MR1-Hequet

Brasilia, 3-6 Setembro 2013

Mesa Redonda 1 – Fibra do Futuro THE FUTURE OF COTTON FIBERS: BREEDING FOR IMPROVED PROCESSABILITY AND END-PRODUCT QUALITY Eric F. Hequet; Brendan Kelly Fiber and Biopolymer Research Institute; Plant and Soil Science Department Texas Tech University- Lubbock, Texas- U.S.A.

Upland cotton, Gossypium hirsutum L., ranks fourth in planted acreage in the United States,

behind corn, wheat, and soybeans. In response to the demand for cotton fabric, worldwide consumption of cotton fiber has more than doubled from 1960 to 2011 (Figure 1). Though cotton fiber consumption has increased, cotton has lost half its market shares to competition from synthetic fibers [1].To improve its competitiveness as compared with man-made fibers; cotton fiber must exhibit reduced variability so that it may perform more predictably at the mill. This can be achieved by breeding for an improved distribution in fiber quality using non-HVI fiber properties.

Figure 1. Cotton fiber market shares vs. non-Cotton fiber alternatives [3].

While consumers demand cotton yarns and fabrics, variability in cotton fiber quality makes it a challenging natural raw material to transform into a consistent industrial product. Natural variability in cotton fiber quality can translate into imperfections in spun yarns [2-5]. Imperfections in the yarns, in turn, result in imperfection in the finished textiles. In addition to impacting the value of finished yarns and textiles, variability in cotton fiber negatively impacts processing ability [6]. Indeed, yarn imperfections translate into weak points which increase yarn breakages and lower productivity at the mill.

Developing cotton varieties with improved spinning performance and yarn quality poses a formidable challenge. Spinning trials demand a great deal of time and money, making them impractical in a sizeable breeding program. Therefore, breeding lines are not screened based on


their spinning performance. Instead, breeders interested in selecting cotton varieties with improved spinning performance make their selections indirectly, based on fiber quality parameters. The most common source of fiber quality parameters is the High Volume Instrument (HVI). HVI is a classification tool originally developed to replace hand classers in cotton marketing. Despite their origins in cotton marketing, HVI fiber quality parameters are used as an evaluation tool in most breeding programs worldwide. HVI results are popular because the test is relatively fast and inexpensive. However, selections based on fiber quality parameters should be done with the aim of improving yarn quality. It is important to ask if fiber quality parameters provided by HVI testing are adequate for selecting elite cotton lines for improved spinning performance. While HVI measurements are fast, they cannot characterize the important within-sample variations in cotton fiber quality. Yet, capturing within-sample variability of a bale is critical for predicting spinning performance [7]. In effect, high-speed HVI fiber quality assessment is achieved at the expense of a more complete characterization of within-sample variability of fiber quality.

While most breeders depend on HVI fiber quality parameters exclusively, many spinning mills have long known of the need for distributional data to augment the HVI data [7]. The main tool for measuring fiber-to-fiber variability is the Advanced Fiber Information System (AFIS). AFIS evaluates the distributions of fiber quality parameters within a loose sample of aligned fibers called a sliver. A sliver is fed into the AFIS which individualizes fibers by pulling them from the sliver with the teeth of a spinning rotor. The individualized fibers are drawn into an airflow which presents each fiber to a series of electro-optical sensors for quality assessment. The signals generated as each fiber passes in front of the sensors are used to assess the length, maturity, and linear density of the fibers. AFIS also characterizes within sample variability of nep, maturity and fineness. During individualization, contaminants in the sample that are heavier and aerodynamically dissimilar to fibers are drawn into a second airflow for characterization. The within sample variability of each fiber property is then summarized by AFIS in a set of fiber quality parameters and individual histograms. Hence, AFIS provides a much more complete characterization of fiber quality within the sample than HVI. AN ILLUSTRATION USING FIBER LENGTH

Three commercial cotton bales were selected to demonstrate the difference between AFIS and HVI measurements [Figure 2]. A sample from each of these three bales was evaluated with HVI and AFIS. HVI classification indicates all three bales have an identical Upper Half Mean Length (1.1inch) and Uniformity Index (81.3%). However, the within sample distribution of fiber length provided by AFIS provides a completely different picture. AFIS results reveal that HVI does not adequately characterize the large differences in fiber length distributions within each bale. Bale 1 and Bale 3 both have a higher short fiber content (SFC) and lower average fiber length when compared to Bale 2. To produce more premium yarns, the shorter fibers must be combed out, making Bale 1 and Bale 3 less desirable in premium spinning markets.


