Genetic studies on iron and zinc concentrations in common bean (Phaseolus vulgaris L.) in Ghana
Bibliographic record
Abstract
Iron and zinc deficiencies cause high health risk to young children and expectant mothers in sub Saharan Africa. The development of biofortified common bean ( Phaseolus vulgaris L.) varieties could address the acute micronutrient deficiencies with associated improvement in the nutrition and health of women, children and adults. The objective of this study was to determine the mode of gene action and genetic advance in iron and zinc levels in common bean. Field experiment was carried out using six generations of two populations made of crosses between pairs of low iron, low zinc and high iron, moderate zinc genotypes (Cal 96 ˣ RWR 2154; MCR-ISD-672 ˣ RWR 2154). Each generation (P 1 , P 2 , F 1 , F 2 , BC 1 P 1 and BC 1 P 2 ) was evaluated on the field in a randomized complete block design with three replications. Generation mean analysis were performed for each trait measured in each of the crosses while iron and zinc levels were quantified by x-ray fluorescence. The study showed that both additive and non-additive gene effects were important in determining the expression of high iron and zinc levels. Iron concentration in the common bean seeds ranged from 60.68 to 101.66 ppm while zinc levels ranged from 25.87 to 34.04 ppm. Broad sense heritability estimates of iron and zinc were high in the two crosses (62–82% for Fe and 60–74% for Zn) while narrow sense heritability ranged from low to high (53–75% for Fe and 21–46% for Zn). Heritability and genetic gain were used as selection criteria for iron and zinc, and it was concluded that doing so would be beneficial for future improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".