Review of Corn Yield Response under Winter Cover Cropping Systems Using Meta‐Analytic Methods
Bibliographic record
Abstract
Extensive research on the use of winter cover crops (WCC) under different agricultural practices in the USA and Canada has shown both negative and positive effects on subsequent corn (Zea mays L.) yield. These contrasting results determine the need for a comprehensive quantitative review. The objective of this study was to use meta‐analytic methods to summarize and quantitatively describe the effects of WCC on corn yield based on peer‐reviewed published research. Thirty‐six studies were included in the analysis representing different regions of the USA and Canada under different agricultural practices (i.e., species, fertilization, kill date, tillage, etc.). The effect‐size used to compare studies was the response ratio, calculated as yield of corn following WCC over yield of corn following no cover. Biculture WCC increased corn yield by 21%, but there is greater variation due to the small number of studies in this group. Overall, grass WCC neither increased nor decreased corn yields and this response was not dependent on the use of N fertilizer. Legume WCC increased corn yield by 37% when no nitrogen (N) fertilizer was applied and this benefit decreased with application of N fertilizer.
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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 | 0.029 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".