Accurate prediction of forest growth and yield is essential for strategic planning in intensive plantation management. This study evaluates whether unsupervised anomaly detection can be used as a systematic data-quality layer in stand-level growth and yield modeling, after standard consistency checks have been applied. We used a multi-regional continuous forest inventory of hybrid Eucalyptus urophylla × Eucalyptus grandis plan...