Foliar Application of Nitrogen Affects Metabolism and Productivity of Soybean
Bibliographic record
Abstract
Soybean is one of the world’s most economically important crops and several factors can affect the productivity of this culture. Among these factors is the supply of needed nitrogen, especially in the reproductive stage, as it acts in photosynthetic activity and in grain filling. In view of this, the objective of our work was to evaluate the effect of foliar application of nitrogen in different reproductive stages in soybean culture. Two sources of nitrogen were used: conventional urea and urea-formaldehyde/triazone, both applied in reproductive stages R2, R3, R4, or R5, as well as a control treatment without foliar application of nitrogen. Plants submitted to foliar application of urea-formaldehyde/triazone showed an increase in nitrogen metabolism (percentage of nitrogen derived from the atmosphere [Ndfa] and nitrate reductase activity [NR]), an increase in peroxidase (POD), and the consequent reduction in hydrogen peroxide (H2O2) in all stages of application of this treatment. When urea-formaldehyde/triazone was applied in R4, it resulted in a 7% increase in yield. The application of conventional urea in reproductive stages R4 and R5 increased nitrogen metabolism and resulted in an increase in yield by 4%. However, conventional urea reduced yield when applied in stages R2 and R3. The use of low doses of foliar nitrogen in stages R4 and R5, increased nitrogen metabolism in soybean plants. The timing of the application has a direct impact on the results with the slow-release nitrogen (urea formaldehyde /triazone) showing better results when applied in stage R4 and better results for conventional urea in R5.
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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 | 0.001 |
| 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".