Tillage and Previous Crop Effects on Dynamics of Nitrogen in a Wheat–Soil System
Bibliographic record
Abstract
The effects of tillage and preceding legume crops on N flux in the soil–plant system require quantification for developing sustainable cropping systems. We measured changes in soil and plant N under the influence of tillage [no till (NT) vs. conventional tillage (CT)] and previous crops [spring wheat (Triticum aestivum L.), red clover (Trifolium pratense L.) green manure, and field pea (Pisum sativum L.)]. The study was conducted from 1994 through 1996 on a well‐drained sandy loam soil (coarse‐loamy, mixed, frigid, Typic Cryoboralf) near Fort Vermilion, Alberta (58°23′N, 116°2′W). Nitrogen uptake by wheat was increased by NT and legume crops. At seeding, CT soil had 28 kg ha−1 more NO3–N to 100‐cm depth than NT soil. Apparent net N mineralization in the growing season was 71 and 22 kg N ha−1, respectively, for the NT and CT systems. Previous crop effect on net N mineralization (kg N ha−1) was red clover (56) > field pea (51) > wheat (34). Approximately 18 kg N ha−1 was net‐mineralized from red clover residues compared with insignificant amounts from pea and wheat residues. Microbial biomass turnover's contribution to net N mineralization (28 to 40 kg N ha−1) was increased by NT and previous legume crop. Soluble organic N decreased by 7 kg ha−1 between seeding and maturity for all experimental treatments. The results indicate that N fertilizer recommendations should allow for greater mineralization of organic N under NT than CT and following a legume green manure.
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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) | 0.000 | 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".