Person: Alvarado Beltrán, G.
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Alvarado Beltrán
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G.
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Alvarado Beltrán, G.
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0000-0002-2139-811319 results
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- Statistical, biometrical and genomic methods III(CIMMYT, 2019) Alvarado Beltrán, G.
Publication - Statistical, biometrical and genomic methods II(CIMMYT, 2019) Alvarado Beltrán, G.
Publication - Common experimental designs in agronomic research and their analysis(CIMMYT, 2018) Alvarado Beltrán, G.
Publication - Statistical, biometrical and genomic methods I(CIMMYT, 2019) Alvarado Beltrán, G.
Publication - Maize responsiveness to Azospirillum brasilense: insights into genetic control, heterosis and genomic prediction(Public Library of Science, 2019) Vidotti, M.S.; Matias, F.I.; Alves, F.C.; Pérez-Rodríguez, P.; Alvarado Beltrán, G.; Burgueño, J.; Crossa, J.; Fritsche-Neto, R.
Publication - Morphological variability of native maize (Zea mays L.) of the west highland of Puebla and east highland of Tlaxcala, Mexico = Variabilidad morfológica del maíz nativo (Zea mays L.) del altiplano poniente de Puebla y altiplano oriente de Tlaxcala, México(Facultad de Ciencias Agrarias Universidad Nacional de Cuyo, 2019) Alvarado Beltrán, G.; López-Sánchez, H.; Santacruz-Varela, A.; Muñoz-Orozco, A.; Valadez-Moctezuma, E.; Gutiérrez-Espinosa, M.A.; López, P.A.; Gil Munoz, A.; Guerrero-Rodríguez, J.D.; Taboada-Gaytán, O.R.
Publication - Empirical comparison of tropical maize hybrids selected through genomic and phenotypic selections(Frontiers, 2019) Beyene, Y.; Gowda, M.; Olsen, M.; Robbins, K.; Pérez-Rodríguez, P.; Alvarado Beltrán, G.; Dreher, K.; Yanxin Gao; Mugo, S.N.; Prasanna, B.M.; Crossa, J.
Publication - Large scale deployment of rAmpSeq genotyping technology in maize genomic selection(CIMMYT, 2018) Olsen, M.; Robbins, K.; Dreher, K.; Ayala Hernández, C.; Crossa, J.; Burgueño, J.; Pérez-Rodríguez, P.; Alvarado Beltrán, G.; Gowda, M.; Beyene, Y.; Makumbi, D.; Xuecai Zhang; San Vicente Garcia, F.M.; Punna, R.; Buckler, E.; Atanda, A.S.; Shibin Gao; Jones, L.
Publication - Chapter 11. RIndSel: selection Indices with R(Springer, 2018) Alvarado Beltrán, G.; Pacheco Gil, Rosa Angela; Pérez-Elizalde, S.; Burgueño, J.; Rodríguez, F.M.; Cerón-Rojas, J.J.; Crossa, J.RIndSel is a graphical unit interface that uses selection index theory to select individual candidates as parents for the next selection cycle. The index can be a linear combination of phenotypic values, genomic estimated breeding values, or a linear combination of phenotypic values and marker scores. Based on the restriction imposed on the expected genetic gain per trait, the index can be unrestricted, null restricted, or predetermined proportional gain indices. RIndSel is compatible with any of the following versions of Windows: XP, 7, 8, and 10. Furthermore, it can be installed on 32-bit and 64-bit computers. In the context of fixed and mixed models, RIndSel estimates the phenotypic and genetic covariance using two main experimental designs: randomized complete block design and lattice or alpha lattice design. In the following, we explain how RIndSel can be used to determine individual candidates as parents for the next cycle of improvement.
Publication - Evaluation and interpretation of interactions(American Society of Agronomy, 2013) Crossa, J.; Vargas Hernández, M.; Cossani, C.M.; Alvarado Beltrán, G.; Burgueño, J.; Mathews, K.L.; Reynolds, M.P.Understanding the factors that define a given interaction is important in agricultural, agronomic, and plant breeding research, where agronomic treatments or genotypes are evaluated under several environmental conditions and where interactions usually complicate a researcher’s decisions. We give examples of how interactions, in common agricultural experiments, can be examined and studied to make use of the rich information available on the interaction term of the model. Examples with different levels of interaction complexity are used to illustrate how to analyze and interpret interactions and how interaction components can be partitioned into comparisons with sensible biological interpretations. It will offer researchers a greater understanding of how to exploit interaction information beyond the standard statistical tests performed in the usual analysis of variance. Simple SAS codes for performing standard interaction contrasts and defining interaction covariables are provided.
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