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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #308629
Title: Field experimental design comparisons to detect field effects associated with agronomic traits in Upland cotton

Author
item BONDALAPATI, KRISHNA - South Dakota State University
item Jenkins, Johnie
item McCarty, Jack
item WU, JIXIANG - South Dakota State University

Submitted to: Euphytica
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/2/2015
Publication Date: 7/16/2015
Citation: Bondalapati, K.D., Jenkins, J.N., Mccarty Jr, J.C., Wu, J. 2015. Field experimental design comparisons to detect field effects associated with agronomic traits in Upland cotton. Euphytica. 206:747-757.

Interpretive Summary: Field variation has a significant impact on genetic analysis. We addressed this problem by merging genetic models with the information from a cotton experimental field layout. We 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 systemic field variation was present the conventional genetic model yielded biased estimates for residual variance component with larger mean square error, whereas, extended genetic model yielded more unbiased estimates. Actual data analysis revealed that lint yield and seed yield were significantly influenced by the systemic variation present in the field. With the extended model, the residual variance associted with these traits was reduced approximately 65% compared to the conventional block model. Accordingly, the averaged heritability estimated was improved for these traits by about 18%. Thus, the results suggested that genetic data analysis can be improved when field variation is considered in the design and analysis of the experimental data.

Technical 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 cotton field layout. Data from a genetic mapping study in Upland cotton 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 extended genetic models yielded similar results. However, when either row and/or column effects were significant, the conventional genetic model yielded biased estimates for residual variance component with larger mean square error (MSE) whereas extended genetic model 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 was increased for these traits by about 18%. Thus, the results suggested that genetic data analysis can be improved when field variation is considered.