Agro-morphological Characterization of Maize (Zea mays L.) Hybrids Under Acid Soils in Two Contrasting Environments
Bibliographic record
Abstract
Acidic soils cover 75 to 80% of the arable soils in the humid forest areas of Cameroon, causing maize yield losses of around 69%. Sixty-four accessions of maize hybrids were developped from “Line × Tester” crosses between twenty tropical inbred lines with three testers (Cam inbgp117, 88069 and 9450) and between testers themselves, and one acid tolerant open pollinated variety (ATP-SR-Y). Those inbred breeding lines were collected from CIMMYT, IITA and IRAD and the derived single hybrids were characterized using agro-morphological maize’s descriptors on a completely randomized block design in two contrasting environments (Nkoemvone and Nkolbisson). The data collected was subjected to multivariate analyses. The Principal Component Analysis showed the first two components being 73.60% and 78.99% of the total variation in Nkoemvone and Nkolbisson, respectively. Furthermore, grain yield showed a positive and highly significant correlation with the plant emergence rate in Nkoemvone (r = 0.61, P < 0.001) and Nkolbisson (r = 0.84, P < 0.001). Hierarchical Clustering Analysis indicated that these accessions forms four distinct groups, where each of the groups showed clear specific features for which the performance differs from that of the others in Nkoemvone and Nkolbisson. Characters such as plant emergence rate, prolificacy, ear appearance and grain yield have been found as important phenotypic markers for assessing agromorphological diversity of maize hybrids. These traits should necessarily be considered in maize breeding programs for varietal discrimination and formulation of cores collection of maize tolerant to aluminum and manganese toxicities in the soil.
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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) | 0.000 | 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 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".