Food vs. Wood: Dynamic Choices for Kenyan Smallholders
Bibliographic record
Abstract
Smallholder farmers in many areas of the semiarid tropics are planting exotic tree species that provide alternative income sources, fuel, and building materials. While providing other benefits, these trees often occupy land that could produce annual food crops. This study uses a polyperiod, linear programming model, to explore the opportunity cost of planting Eucalyptus grandis and Grevillea robusta trees compared to crops in the Nyando watershed of western Kenya. Results of the ten-year period wealth maximization model suggest that a representative farmer’s decisions on farm resource allocation are sensitive to changes in the relative prices of short rotation tree products and annual crops. The model also suggests that there are economic tradeoffs between planting trees and crops, as well as between planting different tree species. Timber production is not likely to replace food crops for two main reasons: (1) the high cost of meeting household subsistence requirements from marketed grains, (2) household cash flow needs met by annual crops. Farmers plant eucalyptus for commercial purposes because they can obtain timber products within four years; however if the prices of these short rotation products go down, farmers will prefer to grow timber from high yield grevillea.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".