Seasonal Analysis of Yield and Loss Factors in Bt Soybean Crops in North Brazil
Bibliographic record
Abstract
Tropical crops face significant challenges from abiotic and biotic stressors, resulting in substantial losses. This study aimed to assess the yield and losses in Bt soybean crops in Tocantins state, northern Brazil, during the 2017/2018 and 2018/2019 growing seasons. We monitored and estimated yield losses and their contributing factors in commercial fields, spanning dry and rainy seasons, from planting to harvest. Our findings revealed that crop yields remained consistent between the dry season (4349.85 kg/ha) and the rainy season (4206.51 kg/ha). Similarly, the overall yield loss showed no significant variation between seasons, with values of 902.86 kg/ha (dry) and 1007.92 kg/ha (rainy). Nevertheless, the factors contributing to these losses exhibited season-dependent variations. We observed higher plant mortality rates during the dry season, whereas insects (particularly stink bugs) and fungi were the primary contributors to grain yield losses during the rainy season. Conversely, losses due to flower abortion and pod malformation remained relatively consistent between the two seasons. Our study underscores the increase in soybean yield in one of Brazil’s agricultural frontiers. While overall yield and losses remained stable between dry and rainy seasons, the distinct seasonal patterns influencing yield losses call for nuanced and season-specific strategies in sustainable crop management.
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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 | 0.001 |
| 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 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".