Improving soil carbon pool, soil fertility and yield of maize (<i>Zea mays</i> L.) in low-fertile tropical Alfisols by combining fertilizers with slow-decomposing organic amendments
Bibliographic record
Abstract
Abstract Amendment of recalcitrant organic materials with high carbon/nitrogen (C/N)-ratio may improve and maintain soil labile C for a longer period, thus enhancing the productivity of soils with low fertility; however, immobilization of N may affect the plant growth negatively. To reduce the negative impacts, recalcitrant organic materials can be pre-incubated with N-rich sources or applied in combination with fertilizers. The current study evaluated sawdust biochar (BC) and pre-incubated cattle manure–sawdust mixture (CS) amendments with synthetic fertilizers in improving soil carbon pool, soil fertility and maize (Zea mays L.) yield on a tropical Alfisol. Four treatments: control, site-specific fertilizer (SSF), site-specific fertilizer with sawdust biochar (BC + SSF) or pre-incubated cattle manure-sawdust mixture (CS + SSF), were evaluated for two seasons with maize. The residual effect was evaluated in the third season. During the year of active C application, lability index, C management index and potentially mineralizable N were significantly greater in CS + SSF than BC + SSF treatment. However, the same indices measured in the third season with no further application of amendments were significantly greater in BC + SSF than in CS + SSF treatment, indicating an increase in more recalcitrant C pool with BC amendment. Application of organic amendments improved soil fertility parameters compared with the application of fertilizer alone. Maize yield was significantly increased by fertilizer, with or without organic amendments; with significantly greater yield in BC + SSF than other treatments. Results suggest that soil amendment with BC had greater potential to improve the soil carbon pool and maintain labile carbon for a longer period than a pre-incubated CS.
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) | 0.001 | 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".