Foliar Application of Boron Improves the Growth and Yield in Barley (Hordeum Vulgare L.)
Bibliographic record
Abstract
Crop productivity is limited by several environmental constraints. Among these, micronutrients availability to plants plays a key role in agricultural crops production. Boron is an important micronutrient for crops and it significantly aids in a grain setting. Soil applied boron become unavailable to plant due to fixation in soil sites and cause nutrients deficiency in plants, immature grain settings, and yield reduction. Thus, the aim of this work was to quantify the impact of the foliar application of boron gradients on the yield and growth of barley (Hordeum vulgare L.). Five treatments were studied, which included; T1= Control (0% B), T2 = 0.5% B, T3 = 1% B, T4 =1.5% B, and T5 =2% B. The results indicated that foliar application of B significantly increased the yield and growth traits in barley. However, 2% foliar application of B showed the highest value for all studied traits including; plant height (5.6%), number of tillers per plant (2.4%), spike length (32%), weight gain per spike (6.2%), seed index (6%), grain yield (10%), and biological yield (4%). Based on these findings, it can be concluded that the foliar application of B at 2% can be used to improve the growth and yield in barley.
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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".