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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Genetics Research » Research » Publications at this Location » Publication #358786

Research Project: Utilizing Conventional and Molecular Approaches to Enhance Seed and Fiber Quality Traits, and Conducting a National Cotton Variety Testing Program

Location: Crop Genetics Research

Title: Analysis of testing locations in regional high quality tests for cotton fiber quality traits

item Zeng, Linghe
item Boykin, Deborah
item ZHANG, JINFA - New Mexico State University
item Bechere, Efrem
item DEVER, JANE - Texas A&M University
item Campbell, Benjamin - Todd
item RAPER, TYSON - University Of Tennessee
item MEEKS, CALVIN - University Of Missouri
item SMITH, WAYNE - Texas A&M University
item MYERS, GERALD - Louisiana State University
item BOURLAND, FRED - University Of Arkansas

Submitted to: Journal of Cotton Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/5/2019
Publication Date: 12/30/2019
Citation: Zeng, L., Boykin, D.L., Zhang, J., Bechere, E., Dever, J.K., Campbell, B.T., Raper, T.B., Meeks, C., Smith, W., Myers, G.O., Bourland, F.M. 2019. Analysis of testing locations in regional high quality tests for cotton fiber quality traits. Journal of Cotton Science. 23:284-291.

Interpretive Summary: Evaluation of cotton varieties for their agronomic performance and fiber quality under multiple environments is a necessary step before their use in cotton production. When designing tests, breeders need to decide an appropriate number of locations to evaluate varieties. More locations included in tests would provide better accuracy in controlling environmental influence, but cost would be higher. In this research, we used the most recent test data in National Cotton Variety Test (NCVT) between 2011 and 2016 to determine an optimum number of locations so that the future design of regional tests for fiber quality in the U.S. Cotton Belt can be optimized for both accuracy and cost. A series of datasets with different numbers of locations were created from the original NCVT tests. The statistical accuracy in controlling environmental influence was estimated in the tests with different numbers of locations. The results showed that the statistical accuracy could be maintained with as few as five locations. When locations were reduced to four or less, the statistical accuracy decreased dramatically. It is concluded that five locations will be an optimum number of testing locations in tests for fiber quality in the U.S. Cotton Belt.

Technical Abstract: Significant genotype (g) by environment (e) interactions affect breeding efficiency and increase the cost of tests for cotton (Gossypium hirsutum L.) fiber quality. Determination of an efficient number of testing locations may allow removal of unnecessary testing locations while maintaining the statistical power in detection of ge. In this study, fiber quality data from Regional High Quality (RHQ) tests of 2011-2016 were used to determine an efficient number of locations in multiple-location tests for fiber quality and relationships among testing locations for their representativeness and ability to discriminate among genotypes. Covariance parameters of genotype (g), location (l), and gl in the original RHQ tests were estimated in a random model. The simulating data with varying number of locations omitted from the original tests were created by performing simulations 100 times. Covariance parameters and their standard deviations (std) were estimated in the simulated data. When locations were reduced to five, the std of gl increased by 18 to 37% compared to the original tests. Further reduction of locations to four or less increased std of gl by 30 to 217% compared to the original tests. Therefore, five locations were determined to be an efficient number of locations in multiple-location tests for fiber quality. Relationships among eight testing locations in the RHQ tests of 2011-2016 were analyzed using GGE biplot software. The discriminating ability and representativeness of the eight locations for fiber properties were calculated as their distances to an ‘ideal environment’ which was designed as a center in GGE biplot graphics for representativeness and discriminating ability. The relationships among testing locations were different across years. However, by averages of the distances across testing years, the locations of Stoneville, MS, Keiser, AR, Lubbock, TX, and College Station, TX were identified as most representative testing sites for fiber properties.