An optimization model for the camel OPU and SCNT-ET system: Decoupling seasonal biological trends from donor variation and operational disruptions in an eight-year longitudinal analysis

This retrospective study isolated underlying seasonal biological trends from donor variation and operational adjustments in a camel cloning workflow over eight years. Ovarian follicular dynamics and oocytes were monitored and retrieved via transvaginal ultrasound-guided ovum pick-up (OPU) following superstimulation in dromedary camel ( Camelus dromedarius ) donors during the cool breeding season (October–April). Recovered oocytes underwent somatic cell nuclear transfer (SCNT); resulting blastocysts were transferred into synchronized recipients. Data were analyzed using a Generalized Linear Mixed Model (GLMM) with repeated measures, treating individual donor identities, recipient identities, and production years as random effects to natively stabilize individual “super-donor” variations. Follicular availability peaked in January and March, while oocyte yields maximized in February (total: 15.95; useable: 13.08 COCs/session). Morphological oocyte quality remained stable, with useable cumulus-oocyte complex (COC) rates hovering between 79.6% (November) and 84.2% (January). Transvaginal ultrasound-monitored Day-35 pregnancy rates formed a stable winter plateau across December (11.3%), January (10.2%), and February (10.4%). Conversely, an “April Wall” collapse occurred; despite transferring 129 high-quality, winter-produced fresh blastocysts (rollover blastocysts), pregnancy rates plummeted to 5.4% ( p < 0.05). Sequential ultrasound monitoring through Day 60 confirmed complete embryonic loss across multiple years. Thus, while camel OPU yields track seasonal follicular waves, intrinsic oocyte competence remains stable. The late-season pregnancy collapse is driven by maternal recipient climate constraints rather than embryo quality variations. These multi-year data were consolidated into a system-wide optimization equation via multi-variable regression ( R 2 = 0.81), validating shifting heavy workloads into February while enforcing a zero-fresh-aspiration “rollover strategy” in April to maximize efficiency.