Evaluation of Seed Quality Attributes of Sorghum Germplasm Accessions from Eastern, Coastal and Nyanza Regions, Kenya
Bibliographic record
Abstract
Sorghum (Sorghum bicolor L.) is an important cereal crop in Kenya. Despite the crops importance, the yields attained by farmers in Eastern, Coastal and Nyanza regions of Kenya remain low. Access to good quality seeds of sorghum is one of the constraints facing the subsistence farmers. Good quality seed is important for increasing yield to attain food security. The aim of this study was to evaluate quality attributes of the seed used by farmers. A total of 108 germplasm accessions were obtained from 76 farmers. The seeds were tested for time and percentage of germination, seed vigour index, shoot and root dry weight. Data collected was subjected to analysis of variance. Means were separated using Fisher’s Least Significance Difference test at p ≤ 0.05. Seed samples of 26 accessions attained germination percentage below stipulated standards by Seeds and Plant Varieties Act CAP 326. Majority of seeds showed longer mean germination time with only nine accessions germinating in less than ten days. Seed vigour index was relatively high in most of the accessions, while biomass accumulation varied from high to very low among accessions. Though most of the seeds attained a high germination percentage, about 92% of seeds showed longer mean germination time. The environmental conditions in the fields, pre and post harvest handling practices impact on the seed quality hence the wide variability in germination percentage, germination time, seed vigour index and dry matter accumulation. Therefore the need to improve quality of seeds used by subsistence farmers by providing extension services on best pre and post harvest handling practices. Increasing production of sorghum in these regions will contribute significantly towards realizing food security. Further analysis could be carried out on genetic and sanitary quality aspects of the seeds planted by farmers in Eastern, Nyanza and Coastal regions.
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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 | 0.001 | 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) | 0.001 | 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".