Investigation of Morphological Diversity and Evaluation of Tomato Lines Yield Using Multivariate Statistical Analysis
Notice bibliographique
Résumé
Introduction Tomato is a product with a wide range of genotypes with different yields and selection based on this trait and its components can accelerate the breeding programs of this plant. The most important goals of tomato breeders have been to increase yield, disease resistance, early maturity, and improve the quality characteristics of the fruit. Therefore, awareness of genetic diversity in the population is an important step in plant breeding, and to achieve this goal, the studied genotypes must first be identified in terms of genetic potential and favorable agronomic traits. The usual approach for describing and evaluating populations requires cultivating sample populations and evaluating their morphological and agronomic characteristics. In this regard, multivariate statistical methods play an important role in studying genetic diversity and selecting appropriate parents. Unfortunately, the tomato cultivars used in Iran are often not at the favorable level in terms of important traits such as the number of days to fruit ripening, fruit weight, fruit yield, fruit length, and width, and few studies have been done on these traits. Therefore, this study was conducted to investigate the morphological diversity, evaluate the yield and its components among some imported tomato lines using analysis of variance, cluster analysis, and principal component analysis. Materials and Methods This study was performed in Sari University of Agricultural Sciences and Natural Resources, Mazandaran, Iran in 2020. The plant material included 24 tomato lines imported from the Canadian Plant Gene Bank and one check variety. The experimental design used for morphological analysis was a randomized complete block design with three replications. Evaluated characteristics were included of the number of days to first flowering, number of days from germination to first fruit coloring (early ripening), plant height (cm), number of fruits per plant, average fruit weight per plant (g), plant yield (g), length and width of the fruit (cm). After measuring the characteristics at the farm and recording the data, analysis of variance was performed to examine the diversity between lines in terms of the studied variables, and Duncan test was used to compare the means and SAS software was used to test the correlation coefficients of the variables. Cluster analysis for grouping of tomato lines was performed based on the mean of the main data of standardized traits, which was determined by Euclidean distance to determine the distance between the lines, and the UPGMA method was used to merge the clusters. Principal component analysis was performed based on the mean of the main data of morphological traits. Results and Discussion The results of the analysis of variance showed that there was a significant difference between all lines in terms of the studied characters. Also, principal component analysis based on morphological traits showed that the first two main components accounted for 75% of the total phenotypic variation in the data and the number of days from germination to first fruit coloring (-0.606), the number of days to first flowering (-0.516), fruit weight per plant (0.492), fruit width (0.480), fruit length (0.472), plant height (-0.445), fruit yield per plant (0.395) and the number of fruits per plant (-0.367) had the highest contribution in yield changes. Therefore, these variables might be taken into consideration for effective selection of parents for hybridization programs for broadening the genetic base in the population as well as to develop elite lines or F1 hybrids. UPGMA cluster analysis also divided the studied lines into nine groups. Group IX lines were in good condition in terms of yield traits and components, group VIII lines in terms of maturity and flowering, and group IV lines in terms of fruit number per plant. And the lines in groups I and V were in moderate condition for all traits. According to these results, the cross of the lines in the more distant groups can produce hybrids with high diversity and maximum heterosis. Conclusion According to the main purpose of this study, which was to evaluate the yield and its components and according to the analysis performed, lines 8, 11, and 17 due to showing the least number of days to flowering and early, lines 10 and 14 due to having the highest yield, the highest fruit weight, and highest fruit length and width and lines 2, 9, 15, 21, and 24 due to having the highest number of fruits per plant and the favorable height are also recommended for use in tomato breeding programs.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».