Adaptation and Validation of the CO2 Balance Method for Ventilation Rate Estimation in Laying Hen Houses
Bibliographic record
Abstract
Highlights Adapted CO2 balance improves ventilation rate prediction in poultry housing systems. Respiration quotient set to 0.9 (day) and 0.85 (night) enhances model accuracy. Adjusting the CO2 production rate (+18% day, +8% night) boosts model performance. Model accuracy declines with inlet-to-exhaust CO2 differences below 150 ppm. ABSTRACT. Assessing the environmental impact of poultry farms requires accurately determining ventilation rates (VR) while minimizing resource use. Accurate VR estimates are crucial for precisely evaluating emissions, making VR a vital factor in enhancing the sustainability of poultry production. This study aims to adapt and validate the carbon dioxide (CO2) balance method for VR prediction in laying hen houses. The adaptation was based on the CO2 balance method from the International Commission of Agricultural and Biosystems Engineering (CIGR), which considers variables such as the CO2 production rate of hens (PRCO2), animal activity (AA), and the respiratory quotient of laying hens (RQ). Data collected from an experiment involving laying hens in conventional cages (CC) under laboratory conditions were used to adapt the VR prediction model. The adapted model was validated under both laboratory and commercial conditions in three housing systems: conventional cages (CC), enriched cages (EC), and cage-free systems (CF). At the laboratory level, validation was performed using data from two studies conducted in the same controlled laboratory conditions but with different housing systems (the first in CF and the second in the three systems simultaneously). At the commercial level, validation was performed using data collected from 30 commercial farms in Quebec, Canada. The resulting adaptations included setting AA to 1, RQ to 0.9 during the day and 0.85 at night, and PRCO2 to 18% during the day and 8% at night. The adjusted model demonstrated an R2 of 0.63 for VR prediction when the difference between exhaled and inlet CO2 was less than 150 ppm. At the experimental level, all evaluated housing systems showed an R2 greater than 0.63 and an average RMSE of 0.35 m3 h-1 hen-1. At the commercial level, housing systems exhibited an average predicted R2 of 0.71 and an RMSE of 1.68 m3 h-1 hen-1. The adapted CO2 balance method presented good predictive values across laboratory and commercial experiments. Keywords: Airflow, Animal activity, Egg production, Gas production, Poultry building, Respiratory quotient.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".