Fiber Maturity and Source-Sink Manipulation:

    Variations in fiber maturity were linked with source-sink modulations related to flowering date , and seed position within the bolls . However, manipulation of source-sink relationships by early-season square (floral bud) removal had no consistently significant effect on upland cotton micronaire in one study . However, selective square removal at the first, second, and third fruiting sites along the branches increased micronaire, compared with controls from which no squares had been removed beyond natural square shedding . The increases in micronaire after selective square removals were associated with increased fiber wall thickness, but not with increased strength of elongation percent. Early-season square removal did not affect fiber perimeter or wall thickness (measured by arealometer) . Partial defruiting increased micronaire and had no consistent effect on upland fiber perimeter in bolls from August flowers.

Fiber Maturity and Water:

    Generous water availability can delay fiber maturation (cellulose deposition) by stimulating competition for assimilates between early-season bolls and vegetative growth . Adequate water also can increase the maturity of fibers from mid-season flowers by supporting photosynthetic C fixation. In a year with insufficient rainfall, initiating irrigation when the first-set bolls were 20-d old increased micronaire, but irrigation initiation at first bloom had no effect on fiber maturity.  Irrigation and water-conservation effects on fiber fineness (millitex) were inconsistent between years, but both added water and mulching tended to increase fiber fineness. Aberrations in cell-wall synthesis that were correlated with drought stress have been detected and characterized by glycoconjugate analysis .

    An adequate water supply during the growing season allowed maturation of more bolls at upper and outer fruiting positions, but the mote counts tended to be higher in those extra bolls and the fibers within those bolls tended to be less mature . Rainfall and the associated reduction in insolation levels during the blooming period resulted in reduced fiber maturity . Irrigation method also modified micronaire levels and distributions among fruiting sites.

    Early-season drought resulted in fibers of greater maturity and higher micronaire in bolls at branch positions 1 and 2 on the lower branches of rainfed plants. However, reduced insolation and heavy rain reduced micronaire and increased immature fiber fractions in bolls from flowers that opened during the prolonged rain incident. Soil water deficit as well as excess may reduce micronaire if the water stress is severe or prolonged .

Fiber Maturity and Genetic Improvement:

    Micronaire or maturity data now appear in most cotton improvement reports . In a five-parent half-diallel mating design, environment had no effect on HVI micronaire . However, a significant genotypic effect was found to be associated with differences between parents and the F1 generation and with differences among the F1 generation. The micronaire means for the parents were not significantly different, although HVI micronaire means were significantly different for the F1 generation as a group. The HVI was judged to be insufficiently sensitive for detection of the small difference in fiber maturity resulting from the crosses.
    In another study, F2 hybrids had finer fibers (lower micronaire) than did the parents, but the improvements were deemed too small to be of commercial value.  Unlike the effects of environment on the genetic components of other fiber properties, variance in micronaire due to the genotype-by-environment interaction can reach levels expected for genetic variance in length and strength . Significant interactions were found between genetic additive variance and environmental variability for micronaire, strength, and span length in a study of 64 F2 hybrids .

    The strong environmental components in micronaire and fiber maturity limit the usefulness of these fiber properties in studies of genotypic differences in response to growth environment. Based on micronaire, fiber maturity, cell-wall thickness, fiber perimeter, or fiber fineness data, row spacing had either no or minimal effect on okra-leaf or normal-leaf genotypes . Early planting reduced micronaire, wall-thickness, and fiber fineness of the okra-leaf genotype in one year of that study. In another study of leaf pubescence, nectaried vs. no nectaries, and leaf shape, interactions with environment were significant but of much smaller magnitude than the interactions among traits .
    Micronaire means for Bt transgenic lines were higher than the micronaire means of Coker 312 and MD51ne when those genotypes were grown in Arizona . In two years out of three, micronaire means of all genotypes in this study, including the controls, exceeded 4.9; in other words, were penalty grade. This apparent undesirable environmental effect on micronaire may have been caused by a change in fiber testing methods in the one year of the three for which micronaire readings were below the upper penalty limit. Genotypic differences in bulk micronaire may either be emphasized or minimized, depending on the measurement method used .

