Nitrogen use efficiency in spring wheat: genotypic variation and grain yield response under sandy soil conditions
Bibliographic record
Abstract
SUMMARY Agricultural practices are likely to lower nitrogen (N) fertilization inputs for economic and ecological limitation reasons. The objective of the current study was to assess genotypic variation in nitrogen use efficiency (NUE) and related parameters of spring wheat (Triticum aestivumL.) as well as the relative grain yield performance under sandy soil conditions. A sub-set of 16 spring wheat genotypes was studied over 2 years at five N levels (0, 70, 140, 210 and 280 kg N/ha). Results indicated significant differences among genotypes and N levels for grain yield and yield components as well as NUE. Genotypes with high NUE exhibited higher plant biomass, grain and straw N concentration and grain yield than those with medium and low NUE. Utilization efficiency (grain-NUtE) was more important than uptake efficiency (total NUpE) in association with grain yield. Nitrogen supply was found to have a substantial effect on genotype; Line 6052 as well as Shandawel 1, Gemmiza 10, Gemmiza 12, Line 6078 and Line 6083 showed higher net assimilation rate, more productive tillers, increased number of spikes per unit area and grains per spike, extensive N concentration in grain and straw, heavier grains, higher biological yield and consequently maximized grain yield. The relative importance of NUE-associated parameters such as nitrogen agronomic efficiency, nitrogen physiological efficiency and apparent nitrogen recovery as potential targets in breeding programmes for increased NUE genotypes is also mentioned.
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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".