Decision model development for application of PRRS mitigation strategies post-weaning


Article by:

E Johnson, J Lowe2014 IPVS Congress p. 321.


Pork production systems routinely capture large amounts of real-time data. Decision tools can be developed to effectively utilize this data in strategic decision making. For example, Porcine Reproductive and Respiratory Syndrome (PRRS) creates economic losses in the period from weaning to market (WTM).1 Several tactics can be employed to mitigate these losses in WTM batches. However, understanding the mean cost of PRRS infection in a batch of pigs and the ability to predict which batches are most likely to suffer those losses are critical in designing mitigation strategies that optimize economic returns. This paper describes an economic decision model for PRRS mitigation strategies in WTM pigs that was constructed and applied within a large production system.

Materials and Methods

A two stage decision model was constructed. The first stage estimates the Marginal Economic Cost (MEC) of PRRSv infection in the WTM period based on the performance differences of infected (p) and non-infected (n) groups.

MEC=[[(weight gain/nADG)-(weight gain/pADG)] x housing cost/day]+ [[(Weight gain*nFCR)-(weight gain*pFCR)] x feed cost] +[[nMortality-pMortality] x value of a dead pig].

Values used in the model are determined with a multivariate approach (ANOVA) that estimates the mean performance differences between n and p batches and accounts for other sources of variation including: Source Farm, Area Pig Farm Density, Site level biosecurity, Time of year at Placement and Timing of PRRS Infection.

The second stage predicts which batches are more likely to suffer losses associated with PRRSv infection through application of multivariate logistic regression using the same variables in a backwards stepwise fashion.

A production system provided a data set containing 503 individual batch records. Of the 503, 53 batches included all of the data points necessary for this analysis. Site level bio-security was estimated with PADRAP2 and an Internal Biosecurity audit. Area density was estimated based on the number of farms per square mile in the site’s county based on 2007 USDA Census of Ag. Serial Oral Fluid PRRS PCR tests that were typically, but not consistently collected at 4 week intervals were used as a way to estimate timing of infection. Twenty seven positive batches (having at least one PCR positive) and 26 negative batches comprised the data set.


PRRSv infection post-weaning increased mortality, impaired feed conversion and reduced growth rate, resulting in an additional 2.8 (1.4, 4.3) days on feed and 4.77 (2.9, 6.6) kg of feed per head. 

Using cost assumtions (mortality= $50/hd, yardage=$0.12/day and feed=$0.33/kg), the MEC of PRRS infection during WTM was estimated to be $2.84 ($1.78, $3.92). The risk of losses due to PRRSv infection in a batch of pigs could not be predicted by placement date, source, area pig density or either measure of site biosecurity assessed by this system.

Conclusions and Discussion

With a PRRS infection cost of $2.84 in roughly 50% of batches and the inability to predict which batches will suffer losses, the system should implement a cost conscious mitigation strategy in a system-wide manner rather than attempt a targeted approach. The ability to measure the biological and systematic sources of variation is critical to make informed decisions. Although in this case just 10.5% of batches had a complete data set; small, accurate, representative data sets can be useful in models to inform decision making and aid in achieving optimal long term performance in production systems.