Potassium fertilization: paradox or K management dilemma?
Bibliographic record
Abstract
Abstract In 2014, Khan et al. presented evidence that soil exchangeable K (Exch-K) increases over time without addition of potassium (K) to the soil despite the removal of K in crops on a soil rich in montmorillonite and illite. The authors term this behavior ‘The potassium paradox’. From their review of the literature, the authors also report a lack of crop response to potassium chloride (KCl) fertilization. Close evaluation of these findings reveals that their observations can be interpreted and predicted using current knowledge of K in soil chemistry and its uptake by plants, and there is no paradox in K behavior in the soil–plant system. There is also no evidence of a detrimental effect of KCl on crop yield or quality. Their conclusion that the widely used Exch-K soil test is inadequate for managing K fertilization is discussed and some possible modifications to improve its performance are included. We believe that measurement of Exch-K is an essential and valuable tool and its use should be continued, along with improvements in recommending K fertilizer application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".