HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Gardunia, B. W. Articles by Smith, C. W. PubMed Articles by Gardunia, B. W. Articles by Smith, C. W. Agricola Articles by Gardunia, B. W. Articles by Smith, C. W. Related Collections Statistics Cotton

Applying Quantile Regression to Analysis of AFIS Cotton Fiber Distribution

^a Ag Alumni Seed Improvement Association, P.O. Box 158, Romney, IN 47981
^b Helena Chemical, San Angelo, TX
^c International Textile Center, Texas Tech University, Lubbock, TX
^d Texas A&M University, Dep. of Soil and Crop Sciences, College of Biology and Agriculture. College Station, TX

^* Corresponding author (gardunia{at}agalumniseed.com).

Varying fiber length distributions of cotton, Gossypium hirsutum L., impacts its spinning performance. Advanced Fiber Information System (AFIS) facilitates the analysis of the length distribution of individual fibers in cotton. Quantile regression is a variant of standard regression with which conditional quantile values can be calculated by minimizing weighted sums of absolute deviations across the entire distribution. Quantile regression was used to analyze AFIS fiber length distribution among five genotypes of upland cotton grown at the Texas Agricultural Experiment Station Research Farm near College Station, TX during 2001 and 2002. The shape of the distribution of ‘CAMD-E’, a short-staple variety, was actually similar to ‘Acala 1517-99’, a long staple variety with good spinning quality, even though CAMD-E had consistently lower fiber lengths. ‘FM 832’, and ‘TAM 94L-25’ had similar mean fiber lengths to Acala 1517-99, but their distribution shape was less skewed. ‘TTU 202’ had high cross entropy values, but little difference was detected in distribution shape by quantile regression. Year had a significant impact on distribution of fiber lengths, affecting distribution scale and location, which may be due to lower fiber fineness and maturity in 2001. Quantile regression was found to be an effective method for analyzing AFIS fiber length distributions, although further testing with a larger set of genotypes and environments with spinning data is needed.

Abbreviations: AFIS, Advanced Fiber Information System • HVI, high volume instruments • IFC, immature fiber content • Ln, length by number • Lw, length by weight • MAT, fiber maturity • UQLw, upper quartile length by weight

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Received for publication March 24, 2008.