Agricultural data for Ethiopia, gathered from research institutes and government sources into one searchable catalogue.
671–680 of 1,112 datasets
CIMMYT Ethiopia
International Durum Screening Nursery (IDSN) distributes diverse CIMMYT-bred spring durum wheat germplasm adapted to irrigated and variable moisture stressed environments. Disease resistance and high industrial pasta quality are essential traits possessed in this germplasm. It is distributed to 100 locations, and contains 150 entries.
CIMMYT Ethiopia
International Durum Screening Nursery (IDSN) distributes diverse CIMMYT-bred spring durum wheat germplasm adapted to irrigated and variable moisture stressed environments. Disease resistance and high industrial pasta quality are essential traits possessed in this germplasm. It is distributed to 100 locations, and contains 150 entries.
CIMMYT Ethiopia
International Durum Screening Nursery (IDSN) distributes diverse CIMMYT-bred spring durum wheat germplasm adapted to irrigated and variable moisture stressed environments. Disease resistance and high industrial pasta quality are essential traits possessed in this germplasm. It is distributed to 100 locations, and contains 150 entries.
CIMMYT Ethiopia
International Durum Screening Nursery (IDSN) distributes diverse CIMMYT-bred spring durum wheat germplasm adapted to irrigated and variable moisture stressed environments. Disease resistance and high industrial pasta quality are essential traits possessed in this germplasm. It is distributed to 100 locations, and contains 150 entries.
CIMMYT Ethiopia
International Durum Screening Nursery (IDSN) distributes diverse CIMMYT-bred spring durum wheat germplasm adapted to irrigated and variable moisture stressed environments. Disease resistance and high industrial pasta quality are essential traits possessed in this germplasm. It is distributed to 100 locations, and contains 150 entries.
CIMMYT Ethiopia
Farmers’ participatory researchers managed long-term trials aimed at improving the productivity, profitability, and sustainability of smallholder agriculture in the EGP by addressing the following objectives: 1. Understand farmer circumstances with respect to cropping systems, natural and economic resources base, livelihood strategies, and capacity to bear the risk and undertake technological innovation. 2. Develop with farmers more productive and sustainable technologies that are resilient to climate risks and profitable for smallholders. 3. Facilitate the widespread adoption of sustainable, resilient, and more profitable farming systems.
CIMMYT Ethiopia
The Fieldbook2020 software provides offline capabilities for managing pedigrees, phenotypic data, seed stocks, and fieldbooks for a maize breeding program. CIMMYT is transitioning toward the use of an online Enterprise Breeding System for managing breeding information and performing analyses that will replace the use of Fieldbook2020. Institutes interested in adopting online breeding management and analysis systems are encouraged to request more information from Liz Jones (ej245@cornell.edu) with the Excellence in Breeding (EiB) Platform Module 5. She can provide more information about available options such as the Enterprise Breeding System, the Breeding Management System of the IBP, and the Breedbase systems. Additional information is available in the EiB Toolbox: https://excellenceinbreeding.org/toolbox . The user takes full responsibility for all manipulations done with Fiedbook2020. NOTE: CIMMYT is operating Fieldbook2020 in a run and maintain mode currently and will not provide support for the maintenance, update, or use of Fieldbook2020 after 2020. Fieldbook2020 users are encouraged to plan to transition to another system by or during 2021.
CIMMYT Ethiopia
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.
CIMMYT Ethiopia
The potential to integrate consistent associations identified from GWAS as fixed variables in GP models to improve prediction accuracy for complex traits (for example, grain yield) has not been investigated comprehensively in wheat. Here, we untangled the genetic architecture of grain yield and yield stability by haplotypes-based GWAS and epistatic scan of the genome. We then integrated robust and stable associations (and interacting loci) as fixed effects in GP models to investigate the importance of these associations in improving prediction accuracies of the said traits. We concluded that the utility of GP incorporating GWAS results is noteworthy for GY when GWAS results identify significant and robust genomic regions.
CIMMYT Ethiopia
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.