Soybean Yield Response to In‐furrow Fungicides, Fertilizers, and Their Combinations
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
Core Ideas Fungicide in‐furrow and starter fertilzer individually did not increase yield. Fungicide and starter fertilizer combined in‐furrow increased yield marginally. Fungicide and starter fertilizer in‐furrow may not be economical without disease. Planting into cool and wet conditions exposes soybean [ Glycine max (L.) Merr.] seed and seedlings to pathogens that may reduce plant populations, resulting in lower yield. Recently, fungicides have been labeled for in‐furrow applications and marketed to provide additional broad‐spectrum protection from soilborne fungi and enhance seedling vigor. Additionally, liquid fertilizers have been promoted recently as a carrier for fungicides to improve yield in some soybean producing areas in the United States. The objective of this study was to evaluate the effect of a fungicide, starter fertilizer, and a combination of fungicide and starter fertilizer on soybean yield. Field experiments were laid out in Arkansas, Indiana, Iowa, and Mississippi in the United States and Ontario, Canada, with a total of 14 site‐years. A positive yield response was observed with the fungicide and starter fertilizer treatment combination in Arkansas in 2014; however, there was no effect of treatment on soybean yield at any other location or year. Overall, a yield benefit of 1.6 bu/acre (107.6 kg/ha) ( P = 0.02) with the fungicide and starter fertilizer treatment was observed across all locations when combined using meta‐analysis. In conclusion, our study suggests that the prophylactic application of fungicide and starter fertilizer may not be profitable without the risk of soilborne diseases and nutrient deficiencies.
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 | 0.000 | 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) | 0.000 | 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