Genetic effects on the efficiency and responsiveness to phosphorus use in popcorn as estimated by diallel analysis
Bibliographic record
Abstract
Agricultural expansion and the need for sustainable cultivation are challenges faced by researchers involved in the generation of new cultivars that can adapt to abiotic stress. Knowledge of the genetic effects of characteristics related to efficiency and responsiveness to phosphorus use must be considered when implementing methods to obtain better genotypes. The aim of this study was to characterize and select popcorn hybrids based on their efficiency and responsiveness to phosphorus use, and estimate their combining abilities and genetic effects via diallel analysis to implement improvement programs for sustainable agriculture. Eight contrasting inbred lines were used to obtain simple hybrids for diallel analysis. Twenty-eight diallelic hybrids plus the popcorn parental lines were evaluated at two different sites under two contrasting environments for soil phosphorus availability (6 × 6 lattice design). Grain yield, popping expansion, and volume of expanded popcorn per hectare were measured. A combined analysis of variance and a test of means were performed. The classification and utilization of the phosphorus use efficiency index, according to the grain yield performance of the hybrids under contrasting environments, was considered. Through model 2 of the Griffing's diallel analysis method, the general and specific combining abilities were estimated, along with their environmental interactions. The best strategy to obtain genotypes that are efficient and responsive to phosphorus involves exploring popcorn hybrids using genitors that result in the accumulation of additive genes that promote popping expansion. Hybrids P7 × L80, P7 × L59, P7 × L76, and P6 × L80 presented promising results and may be evaluated as cultivation options in phosphorus-deficient soils.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".