Person: Burgueño, J.
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Burgueño
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Burgueño, J.
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- SAS macro for analysing unreplicated designs(CIMMYT, 2000) Burgueño, J.; Crossa, J.Augmented or unreplicated experiment designs have two type of treatments, the replicated checks and the unreplicated new entries. The latter are usually considered to be random effects while the checks treatments are considered as fixed effects. Augmented designs have several advantages over the systematic check arrangement such as more than one check can be included and standard errors of differences between unreplicated entries and between unreplicated entries and checks are available. The SAS macro presented in this manual analyzes unreplicated design when the repeated checks are arranged in incomplete blocks. Usually 4-6 different checks are repeated several times throughout the experiment and 2-3 checks are arranged in each incomplete block. The incomplete block has plots for checks and plots for the unreplicated genotypes.
Publication - Diseños experimentales con testigos repetidos(Colegio de Postgraduados, 2005) Burgueño, J.; Martinez-Garza, A.; Crossa, J.; Mastache-Lagunas, A.In some stages of the breeding of crops, breeders must select the most promising lines from a very large number of sets of new varieties. Selection is conducted via direct comparison of new varieties that are tested within a single experimental unit, with control varieties systematically intercropped among them and replicated over a large number of plots. Researchers often do not realize that, by following some simple design rules, they might be susceptible of a precise and accurate statistical analysis. This paper discusses the subject with precision, and establishes rules of design and a statistical analysis technique appropriate for completely randomized experimental designs or randomized complete blocks with blocks of the same size.
Publication - User's guide for spatial analysis of field variety trials using ASREML(CIMMYT, 2000) Burgueño, J.; Cadena, A.; Crossa, J.; Banziger, M.; Gilmour, A.; Cullis, B.This manual describes an approach to the spatial analysis of field experiments based on the software package AS residual maximum likelihood (ASREML; Gilmour et al. 1999). It describes common sources of spatial variation and explains how these can be identified and accounted for in an analysis.
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