Water Use Efficiency Differences in Maize Varieties under Every Furrow and Alternate Furrow Irrigation
Bibliographic record
Abstract
Water-use efficiency (WUE) differences of selected maize varieties under alternate and every furrow irrigation were investigated in a split-plot design trials with three replicates. Alternate furrow (AFI) and Every furrow irrigation (EFI) were main treatments and twenty maize varieties were sub-treatments. Plots were 64 m2 with one maize seed per station spaced at 0.25m apart. Crop water use results indicated that EFI consumed more water than the AFI. The AFI reduced crop water consumption by 38 - 45% compared to EFI.  Differences were also prominent in maize varieties’ response to AFI. Late maturing maize varieties proved to have minor yield reduction with AFI compared to early and medium maturing maize varieties. WUE (kg m-3) differed with irrigation water application strategy (P<0.001). AFI had high WUE. A combination of AFI with selection of water efficient maize varieties was a good strategy for improving WUE. The AFI is a promising furrow irrigation water management strategy for water saving. According to farmers experience at five irrigation schemes and on station research, it was concluded that AFI is one of the climate smart irrigation technique that farmer can easily adopt and apply as it saves labour, time water whilst reducing conflict for water among irrigators. It was recommended that AFI be applied fully on early and medium maturing maize varieties within an irrigation interval of 7 days. For late maturing maize varieties, AFI technique should be applied from initial stage to mid - stage (up 55 days from planting) then apply EFI at tasselling and silking stages to reduce water stress at this critical 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".