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 Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Hooks, T. Articles by Vogel, K. P. Search for Related Content PubMed Articles by Hooks, T. Articles by Vogel, K. P. Agricola Articles by Hooks, T. Articles by Vogel, K. P. Related Collections Sorghum Data Management Plant Genetic Resources

PLANT GENETIC RESOURCES

Variation in the U.S. Photoperiod Insensitive Sorghum Collection for Chemical and Nutritional Traits

^a Dep. of Statistics, Univ. of Nebraska-Lincoln, Lincoln, NE 68583
^b USDA-ARS, NPA Wheat, Sorghum and Forage Research, 344 Keim Hall, Univ. of Nebraska-Lincoln, Lincoln, NE 68583-0937

^* Corresponding author (jfp{at}unlserve.unl.edu)

Screening germplasm for chemical and nutritional content can be expensive and time consuming. Near infrared spectroscopy (NIRS) and application of geostatistical models can make screening more efficient. The objectives of this study were to utilize these technologies to: (i) generate chemical and nutritional values for the U.S. photoperiod insensitive sorghum collection, (ii) describe variability for those traits, (iii) identify accessions in the highest and lowest 1% for each trait, and (iv) describe relationships among the accessions. Accessions were grown at Ithaca, NE, during 2001 and 2002. Samples of grain were scanned and NIRS equations developed for starch, fat, crude protein, acid detergent fiber, and phosphorus. The NIRS generated values for each accession can be accessed on GRIN at http://www.ars-grin.gov/cgi-bin/npgs/html/desclist.pl?69 ; verified 22 November 2005. The highest and lowest 1% of accessions was identified for each trait by best linear unbiased predictors (BLUPs). Means and standard deviations for observed values and variances due to accessions were calculated. Rank correlations between BLUPs and observed values ranged from r = 0.82 to r = 0.92. Principal component analysis showed that much of the variation is attributable to a contrast of starch with a weighted average of fat, crude protein, acid detergent fiber, and phosphorus. Cluster analyses showed clusters on the basis of canonical values, but no geographical, taxonomical, or morphological interpretation of the clusters was apparent.

Abbreviations: ADF, acid detergent fiber • BLUPs, best linear unbiased predictors • Ca, calcium • CP, crude protein • GRIN, Germplasm Resources Information Network • NIRS, near infrared spectroscopy • P, phosphorus • SEC, standard error calibration • SECV, standard error cross validation