Potassium and Nitrogen Fertigation Frequency on Pineapple Yield and Fruit Quality
Bibliographic record
Abstract
Pineapple is a nutritionally demanding crop, with emphasis on potassium and nitrogen nutrients. In this context, the aim of this paper was to study the effect of nitrogen (N) and potassium (K) fertigation frequencies on the physical-chemical fruit characteristics of ‘Pérola’ pineapple crop. The experiment was conducted in a randomized block design, with 18 treatments and 4 replications. It was used a factorial scheme (4 × 4) + 1 + 1, which represents: four N fertigation frequencies applied by surface drip irrigation (4, 7, 27 and 54 times throughout the crop cycle); four K fertigation frequencies (4, 9, 35 and 70 times); one additional treatment (irrigated, but without fertigation); and one control (non irrigated and non fertigated). The fruit characteristics analyzed were: fruit mass with crown, yield, soluble solids, pH, titratable acidity and SS/TA ratio. The N fertigation frequencies had no effect on variables evaluated, however, the K frequencies had a significant influence on fruit mass with crown, yield and pH. The results showed that the effect of K frequencies applied through fertigation on pineapple yield and fruit physical-chemical quality was more pronounced in comparison to the effect of N applications. Monthly potassium fertigations, followed by four applications throughout the crop cycle, provided the greatest increase in fruit quality, allowing higher values of fruit mass, yield and pH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".