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This study aimed to assess the effect of fertilizer rates on agro-morphological traits, nutrient uptake and use efficiency, and economic feasibility of fodder yield in oat. The study was carried out using a factorial arrangement of eight fertilizer rates (0, 25, 50, 75, 100, 125, 150, and 175 %) and three oat varieties (CI-8251, SRCPX80Ab2291, and Sorataf) in a randomized complete block design with three replications. The results revealed that variety, fertilizer, and year had a significant effect on fodder yield. The fodder yield increased up to 175 % of the blanket recommended fertilizer and application of 100, 125, 150, and 175 % produced fodder yield of 14.0, 15.6, 15.8, and 15.9 t/ha, respectively. The fodder type oat (CI-8251) and dual type oat (SRCPX80Ab2291) variety had 15.4 and 10.6 % fodder yield advantages over grain type oat (Sorataf), respectively. Significantly higher fodder yield was obtained in 2021 than 2020 cropping season. Moreover, the interaction effect of variety by fertilizer by year was significant for nitrogen and phosphorus concentrations, nitrogen and total nutrient uptake, partial factor productivity, internal utilization efficiency, agronomic efficiency, and agro-physiological efficiency. The Pearson correlation analysis indicated that the number of tillers per plant, yields of morphological fractions (leaf, stem, and panicle), forage yield production rate, nutrient concentration, and uptake had significant positive associations with fertilizer levels and fodder yield. In contrast, all nutrient use efficiency except agro-physiological efficiency had inverse associations with fertilizer levels and fodder yield. The combined partial budget analysis for the three oat varieties showed that the application of 125 % fertilizer produced a higher net benefit (83,440 ETB) and marginal rate of return (12,635.6 %). Therefore, the blanket recommended fertilizer rate of 125 % (51.3 kg N + 25 kg P/ha) could increase fodder yield and farm profitability in the study area.

Abstract Background The performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement. Methods The study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times. Results The pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression ( S d i 2 ), the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance ( S ( 2 ) i ), indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones. Conclusions Therefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.

This study aimed to evaluate the genotype by environment interaction for agro-morphological traits and fodder nutritive values of oat (Avena sativa L.) genotypes under diverse environmental conditions and to identify high fodder yielding and consistently performing genotypes in Ethiopia. Oat genotypes usually demonstrate inconsistent performance in different environments due to variations in growing environments and the existence of significant genotype by environment interaction complicates the selection process. Therefore, the genotype by environment interaction study was executed using the additive main effects and multiplicative interactions model to select superior and stable genotypes. The effects of genotype by environment interaction on fodder yield stability in twenty-four oat genotypes were investigated in nine environments using a randomized complete block design. The additive main effects and multiplicative interaction analysis of variance for fodder yield showed significant variation for genotype, environment, and genotype by environment interaction effects and the highest contributor for the total variation was the environment (67.45 %) main effect followed by genotype by environment interaction (22.73 %) and the genotype (9.82 %). The first (44.11 %) and the second (26.79 %) interaction principal component axes were significant and cumulatively accounted for 70.91 % of the total interaction variance. Based on the first two additive main effects and multiplicative interaction analyses, E6, E4, and E2 were located far from the biplot origin and had a high contribution to the total variation of genotype by environment interaction. The fodder yield of the genotypes G6, G10, G23, G7, G9, G18, G13, G1, G24, G15, G2, G3, G19, G4, and G11 were above the grand mean. The fodder yield stability result obtained from the second additive main effects and multiplicative interaction is usually more accurate compared to the first additive main effects and multiplicative interaction. Consequently, genotypes which had mean fodder yield above the grand mean and relatively stable performance were observed for G23, G6, G10, G7, G11, G1, G9, G15, G2, G3, G4, and G19. However, G2, G3, G4, G11, and G19 have released varieties while the remaining genotypes have not been yet released. Therefore, G6, G10, G23, G7, and G9 were selected for verification and cultivation of these selected genotypes would contribute more for oat fodder yield improvement programs in Ethiopia.

The study aimed to estimate the extent of genetic variability, heritability, and expected genetic gain attributable to agro-morphological traits and to suggest an effective selection scheme and specify promising genotypes for future oat breeding programs. One hundred twenty oat (Avena sativa L.) genotypes were evaluated at two locations in the central highlands of Ethiopia. The analysis of variance indicated that the mean squares due to locations and genotype x location interaction varied significantly for some measured traits while that of genotypes varied significantly (P ≤ 0.001) among all studied traits. Moreover, the estimates of genetic variability, heritability, and expected genetic advance indicate the presence of remarkable genetic diversity in oat genotypes and the magnitude of selection response for these traits in the population to develop superior genotypes for oat improvement. The result revealed that very high to moderately high heritability in a broad sense (h2b) coupled with high to moderate genetic advance as percent of mean (GAM) were recorded for forage dry matter yield (h2b = 64.4%; GAM = 19.3%), number of leaves at forage harvest (h2b = 86.4%; GAM = 19.9%), stem dry matter yield (h2b = 62.9%; GAM = 18.3%), forage yield production rate (h2b = 69.7%; GAM = 21.7%), grain yield (h2b = 64.2%; GAM = 26.1%), panicle length at grain harvest (h2b = 80.9%; GAM = 21.9%), number of spikes per panicle (h2b = 68.8%; GAM = 16.0%), and harvest index (h2b = 80.9%; GAM = 26.0%). The results suggested that these traits are governed more by additive gene action, and selection based on these traits could be effective to achieve the desired genetic gains for improvement. Therefore, high forage dry matter and grain yielding genotypes along with these desirable yield-related traits could be considered for selection in oat genotypes.

Abstract A total of 24 oat genotypes were evaluated across 9 environments using a randomized complete block design with 3 replications. The combined analysis of variance indicated that grain yield was significantly affected by genotype, environment, and genotype by environment interaction (GEI). The environment's main effect explained 44.62% of the total variation, whereas the genotype and GEI captured 28.84% and 26.54%, respectively. The genotype plus genotype by environment (GGE) biplot of the first two principal components also explained (PC1 = 68.46%) and (PC2 = 11.84%) of the GEI sum of squares. G8 (SRCPX80Ab2267), G17 (SRCPX80Ab2310), G5 (Sorataf), G14 (Was), G19 (Bonsa), G22 (SRCPX80Ab2691), G12 (SRCPX80Ab2291), G11 (SRCPX80Ab2806), G4 (CI‐8251), and G16 (Jassari), which were located at the left side of the PC1, showed better grain yield performance. The smaller PC1 values were observed for G19, G14, G8, G11, G12, G17, and G22 showing better grain yield stability. The ideal genotype located at the center of the concentric circles was G19, whereas the desirable genotypes were G14, G8, G11, G12, G17, and G22. The polygon view of the GGE biplot showed that the first mega‐environment contains environment E3 (Ginchi) with G16, whereas the second mega‐environment contains environments E7 (Bekoji) and E8 (Kuyu) with G12 being the winning genotypes. The vertices of the fourth mega‐environments (E6—Kulumsa) were G8 and G17, whereas the remaining environments grouped in the third mega‐environment without vertex genotype. Furthermore, E2 (Ada‐Berga) was an ideal test environment, whereas E5 (D/Zeit), E1 (Holetta), and E4 (Jeldu) were identified as favorable environments. Regarding the mean grain yield and stability, G19, G14, G8, G11, G12, G17, and G22 were the best genotypes that can be considered adapted genotypes to wider environments.