Effect of potassium fertilization on soil potassium pools and rice response in an intensive cropping system in China
Bibliographic record
Abstract
Abstract In order to assess the changes in soil K pools as affected by K‐fertilizer application and the impact of the changes on K balance, grain yield, and K uptake, an experiment was conducted in Central Zhejiang Province, E China, in a continuous double‐cropping rice system. Two sites were selected: (1) the Agricultural Research Institute of Jinhua (ARI) where soil is calcareous and (2) the Shimen Research Farm (SM) where soil is acidic. Eight consecutive crops were grown (1997–2000) in ARI and five consecutive crops (1998–2000) at SM. Treatments included unfertilized control (CK) and three different fertilizer treatments (NP, NK, and NPK). Potassium extracted by ion‐exchange resin decreased from 26 mg kg–1 to 5–10 mg kg–1 after eight consecutive seasons of growth at the ARI site. Addition of 100 kg K ha–1 for each rice crop was not enough to maintain initial K availability, especially in the calcareous soil at ARI site. In treatments with K, a small increase in readily available K was observed only in SM soil. The K extracted by HNO3 also decreased significantly in the treatments without K addition and was increased slightly in the treatments with K application. In the NP treatment, the decrease in HNO3‐K was several times greater than resin‐K, indicating that nonexchangeable K may be the major source of K supply to rice. Soil K depletion was greater for hybrid rice than for inbred rice, and this difference in K demand should be taken into account in developing fertilizer recommendations for irrigated rice.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 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".