Maize Intensification among Smallholder Farmers in Kenya: Understanding the Impacts of Climate
Bibliographic record
Abstract
This chapter distinguishes the effects of climatic shocks (droughts), weather (during one year) and climate normals (long-term average weather conditions) on Kenyan smallholder farmers’ decisions to intensify maize production, measured as the share of maize area per farm allocated to hybrid seeds. We also ask how such intensification affects the vulnerability of expected crop income, crop income variability, and downside risk. We find that maize intensification is strongly affected by weather, climate shocks and climate normals. We also find that maize intensification has a positive effect on expected crop income but no significant effect on crop income variability or downside risk. Moreover, relying on a higher proportion of hybrid seed use, which is negatively associated with persistent climatic shocks, is not enough to statistically significantly reduce the likelihood that crop income falls below a given threshold (downside risk). Importantly, cropping system decisions are related to longer-term investment choices, while decisions on specific hybrid types are annual decisions. Thus, maize intensification alone is not an effective strategy. Further, our results suggest that farmers are not adapting optimally to climate change. Suboptimal choices might reflect market failures, such as credit constraints, poor access to input and output markets, and information asymmetries. Our results also suggest that rising population density provides incentives to shift toward more intensive farming systems. Finally, we find trade-offs between nonfarm employment and crop income. Our findings lead us to recommend that the Government of Kenya play an active role in encouraging smallholder adaptation to changing climate patterns and climate shocks. Not only do smallholders need better access to hybrid seeds and other inputs through decentralized, competitive markets, but also effective, widely-diffused market information services and other insurance mechanisms.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".