Characterizing best practices for tonsil-oral-scrubbing (TOSc) collection for PRRSV RNA detection in sows.

BACKGROUND: A Tonsil-Oral-Scrubbing (TOSc) method was developed to sample the sow's oropharyngeal and tonsillar area without snaring and has shown comparable porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection rates with tonsil scraping in infected sows. This study investigated the effect of specific TOSc collection factors on the PRRSV RT-rtPCR results (detection rates and Ct values). Those factors include whether the sow was snared or not snared at TOSc collection ("snared" vs. "not snared"); whether the sow was laying down or standing at collection ("laying down" vs. "standing"); and type of collectors used for TOSc collection ("TOSc prototype" vs. "Spiral-headed AI catheter (SHAC)"). Volume of fluid was compared between "snared" and "not snared" groups, and collection time was compared between "laying down" and "standing" groups as well. RESULTS: The effect for each factor was assessed in three independent studies following the same design: TOSc was collected twice from each studied sow, once with the baseline level for a factor ("not snared", or "standing", or "TOSc prototype"), and another time followed by the other level of the paired factor ("snared", "laying down", or "SHAC", correspondingly). Results showed that "not snared" TOSc had numerically higher PRRSV RNA detection rate (60.7% vs. 52.5%, p = 0.11), significantly lower median Ct values (31.9 vs. 32.3, p < 0.01), and significantly higher volume of fluid than "snared" samples (1.8 mL vs. 1.2 mL, p < 0.01); "laying down" TOSc samples did not differ statistically (60.7% vs. 60.7%) in the PRRSV RNA detection rate, obtained numerically lower median Ct values (30.9 vs. 31.3, p = 0.19), but took 40% less collection time compared to "standing" TOSc samples; samples collected using the "TOSc prototype" had numerically higher PRRSV RNA detection rate (91.7% vs. 88.3%, p = 0.27) and significantly lower median Ct values (32.8 vs. 34.5, p < 0.01) than that from "SHAC". CONCLUSIONS: Under the conditions of this study best practices for TOSc collection aiming higher detection rate of PRRSV RNA while minimizing time for collection were suggested to be sampling TOSc without snaring, when sows are laying down, and using a prototype TOSc collector.

Assessment of changes in antibiotic use in grow-finish pigs after the introduction of PRRSV in a naïve farrow-to-finish system.

Responsible antibiotic usage (ABU) is crucial for both animal and human health and requires constant improvement of antimicrobial stewardship (AMS). The presence of porcine reproductive and respiratory syndrome virus (PRRSV), a viral pathogen with immunosuppressive effects on swine, can intensify bacterial co-infections, alter antibiotic pharmacokinetics, and potentially lead to increased ABU. This study aimed to measure ABU changes in the grow-finish population associated with PRRSV infection and describe the antibiotic classes employed to manage clinical signs from a farrow-to-finish genetic multiplier system. Three PRRSV statuses (naïve, positive epidemic, and positive endemic) were established to classify the lots based on PRRSV circulation, with a total of 135,063 animals evaluated. The number of pig treatments per animal days at risk (PTDR) was calculated by administration route to quantify ABU across PRRSV status using negative binomial regression and non-parametric tests (P-value < 0.05). Moreover, to improve ABU comparability in the international scenario, the milligrams per population correction unit (mg/PCU) was calculated according to the European Medicines Agency guidelines. In the nursery phase, there was a statistically significant difference between PRRSV statuses for the overall PTDR (injectable and water routes of administration), with an ABU increase of 3.79 and 2.51 times the naïve PTDR for positive epidemic and endemic status, respectively. For the finishing phase, there was a statistically significant difference between PRRSV statuses in the injectable PTDR, with an ABU increase of 2.74 and 2.28 times the naïve PTDR level for positive epidemic and endemic statuses, respectively. In the nursery phase, the mean mg/PCU was 22.27 mg/PCU for naïve, 86.71 for positive epidemic, and 33.37 for positive endemic statuses; in the finishing phase, 81.31, 76.55, and 67.09 mg/PCU, respectively. The most frequently injected antibiotic in the nursery phase was ampicillin, with 49 % of total injections, followed by lincomycin (31 %) and enrofloxacin (20 %), and in the finishing phase, 72 % of injections were lincomycin, followed by enrofloxacin (28 %). The results highlight that the PRRSV outbreak in the source was associated with a grow-finish ABU increase, revealing the importance of preventing PRRSV infection to potentially decrease ABU and improve AMS within swine production systems.

Development of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions.

