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Published in Crop Sci. 44:741-747 (2004).
© 2004 Crop Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA

CROP BREEDING, GENETICS & CYTOLOGY

Yield Evaluation of Maize Cultivars across Environments with Different Levels of Pink Stem Borer Infestation

A. Butrón*, P. Velasco, A. Ordás and R. A. Malvar

Misión Biológica de Galicia, Spanish Council for Scientific Research, Apartado 28, 36080 Pontevedra, Spain

* Corresponding author (abutron{at}mbg.cesga.es).

Yield under infestation by the pink stem borer [Sesamia nonagrioides (Lefebvre)] has been proposed as the best estimator of maize (Zea mays L.) performance under pink stem borer attack. Yield is a complex trait that greatly interacts with the environment. Several methods could be used to study the genotype x environment (GE) interaction. The objective of this work was to study the GE interaction for yield of 49 maize hybrids in five different environments by the Site Regression (SREG) and factorial regression methods. Locations presented different levels of natural infestation by the pink stem borer. The biplot obtained by applying the SREG method allowed visual cultivar evaluation. The factorial regression method incorporated genotypic and environmental covariates that enhanced biological interpretation of GE interaction. Hybrid A637 x EP42 would be recommended in northwestern Spain under medium and high natural infestation by the pink stem borer because it showed high and stable yielding ability. Genotypic and environmental covariates explained approximately 75% of the GE interaction variation, but other genotypic covariates could be introduced in the model to reduce GE residual variation to a nonsignificant level. In general, GE effects for grain yield were mainly due to earliness, vigor effects, and environmental yield limiting factors such as the mean of minimum temperature and percentage of air humidity.

Abbreviations: E, Environmental main effects • G, Genotype main effects • GE, Genotype x environment • GGE, G plus GE interaction • SREG, Sites Regression Model


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Crop Science 2004 44: 707-710. [Full Text]  



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R. A. Malvar, P. Revilla, A. Butron, B. Gouesnard, A. Boyat, P. Soengas, A. Alvarez, and A. Ordas
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