Spatiotemporal Heterogeneity of Soil Available Nitrogen During Crop Growth Stages on Mollisol Slopes of Northeast China
Bibliographic record
Abstract
Abstract Spatiotemporal heterogeneity of soil available nitrogen (AN) (sum of NO3−–N and NH4+–N) is the essential basis for soil management and highly correlates to crop yield. Both geostatistical and traditional analyses were used to describe the spatiotemporal distribution of AN in the 0–20‐cm soil depth on typical Mollisol slopes (S1 and S2) in Northeast China. The concentration of NO3−–N dynamics at slope positions was typically opposite to NH4+–N. The peak values of AN typically moved from the summit of the slope to the bottom from spring to autumn and were mainly influenced by the content of NO3−–N (S1, 7·9–18·9 mg kg−1; S2, 1·2–103·6 mg kg−1), both of NO3−–N (S1, 3·9–8·3 mg kg−1; S2, 2·2–28·0 mg kg−1) and NH4+–N (S1, 21·4–30·5 mg kg−1; S2, 2·1–23·3 mg kg−1), and NH4+–N (S1, 10·5–28·9 mg kg−1; S2, 5·0–39·0 mg kg−1) in the seedling stage, vegetative growth stage, and reproductive growth stage, respectively. The spatial autocorrelation of AN was strong and was mainly influenced by structural factors during crop growth stages. This was mainly determined by soil erosion–deposition (SED) and soil temperature–moisture (STM) in the seedling stage; this was also mainly influenced by SED, STM, crop type, and crop growth in the vegetative growth stage and by early STM and early SED in the reproductive growth stage. Generally, the content of AN, NO3−–N, and NH4+–N on the whole slope was mainly determined by the early SED and local fertilizer application, while their spatiotemporal heterogeneity, especially the evenness, was mainly changed by SED, STM, crop growth, and crop types on the slope scale. In order to increase more crop yields, additional N fertilizer application on both the summit and the bottom during the vegetative growth stage and conservation tillage systems or additional soil amendments on the back slopes was necessary. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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".