Inheritance of Secondary Metabolites Associated With Cowpea Resistance to Flower Bud Thrips
Bibliographic record
Abstract
Plant secondary metabolites are part of defense mechanism that form host plant resistance. Understanding the inheritance of secondary metabolites is preliquisite for conducting an effective plant breeding program. The objective of this study was to determine the mode of inheritance of secondary metabolites associated with resistance of cowpea to flower bud thrips. Five cowpea genotypes with varying level of resistance to flower thrips were crossed in full diallel method 1. The F2 were evaluated for secondary metabolites and flower thrips damage in field and screen house. The results confirmed genotype TVU-3804 as resistant, and TVU-9820 and TVU-201 as susceptible to flower bud thrips. Flavonoids, antioxidants, proteins and reducing sugars had significant (p < 0.001) GCA and SCA effects indicating the importance of additive and dominance effect in controlling resistance of flower bud thrips. In addition, significant reciprocal observed for crosses such as Lori Niebe × Sanzi and Sanzi × TVU-3804 for flavonoids and antioxidants is an indication that maternal effect is key in governing resistance of cowpea to thrips. The broad sense heritability was low for secondary metabolites except flavonoid which had moderate value, an indication that delaying selection to a later generation would yield better results while breeding for resistance to flower thrips based on secondary metabolites. The results of this experiment showed that it is possible to use secondary metabolites to breed for improved cowpea resistance to flower bud thrips.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".