The Role of Anticipated Regret in Farmers’ Land Conversion Decisions
Bibliographic record
Abstract
Conversion of grassland to cropland in the Prairie Pothole Region of North and South Dakota has many environmental consequences, including the loss of important migratory bird breeding grounds, increased agricultural chemical use, and release of sequestered carbon into the atmosphere. While conversion has negative ecological consequences, in years of high crop prices, cropland can generate higher returns than grassland, and farmers therefore face economic incentives for conversion in these years. However, recent research suggests that farmers may not convert land despite the economic incentives to do so. In this paper, we used the results of a framed economic experiment to explore the role of anticipated regret in farmers’ land conversion decisions. We used duration analysis to investigate the effect of anticipated regret salience on the risk of grass-to-crop land conversion and examined the regret participants express ex post about their land use decisions. Our results show that conversion risk from grassland to cropland was lower when anticipated regret was made salient than when it was not. Additionally, farmers expressed more regret about decisions to convert their land than when they left their land in grass. These results suggest that anticipated regret may play a role in farmers’ land conversion decisions, and that encouraging farmers to consider how they might feel about their decisions in the future may lead to lower rates of grass-to-crop conversion. We propose operational policy strategies based on our findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".