Effect of growing season rainfall and tillage on the uptake and recovery of <sup>15</sup>N-labelled urea fertilizer by spring wheat in a semi-arid environment
Bibliographic record
Abstract
Growing season rainfall affects fertilizer N recovery, particularly in semi-arid environments. However, the influence of rainfall distribution during the growing season is not well-understood. We conducted a 7-yr study (from 1997 to 2006) to assess this effect, and that of no-till (NT) vs. conventional tillage (CT), on fertilizer N recovery by spring wheat (Triticum aestivum L.) fertilized with 15N-labelled urea at 40 kg N ha–1 and grown on stubble on a Dark Brown Chernozem soil in Saskatchewan, Canada. Two of the seven experimental years had growing season rainfall close to normal, one was above normal and four were below normal. Tillage treatment did not affect 15N recovery by wheat; however, 15N recovery in the top 15 cm of soil averaged 47% under NT vs. 39% under CT (P = 0.02). Total N and 15N uptakes were most affected by "year" due to variation in growing season rainfall distribution. Excluding an ultra-low value of 3.8% (or 1.5 kg N ha–1) in 2002, due to extreme drought, 15N recovery by wheat averaged 47.5% (range 30–57%), and percent N derived from fertilizer was 12–20%. Rainfall in May correlated significantly with 15N and total N uptake (r = 0.605 and 0.699, respectively). The recovery of 15N in wheat head correlated negatively with June rainfall (r = –0.624), probably because more moisture increased soil N mineralization, which diluted the 15N pool. During grain filling, soil N uptake was 12–30 kg ha–1, compared with negligible amounts (< 7%) of 15N; however, about 15 kg ha–1 of 15N were remobilized vs. 34–74 kg ha–1 of soil N. It is concluded that, in this semi-arid region, fertilizer N uptake is influenced more by rainfall in May than other months of the growth period.Key words: 15N-labelled urea, fertilizer N recovery, N uptake, rainfall, remobilized N, 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".