Postharvest Management Practices of Grains in the Eastern Region of Kenya
Bibliographic record
Abstract
Cereals and legumes play a major role in the production systems and diets of farmers in the semi-arid eastern region of Kenya. Efficient postharvest management can tremendously contribute to food security in these regions. A study was carried out in three counties in eastern Kenya to assess pre and postharvest management practices among farmers. Data was collected using semi-structured questionnaires designed and administered using Kobo Toolbox via android tablets. Results showed that farmers cultivated three main crops: maize (98%), beans 66%), and pigeon peas (28%). The most saved seed crops were beans (80%) and pigeon peas (50%). Majority of the farmers (80%) experienced pre-drying losses due to insects (48%), rodents (40%) and birds (39%). Farmers stored grain for consumption (80%) and for sale (19%). About 48% of farmers stored the grain for more than 9 months. Challenges during grain storage were insects (57%) and rodents (43%). Primary methods of grain preservation included hermetic methods (61%) followed by insecticides (33%). While progress is being made in addressing storage challenges, there still a need to continue building awareness about improved storage technologies and find solutions for pest infestations in the field and drying after harvest.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".