Effects of Irrigation Frequency on Dry Matter Accumulation and Water Use Efficiency of Wheat
Bibliographic record
Abstract
Wheat ( Triticum aestivum L.) is a globally essential cereal crop whose productivity is closely linked to water availability, particularly in water-limited regions. This study explores the effects of different irrigation frequencies on dry matter accumulation and water use efficiency (WUE) in wheat cultivation. We examined the physiological basis of biomass accumulation and analyzed how irrigation intervals influence partitioning among organs and developmental stage-specific responses. Further, we evaluated WUE in relation to irrigation frequency, considering agronomic implications and the interplay of root development, leaf structure, and molecular signaling pathways. A case study from a semi-arid wheat-growing region provided field-based insights into the impacts of irrigation frequency on yield, soil health, and practical outcomes. Our analysis highlights the trade-offs between water input and biomass productivity, emphasizing the importance of optimized irrigation scheduling. We conclude that moderate irrigation intervals can enhance WUE without severely compromising yield, though outcomes depend on local climate and soil conditions. Future research should focus on site-specific strategies using precision agriculture to improve sustainability under climate variability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".