Supplementary Nitrogen Fertilization in Sugarcane
Bibliographic record
Abstract
In Brazil, sugarcane (Saccharum spp.) is considered one of the most economically important crops. Nitrogen (N) is one of the most required elements in sugarcane cultivation. Nevertheless, the information about the soil and foliar applications of this nutrient in crops are discrepant. Therefore, the importance of this study is evident. Given the above, this study aimed to evaluate the soil-applied and foliar N fertilization of sugarcane. The experiment was conducted at the Araporã Bioenergia S.A. power plant, located at Fazenda Santa Rita, in the municipality of Itumbiara-GO. The 5 × 5 factorial design was adopted, with four repetitions, including five doses of soil-applied ammonium nitrate and five doses of foliar Amidic N polymer. The nutrient extraction, the experiment’s initial and final total chlorophyll content, the biometric indexes and the industrial quality indexes were analyzed for sugarcane. The ammonium nitrate doses caused differences in fiber, sacarose content, total recoverable sugar, sugar cane Brix, magnesium, and zinc, which was statistically different for the foliar polymer doses. There was no increment of the production variables with the increase of the nitrogen supply in the soil. On the other hand, the levels of zinc and magnesium in the leaves increased 12% and 27%, respectively, reflecting the importance of this fertilization in sugarcane cultivation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".