Effects of soil water and nitrogen on drought resilience, growth, yield, and grain quality of a spring wheat
Bibliographic record
Abstract
Drought imposes a significant challenge for crop production. However, little is known about the impact of drought priming and nitrogen (N) application and their interactive effects on drought resilience, yield, and grain quality in wheat. Spring wheat (cv. Stettler) was grown in plastic pots (25 cm diameter) with high, moderate, and low soil water levels and received N (added N) or without N (no N added), and subjected to acute drought for 10 days, then rewatering at the tillering stage. Canopy temperature, maximum efficiency of photosystem II, and normalized difference vegetation index were measured at 3-day intervals during drought-recovery periods to quantify drought resistance and resilience. Above-ground dry matter, straw dry matter, seed dry matter, harvest index, and grain N, phosphorus (P), and zinc (Zn) concentrations were determined. Both moderate- and low-water-grown plants had higher drought resistance than high-water-grown plants. The addition of N alleviated acute drought stress in high- and moderate-water-grown plants but exacerbated drought stress in low-water-grown plants. Both high and moderate water resulted in higher grain yields, but had a lower harvest index than low water. The highest and lowest grain N were observed in the low- and high-water-grown plants, respectively. The addition of N increased N and N:P in grains but decreased grain Zn:N. This study showed that moderate drought priming along with N application can improve drought resistance, yield, and grain quality. The results also indicated that canopy thermal imaging is a useful tool for high-throughput quantification of the drought resistance of 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 | 0.000 |
| 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".