Break-Even Profitability for Food-Grade Specialty Soybeans
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it