Field experimental design comparisons to detect field effects associated with agronomic traits in upland cotton

Abstract

Field variation is one of the important factors that can have a significant impact on genetic data analysis. Ineffective control of field variation may result in an inflated residual variance and/or biased estimation of genetic variations and/or effects. In this study, we addressed this problem by merging genetic models with the information from a rectangular cotton field layout (referred to row and column directions). Data from a genetic mapping study in Upland cotton (Gossypium hirsutum L.) was used to validate the proposed methodology. This study included model evaluation based on simulations and actual data analysis on four agronomic traits (seed yield, lint yield, lint percentage, and boll weight) in cotton. Results based on simulations suggested that when there were no row and column effects, the conventional and the extended genetic models yielded similar results. However, when either field row and/or column effects were significant, the conventional genetic model yielded biased estimates for residual variance component with larger mean square error whereas the extended genetic models yielded more unbiased estimates. Actual data analysis revealed that lint yield and seed yield were significantly influenced by the systematic variation present in the field. With the extended model, the residual variance associated with these traits was reduced approximately 65 % compared to the conventional block model. Accordingly, the averaged heritability estimate increased by about 18 % for these traits. Thus, the results suggested that genetic data analysis can be improved when field variation is considered.