Biochemical Fractionation of Soil Organic Matter after Incorporation of Organic Residues
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Bibliographic record
Abstract
Soil organic matter (SOM) is a key factor for building and maintaining soil quality. The SOM quality is commonly assessed using densitometric and sieving separation methods, but such methods do not inform on the biochemical composition of SOM. Our objective was to evaluate the van Soest extraction procedure for soluble (SOL), holocellulose (HOLO) and lignin/cutin (LIC) fractions of SOM after incorporating crop residues and animal wastes into a C-depleted loamy sand. Millet cuttings, oat straw, fresh cattle manure and cattle manure compost were dried, sieved to obtain 53 - 250 and 250 - 2000 μm size fractions and characterized biochemically using a modified NDF-ADF-ADL van Soest method. Soil was also sieved into 53 - 250 and 250 - 2000 μm fractions. On a dry mass basis, crop residues contained 60% - 70% holocellulose while animal wastes contained more than 40% ash. Each soil fraction was combined with three rates of the corresponding organic fraction (2, 4, and 6 Mg·haǃ millet forage cuttings or oat straw and 5, 10, and 15 Mg·haǃ of cattle manure or cattle manure compost). Changes in soil biochemical components were analyzed using the balance method of compositional data analysis. Amendment, application rate and size fraction influenced significantly (p < 0.05) the [SOL | HOLO] balance but did not significantly affect the [SOL,HOLO | LIC] balance. The [SOL | HOLO] increased linearly with addition rate of crop residues, and decreased linearly with addition rate of animal wastes. This approach of balancing biochemical SOM components is a promising method to monitor the changes in SOM quality after the incorporation of organic residues and to elaborate beneficial practices for managing crop residues and animal wastes in agro-ecosystems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it