A Diagnostic Appraisal of Sorghum Farming in Striga Endemic Areas of Eritrea
Bibliographic record
Abstract
Sorghum (Sorghum bicolour (L.) Moench), the most important cereal crop in Eritrea, is grown in more than 210,000 ha annually but its productivity is hampered by many factors including: farmer perception, lack of appropriate varieties, undeveloped seed systems, drought stress and Striga infestation. This study was aimed at understanding the sorghum smallholder livelihoods, farming systems, Striga incidence and infestation levels, and types of varieties grown in Striga infested sorghum growing areas. A semi structured questionnaire and focused group discussions were used to gather information from a total of 136 randomly selected farmers from three Striga endemic sub-regions of Eritrea namely, Hamelmalo, Goluj, and Tesseney. The results indicated that the most important cereal crops in the study area were sorghum, pear millet, ground nut and sesame. The most important constraint to sorghum production was identified as drought stress followed by Striga infestation. The majority of the respondents (81.6%) reported that their sorghum was affected by Striga and the level of infestation varied from mild (10%) to severe (70% and above). Small-holder farmers also indicated that high grain yield, drought tolerance/resistance and Striga resistance were their most important criteria when selecting sorghum varieties. Up to 31 different landraces were listed as the most popular in the study area. The dominant source of seed for cultivation was the local varieties retained by farmers from previous harvests. The information documented from this study may be used in future as a basis for a participatory farmer-oriented sorghum breeding program.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".