Comparative Response of Cabbage to Irrigation in Southern Malawi
Bibliographic record
Abstract
An experiment was conducted at Kasinthula and Masenjere in Chikwawa district in Malawi in the dry seasons (May through August) of 2006 and 2007 to evaluate yield response of cabbage (Brassica oleraceae) to irrigation frequency. The study was laid out in a randomized complete block design (RCBD) where three irrigation frequencies served as treatments: F1-Irrigated twice a week, F2-Irrigated once a week and F3-Irrigated once a fortnight. At Kasinthula, weight of marketable heads and water-use efficiency (WUE) were significantly different (P<0.05) across the irrigation frequencies. At Kasinthula and Masenjere, F1 resulted in highest yield of 32.9 and 23.0 t ha-1 in 2006 and 2007 seasons. There was a 50% and 25% reduction in yield in 2007 at Kasinthula and Masenjere Research sites. WUE peaked in F1 to 83.6 and 57.5 kg ha-1 mm-1 in 2006 and 2007 while lowest values were noted using F3 resulting in WUE of 57.9 and 39.4 kg ha-1 mm-1. Water productivity (WP) was significantly different across irrigation frequency (P<0.05). F3 resulted in the highest WP of 11.8 and 7.4 kg m-3 in 2006 and 2007, respectively. The lowest WP of 6.7 and 5.2 kg m-3 were observed in F1 in the two years. Comparing all the irrigation frequencies, F3 turns out to be the most effective water saving irrigation frequency suggesting that in the face of competing water needs and dwindling water resources, the longer duration F3 irrigation frequency is preferred to shorter duration ones. Where water is considered ample, F1 is recommended.
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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 | 0.000 |
| 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 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".