Submitted to: Journal of Cotton Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/29/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary: At U.S. cotton gins, ginning rate has been shown to be related to various fiber quality attributes. The varieties of cotton also play a role. From our studies, we've found that slower ginning rate was associated with extraneous matter and percentage of lint in a cotton tuft—often linked to smaller seeds and other factors like color are associated with improved ginning rate. We created parametric models to help predict the ginning rate (measured in bales per hour) based on these lint quality and variety characteristics, although the precision can vary. To make things easier for farmers and gin operators, a user-friendly ginning rate calculator was developed. This handy tool can help the ginners and farmers to understand the ginning rate for the cotton variety they are working with thereby justifying the variable ginning rates.

Technical Abstract: In this project, an extensive data analysis was performed using the data collected from 8 commercial gins to better understand the relationship between variety and quality attributes and gin performance and develop a generalizable model to predict ginning rate. Although the model was unable to explain most of the variance of commercial time from module cut to bale, results quantified impact of fiber and variety characteristics on ginning rate. Post-ginning fiber quality attributes extraneous matter negatively influenced ginning, micronaire positively influenced ginning rate (within the range sampled), and reflectance (RD), and yellowness (PlusB or +b) positively influenced ginning rate conditioned by a negative (antagonistic) interaction between the two for ginning rate. Variety characteristics include Lint Percent (LP, associated with smaller seeds) and Bract Trichomes (BTri) negatively influenced ginning rate while Fiber density (FDen) positively influenced it. A causal model of variety characteristics was developed for a larger-seeded variety reputed to be slow ginning that supported an explanation of lower fiber density and fewer fibers per seed. Other findings include that there are variety-brand specific associations with ginning rate unrelated to LP, Btri and FDen, there is a steep drop in the ginning rate at moisture greater than 8.5%; the initial weeks of ginning have a slower ginning rate compared to the latter part of the ginning season. While this research may be useful to quantify how crop management, harvest and seed-selection factors influence ginning rate, any model to effectively predict ginning rate ought to factor in gin operations characteristics.