Water Use Efficiency of Maize Varieties under Rain-Fed Conditions in Zambia
Bibliographic record
Abstract
This study evaluated water use efficiency (WUE) of selected hybrid maize (Zea Mays L.) varieties in Zambia under rain-fed conditions. A randomized complete block field experiment was carried out during the 2014/2015 rainy season at the University of Zambia Agricultural Demonstration Centre. Treatments were 30 maize varieties from the early, medium and late maturity classes. WUE was calculated as the ratio of yield to evapotranspiration (ET) and transpiration (T). Results showed significant differences in WUE dry matter (DM) for transpiration (T) of early maturing varieties. However, no significant differences were observed in WUEDM for evapotranspiration (ET), WUE grain yield (GY), T and WUEGY, ET. WUEDM, T, WUEDM, ET, WUEGY, T, and WUEGY, ET were statistically the same among medium maturing varieties. Results further showed that among the late maturing varieties, WUEDM, T, WUEDM, ET and WUEGY, T showed significant differences but no significant differences were observed in WUEGY, ET. It was concluded that maize varieties from the same maturity classes have different WUEs. The study thus provided options in variety selection based on which varieties performed better, particularly SC 525, SC 513 and PAN 4M 21 from the early maturity class; PHB 30G19, ZMS 606, MRI 634 and SC 637 from the medium maturity class; and PAN ZM 83, SC 709, PAN 8M 93 and SC 719 from the late maturity class. It was recommended however, that repeated experiments over time should be done to validate the findings given that the trial was only conducted in one season.
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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 | 0.000 | 0.000 |
| 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 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".