Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: January 8, 2003
Publication Date: January 8, 2003
Agronomic and fiber quality cotton (Gossypium hirsutum L.) Traits were collected and evaluated from generation F2.3 (field progeny rows). F2.4 (greenhouse single plants), F2.5 (field single plants), and F2.6 (field progeny rows) on a population of 208 families, developed by a cross of A1006 x ¿MD51ne¿ (A1006 known as ¿Fiber Max 832"). Generations were advanced from F2.3 to F2.6 by the single seed descent method. We used SAS PROC GLM and PROC CORR to perform the analyses of variance and mean separations, and for the association of the above traits within and across generations, respectively. Thirty-four traits were assessed, and only ten have been discussed herein. Lint percentage and boll weight were correlated with all agronomic and fiber quality traits in at least one generation, except for fiber elongation. In addition, lint percentage and boll weight were correlated across generations, indicating some level of association among different environments of collected data. For fiber quality traits, Starlab, Inc. Conventional measurements were for the most part correlated with measurements taken by the Advanced Fiber Information System (AFISR Uster), e.g., 2.5% fiber span length from Starlab and AFIS with r = 0.851 and r = 0.899 for generation F2.3 and F2.6, respectively. A smaller number of trait measurements, from the AFIS multidata measurement module, was correlated with agronomic traits within generations. Across generations, AFIS analyzed measurements were also less correlated, possibly detecting differences between environments, and/or indicating less inheritable traits. The greenhouse fiber data from F2.4 generation was found to possess the highest values for neps (147.8±49.6), seed coat (14.9±6.4), and percentage of immature fiber content (7.7±4.9). Multiple traits can be correlated due to linkage or pleiotropy; or the correlated traits may be components of a more complex variable. The above trait correlation may be useful in developing selection criteria to simultaneously improve yield and fiber quality traits.