Farms.com Home   Ag Industry News

Getting “triggered” can be a good thing

Getting “triggered” can be a good thing

Time to look forward to being able to act in a positive way when we see a trigger in pig production data.

Image and article by Marie Culhane, University of Minnesota

The word “triggered” frequently has a negative meaning. Rather than focus on the downsides of being triggered - such as getting furiously angry by images we see on social media - it is time to look forward to being able to act in a positive way when we see a trigger in pig production data.

Many farms use indicators or signals for animal health that require pig caretakers to take action.

For example, when a sow in her first three days of lactation does not get up to eat and her piglets appear to be losing weight, she is examined for mastitis. However, the ability to act in a timely manner when a signal occurs may be difficult when resources such as time and labor are in short supply.

Regardless, it is still important to monitor your data in case your production moves away from your benchmarks or is trending in the wrong direction.

Frequently examining your data may be a good way to alert yourself to a potential problem.

The proactive use of a production trigger can be used as an early detector of disease within a herd. This example was recently published for finisher sites [1].

To simulate when there would be enough sick or dead finisher pigs to indicate that African Swine Fever (ASF) virus had infected a pig site, the normal mortality associated with routine production causes unrelated to ASF had to be determined.

To determine “normal” mortality, the authors used distributions based on weekly mortality data for 248 pig herds from four pig farming systems in North America.

This allowed the authors to estimate an average weekly mortality rate of 0.3% - i.e., every week “normally” three pigs in a herd of 1,000 die.

In addition to using that mortality rate as an average, other information taken into account for the simulation included data such as the common sizes of grow-finish sites in the Midwest - those with populations of 1,000, 2,496, and 5,000 pigs [2], along with expert opinions from pig farmers and swine veterinarians on the average weekly morbidity (i.e., sick pigs) rate.

Using the above data along with some sophisticated mathematical models, the results indicated that it may take two weeks or longer to detect ASF in a finisher swine herd via mild clinical signs or increased mortality beyond levels expected in routine production.

To read the Full Article as it appeared in our Benchmark swine magazine, click HERE.


Trending Video

How Data Predicts Swine Outbreaks - Swaminathan Jayaraman

Video: How Data Predicts Swine Outbreaks - Swaminathan Jayaraman


In this episode of The Swine it Podcast Show Canada, Swaminathan Jayaraman, Research Assistant and PhD Candidate at Iowa State University College of Veterinary Medicine, explains how integrated data systems can improve disease surveillance in swine production. He discusses combining diagnostics, animal movement data, production records, and spatial analytics to identify risks earlier and support proactive decision-making for PRRS, PED, and swine influenza. Listen now on all major platforms!

"Disease surveillance remains largely reactive because outbreaks are often confirmed only after transmission has already occurred across multiple connected production sites."

Meet the guest: Swaminathan Jayaraman / swamjay is a Graduate Research Assistant at Iowa State University College of Veterinary Medicine. With academic training in management information systems and engineering, he focuses on integrating diagnostic, production, and movement data to improve disease surveillance and decision support in swine production systems. Listen to Swaminathan Jayaraman on The Swine it Podcast Show Canada, available on all major platforms.