How Raised Beds and Fe-Chelate Affect Soybean Iron Deficiency Chlorosis and Yield
Bibliographic record
Abstract
Water-logging and the inability to take up sufficient iron (Fe), causing iron deficiency chlorosis (IDC) in soybean (Glycine max, L. Merr.), can be major yield reducing factors in certain soils in the northern USA and Manitoba, Canada, soybean growing regions. The objective of this research was to evaluate soybean IDC, biomass production, and yield with seeding on raised beds and seed application of the Fe-chelate compound ortho-ortho-Fe-EDDHA. In six environments, soybean were seeded on raised beds and conventionally prepared seedbeds (flat) and with a factorial arrangement of five cultivars (within adapted maturity group 0.1 to 0.9 and variable IDC tolerance) and seed applied Fe-EDDHA using rates of 0 kg·ha−1 and 3.36 kg·ha−1. There were no significant interactions between the factors tested. The plant population was 27% higher on the raised beds compared with flat, and yield was 6.3% higher (2893 kg·ha−1 vs. 2722 kg·ha−1). Total dry plant biomass on raised beds was 9.8% greater compared with flat. The plant population with seed applied Fe-EDDHA was 10.6% lower compared with no application. However, the IDC score was significantly lower 2.2 vs 2.4 (1 = green, 5 = dead) for Fe-EDDHA seed application. Yield and plant biomass were not significantly different between Fe treatments. Raised beds offer an opportunity for soybean growers to reduce the negative influence of excessive water. Further research is needed to determine the long-term effect of raised beds on plant development, IDC expression, and yield. The application of Fe-EDDHA remains a partial solution and should therefore be combined with other methods to reduce IDC. Further research should study other Fe-EDDHA application rates and methods.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".