk production, economics, antimicrobial use, and antimicrobial resistance. Various methods to identify infections at drying-off that could benefit from antimicrobial treatment are described for selecting cows or mammary quarters for treatment, including utilizing somatic cell count thresholds, pathogen identification, previous clinical mastitis history, or a combination of criteria. Selection methods may be enacted at the herd, cow, or quarter levels. Producers' and veterinarians' motivations for antimicrobial use are discussed. Based on review findings, SDCT can be adopted without negative consequences for udder health and milk production, and concurrent teat sealant use is recommended, especially in udder quarters receiving no intramammary antimicrobials. Furthermore, herd selection should be considered for SDCT implementation in addition to cow or quarter selection, as BDCT may still be temporarily necessary in some herds for optimal mastitis control. Costs and benefits of SDCT vary among herds, whereas impacts on antimicrobial resistance remain unclear. In summary, SDCT is a viable management option for maintaining udder health and milk production while improving antimicrobial stewardship in the dairy industry.

Bayesian estimation of sensitivity and specificity of a rapid mastitis test kit, bacterial culture, and PCR for detection of Staphylococcus aureus, Streptococcus species, and coliforms in bovine milk samples

Our objectives were to evaluate the diagnostic accuracy of a rapid and novel immunochromatography-based mastitis kit that includes 3 independent tests to detect coliforms (Escherichia coli or Klebsiella pneumoniae), Streptococcus spp., and Staphylococcus aureus. The kit was developed to facilitate diagnostic-based mastitis treatment. Validation of the kit was based on 154 aseptically collected mastitis samples from 2 clinical herds (clinical population) and 120 milk samples from 3 nonclinical herds (nonclinical population) without clinical cases at the time of enrollment. One herd sampled at different times was common to both populations. A 3-test in 2-population Bayesian latent class model with uniform priors for all test parameters except specificity of culture, which was modeled informatively, was used to estimate sensitivity (Se) and specificity (Sp) of the test kit, culture, and PCR at the cow level. The mastitis test kit's 96.9% Sp for Streptococcus spp. had a low false positive percentage (3.1%), which, together with the kit's rapid turnaround time for results, makes it a suitable initial screening test that producers can use to identify clinical cows to treat based on Streptococcus spp. mastitis in kit-positive results. Due to the 60.4% kit Se, producers should follow up on Streptococcus spp. kit-negative cows using a confirmatory test such as PCR (Sp of 98.4%) or culture (Sp of 99.6%). In contrast, aerobic culture had Se of 76.5% and Sp of 99.6% for Streptococcus spp. Similarly, the Sp of the kit (98.2%) and culture (99.8%) for Staph. aureus were particularly high, and even though the kit's Se (61.0%) was lower than culture (88.4%; posterior probability of difference 98%), the kit could be beneficial before use of a confirmatory test for kit-negative samples due to its ease and rapid turnaround time. Mostly, quantitative real-time (q)PCR outperformed the kit's Se (37.7%) and Sp (92.9%) for coliforms, as well as the kit's Se (60.4%) for Streptococcus spp. However, qPCR may require more technical skills and turnaround time for final results. Use of the on-farm mastitis test kit evaluated in the present study could enhance sustainable antimicrobial drug use by rapidly identifying Streptococcus mastitis for targeted treatment. Furthermore, the kit may be used in a Staph. aureus outbreak where cows can be rapidly screened to identify cases for segregation or culling during an outbreak and kit-negative cows further confirmed by milk culture or qPCR. However, the cost-effectiveness of such an approach has not been investigated.

Calculating clinical mastitis frequency in dairy cows: Incidence risk at cow level, incidence rate at cow level, and incidence rate at quarter level

