Author: Michael Olsen

Replication Data for: Incorporating male sterility increases hybrid maize yield in low input African farming systems

Hugo De Groote Jill Cairns Michael Olsen (2021)

In sub-Saharan Africa, maize is a staple crop but yields remain sub-optimal. A novel hybrid seed technology offers the opportunity to reduce seed production costs and increase yields. This dataset contains data from on-farm and on-station trials collected in 2017 to 2019 in South Africa, Kenya and Zimbabwe to assess this hybrid seed production technology. The results of the analysis are presented in the accompanying article.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Genomic prediction of resistance to Tar Spot Complex of maize in multiple populations using genotyping-by-sequencing SNPs

Michael Olsen Prasanna Boddupalli Felix San Vicente Garcia XUECAI ZHANG (2021)

Tar spot complex (TSC) is an important foliar disease for tropical maize. The data provided in this dataset were used to estimate the effectiveness of genomic selection for improving TSC resistance. The results of the analysis are reported in the accompanying journal article

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Genetic dissection of quantitative resistance to common rust (Puccinia sorghi) in tropical maize (Zea mays L.) by combined GWAS, linkage mapping, and genomic prediction

Michael Olsen Prasanna Boddupalli Felix San Vicente Garcia XUECAI ZHANG (2021)

Significant grain yield losses and poor grain quality can be caused by Common rust a major foliar disease in maize. This dataset provides the genotypic and phenotypic data that were used to perform genome-wide association studies (GWAS) and linkage analysis mapping to dissect the architecture of common rust resistance.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Replication Data for: Genomic prediction of kernel zinc content in multiple maize populations using genotyping-by-sequencing and repeat amplification sequencing markers

Thanda Dhliwayo Edna Mageto Michael Olsen Jose Crossa Prasanna Boddupalli XUECAI ZHANG (2020)

An association-mapping panel (DTMA) and two DH populations (DH1 and DH2) were used in the current study, which in total includes 487 materials. The dataset includes three types of files. One is the genotype of 487 lines sequenced by GbS, named DTMA_DH2_DH3-955690.hmp.txt; one is the genotype of 487 lines sequenced by rAmpSeq named genotype-rAmpSeq.csv; and the third type of files are the phenotypic data files named DH1-phenotype.csv, DH2-phenotype.csv and DTMA-phenotype.csv.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA