Is crop insurance to blame for narrow crop rotations in Saskatchewan?
Bibliographic record
Abstract
My research uses field-level data to empirically quantify the relationship between crop insurance and agricultural producers’ crop rotation decisions. I answer the question: is crop insurance aggravating the trend away from agronomically advised diverse crop rotations towards narrower rotations in Saskatchewan? I use field- and producer-specific yield and insurance coverage level data from the Saskatchewan Crop Insurance Corporation in a three-part empirical approach to study the relationship between crop insurance and crop rotations. I first develop and estimate a field-level expected profit model for popular crops that uses yield observations, rotation variables, and fixed effects to estimate expected yield before being combined with spring prices and soil zone costs to predict producers’ expected profit and risk. The predicted expected profit and risk are used in a multinomial logit crop choice model that predicts producers’ crop choices based on random utility theory. The crop choice model is used to predict producers’ acreage response to changing insurance coverage levels. My results suggest that crop insurance only has a marginal effect on farmers’ crop rotation decisions, even when crop insurance is completely denied to producers who plant narrow rotations. Instead, crop specific characteristics, previously planted crops, and geographic crop compatibility appear to be far more important factors to producers when they are making crop rotation decisions. These results suggest that crop insurance is not the driving force behind the trend towards narrower crop rotations. This is an important finding for policy makers looking to encourage producers to adopt agronomically advised diverse rotations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".