Long-term organic material application enhances black soil productivity by improving aggregate stability and dissolved organic matter dynamics
Bibliographic record
Abstract
Understanding the long-term effects of organic material application on soil structure and organic matter dynamics is important for sustainable agriculture . We investigate interactions between organic materials, soil aggregates, dissolved organic matter (DOM), and crop yield after long-term (34-year) experimental field conditions. The effects of fertilization regimes on soil aggregates, DOM characteristics, and soil organic carbon from 0 to 20 cm and 20–40 cm depth, and their impacts on crop yield are explored for various treatments (no fertilizer, chemical fertilizer , and chemical fertilizer combined with low straw, high straw), and organic manure (OM)). Organic amendments increased proportions of soil aggregates > 0.25 mm by 2.5 %-5.4 % and soil organic carbon contents within aggregates by 4.5 %-21.2 %. The OM treatment had the highest mean weight diameter and geometric mean diameter. DOM concentration in soil aggregates increased by 11.8 %-42.7 % in organic material treatments, and shifted in composition. Fulvic-like and humic-like components increased and protein components decreased, suggesting a transition towards microbial-derived organic matter, enhancing soil humification and bioavailability. Analyses reveal DOM influences aggregate stability and carbon sequestration by changing fluorescence components and structure in soil layers. Straw treatments primarily improved crop yields by enhancing soil aggregate stability, and OM boosted yield. We demonstrate the benefits of applying different organic materials to soil to sustain agricultural productivity, improve soil structure , enhance organic matter quality and quantity, and increase crop yield , revealing ways to optimize organic amendment in different agricultural contexts for more resilient and productive farming systems .
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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".