Efficiency of high-nitrogen selection environments for improving maize for low-nitrogen target environments

Most maize (Zea mays L.) in the tropics is grown under low‐nitrogen (N) conditions, raising the need to assess efficient breeding strategies for such conditions. This study assesses the value of low‐N vs. high‐N selection environments for improving lowland tropical maize for low‐N target environment...

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Detalles Bibliográficos
Autores principales: Bänziger, M., Betrán, F.J., Lafitte, H.R.
Formato: Journal Article
Lenguaje:Inglés
Publicado: Wiley 1997
Acceso en línea:https://hdl.handle.net/10568/167236
Descripción
Sumario:Most maize (Zea mays L.) in the tropics is grown under low‐nitrogen (N) conditions, raising the need to assess efficient breeding strategies for such conditions. This study assesses the value of low‐N vs. high‐N selection environments for improving lowland tropical maize for low‐N target environments. Fourteen replicated trials grown under low (no N applied) and high (200 kg N Ha−1 applied) N at CIMMYT, México, between 1986 and 1995 were analyzed for broad‐sense heritability of grain yield, genetic correlation between grain yields under low and high N, and predicted response of grain yield under low N to selection under either low or high N. Broad‐sense heritabilities for grain yield under low N were on average 29% sm ller than under high N because of lower genotypic variances under low N. Error variances were similar at low and highN. Genetic correlations between grain yields under low and high N were generally positive. They decreased with increasing relative yield reduction under low N, indicating that specific adaptation to either low or high N became more important the more low‐N and high‐N experiments differed in grain yield. Selection under high N for performance under low N was predicted significantly less efficient than selection under low N when relative yield reduction due to N stress exceeded 43%. Maize breeding programs targeting low‐N environments in the tropics should include low‐N selection environments to maximize selection gains.