Inoculation Timing Effect on Biological Nitrogen Fixation and Soybean Productivity
Bibliographic record
Abstract
Soybean [Glycine max (L.) Merr.], as other legume species, has the characteristic of fixing nitrogen (N) from the atmosphere via the biological N fixation (BNF) process. When a proper symbiosis relationship between soybeans and specific bacteria has been established, the plants can obtain up to 98% of the total N need. However, several factors can negatively affect BNF, impairing its contribution to nutrient demand and reducing crop productivity. In this scenario, additional inoculation could help the plant to overcome potential N gaps in BNF. Therefore, the goal of this project was to investigate if additional inoculation at different growth stages of the soybean growing season could increase nodulation, improve BNF (N contribution) and productivity of two varieties from maturity groups (MG) III and IV. To address this objective, different strategies for N supply were tested in a greenhouse and two field locations (Ashland Bottoms and Ottawa, KS) during the 2018 growing season. Trials were arranged in a complete randomized block design with four replications. The main outcomes of this study were that nodulation (total number of nodules per plant), plant dry biomass, relative abundance of ureide (RAU (%), indirect measurement of BNF), and productivity did not differ between inoculated treatments.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".