Introduction of dry yield-related traits to screen low-irrigation tolerant ecotypes in alfalfa (Medicago sativa L)

Abstract

Alfalfa is one of the most important forage crops in the world and Iran. Due to the adverse effect of drought on alfalfa yield, screening drought- tolerant genotypes is essential in breeding efforts. In the present study, 11 alfalfa ecotypes were evaluated during two years under the low-irrigation stress condition. The statistical analyzes were done on the average of two-years data. The first and second factors, respectively as "forage- quantity factor" and "forage-quality factor", explained 70.40% of the data total variance. Factor analysis showed that, the traits of fresh-forage yield, plant height, stem fresh weight and regrowth rate, had the most positive effect on dry-forage yield, respectively. Qharaghezlou ecotype with the highest dry-forage yield, and Sedghiyan ecotype, with the highest forage quality, were the most drought-tolerant and high-quality ecotypes, respectively. Ecotypes of Mohajeran and Famenein showed the lowest dry-forage yield. Ecotypes were grouped in three separate clusters. The first and third clusters were identified as "dry-forage quality" and "dry-forage quantity" cluster, respectively. These two clusters had the highest genetic distance. Correctness of cluster grouping was confirmed by the discriminant function analysis. Fresh-forage yield, dry to fresh-forage yield ratio, plant height and regrowth rate were entered into the regression model respectively, as the most important traits affecting on dry-forage yield. The traits of fresh-forage yield and dry to fresh-forage yield ratio showed the most positive direct effect on dry-forage yield. Also, plant height, through increasing fresh-forage yield, and regrowth rate, through decreasing the dry to fresh forage yield ratio, had the largest positive and negative indirect effects on dry-forage yield, respectively. According to the results, the ecotypes showed a high diversity, which suggests the use of desirable traits and superior genotypes identified for use in future alfalfa breeding programs.

Keywords

• alfalfa
• factor analysis
• cluster analysis
• discriminant function analysis
• stepwise regression
• causality analysis