Voluntary culling decisions are inherently uncertain because they involve comparing the variable future performance of a current sow with that of a prospective gilt. This uncertainty often leads to hesitation, and marginal sows may remain in the herd for a very long time.
As a result, underperforming animals can quietly reduce overall herd efficiency. A structured decision-making approach can help address this challenge.
The goal is to outline a practical framework to support voluntary culling decisions. Voluntary culls are sows that could be bred again but are removed because a replacement gilt is expected to generate greater value. This differs from involuntary culling, which is driven by age, health or reproductive failure, situations where a sow’s future value has effectively dropped to zero. The emphasis is on value rather than individual performance metrics.
By focusing on value, additional factors such as replacement cost, cull value, genetic progress, etc. can be incorporated in a consistent way.
In this framework, total pigs weaned serves as the foundation for value. The comparison is between a sow’s cumulative production, plus her expected next litter, and the projected output of a replacement gilt over the same period.
To make comparisons straightforward, productivity is expressed using a standardized weaned-pig value for both the sow and replacement gilt. However, a few practical challenges need to be addressed.
Evaluating Estimates
Cross-fostering can complicate individual sow records, making pigs weaned less reliable as a direct measure. In these cases, pigs born alive minus average preweaning mortality provides a reasonable proxy for individual performance.
Estimating future output is another challenge, but simple methods are often sufficient. For example, if a sow consistently produces litters 10% below her parity group average, it is reasonable to expect similar performance in the near term. A sow making up for lost production is the exception, not the rule.
Estimating gilt performance is more straightforward. Historical herd data can establish the typical number of litters per gilt and total pigs weaned. This baseline can be adjusted if desired to account for genetic improvement. From there, two complementary evaluation methods can be applied.
The first is a probability-based approach. It estimates the likelihood that a gilt will remain in the herd long enough to match or exceed the performance of the cull candidate. For example, though the average gilt makes it to Parity 4, records show 80% of P4s become P5s, from there 75% of P5s farrow again. Applying these probabilities to expected litter performance beyond the average provides a weighted estimate of gilt output.
The second method assumes multiple replacements over time instead of comparing one gilt to one sow. Because sequential average gilts are compared against the extended production of the current sow, the calculation is simple. Where the nuance comes in is factoring in replacement costs, cull values, and genetic improvement over generations.
Using both methods provides a more robust perspective. As conditions change, such as replacement costs, cull values, survivability or genetic gain, the relative advantage of keeping or replacing a sow will shift. This adaptability is key. A clear, value-based framework can reduce hesitation, support more timely decisions and ultimately improve herd profitability.