Mortality during the post-weaning phase is a critical indicator of swine production system performance, influenced by a complex interaction of multiple factors of the epidemiological triad. This study leveraged retrospective data from 1723 groups of pigs marketed within a US swine production system to develop a Wean-Quality Score (WQS) using machine learning techniques. The study evaluated three machine learning models, Random Forest, Support Vector Machine, and Gradient Boosting Machine, to classify groups having high or low 60-day mortality, where high mortality groups represented 25 % of the groups among the study population with the highest mortality values (n=431; 60-day mortality=9.98 %), and the remaining 75 % of the groups were of low mortality (n=1292; 60-day mortality=2.75 %). The best-performing model, Random Forest (RF), outperformed the other ML models in terms of accuracy (0.90), sensitivity (0.84), and specificity (0.92) metrics, and was then selected for further analysis, which consisted of creating the WQS and ranking the most important factors for classifying groups as high or low mortality. The most important factors ranked through the RF model to classify groups with high mortality were pre-weaning mortality, weaning age, average parity of litters in sow farms, and PRRS status. Additionally, stocking conditions such as stocking density and time to fill the barn were important predictors of high mortality. The WQS was developed and correlated (r = 0.74) with the actual 60-day mortality of the groups, offering a valuable tool for assessing post-weaning survivability in swine production systems before weaning. This study highlights the potential of machine learning and comprehensive data utilization to improve the assessment and management of weaned pig quality in commercial swine production, which producers can utilize to identify and intervene in groups, according to the WQS.

Risk factors associated with piglet pre-weaning mortality in a Midwestern U.S. swine production system from 2020 to 2022.

Piglet pre-weaning mortality (PWM) is a significant issue in the U.S. swine industry, causing economic losses and raising sustainability and animal welfare concerns. This study conducted a multivariable analysis to identify factors associated with PWM in a Midwestern U.S. swine production system. Weekly data from 47 sow farms (7207 weaning weeks) were captured from January 2020 to December 2022. Initially, 29 variables regarding farm infrastructure, productivity parameters, health status, and interventions were selected for univariate analysis to assess their association with PWM. The initial multivariable analysis included the variables with P < 0.20 in the univariate analyses. A backward stepwise model selection was conducted by excluding variables with P > 0.05, and the final multivariable model consisted of 19 significant risk factors and 6 interaction terms. The overall average PWM for the study population was 14.02 %. Yearly variations in PWM were observed, with the highest recorded in 2020 (16.61 %) and the lowest in 2021 (15.78 %). Cohorts with a pond water source, lower farrowing rate (71.9 %), higher farrowing parity (5.1), shorter gestation length (116.2 days), and using oxytocin during farrowing had increased PWM. The higher productivity parameters such as mummies rate, stillborn rate, and average total born, the higher the PWM was. Additionally, health status and intervention-related factors were associated with PWM, where higher PWM rates were observed in herds facing porcine reproductive and respiratory syndrome virus (PRRSV) outbreaks, porcine epidemic diarrhea virus (PEDV) positive, the weeks before and during feed medication, and weeks without using Rotavirus vaccine or Rotavirus feedback. Altogether, these results corroborate that PWM is a multifactorial problem, and a better understanding of the risk factors is essential in developing strategies to improve survival rates. Therefore, this study identified the major risk factors associated with PWM for groups of pigs raised under field conditions, and the results underscore the significance of data analysis in comprehending the unique challenges and opportunities inherent to each system.

Monitoring emerging pathogens using negative nucleic acid test results from endemic pathogens in pig populations: Application to porcine enteric coronaviruses.

This study evaluated the use of endemic enteric coronaviruses polymerase chain reaction (PCR)-negative testing results as an alternative approach to detect the emergence of animal health threats with similar clinical diseases presentation. This retrospective study, conducted in the United States, used PCR-negative testing results from porcine samples tested at six veterinary diagnostic laboratories. As a proof of concept, the database was first searched for transmissible gastroenteritis virus (TGEV) negative submissions between January 1st, 2010, through April 29th, 2013, when the first porcine epidemic diarrhea virus (PEDV) case was diagnosed. Secondly, TGEV- and PEDV-negative submissions were used to detect the porcine delta coronavirus (PDCoV) emergence in 2014. Lastly, encountered best detection algorithms were implemented to prospectively monitor the 2023 enteric coronavirus-negative submissions. Time series (weekly TGEV-negative counts) and Seasonal Autoregressive-Integrated Moving-Average (SARIMA) were used to control for outliers, trends, and seasonality. The SARIMA's fitted and residuals were then subjected to anomaly detection algorithms (EARS, EWMA, CUSUM, Farrington) to identify alarms, defined as weeks of higher TGEV-negativity than what was predicted by models preceding the PEDV emergence. The best-performing detection algorithms had the lowest false alarms (number of alarms detected during the baseline) and highest time to detect (number of weeks between the first alarm and PEDV emergence). The best-performing detection algorithms were CUSUM, EWMA, and Farrington flexible using SARIMA fitted values, having a lower false alarm rate and identified alarms 4 to 17 weeks before PEDV and PDCoV emergences. No alarms were identified in the 2023 enteric negative testing results. The negative-based monitoring system functioned in the case of PEDV propagating epidemic and in the presence of a concurrent propagating epidemic with the PDCoV emergence. It demonstrated its applicability as an additional tool for diagnostic data monitoring of emergent pathogens having similar clinical disease as the monitored endemic pathogens.

