AMMI and GGE Biplot Analyses for Mega-Environment Identification and Selection of Some High-Yielding Oat (Avena sativa L.) Genotypes for Multiple Environments.

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.

Increased number of large non-atretic follicles and co-dominance effects account for high litter sizes in Bonga sheep.

To understand the ovarian basis for prolificacy of Bonga sheep, a total of 31 ewes were selected based on litter size (LS) records and divided into two groups: High Prolificacy (HP) (n = 20) with LS ≥ 2 and Low Prolificacy (LP) (n = 11) with LS = 1. At a synchronized estrus, follicular dynamics were determined using transrectal ultrasonography. Plasma estradiol concentrations were also monitored. In total 27 ewes were observed in estrus being 9/11 LP (82%) and 18/20 HP (90%). On the day of estrus (day 0), the mean number of large follicles was higher (p < .05) in HP (1.78 ± 0.19) than in LP (1.0 ± 0.28) ewes. Prior to estrus, more (p < .05) medium follicles were visible for HP compared to LP ewes. Plasma estradiol concentrations were higher in HP compared to LP ewes (18.91 ± 0.41 vs. 14.51 ± 0.65 pg/ml; p < .05) and similarly was ovulation number (2.3 ± 0.15 vs. 1.28 ± 0. 14; p < .05). Higher ovulation rates and litter size in Bonga sheep are evidenced by the previous presence of more large follicles and the existence of co-dominance effects as most likely medium follicles are selected to ovulate.