Abstract
Buffalo milk production in Egypt has steadily declined since 2014, mainly due to climate-driven heat stress (HS) and rising temperature–humidity index (THI). This quasi-field study randomly evaluated twelve lactating buffalo during peak summer, introducing a biometeorological approach to define and predict HS impacts precisely. Heat stress in buffalo was classified according to THI ranges as follows: non-HS zone (NHSZ, 56.7–73.2), moderate HS zone (MHSZ, 73.2–75.4), severe HS zone (SHSZ, 75.4–80.3), and critical HS zone (CHSZ, ≥ 80.3). Two models were compared: Model I (natural, group A) and Model II (adaptive, group B), which received targeted environmental and management interventions. Continuous monitoring of THI alongside daily milk yield (DMY), physiological responses, oxidative stress biomarkers, and the expression of key energy homeostasis genes was assessed in both groups. Adaptive interventions effectively reduced THI exposure, shifted animals from critical HS to non-HS zones, improved physiological parameters, increased milk yield by 53%, lowered oxidative stress, and enhanced milk quality ( p < 0.05). The study presents the first transcriptional analysis of stress-responsive energy-regulating genes in buffalo, revealing higher AMPK , HRH1 , and mTOR expression in HS-Model I buffalo, which reflects the metabolic strain associated with unmanaged thermal stress. Regression analysis showed that for every one-unit increase in THI above 69, milk yield decreased by 0.17–0.23 kg/day. These findings underscore the value of integrated biostatistical modeling and targeted adaptation strategies for sustaining buffalo productivity under the pressures of subtropical climates. Adaptive housing, nutritional support, and management interventions effectively mitigate the impacts of HS. At the molecular level, evidence of oxidative stress and altered energy regulation highlights the physiological toll of thermal load, emphasizing the need for holistic approaches to protect productivity and herd resilience in heat-stressed regions.