Potential of Tropically-Adapted Exotic Acid Tolerance White Maize Donor Lines in Sub-tropical Breeding Programmes for Low pH Adaptation
Bibliographic record
Abstract
Low pH in soils is attributed as the main contributor to the low yields characteristic in maize production regions of Angola. Here, eight white-kernel acid soil tolerant donor lines (ASTDLs) sourced from CIMMYT-Colombia (testers) were crossed with eight white elite lines adapted to the mid-altitude climatic conditions from CIMMYT-Zimbabwe, in order to identify donor lines, which can be potential sources of acid tolerance genes in breeding programmes in Angola. The two groups of parents were crossed using a line by tester (L×T) mating design, yielding 47 crosses with sufficient seed, which were evaluated alongside eight acid tolerant commercial hybrids, during the 2014-16 cropping seasons at nine sites, representing acid and non-acid soils in Angola and Zimbabwe. Effects of general combining ability (GCA) due to lines and testers, as well as specific combining ability (SCA) on grain yield were significant (P < 0.05) under acid soils. From the CIMMYT-Zimbabwe breeding programme, inbred lines identified as ZW1, ZW4 and ZW5 together with the CIMMYT-Colombia ASTDLs (i.e., CW4 and CW8) seemed to be ideal parents for crosses that can do well under both the acid and non-acid soils. The best specific cross for acid soils was identified as ZW1 × CW8 (CH142512), whereas for non-acid soils, ZW3 × CW4 (CH142500) was identified. Interestingly, the cross ZW1 × CW8 was also observed as stable under both acid and non-acid soil conditions. Overall, data showed potential of exotic acid tolerance donor lines for enhancing low-pH adaptation in sub-tropical maize populations.
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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 | 0.000 | 0.000 |
| 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".