Getting “triggered” can be a good thing

By Marie Culhane

Marie Culhane1, Amos Ssematimba1,2, and Benjamin Blair1
1University of Minnesota, Saint Paul, Minnesota, USA
2Gulu University, Gulu, Uganda

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].

“The more stringent the triggers are set, the quicker one can notice disease or shifts in production.”

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.

Those results are not meant to scare or “trigger” anyone in a negative way, but instead, it should prompt some thinking about how important it is to look at the production data since it can help you take action.

Looking at the benchmark data for sow herds from 2015 to 2022, can we set a level for pre-weaning mortality or sow/gilt death rate that would cause farmers to take action at the sow or piglet level?

The PigCHAMP Benchmark Summary data for mean pre-weaning mortality and death rate depicted in the chart suggests that a trigger for pre-weaning mortality could be set to 16, but this is an oversimplification of a complicated issue.

Chart - Mean Death rate (sow) vs Mean pre-weaning mortality data

The mean death rate of sows shows a steady increase over the past seven years. However, if examined on a weekly basis across multiple herds, a trigger set to 12 might be appropriate.

The more stringent the triggers are set, the quicker one can notice disease or shifts in production within the herd, allowing for more timely treatment and a rapid return to normal production.

While more analysis is needed to determine the right trigger for each production metric for a herd, a disease, or a situation, the data already exists in the form of Benchmark’s, allowing producers to get started in the right direction and with a positive outlook.

To help determine triggers within your own organization, the PigCHAMP team recommends using the Performance Trend Analysis. For larger organizations, the Comparative Production Summary should be utilized. For Grow-Finish triggers, use the Cohort Summary Report (real-time group analysis) and Current Inventory Analysis.

For additional PigCHAMP reports to help further evaluate female/sow mortality, use the Female Removal Analysis, Culls and Death List, and Gilt Retention Report. For the pig/piglet morality, it is suggested you use the Death Loss Analysis and Death Loss Chart for Grow-Finish clients, and the Piglet Loss Analysis for Reproductive customers.

As a reminder - once you identify the triggers for your farm(s) don’t forget to update your Targets in the program to help monitor the situation and progress.

1. Malladi, S., Ssematimba, A., Bonney, P.J. et al. Predicting the time to detect moderately virulent African swine fever virus in finisher swine herds using a stochastic disease transmission model. BMC Vet Res 18, 84 (2022). https://doi.org/10.1186/s12917-022-03188-6

2. USDA, NASS. 2017 census of agriculture. Geographic area series. Washington, DC: USDA Department, National Agricultural Statistics Service; 2019.https:// www. nass. usda. gov/ Quick_ Stats/ CDQT/ chapt er/1/ table/ 25/ state/MN

Marie Culhane
Marie is a veterinarian working with all species, but having a special interest in diseases of food animals, especially swine. Her research involves the antigenic and genetic characterization of influenza A viruses of swine and turkeys, pathogenesis of unique influenza A virus infections in swine and turkeys, and the possible impact these viruses may have on current vaccination protocols in place in the US livestock industry. She is also involved in animal disease emergency planning at local, national, and global levels.