Staking Options for Sustainable Yam Production in Ghana
Bibliographic record
Abstract
The study evaluated staking options to address the problem of deforestation for sustainable yam production in the Forest and Forest-Savannah Transition zones of Ghana. A split-plot design with three yam varieties (Dente, Water Yam and TDR95/19177 line) and three staking options (No staking, Vertical staking and Trellis with 50% and 30% number of vertical stakes for 2012 and 2013 respectively) as main plots and subplots respectively were used. Results revealed a significant (P < 0.05) interaction between yam variety and staking options on yam tuber yields in both locations and years. While water yam had similar tuber yields under all staking options, Dente and TDR95/19177 under no staking had significant yield reductions ranging from 37 to 65% compared to the other staking options. The observed yield reduction under no staking of Dente and TDR95/19177 could be attributed to higher incidence of yam mosaic virus leading to significantly lower fresh leaf biomass production. Reducing the number of stakes in trellis to as low as 30% of the vertical/optimum staking option did not result in a significant reduction in tuber yields for TDR95/19177 and Water yam. The economic analysis revealed that it is more profitable to produce water yam and TDR95/19177 under no staking and trellis (50% and 30% number of optimum staking) respectively in both locations. The results suggest trellis/minimum staking can be used to minimize the use of stakes, yam mosaic virus disease infection and for sustainable yam production in the face of climate change.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".