    In U.S. cotton classing, nonmandatory grade standards were first established in 1909, but compulsory upland grade standards were not set until 1915 . Official pima standards were first set in 1918. Grade is a composite assessment of three factors - color, leaf, and preparation . Color and trash (leaf and stem residues) can be quantified instrumentally, but traditional, manual cotton grade classification is still provided by USDA-AMS in addition to the instrumental HVI trash and color values. Thus, cotton grade reports are still made in terms of traditional color and leaf grades; for example, light spotted, tinged, strict low middling.


    There is no approved instrumental measure of preparation - the degree of roughness/smoothness of the ginned lint. Methods of harvesting, handling, and ginning the cotton fibers produce differences in roughness that are apparent during manual inspection; but no clear correlations have been found between degree of preparation and spinning success. The frequency of tangled knots or mats of fiber (neps) may be higher in high-prep lint, and both the growth and processing environments can modulate nep frequency . However, abnormal preparation occurs in less than 0.5% of the U.S. crop during harvesting and ginning.

Trash or Leaf Grade:

    Even under ideal field conditions, cotton lint becomes contaminated with leaf residues and other trash . Although most foreign matter is removed by cleaning processes during ginning, total trash extraction is impractical and can lower the quality of ginned fiber. In HVI cotton classing, a video scanner measures trash in raw cotton, and the trash data are reported in terms of the total trash area and trash particle counts (ASTM, D 4604-86, D 4605-86). Trash content data may be used for acceptance testing. In 1993, classer's grade was split into color grade and leaf grade . Other factors being equal, cotton fibers mixed with the smallest amount of foreign matter have the highest value. Therefore, recent research efforts have been directed toward the development of a computer vision system that measures detailed trash and color attributes of raw cotton .

    The term leaf includes dried, broken plant foliage, bark, and stem particles and can be divided into two general categories: large-leaf and pin or pepper trash . Pepper trash significantly lowers the value of the cotton to the manufacturer, and is more difficult and expensive to remove than the larger pieces of trash.Other trash found in ginned cotton can include stems, burs, bark, whole seeds, seed fragments, motes (underdeveloped seeds), grass, sand, oil, and dust. The growth environment obviously affects the amount of wind-borne contaminants trapped among the fibers. Environmental factors that affect pollination and seed development determine the frequency of undersized seeds and motes.

    Reductions in the frequencies of motes and small-leaf trash also have been correlated with semi-smooth and super-okra leaf traits . Environment (crop year), harvest system, genotype, and second order interactions between those factors all had significant effects on leaf grade . Delayed harvest resulted in lower-grade fiber. The presence of trash particles also may affect negatively the color grade.

Fiber Color:

    Raw fiber stock color measurements are used in controlling the color of manufactured gray, bleached, or dyed yarns and fabrics .  Of the three components of cotton grade, fiber color is most directly linked to growth environment. Color measurements also are correlated with overall fiber quality so that bright (reflective, high Rd), creamy-white fibers are more mature and of higher quality than the dull, gray or yellowish fibers associated with field weathering and generally lower fiber quality . Although upland cotton fibers are naturally white to creamy-white, pre-harvest exposure to weathering and microbial action can cause fibers to darken and to lose brightness.

    Premature termination of fiber maturation by applications of growth regulators, frost, or drought characteristically increases the saturation of the yellow (+b) fiber-color component. Other conditions, including insect damage and foreign matter contamination, also modify fiber color.   

The ultimate acceptance test for fiber color, as well as for finished yarns and fabrics, is the human eye. Therefore, instrumental color measurements must be correlated closely with visual judgment. In the HVI classing system, color is quantified as the degrees of reflectance (Rd) and yellowness (+b), two of the three tri-stimulus color scales of the Nickerson-Hunter colorimeter.

