sow farm – Swine in Minnesota

Breeding herd Senecavirus A infection: understanding its persistence

This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.

This week, Dr. Guilherme Preis, PhD candidate working with Drs. Cesar Corzo and Fabio Vannucci, shares his latest results about Senecavirus A persistence in sow farms after an outbreak.

Key points

Senecavirus A (SVA) continues to be responsible for an important number of FAD investigations.
SVA continues to circulate in breeding herds for up 21 weeks after clinical signs had been detected.
Heat check boars may contribute to population persistence of this virus.

Development of an automated model to capture and analyze whole-herd parameters associated with wean-to-finish mortality

This week, Dr. Linhares’s team from Iowa State University is proposing a model to analyze wean-to-finish mortality based on whole-herd parameters such as farrowing rate, PRRS status or even management factors.

Key Points

An automated model was developed to consolidate multiple data streams from weaning cohorts to their respective closeouts.
Sow farm productivity and health are highly associated with wean to finish mortality.
Sow farm-related data explained 74.1% of the variation observed on wean to finish mortality.

PRRSv ORF5 difference from VR2332 by herd type

This week, the MSHMP team assessed differences in ORF5 sequences compared to VR2332 based on the type of farm, the sequence was collected at.

Key points

Breeding herd sequences differ 8%-16% while in other herd types they differ 1%-15% from VR2332 at the ORF5 level.
The larger nucleotide identity (%) range compared to VR2332 in growing pigs suggests a higher viral diversity within this group.

Science Page: Assessing the relative vulnerability of swine breeding herds to the introduction of PRRS virus

This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.

This week we are sharing a report by Dr. Daniel Linhares’ lab at Iowa State University. The report summarizes the findings of his study regarding the factors making a sow farm vulnerable for PPRS introduction.

Key Points:

A model to quantify and identify biosecurity vulnerability in breeding herds is now available.
Events related to swine movements, transmission by air and water, and people movements were the variables most associated with PRRS outbreak.
Biosecurity vulnerability scores may help producers/veterinarians prioritize biosecurity investments.

Study Summary:

Herd-specific biosecurity assessments are needed to determine herd-specific risk for PRRS outbreaks. Thus, we developed and validated a biosecurity vulnerability score (BVS) that measures the relative vulnerability of swine breeding herds to PRRSv introduction. The BVS was based on a multi-criteria decision algorithm that ranked risk events associated with outbreaks. A comprehensive biosecurity assessment was used to obtain information of the biosecurity practices from each participating herd. The practices performed in each herd were weighted by the relative importance of each event obtained from an expert opinion panel resulting in a score that identifies the events that should be prioritized. In two independent data sets, the scores consistently revealed that farms with higher scores had a higher frequency of PRRS outbreaks. In addition, results suggest that events related to swine movements,transmission by air and water, and people movements should be prioritized.

Follow-up study:

We are developing a new screening tool to validate the minimum number of questions associated with frequency of PRRS outbreak. Study farms will be asked to fill out a short survey. This can help producers and veterinarians to identify sites at relatively higher risk of PRRSv introduction.

To enroll or to request additional clarification please contact: Gustavo Silva at Iowa State University (gustavos-at-iastate.edu)

Science Page: Within farm PRRS time-to-stability differences in sow farms in the Midwest

This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.

This week, we are sharing a report by the MSHMP team regarding PRRS time-to-stability differences in sow farms.

Keypoints:

There is significant within farm PRRS time-to-stability variation.
Several factors contribute to PRRS time-to-stability variability; however, there is still a significant amount of unexplained variability.
The role of within farm management practices and internal biosecurity measures should be further explored.

Introduction

Porcine reproductive and respiratory syndrome (PRRS) stability is reached when no evidence of infection is observed in wean-age piglets. Sample size to detect PRRS virus in wean-age piglets usually involves blood sampling of 30 piglets, at least four times, 30 days apart (Holtkamp et al., 2011). The cumulative time from the intervention (i.e. whole herd exposure, herd closure) to PRRS stability is usually referred to as time-to-stability (TTS).

Here we summarize differences in TTS in MSHMP participating farms located in the Midwest that have had at least two PRRS outbreaks.

Methods

Six systems that are similar in the way they test to classify a herd as stable were selected for inclusion in the study. PRRS outbreaks reported from 2011 to 2017 were used for analysis.

TTS was defined as the time period from the date of outbreak reporting to the date when PRRS stability was reported (last consecutive negative PCR result). To assess the variability in TTS, only farms that had at least two PRRS outbreaks were selected.

Results

Overall, 133 PRRS outbreaks in 53 farms were recorded withtwo, three, four and five outbreaks in 35, 11, 5, 2 farms, respectively. The median TTS standard deviation of PRRS outbreaks within the same farm was 12 weeks (minimum = 0 weeks, maximum=88 weeks).

After accounting for the effect of the intervention using MLV or FVI, the RFLP pattern of the virus associated with the outbreak and previous PRRS outbreaks in the farm, the PRRS time-to-stability correlation of outbreaks in the same farm and system was only 1.2%.

In other words, TTS of two given outbreaks in the same farm were not correlated indicating that TTS within farm is highly variable.

Conclusion

There is a high TTS variability after a PRRS outbreak within the same farm that is not accounted for by the effect of the intervention used, the virus (i.e RFLP), previous PRRS outbreaks in the farm and system.