Nitrogen supply of a Dark Brown Chernozem soil and its utilization by wheat
Bibliographic record
Abstract
Environment and management effects on the N supply to crops are not well understood. We assessed the influence of tillage system (conventional tillage or no-till), N fertilizer (0 or 60 kg N ha-1) and year on N sources and supply of a Dark Brown Chernozem loam soil, and N utilization by spring wheat (Triticum aestivum L.) in 2 yr. The main N source was mineralized N; only 14–23% of wheat N was derived from fertilizer, and non-exchangeable ammonium made no measurable contribution. Soil NO3 and exchangeable NH4 content at sowing and net N mineralization during the growing season (Nmin) were influenced more by year than by N addition and tillage. Nmin was 90–100 kg N ha-1 in 1999, a moist growing season but only 21–39 kg N ha-1 in 2000, a drier year. In both years, soil inorganic N to 60 cm at sowing averaged about 60 kg N ha-1 of which half was N mineralized since the previous harvest. Year accounted for 65–81% of the variation in N uptake. Fertilization increased N uptake and wheat yields, especially in 2000, but fertilization and tillage had no effect on post-heading N uptake and N translocation. Nitrogen use efficiency (NUE) and N recovery were lower with N applied and not affected by tillage. Our study indicated that available N was affected more by environment than management. In dry conditions, when Nmin is low, N application may be more effective in increasing yield and N uptake than in wet years, an observation that may merit further attention. Key words: Available N, N mineralization, N recovery, N use efficiency, non-exchangeable ammonium, tillage
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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".