Energy efficiency of heating machines and its effects on broiler's performance and welfare
Bibliographic record
Abstract
The increasing global demand for animal protein has driven the broiler industry to optimize production systems and better understand limiting factors affecting performance. This study evaluated four different heating systems to determine their correlation with climatic variables, zootechnical performance, pellet fuel consumption, and energy usage. Data were obtained from a private broiler integration company in Southwest Paraná, Brazil, specializing in the griller broiler category. The study covered a 28-day housing period for 12 flocks (both male and female), each consisting of approximately 120,000 birds, for 28 days. The analyzed variables included indoor and outdoor environmental temperature, relative air humidity, carbon dioxide (CO₂) concentration inside the poultry houses, feed conversion ratio, weight gain, pellet fuel consumption, and energy consumption. Statistical analyses were performed using descriptive statistics and Principal Component Analysis (PCA) in R software. Results indicated that correlations among variables were generally weak. However, environmental conditions had the greatest influence on broiler performance. The first principal component explained 74.1% of the total variance, with minimum CO₂ concentration, external temperature, minimum and maximum internal temperature, and pellet fuel consumption being key contributing factors. The second principal component included maximum CO₂ concentration, weight gain, and minimum internal and external relative humidity. Among the evaluated heating systems, the fourth machine tested exhibited the lowest pellet fuel consumption while maintaining satisfactory weight gain and feed conversion rate despite its relatively high energy consumption. These findings suggest that temperature control and pellet fuel consumption are critical factors in optimizing broiler production efficiency, ultimately contributing to improved growth performance and resource utilization.
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".