The distribution of returns from land efficiency improvement in multistage production systems
Bibliographic record
Abstract
Abstract This paper assesses the distributional consequences of technical changes that improve the efficiency of land and of other inputs in a multifactor crop‐production system. We introduced an equilibrium displacement model (EDM) by using the specification of a factor‐augmenting approach. Given the uncertainty about the EDM parameters, a Monte Carlo simulation is used to produce a distribution of possible return measures. We found that land suppliers (likely farmers) receive a larger share (73%) of total benefits from the adoption of land‐technical change than they do from the adoption of other input technologies. Each input supplier receives a larger share of total benefits from technical change in her own input. However, this result is sensitive to the value of the parameters, especially the value of the elasticity of substitution. We applied the EDM to the case of no‐tillage (NT) to provide insight into how the aggregate return from the adoption of NT was distributed among different groups on the Canadian Prairies. The results of this study can be used by policymakers and funding agencies in order to influence landowners and farming communities to adopt environmentally sound land technologies to achieve both greater agricultural productivity and sustainability.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".