Traditional knowledge on post-harvest practices used by maize farmers against infestation by maize weevils Sitophilus zeamais and Prostephanus truncatus in Arumeru district, Tanzania
Bibliographic record
Abstract
Introduction: Maize (Zea mays L.) is a crucial staple crop in Tanzania and Africa. However, post-harvest losses of maize grains due to insect pests, particularly Sitophilus zeamais and Prostephanus truncatus, pose significant economic challenges. Traditional knowledge and practices among farmers play a pivotal role in mitigating these losses. The present study explored the conventional understanding of post-harvest practices used by maize farmers against infestation by maize weevils in the Arumeru district, Tanzania.Methods: A qualitative cross-sectional study was conducted in Ngongongare village, Arumeru district, in the Arusha region of Tanzania. Key informants participated in in-depth interviews and focus group discussions. Data were collected using interview schedules, observation checklists, and focused group discussion guides. Pesticidal plants used by the community were taxonomically identified.Results: 53 participants were interviewed, with the majority (47.2%) aged 18-35 years, and 54.7% were female. Participants demonstrated knowledge of common post-harvest insect pests of maize, which severely affect maize quality. Traditional methods for maize storage, such as hanging cobs and using traditional silos (kihenge), were commonly practiced. Indigenous pesticidal plants, animal by-products, and mineral dust were used for pest control. Participants highlighted the effectiveness of traditional methods compared to synthetic pesticides, which were perceived as less efficient, expensive, and associated with health risks.Conclusion: Traditional maize storage practices were preferred due to their accessibility, cost-effectiveness, and perceived safety. Variability in herbal preparations' efficacy and modernization's impact on traditional practices were highlighted. The study underscores the importance of documenting and validating indigenous knowledge to enhance food security strategies, particularly in protecting maize grains against storage pests. Strengthening local capacities in pest management through scientific validation and community involvement is crucial for minimizing postharvest losses, sustainable agricultural development, and food security in Tanzania.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".