Managing Soil Biota-Mediated Decomposition and Nutrient Mineralization in Sustainable Agroecosystems
Bibliographic record
Abstract
Transformation of organic residues into plant-available nutrients occurs through decomposition and mineralization and is mediated by saprophytic microorganisms and fauna. Of particular interest is the recycling of the essential plant elements—N, P, and S—contained in organic residues. If organic residues can supply sufficient nutrients during crop growth, a reduction in fertilizer use is possible. The challenge is synchronizing nutrient release from organic residues with crop nutrient demands throughout the growing season. This paper presents a conceptual model describing the pattern of nutrient release from organic residues in relation to crop nutrient uptake. Next, it explores experimental approaches to measure the physical, chemical, and biological barriers to decomposition and nutrient mineralization. Methods are proposed to determine the rates of decomposition and nutrient release from organic residues. Practically, this information can be used by agricultural producers to determine if plant-available nutrient supply is sufficient to meet crop demands at key growth stages or whether additional fertilizer is needed. Finally, agronomic practices that control the rate of soil biota-mediated decomposition and mineralization, as well as those that facilitate uptake of plant-available nutrients, are identified. Increasing reliance on soil biological activity could benefit crop nutrition and health in sustainable agroecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".