Evaluation of sorghum (Sorghum bicolor (l.) Moench) genotypes for striga resistance and associations among yield and striga related traits
Bibliographic record
Abstract
Striga stands as one of the most significant biotic constraints, severely impacting sorghum production and productivity in the northern and northeastern regions of the country. This study aimed to assess the performance of 49 sorghum genotypes against Striga hermonthica and examine the relationship between yield and traits associated with Striga. The experiment was carried out in Kobo, located in north eastern Ethiopia, during the main cropping season of 2022, utilizing a simple lattice design. The variance analysis indicated highly significant differences (p < 0.01) among genotypes across all traits. Genotype E17024-2 exhibited the highest Striga severity mean of 58.9, while the lowest mean of 1.4 was observed in genotype E17008-1. The grain yields recorded were highest at 6.5 tons per hectare for E17096-2 and lowest at 2.5 tons per hectare for E17065-2. At the genotypic level, grain yield showed highly significant positive correlation with panicle length (0.68), head count (0.83), head weight (0.95), biomass yield (0.77) and harvest index (0.8). Both at phenotypic and genotypic level, grain yield was highly significant negative correlated to striga count (-0.9,-0.94), striga vigorisity (-0.24, -0.36) and striga severity (-0.89, -0.93. The first five principal components of analysis explained 86.8% of the total variation and the traits Panicle width (0.76), Striga vigorisity (0.69), days to maturity (0.59), days to flowering (0.55) and plant height (0.46) captured most of the variability. Cluster analysis was done to group the genotypes based on multiple traits, forming five distinct clusters. The highest Striga severity score was recorded in Cluster IV (50.9) and Cluster II (48.2) but the minimum score was recorded in Cluster III (7.9). Hence, genotypes from cluster III can be used in sorghum breeding programs for grain yield improvement under Striga infested areas. The maximum inter-cluster distance was recorded between cluster III and cluster V (666.2). Accordingly, resistance, tolerance and susceptible genotypes were identified.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".