Yield Responses to Planting Density for US Modern Corn Hybrids: A Synthesis‐Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it