Impact of lowering nitrogen content in pig manure through low crude protein diets on anaerobic digestion process stability, biogas yields, and digestate composition
Bibliographic record
Abstract
Lowering crude protein in pig diets can reduce nitrogen (N) excretion and alter manure characteristics. Anaerobic digestion (AD) offers potential for converting pig manure into biogas and bio-based fertilizers (i.e., digestate). However, limited research exists on the effects of N content in pig manure on AD when pigs are fed diets with varying crude protein levels. This study investigated how lowering N content in pig manure through low crude protein diets may affect AD process stability, biogas generation, and digestate properties. Manures from different dietary treatments, named as control (CON), low N (LN), and very low N (VLN), with Total Kjeldahl Nitrogen concentrations of 5.87, 5.42, and 5.15 g/L, respectively, were investigated. Daily biogas production, composition (CH 4 , CO 2 , and H 2 S), and digestate properties were monitored over 13 fed-batch cycles (25 ± 4 days per cycle). The experiment was conducted at 20 ± 1 °C, a condition suited for milder climate regions, using six single-stage digesters operated in sequencing fed-batch mode. Data were analyzed by ANOVA using PROC MIXED with repeated measures. Results showed that the differences in N content in pig manure due to the three dietary treatments had a limited impact on biogas generation, with specific methane yields remaining similar over time. CH 4 concentrations remained stable between 60 and 65 %, ensuring high-quality biogas despite dietary variations. Differences between treatments became more pronounced with increased organic loading rates (OLRs) due to variations in the amount of volatile solids fed. AD also remained stable (Total Volatile Fatty Acids/Total Alkalinity <0.25) even at an OLR of 2.15 g of chemical oxygen demand L −1 day −1 , highlighting AD's robustness at lower temperatures. Digestate samples contained essential minerals beneficial for plant growth. More research is needed to explore varied manure compositions and feeding strategies to better understand the interactions of animal nutrition with AD.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".