Response of Haricot Bean Varieties to Different Levels of Iron Application in Selected Areas of Ethiopia
Bibliographic record
Abstract
Haricot bean (Phaseolus vulgaris L.) can be an important source of Fe for human nutrition, particularly in regions in which human Fe deficiencies are known to occur.A study using replicated field and greenhouse experiments was conducted in Ethiopia to evaluate the yield and Fe uptake response of different haricot bean varieties (Nasir, Ibado, Hawassa Dume, and Sari-1) to different levels of foliar-applied iron (Fe) fertilizer (0, 1, 2, and 3% solution).Pot experiment results indicated yield, yield components, and tissue Fe concentrations varied among varieties and across soils.The variety Ibado yielded the highest leaf Fe concentration (290.19 mg kg -1 ) whereas Hawassa Dume had the highest number of pods per plant (7.28) and grain yield (15.85 g per pot).Varieties Sari-1 and Nasir produced the highest number of seeds per pod (4.94) and seed Fe concentration (59.02 mg kg -1 ), respectively.Levels of Fe fertilization did not significantly influence yield and yield components, but significantly increased both leaf and seed Fe concentrations.Application of 3% FeSO 4 .7H 2 O produced the highest concentration of both leaf (339.50 mg kg -1 ) and seed Fe (53.46 mg kg -1 ).Field experiments revealed that haricot bean varieties significantly varied in yield, yield components, and leaf and seed Fe concentration.Highest grain yield (3099.55 kg ha -1 ) was observed with variety Hawassa Dume.Production was significantly influenced by planting season and location.Overall, 3% FeSO 4 .7H 2 O fertilizer application best improved the quality of haricot bean produced.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".