Investigating the uses of machine learning algorithms to inform risk factor analyses: The example of avian infectious bronchitis virus (IBV) in broiler chickens.

Infectious bronchitis virus (IBV) is a contagious coronavirus causing respiratory and urogenital disease in chickens and is responsible for significant economic losses for both the broiler and table egg layer industries. Despite IBV being regularly monitored using standard epidemiologic surveillance practices, knowledge and evidence of risk factors associated with IBV transmission remain limited. The study objective was to compare risk factor modeling outcomes between a traditional stepwise variable selection approach and a machine learning-based random forest Boruta algorithm using routinely collected IBV antibody titer data from broiler flocks. IBV antibody sampling events (n = 1111) from 166 broiler sites between 2016 and 2021 were accessed. Ninety-two geospatial-related and poultry-density variables were obtained using a geographic information system and data sets from publicly available sources. Seventeen and 27 candidate variables were screened to potentially have an association with elevated IBV antibody titers according to the manual selection and machine learning algorithm, respectively. Selected variables from both methods were further investigated by construction of multivariable generalized mixed logistic regression models. Six variables were shortlisted by both screening methods, which included year, distance to urban areas, main roads, landcover, density of layer sites and year, however, final models for both approaches only shared year as an important predictor. Despite limited significance of clinical outcomes, this work showcases the potential of a novel explorative modeling approach in combination with often unutilized resources such as publicly available geospatial data, surveillance health data and machine learning as potential supplementary tools to investigate risk factors related to infectious diseases.

Assessment of temperature and time on the survivability of porcine reproductive and respiratory syndrome virus (PRRSV) and porcine epidemic diarrhea virus (PEDV) on experimentally contaminated surfaces.

Fomites might be responsible for virus introduction in swine farms, highlighting the importance of implementing practices to minimize the probability of virus introduction. The study's objective was to assess the efficacy of different combinations of temperatures and holding-times on detecting live PRRSV and PEDV on surfaces commonly found in supply entry rooms in swine farms. Two PRRSV isolates (MN 184 and 1-4-4 L1C variant) and one PEDV isolate (NC 49469/2013) were inoculated on cardboard and aluminum. An experimental study tested combinations of four temperatures (20°C, 30°C, 40°C, and 50°C) and six holding-times (15 minutes, 60 minutes, 6 hours, 12 hours, 24 hours, and 36 hours) for the presence of the viruses on each surface type. After virus titration, virus presence was assessed by assessing the cytopathic effects and immunofluorescence staining. The titers were expressed as log10 TCID50/ml, and regression models; half-lives equations were calculated to assess differences between treatments and time to not detect the live virus. The results suggest that the minimum time that surfaces should be held to not detect the virus at 30°C was 24 hours, 40°C required 12 hours, and 50°C required 6 hours; aluminum surfaces took longer to reach the desired temperature compared to cardboard. The results suggest that PRRSV 1-4-4 L1C variant had higher half-lives at higher temperatures than PRRSV MN 184. In conclusion, time and temperature combinations effectively decrease the concentration of PRRSV and PEDV on different surfaces found in supply entry rooms in swine farms.

Active Participatory Regional Surveillance for Notifiable Swine Pathogens.

