Location: Crop Genetics ResearchTitle: Genetic improvement of lint yield by selections of within-boll yield components based on commonality analysis
Submitted to: Euphytica
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/7/2022
Publication Date: 8/13/2022
Citation: Zeng, L., Wu, J., Delhom, C.D. 2022. Genetic improvement of lint yield by selections of within-boll yield components based on commonality analysis. Euphytica. 218. https://doi.org/10.1007/s10681-022-03071-3.
Interpretive Summary: In traditional cotton breeding, selection for high lint percent is a major approach to increase lint yield. However, extensive selections of lint percent have led to some issues such as small cotton seed, shorter fiber, and higher micronaire. It was known that small cotton seed caused difficulty in ginning and germination in cotton fields. In recent years, cotton breeders are looking for different breeding methods to improve selection efficiency. In this study, a statistical method was employed to assist breeding for higher yield by selections of different yield traits. This new statistical method calculated all possible combinations among these yield traits for their contributions to lint yield, and the desirable combinations which contributed most to yield were chosen in breeding. Results in the breeding during 2017 and 2018 showed that the chosen combinations of yield traits increased lint yield and there was no penalty on cotton seed size and fiber quality. This study is the first application of this statistical method in crop breeding and showed potential of its use in cotton breeding for improvement of yield.
Technical Abstract: Extensive selection of lint percentage (LP) led to decrease of seed size in Upland cotton (Gossypium hirsutum L.). The issues with small seed size in ginning and germination have become more aware in cotton industry in recent years and demand for improving selection criteria is increasing. Relationships between yield components and lint yield are complicated because of interrelationships among them. Commonality analysis is a method of multiple regression to dissect the total effects of yield components to yield into direct effects and indirect effects. This study was designed to dissect relationships of yield components to yield based on commonality analysis and determine correlated selection responses of yield to selections of different yield components. Selections were made within 300 F3 plants in 2017 for the top seventy-five plants by LP and six within-boll yield components, lint weight per seed (LPS), seeds per boll (SPB), seed surface area (SSA), lint weight per fiber (LWF), lint weight per unit seed surface area (LWSA), and lint number per unit seed surface area (LNSA). F4 progenies were evaluated in field with two replicates in 2018. Direct coefficients of the seven single yield components ranged from 0.00 to 0.18 with LP as the largest contributor. Indirect coefficients of multiple components ranged from -0.04 to 0.08. Five single yield components and five multiple yield components were chosen for selections based on their relatively large coefficients to yield. The correlated selection response (CR) of lint yield, 1684 kgha-1, in F4 was significantly positive to selections by LP, but the CR of seed size and fiber length was negative to the selection by LP. The CR of lint yield, 1769 kgha-1, to selections by multiple yield components of LP-LWF, with 5% increase compared with the selection by LP alone. There was no significant CR of seed size and fiber length to these selections. This study was a first application of commonality analysis in crop breeding and these results indicated an efficiency of LP-LWF as multiple selection criteria in selection for lint yield.