Bibliographic record
Abstract
Livestock production, including the storage, handling, and spreading of manure, are among the largest contributors to greenhouse gas emissions from the agricultural sector. Liquid dairy manure storages are hot spots of methane (CH4), nitrous oxide (N2O) and ammonia (NH3). Both CH4 and N2O are greenhouse gases (GHG) which contribute to global warming, while NH3 is an indirect source of N2O and a risk to human health. Reducing emissions from manure storages is important not only for protection of environment and humans, but also for conserving the nutrients in manure making it valuable as a fertilizer. This thesis contributed to the advancement of GHG reducing strategies for liquid dairy manure by: i) testing gradual and batch fillings methods with inoculum stored manure ii) field-scale and lab-scale studies of dairy manure acidification, and iii) a quantitative and qualitative review of 12 years of research from a meso-scale manure storage facility. Gradually-filled and batch-filled meso-scale manure tanks with inoculum (0%, 10% or 20%) were compared on their GHG emissions. On average, gradually-filled tanks had 1.4°C higher manure temperature, which may have contributed to a 12% increase in total CH4 (6.26 kg m-3) and 28% increase in NH3 emissions (358 g m-3). The 10% and 20% inoculum tanks produced comparable emissions, while the 0% tanks (4.84 kg m-3) produced markedly lower CH4 (24%). Acidification using H2SO4 was explored at different rates of application, with or without inoculum, in a laboratory incubation and in meso-scale storages. The novelty of this research was reducing the frequency of acidification, acidifying only once throughout the storage period and an overall focus on reducing cost. Acidification had up to 89% CH4 reduction and 53% NH3 reductions using 1.1 – 2.4 mL acid L−1 manure. In laboratory incubations, H2SO4 reduced CH4 production by 80% at 17°C, 90% at 20°C, and 19% at 23°C. Results also indicated that residual slurries of acidified manure were a poor inoculant in subsequent storage periods, hence manure acidification reduced CH4 for two fill-empty cycles. Lastly, analysis of meso-scale trials (2006-18) compared treatment differences using Cohen’s d effect size. Manure acidification had the largest effect size (up to 6.03) compared to using manure covers, inoculum removal, and dilution which had effect sizes as low as 0.096. Overall, this thesis contributed to the advancement of reducing GHG emissions from liquid dairy manure through original research by: i) highlighting the bias in batch-filling experimental storages ii) creating strategies for reducing cost of acidification while retaining good treatment effects iii) compared GHG reducing strategies from over a decade of research, highlighting acidification as having the best treatment potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".