Risk aversion and land allocation between annual and perennial crops in semisubsistence farming: a stochastic optimization approach
Bibliographic record
Abstract
Abstract This article analyzes the effect of production uncertainty on farmland allocation decisions between perennial and annual crops, focusing on a representative farmer's attitude toward risk. A dynamic stochastic optimization model that considers net planting—the difference between new plantings and removals of perennial crops that achieve full production cycle—is used. The effect of uncertainty on the representative farmer's decisions to increase or decrease perennial crops’ acreage, on the optimal path, is examined. Our results reveal that the response of optimal path of net planting to uncertainty related to perennial crop production is highly affected by the farmer's attitude toward risk. A risk‐averse or a low‐risk loving farmer tends to reduce land allocation to perennial crops under uncertainty, while a high‐risk loving farmer will do exactly the opposite. Also, due to disutility of farming, the farmer tends to reduce land allocation to perennial crops when prices are high enough for him to attain a desired income level expectation. One implication of this research is the need for mechanization—in sub‐Saharan countries in particular—that increases per‐acreage yield and output in semisubsistence agriculture.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".