Nitrogen and Potassium Fertilization in a Guava Orchard Evaluated for Five Cycles: Effects on the Plant and on Production
Bibliographic record
Abstract
Guava response to fertilization can be monitored through plant tissue analysis. Correct interpretation of these results, based on standard levels, is of great importance for correct nutrient management of the crop. However, standard levels are constantly criticized for not considering interactions among elements. To improve the nutritional diagnosis of 'Paluma' guava (Psidium guajava L., Myrtaceae), an experiment was conducted using nitrogen fertilization (0, 0.5, 1.0, and 2.0 kg per plant per cycle of N, with urea as a source with 45 % N), and potassium fertilization (0, 0.55, 1.1, and 2.2 kg per plant per cycle of K2O, with potassium chloride as a source with 60 % K2O) in an irrigated commercial area for five consecutive cycles, 2009 through 2012, observing the influence of fertilizers and climate and assessing yield and leaf element content, using the concept of isometric log ratios (ilr) to interpret leaf analysis results (N, P, Ca, Mg, K, and S). This paper showed that nutrient balances and nutrient concentration values can be interpreted coherently using compositional data analysis. Ranges of nutrient balances also were established for "Paluma" guava and validated through ranges grounded in nutrient contents currently used in Brazil. Nitrogen fertilization increased "Paluma" guava yield. The 0.5 kg N application rate per plant and the other studied treatments practically showed the same results, and their values were affected by pruning time as well as the nutrient balances.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".