CIMMYT Institutional Multimedia Publications Repository
- » Grain Brain: Myths and facts about the benefits of wheat and grain
- » Food and nutrition security in changing environments
- » Office of Special Studies (OSS), El Meche, Hidalgo
- » Office of Special Studies (OSS), San Jacinto
- » Herramientas Bioinformáticas
- » Office of Special Studies (OSS), La Cal Grande
- » Office of Special Studies (OSS), Guadalajara, Jalisco
- » Office of Special Studies (OSS), Tepalcingo, Morelos
- » Office of Special Studies (OSS), Huamantla, Tlaxcala
- » Office of Special Studies (OSS), Santa Monica
- » Scientists visiting experimental fields, India
- » Scientists visiting experimental fields, Pakistan
- » Breeding Strategy and use of HTPG in the CIMMYT Global Wheat Program (GWP)
- » Stem rust resistance in a geographically diverse collection of spring wheat lines collected from across Africa
- » CIMMYT Wheat Molecular Genetics: laboratory protocols and applications to wheat breeding
- » Breeding value of primary synthetic wheat genotypes for grain yield
- » Effect of the few-branched-1 (Fbr1) tassel mutation on performance of maize inbred lines and hybrids evaluated under stress and optimum environments
- » A standard methodology for evaluation of mechanical maize seed meters for smallholder farmers comparing devices from Latin America, Sub-Saharan Africa, and Asia
- » Genomic regions associated with root traits under drought stress in tropical maize (Zea mays L.)
- » But what do rural consumers in Africa think about GM Food?
- » Bulked sample analysis in genetics, genomics and crop improvement
- » Mining centuries old In situ conserved turkish wheat landraces for grain yield and stripe rust resistance genes
- » Strand-specific RNA-Seq transcriptome analysis of genotypes with and without low-phosphorus tolerance provides novel insights into phosphorus-use efficiency in maize
- » Evaluation of CIMMYT drought tolerant maize germplasm for resistance to weevil (Sitophilus zeamais Motschulky) damage
- » A bayesian poisson-lognormal model for count data for multiple-trait multiple-environment genomic-enabled prediction