Decrease in soil inorganic nitrogen supply capacity under long‐term areca nut plantations in the tropics
Bibliographic record
Abstract
Abstract Understanding the dynamics and availability of soil nitrogen (N) affected by the conversion of cropping patterns is critical for environmental sustainability, especially in tropical soils with low fertility and high N loss. In this study, the 15 N tracing technology combined with the 15 N tracer model was used to explore the dynamic change of soil N transformation in long‐estabished areca nut ( Areca catechu ) plantations. Areca nut plantations with different ages (2, 5, 10, 14, and 17 years) and paddy fields in the tropical region of China were studied. The results demonstrated that the gross N mineralization rate ( M ) of areca nut plantation soil was much lower than that of paddy soil. The NH 4 + immobilization ( I NH4 ) rate was also significantly reduced in areca nut plantations. Besides, the O NH4 (autotrophic nitrification) in long‐term areca nut planted soil decreased significantly with decreasing ammonia‐oxidizing archaea (AOA) and ammonia‐oxidizing bacteria (AOB) abundance. Inorganic N supply (INS) capacity of areca nut planted soil was much lower than the paddy soil, indicating declined N supply in long‐term areca nut plantations soil. The decline in soil gross N transformations rate and INS capacity of areca nut plantations soil was significantly correlated to reduced levels of TN and soil pH. Thus, agricultural practices that increase the soil pH (e.g., biochar or lime application) and the soil organic matter content (e.g., organic fertilizers) could improve soil INS capacity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".