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Book

Maize production environments revisited: A GIS-based approach

Hartkamp, A.D.; White, J.W.; Rodriguez, A.; Bänziger, M.; Srinivasan, G.; Granados, G.; Crossa, J. (CIMMYT, 2000)

This publication presents a GIS-based approach for revising the descriptions of global maize production environments, called "mega-environments" (MEs), used by CIMMYT and its partners. A cluster analysis was performed on ...

Article

A benchmarking between deep learning, support vector machine and bayesian threshold best linear unbiased prediction for predicting ordinal traits in plant breeding

Montesinos-Lopez, O.A.; Martin-Vallejo, J.; Crossa, J.; Gianola, D.; Hernández Suárez, C.M.; Montesinos-Lopez, A.; Juliana, P.; Singh, R.P. (Genetics Society of America, 2019)

Presentation

Large scale deployment of rAmpSeq genotyping technology in maize genomic selection

Olsen, M.; Robbins, K.; Dreher, K.A.; Ayala, C.; Crossa, J.; Burgueño, J.; Perez-Rodriguez, P.; Alvarado Beltrán, G.; Gowda, M.; Beyene, Y.; Makumbi, D.; Zhang, X.; San Vicente, F.M.; Punna, R.; Buckler, E.; Atanda, A.S.; Gao, S.; Jones, L. (CIMMYT, 2018)

Book Chapter