We evaluated an active participatory design for the regional surveillance of notifiable swine pathogens based on testing 10 samples collected by farm personnel in each participating farm. To evaluate the performance of the design, public domain software was used to simulate the introduction and spread of a pathogen among 17,521 farms in a geographic region of 1,615,246 km2. Using the simulated pathogen spread data, the probability of detecting ≥ 1 positive farms in the region was estimated as a function of the percent of participating farms (20%, 40%, 60%, 80%, 100%), farm-level detection probability (10%, 20%, 30%, 40%, 50%), and regional farm-level prevalence. At 0.1% prevalence (18 positive farms among 17,521 farms) and a farm-level detection probability of 30%, the participatory surveillance design achieved 67%, 90%, and 97% probability of detecting ≥ 1 positive farms in the region when producer participation was 20%, 40%, and 60%, respectively. The cost analysis assumed that 10 individual pig samples per farm would be pooled into 2 samples (5 pigs each) for testing. Depending on the specimen collected (serum or swab sample) and test format (nucleic acid or antibody detection), the cost per round of sampling ranged from EUR 0.017 to EUR 0.032 (USD 0.017 to USD 0.034) per pig in the region. Thus, the analysis suggested that an active regional participatory surveillance design could achieve detection at low prevalence and at a sustainable cost.

Detection and disease diagnosis trends (2017-2022) for Streptococcus suis, Glaesserella parasuis, Mycoplasma hyorhinis, Actinobacillus suis and Mycoplasma hyosynoviae at Iowa State University Veterinary Diagnostic Laboratory.

BACKGROUND: Accurate measurement of disease associated with endemic bacterial agents in pig populations is challenging due to their commensal ecology, the lack of disease-specific antemortem diagnostic tests, and the polymicrobial nature of swine diagnostic cases. The main objective of this retrospective study was to estimate temporal patterns of agent detection and disease diagnosis for five endemic bacteria that can cause systemic disease in porcine tissue specimens submitted to the Iowa State University Veterinary Diagnostic Laboratory (ISU VDL) from 2017 to 2022. The study also explored the diagnostic value of specific tissue specimens for disease diagnosis, estimated the frequency of polymicrobial diagnosis, and evaluated the association between phase of pig production and disease diagnosis. RESULTS: S. suis and G. parasuis bronchopneumonia increased on average 6 and 4.3%, while S. suis endocarditis increased by 23% per year, respectively. M. hyorhinis and A. suis associated serositis increased yearly by 4.2 and 12.8%, respectively. A significant upward trend in M. hyorhinis arthritis cases was also observed. In contrast, M. hyosynoviae arthritis cases decreased by 33% average/year. Investigation into the diagnostic value of tissues showed that lungs were the most frequently submitted sample, However, the use of lung for systemic disease diagnosis requires caution due to the commensal nature of these agents in the respiratory system, compared to systemic sites that diagnosticians typically target. This study also explored associations between phase of production and specific diseases caused by each agent, showcasing the role of S. suis arthritis in suckling pigs, meningitis in early nursery and endocarditis in growing pigs, and the role of G. parasuis, A. suis, M. hyorhinis and M. hyosynoviae disease mainly in post-weaning phases. Finally, this study highlighted the high frequency of co-detection and -disease diagnosis with other infectious etiologies, such as PRRSV and IAV, demonstrating that to minimize the health impact of these endemic bacterial agents it is imperative to establish effective viral control programs. CONCLUSIONS: Results from this retrospective study demonstrated significant increases in disease diagnosis for S. suis, G. parasuis, M. hyorhinis, and A. suis, and a significant decrease in detection and disease diagnosis of M. hyosynoviae. High frequencies of interactions between these endemic agents and with viral pathogens was also demonstrated. Consequently, improved control programs are needed to mitigate the adverse effect of these endemic bacterial agents on swine health and wellbeing. This includes improving diagnostic procedures, developing more effective vaccine products, fine-tuning antimicrobial approaches, and managing viral co-infections.

Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System.

The performance of five forecasting models was investigated for predicting nursery mortality using the master table built for 3242 groups of pigs (~13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model's performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE = 0.406), Mean Absolute Error (MAE = 0.284), and Coefficient of Determination (R2 = 0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2 = 0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (>5%) or low (<5%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking condition variables collected post-placement in nursery sites.

A cross-sectional assessment of PRRSV nucleic acid detection by RT-qPCR in serum, ear-vein blood swabs, nasal swabs, and oral swabs from weaning-age pigs under field conditions.

