Effect of Planting Date, Seed Treatment, and Cultivar on Plant Population, Sudden Death Syndrome, and Yield of Soybean
Bibliographic record
Abstract
A 2-year study was conducted in Illinois, Indiana, Iowa, and Ontario in 2013 and 2014 to determine the effects of planting date, seed treatment, and cultivar on plant population, sudden death syndrome (SDS) caused by Fusarium virguliforme, and grain yield of soybean (Glycine max). Soybean crops were planted from late April to mid-June at approximately 15-day intervals, for a total of three to four plantings per experiment. For each planting date, two cultivars differing in SDS susceptibility were planted with and without fluopyram seed treatment. Mid-May plantings resulted in higher disease index compared with other planting dates in two experiments, early June plantings in three, and the remaining six experiments were not affected by planting date. Soil temperature at planting was not linked to SDS development. Root rot was greater in May plantings for most experiments. Resistant cultivars had significantly lower disease index than the susceptible cultivar in 54.5% of the experiments. Fluopyram reduced disease severity and protected against yield reductions caused by SDS in nearly all plantings and cultivars, with a maximum yield response of 1,142 kg/ha. Plant population was reduced by fluopyram seed treatment and early plantings in some experiments; however, grain yield was not affected by these reductions. Yields of plots planted in mid-June were up to 29.8% less than yields of plots planted in early May. The lack of correlation between early planting date and SDS severity observed in this study indicates that farmers do not have to delay planting in the Midwest to prevent yield loss due to SDS; cultivar selection combined with fluopyram seed treatment can reduce SDS in early-planted soybean (late April to mid May).
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 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 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".