Bibliographic record
Abstract
Agriculture, the ‘backbone’ of the Kenyan economy, is dominated by smallholder farmers who account for 65 per cent of total agricultural production and 51 per cent of labour force in the sector. However, with 80 per cent of land classified as arid or semi-arid land (ASAL), smallholding in Kenya exists against a backdrop of poor agro-ecological conditions. In ASALs, 86 per cent of women and children in households are classified as ‘food insecure’. Climate change, characterised by increased frequency and intensity of drought in the region, inserts new layers of urgency and complexity into this already-great crisis. Responding to these immediate food security challenges, cross-sectional household data from smallholder farmers in three Counties of semi-arid Kenya is analysed with the goal of advancing the knowledge of semi-arid smallholder agriculture and improving household food security in the region. Specifically, household agro-economic data from two Agro-Ecological Zones (AEZ), Lower Midlands 4 (LM4) and Lower Midlands 5 (LM5), in Tharaka-Nithi, Machakos and Makueni Counties is examined in the light of two themes: frailty and complexity. Using existing literature and anecdotal evidence for hypothesis testing, I present multiple and logistic regression analyses, exposing the entrenched frailty of a smallholder agricultural system whereby even in a good season, only 52 per cent of plots break even. Three trends emerge: (i) plots in the LM4 Agro-Ecological Zone, characterised by lower mean temperature and higher annual precipitation, are more likely to break even in both good and bad seasons than farmers in the drier LM5 zone; (ii) despite anecdotal evidence provided by farmers, monocrop plots outperform mixed crop plots, offering a higher probability of breaking even in both good and bad seasons largely due to economies of scale; (iii) High Value Traditional Crops (HVTCs), notably green grams and millet, increase farmers’ likelihood of breaking even in both good and bad seasons, while maize, our sample’s cash crop, performs the most poorly. Reflecting on these statistical trends, data-based policy inferences - including greater HVTC adoption, ‘mosaic monocropping’, collective pest-management and microinsurance - are offered vis-à-vis how agricultural policy in Kenya can better contribute to food security, drawing heavily on the FAO’s climate-smart agriculture (CSA) framework.Adopting a ‘magnifying glass’ approach, I then offer a theoretical (and at times humorous) analysis of one particular barrier to the greater adoption of HVTCs, bird scaring by farmers of millet and sorghum, demonstrating the inherent complexity of even the most seemingly-simple development intervention. 100 per cent of millet and sorghum farmers in the Tharaka-Nithi study area report scaring birds as a labour input, devoting on average 24-66 per cent of all labour time to this activity – a stark contrast to farmers of all other crops, almost zero per cent of which report scaring birds. However, it is farmers’ behaviour with respect to birds, and not the pests themselves, that provide the greatest insight. Individually scaring birds from their land, farmers within a community continuously shift the cost of pests from one plot to the next, creating what I describe as a ‘ripple effect’ externality. Environment and resource economic’s (ERE) prescriptions are overviewed and rendered inadequate for addressing this bird scaring tragedy. In turn, a collective action approach is proposed, incorporating farmer groups, collective planting and scaring schedules and community feeding plots.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".