Introduction: The porcine reproductive and respiratory syndrome virus (PRRSV) continues to challenge swine production in the US and most parts of the world. Effective PRRSV surveillance in swine herds can be challenging, especially because the virus can persist and sustain a very low prevalence. Although weaning-age pigs are a strategic subpopulation in the surveillance of PRRSV in breeding herds, very few sample types have been validated and characterized for surveillance of this subpopulation. The objectives of this study, therefore, were to compare PRRSV RNA detection rates in serum, oral swabs (OS), nasal swabs (NS), ear-vein blood swabs (ES), and family oral fluids (FOF) obtained from weaning-age pigs and to assess the effect of litter-level pooling on the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) detection of PRRSV RNA. Methods: Three eligible PRRSV-positive herds in the Midwestern USA were selected for this study. 666 pigs across 55 litters were sampled for serum, NS, ES, OS, and FOF. RT-qPCR tests were done on these samples individually and on the litter-level pools of the swabs. Litter-level pools of each swab sample type were made by combining equal volumes of each swab taken from the pigs within a litter. Results: Ninety-six piglets distributed across 22 litters were positive by PRRSV RT-qPCR on serum, 80 piglets distributed across 15 litters were positive on ES, 80 piglets distributed across 17 litters were positive on OS, and 72 piglets distributed across 14 litters were positive on NS. Cohen's kappa analyses showed near-perfect agreement between all paired ES, OS, NS, and serum comparisons (). The serum RT-qPCR cycle threshold values (Ct) strongly predicted PRRSV detection in swab samples. There was a ≥ 95% probability of PRRSV detection in ES-, OS-, and NS pools when the proportion of positive swab samples was ≥ 23%, ≥ 27%, and ≥ 26%, respectively. Discussion: ES, NS, and OS can be used as surveillance samples for detecting PRRSV RNA by RT-qPCR in weaning-age pigs. The minimum number of piglets to be sampled by serum, ES, OS, and NS to be 95% confident of detecting ≥ 1 infected piglet when PRRSV prevalence is ≥ 10% is 30, 36, 36, and 40, respectively.

In-silico characterization of the relationship between the Porcine reproductive and respiratory syndrome virus prevalence at the piglet and litter levels in a farrowing room.

BACKGROUND: Family oral fluids (FOF) sampling has been described as a sampling technique where a rope is exposed to sows and respective suckling litters and thereafter wrung to obtain fluids. PCR-based testing of FOF reveals presence of PRRS virus RNA only at the litter level, as opposed to conventional individual-animal-based sampling methods that demonstrate PRRSV RNA at the piglet level. The relationship between the PRRSV prevalence at the individual piglet level and at the litter level in a farrowing room has not been previously characterized. Using Monte Carlo simulations and data from a previous study, the relationship between the proportion of PRRSV-positive (viremic) pigs in the farrowing room, the proportion of litters in the farrowing room with at least one viremic pig, and the likely proportion of litters to be positive by a FOF RT-rtPCR test in a farrowing room was characterized, taking into account the spatial distribution (homogeneity) of viremic pigs within farrowing rooms. RESULTS: There was a linear relationship between piglet-level- and litter-level prevalence, where the latter was always larger than the former. When the piglet-level prevalence was 1%, 5%, 10%, 20%, and 50%, the true-litter level prevalence was 5.36%, 8.93%, 14.29%, 23.21%, and 53.57%, respectively. The corresponding apparent-litter prevalence by FOF was 2.06%, 6.48%, 11.25%, 21.60%, and 51.56%, respectively. CONCLUSION: This study provides matching prevalence estimates to help guide sample size calculations. It also provides a framework to estimate the likely proportion of viremic pigs, given the PRRSV RT-rtPCR positivity rate of FOF samples submitted from a farrowing room.

Risk factors associated with sow mortality in breeding herds under one production system in the Midwestern United States.

Sow mortality has significantly increased throughout the world over the past several years, and it is a growing concern to the global swine industry. Sow mortality increases economic losses, including higher replacement rates, affects employees' morale, and raises concerns about animal well-being and sustainability. This study aimed to assess herd-level risk factors associated with sow mortality in a large swine production system in the Midwestern United States. This retrospective observational study used available production, health, nutritional, and management information between July 2019 and December 2021. A Poisson mixed regression model was used to identify the risk factors and to build a multivariate model using the weekly mortality rate per 1000 sows as the outcome. Different models were used to identify the risk factors according to this study's main reasons for sow mortality (total death, sudden death, lameness, and prolapse). The main reported causes of sow mortality were sudden death (31.22 %), lameness (28.78 %), prolapse (28.02 %), and other causes (11.99 %). The median (25th-75th percentile) distribution of the crude sow mortality rate/1000 sows was 3.37 (2.19 - 4.16). Breeding herds classified as epidemic for porcine reproductive and respiratory syndrome virus (PRRSV) were associated with higher total death, sudden death, and lameness death. Open pen gestation was associated with a higher total death and lameness compared with stalls. Pulses of feed medication was associated with lower sow mortality rate for all outcomes. Farms not performing bump feeding were associated with higher sow mortality due to lameness and prolapses, while Senecavirus A (SVA)-positive herds were associated with a higher mortality rate for total deaths and deaths due to lameness. Disease interactions (herds Mycoplasma hyopneumoniae positive and epidemic for PRRSV; SVA positive herds and epidemic for PRRSV) were associated with higher mortality rates compared to farms with single disease status. This study identified and measured the major risk factors associated with total sow mortality rate, sudden deaths, lameness deaths, and prolapse deaths in breeding herds under field conditions.

