Inclusion of sulphur in fungicide programmes for integrated disease management of soybean
Bibliographic record
Abstract
Soybean rust (SBR) and late-season diseases (LSD) significantly threaten soybean production worldwide. This study aimed to evaluate the effectiveness of incorporating sulphur into fungicide programmes for managing SBR and LSD in soybeans and to optimize these programmes with novel fungicide combinations. Field experiments were conducted over two phases across multiple localities and seasons in Paraguay. Phase 1 assessed the impact of sulphur-enriched fungicide programmes on disease control efficacy and soybean yield. Phase 2 optimized fungicide programmes by integrating sulphur with new fungicide combinations. Disease severity, area under the disease progress curve (AUDPC), thousand-grain weight (TGW) and yield were measured. Treatments combining sulphur with multi-site and site-specific fungicides significantly reduced the severity and progression of SBR and LSD compared to treatments without sulphur or the untreated control. Control efficacies exceeded 86% for SBR and were around 77% for LSD. Despite significant reductions in disease severity, yield and TGW did not always show significant increases, likely due to environmental factors. Incorporating sulphur into fungicide programmes effectively reduces SBR and LSD in soybean crops, enhancing disease control efficacy. Environmental conditions influenced treatment efficacy, underscoring the need for site-specific disease management strategies. Integrating sulphur with fungicides represents a viable approach to optimize soybean disease management and promote sustainable production systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".