Forage and Grain Quality Evaluation Project: Phenotypic Dataset of Oat Germplasm Evaluated in Ethiopian Highland Environments

Citation

Ashenafi, L. Dejene, M. Mohammed, K. Mekonnen, T. Feyisa, F. Lire, H. Negawo, A. T. Rahman, A. Barth, S. Vega, J. de, Jones, C. S. and Gari, A. 2026. Forage and Grain Quality Evaluation Project: Phenotypic Dataset of Oat Germplasm Evaluated in Ethiopian Highland Environments. Phenotypic field evaluation data.

Abstract/Description

This dataset contains phenotypic field evaluation data generated from a germplasm collection assessed under Ethiopian highland agroecological conditions. The study was conducted as part of ILRI–CGIAR research activities focusing on forage and grain quality, improvement, climate resilience, and accelerated breeding for livestock feed systems in Eastern Africa. The dataset includes phenotypic, and nutritional measurements collected from diverse oat accessions evaluated across experimental environments and/or growing seasons. Traits recorded include agronomic performance, biomass production, grain yield, forage-related characteristics, and quality-associated traits. The dataset also include morphological descriptors and related experimental observations used for genetic analysis and breeding applications. The data were generated to support: genetic diversity and population structure analysis, genome-wide association studies (GWAS), marker–trait association studies, genomic selection and breeding applications, forage and grain quality improvement research, climate-resilient crop development for mixed crop–livestock systems. Researchers, breeders, students, and agricultural development practitioners may use the dataset for comparative analyses, breeding program support, statistical modelling, genotype evaluation, and integrative genomics research in oats and related forage crops. The dataset is provided in Microsoft Excel (.xlsx) format and contains curated phenotypic observations for further analyses.

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Contributes to SDGs

SDG 2 - Zero hunger
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Lidya Ashenafi, Mesfin Dejene, Kedir Mohammed, Tilahun Mekonnen, Fekede Feyisa, Hailu Lire, Alemayehu Teressa Negawo, Atikur Rahman, Susanne Barth, Jose De Vega, Chris S. Jones, Abel Teshome bioRxiv 2025.11.26.690820; doi: https://doi.org/10.1101/2025.11.26.690820

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