High-Yielding Soybean: Genetic Gain × Fertilizer Nitrogen Interaction
Bibliographic record
Abstract
The U.S. accounts for 35% of the global soybean production. Potential soybean yields are determined by the interaction of genotype, environment, and management practices (G × E × M). The question “Do high yielding soybean need to be fertilized with nitrogen (N)?” is still a valid one. The overall objective of this project is to study the contribution of N via utilization of varying N strategies under historical and current soybean genotypes. Two field experiments were conducted during the 2015 growing season at Ottawa (east central KS) and at Ashland Bottoms (central KS). Three soybeans varieties were used (1990s = non-RR, 2000s = RR-1, and 2010s = RR-2) under three N systems (non-N applied; late-N, 50 lb N/a; and 550 lb N/a, split in 3 timings) with all seeds inoculated. At Ottawa, the study was planted in an area without soybean history, with yields ranging from 14 to 37 bushels per acre. Superior yields were recorded for the modern soybean variety Roundup Ready (RR-2) relative to the RR-1 and non-RR materials. As related to the N management approach, slightly higher soybean yields occurred when N nutrition was based on fertilizer N application. At the Ashland Bottoms site, yields ranged from 44 to 76 bushels per acre. High yields were with the oldest soybean genotype (non-RR) when N nutrition was based on the fertilizer N application; while low yields were when the N nutrition of the modern soybean variety (RR-2) was based on the inoculation. There was no variety by N factor interaction with yield. The variety (P < 0.05) was the main significant single effect, which presented the following order from high to low productivity: non-RR >> RR-1 = RR-2. A conclusion from the first year of this experiment was the field where soybean had not been previously planted (Ottawa) had a lower yield capacity compared to the site with a soybean history (Ashland Bottoms).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".