KIBREAB YOSEFE .

Dr. KIBREAB YOSEFE .

Jimma University, Jimma, Ethiopia
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Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 August 2023 in Plants
Verified
Journal Article
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KIBREAB YOSEFE .KIBREAB YOSEFE .
KIBREAB YOSEFE .
TTTaye Tolemariam
Taye Tolemariam
SDSolomon Demeke
Solomon Demeke
WGWeyessa Garedew
Weyessa Garedew
TATessema Atumo
Tessema Atumo
Mamta SharmaMamta Sharma
Mamta Sharma
This paper reports an evaluation of eleven oat genotypes in four environments for two consecutive years to identify high-biomass-yielding, stable, and broadly adapted genotypes in selected parts of Ethiopia. Genotypes were planted and evaluated with a randomized complete block design, which was repeated three times. The additive main effect and multiplicative interaction analysis of variances revealed that the environment, genotype, and genotype–environment interaction had a significant (p ≤ 0.001) influence on the biomass yield in the dry matter base (t ha−1). The interaction of the first and second principal component analysis accounted for 73.43% and 14.97% of the genotype according to the environment interaction sum of squares, respectively. G6 and G5 were the most stable and widely adapted genotypes and were selected as superior genotypes. The genotype-by-environment interaction showed a 49.46% contribution to the total treatment of sum-of-squares variation, while genotype and environment effects explained 34.94% and 15.60%, respectively. The highest mean yield was obtained from G6 (12.52 kg/ha), and the lowest mean yield was obtained from G7 (8.65 kg/ha). According to the additive main effect and multiplicative interaction biplot, G6 and G5 were high-yielding genotypes, whereas G7 was a low-yielding genotype. Furthermore, according to the genotype and genotype–environment interaction biplot, G6 was the winning genotype in all environments. However, G7 was a low-yielding genotype in all environments. Finally, G6 was an ideal genotype with a higher mean yield and relatively good stability. However, G7 was a poor-yielding and unstable genotype. The genotype, environment, and genotype x environment interaction had extremely important effects on the biomass yield of oats. The findings of the graphic stability methods (additive main effect and multiplicative interaction and the genotype and genotype–environment interaction) for identifying high-yielding and stable oat genotypes were very similar.

9 January 2023 in Sustainability
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Journal Article
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KIBREAB YOSEFE .KIBREAB YOSEFE .
KIBREAB YOSEFE .
TTTaye Tolemariam
Taye Tolemariam
SDSolomon Demeke
Solomon Demeke
WGWeyessa Garedew
Weyessa Garedew
JWJane Wamatu
Jane Wamatu
MEMuluken Zeleke Ekule
Muluken Zeleke Ekule
The objective of this study was to investigate the fermentation characteristics and nutritional value of A. sativa genotypes ensiled either solely or in combination with P. purpureum 16791. Three different A. sativa genotypes (SRCPX80AB2806, ILRI_5527A, and ILRI_5526A) were harvested at the dough stage and ensiled solely or in combination with equal parts of P. purpureum 16791 with the addition of 3% molasses for all treatments in a completely randomized design with three replications over a period of 45 days. P. purpureum harvested at 60 days was ensiled and used as a control treatment. All treatments were evaluated for fermentation characteristics (pH, temperature, physical properties, and flieg point) and subjected to chemical analysis. The results showed that ensiling A. sativa ILRI_5527A, in combination with equal parts of P. purpureum 16791, produced the best silage with a significantly lower pH of 3.52. Optimal temperature (25 °C), nutrient losses based on the total dry matter (2.17%), gas (3.74%), and effluent (4.28%) were significantly (p < 0.0001) lower for T6 compared to the others. The dry matter recovery rate of T6 was significantly (p < 0.0001) higher than that of the others. T6 ranked first in the quality of physical properties (smell, color, mold, and texture). The highest dry matter (24%), organic matter (96.80%), crude fat (3.32%), and metabolizable energy (10.05 MJ/kg DM) were recorded for T6. The flieg score for T6 silage (96.6%) was also better than the others. In conclusion, ensiling A. sativa ILRI_5527 with equal parts of P. purpureum 16791 and the addition of 3% molasses improved fermentation characteristics and silage quality.

24 September 2026 in Veterinary and Animal Science
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Journal Article
Open access
KIBREAB YOSEFE .KIBREAB YOSEFE .
KIBREAB YOSEFE .
TETaye Tolemariam Ejeta
Taye Tolemariam Ejeta
WTWeyessa Garedew Terefe
Weyessa Garedew Terefe
MEMuluken Zeleke Ekule
Muluken Zeleke Ekule
JWJane Ambuku Wamatu
Jane Ambuku Wamatu
Evaluating forage genotypes is crucial for mitigating nutritional deficiencies and optimizing production in tropical small horn systems. This study investigated the effects of incorporating different Avena sativa genotypes (5526A, SRCPX80AB2806, and 5527A) into total mixed rations on nutrient intake, apparent digestibility, growth performance, and economic returns in yearling Bonga lambs under specific experimental conditions. Thirty intact male yearling lambs were assigned to three dietary treatments in a randomized complete block design over a 90-day feeding trial. The diets were formulated to be isocaloric and isonitrogenous (18.5% crude protein and 10.34 MJ/kg metabolizable energy). The results demonstrated that genotype 5527A (Treatment 3) significantly improved dry matter intake, apparent digestibility, and average daily gains compared to the other evaluated genotypes. Specifically, lambs on the 5527A-based diet exhibited superior average daily gains and enhanced feed conversion efficiency. Furthermore, partial budget analysis indicated higher net economic returns under the conditions of this trial. However, these findings are limited to the specific breed, forage management practices, and production system evaluated. While these results highlight the potential of utilizing superior forage genotypes to improve smallholder productivity, further multi-environment evaluations are necessary before broader generalizations can be made.