Corn yield response to winter cover crops: An updated meta-analysis
Bibliographic record
Abstract
Winter cover crops (WCCs) provide agronomic and environmental benefits, although their impacts on subsequent crop yields have been reported to vary across regions, soils, or under different farm practices. To address the variability in response, previous qualitative and quantitative reviews have summarized the overall yield effects of WCCs. However, the results from such reviews need constant revision as new research is published and interest in the conservation benefits of WCCs increases. Here, we update a previous meta-analysis of WCC effects on corn (Zea mays) yields, which summarized peer-reviewed research from the United Sates and Canada that was published between 1965 and 2004. Our updated data set (1965 to 2015) comprises 268 observations from 65 studies conducted in different regions of the United States and Canada, and includes information about the management practices utilized (i.e., WCC species, nitrogen [N] fertilization, termination date, tillage, etc.). The effect-size was the response ratio (RR), defined as corn yield following WCCs relative to yield after no cover crop (NC). As in the previous meta-analysis, our results showed a neutral to positive contribution of WCCs to corn yields. On average, grass WCCs neither increased nor decreased corn yields, although corn grown for grain yielded relatively higher than silage corn after grass WCCs. Legume WCCs resulted in subsequent higher corn yields by 30% to 33% when N fertilizer rates were low or the tillage system shifted from conventional tillage (CT) to no-tillage (NT). Mixture WCCs increased corn yields by 30% when the cover crop was late terminated (zero to six days before subsequent corn). Evidence of 65 years of research showed that uncertainty around the RR has decreased and corn yield response to WCCs has stabilized over time. Our results suggest that benefits of WCCs do not result in reduced corn productivity if properly managed.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.043 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".