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Article Excerpt
SCREENING large germplasm collections for quantitative traits such as chemical and nutritional content can be prohibitively expensive and time consuming by traditional experimental designs and analytical techniques. For example, the current U.S. sorghum collection contains over 42 000 accessions (USDA-ARS, 2005a). Growing each sorghum accession in one single-row plot 7.6 m long spaced 76 cm apart would require over 24 ha of research plots per replication. Total per sample analysis costs for crude protein (CP), acid detergent fiber (ADF), phosphorus (P), calcium (Ca), starch, fat, and various calculated values can total $35/sample for wet chemistry (Ward Laboratories, 2005). Few research projects have the resources to undertake screening of such a large collection by traditional replicated field studies and wet chemical analysis.
Two technologies, NIRS and the use of new statistical models made possible by the increasing speed and power of computers, can be used to generate predicted values for chemical and nutritional content on the basis of a relatively small subset of samples and to account for spatial and temporal variability, reducing the need for replication of accessions or replicated check plots in augmented designs. NIRS predicts chemical and nutritional values from spectral data and is used to generate chemical and nutritional values for grain and forage samples. Time required to scan a sample is approximately 1 min, and cost per sample is low compared with the wet chemistry methods. The technology is commonplace in many analytical labs and has been approved for use in testing ADF and protein in feeds for over a decade (AOAC, 1990). More recently, it was approved for testing of corn grain (Zea mays L.) for oil, protein, and starch content by the USDA Grain Inspection, Packers and Stockyards Administration (USDAGIPSA, 1999).
Numerous statistical approaches to make large early generation or germplasm screening efforts more efficient have been developed during the past century. Augmented designs (Federer, 1956; Steel, 1958; Searle, 1965) and nearest neighbor analysis (Papadakis, 1937; Bartlett, 1937) are both examples that have been widely used. However, neither offered the needed increase in efficiency for large germplasm screening experiments, and neither specified the nature of the relationship between neighboring plots. Applications of geostatistical models to account for various correlation structures in field experiments have led to increases in accuracy and precision (Brownie et al., 1993; Cullis and Gleeson, 1991; Zimmerman and Harville, 1991). Mixed model equations developed by Henderson (1975) have become useful with the widespread availability of analytical software (Littell et al., 1996) and more powerful computers, and they have proven useful for the analysis of spatially correlated data (Henderson, 1975; Marx and Stroup, 1993).
Recent research in our laboratory using PROC MIXED (SAS, 2003) and simulated data to compare the efficiency of germplasm screening experiments with varying levels of check plots demonstrated that the use of BLUPs was superior to the use of least square means (LS means) and observed values for selecting the highest proportion of true top ranking genotypes and that BLUPs were influenced little by the proportion of check plots to experimental plots (Sebolai et al., 2005). Incorporation of the correlation structure and the use of BLUPs to select superior accessions may be especially well suited to screening germplasm collections since they were originally developed for ranking and selection (Robinson, 1991). The use of BLUPs is, however, premised on treatment (accession) effects being assumed random. In screening experiments involving a subpopulation of accessions from a collection, this would be valid if the subsetting criteria were independent of screening criteria.
A subset of the U.S. sorghum collection that are of particular interest for sorghum improvement is comprised of approximately 4000 photoperiod insensitive accessions that will flower at temperate latitudes. The objectives of this study were to (i) generate chemical and nutritional values for grain from the U.S. photoperiod insensitive sorghum collection, (ii) describe the variability for those chemical and nutritional values, (iii) identify accessions in the highest and lowest 1% for each trait after accounting for spatial and temporal variation, and (iv) describe relationships among the accessions using these values.
MATERIALS AND METHODS
Approximately 75% of the accessions in the U.S. photoperiod insensitive sorghum collection were grown during 2001 and 2002 at the University of Nebraska Field Laboratory, Ithaca, NE, on a predominantly Sharpsburg silty clay loam (fine, montmorillonitic, mesic Typic Arguidolls) site. Plots consisted of a single 7.6-m row of each accession spaced 76 cm apart, and individual accessions were planted in only 1 yr. The variety 'Wheatland' was interspaced throughout the nurseries at a density of 16% of the plots. Each plot was seeded with a precision vacuum planter calibrated to deliver 120 seeds per row (240 000 seeds ha i). Because of limitations on field space, 1990 accessions were planted in 2001 and 1215 accessions were planted in 2002. Germination was generally adequate for plot establishment. Plots that did not...
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