Enhancing Cabbage (Brassica oleraceae Var capitata) Yields and Quality Through Microclimate Modification and Physiological Improvement Using Agronet Covers
Bibliographic record
Abstract
Adverse environmental conditions have contributed to perpetual poor cabbage (Brassica oleraceae var. capitata) yields in sub-Saharan Africa. Elsewhere, net covers have been reported to provide a low-cost technology with the potential of modifying the microclimate around a crop for better performance. Two experiments were therefore conducted over a span of two seasons to determine the effects of agronet covers on microclimate modification and subsequent cabbage yield and quality. The treatments comprised cabbage plants grown under either fine mesh (0.4 mm pore diameter) or large mesh (0.9 mm pore diameter) agronet covers maintained permanently closed, or opened thrice weekly from 9 am to 3 pm and a control treatment where cabbage was grown in the open field. Net covering generally modified the microclimate by raising temperatures, relative humidity and volumetric water content but lowering photosynthetic active radiation and diurnal temperature range compared to control. The use of agronet covers resulted in better cabbage performance. The large mesh (0.9 mm) enhanced leaf stomatal conductance and chlorophyll content, and improved fresh and dry weight as well as head quality. Results of this study present the use of agronet covers as a potentially effective technology for use by small-scale farmers in protected cabbage culture in sub-Saharan Africa.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".