However, unlike other herd choices that may require a small time commitment to alter, changing genetics to meet your operational goals today and into the future requires significant time and planning to execute.

So, how should a commercial producer decide which lines are best?
Bad data, made better: Oftentimes, producers will rely on closeout data to make genetic decisions. This can be misleading due to the inherent variation between groups of pigs as the differences in genetic performance will most likely be completely outweighed by the multiple other sources of variation. If closeout data is your only mechanism for making decisions, use these guidelines for setting up the evaluation:
  • Evaluate within common flow(s) of pigs. The variation in performance between flows can be significant due to sow farm variation that affects growth performance downstream.
  • Account for seasonality. Rotate lines randomly over time versus doing strictly a “before and after” evaluation.
  • More data is better. With inherently noisy data, it is best to replicate as many times as possible. For our internal evaluations, we would typically use 20 or more groups to inform decisions using closeout data.
Good data: A head-to-head comparison trial of genetic lines in commercial conditions is the best and most accurate way to do an evaluation. Benchmarking trials that compare the performance of multiple lines with enough replication to give statistical confidence in the outcome is ideal. If you don’t have this capability, a partner that does is a close second.
The best data: Doing head-to-head line comparisons gives you a good assessment of genetic capabilities at that point in time but doesn’t necessarily demonstrate future performance. To better understand the trajectory of the line, we look at how boars from different indexes within the line perform. Penning pigs by sire and collectively evaluating multiple boars from multiple lines in a head-to-head evaluation gives us a snapshot of the individual line and also demonstrates the differences in the value of index points between lines.

Each genetic company establishes their index slightly differently. Indexes can be made up of different component traits and/or have drastically different weighting on each trait. Understanding how boars in each line with differing indexes perform in your system can give you an indication of where the line is headed. Lines that have an index that ties closer to value creation should add more value in the long run. Ultimately, if you know the value of an index point of all the lines you are considering, you will be able to deploy genetics in a targeted fashion that delivers the most value for your system.
The constant across all genetic evaluations — keep it up. Having a realistic understanding of where you are and setting goals for where you want to be in the future is key to finding the right genetic solution for your herd. Genetic companies experience change similar to producers and the outcome of their genetic products change over time. The old saying, “you can’t manage what you don’t measure” certainly applies to genetics. Routine evaluations are key to demanding accountability and making good genetic decisions.
Technical Expert: Caleb Shull, Ph.D., director of research & innovation for The Maschhoffs
As director of research & innovation for the Maschhoffs, Caleb oversees the research and development to innovate new approaches for genetic evaluation and progress. Caleb studied at the University of Illinois for his undergrad and graduate programs. He partnered with the Maschhoffs in 2009 to do his thesis: Effect of floor space in the nursery and grow-finish periods on the growth performance of pigs. His work has been influential in the creation of Acuity’s Commercial Test Herd and in evaluation techniques to drive genetic progress in commercial herds.
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Acuity was created in response to a need for genetic improvement with a systems-based focus. For nearly a decade, our technical team has worked to develop a platform capable of delivering solutions that increase profitability throughout the supply chain. Our focus is different: commercially-derived data supports decisions that enable value realization.
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