A Ricardian Analysis of the Impact of Climate Change on South American Farms
Bibliographic record
Abstract
This study estimates the impact of climate change on South American agriculture taking into account farmer adaptations. The study used a Ricardian analysis of 2300 farms to explore the effects of global warming on land values. In order to predict climate change impacts for this century, were examined climate change scenarios predicted by three Atmospheric Oceanic General Circulation Models (AOGCM): the Canadian Climate Center (CCC), the Centre for Climate System Research (CCSR), and the Parallel Climate Model (PCM) models. Several econometric specifications were tested, and five separate regressions were run: for all farms, small household farms, large commercial farms, rainfed farms, and irrigated farms. Farmland values will decrease as temperature increases, but also as rainfall increases except for the case of irrigated farms. Under the severe Canadian Climate Center (CCC) scenario, South American farmers will lose on average 14% of their income by the year 2020, 20% by 2060, and 53% by 2100, but half of these estimates under the less severe Centre for Climate System Research (CCSR) scenario. However, farms will lose only small amounts of income under the mild and wet Parallel Climate Model (PCM) scenario. Both small household farms and large commercial farms are highly vulnerable, but small farms are more vulnerable to warming, while large farms are more vulnerable to rainfall increases. Both rainfed and irrigated farms will lose their incomes by more than 50% by 2100, with slightly more severe damage to irrigated farms, but the subsample analysis treats irrigation as exogenous.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".