Effects of Iron Fertilization on Yield and Tissue Micronutrients Concentrations of Different Haricot Bean (Phaseolus Vulgaris L.) Varieties in Southern Ethiopia
Bibliographic record
Abstract
Although required in smaller quantity, micronutrients are as essential as macronutrients for optimum growth and yield for beans. This study was conducted under field conditions at Halaba, Butajira and Taba, and under greenhouse conditions with soils collected from the aforementioned locations to evaluate the effect of Fe fertilization on tissue micronutrients (Zn, Fe, Cu and Mn) contents of different haricot bean varieties. The treatments include two factors, haricot bean varieties (Nasir, Ibado, Hawassa Dume, and Sari-1) and levels of foliar-applied iron (Fe) fertilizer (0, 1, 2, and 3% solution). Both the pot and field experiments indicated that yield and tissue micronutrients (Zn, Fe, Cu and Mn) concentrations of haricot bean varied significantly across soils (locations) and among varieties. The highest seed Fe concentration (59.04 mg kg -1 ) was recorded in Taba soil, whereas the lowest value, 35.63 mg kg -1 , was observed in Halaba soil. Hawassa Dume and Nasir produced the highest and equal grain yield, whereas Nasir produced the highest seed Fe concentration (59.02 mg kg -1 ). The highest leaf Fe concentration (290.19 mg kg -1 ), was observed with Ibado. Foliar application of FeSO 4 .7H 2 O did not significantly influence tissue Zn concentration and leaf Cu concentration, but seed Cu, tissue Mn and tissue Fe were significantly affected by Fe fertilization. The application of different levels of Fe fertilizer did not significantly influence yield of haricot bean varieties, but it significantly increased both leaf and seed Fe concentrations. Consequently, Nasir and 3% FeSO 4 .7H 2 O were found to be the best variety and rate, respectively, for quality production of haricot bean. Keywords: Manganese, Iron, Copper, Zinc, Concentration and Haricot bean
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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 | 0.000 |
| 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".