Intermittent Flooding and Organic Fertilizer Improve Biological Phosphorus Cycling and Crop Yield in a Low‐Fertility Tropical Rice Cropping System
Bibliographic record
Abstract
Manipulating the water regime and applying organic fertilizer can conserve water, reduce greenhouse gas emissions, and improve soil quality in rice cultivation. However, rice yields depend on soil phosphorus (P) availability, which fluctuates with water and fertilizer manipulations on the low‐fertility soils that characterize many tropical rice cropping regions. We conducted a field experiment to determine the effects of water regime and fertilizer source on rice yield and associated P dynamics in a low‐P tropical soil under a double‐cropping system in Panama. Water regime (continuously vs. intermittently flooded) and fertilizer source (mineral fertilizer [NK, NPK, and none] or composted cow manure) were manipulated in a randomized complete block split‐split plot design. We used the Diagnostic and Recommendation Integrated System (DRIS) to determine nutrient limitation of rice yield. Organic fertilizer increased plant P uptake, soil available P, microbial P, and phosphomonoesterase activity, but the changes were greater under intermittent flooding than continuous flooding. In the first cropping period (dry season), plant growth was limited by P availability, and yields were greater with combined NPK + organic fertilizer (6.6–6.8 t·ha −1 ) compared with NPK only (4.9–6.2 t·ha −1 ). In the second cropping period (rainy season), plant growth was limited by nitrogen, and yields were greater under continuous flooding than intermittent flooding unless organic fertilizer was added (i.e., a significant water regime × fertilizer interaction). These findings demonstrate that organic fertilizer can maintain P availability and yield in water‐conserving rice production systems with intermittent flooding in low‐fertility systems. This is of particular importance given the challenges faced by resource‐poor farmers on low‐fertility soils in the face of unpredictable water availability and rainfall responses to climate change.
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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) | 0.001 | 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".