Strengthening Public Breeding Pipelines by Emphasizing Quantitative Genetics Principles and Open Source Data Management
Overview
Affiliations
Optimization of breeding program design through stochastic simulation with evolutionary algorithms.
Hassanpour A, Geibel J, Simianer H, Rohde A, Pook T G3 (Bethesda). 2024; 15(1.
PMID: 39495659 PMC: 11708219. DOI: 10.1093/g3journal/jkae248.
Tolerance to spittlebugs () in spp. and grasses: A dataset for plant damage phenotyping.
Ruiz-Hurtado A, Espitia-Buitrago P, Hernandez L, Jauregui R, Cardoso J Data Brief. 2024; 56:110857.
PMID: 39281012 PMC: 11399710. DOI: 10.1016/j.dib.2024.110857.
Genetic trends in the Zimbabwe's national maize breeding program over two decades.
Mukaro R, Chaingeni D, Sneller C, Cairns J, Musundire L, Prasanna B Front Plant Sci. 2024; 15:1391926.
PMID: 38988630 PMC: 11234322. DOI: 10.3389/fpls.2024.1391926.
Mathew J, Delavarpour N, Miranda C, Stenger J, Zhang Z, Aduteye J Sensors (Basel). 2023; 23(14).
PMID: 37514799 PMC: 10384073. DOI: 10.3390/s23146506.
Genetic trends in CIMMYT's tropical maize breeding pipelines.
Prasanna B, Burgueno J, Beyene Y, Makumbi D, Asea G, Woyengo V Sci Rep. 2022; 12(1):20110.
PMID: 36418412 PMC: 9684471. DOI: 10.1038/s41598-022-24536-4.