Response of Maize Yield Components to Growth Stage‐Based Deficit Irrigation
Bibliographic record
Abstract
In the face of declining water resources and climatic variability, growth stage‐based deficit irrigation may be a feasible approach to enhance agricultural system resilience. A 3‐yr experiment was conducted to evaluate the impact of deficit irrigation on maize ( Zea mays L.) in the late vegetative (Lveg) and maturation (Mat) growth stages, where phenology, dry leaf weight, aboveground biomass, yield, kernel number, 1000 kernel weight, and grain‐filling rate were evaluated. Water deficit during the Lveg stage decreased the kernel number and dry leaf weight, thus decreasing the potential grain‐filling rate (less photosynthetic tissue). In contrast with deficit during the Lveg stage, deficit during the Mat stage directly reduced the grain‐filling rate and duration and thus had the strongest effect on grain yield. A growth stage interaction was evident, such that the reduction in yield associated with water deficit applied during the Lveg stage was exacerbated by water deficit applied during the Mat stage. Yield reduction was proportional with the severity of the water deficit, in all cases. Nevertheless, water deficit applied during the Mat stage had a larger impact on maize yield compared with water deficit applied during the Lveg stage. If farmers have reduced water allocations but seasonal flexibility in the timing of irrigation water application, they will maximize yield by saving water for reproductive and maturation growth stages. Core Ideas Water deficit during the late vegetative stage decreased the kernel number and dry leaf weight. Deficit applied during the maturation stage directly reduced the grain‐filling rate. Yield reduction was proportional with the severity of the water deficit. Water deficit during the maturation stage had a larger impact on maize yield compared with that at the late vegetative stage.
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 | 0.000 |
| 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 | 0.001 |
| 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".