Growing Crops in Arid, Drought-Prone Environments: Adaptation and Mitigation
Bibliographic record
Abstract
Drought poses significant risks to society, in particular irrigated-crop production, which accounts for a large share of global freshwater use. Given its key role in the production of food, feed and fiber crops, there exists a need for policy measures to prevent and mitigate the impacts of drought on irrigated agriculture. This paper proposes that the design of drought policy should take into account actual farmer behavior in response to water scarcity. To this end, we offer a detailed analysis of land allocation and crop-choice decisions over time in an irrigation district located in the dry plains of Northern Mexico. We find that farmers systematically change their crop mix in response to water availability. In particular, in times of drought, irrigation water flows to higher-yield and higher-price crops (which also require more intense irrigation) to the detriment of less water-demanding (but lower value) crops. Farmers use water with the goal of earning a living—economizing on water per se has no relevance in that context. Policies that do not explicitly recognize this may result in ineffective, inefficient and/or unfair outcomes.
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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".