Figure 2. AFIS length-by-number distribution of three commercial bales exhibiting identical HVI length measurements (Upper Half Mean Length = 1.1in; Uniformity Index = 81.3). IMPLICATIONS FOR COTTON BREEDING PROGRAMS

Cotton producers require varieties which have the potential to produce fiber of a quality that enables them to sell into the highest-valued markets possible. The basic objective of this paper is to present the limitations of bundle testing while highlighting the efficiency of alternative fiber quality evaluation systems in breeding programs. This will be done through one example designed to reveal the practical advantages and limitations of augmenting HVI data with additional fiber measurements. The efficacy of fiber quality evaluation systems for improving yarn quality.

In this example, 110 cotton bales were selected to represent a wide range in variability of fiber quality within and between bales. The commercial bales represent several years and locations from across the United States Cotton Belt. Each commercial bale was tested on HVI to obtain the standard bundle properties. In addition to HVI testing, each bale was tested for within-sample variability in fiber quality on the AFIS. The tests confirmed that the bales covered a wide range of fiber properties. Each bale was spun into carded 30Ne ring spun yarns, which were tested for tensile properties on the Uster Tensorapid and for evenness and imperfections on the Uster Tester 3.

The relationship between the fiber quality profile for each bale and yarn quality produced was investigated with a partial least squares regression (PLSR). First, the fiber quality attributes were grouped into two subsets. The first subset, HVI, is composed of the most commonly used HVI

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fiber quality parameters. The second subset, HVI&AFIS, also includes the basic HVI parameters with the addition of AFIS fiber quality parameters. Each of the two fiber quality subsets were then used to separately characterize the fiber and yarn quality complex.

Because HVI data are so widely available, both regression models of the fiber and yarn quality complex include HVI fiber quality parameters as predictor variables. In this way, any differences in the two models must be attributed to the addition of non-HVI fiber quality parameters in the second model.

The amount of variation in yarn tensile properties explained by both sets of fiber qualities is compared in Figure 3, where the improvements provided by the non-HVI fiber properties are apparent. There are clear differences in the performance of HVI and non-HVI fiber quality parameters when predicting yarn tensile properties. The model constructed with HVI bundle parameters characterizes anywhere from 61% of the variation in yarn elongation to 72% of the variation in yarn strength. However, augmenting the model with non-HVI fiber qualities provided by AFIS helps explain from 80% of variation in yarn elongation, up to 87% of variation in yarn strength. The augmented model explains 31% more variation in yarn elongation than the traditional HVI classification parameters. This translates into 76% of the variation explained in yarn work-to-break.

Figure 3: Explained variation in yarn tensile properties. R2 of 1 indicates the fiber quality parameters used in the model explain all of the variability observed.

The differences are even larger when considering explained variation in yarn imperfections (Figure 4). The model constructed with HVI fiber quality parameters alone fails to explain even 50% of the total variation of two critical yarn imperfection parameters, thick places and neps. In contrast, the model augmented with non-HVI fiber quality attributes explains 82.8% of the variation in yarn CVm% and at least 78% of the remaining yarn imperfection parameters considered in this study. The model augmented with non-HVI fiber qualities explains 79% more of the variation in yarn neps over traditional HVI parameters.


Figure 4: Explained variation in yarn imperfections. R2 of 1 indicates the fiber quality parameters used in the model explain all of the variability observed.

This model could be a very valuable tool for developing the cotton of the future. Indeed, breeders and biotechnologist could use the model to investigate the potential impact of modifying a single fiber quality attribute on ring spinning performance. To illustrate that possibility, the model is used to characterize the potential impact of modifying fiber standard fineness (equivalent to biological fineness) on two yarn properties, tenacity and number of thin places, in Figures 5 and 6. A known cotton with relatively poor fiber length distribution (HVI UHML = 1.09, Uniformity Index = 78.3) is used in this example. The model reveals that standard fineness (fiber diameter) has a very large impact on yarn tenacity, i.e., smaller fiber diameter leads to stronger yarns while reducing the number of thin places in the yarn.

Figure 5: Impact of standard fineness and maturity ratio on yarn tenacity (ring spun yarn 30Ne). The grey line represents the Uster Statistics 50% level for a ring spun 30Ne carded with a knitting twist.


Figure 6: Impact of standard fineness and maturity ratio on yarn thin places (ring spun yarn 30Ne). The grey line represents the Uster Statistics 50% level for a ring spun 30Ne carded with a knitting twist. Cotton Fiber Phenomics

