Impact of Tillage and Rotation on Yield and Economic Performance in Corn‐Based Cropping Systems
Bibliographic record
Abstract
The objective of our research was to identify economically efficient corn (Zea mays L.) based tillage‐rotation combinations using a 20‐yr data set from a long‐term experiment in Ontario, Canada. Seven rotations in two tillage systems (moldboard and chisel plow) were analyzed. We found multiple benefits associated with diversifying rotations in both tillage systems The integration of soybean [Glycine max (L.) Merr.] or soybean and wheat (Triticum aestivum L.) resulted in 7 to 11% higher corn yields in the chisel tillage system. In the plow tillage system corn yield in rotation with soybean and wheat increased by 5%, when wheat was underseeded with red clover (Trifolium pratense L.). These diversified rotations resulted in an increase in yearly net returns of $51 to $64 in the moldboard tillage system and $96 to $108 in the chisel tillage system. The diversification of rotations reduces variance of net return and thus makes the rotations attractive to risk averse producers. Furthermore diversified rotations showed less response to price changes. Diversified rotations evaluated in this study also proved to be less affected by increasing energy costs. Red clover seeded into wheat resulted in 5% higher yields for the following corn crop in the moldboard system. Rotations that included red clover cover lowered production risk but did not have higher net returns than comparable rotations without red clover. However, the potential for red clover to reduce N fertilization requirements for the following corn, was not considered in this study. Yield penalties due to chisel plowing with financial consequences were only observed in continuous corn. In all other rotations the effect of tillage was negligible. An increase in energy costs forces farmers to switch to crops with lower inputs rather than switch to reduced tillage.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".