Genetic analysis of single cross Quality Protein Maize (QPM) hybrids | Zendy

Atta Ofori | Zendy; K. Ofori | Zendy; K Obeng Antwi | Zendy; K. Tengan | Zendy; Adelaide Agyeman | Zendy; Baffour BaduApraku | Zendy

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Genetic analysis of single cross Quality Protein Maize (QPM) hybrids

Author(s) -

Atta Ofori,

K. Ofori,

K Obeng Antwi,

K. Tengan,

Adelaide Agyeman,

Baffour BaduApraku

Publication year - 2015

Publication title -

journal of plant breeding and crop science

Language(s) - English

Resource type - Journals

ISSN - 2006-9758

DOI - 10.5897/jpbcs2014.0515

Subject(s) - ammi , hybrid , biology , randomized block design , grain yield , selection (genetic algorithm) , stability (learning theory) , yield (engineering) , gene–environment interaction , correlation , microbiology and biotechnology , statistics , agronomy , inbred strain , mathematics , genotype , genetics , computer science , gene , materials science , geometry , artificial intelligence , machine learning , metallurgy

Correlation coefficients and stability of grain yield were determined using 6 extra-early quality protein maize (QPM) parental inbred lines and their F1 (15) single crosses evaluated in selected ecological zones of Ghana. The objectives were; to estimate the genetic correlation between grain yield and other agronomic traits and to determine the stability of the single cross hybrids across four locations. Randomized Complete Block Design (RCBD) with three replications was used for each location. Estimates of correlation coefficients and stability analysis of grain yield was done using Genstat 9.2 and additive main effects and multiplicative interaction (AMMI) statistical model (MATMODEL 2.0). Results from phenotypic correlation of grain yield showed highly positive correlation with thousand grain weight (TGW) and number of kernels per row (NKR) across all locations suggesting that selection efficiency could be improved through indirect selection. AMMI analysis revealed non-significant genotype by environment interaction (GEI) for grain yield whilst genotypic and environmental main effects were highly significant. However, the contribution of the environment was higher which suggests that anyone of the locations used in this study can be used for subsequent evaluations in order to manage the limited resources available for the testing program.

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