    Fiber maturity has been associated with dye-uptake variability in finished yarn and fabric, but the color grades of raw fibers seldom have been linked to environmental factors or agronomic practices during production. 
Other Environmental Effects on Cotton Fiber Quality:

    Although not yet included in the USDA-AMS cotton fiber classing system, cotton stickiness is becoming an increasingly important problem . Two major causes of cotton stickiness are insect honeydew from whiteflies and aphids and abnormally high levels of natural plant sugars, which are often related to premature crop termination by frost or drought. Insect honeydew contamination is randomly deposited on the lint in heavy droplets and has a devastating production-halting effect on fiber processing.

    The cost of clearing and cleaning processing equipment halted by sticky cotton is so high that buyers have included honeydew free clauses in purchase contracts and have refused cotton from regions known to have insect-control problems. Rapid methods for instrumental detection of honeydew are under development for use in classing offices and mills .


    Like all agricultural commodities, the value of cotton lint responds to fluctuations in the supply-and-demand forces of the marketplace.  In addition, pressure toward specific improvements in cotton fiber quality - for example, the higher fiber strength needed for today's high-speed spinning - has been intensified as a result of technological advances in textile production and imposition of increasingly stringent quality standards for finished cotton products.

    Changes in fiber-quality requirements and increases in economic competition on the domestic and international levels have resulted in fiber quality becoming a value determinant equal to fiber yield . Indeed, it is the quality, not the quantity, of fibers ginned from the cotton seeds that decides the end use and economic value of a cotton crop and, consequently, determines the profit returned to both the producers and processors.
    Wide differences in cotton fiber quality and shifts in demand for particular fiber properties, based on end-use processing requirements, have resulted in the creation of a price schedule, specific to each crop year, that includes premiums and discounts for grade, staple length, micronaire, and strength . This price schedule is made possible by the development of rapid, quantitative methods for measuring those fiber properties considered most important for successful textile production . With the wide availability of fiber-quality data from HVI, predictive models for ginning, bale-mix selection, and fiber-processing success could be developed for textile mills .
    Price-analysis systems based on HVI fiber-quality data also became feasible . Quantitation, predictive modeling, and statistical analyses of what had been subjective and qualitative fiber properties are now both practical and common in textile processing and marketing.

     Field-production and breeding researchers, for various reasons, have failed to take full advantage of the fiber-quality quantitation methods developed for the textile industry. Most field and genetic improvement studies still focus on yield improvement while devoting little attention to fiber quality beyond obtaining bulk fiber length, strength, and micronaire averages for each treatment . Indeed, cotton crop simulation and mapping models of the effects of growth environment on cotton have been limited almost entirely to yield prediction and cultural-input management.

         Plant physiological studies and textile-processing models suggest that bulk fiber-property averages at the bale, module, or crop level do not describe fiber quality with sufficient precision for use in a vertical integration of cotton production and processing. More importantly, bulk fiber-property means do not adequately and quantitatively describe the variation in the fiber populations or plant metabolic responses to environmental factors during the growing season. Such pooled or averaged descriptors cannot accurately predict how the highly variable fiber populations might perform during processing.

    Meaningful descriptors of the effects of environment on cotton fiber quality await high-resolution examinations of the variabilities, induced and natural, in fiber-quality averages. Only then can the genetic and environmental sources of fiber-quality variability be quantified, predicted, and modulated to produce the high-quality cotton lint demanded by today's textile industry and, ultimately, the consumer.

    Increased understanding of the physiological responses to the environment that interactively determine cotton fiber quality is essential. Only with such knowledge can real progress be made toward producing high yields of cotton fibers that are white as snow, as strong as steel, as fine as silk, and as uniform as genotypic responses to the environment will allow.

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