Impact of Genetically Engineered Varieties on the Cost Structure of Corn and Soybean Production in Canada
Bibliographic record
Abstract
This paper investigates the impact of genetically engineered (GE) varieties on the cost structure of corn and soybean production in Canada. Employing an adoption index for each farm and a time trend with farm‐level data on production costs of grain corn and soybeans from 2000 to 2007, a translog cost function and the associated input‐share equations are estimated. The use of the adoption index improves the estimates of technological change and multifactor productivity (MFP) growth. The results demonstrate that the adoption of GE corn and soybean reduced the variable costs of production by 0.62% per year. The MFP of corn and soybean grew by 2.0% per year during the study period, and 31% of this growth is attributable to GE varieties of these crops. The results also reveal that the adoption of GE varieties reduced the cost shares of fertilizer, herbicides and pesticides, and machinery in corn and soybean production. While the adoption of GE varieties increased the cost shares of seeds and custom works including labor, only the former was statistically significant.
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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) | 0.001 | 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".