Corn and soybean yields and returns are greater in rotations with wheat
Bibliographic record
Abstract
Abstract Simple rotations containing only corn (Zea mays L.) and/or soybean (Glycine max L.) dominate landscapes despite agronomic, soil health, and environmental benefits associated with diversification. We hypothesize that inclusion of wheat (Triticum aestivum L.) in corn–soybean (CS) rotations increases yield and net returns and that this benefit is becoming larger over time. Yields from rotation–tillage trials located near Elora and Ridgetown, ON, Canada, respectively, were used to investigate the yield and return effects of diversifying CS rotation using wheat with/without red clover (Trifolium pratense L). At Elora, wheat inclusion increased rate of yield increase in corn and soybean over 36 yr. During the latter 16 yr, adding wheat to CS rotation increased conventional tillage (CT) 1st and 2nd‐year corn yields (with red clover) by 0.43 Mg ha−1 (4.2%) and 0.98 Mg ha−1 (11.8%), respectively, no‐till 1st and 2nd‐year corn yields (no red clover) by 0.78 Mg ha−1 (9.9%) and 0.45 Mg ha−1 (5.3%), respectively, and 1st‐year soybean yield by 0.34 Mg ha−1 (11.8%). At Elora, net returns in the 4‐yr wheat‐containing rotations were 10% greater compared to the CS rotation. Similar corn and soybean yield responses including wheat in 2 or 3 yr rotations also occurred at the greater‐yielding Ridgetown trial, however net returns were not increased relative to the CS rotation. Inclusion of wheat in CS rotation once every 4–5 yr may provide the optimal balance between accruing higher net returns from corn and soybean while minimizing direct net revenue reductions associated with wheat.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".