Bibliographic record
Abstract
Many studies report the yield benefits of narrow row soybean [Glycine max (L.) Merr.] and corn (Zea maize L.), but few focus on the profitability of switching to narrow rows. Based on yield data from 10 states in the north‐central USA and one province in Canada, this study considers the economic benefits of narrow row corn and soybean as a combined cropping system. The objectives of this study are to: (i) estimate the costs of switching from a wide to narrow row corn and soybean production system; (ii) determine the net benefits of making this change; and (iii) to quantify the risks associated with switching from wide to narrow rows. Narrow row systems where corn and soybean are planted using the same narrow row spacing with the same planting equipment are compared with (i) a system where soybean are drilled and corn is planted in conventional, 76‐cm rows (30‐inches), and (ii) a system where the same equipment is used to plant corn and soybean in 76‐cm rows. Sensitivity analyses consider net returns (i) to each system calculated at loan rates, (ii) to each system when glyphosate‐resistant soybean are included in the production set, and (iii) taking into consideration regional price and plant response effects. Expected profits, equipment costs, and the economic risks involved in the choice between alternatives are quantified using partial budget analysis, a mean‐variance criterion, stochastic dominance, and certainty equivalents. In all comparisons, strategies with narrow row soybean were always more profitable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".