Drought is a devastating factor for crop production worldwide. Therefore, an experiment was conducted to study genetics for some agro-physiological traits in cotton under drought stress. The 13 parental cotton genotypes along with their 30 F1 hybrids were planted under normal and drought conditions. The mean performance of the genotypes was assessed through principal component and heat map analyses. The principal component analyses revealed 53.99 and 53.15% in the first two principal components of variability for normal and drought conditions, respectively. Heat map analysis revealed that three cotton genotypes i.e. FH-207 × NS-131, FH-207 × KZ-191 and S-15 × AA-703 attained higher values for all the traits except for canopy temperature under drought conditions. These crosses may proliferate to further filial generations to identify transgressive segregates for drought tolerance. The heritable differences of F̅1 and mid-parent showed dominance and non-additive gene action under drought conditions. Heritable differences between F̅1 and P̅1 showed over dominance and partial dominance under drought conditions. Heritable differences between F̅1 and P̅2 indicated negative over dominance and partial dominance for all traits under drought conditions. Proline contents and the bolls per plant showed high heritability and genetic advance through additive gene action. Therefore, these two traits can be used as a means of selection in future breeding programmes of drought tolerance.

The aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.