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

This Article Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Wu, J. Articles by Watson, C. E. Search for Related Content PubMed Articles by Wu, J. Articles by Watson, C. E. Agricola Articles by Wu, J. Articles by Watson, C. E. Related Collections Statistics Crop Ecology Crop Genetics Other Crop Management Cotton Experiment Design Biometrics

Cotton

Comparisons of Two Statistical Models for Evaluating Boll Retention in Cotton

^a Dep. of Plant and Soil Sci., Mississippi State Univ., Mississippi State, MS 39762
^b Crop Sci. Res. Lab., USDA-ARS, Mississippi State, MS 39762
^c MAFES Administration, Mississippi State Univ., Mississippi State, MS 39762

^* Corresponding author (jnjenkins{at}msa-msstate.ars.usda.gov)

Received for publication August 13, 2004. Boll number is one of the most important traits related to yield of upland cotton (Gossypium hirsutum L.). Evaluation of boll retention properties at different fruiting sites would provide useful information for cotton breeding and cotton growth management. The presence or absence of a boll at each fruiting position can be considered as binomially distributed. In this study, 188 upland cotton recombinant inbred (RI) lines, two parental lines, and a control cultivar, Stoneville 474, were used. These lines were planted at Mississippi State, MS in 1999. The data set was analyzed by the mixed linear model and logistic regression model. The results showed that the boll retention for the first position was significantly different among nodes but expressed similar total numbers from the first position among RI lines. Estimates for boll retention were similar for both models; however, the logistic regression model gave smaller confidence intervals for each estimate than the mixed linear model.

Abbreviations: CIL, confidence interval length • RI, recombinant inbred (lines)