The lack of standardization in reporting clinical mastitis incidence limits the ability to compare results across multiple studies without additional calculations. There is both a biological and statistical rationale for evaluating the at-risk period at the quarter level. This study aimed to: (1) to outline an applied method for calculating clinical mastitis (CM) incidence rate at the quarter level using currently available software; and (2) to present the results of three different measurements: incidence risk at cow level, incidence rate at cow level, and incidence rate at quarter level. In an open population prospective cohort of eight commercial dairy farms monitored from May 15, 2016, to May 31, 2017, all CM cases (n = 7513) were identified by trained on-farm personnel, who collected all milk samples from all quarters with visibly abnormal milk. Microbiological identification was determined by culture and MALDI-TOF. All lactating quarters were at risk for CM. A quarter was at risk for a new CM case if there was at least 14 d between a previously diagnosed case and the current case in the same quarter, or if a different pathogen was isolated in the same quarter within 14 d. A total of 17,513,429 quarters days at risk (QDAR) were estimated. A statistical software macro and Structured Query Language (SQL) were used to bring all data together. The monthly incidence rate at the cow level was 16.6 cases per 10,000 cow-days, the monthly incidence rate at the quarter level was 4.4 cases per 10,000 QDAR and the monthly incidence risk at the cow level was 4.8 cases per 100 cows. Although the evaluation of QDAR requires additional computation when compared to other methods, it might allow for a more precise evaluation of the data and a more accurate evaluation of mastitis incidence. Clearly defining the methods used to report mastitis incidence will improve our ability to discuss and learn about the differences and similarities across studies, regions, and countries.

Novel ways to use sensor data to improve mastitis management

Current sensor systems are used to detect cows with clinical mastitis. Although, the systems perform well enough to not negatively affect the adoption of automatic milking systems, the performance is far from perfect. An important advantage of sensor systems is the availability of multiple measurements per day. By clearly defining the need for detection of subclinical mastitis (SCM) and clinical mastitis (CM) from the farmers' management perspective, detection and management of SCM and CM may be improved. Sensor systems may also be used for other aspects of mastitis management. In this paper we have defined 4 mastitis situations that could be managed with the support of sensor systems. Because of differences in the associated management and the epidemiology of these specific mastitis situations, the required demands for performance of the sensor systems do differ. The 4 defined mastitis situations with the requirements of performance are the following: (1) Cows with severe CM needing immediate attention. Sensor systems should have a very high sensitivity (>95% and preferably close to 100%) and specificity (>99%) within a narrow time window (maximum 12 h) to ensure that close to all cows with true cases of severe CM are detected quickly. Although never studied, it is expected that because of the effects of severe CM, such a high detection performance is feasible. (2) Cows with mastitis that do not need immediate attention. Although these cows have a risk of progressing into severe CM or chronic mastitis, they should get the chance to cure spontaneously under close monitoring. Sensor alerts should have a reasonable sensitivity (>80%) and a high specificity (>99.5%). The time window may be around 7 d. (3) Cows needing attention at drying off. For selective dry cow treatment, the absence or presence of an intramammary infection at dry-off needs to be known. To avoid both false-positive and false-negative alerts, sensitivity and specificity can be equally high (>95%). (4) Herd-level udder health. By combining sensor readings from all cows in the herd, novel herd-level key performance indicators can be developed to monitor udder health status and development over time and raise alerts at significant deviances from predefined thresholds; sensitivity should be reasonably high, >80%, and because of the costs for further analysis of false-positive alerts, the specificity should be >99%. The development and validation of sensor-based algorithms specifically for these 4 mastitis situations will encourage situation-specific farmer interventions and operational udder health management.

Evaluation of 4 predictive algorithms for intramammary infection status in late-lactation cows

The objective of this observational study was to compare 4 cow-level algorithms to predict cow-level intramammary infection (IMI) status (culture and MALDI-TOF) in late-lactation US dairy cows using standard measures of test performance. Secondary objectives were to estimate the likely effect of each algorithm, if used to guide selective dry cow therapy (SDCT), on dry cow antibiotic use in US dairy herds, and to investigate the importance of including clinical mastitis criteria in algorithm-guided SDCT. Cows (n = 1,594) from 56 US dairy herds were recruited as part of a previously published cross-sectional study of bedding management and IMI in late-lactation cows. Each herd was visited twice for sampling. At each farm visit, aseptic quarter-milk samples were collected from 20 cows approaching dry-off (>180 d pregnant), which were cultured using standard bacteriological methods and MALDI-TOF for identification of isolates. Quarter-level culture results were used to establish cow-level IMI status, which was considered the reference test in this study. Clinical mastitis records and Dairy Herd Improvement Association test-day somatic cell count data were extracted from herd records and used to perform cow-level risk assessments (low vs. high risk) using 4 algorithms that have been proposed for SDCT in New Zealand, the Netherlands, United Kingdom, and the United States. Agreement between aerobic culture (reference test; IMI vs. no-IMI) and algorithm status (high vs. low risk) was described using Cohen's kappa, test sensitivity, specificity, negative predictive value, and positive predictive value. The proportion of cows classified as high risk among the 4 algorithms ranged from 0.31 to 0.63, indicating that these approaches to SDCT could reduce antibiotic use at dry-off by 37 to 69% in the average US herd. All algorithms had poor agreement with IMI status, with kappa values ranging from 0.05 to 0.13. Sensitivity varied by pathogen, with higher values observed when detecting IMI caused by Streptococcus uberis, Streptococcus dysgalactiae, Staphylococcus aureus, and Lactococcus lactis. Negative predictive values were high for major pathogens among all algorithms (≥0.87), which may explain why algorithm-guided SDCT programs have been successfully implemented in field trials, despite poor agreement with overall IMI status. Removal of clinical mastitis criteria for each algorithm had little effect on the algorithm classification of cows, indicating that algorithms based on SCC alone may have similar performance to those based on SCC and clinical mastitis criteria. We recommend that producers implementing algorithm-guided SDCT use algorithm criteria that matches their relative aspirations for reducing antibiotic use (high specificity, positive predictive value) or minimizing untreated IMI at dry-off (high sensitivity, negative predictive value).

