Comparing Extraction and Quantity/Intensity Based Recommendations for Nitrogen, Phosphorus and Potassium Recommendation
Bibliographic record
Abstract
Conventional, extraction based fertilizer recommendations for phosphorus and potassium have been shown to lack mechanistic basis, thus unreliable. This has led to an urgent need for the development and evaluation of accurate and consistent phosphorus and potassium recommendations approaches with mechanistic basis. Also it has been shown that integrating nitrogen mineralization on nitrogen recommendations, has a potential of improving nitrogen recommendations. We established two parallel pot trial studies with the objective of comparing between extraction based fertilizer recommendations with alternative strategies. The first study was to compare the effect of integrating nitrogen mineralization on N recommendations. Second pot experiment in addition to N being recommended after integrating N mineralization; P and K were also recommended with an alternative strategy, which was derived from quantity/intensity relations. No negative impacts were observed on crop growth and nutrient uptake due to the integration of mineralizable nitrogen, despite nitrogen amounts being lower compared to treatments where N was applied without adjusting for mineralizable N. The same was true for the second pot trial, P and N recommended by conventional approach were higher, yet the crop response was not concurrently improved by higher rates. Potassium rates recommended by alternative strategy were higher and this was concurrent with potassium uptake. We therefore concluded, that this NPK recommendation experimental approach (NePeKe) is superior to its conventional counterpart (NcPcKc). Hence, more reliable recommendations can be developed using this approach and this might reduce environmental footprint of agro-ecosystems, and reduce input cost for farmers where warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".