MODULATION OF NITROGEN-UTILIZATION EFFICIENCY IN WHEAT GENOTYPES DIFFERING IN NITRATE REDUCTASE ACTIVITY
Bibliographic record
Abstract
The plants growing in natural field conditions do not express their full genetic potential of nitrogen (N) utilization due to a limiting availability of N at later stages of growth. Their full potential is likely to manifest under non-limiting nitrogen supply wherein the high nitrate reductase (HNR) and the low nitrate reductase (LNR) genotypes should differ significantly in their N-utilization efficiencies. In a sand culture experiment, using IC 321157 (HNR) and C 306 (LNR) genotypes of Triticum aestivum L. under controlled conditions, 15-day-old plants were collected in triplicate and analyzed for nitrate content, N-metabolizing enzymes and N harvest. Kinetic studies were conducted to obtain the Km and Vmax values for enzymes. The values for nitrate content, activities of the nitrate- and the ammonium- assimilating enzymes, biomass and N harvest were higher in the HNR than in the LNR genotype. The higher affinities of enzymes to their substrates in the HNR genotype indicated a greater potential of this genotype for N utilization under non-limiting N supply with a well-coordinated system of N uptake and assimilation. The study suggests that the N-utilization efficiency of plants can be improved by exploiting their full genetic potential under non-limiting N supply, which may be achieved by synchronizing the supply with demand during late stages of plant growth. It also shows that the enzymes responsible for N assimilation act in a coordinated way, thus necessitating the need of a holistic approach for the study of the N-metabolic pathways.
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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".