Farmer Perception and Adaptation Strategies on Climate Change in Lower Eastern Kenya: A Case of Finger Millet (Eleusine coracana (L.) Gaertn) Production
Bibliographic record
Abstract
Eastern Kenya, a semi-arid region, is characterized by low and erratic rainfall, high temperatures, and low soil fertility. Climate change has further worsened the situation leading to frequent droughts and hence increased food insecurity. Traditional crops like finger millet are possible solutions to combating changing climate due to their drought resistance nature, ability to produce high yields with little inputs and high nutritional content. It is against this backdrop that a survey was carried out in Mwala and Katangi divisions of Machakos and Kitui counties, respectively, to assess farmer’s perception on climate change, coping and adaptation mechanisms in finger millet production systems in smallholder farming systems of lower eastern Kenya. Data was collected, using semi-structured questionnaire, from 120 farmers i.e. 60 in each division. A stratified random sampling procedure, with location as a stratum was used to select respondent’s households. A computer random number generator was used to select number of households in each stratum. Maize and beans were the most popular crops grown by over 98% of the farmers in both sub-counties. Farmers also grew drought tolerant legumes; cow peas, green grams pigeon peas and cereals; sorghum and finger millet. Temperature rise was ranked highest with 88% and 98%, followed by prolonged drought with 70% and 72%, irregular rainfall at 69% and 81% and increased wind intensity at 22% and 28% at Machakos and Kitui, respectively, as aspects of climate change perceived by farmers. Farmers had taken up early planting at 88.6% and 93.7%, use of organic inputs at 89% and 92%, introduced new tillage practices, by applying ridges and furrows and tied ridges at 45% and 54%, and by adopting irrigation at 13%, and 9%, as coping strategies to climate change in Machakos and Kitui, respectively.It can be concluded that farmers in Machakos and Kitui are aware of climate change and its negative effects on crop production. In a bid to minimize crop loss and food insecurity, they have taken up various soil moisture conservation and soil fertility enhancement technologies.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".