Investigation of Morphological Diversity and Evaluation of Tomato Lines Yield Using Multivariate Statistical Analysis
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".