Comparison of Foliar Verses Soil Application of Micronutrients on the Production of Wheat (Triticum aestivum. L) Crop.
Bibliographic record
Abstract
The present work was laid out to compare the effect of foliar verses soil application of micronutrients on the production of wheat crop at experimental side of southern wheat station Agriculture Research Institute Tandojam, during Rabi season 2016. There were ten fertilizer treatments viz T1= K2%, T2= 1% Zn, T3= B 0.2%, T4= Cu 2%, T5= Mg 1% as foliar application while T6= 6Kg Zn ha-1, T7= 3.5Kg B ha-1 (Borax) T8= 5Kg Cu ha-1 (CuSo4), untreated T9 tried with an standard dose of 230-115 Kg and NP ha-1 was (T10). The experiment was laid out in three replicated Randomized Complete Block Design. It was observed that plant height, tillers plant-1, spike length, grains spike-1, 1000 grain weight and grain yield ha-1 differed significant between all the treatments. Soil application of 6 Kg ha-1 Zn gave maximum grain yield of 5113.33 Kg ha-1, this increscent in yield was associated with significant increase in tillers plant-1 of 20.81.Spike length of 13.84 cm, grain spike-1 of 71.95 and 1000 seed weight was 68.66 respectively. It is concluded that soil application of micronutrients were relatively more effective than foliar application in local soil condition. Among the micronutrients Zn applied at 6 Kg ha-1, followed by 3 Kg Mg ha-1 and 3.5 Kg B ha-1 gave higher grain yield due to increased values in all yield related parameters.
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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".