Dairy manure nutrient recovery reduces greenhouse gas emissions and transportation cost in a modeling study
Bibliographic record
Abstract
Technologies that separate manure or digestate into fractions with different solids and nutrient contents present interesting options to mitigate manure storage emissions (by reducing the quantity of carbon stored anaerobically) and to improve nutrient distribution (by reducing the quantity of water transported with nutrients). In this study, the dairy farm model, DairyCrop-Syst, was used to simulate storage emissions of methane (CH 4 ), nitrous oxide (N 2 O), and ammonia (NH 3 ), and to simulate nutrient distribution for a case-study farm in Canada. The farm used several types of manure processing, including: anaerobic digestion (AD), solid-liquid separation (SLS), and nutrient recovery (NR). Simulations were done with combinations of the above technologies, i.e., a baseline with only AD that produced a single (unseparated) effluent, compared to AD+SLS, and AD+SLS+NR that produced two separate fractions. With AD+SLS+NR, the processing system isolated a solid fraction with a high concentration of N and P, and a liquid fraction containing less nutrients. Compared to the baseline system, the addition of solid liquid separation and nutrient recovery (i.e. SLS+NR) reduced CH 4 emissions from outdoor liquid digestate storage by 87%, with only a small offset from higher N 2 O and NH 3 emissions from storing the solid fraction. The solid fraction was simulated to be transported to fields at least 30 km away from the dairy barns, while the liquid fraction was transported by dragline to fields adjacent to the barn. The advanced nutrient separation system resulted in much lower transport costs for manure nutrients and the ability to transport N and P to greater distances.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".