Influence of Tillage on Corn and Soybean Yield in the United States and Canada
Bibliographic record
Abstract
An extensive literature review was conducted of corn and soybean research that compared yields of no‐tillage to conventional fall tillage systems. The objective was to test the hypothesis that no‐till has a different effect on corn and soybean yields in different regions of the United States and Canada. The trial results were mapped to look for geographic and environmental patterns in the relative performance of no‐tillage to conventional tillage on corn and soybean yield. The national average difference in corn and soybean yield between no‐tillage and conventional tillage was negligible. A map of the tillage yield comparisons was created for the U.S. and Canada. No‐till tended to have greater yields than conventional tillage in the south and west regions. The two tillage systems had similar yields in the central U.S., and no‐till typically produced lower yields than conventional tillage in the northern U.S. and Canada. No‐tillage had greater corn and soybean yields than conventional tillage on moderate‐ to well‐drained soils, but slightly lower yields than conventional tillage on poorly drained soils. Corn and soybean yields tended to benefit more from crop rotation in no‐till as compared to continuous cropping. Future tillage research should focus on optimizing successful high residue no‐tillage or strip‐tillage production systems instead of making comparisons to conventional tillage systems.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".