Assessment of Nutrient Balance in Sugarcane Using DRIS and CND Methods
Bibliographic record
Abstract
Many methods of nutritional diagnosis present discordant reports. It is necessary to study how these diagnoses relate to agricultural productivity and nutrient balance for a more efficient nutritional monitoring of the crops. This study had two objectives: (1) evaluate and compare Diagnosis and Recommendation Integrated System (DRIS) and Compositional Nutrient Diagnosis (CND) methods for nutritional diagnosis of sugarcane cultivated in the Northeast of Brazil; (2) establish standards, identify and hierarchize nutritional limitations. The database consisted of 183 samples, in which 31 were in areas with high productivity (³ 80 Mg ha-1) and 152 of areas with low productivity (< 80 Mg ha-1). Sugarcane leaves were collected and contents of N, P, K, Ca, Mg, S, Fe, Zn, Cu, Mn and B were determined. The DRIS indexes were calculated by methods DRIS-Beaufils, DRIS-Jones, DRIS-Elwali and Gascho, M-DRIS Beaufils, M-DRIS Jones, and the indexes CND too were calculated. The DRIS-Beaufils, DRIS-Jones, M-DRIS Beaufils and M-DRIS Jones methods tended to agree on the nutritional diagnosis of sugarcane. The nutritional diagnosis of the CND method interpreted by the Potencial Fertilization Response (PFR) was different from the DRIS methods for N and Mn nutrients. The M-DRIS Beaufils and M-DRIS Jones methods showed a higher correlation with nutrient contents. However, there was no significant correlation between agricultural productivity and nutrient balance index mean (NBIm), suggesting that other factors influenced sugarcane production more than nutritional factors. The nutritional diagnosis methods identified excessive fertilization with N and limitations of Ca, Mg, K, S, Mn, Cu, Zn and B in sugarcane in the Northeast of Brazil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".