Effects of Varied Nitrogen Supply and Irrigation Methods on Distribution and Dynamics of Soil NO3-N during Maize Season
Bibliographic record
Abstract
A field experiment was carried out to investigate the effects of different supply methods of nitrogen (N) fertilizer and irrigation on the spatial distribution and dynamics of soil NO3-N for maize (Zea mays L.) grown in northwest China in 2012 and 2014. In 2012, there were three irrigation methods: alternate furrow irrigation (AI), fixed furrow irrigation (FI) and conventional furrow irrigation (CI). Three N supply methods: alternate N supply (AN), fixed N supply (FN) and conventional N supply (CN), were applied at each irrigation method. In 2014, the fixed treatments were excluded. Soil NO3-N in horizontal direction was measured to 100 cm soil profile. For 2012, at filling stage, compared to CI, AI increased soil NO3-N concentration under the plant by 4.5 to 7.4% in 0-40 cm soil profile and decreased that by 9.9 to 14.4% in 40-80 cm for three N supply methods. NO3-N concentration between two sides of the ridge was comparable for AN and CN coupled with AI or CI. When compared to CI, AI reduced soil NO3-N concentration in 60-100 cm by 4.8 to 8.7% from 12 collars stage to maturity over different positions when coupled with CN. Soil residual NO3-N at maturityin 0-100 cm was the lowest in AI coupled with CN or AN. The 2014 experiment verified the above results. Therefore, alternate furrow irrigation coupled with conventional or alternate N supply brought an optimum spatial distribution of soil NO3-N during maize season, resulting in little soil residual NO3-N at maturity.
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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 | 0.001 |
| 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".