Bibliographic record
Abstract
Cultivar selection for specialty soybeans is mainly based on seed-yield performance, disease resistance, and value-increasing seed attributes. However, adoption of food-grade specialty soybean cultivars by farmers for commercial production requires studies on profitability and economic factors. This research evaluated the profitability of small-seeded, large-seeded, and high-protein specialty soybeans using break-even (BE) analysis to establish guidelines for cultivar selection and adoption based on economic feasibility. Differential costs for seed and weed control were considered in the BE analysis of two different planting systems: conventional (Scenario I) and herbicide tolerant (Scenario II) soybeans. Average BE premiums were $2.74, $4.26, and $1.30 bu-¹ under Scenario I, and $2.02, $4.57, and $0.66 bu-¹ under Scenario II for small seeded, large seeded, and high-protein test lines, respectively. At current premium level of $3.50 bu-¹ for small seeded, $2.50 bu-¹ for large seeded, and $1.50bu-¹ for high-protein specialty soybean, BE yields for these three types of specialty soybean should be 76.46, 85.21, and 89.28% of the check’s yield when compared with conventional soybean; and 77.47, 92.92, and 90.71% of the check’s yield when compared with Roundup Ready soybean, respectively. Additional positive returns will be expected when the current premiums offered in the market are higher than the BE premium of a specialty soybean cultivar, or when the actual yields of this cultivar are higher than the BE yield at current premiums. Based on the economic feasibilities, the present study proposed a new model for the selection and adoption of specialty soybean cultivars, both in breeding programs and for commercial 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 | 0.001 |
| 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 | 0.001 | 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".