The Effects of Fe, Mn and Zn Foliar Application on Yield, Ash and Protein Percentage of Forage Sorghum in Climatic Condition of Esfahan
Bibliographic record
Abstract
Improving forage yield and quality remains a major concern of the producer. Among the various ways of supplying nutrient to the crops, the efficient utilization of nutrients by the plants is made through foliar application. Foliar application had significant effect on plant height, the number of leaf, the number of tiller, LAI, fresh forage yield, dry leaf yield, dry stem yield, total dry yield, dry leaf weight/dry stem weight ration and ash percentage. Application methods of micronutrients are very important to attain the best absorption. The results of this study demonstrated that, Fe, Zn and Mn had positive effect on yield and quality of forage sorghum. The highest plant height, LAI, Fresh forage yield, dry leaf and stem yield, total dry yield and dry leaf weight/dry stem weight was obtained in Zn+Fe+Mn application. The highest number of tiller was related to combination of Zn and Mn. The maximum ash percentage and appropriate protein percentage also was achieved in application of Zn+Fe+Mn. So, on the basis of the results, it seems that application Zn+Fe+Mn was suitable to gain high forage yield and gain to high 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) | 0.000 | 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) | 0.001 | 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".