Impact of Different Water Management Scenarios on Corn Water Use Efficiency
Bibliographic record
Abstract
Abstract. This study investigated the water balance, crop yield, and water use efficiency (WUE) of a water table management system compared to a conventional drainage system at three nitrogen levels. A two-year field study was conducted using three blocks; each block was composed of two water management treatments: controlled drainage with subirrigation (CD-SI) and conventional or free drainage (FD). The water table depth was maintained at 60 cm below the soil surface in the CD-SI plots. Three nitrogen treatments (low, medium, and high) were applied in strips across all blocks. The seasonal water balance indicated surplus water conditions in the CD-SI plots, while the FD plots had deficit conditions. In 2008 and 2009, the corn grain WUE for the FD plots was 2.49 and 2.46 kg m-3 respectively. The corn grain WUE for the CD-SI plots was 2.43 and 2.26 kg m-3 in 2008 and 2009, respectively. The WUE of corn grain responded to the water treatments (p < 0.05) in 2009 but not in 2008. In 2009, at low and high nitrogen levels, the water management treatments demonstrated significant differences (p < 0.05) in grain yields. However, water management demonstrated no significant effect (p > 0.05) on grain yields at the normal nitrogen level. Furthermore, the two water treatments had no effect on the aboveground dry biomass yields in both years.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".