Agronomic Optimal Plant Density by Yield Environment in Soybean
Bibliographic record
Abstract
This research report presents a summary of a peer-reviewed publication: Carciochi W.D.; Schwalbert R.; Andrade F.H.; Corassa G.M.; Carter P.; Gaspar A.P.; Schmidt J.; Ciampitti I.A. 2019. Soybean seed yield response to plant density by yield environment in North America. Agronomy Journal. Recent economic and productive circumstances have caused interest in within-field variation of the agronomic optimal plant density (AOPD) for soybean [Glycine max (L.) Merr.]. Thus, the objective of this study was to determine the AOPD by yield environment (YE) for soybean. During 2013 and 2014, nine site-years with a total of 78 yield-to-plant density responses were evaluated in different regions of the United States and Canada. A soybean database evaluating seeding rates ranging from 69,000–271,000 seeds/a was utilized, including the final number of plants and seed yield. The data were classified in YEs: low (LYE, <59.6 bu/a), medium (MYE, 59.6-64.1 bu/a), and high (HYE, >64.1 bu/a). The main outcomes for this study were: 1) AOPD decreased by 24% from LYE (127,000 plants/a) to HYE (97,000 plants/a); 2) greater AOPD in a LYE was not related to a low plant survival rate; and 3) cumulative precipitation during soybean reproductive growth period was 39% lower in LYE compared with MYE and HYE, possibly reducing its reproductive ability. This study presents the first attempt to investigate the seed yield-to-plant density relationship via understanding final plant establishment and by exploring the influence of weather defining soybean YEs in North America.
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.
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 | 0.001 |
| 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) | 0.000 | 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 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".