This study was conducted to assess sources of genetic and phenotypic variability of eight different alfalfa populations. Thirteen morphological and agronomic traits were measured on studied populations. The traits included plant Height, leaf chlorophyll content, total fresh weight, total dry weight, leaf fresh weight, leaf dry weight, stem wet weight, stem dry weight, number of stems, number of leaves, ratio of number of leaves to number of stem, ratio of leaf dry weight to stem dry weight and total dry weight: total wet weight ratio.Multivariate data set was analyzed by CANONICAL DISCRIMINANT ANALYSIS (CDA) in combination with a clustering procedure. The first two CANONICAL variables were significant and CANONICAL variables indicated that leaf dry weight, total dry weight, total fresh weight, leaf fresh weight and stem fresh weight are the most differentiating traits among the studied traits of plant populations. The CANONICAL variables were used for clustering the populations into three main groups. CDA was useful in identifying the genetic variation and the traits that better describe the variation among the alfalfa populations. Effective traits identified in the variation between populations included that leaf dry weight, total dry weight, total fresh weight, leaf fresh weight and stem fresh weight and plant height. Recognize the diversity of these traits among the cultivated alfalfa allow to plant breeders that focus on specific traits that contribute to variation. Cluster ANALYSIS was successful in differentiating the populations into similar main groups on the basis of the measured traits.

This study was conducted to assess the sources of genetic and phenotypic variability of peanut varieties. 15 morphological and agronomic traits were measured on 39 peanut varieties. These traits include oil yield, 100 grain weight, 100 pod weight, grain length, grain width, pod length, pod width, grains in pod, grain: pod weight ratio, grain: pod volume ratio, oil%, pods per plant, leaflet length, leaflet width and leaflet length: width ratio. The multivariate data set was analyzed by CANONICAL DISCRIMINANT ANALYSIS (CDA) in combination with a clustering procedure. In this ANALYSIS, the first two CANONICAL variates were significant and CANONICAL variates indicated that 100 grain weight, oil yield, 100 pod weights, grain: pod volume ratio and pods per plant are the most differentiating traits among the varieties. The CANONICAL variates were used to cluster the varieties into three subgroups. CANONICAL DISCRIMINANT ANALYSIS was useful in identifying the genetic variation and the traits that better describe the variation among Peanut varieties. Cluster ANALYSIS was successful in differentiating the varieties into similar subgroups on the basis of the measured traits.

Licorice (Glycyrrhiza glabra L. ) is one of the oldest and most important medicinal plants in Fabaceae, used for curing many diseases since 4000 years ago. This study was conducted to evaluate the genetic diversity of 22 different licorice populations based on morphological and yield traits at the research field of the Faculty of Agriculture, Zanjan University, during 2016 to 2018. Morphological and yield traits including plant height and width, leaf length and width, number, length and width of leaflets, number of lateral branches, main stem diameter, aerial parts fresh and dry weight, root fresh and dry weight, root to aerial parts ratio and aerial parts and root yields (per m2) were measured. CANONICAL DISCRIMINANT (CDA) and cluster (CA) analyses were used to group the populations. In CDA, the first two CANONICAL variables were significant. The first CANONICAL variable included plant height and width, main stem diameter, leaf length and the number of leaflets, and the second one included aerial parts fresh and dry weight, root fresh and dry weight, root and aerial parts yields. The second CANONICAL variable had the greatest role in population separation and grouping. CANONICAL variables divided populations into four main groups and confirmed CA clustering results. In general, the results indicated the good potential of CANONICAL DISCRIMINANT ANALYSIS in evaluating the genetic diversity and identifying the index traits in licorice.

This study was conducted to assess the sources of genetic and phenotypic variability of Common bean genotypes. Fourteen morphological and agronomic traits were measured on 49 common bean genotypes. These traits included: days to 50% flowering, days to 50% Podding, pod length, pod wide, seeds per pod, seed length, seed wide, stem diameter, seed and pod weight, number of full pod, number of total pod, seeds per plant, 100-seeds weight and seed yield per plant. The multivariate data set was analyzed using CANONICAL DISCRIMINANT ANALYSIS (CDA) in combination with a clustering procedure. In this ANALYSIS, the first two CANONICAL varieties were significant and justified 88.5% and 94.7% of variances in normal condition and drought stress, respectively. According to cluster ANALYSIS, genotypes were divided in 4 categories. CANONICAL DISCRIMINANT ANALYSIS is useful in identifying the genetic variation and describes the variation among common bean genotypes. Cluster ANALYSIS was successful in differentiating the varieties into similar subgroups based on the measured traits.

In order to identify the correlations and relationships between different traits in the bread wheat, 30 genotypes were investigated as sub-factors under the split plot experiment in the completely randomized design in greenhouse and the randomized complete block design in the field with three replications under the normal irrigation and post-anthesis water stress conditions. The results of the CANONICAL correlation ANALYSIS showed that at both normal and moisture stress levels, there was a significant correlation between the pair of CANONICAL variables obtained from the phenological traits and the yield components. Under the normal conditions in the greenhouse, to increase the spike density and length, the number of days to 50% flowering can be considered as a suitable selection criterion. According to the results of CANONICAL correlation ANALYSIS, under the stressed conditions in greenhouse, to increase the spike length, the number of days to the heading, maturity and 50% of flowering were among the important and effective factors. Under the optimum irrigation conditions in the field, the number of days to maturity was considered as one of the factors influencing the seed weight per spike. The results of CANONICAL correlation ANALYSIS under the field stress conditions showed that to increase the 1000-seed weight, the number of days to maturity can be considered an appropriate selection criterion.

CANONICAL DISCRIMINANT ANALYSIS (CDA) in combination with cluster ANALYSIS and genotype by trait (GT) biplot ANALYSIS methods were used to assess 9 wheat cultivars having different degrees of tolerance along with 36 F1 hybrids derived from partial diallel crosses, using stress tolerance indices, in two levels (well watered and cessation of irrigation at pollination stage). Cluster ANALYSIS classified genotypes into three clusters and the results used to CDA as classifying variable. The first CANONICAL variable was under the influence of high CANONICAL coefficients for STI, HM, MP, GMP, Yp and Ys. CANONICAL coefficients for SSI and TOL in the second CANONICAL variable were remarkable. The first CANONICAL variable differentiated genotypes based on yield potential and stress tolerance and the second CANONICAL variable distinguished stress tolerant genotypes from the sensitive ones. The scatter plot of two significant CANONICAL variables characterized three distinct groups and all pairwise Mahalanobis distances among groups were significant. The genotypes in group 1 had high yield in stress and non-stress conditions and the genotypes of group 3 had more stress tolerance and less susceptibility to stress. To identify tolerant and high yielding genotypes in both stress and non-stress conditions, GT biplot ANALYSIS based on combination of STI and SSI was used and superior genotypes which simultaneously have higher yield potential and stress tolerance were introduced.