Optimizing Foliar Iron Application: Effects of Rate and Frequency on Maize Growth, Yield and Grain Quality
Bibliographic record
Abstract
A field experiment was conducted at the Micronutrient Research Farm, Anand Agricultural University, Anand, Gujarat, India to evaluate the influence of foliar iron (Fe) application at varying rates and frequencies on the growth, yield, and quality of maize. The study comprised nine treatments arranged in a Randomized Block Design with three replications. The results revealed that foliar application of iron significantly affected cob length, the number of cobs per plant, and the grain and stover yields of maize. The treatment comprising recommended dose of fertilizer (RDF) + 0.75% FeSO4 (two sprays at 25 and 50 days after sowing (DAS)) recorded the highest grain yield (3846 kg ha-1;) and stover yield (7543 kg ha-1), showing a significant improvement over the control. The Fe and sulphur (S) content were measured in maize leaves one week after the first and second foliar sprays, and also in the grain and stover at harvest. The treatment RDF + 1.00% FeSO4 (two sprays at 25 and 50 DAS) resulted in significantly higher Fe and S content in maize leaves after both sprays. Similarly, Fe and S uptake by maize grain and stover were significantly influenced by foliar Fe application, with the highest uptake observed under the RDF + 1.00% FeSO4 (two sprays) treatment. These findings highlight the dual benefit of foliar Fe application in improving not only maize productivity but also its nutritional quality.
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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) | 0.001 | 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) | 0.001 | 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".