Tracing nitrogen use efficiency of diverse Canadian spring wheat cultivars
Bibliographic record
Abstract
Decades of wheat breeding have provided growers with numerous high-yielding options, but it is unknown if these yield improvements are likewise characterized with improved nitrogen use efficiency (NUE). Fertilizer nitrogen (N) is an ever-increasing expense, so improving NUE by reducing the requirement for N fertilizer without risking yield and quality is necessary. The goal of our research is to identify cultivars and associated traits that may improve NUE while maintaining productivity. We compared 25 spring wheat cultivars over a three-year period (2020, 2021, 2022) at two field sites differing in background soil N level for the ability to use fertilizer-N and allocate it to the grain. To do so, we employed the 15N stable isotope technique to trace the flow of fertilizer-N and determine the 15N recovery efficiency (15NRE). The 15NRE in the grain averaged 25.0% at the higher soil N site, and 15.5% at the lower soil N site. At the higher soil N site only, dwarfing alleles (Rht-B1b) were associated with greater 15NRE. Grain 15NRE was positively associated with yield, grain N content, and the 15N harvest index (15NHI) at the high soil N environment, but never at the low soil N environment. Our findings support the notion that the genetic development of high yielding semi-dwarf cultivars also translates into an improved ability to recover fertilizer-N—but this outcome is only expressed only under rich soil N conditions. Cultivars that simultaneously produced higher 15NRE and yields, grain N, or 15NHI differed by environment; possibly suggesting different mechanisms for improving crop NUE depending on background soil N level. Ultimately, cultivar-specific 15NRE information, including that presented here, will be useful breeders to design new crosses and approaches aimed at increasing NUE for spring wheat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".