Assessment of production and marketing constraints and value chain of sweet potato in the municipalities of Dangbo and Bonou
Bibliographic record
Abstract
Sweet potato [ Ipomoea batatas (L.) Lam.] plays an important role in ensuring food security and incomes for local communities. It is particularly grown in southern region of Benin, and its production over the last few years has declined due to disease and severe pest infestation. Therefore, it was imperative to carry out this study to identify the main constraints related to the recent decline in sweet production and more specifically analyzing the production and marketing constraints of sweet potato in the townships of Dangbo and Bonou. The results of the survey revealed that many varieties of sweet potato were grown, mainly “Vobodouaho” (local name in Goun language), the most preferred variety by producers. Weedicides and insecticides were used to control weeds and insect pests, respectively. There was no efficient control methods against black rot, Alternaria blight, cylas spp., and caterpillar among the pathogens and insects that damaged sweet potato production in the study areas. The main constraints reducing sweet potato production in the townships of Dangbo and Bonou are: lack of adequate methods for controlling pest and diseases, lack of financial resources due to no access to credit, absence of modern equipment, scarcity and high cost of labor and absence of best methods for long-term storage. The average yield of sweet production per hectare ranged from 6.5 to 15.1 tons fresh matter/ha. The market value of sweet potato is still low at farmers’ level when compared to that of other stakeholders along the value chain. This study calls for the attention of public and private sectors and policy makers for more investment in sweet potato research. Avenues for further research on sweet potato have also been suggested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".