Nitrogen dynamics and yield performance of an elite bread wheat line with BNI capacity expressed in an alkaline soil
Bibliographic record
Abstract
Abstract Wheat with biological nitrification inhibition (BNI) has been shown to be effective in acidic soils. BNI expressions in alkaline soil have not been documented in field studies. We present the first observation of BNI effects in alkaline soil trials (pH 8.6-8.7) of elite spring wheat that carries the Lr#N short arm from Leymus racemosus . BNI was expressed as lower soil nitrate in three trials conducted in an irrigated wheat system in northwestern Mexico. In one year, we observed 24-37% lower nitrate in soil of the translocation line compared to the control after the second ammonium-N fertilizer application. Lower nitrate was observed in the row and in the furrow. The largest difference between the control and the translocation line was approximately one month after the second ammonium-N split application (73-77% less nitrate). Potential nitrification rates were 27-32% lower in soil from the translocation line compared to the control, one week after a high ammonium-N application. Higher flag leaf nitrate concentration of the translocation line could be related to the strongly reduced soil nitrate. The translocation line took longer to reach anthesis and flowering. In two experiments, the translocation line was equivalent in terms of grain yield, whereas in one experiment it produced lower yields, with fewer grains per spike and reduced number of spikes per area. Protein content and grain N uptake of the translocation line were similar to the control. We conclude that BNI reduces nitrification with a spatially and temporally significant impact under alkaline soil and high N input conditions. This finding points to the potential major environmental benefits that could be achieved also in non-acidic soil spring wheat systems worldwide. The effect of the Lr#N short arm on yield quantity and quality in other elite lines needs further investigation.
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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) | 0.001 | 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".