## 2 Results and Discussion
### Morphological Characterisation
#### 2.1.2 Descriptive Analysis of Quantitative Morphological Traits Studied
In addition, most of the quantitative characters studied show greater variability between the two varieties than within the variety. In general, significant variations between the two varieties were found (Table 4); the analysis of variance revealed highly significant differences (\(p<0.001\)). The results indicate that among the 16 traits examined, the 8 most discriminating traits were the number of seeds per capitula, the weight of 100 seeds, the number of tubular flowers per capitula, the \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **Variation Coefficient** & **Variation Coefficient** & **Variation Coefficient** & **Significant** \\  & **between Individuals** & **between Individuals** & **between the Two** & **between the Two** \\  & **of** _A.P_ var. _depressus_ & **of** _A.P_ var. _pyrethrum_ & **Varieties** & **\(p=0.001\)** \\ \hline Roots & & & & \\ Length (cm) (LOR) & 16.72\% & 15.65\% & 39.45\% & *** \\ Width (cm) (LAR) & 13.37\% & 19.81\% & 23.02\% & ns \\ \hline Leaves & & & & \\ Number of branches/individual & & & & \\ (FNR) & 24.86\% & 31.62\% & 34.71\% & *** \\ \hline Capitula & & & & \\ Number/individual (NC) & 33.36\% & 21.78\% & 45.47\% & ** \\ Length (cm) (LOC) & 14.55\% & 13.83\% & 33.67\% & ns \\ Width (cm) (LAC) & 14.31\% & 13.11\% & 31.06\% & ns \\ \hline Lysulate flowers & & & & \\ Number/capitula (NFL) & 7.43\% & 11.76\% & 13.25\% & ns \\ Length (mm) (LOFL) & 16.05\% & 6.67\% & 48.83\% & *** \\ Width (mm) (LAFL) & 11.72\% & 24.96\% & 26.35\% & * \\ Tubular flowers & & & & \\ Number/capitula (NFT) & 33.21\% & 23.44\% & 33.92\% & ns \\ Length (mm) (LOFT) & 21.50\% & 7.93\% & 29.11\% & * \\ Width (mm) (LAFT) & 5.94\% & 16.08\% & 33.83\% & ns \\ \hline Seeds & & & & \\ Number/capitula (NG) & 25.59\% & 18 \% & 29.50\% & *** \\ Length (mm) (LOG) & 12.36\% & 3.79\% & 14.35\% & ns \\ Width (mm) (LAG) & 8.54\% & 5.52\% & 23.40\% & ns \\ Weight of 100 seeds (g) (PG) & & & & \\ \hline \hline \end{tabular}  
* \(p<0.05\); ** \(p<0.01\); *** \(p<0.001\); ns: not significant.  
\end{table} 
Table 4: Descriptive statistics for quantitative morphological characteristics. length and width of Ligulate flowers, the number of capitula per individual, the number of branches, and the length of the roots. * Correlation between quantitative morphological characteristics  
The correlation coefficient quantifies the degree of association or variation between the two descriptors. The sign of the coefficient indicates the type of association: positive (+) if the relationship is direct and negative (-) If the relationship is inverse. If the coefficient approaches 1, the two descriptors are closely correlated [19]. Table S1 shows the correlation coefficients obtained between the 16 quantitative traits measured. These analyses show the presence of significant positive and negative correlations between all the characteristics studied. In particular, they show the presence of highly significant positive correlations between characteristics describing the same variety and a highly significant negative correlation between characteristics concerning the variety _A.P_ var. _pyrethrum_ and those concerning the variety _A.P_ var. _depressus_. As the significant correlations generally concern different parts of the two varieties, no characteristics could be eliminated as a result of this analysis. The highest positive correlation coefficient (r = 0.99) was observed between the length and width of capitula (LOCP, LACP, LOCD, and LACD). The highest positive correlation coefficient (r = 0.99) was observed between the length and width of flower capitula (LOCP, LACP, LOCD, and LACD) and the number, length, and width of ligulate flowers (NFLP, LOFLP, and LARFLP) (Table S1). Furthermore, the results of [PERSON]'s sphericity test and the overall KMO index for the matrix are significant, which confirms that the data matrix can be subjected to exploratory factor analysis. In our PCA analysis, the first principal component explains 71.71% of variability and the second 22.41%.