Growing Seed Yams in the Air: the Agronomic Performance of Two Aeroponics Systems Developed in Ghana
Bibliographic record
Abstract
Aeroponics has been perceived as a technology crammed innovation, far out of reach of the ordinary farmer. Apart from its continuous dependency on electrical power, the technology comes with very sophisticated inputs such as solenoid valves, timers, misters, CO2 tanks, and air and water pumps. The main objective of this study was to evaluate the option of using gravity-fed and pressurised aeroponics system for propagating seed yams from vine cuttings. The study was setup at the CSIR–Crops Research Institute in collaboration with the Agricultural Engineering Department of the Kwame Nkrumah University of Science and Technology. The basic advantage of the gravity-fed system is its non-dependency on electrical power, pumps or timers and its ability for continuous production. The two systems were set-up using conventional materials and equipment available on the local market. The treatments were arranged in a split-split-plot design with the two aeroponic units as the main plot, four nutrient concentration levels (C1 - , C2 - , C3 - and C4 -) as the sub plots and vine cuttings from three Dioscorea rotundata varieties (Dente, Pona and Mankrong Pona) as sub-sub plots respectively. Results showed there were significant differences (P<0.05) in minituber weight and days to emergence of planted miniubers. The agronomic response of the two systems in producing mini-tubers was suggestive of the fact that both systems could be used to effectively produce mini-tubers.
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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) | 0.001 | 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".