Poultry gut health is a cornerstone of flock performance, but turning microbiome data into farm-level decisions is a persistent challenge for the poultry industry. Artificial intelligence (AI) could provide the key.
For all the industry's growing awareness of the microbiome's importance, a fundamental problem has remained.
"There is still a disconnect between modulating the gut microbiome and how that translates into something the producer can actually see and use," Fernanda Castro, micronutrition and health technical lead at Cargill, said.
Gut health directly affects nutrient digestibility, absorption efficiency and feed conversion. This means gaps in microbiome management carry real economic consequences.
AI finds the patterns
Combining AI models, databases full of microbiome data and performance information could help identify bacterial patterns and specific biomarkers associated with high-performing flocks, as well as those linked to poor outcomes.
Factoring diet composition, geographic region, season, body weight, vaccination records and production performance into the analysis allows the AI to account for variability that exists between farms, regions and production systems. A poultry gut microbiome in the U.S. looks different from one in the Netherlands, and models built without that context will always fall short.
Rethinking what we know
AI-driven analysis is also challenging some long-held assumptions. Lactobacillus, for example, has been widely promoted as a reliable marker of good gut health, but the reality – as shown by AI analysis – is more complicated.
Early in a bird's life, around seven days of age, high Lactobacillus abundance is indeed a positive sign. But as birds mature, a healthy microbiome should shift toward greater populations of short-chain fatty acid-producing bacteria.
However, AI-powered analysis revealed that certain Lactobacillus strains that proliferate in the cecum later in production can actually compete with and suppress those beneficial bacteria by reducing pH, quietly working against the performance outcomes producers are trying to achieve.
Precision over complexity
Rather than attempting to map the full complexity of the microbiome at once with AI, focus instead on key biomarkers. By narrowing the analysis to what matters most and increasing the number of technical replicates, the analysis can raise its statistical power and make bacterial differences between flocks more detectable, advised Luisa Gene, MSc, Galleon technology lead, Cargill.
For some producers, microbiome testing already provides a scientific foundation for nutritional and management decisions across seasons and production cycles. The more data the system receives, the sharper its models become.
"You can only manage what you measure," Gene said. "The microbiome — how are you going to manage it if you don't measure it?"