Compliance of biosecurity practices for compartmentalization to foot-mouth disease and classical swine fever viruses in commercial swine companies from southern Brazil.

Classical swine fever (CSF) and foot-mouth disease (FMD) are both highly contagious disease and disruptive to commercial trades, but they are examples of foreign animal diseases that biosecurity-based compartmentalization could be used to support trade in free zones in response to an outbreak. This study aimed to evaluate biosecurity compliance to the Federal Normative Instruction #44 from December 4th, 2017 (BRAZIL, 2017) in commercial swine farms located in southern Brazil. A total of 604 swine farms from 10 commercial swine companies were sampled, from which 28.5% were breeding farms, 29.1% nursery, 32.8% finishing, 6.8% multipliers, and 2.8% farrow-to-finish. Cluster analyses revealed that farms with high compliance (n = 303, Cluster 1) performed 71% of the practices, moderate (n = 219, Cluster 2) 47%, and the low (n = 82, Cluster 3) 33%. A spatial logistic regression model estimated that biosecurity compliance was highest in only one of 10 commercial swine companies, and within a company, multipliers (when present) obtained the highest biosecurity compliance (p-value < 0.01). These results suggest that major improvements in biosecurity practices are needed in breeding herds, nursery, and grow-finish farms to be compliant to the Federal Instruction #44. Based on the combination of these analyses, only one commercial swine company was more suitable to establish compartments for CSF and FMD with minimal investments. Still, this study revealed that the majority of commercial swine companies needs to improve biosecurity practice protocols to then target compartmentalization.

Effects of nursing a large litter and ovarian response to gonadotropins at weaning on subsequent fertility in first parity sows.

Post-weaning fertility failures occur more often in parity 1 (P1) sows due to high metabolic demands for lactation and their inability to meet energy requirements for maintenance, growth, and reproduction. We hypothesized that body condition loss occurs more frequently in P1 sows nursing a large litter, resulting in impairment of ovarian follicle development during lactation and post-weaning, which can negatively impact estrus and subsequent fertility. At 24 h post-farrowing, P1 sows (n = 123) were assigned to treatment (TRT) based on sow weight and the number of functional teats to receive a high number (HN, 15 to 16) or low number (LN, 12) of nursing piglets. At weaning, sows in each TRT were assigned to receive PG600 or None (Control). During lactation, sow body measures were obtained and ovarian follicles were assessed in mid-lactation and post-weaning. Lactation data were analyzed for the effects of TRT, and fertility data after weaning were assessed for TRT x PG600, but there were no interactions (P > 0.10). During lactation, 22.2 % of HN sows lost ≥ 4 piglets due to death or removal, and so these sows were excluded from further analysis. The HN sows were lighter (-6.2 kg), had less backfat (-1.0 mm), had lower body condition score (-0.4), and lost more nursing piglets (-1.2) than LN sows (P < 0.05). However, HN sows weaned more pigs (14.0) than LN sows (11.0). There was no effect of TRT on wean to estrus interval (4.2 d), but the interval was 0.5 days shorter for PG600 (P = 0.004) than control. There were no effects of TRT or PG600 on estrus within seven days after weaning (87.3 %), but PG600 induced smaller (P = 0.002) follicles at estrus (6.7 mm) than control (7.3 mm). In the subsequent parity, there were no effects of TRT or PG600 on farrowing rate (93.9%) and total born (13.2). Overall, HN sows lost more piglets and body condition but still weaned more pigs without any detrimental effects on subsequent reproductive performance.

The motivation for this study was to determine if assigning parity one (P1) sows to nurse a large number of piglets (15 to 16) would cause substantial body condition loss and have detrimental consequences for fertility after weaning. The results showed that when P1 sows nurse 15 to 16 piglets, they lose more body condition and more piglets than P1 sows nursing 12 piglets. However, there was no impact on the fertility of these sows. This study demonstrates that P1 sows can nurse a high number of piglets and still have a high potential to be fertile after weaning their piglets. Still, there is potential to improve management to avoid excessive weight loss in sows and piglet losses.

Utilizing productivity and health breeding-to-market information along with disease diagnostic data to identify pig mortality risk factors in a U.S. swine production system.

