Defoliation practices such as scalping and flails are valuable tools in commercial operations of US sugarbeet growers. It’s well documented that the top of the beet (crown, petiole, leaves) crown carries more impurities that increase loss to molasses and negatively impact storability. Commercial growers, who generally have a single variety in each field, easily adjust their defoliators and scalpers for the most effective removal of material that would negatively impact their sugar payment while preserving as much of their yield as possible. Research trials are utilized to test many different varieties side by side, which, due to genetics, can vary drastically in crown height and shape. Scalping each variety effectively in yield trials is difficult, as varieties with taller crowns are scalped more aggressively than those with shorter crown heights. Additionally, varieties with narrow crowns have less material removed compared to varieties that produce wider crowns. These differences can be further magnified seed quality differences, as gaps in stands contribute to more extreme crown height variance. Many research trials utilize a subsample system for quality analysis. In this system, 20 to 30 pounds, or about 10-15 roots, are sampled from a research plot. These samples are brought to a tare lab for analysis to determine the quality of the variety. Tare labs handle these samples differently from market to market. Some labs hand trim the roots of petiole material prior to processing that may have been missed by the defoliator/scalper in the field, while other labs simply process the roots as they are. Due to genetic diversity, it’s very difficult to evenly scalp each variety in today’s research trials. Our studies show losses of over 2 tons/acre due to over scalping varieties with taller or wider crowns. On the contrary, lower profile varieties often get very little scalping which increases the impurity aspect of those varieties and subsequently reduces recoverable sugar per ton. Research teams always try to minimize variables that can impact results. Our findings show that scalping variance can impact a research team’s ability to report the most accurate assessment of a variety’s genetic potential.