Dataset for: Assessment of EMS-induced sesame (Sesamum indicum L.) mutants for salinity tolerance during germination and early seedling growth
Description
The data were generated from controlled in vitro germination experiments using replicated treatments and standardized measurement protocols. They can be used to compare the salinity tolerance of different sesame genotypes during early developmental stages, investigate genotype × salinity interactions, validate statistical analyses, and support future studies on the physiological and genetic mechanisms underlying salt tolerance. Furthermore, these data provide a valuable resource for mutation breeding programs aimed at developing sesame cultivars with improved resilience to saline environments.
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Steps to reproduce
Germination was monitored every 48 hours over an 8-day period. Germination Percentage (GP) was calculated on day 8 as the proportion of germinated seeds relative to the total number of seeds. Germination Rate (GR) was calculated from the cumulative number of germinated seeds recorded at each observation, while Mean Germination Time (MGT) was determined using the weighted average of germination time based on the number of newly germinated seeds at each observation. At the end of the experiment (day 8), ten seedlings were randomly selected from each Petri dish and individually measured using a digital caliper. Root Length (RL) and Shoot Length (SL) were recorded, and the average value for the ten seedlings from each Petri dish was used for statistical analysis, with each Petri dish considered as one experimental unit. Root-to-Shoot Ratio (RSR) was calculated as the ratio of root length to shoot length, and Seedling Vigor Index (SVI) was calculated by multiplying total seedling length by germination percentage and dividing by 100. Statistical analyses were performed using SPSS Statistics version 25.0 and GraphPad Prism 9. Data were analyzed using a two-way analysis of variance (ANOVA), considering genotype and salinity treatment as fixed factors. Percentage data were analyzed without transformation because the assumptions of ANOVA were met. When significant differences were detected (p < 0.05), means were separated using Duncan's Multiple Range Test (DMRT) implemented in SAS version 9.1.3. Graphs were generated with standard deviation (SD) as the measure of variability, and significant differences among means were indicated by different letters. To further investigate the relationships among genotypes and measured traits, Principal Component Analysis (PCA) was performed using all germination and seedling growth parameters, providing a multivariate assessment of salinity tolerance.
Institutions
- Sidi Mohamed Ben Abdellah UniversityFès-Meknès, Fes