Customized nutrient management of onion (Alium cepa) agroecosystems
Bibliographic record
Abstract
Abstract While onion cultivars, irrigation and soil and crop management practices have been given much attention in Brazil, nutrient management at growers’ scale is still challenging. Our objective was to customize the fertilization of onion crops. We attempted to adjust nutrient management to the complexity of onion cropping systems by combining ML and compositional methods. We assembled climatic, edaphic, and managerial features as well as tissue tests into a data set of 1182 observations collected across fertilizer experiments conducted over 13 years. Data were processed using machine learning methods. Fertilization (NPK) treatments as well as edaphic and managerial features that are easy to acquire by stakeholders sufficed to explain 93.5% of total variation in marketable onion yields. Customized crop response models differed from state-base fertilizer recommendations, indicating potential benefits to customize fertilizer recommendations using a median experimental site condition in southern Brazil. Foliar nutrient standards to reach > 50 Mg bulb ha− 1 differed among cultivars grown under a large range of edaphic and managerial features, supporting local nutrient diagnosis. Larger and more diversified observational and experimental data sets could be acquired to customize fertilization across more Brazilian onion agroecosystems and document successful combinations of growth-impacting features through close ethical collaboration among stakeholders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".