Assessment of Low-cost Postharvest Techniques to Reduce Storage Losses in Sweet Potato
Bibliographic record
Abstract
Sweet potato is now classified among the high-priority crops targeted at reducing food insecurity and malnutrition in many countries. Despite this growing interest, high postharvest losses caused by poor handling, physiological weight loss, sprouting, weevil (Cylas spp.) damage and microbial decay remain a challenge. Decay losses due to tip rot and other pathogenic fungi are a challenge in Ghana. Incidence of tip rot, which is characterized by a dry decay at either or both ends of roots, is severe due to late harvesting of roots which are intended for storage. This study evaluated a series of postharvest techniques which can be integrated to extend shelf-life in small scale storage. These include harvest maturity, root tip coppicing, hot water treatment, honey waxing and sanitizing in postharvest chemicals. Tip coppicing significantly (P<0.001) reduced the incidence of tip rot across the varieties when stored for 8 weeks, but dipping roots in different oils did not significantly (P<0.05) reduce tip rot incidence. Decay losses in the control and hot water treated roots were similar during the first 4 weeks after storage, but later losses were significantly severe in hot water treated roots. The range of fungi genera identified in this study are generally fungal endophytes; often symptomless in roots but become pathogenic when conditions are favourable. Due to the root thin skin, mechanical injuries during harvesting and handling become infection pathways for such pathogens. Therefore, integration of best practices at each step of harvesting and postharvest handling operations can significantly minimize decay losses.
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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) | 0.000 | 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".