Partial budget analysis of culture- and algorithm-guided selective dry cow therapy

The objectives of this study were to (1) use partial budget analysis to estimate the cash impact for herds that switch from blanket dry cow therapy (BDCT) to culture- or algorithm-guided selective dry cow therapy (SDCT) and (2) conduct a sensitivity analysis to investigate effects in situations where SDCT increased clinical and subclinical mastitis risk during the subsequent lactation. A partial budget model was created using Monte Carlo simulation with @Risk software. Expenditures associated with dry-off procedures and health outcomes (clinical and subclinical mastitis) during the first 30 d in milk were used to model herd-level effects, expressed in units of US dollars per cow dry-off. Values for each economic component were derived from findings from a recent multisite clinical trial, peer-reviewed journal articles, USDA databases, and our experiences in facilitating the implementation of SDCT on farms. Fixed values were used for variables expected to have minimal variation within the US dairy herd population (e.g., cost of rapid culture plates) and sampling distributions were used for variables that were hypothesized to vary enough to effect the herd net cash impact of one or more DCT approach(es). For Objective 1, herd-level udder health was assumed to be unaffected by the implementation of SDCT. For culture-guided SDCT, producers could expect to save an average of +$2.14 (-$2.31 to $7.23 for 5th and 95th percentiles) per cow dry-off as compared with BDCT, with 75.5% of iterations being ≥$0.00. For algorithm-guided SDCT, the mean net cash impact was +$7.85 ($3.39-12.90) per cow dry-off, with 100% of iterations being ≥$0.00. The major contributors to variance in cash impact for both SDCT approaches were percent of quarters treated at dry-off and the cost of dry cow antibiotics. For Objective 2, we repeated the partial budget model with the 30-d clinical and subclinical mastitis incidence increasing by 1, 2, and 5% (i.e., risk difference = 0.01, 0.02, and 0.05) in both SDCT groups compared with BDCT. For algorithm-guided SDCT, average net cash impacts were ≥$0.00 per cow dry-off (i.e., cost effective) when mastitis incidence increased slightly. However, as clinical mastitis incidence increased, economic returns for SDCT diminished. These findings indicate that when SDCT is implemented appropriately (i.e., no to little negative effect on health), it might be a cost-effective practice for US herds under a range of economic conditions.

Postcalving udder health and productivity in cows approaching dry-off with intramammary infections caused by non-aureus Staphylococcus, Aerococcus, Enterococcus, Lactococcus, and Streptococcus species

