International Comparison of Cost and Efficiency of Corn and Soybean Production
Bibliographic record
Abstract
The objective of this paper was to examine the cost efficiency of corn and soybean production for typical farms involved in the cash crop agri benchmark network. Using a data envelopment analysis (DEA) approach, efficiency indices were computed for 35 corn farms, representing 15 countries including Argentina, Bulgaria, Brazil, China, Czech Republic, France, Hungary, Italy, Poland, Russia, Ukraine, United States, Uruguay, Vietnam, and South Africa. Average technical efficiency was 0.497, average allocative efficiency was 0.487, and average cost efficiency was 0.310. Efficiency indices were also found for 18 soybean farms, representing 9 countries, including Argentina, Brazil, Canada, China, Italy, Ukraine, United States, Uruguay, and South Africa. Average technical efficiency was 0.533, average allocative efficiency was 0.553, and average cost efficiency was 0.340. Correlation analysis shows that seed input cost shares were the most correlated with cost efficiency for soybeans, while fixed capital cost shares were the most correlated with cost efficiency for corn production. OLS regression indicated that land, labor and other direct services were under-utilized for corn production, and that seed was over-utilized for soybean production.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".