Soil total phosphorus content is a driver of P forms in continuously flooded paddy soils
Bibliographic record
Abstract
• Soil total phosphorus (TP) concentration affects soil P pools before and after rice growth. • Redox-sensitive inorganic P (P i ) and organic P (P o ) are the predominant P pools. • In high-P soils, P o forms do not undergo significant changes with rice growth. • Low-P soils contain labile P o species (diesters) that decompose with rice growth. Redox fluctuations in submerged paddy soils strongly influence the transformation and availability of inorganic (P i ) and organic phosphorus (P o ) forms. However, the extent to which these redox-driven processes affect P i and P o pools and speciation, and their contribution to phosphorus (P) availability for rice, remains poorly understood. This study examined P i and P o dynamics in twelve paddy soils with different total P (TP) content, classified as high-P (>800 mg P kg −1 ), medium-P (500–800 mg P kg −1 ), and low-P (<500 mg P kg −1 ). Soils were analysed before and after 60 days of rice growth using sequential P fractionation, liquid-state 31 P nuclear magnetic resonance ( 31 P NMR) spectroscopy, and phosphomonoesterase activity assays to assess P pools (soluble, exchangeable, redox-sensitive, and residual), organic P composition, and enzymatic hydrolysis potential. Redox-sensitive P i and P o were the dominant pools across all soils, accounting for ∼50 % and ∼18 % of total P, respectively. Soluble and exchangeable P pools remained minor. Concentrations of P i and P o were highest in high-P soils and lowest in low-P soils. In high-P soils, orthophosphate monoesters dominated and remained quite stable during plant growth, likely due to selective accumulation of inositol phosphates under repeated Fe redox cycles. In contrast, orthophosphate diesters in medium- and low-P soils represented the most labile component of P o and were rapidly hydrolyzed during rice growth to alleviate P limitation. These findings highlight how TP content modulates the contribution of P i and P o pools to rice nutrition, emphasizing the need to account for P o dynamics when evaluating P availability in paddy systems under fluctuating redox conditions.
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 | 0.000 | 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".