The objective of this prospective cohort study was to explore associations between intramammary infection (IMI) in late-lactation cows and postcalving udder health and productivity. Cows (n = 2,763) from 74 US dairy herds were recruited as part of a previously published cross-sectional study of bedding management and IMI in late-lactation cows. Each herd was visited twice for sampling. At each visit, aseptic quarter milk samples were collected from 20 cows approaching dry-off (>180 d pregnant), which were cultured using standard bacteriological methods and MALDI-TOF for identification of isolates. Quarter-level culture results were used to establish cow-level IMI status at enrollment. Cows were followed from enrollment until 120 d in milk (DIM) in the subsequent lactation. Herd records were used to establish whether subjects experienced clinical mastitis or removal from the herd, and DHIA test-day data were used to record subclinical mastitis events (somatic cell count >200,000 cells/mL) and milk yield (kg/d) during the follow-up period. Cox regression and generalized estimating equations were used to evaluate the associations between IMI and the outcome of interest. The presence of late-lactation IMI caused by major pathogens was positively associated with postcalving clinical mastitis [hazard ratio = 1.5, 95% confidence interval (CI): 1.2, 2.0] and subclinical mastitis (risk ratio = 1.5, 95% CI: 1.3, 1.9). Species within the non-aureus Staphylococcus (NAS) group varied in their associations with postcalving udder health, with some species being associated with increases in clinical and subclinical mastitis in the subsequent lactation. Late-lactation IMI caused by Streptococcus and Streptococcus (Strep)-like organisms, other than Aerococcus spp. (i.e., Enterococcus, Lactococcus, and Streptococcus spp.) were associated with increases in postcalving clinical and subclinical mastitis. Test-day milk yield from 1 to 120 DIM was lower (-0.9 kg, 95% CI: -1.6, -0.3) in late-lactation cows with any IMI compared with cows without IMI. No associations were detected between IMI in late lactation and risk for postcalving removal from the herd within the first 120 DIM. Effect estimates reported in this study may be less than the underlying quarter-level effect size for IMI at dry-off and postcalving clinical and subclinical mastitis, because of the use of late-lactation IMI as a proxy for IMI at dry-off and the use of cow-level exposure and outcome measurements. Furthermore, the large number of models run in this study (n = 94) increases the chance of identifying chance associations. Therefore, confirmatory studies should be conducted. We conclude that IMI in late lactation may increase risk of clinical and subclinical mastitis in the subsequent lactation. The relationship between IMI and postcalving health and productivity is likely to vary among pathogens, with Staphylococcus aureus, Streptococcus spp., Enterococcus spp., and Lactococcus spp. being the most important pathogens identified in the current study.

Randomized controlled non-inferiority trial investigating the effect of 2 selective dry-cow therapy protocols on antibiotic use at dry-off and dry period intramammary infection dynamics

Selective dry-cow therapy (SDCT) could be used to reduce antibiotic use on commercial dairy farms in the United States but is not yet widely adopted, possibly due to concerns about the potential for negative effects on cow health. The objective of this study was to compare culture- and algorithm-guided SDCT programs with blanket dry-cow therapy (BDCT) in a multi-site, randomized, natural exposure, non-inferiority trial for the following quarter-level outcomes: antibiotic use at dry-off, dry period intramammary infection (IMI) cure risk, dry period new IMI risk, and IMI risk at 1 to 13 d in milk (DIM). Two days before planned dry-off, cows in each of 7 herds were randomly allocated to BDCT, culture-guided SDCT (cult-SDCT), or algorithm-guided SDCT (alg-SDCT). At dry-off, BDCT cows received an intramammary antibiotic (500 mg of ceftiofur hydrochloride) in all 4 quarters. Antibiotic treatments were selectively allocated to quarters of cult-SDCT cows by treating only quarters from which aseptically collected milk samples tested positive on the Minnesota Easy 4Cast plate (University of Minnesota, St. Paul, MN) after 30 to 40 h of incubation. For alg-SDCT cows, antibiotic treatments were selectively allocated at the cow level, with all quarters receiving antibiotic treatment if the cow had either a Dairy Herd Improvement Association test somatic cell count >200,000 cells/mL during the current lactation or 2 or more clinical mastitis cases during the current lactation. All quarters of all cows were treated with an internal teat sealant. Intramammary infection status at enrollment and at 1 to 13 DIM was determined using standard bacteriological methods. The effect of treatment group on dry period IMI cure, dry period new IMI, and IMI risk at 1 to 13 DIM was determined using generalized linear mixed models (logistic), with marginal standardization to derive risk difference (RD) estimates. Quarter-level antibiotic use at dry-off for each group was BDCT (100%), cult-SDCT (45%), and alg-SDCT (45%). The crude dry period IMI cure risk for all quarters was 87.5% (818/935), the crude dry period new IMI risk was 20.1% (764/3,794), and the prevalence of IMI at 1 to 13 DIM was 23% (961/4,173). Non-inferiority analysis indicated that culture- and algorithm-guided SDCT approaches performed at least as well as BDCT for dry period IMI cure risk. In addition, the final models indicated that the risks for each of the 3 IMI measures were similar between all 3 treatment groups (i.e., RD estimates and 95% confidence intervals all close to 0). These findings indicate that under the conditions of this trial, culture- and algorithm-guided SDCT can substantially reduce antibiotic use at dry-off without negatively affecting IMI dynamics.