Submitted to: Journal of Cotton Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 23, 2011
Publication Date: April 1, 2011
Citation: Bechere, E., Boykin Jr, J.C., Meredith Jr, W.R. 2011. Evaluation of cotton genotypes for ginning energy and ginning rate. Journal of Cotton Science. 15:11-21. Interpretive Summary: Efficient use of energy in the gins has become very important in the United States in light of the high cost of energy that has been evident in the last few years. Optimizing energy use is critical if the U.S.A. cotton producer has to successfully compete in the international market. The objectives of this research were (1) to identify genotypes that gin faster with reduced ginning energy requirements while maintaining fiber quality through the ginning process and (2) to evaluate the relationships between ginning energy, ginning rate, and various quality and physical cotton properties to aid in breeder variety selection and development. Forty-six conventional and transgenic genotypes were planted at two locations each in Stoneville, Mississippi during 2008 and 2009. Lint from fifty random bolls was hand-picked from each entry. Data on ginning energy requirement, rate of ginning, fiber quality, fuzz percent, lint percent, fiber per seed, and boll weight were collected. Ginning efficiency was based on measurements of ginning energy (watt hours/kg of lint) and ginning rate (gms lint/second)). Correlations between all parameters were conducted to establish the level of inter-dependence between them. Ginning rates among the tested varieties ranged from 2.37 to 3.35 gms lint/second. Net ginning energy for the tested cultivars ranged from 30.1 to 47.8 wh/kg lint. The cultivars with the low energy/fast ginning requirements, as a group, took about 14% less energy and ginned 19% faster than the cultivars in the high energy/slow ginner group. This group also had lower fiber strength, fiber length, nep size, nep number, short fiber content, and higher fineness than the high energy, low ginning rate group. Net ginning energy was positively correlated with fuzz percent, boll weight, fiber strength, and fiber length. Ginning energy was also positively associated with AFIS (Advanced Fiber Information System) nep size, seed coat nep (SCN) size, SCN count. The strongest positive trend was with nep count (0.07**), where more energy was required for the saw to pass through and remove entangled fibers. Fuzz percent and nep count were two components that negatively affected ginning rate. The more fuzz on the seed and neps in the lint, the slower the cotton gin will turn. Strong and positive associations between ginning rate and fibers per seed, lint percent, boll weight, fiber strength and AFIS maturity ratio were also observed. Significant variability for ginning efficiency (lower ginning energy and faster ginning) was observed among the tested genotypes. This diversity can be utilized more effectively by breeding this trait into more productive genotypes.
Technical Abstract: Reducing ginning energy use through cultivar improvement could reduce ginning and energy cost. The objective of this study was to detect genetic variability for ginning energy and ginning rate. Thirty four conventional and twelve transgenic genotypes were evaluated in 2008 and 2009 for ginning energy requirements and ginning time rate. The genotypes were selected because of their diverse breeding backgrounds and phenotypic differences for yield components and fiber traits. The experiments were conducted at two sites near Stoneville, Mississippi for both 2008 and 2009. Field plots were one row 12.2 m in length and 1.0 m between rows. Ginning efficiency was based on measurements of ginning energy (Wh kg-1 lint) and ginning rate (g lint sec.-1). The Generalized Linear Model Procedure (GLM) in the Statistical Analysis System Program (SAS) was used to analyze the data and Pearson’s Correlation Coefficient was used to estimate the level of inter-dependence between all measured traits. Large differences in both ginning energy and ginning rate were detected. The mean square values for genotypes were highly significant for all traits studied. The two genotypes with least ginning energy were ‘AR 9317-26’ and ‘Yugo 8’ with average ginning energy of 30.1 and 31.6 Wh kg-1 lint, respectively. The fastest ginners were ‘MD 25’ and ‘FiberMax 960 B2R’ with 3.35 and 3.32 g sec.-1 lint, respectively. There was no detectable relationship between ginning energy and ginning rate (r = -0.15). Five traits that were highly correlated with ginning energy were fuzz percent, fiber strength, fiber length, neps, and fineness. Five traits that were highly correlated with ginning rate were fuzz percent, fibers seed-1 , lint percent, boll weight, and neps. Correlation between net energy and fuzz percent decreased from 0.62 to 0.32 when the semi-naked seed genotypes were removed from the group. Because of the ease in measurement, the correlations of fuzz percent with ginning energy, r=0.62, and ginning rate, r=-0.40, appears to be useful tools in improving overall ginning efficiency. These evaluations show great potential for reducing energy and ginning time rate.