Integrating scientific and farmers perception towards evaluation of rain-fed agricultural technologies for sorghum and cowpea productivity in Central Kenya
Bibliographic record
Abstract
Soil fertility degradation remains the major biophysical cause of declining per capita crop production on smallholder farms in Central Kenya highlands. A study was conducted to compare farmers’ perception and biophysical data on selected water harvesting and integrated soil fertility management technologies on sorghum (Sorghum bicolor (L.) Moench) and cowpea (Vigna unguiculata L.) production in Central highlands of Kenya. Three hundred and seventy one smallholder farmers were invited to evaluate thirty six plots laid out in Partially Balanced Incomplete Block Design (PBIBD) replicated three times. The treatment which was ranked best overall rated as ‘good’ by the farmers was farmers practice with a mean score of (2.78) and yielding (3.5 t/ha) under sorghum alone plus external soil amendment of 40 kg P /ha+20 kg N /ha. This was closely followed by tied ridges and contour furrows overall rated as ‘good’ by the farmers under sorghum alone plus external soil amendment of 40 kg P /ha+20 kg N /ha+manure 2.5 t/ha and 40 kg P /ha+40 kg N /ha+manure 5 t/ha both with a mean score of (2.7) and yielding (3.0 t/ha) and (2.9 t/ha) respectively. Generally, all experiment controls were overall scored as ‘poor’ yielding as low as 0.3 t/ha to 0.6 t/ha. Therefore, integration minimal addition of organic and inorganic inputs on highly valued traditional crops with adequate rainfall under normal farmers practice in semi arid lands could be considered as an alternative option contribution to food security in central highland of Kenya. Key words: Food security, water harvesting, integrated soil fertility management, Central Kenya.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".