Yield Responses to Planting Density for US Modern Corn Hybrids: A Synthesis‐Analysis
Bibliographic record
Abstract
Identifying an optimal plant density is a critical management decision for corn (Zea mays L.) production. The main objectives of this study were to: (i) investigate the grain yield responses to plant density (yield–density relationship), (ii) identify best fitted yield–density response curves, and (iii) explore genotype (G) × environment (E) interaction effect on yield–density response models. Analysis was conducted on meta‐data (124,374 observations) gathered from 22 US states and 2 Canadian provinces, diverse sites (E), for years from 2000–2014 on multiple hybrids (G). Yield data were further grouped into four yield environments (low [LY], <7 Mg ha−1; medium [MY], 7–10 Mg ha−1; high [HY], 10–13 Mg ha−1; and very high [VHY], >13 Mg ha−1 yielding groups). Primary outcomes from this analysis were: (1) strong G × E interaction; (2) a quadratic model best fitted yield–density relationship; (3) four contrasting yield–density responses identified as dominant in each yield productivity environment, i.e., a declining, a constant, an increasing, and ever‐increasing type; (4) the yield productivity environment varied for the different corn comparative relative maturity (CRM) groups, i.e., the LY environment for long‐maturing hybrids matched with a MY or HY environment for short maturing hybrids; and (5) maximum yielding plant density (MYPD) was lower but maximum yield was greater for long‐ versus short‐maturing hybrids. In summary, optimal plant density should be decided based on detailed G × E analysis of production conditions that include factors such as CRM, yield productivity environment (weather–soil × management practices), and site information.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".