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Genetic Components of Yield Stability in Maize Breeding Populations
Author(s) -
Lee E. A.,
Doerksen T. K.,
Kannenberg L. W.
Publication year - 2003
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2003.2018
Subject(s) - diallel cross , biology , agronomy , selection (genetic algorithm) , trait , population , stability (learning theory) , grain yield , additive genetic effects , plant breeding , genetic gain , genetic variability , genetic variation , microbiology and biotechnology , genetics , genotype , heritability , gene , hybrid , demography , artificial intelligence , machine learning , sociology , computer science , programming language
Phenotypic stability has long been recognized as an important target in plant breeding. Stability is influenced in part by the genetic structure, i.e., level of heterogeneity and heterozygosity, of the cultivar. Yet, very little is known about the genetic components underlying stability, and how population improvement strategies influence stability. We examined 12 maize ( Zea mays L.) breeding populations selected via reciprocal recurrent selection (RRS), selfed progeny recurrent selection (S), or a method combining RRS and S (COM), to examine changes in the genetic structure of the phenotypic stability of three traits (grain yield, grain moisture, and broken stalks), and two associated selection indices. Partitioning of the genotype × environment sums of squares from diallel matings of the original (C 0 ) and advanced (C A ) cycle populations into linear trends indicated that only grain yield and the unadjusted performance index (UPI) followed a predictable linear response. Grain yield and UPI linear trends were further partitioned by Gardner and Eberhart Analysis III to examine the genetic components of stability. We found that recurrent selection (RS) improved grain yield stability, and that this trait is heritable, predictable, and mostly controlled through additive gene action. Improvement in grain yield stability was observed both in cross and per se performance and was accompanied by significant improvement in the mean performance of the populations. However, the improvement in grain yield stability did not result in substantial changes in the general combining ability (g i ) estimates of most populations. Our results indicate that grain yield stability can be improved through RS by selecting solely for mean performance across multiple environments.