Optimizing the manure substitution rate based on phosphorus fertilizer to enhance soil phosphorus turnover and root uptake in pepper (Capsicum)
Bibliographic record
Abstract
Introduction: In contemporary agriculture, the substitution of manure for chemical fertilizer based on phosphorus (P) input in vegetable production has led to a significant reduction in P fertilizer application rates, while, the effect of manure substitution rates on soil P transformation and uptake by root remain unclear. Methods: This research conducts a pot experiment with varying manure substitution rates (0%, 10%, 20%, 30%, 40%, 50%, 75% and 100%) based on P nutrient content to elucidate the mechanisms through which manure substitution affects P uptake in pepper. Results and discussion: The result showed that shoot and root biomass of pepper gradually increased as manure substitution rate from 10% to 40%, and then gradually decreased with further increases in the substitution rate. Soil alkaline phosphatase activity and arbuscular mycorrhizal (AM) colonization gradually increased with manure substitution rates improvement. Specifically, when the substitution rate reached 30%-40%, the alkaline phosphatase activity increased by 24.5%-33.8% compared to the fertilizer treatment. In contrast, phytase activity and the relative expression of phosphate transporter protein genes in the root system was declined after peaking at 30% manure substitution. Additionally, soil available P remained moderate under 30%-40% substitution rate, which was reduced by 8.6%-10.2% compared to that in chemical fertilizer treatment, while microbial biomass P was comparable. In the current study, soil labile P similar to or even higher than that in chemical fertilizer treatment when the substitution rate was ≤40%. Correlation heatmaps demonstrated a significant and positive relationship between soil available P and factors related to labile P and moderately labile P. Conclusion: This finding suggested that substituting 30%-40% of chemical P with manure can effectively enhance root length, AM colonization, soil enzyme activity, soil labile P, and consequently improve P uptake in pepper. These findings provide valuable insights for future organic agricultural practices that prioritize P supply, aiming to standardize organic P management in farmland and achieve high crop yields and maintain soil health.
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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) | 0.001 | 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) | 0.001 | 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".