Economic and Technical Feasibility of Grain Chilling in Brazil
Bibliographic record
Abstract
Grain quality is critical due to the more stringent food-safety demands. Chilled aeration has become a popular technology for preventing grain spoilage during storage, mainly in warmer regions. However, a limiting factor in broad-scale adoption of chilling is the general belief that this technology is much more expensive than other post-harvest methods, such as the aeration with ambient air. In this work, ambient and chilled aeration were simulated considering the three major grain-producing regions in Brazil. Also, three storage capacities (95, 5000, 10500 t), five-grain types (corn, coffee, rice, bean, soybean), and two storage periods (beginning at the first and the last months of the harvest period) were used in the study, totalling 180 simulation scenarios. Based on these simulations a comparative cash flow analysis was performed aiming at evaluating the influence of the product, storage period, region, and silo size on the costs and profits from using these technologies. Results were strongly affected by the weather patterns of the studied regions, market values of grain, storage sizes, and fan operation hours. Chilled aeration should be economically competitive with ambient aeration, and the two technologies appeared as low-risk investments in Brazil, achieving average profits for 20 years by considering the time of money of US$ 68 and 59.4 million, respectively. Considering the technical factors, chilling presented higher energy consumption, but showed a greater potential for reducing grain temperatures and resulted in grain dry matter losses around 58% smaller than those verified when using ambient air.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".