Seven‐year straw and biochar amendments modulate soil pore structure, nutrient availability, and nitrogen partial factor productivity
Bibliographic record
Abstract
Abstract Straw and biochar have shown potential to enhance soil structure, increase nutrient availability, improve crop productivity, and reduce reliance on chemical fertilizers. However, their cumulative effectiveness as partial fertilizer substitutes over extended periods remains unclear. This study evaluated rice ( Oryza sativa L.) straw and its biochar as partial fertilizer substitutes on soil pore structure, nutrient supply, pH, root growth, yield, and nitrogen partial factor productivity (PFP N‐chem ) in a 7‐year field trial in Northeast China. The experiment included five treatments: (1) 100% chemical NPK fertilizer (NPK), (2) low‐dose biochar (LB: 1.5 Mg ha −1 year −1 ), (3) high‐dose biochar (HB: 3.0 Mg ha −1 year −1 ), (4) low‐dose straw (LS: 4.5 Mg ha −1 year −1 ), and (5) high‐dose straw (HS: 9.0 Mg ha −1 year −1 ). Chemical NPK application rates in the straw and biochar treatments were adjusted to maintain equivalent total nutrient level. After 7 years, both LB and LS attained average rice yields (LB: 6.7; LS: 7.6 Mg ha −1 ) similar to NPK (7.3 Mg ha −1 ), though initial yields were lower than NPK. This parity resulted from enhanced macroporosity and pore connectivity, which promoted root growth to compensate for reduced nitrogen availability. Specifically, LS exhibited 42.4% greater macroporosity (100–500 µm), 19.3% longer roots, and 54.8% higher root biomass than LB, yielding superior PFP N‐chem (+27.3%) with a 16% chemical N fertilizer reduction. However, high doses (HB/HS) led to average yield declines (22.8% and 13.1% lower than NPK). These findings highlight the potential of low‐dose straw and biochar as sustainable strategies for improving soil quality and reducing fertilizer dependency.
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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".