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Journal of Crop Science and Biotechnology

, Volume 22, Issue 3, pp 205–211 | Cite as

Efficiency of Index-Based Selection Methods for Stem Borer Resistance in Maize (Zea mays L.)

  • Qudrah Olaitan Oloyede-KamiyoEmail author
Research Article
  • 5 Downloads

Abstract

Breeders need a simple and reliable index that could be used to make selection quickly in a breeding program. A study was conducted at the International Institute of Tropical Agriculture, Nigeria, to compare efficiency of four selection indices in selecting the best genotypes in a population improvement program involving a yellow-grained (DMR ESR-Y) and white-grained (DMR ESR-W) maize (Zea mays L.) population under artificial stem borer infestation. Rank summation index (RSI), Base index (BSI), Multiplicative index (MI), and Optimum index that uses heritability as weight (OI) were compared using grain yield, plant aspect, ear aspect, stalk breakage, and tolerance to stem borer as selection criteria. The selection criteria had moderate to high narrow-sense heritability. Each of the traits had a significant correlation with all selection indices, except MI in DMR ESR-Y. The BSI had the highest selection differential (23.32%) followed by OI (21.45%) in the white maize population. It also had the highest (29.11%) followed by RSI (20.99%) in the yellow maize population. The BSI also had strongest correlation with RSI in the white (−0.95**) and the yellow maize population (−0.90**). Percentage similarity was highest between BSI and RSI in the white (82.6%) and yellow maize populations (72.3%). This suggested that both BSI and RSI are good selection indices that could be used in improvement programs for stem borer resistance in maize.

Key words

Correlation economic weight population improvement predicted gains selection differential 

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Notes

Acknowledgements

This work was supported by the International Institute of Tropical Agriculture (IITA), Nigeria.

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Copyright information

© Korean Society of Crop Science and Springer 2019

Authors and Affiliations

  1. 1.Institute of Agricultural Research and Training (IAR&T)Obafemi Awolowo UniversityIbadanNigeria

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