Aggregated diagnostic data collected over time from swine production systems is an important data source to investigate swine productivity and health, especially when combined with records concerning the pre-weaning and post-weaning phases of production. The combination of multiple data streams collected over the lifetime of the pigs is the essence of the whole-herd epidemiological investigation. This approach is particularly valuable for investigating the multifaceted and ever-changing factors contributing to wean-to-finish (W2F) swine mortality. The objective of this study was to use a retrospective dataset ("master table") containing information on 1,742 groups of pigs marketed over time to identify the major risk factors associated with W2F mortality. The master table was built by combining historical breed-to-market performance and health data with disease diagnostic records (Dx Codes) from marketed groups of growing pigs. After building the master table, univariate analyses were conducted to screen for risk factors to be included in the initial multivariable model. After a stepwise backward model selection approach, 5 variables and 2 interactions remained in the final model. Notably, the diagnosis variable significantly associated with W2F mortality was porcine reproductive and respiratory syndrome virus (PRRSV). Closeouts with clinical signs suggestive of Salmonella spp. or Escherichia coli infection were also associated with higher W2F mortality. Source sow farm factors that remained significantly associated with W2F mortality were the sow farm PRRS status, average weaning age, and the average pre-weaning mortality. After testing for the possible interactions in the final model, two interactions were significantly associated with wean-to-finish pig mortality: (1) sow farm PRRS status and a laboratory diagnosis of PRRSV and (2) average weaning age and a laboratory diagnosis of PRRS. Closeouts originating from PRRS epidemic or PRRS negative sow farms, when diagnosed with PRRS in the growing phase, had the highest W2F mortality rates. Likewise, PRRS diagnosis in the growing phase was an important factor in mortality, regardless of the average weaning age of the closeouts. Overall, this study demonstrated the utility of a whole-herd approach when analyzing diagnostic information along with breeding-to-market productivity and health information, to measure the major risk factors associated with W2F mortality in specified time frames and pig populations.

Porcine reproductive and respiratory syndrome virus RNA detection in tongue tips from dead animals.

The control of porcine reproductive and respiratory syndrome virus (PRRSV) hinges on monitoring and surveillance. The objective of this study was to assess PRRSV RNA detection by RT-PCR in tongue tips from dead suckling piglets compared to serum samples, processing fluids, and family oral fluids. Tongue tips and serum samples were collected from three PRRSV-positive breeding herd farms (farms A, B, and C) of three different age groups: newborns (<24 h), processing (2 to 7 days of age), and weaning (18 to 22 days of age). Additionally, processing fluids and family oral fluids were collected from 2-7 days of age and weaning age, respectively. In farms A and B, PRRSV RNA was detected in tongue tips from all age groups (100 and 95%, respectively). In addition, PRRSV RNA was detected in pooled serum samples (42 and 27%), processing fluids (100 and 50%), and family oral fluids (11 and 22%). Interestingly, the average Ct value from tongue tips was numerically lower than the average Ct value from serum samples in the newborn age. In farm C, PRRSV RNA was only detected in serum samples (60%) and family oral fluids (43%), both from the weaning age. Further, no PRRSV RNA was detected in tongue tips when pooled serum samples from the same age group tested PRRSV RNA-negative. Taken together, these results demonstrate the potential value of tongue tips for PRRSV monitoring and surveillance.

Physical Traits and Reproductive Measurements Associated with Early Conception in Beef Replacement Heifers.

Developing and raising replacement heifers requires a large capital investment for producers. Therefore, it is imperative to discover traits and management practices to eliminate subfertile heifers prior to breeding and pregnancy determination. In this study, four years of data was analyzed from a centralized beef heifer development yard in the Midwest of the United States. The objective of this study was to analyze various heifer physical characteristics and management practices in order to quantify their impact on pregnancy and date of conception. Logistic regression models were built to investigate risk factors associated with conception to artificial insemination (AI), pregnancy by natural service after AI exposure, and pregnancy in the first 21-days of the breeding season. Age at entry, average daily gain from entry to breeding, pelvic width, and year were associated with AI pregnancy (p < 0.05). On the second model, average daily gain from entry to yearling weight, weight at breeding, weight at pregnancy diagnosis, and age at AI were significantly associated with pregnancy. There were no associations with reproductive tract score with any of the response variables analyzed. These results indicate there are physical measurements that can be used to improve the ability to select and develop heifers for improved reproductive performance.

Effect of pooling family oral fluids on the probability of PRRSV RNA detection by RT-rtPCR.

