Interactive Effects of Tillage and Mineral Fertilization on Soil Carbon Profiles
Bibliographic record
Abstract
Both tillage and fertilizer management influence soil organic C (SOC) storage, but their interactive effects remain to be determined for various soil and climatic conditions. We evaluated the long‐term effects of tillage (no‐till, NT, and moldboard plowing, MP), and N and P fertilization on SOC stocks and concentrations in profiles of a clay loam soil (clayey, mixed, mesic Typic Humaquept). Corn ( Zea mays L.) and soybean [ Glycine max (L.) Merr.] were grown in a yearly rotation for 14 yr. Our results showed that NT enhanced the SOC content in the soil surface layer, but MP resulted in greater SOC content near the bottom of the plow layer. When the entire soil profile (0–60 cm) was considered, both effects compensated each other, which resulted in statistically equivalent SOC stocks for both tillage practices. Nitrogen and P fertilization with MP increased the estimated crop C inputs to the soil but did not significantly influence SOC stocks in the whole soil profile. At the 0‐ to 20‐cm depth, however, lower C stocks were measured in the plowed soil with the highest N fertilizer level than in any other treatment, which was probably caused by a greater decomposition of crop residues and soil organic matter. Conversely, the highest SOC stocks of the 0‐ to 20‐cm soil layer were observed in the NT treatment with the highest N rates, reflecting a greater residue accumulation at the soil surface. When accounting for the whole soil profile, the variations in surface SOC due to tillage and fertilizer interactions were masked by tillage‐induced differences in the 20‐ to 30‐cm soil layer.
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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 | 0.001 |
| 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".