Fiber Length Distribution in Cotton Processing: Dominant Features and                 Interaction Effects

Krifa, Mourad

doi:10.1177/0040517506062616

Cited by 57 publications

(52 citation statements)

References 16 publications

(19 reference statements)

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“…Our AFIS-determined fi ber length data are diff erent from commercial cotton production in that bolls in our study were hand-ginned, while most harvested cotton is sawginned. Histograms of the distribution of fi ber length of saw-ginned cotton are often bimodal, with a peak occurring in the short fi ber region and another peak near the mean fi ber length of the sample (Krifa, 2006). It is often assumed that the peak in the lower fi ber length is at least partially associated with fi bers broken during ginning.…”

Section: Resultsmentioning

confidence: 99%

A Comparison of Two Cotton Cultivars Differing in Maturity for Within‐Canopy Fiber Property Variation

et al. 2009

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A verage cotton (Gossypium hirsutum L.) fi ber physical properties are measured on every bale marketed in the United States by the USDA-Agricultural Marketing Service using highvolume instrumentation (HVI). Average fi ber properties provide critical information for successfully spinning cotton into yarn and providing a measure of potential yarn quality. Most agronomic research on assessing and improving fi ber properties has focused on improving average fi ber quality measures. Less attention has been paid to quantifying the amount and causes of variation of fi ber properties, although it has long been recognized that cotton fi bers are naturally variable (Balls, 1928). Reducing the variability for individual fi ber properties within bales appears important for further quality improvements of cotton crops. Lewis (1999) suggested that to improve cotton quality for modern spinning mill operations, as much attention should be paid to the amount of variability within a bale of cotton for fi ber length, strength, and micronaire as to the average values of these properties. ) in either year. Averaged over all mainstem node positions, DPL 555 had fi bers that were longer and fi ner than fi ber of PM 1218 in both years. PM 1218 had higher fi ber glucose and fi ber extract conductivity than DPL 555 among FP1 bolls both years. First-branchposition bolls that developed early during a prolonged rain-free period in 2005 had shorter fi bers that were coarser than bolls that began developing near the end of the rain-free period. The data suggest water-defi cit stress conditions during boll development affects fi ber length of these two cultivars similarly, but water-defi cit stress effect on fi ber secondary wall characteristics is genotype dependent. A Comparison of Two Cotton Cultivars

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Section: Resultsmentioning

confidence: 99%

A Comparison of Two Cotton Cultivars Differing in Maturity for Within‐Canopy Fiber Property Variation

et al. 2009

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“…Selection work establishing the importance of the AFIS length distribution in breeding for improved yarn quality was performed by Kelly et al (2013). If the differences of shape characteristics in the length distribution prove to contribute to better yarn quality, they could be quantified and targeted for improvement through breeding (Wakeham 1955;Krifa 2006;Kelly and Hequet 2017).…”

Section: Introductionmentioning

confidence: 99%

Stability, variation, and application of AFIS fiber length distributions

2020

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Background: Fiber length is one of the primary quality parameters for the cotton industry when considering the textile performance and end-use quality of cotton. Currently, many decisions regarding cotton fiber length utilize the industry standard measurement device, i.e., the High Volume Instrument (HVI). However, it is documented that complete fiber length distributions hold more information than the currently reported HVI length parameters, i.e., upper half mean length (UHML) and uniformity index (UI). An alternative measurement device, the Advanced Fiber Information System (AFIS), is able to capture additional information about fiber length distribution. What is currently not known is how much additional information the AFIS length distribution holds. Results: The stability of differences in within-sample variation in fiber length captured by the AFIS length distribution by number characterizing differences between samples was deemed stable across the extended testing period. A diverse breeding population was evaluated and four significant sources of within sample variation in length were identified. A comparison of the ability between HVI length parameters and AFIS fiber length distribution to correctly categorize breeding lines to their family was performed. In all cases, the AFIS fiber length distribution more accurately identified germplasm families. Conclusions: The long-term stability test of the AFIS fiber length distribution by number shows that the measurement is stable and can be used to assess differences across samples. However, more information about within-sample variation in fiber length than that can be captured by length parameters is needed to assess differences across samples in many applications. Four length parameters outperform two length parameters when trying to identify the familial background of the samples in this set. These parameters characterize distributional shape differences that are not captured by the standard AFIS length parameters, UQL and short fiber content by number (SFCn). These findings suggest that additional types of variation in cotton fiber length are not captured and are therefore not currently used in most cotton breeding programs.

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“…The results indicate very good agreement between the two different procedures. Additional computations produce fiber-length distributions having a bimodal structure and bimodal distributions are commonly seen in fiberlength data [77]. Results of an example calculation are illustrated in Fig.…”

Section: Cotton Fiber Breakagementioning

confidence: 94%

“…Fiber length is a good indicator of spinning efficiency, yarn strength, and yarn uniformity. Fiber length distribution is affected by breakage during processing [77,88]. In cotton processing, fiber breakage occurs in ginning and carding.…”

Section: Cotton Fiber Breakagementioning

confidence: 99%

Modeling with Itô Stochastic Differential Equations

Allen¹

2007

Mathematical Modelling: Theory and Applications

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This series is aimed at publishing work dealing with the definition, development and application of fundamental theory and methodology, computational and algorithmic implementations and comprehensive empirical studies in mathematical modelling. Work on new mathematics inspired by the construction of mathematical models, combining theory and experiment and furthering the understanding of the systems being modelled are particularly welcomed.Manuscripts to be considered for publication lie within the following, nonexhaustive list of areas: mathematical modelling in engineering, industrial mathematics, control theory, operations research, decision theory, economic modelling, mathematical programming, mathematical system theory, geophysical sciences, climate modelling, environmental processes, mathematical modelling in psychology, political science, sociology and behavioural sciences, mathematical biology, mathematical ecology, image processing, computer vision, artificial intelligence, fuzzy systems, and approximate reasoning, genetic algorithms, neural networks, expert systems, pattern recognition, clustering, chaos and fractals.Original monographs, comprehensive surveys as well as edited collections will be considered for publication.

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Fiber Length Distribution in Cotton Processing: Dominant Features and Interaction Effects

Cited by 57 publications

References 16 publications

A Comparison of Two Cotton Cultivars Differing in Maturity for Within‐Canopy Fiber Property Variation

A Comparison of Two Cotton Cultivars Differing in Maturity for Within‐Canopy Fiber Property Variation

Stability, variation, and application of AFIS fiber length distributions

Modeling with Itô Stochastic Differential Equations

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