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ARS Home » Plains Area » Lincoln, Nebraska » Wheat, Sorghum and Forage Research » Research » Publications at this Location » Publication #171242
Title: EFFECTS OF GRID SIZE, CONTROL PLOT DENSITY, CONTROL PLOT ARRANGEMENT AND ASSUMPTION OF RANDOM OR FIXED EFFECTS ON NON-REPLICATED EXPERIMENTS FOR GERMPLASM SCREENING

Author
item SEBOLAI, BOI - BOTSWANA COLLEGE OF AG
item Pedersen, Jeffrey
item MARX, DAVID - UNI OF NE
item Boykin, Deborah

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/19/2005
Publication Date: 8/26/2005
Citation: Sebolai, B., Pedersen, J.F., Marx, D., Boykin, D.L. 2005. Effects of grid size, control plot density, control plot arrangement and assumption of random or fixed effects on non-replicated experiments for germplasm screening. Crop Sci. 45:1978-1984.

Interpretive Summary: U.S. germplasm collections include thousands of lines of important crop species, making selection of lines with beneficial traits from the collections a difficult task. Although geneticists recognize that repeating measurements of traits to ensure accuracy is important, the volume of lines and limited seed stocks can make replication of early generation selection experiments impractical. Statisticians and plant breeders have developed and utilized various techniques to increase effectiveness and efficiencies in such experiments. Some recent techniques utilize the ability to account for differences in traits due to location in the field, called spatial statistics. In this study, mixed model equations were used to provide least squares means (LSMEANs) and best linear unbiased predictors (BLUPs) and compare selection effectiveness and efficiencies to observed (Y) and true values in simulated experiments. In experiments in which the simulated land areas were highly variable, none of the predictors, Y, LSMEAN, or BLUP, was very effective in identifying the true superior genotypes. When the simulated land areas were less variable, use of BLUPs consistently resulted in the highest proportion of true top ranking genotypes identified across all control plot densities, while using the observed values consistently resulted in identification of the lowest proportion of the true top ranking genotypes. Effectiveness of LSMEANs was dependent upon control plot density, reducing selection efficiency. Use of BLUPs by geneticists for early generation germplasm screening experiments should result in a high effectiveness in selecting truly superior germplasm and high efficiency due to the ability to account for spatial variability with the use of few or no control plots.

Technical Abstract: Early generation selection experiments typically involve several hundred to thousands of lines. Various systematic and statistical techniques have been developed to increase effectiveness and efficiencies in such experiments, including the development and application of spatial statistical models. In this study, mixed model equations were used to provide least squares means (LSMEANs) and best linear unbiased predictors (BLUPs) and compare selection effectiveness and efficiencies to observed (Y) and true values in simulated experiments varying in size (10x10, 20x20 and 30x30 grids), control plots densities (0%, 10%, 20%, and 50%), control plot arrangements (high, medium, and low A-optimality) and spatial range of influence (short and long). Results were similar for all grid sizes. In experiments in which the simulated land areas were highly variable (short range), none of the predictors, Y, LSMEAN, or BLUP, were very effective in identifying the true superior genotypes. When the simulated land areas were less variable (long range), use of BLUPs consistently resulted in the highest proportion of true top ranking genotypes identified across all control plot densities, while using the observed values consistently resulted in identification of the lowest proportion of the true top ranking genotypes. Effectiveness of LSMEANs was dependent upon control plot density and arrangements. Use of BLUPs for early generation germplasm screening experiments should result in a high effectiveness in selecting truly superior germplasm and high efficiency due to the ability to account for spatial variability with the use of few or no control plots.