Labile organic matter fractions as early‐season nitrogen supply indicators in manure‐amended soils
Bibliographic record
Abstract
Abstract Soil test indicators are needed to predict the contribution of soil organic N to crop N requirements. Labile organic matter (OM) fractions containing C and N are readily metabolized by soil microorganisms, which leads to N mineralization and contributes to the soil N supply to crops. The objective of this study was to identify labile OM fractions that could be indicators of the soil N supply by evaluating the relationship between the soil N supply, the C and N concentrations, and C/N ratios of water extractable OM, hot‐water extractable OM, particulate OM, microbial biomass, and salt extractable OM. Labile OM fractions were measured before planting spring wheat (Triticum aestivum L.) in fertilized soils and the soil N supply was determined from the wheat N uptake and soil mineral N concentration after 6 weeks. Prior to the study, fertilized sandy loam and silty clay soils received three annual applications of 90 kg available N (ha · y)−1 from mineral fertilizer, liquid dairy cattle manure, liquid swine manure or solid poultry litter, and there was a zero‐N control. Water extractable organic N was the only labile OM fraction to be affected by fertilization in both soil types (P < 0.01). Across both test soils, the soil N supply was significantly correlated with the particulate OM N (r = 0.87, P < 0.001), the particulate OM C (r = 0.83, P < 0.001), and hot‐water extractable organic N (r = 0.81, P < 0.001). We conclude that pre‐planting concentrations of particulate OM and hot‐water extractable organic N could be early season indicators of the soil N supply in fertilized soils of the Saint Lawrence River Lowlands in Quebec, Canada. The suitability of these pre‐planting indicators to predict the soil N supply under field conditions and in fertilized soils from other regions remains to be determined.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".