The Phosphorus Compositional Nutrient Diagnosis Range for Potato
Bibliographic record
Abstract
Leaf analysis could assist in adjusting the P fertilization of potato (Solanum tuberosum L.) to specific soil–plant systems to achieve high yield conditions. Our objective was to derive and validate Compositional Nutrient Diagnosis (CND) norms and ranges for a potato cultivar (Superior) and to compare CND to Diagnosis and Recommendation Integrated System (DRIS) and the Critical Value Approach (CVA). Survey data were obtained from 563 field observations and validated using 100 independent samples and across four P fertilizer trials. The databases included tuber yield and analyses (N, P, K, Ca, and Mg) of the upper fully expanded leaf collected at beginning of bloom. Yield cutoff between low‐ and high‐yield subpopulations, selected from cumulative variance functions across survey data, was 34.1 Mg ha−1. The sum of squared values of CND indexes was distributed like a chi‐square value. The critical chi‐square value was 4.2. The critical CND P index range was between −0.80 and 0.80. Similar critical values were obtained for the validation population and fertilizer trials. The CND P index appeared symmetrical about the zero nutrient balance and was more closely related to yield compared with DRIS and CVA. The CND provides an inferential (as chi‐square) and symmetrical (about zero balance) diagnosis at low cost, could provide nutrient index ranges adjusted to yield goal, and could thus be developed advantageously for specific soil–plant systems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".