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Universidad Nacional de Córdoba (2012)

Predicción del mérito genético de híbridos de maíz con adaptación específica a condiciones de semiaridez

Biasutti, Carlos A.

Titre : Predicción del mérito genético de híbridos de maíz con adaptación específica a condiciones de semiaridez

Auteur : Biasutti, Carlos A.

Université de soutenance : Universidad Nacional de Córdoba

Grade : Doctor en Ciencias Agropecuarias 2012

Présentation
The identification of inbred lines for the production of superior, high yielding hybrids is of paramount importance in maize breeding. Field evaluation of all possible crosses among inbred lines requires extremely large resources and time. Therefore, efforts have been made to predict hybrid performance by using field data and genotype relatedness. The best linear unbiased predictions (BLUP) procedure, in which predictions are made on known genetic relationships among genotypes and available yield data, has been found useful in various studies. However, in semi-arid environments, where the incidence of environmental factors is high and unpredictable, the accuracy of BLUP based predictions remains to be tested. In the present study the main objective was to predict maize hybrid performance by using field data and known genetic relationships among hybrids. Data from 2006 to 2009 were obtained from hybrid testing trails of maize breeding program of the Plant Breeding Department at the Faculty of Agronomy of Córdoba National University, Argentina. Yield data from forty-nine hybrids from different genetic background were utilized. Yield data were analyzed by the Proc Mixed utilizing average data with and without incorporation of relatedness information. The performance of hybrids formed by crossing lines developed from different sources and selection criteria were also analyzed. Two other characteristics, the canopy temperature depression and leaf elongation and their correlations with yield were additionally investigated. The performance of new, untested single crosses was predicted using both data from : high and low relatedness between predictors and predicted hybrids and by employing data from low and high yielding environments. The best predictions of untested new hybrids were reached by using maximum relatedness information combined with data obtained in the better yielding environments

Présentation

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Page publiée le 27 août 2016, mise à jour le 3 juillet 2017