Scientific investigation of the cotton genome is expanding rapidly. The recent release of the genome for the New World species Gossypium raimondii is just the beginning of a larger project to characterize the cotton genome of upland cotton [8, 9]. In marker assisted selection, swaths of plants are screened based on genetic markers that are tightly linked to a trait of interest. Genetic markers for fiber quality are mostly based on HVI fiber quality attributes. We have seen that those are not good estimates of cotton spinning potential. Therefore, in order to take advantage of the progresses in genomics, genetic markers for AFIS fiber properties and more detailed fiber quality attributes are needed. Biotechnology has provided farmers with cotton varieties that express novel agronomic traits such as insect resistance and herbicide tolerance. In the future, non-native genes may be introduced into cotton plants that provide value-added quality to the fiber itself. While primarily academic, a melanin gene [10] and Green Florescent Protein (GFP) [11] have both been used to express novel colors in cotton fibers. In the future, genes may be introduced that increase fiber strength, elongation, or introduce novel fiber characteristics desirable in high-value markets. However, the introduction of a single novel trait may impact the whole array of fiber quality attributes and influence spinning performance. Therefore, selecting elite fiber quality events depends on understanding the impact of the trait on target markets. Fiber quality phenomics may provide the industry with the proper tools for understanding how variations in fiber quality impact the value of products downstream.


Conclusion This paper has shown the following:

HVI fiber properties may not be sufficient for detecting substantial differences in spinning performance. HVI fiber properties data should be augmented with non-HVI fiber properties in order to select lines that perform well in high-value spinning markets.

Including AFIS fiber quality parameters provides a substantial improvement over HVI classification alone for screening breeding lines. AFIS parameters are able to increase the amount of explained variation in yarn tensile properties and in yarn evenness.

New phenotyping tools are needed for developing the cotton of the future. Advanced fiber quality phenotyping tools of the future must: o Expediently assess fiber quality performance in existing markets. o Characterize the effects of transgenic events aimed at augmenting fiber quality. o Predict cotton fiber performance of fiber quality in future high-value markets.

The experiment related here is part of a large and growing body of data showing that non-HVI fiber property measurements are needed to achieve future genetic breakthroughs in fiber quality. These breakthroughs will be necessary to strengthen cotton’s competitiveness vis a vis the large and growing array of synthetic fibers vying to serve the global yarn spinning industry. Acknowledgement The authors would like to thank Cotton Incorporated and the Plains Cotton Improvement Committee (Plains Cotton Growers Inc.) for their financial support. REFERENCES [1] International Cotton Advisory Committee (2013). World Textile Demand. http://www.icac.org/login?url=%2Fpubdetail.php%3Fid%3DP0000102, ISBN: 9780979390333 [2] El Mogahzy, Y. E., Broughton, R., and Lynch, W. K., (1990). A Statistical Approach for Determining the Technological Value of Cotton Using HVI Fiber Properties. Textile Research Journal, 60(9), 495-500 [3] El Sourady, A. S., Worley, S., Stith, JR, Stith, L. S., (1974). The Relative Contributions of Fiber Properties to Variations in Yarn Strength in Upland Cotton, Gossypium hirsutum L. Textile Research Journal. 44, 301-306 [4] Hertel, K. L., & Craven, C. J., (1956). Cotton Fiber Bundle Elongation and Tenacity as Related to Some Fiber and Yarn Properties. Textile Research Journal, 26(6), 479-484 [5] Meredith, W. R., Culp, T. W., Robert, K. Q., Ruppenicker, G. F., Anthony, W. S., and Williford, J. R., (1991). Determining Future Cotton Variety Fiber Quality Objectives. Textile Research Journal, 61(12), 715-720

http://www.icac.org/login?url=%2Fpubdetail.php%3Fid%3DP0000102


[6] Backe, E., (1986). Effect of Short Fiber Content in Cotton on Plant Performance and Quality. Textile Research Journal. 56. 112-115 [7] Wakeham, H. (1955). Cotton Fiber Length Distribution -- An Important Quality Factor. Textile Research Journal. 25, 422-429 [8] Wang, K., Wang, Z., Li, F., Ye, W., Wang, J., Song, G., Yue, Z., Cong, L., Shang, H., Zhu, S., Zou, C., Li, Q., Yuan, Y., Lu, C., Wei, H., Gou, C., Zheng, Z., Yin, Y., Zhang, X., Liu, K., Wang, B., Song, C., Shi, N., Kohel, R., Percy, R., Yu, J., Zhu, Y., Wang, J., and Yu, S., (2012). The draft genome of a diploid cotton Gossypium Raimondii. Nature. 44. 1098-1104. [9] Paterson A.H. et al., (2012). Repeated polyploidization of Gossypium genomes and the evolution of spinnable cotton fibers. Nature. Vol. 492. 423-428 [10] Xu, X., Wu, M., Zhao, Q., Li, R., Chen, J., Ao, G., and Yu, J., (2007). Designing and Transgenic Expression of Melanin in Tobacco Trichome and Cotton Fiber. Plant Biol. 9. 41-48 [11] Sunikumar, G., Mohr, L., Lopata-Finch, E., Emani, C., and Rathore, K., (2002) Developmental and tissue-specific expression of CaMV 35s promoter in cotton as revealed by GFP. Plant Molecular Biology. 50. 463-474.

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