Mitigating Greenhouse Gas and Ammonia Emissions in Litter-Based Pig Farming with Microbial Consortia
Bibliographic record
Abstract
<b><sc>Abstract.</sc></b> Pig production contributes to greenhouse gas (GHG) and ammonia (NH₃) volatilization, posing significant environmental, social, and health challenges. Litter-based pig farming systems are increasingly considered as an alternative to conventional slatted systems due to their potential to improve animal welfare. However, these systems still face challenges related to air quality, nutrient management, and gaseous emissions. Microbial consortia, which leverage the synergistic interactions of diverse microorganisms, offer a promising solution to enhance nutrient cycling and mitigate pollutant emissions in such systems. This study investigates the potential of microbial consortia to mitigate GHG and NH₃ emissions in litter-based pig farming systems. Two microbial consortia, Effective Microorganisms (EM) and Indigenous Microorganisms (IMO), were selected for their potential in GHG and NH₃ reductions. A five-week pilot experiment was conducted in a controlled laboratory setting at the Research and Development Institute for the Agri-environment (IRDA) in Deschambault, QC, Canada. Twelve hermetically sealed chambers were used, each housing three growing pigs and equipped to monitor environmental parameters and gas emissions continuously. The experimental design included three treatments: a control without microbial inoculation, T-EM (500 mL/day EM culture applied to the litter), and T-IMO (500 mL/day IMO culture applied to the litter). The microbial consortia were diluted at 5% for IMO and 1% for EM before application on the bedding. Each treatment was replicated four times, and litter management mimicked commercial practices, including weekly additions based on NH₃ levels and environmental observations. Gas concentrations (NH₃, CO₂, CH₄, and N₂O) were measured at 15-min intervals using advanced spectroscopic instruments. Emission differences between treatments were calculated using ventilation flow data. In addition, odor intensity test and litter physicochemical characterization were conducted. According to the results, gas emissions were consistently lower in the T-IMO treatment compared to the Control and T-EM treatments, with mean reductions of 16% for CO₂, 21% for CH₄, and 29% for NH₃. In odor intensity tests, T-IMO was associated with the lowest n-butanol concentration (875 ppm), compared to 1219 ppm for T-EM and 1110 ppm for Control. Additionally, T-IMO exhibited higher total Kjeldahl nitrogen (NTK) values than the other treatments, indicating improved nitrogen retention within the bedding system. Future research will focus on scaling up these results and evaluating the long-term feasibility of microbial applications and their potential to mitigate GHG and NH<sub>3</sub> emissions in liquid manure management systems.
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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".