Family oral fluids (FOFs) are an aggregate sample type shown to be a cost-efficient and convenient option for determining the porcine reproductive and respiratory syndrome virus (PRRSV) status of weaning age pigs. This study investigates the effect of pooling PRRSV-positive FOF samples with PRRSV-negative FOF samples at different levels (1/3, 1/5, 1/10, 1/20) on the probability of PRRSV RNA detection by reverse-transcription real-time polymerase chain reaction (RT-rtPCR). Mathematical models were built to assess how much the probability of RT-rtPCR PRRSV detection changed with increasing proportion of PRRSV-positive samples present within pools and how partially sampling a farrowing room influenced the probability of RT-rtPCR detection of PRRSV RNA in pooled samples at different prevalence scenarios. A general example of a guideline for FOF-based sampling under different prevalence scenarios to detect PRRSV RNA by RT-rtPCR with at least 95 % certainty is presented. At the sample level, the probability of detecting PRRSV RNA by RT-rtPCR decreased from 100 % to 87 %, 68 %, and 26 % when diluting up to 1/20 for PRRSV positive FOF having an initial Cycle threshold (Ct) below 34, between 34 and 36, or above 36, respectively. When PRRSV prevalence is near-zero (1 or 2 litters positive out of 56), the most cost-efficient farrowing room sampling strategy to detect PRRSV RNA with at least 95 % certainty was pooling FOF samples up to 1/10; at higher prevalence (≥ 3 of 56 litters positive), the most cost-efficient strategy was submitting samples in pools of 20. Subsampling a farrowing room for FOF pools was also demonstrated to be a valuable cost-saving strategy. Overall, based on the conditions of this study, pooling FOFs up to 1/20 is a valid option in situations of cost constraint and regardless of pooling level chosen, capturing as many litters as possible improves the probability of PRRSV detection.

Comparison of virus detection, productivity, and economic performance between lots of growing pigs vaccinated with two doses or one dose of PRRS MLV vaccine, under field conditions.

Porcine reproductive and respiratory syndrome virus (PRRSV) causes a significant economic impact on swine production. It has been demonstrated that PRRS modified-live virus (MLV) vaccination of pigs, with one full dose, significantly reduces clinical consequences of wild-type PRRSV infection compared to non-vaccinates. However, there is limited information about the effect that two doses of PRRSV MLV vaccine have on the performance of growing pigs, compared to vaccination with a single dose. This study was conducted with the objectives to compare (a) the wild-type PRRSV detection in oral fluids over time, (b) key closeout productivity indicators, and (c) economic performance between lots of growing pigs vaccinated with two doses of Ingelvac PRRS® MLV vaccine and lots vaccinated with a single dose of the same vaccine. This randomized field trial included 15 lots of growing pigs from PRRSV positive-unstable sow farms and 66 lots from PRRSV positive-stable sow farms, according to the American association of swine veterinarians' terminology. All pig lots received the first vaccination either around processing or weaning age. Lots allocated in the two doses group received the second vaccination three to four weeks after the first vaccination. The pig lots were monitored for PRRSV detection over time. Six oral fluids samples were collected in three weeks intervals and were tested for wild-type PRRSV-2 RNA by RT-qPCR and open reading frame 5 (ORF)- 5 sequencing. Regression models were used to compare wild-type PRRSV detection dynamics on oral fluids samples and to compare key closeout performance indicators between one dose group and two doses group. Additionally, a benefit-cost ratio analysis compared economic performance between one dose group and two doses group. The proportion of wild-type PRRSV detection on oral fluids samples and the log counts of viral RNA per ml of oral fluids from the two doses group was lower than the one dose group on lots originated from PRRSV positive-stable sow farms, with a risk ratio of 1.24 and a rate ratio of 1.17, respectively. The two doses group had a significantly lower mortality rate than the one dose group, with a rate ratio of 1.21. The effect size increased on lots originated from PRRSV positive-unstable sow farms, and on lots with higher frequency and diversity of wild-type PRRSV detection during the growth phase. No differences in growth performance were detected between two doses group and one dose group. The second MLV vaccination dose had a benefit-cost ratio of 1.83. For lots originated from PRRSV positive-unstable farms, the benefit-cost ratio was 4.45, and for lots originated from PRRSV positive-stable farms, the benefit-cost ratio was 0.45. Under study conditions, vaccinating growing pig lots with two doses of PRRS MLV vaccine was a useful strategy to immunize growing pigs against PRRSV, lowering the wild-type PRRSV detection, lowering mortality rate, and increasing profitability, compared to lots of growing pigs that received a single dose of the same vaccine.