Customized Nutrient Standards to Diagnose Nutrient Imbalance in Fertigated ‘Nanica’ Banana Groves
Bibliographic record
Abstract
Banana (Musa spp.) is an important fruit production in Brazil, but crop productivity is still too low. The ‘Nanica’ cultivar and fertigation have been introduced, but more accurate guidelines are needed to support fertilization decisions at the orchard scale. This study aimed to develop customized nutrient standards for fertigated ‘Nanica’. A commercial ‘Nanica’ orchard provided 129 observations on yield and foliar nutrient concentrations from 2010 to 2017 in eight groves of 3.26 ha each. Plant density averaged 1479 plants ha−1. The diagnostic leaf was analyzed for 13 elements. Concentration values were transformed into centered log ratios (clr), weighted log ratios (wlr), and isometric log ratios (ilr) to account for nutrient interactions and normalize the data. Yield cutoff between low- and high yielders was set at 27 t ha−1 semester−1. The XGBoost classification models relating yield to tissue composition returned an area under curve averaging 0.715 for log ratio expressions. Nutrient standards were expressed as clr, wlr, and raw concentration means and standard deviations of performing specimens. The clr and wlr diagnoses of a low-yielding and imbalanced specimen against a benchmark specimen (Euclidean distance = 2.5) or the performing subpopulation (Mahalanobis distance = 37.6, p < 0.01) indicated Mn shortage and Na excess. Sufficiency concentration ranges may not agree with log ratio diagnoses, especially for Mn. The clr and wlr nutrient standards were site-specific, supporting precision farming. The concept developed in this paper is applicable to endogenous research conducted by stakeholders in orchards worldwide.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".