1. Relationships of Ginning Energy Use, Ginning Rate and Fiber Quality in Upland Cotton
ABSTRACT
RESULTS AND DISCUSSION
Cotton genotypes that gin faster and with less energy presumably gin more gently with less stress on the fiber and less damage. The objective of this research was to determine if ginning energy and ginning rate affect the fiber qualities of Upland cotton genotypes. Thirty four conventional and twelve transgenic genotypes were evaluated in 2008 and 2009 for ginning energy requirements, ginning rate and the association of these traits with fiber quality as determined by High Volume Instrument (HVI) and Advanced Fiber Information (AFIS) evaluations. The experiment was conducted at two sites near Stoneville, MS for both 2008 and Field plots were one row 12.2 m in length and 1.0 m between rows. Ginning energy was measured with a power meter attached to a 10-saw laboratory gin stand. Fiber properties were measured at STARLAB INC., Knoxville, TN, using HVI and at Stoneville Research Unit using AFIS analyses. Statistical analyses were performed using Proc GLM of the SAS system. Mean square values indicated that genotypic variances were high and significant for net energy, fiber strength and length, nep size and nep count, short fiber content and fineness. This is encouraging because this indicates that effective selections for these traits can be made in fewer environments. Genotypes that ginned faster and required less net energy to gin had, in general, lower nep size, nep count, and short fiber content when compared to genotypes that required higher net energy and ginned slower. Fast ginners, as a group, had higher fiber strength and fiber length than slow ginners.
. Mean square values indicated that genotypic variances were high and significant for net energy, ginning rate, fiber strength
and length, nep size, short fiber content and fineness.
. This is encouraging because this indicates that effective selections for these traits can be made in fewer environments.
. Genotypes that ginned faster and required less net energy to gin had, in general, lower nep size, seed coat nep, and short
fiber content.
. Fast ginners, as a group, had higher fiber strength and fiber length than slow ginners.
. Net energy was positively correlated with nep size, and seed coat nep size and number.
. Net ginning energy increased with fuzz %, while ginning rate decreased with fuzz %.
INTRODUCTION
Table 3. Pearson’s correlation coefficients between ginning energy, ginning rate and quality parameters
Genotypes with high fiber attachment strength tend to reduce gin productivity by increasing power requirements, slowing the system and increasing fiber damage as measured by short fiber content and neps. On the other hand, cotton genotypes that gin faster and with less energy gin more gently with less stress on the fiber and less damage.
OBJECTIVE
The objective of this research was to determine if ginning energy and ginning rate affect the fiber qualities of Upland cotton genotypes.
MATERIALS AND METHODS
. Thirty four conventional and twelve transgenic genotypes were evaluated in 2008 and 2009 for ginning energy
requirements, ginning rate and the association of these traits with fiber quality.
. The experiment was conducted at two sites near Stoneville, MS in both 2008 and 2009.
. Field plots were one row 12.2 m long and 1.0 m between rows, replicated 4 times.
. Ginning energy was measured with a power meter attached to a 10-saw laboratory gin stand.
. Fiber properties were measured at STARLAB INC., Knoxville, TN, using HVI and Stoneville Research Unit using AFIS analyses.
. Statistical analyses were performed using Proc GLM of the SAS system.
Figure 1. Relationship between net ginning energy and nep size for fuzzy seeded and naked seeded cultivars.
Table 1. Mean square values for ginning efficiency, HVJ and AFIS quality for 46 genotypes grown in 4 environments during 2008 and 2009 at Stoneville, MS.
Figure 2. Relationship between fuzz %, net ginning energy, and ginning rate for fuzzy seeded and naked seed cultivars.
Table 2. Summary of ginning energy, ginning rate, and quality data by groups.