Physiological and biochemical evaluations and the use of machine learning to elucidate thermoregulatory resilience in Holstein x Nigerian White Fulani crossbred cows

Climate change-induced heat stress poses a global threat to livestock productivity, particularly in tropical agroecologies where smallholder dairy systems dominate. This study investigates the thermoregulatory, metabolic, and productive responses of Nigerian White Fulani × Holstein Friesian crossbred dairy cows (n = 45) to heat stress under natural farm conditions. The study used Temperature-Humidity Index (THI), physiological parameters (respiration rate, pulse rate, rectal temperature), milk yield, biochemical markers (ammonia, pyruvate, tyrosine) alongside machine learning modelling to elucidate heat stress effect on performance of the cows. Under severe heat stress (THI ≥ 80), physiological stress indicators significantly increased (p < 0.001), while milk yield declined by 23% (p < 0.01). There were observations of biochemical disruptions, including elevated ammonia (+ 35%, p < 0.01) and tyrosine (+ 45%, p < 0.01), which highlighted metabolic strain. The machine learning tool (random forest model) integrating THI, feed intake, and pyruvate achieved a robust milk yield prediction (R² = 0.82), outperforming traditional regression approaches. This study presents a key link of White Fulani crossbred thermotolerance to milk production resilience under farm conditions while demonstrating machine learning’s utility in heat stress prediction. The findings emphasise the potentials of strategic crossbreeding and precision management to sustain dairy productivity in warm climates, offering actionable insights for tropical smallholder systems and genomic selection programmes targeting metabolic heat resilience.