Implementation new tools and technologies in the GMP Africa Breeding Pipelines

Yoseph Beyene (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA NEW TECHNOLOGY BREEDING PIPES GERMPLASM PHENOTYPING

Breeding for biotic and abiotic stresses

Yoseph Beyene (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING BIOTIC STRESS ABIOTIC STRESS DROUGHT TOLERANCE DISEASE RESISTANCE PEST RESISTANCE

Efficacy of drought-tolerant and insect-protected transgenic TELA® maize traits in Nigeria

Yoseph Beyene (2023)

Artículo

Stem Borer TELA® Hybrids CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FALL ARMYWORMS GENOTYPE ENVIRONMENT INTERACTION STEM EATING INSECTS TRANSGENIC PLANTS MAIZE HYBRIDS

Product profile development and prioritization: Important considerations

Yoseph Beyene (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE PRODUCTS BREEDING PROGRAMMES MARKET SEGMENTATION TECHNOLOGY GERMPLASM

Use of DH lines in maize breeding programs: CIMMYT experience

Yoseph Beyene (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BREEDING PROGRAMMES HYBRIDS MARKER-ASSISTED SELECTION GRAIN YIELDS

AGG-maize year 3 major achievements and next steps

Yoseph Beyene (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BREEDING PROGRAMMES INNOVATION HYBRIDS GERMPLASM

Introduction to line and hybrid development

Yoseph Beyene (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INBRED LINES HYBRIDS MAIZE GERMPLASM

TELA Project: An update

Yoseph Beyene (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE HYBRIDS PEST INSECTS RESEARCH

Sparse designs for genomic selection using multi-environment data

Yoseph Beyene Juan Burgueño Jose Crossa (2020)

This research study the genomic-enabled prediction accuracy of the composition of the following sparse testing allocation design: (1) all non-overlapping (0 overlapping) lines in environments, (2) all overlapping (0 non-overlapping) lines tested in all the environments, and (3) combinations of the two previous cases where certain numbers of non-overlapping (NO)/overlapping (O) lines were distributed in the environments. We also studied cases where the size of the testing population was decreased. The study used two large maize data sets (T1 and T2). Four different genomic-enabled prediction models were studied, two models (M1 and M2) that do not include the genomic × environment interaction (GE), whereas models M3 and M4 incorporate two forms of modeling GE. The results show that genome-based models including GE (M3 and M4) captured more genetic variability with the GE component than the other models for both data sets. Also, models M3 and M4 provide higher prediction accuracy than models M1 and M2 for the different allocation designs comprising different combinations of NO/O lines in environments. Results indicate that substantial savings of testing resources can be achieved by optimizing the allocation design using genome-based models including genomic × environment interaction.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Tackling Maize Lethal Necrosis (MLN) in eastern Africa through effective phytosanitary approaches

Suresh L.M. Yoseph Beyene Dan Makumbi Manje Gowda Prasanna Boddupalli (2023)

Objeto de congreso

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE NECROSIS DISEASE MANAGEMENT PLANT HEALTH GENE EDITING GERMPLASM