Coccidiosis control programs are becoming more complex as production systems shift between NAE and NAIHM, litter reuse increases and sensitivity to anticoccidials continues to evolve. In this environment, anticoccidial sensitivity testing (AST) is often discussed as a decision-making tool, but how it is designed, interpreted and applied in practices determines if it truly helps protect performance.
A good AST generates reliable, field-relevant data by evaluating how a farm’s current Eimeria isolates respond to specific anticoccidial programs. The biggest non-negotiable is that the isolates must reflect the Eimeria population circulating across the complex, not just a single problem flock. That means collecting from used litter, during the active cycling window and across different performance levels.
Trial design also matters. Proper controls, testing products at label dose and selecting realistic program alternatives are essential. Endpoints should include both performance metrics such as weight gain, feed conversion, mortality and uniformity, along with lesion scores and measures of intestinal cycling. Both pathology and performance are needed to make confident program decisions at the complex level.
Interpreting AST results requires looking at patterns rather than single data points. Strong sensitivity is seen when performance is close to the non-challenged control and lesion scores are clearly reduced. Poor sensitivity reflects performance similar to the challenged control with high lesion scores, while partial sensitivity falls in between.
Program changes are rarely driven by a single result. Instead, consistent signals across the complex should guide decisions. Increasing lesion trends, especially Eimeria maxima, higher cycling observed in scrapings and subtle performance drift across multiple farms are indicators of a potential program-level issue.
AST is useful in both NAE and NAIHM systems, but for different reasons. In NAE systems, it is most decision-worthy when focused on chemical products, which can lose sensitivity relatively quickly. In NAIHM systems, interpretation depends on product class. Ionophores may appear weaker in short-term ASTs because they allow controlled cycling and immunity, making field context essential. Strong chemicals are where AST is most powerful, as resistance is more clearly reflected in performance.
When used as part of a standard decision framework, with consistent isolate collection, clear objectives and alignment with field observations, AST helps track sensitivity trends over time and supports informed, timely program decisions without overreacting to a single result.
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