Greater Impacts of Incubation Temperature and Moisture on Carbon and Nitrogen Cycling in Poultry Relative to Horse Manure‐based Soil Amendments
Bibliographic record
Abstract
Manure‐based soil amendments (MBSAs) must be managed optimally to maximize N concentration and availability while minimizing environmental impacts (e.g., greenhouse gas [GHG]) emissions. We conducted an 83‐d incubation study to determine the effects of different moisture (60 or 120% of water‐holding capacity [WHC]) and temperature (4 or 20°C) conditions during the decomposition of MBSAs. We measured CO 2 , CH 4 , and N 2 O emissions and total C, total N, NH 4 + , and NO 3 − during the decomposition of chicken MBSA and two understudied MBSAs (turkey and horse). Total N decreased by 38 to 50% after 83 d in poultry MBSAs incubated at 20°C and 120% WHC, whereas NH 4 + concentration peaked at 30 d. In contrast, poultry MBSAs incubated at 60% WHC or 4°C had limited N losses but higher CO 2 and/or N 2 O emissions. Horse MBSA incubated for 83 d at 20°C and 60% WHC had two‐ to threefold higher C losses, 53 to 68% higher total N, and two to three orders of magnitude higher NO 3 − concentrations than at wetter and/or colder incubation conditions. Horse MBSA incubated at 20°C and 60% WHC had 13‐ to 130‐fold (CH 4 ) and 4‐ to 70‐fold (N 2 O) higher emissions than horse MBSA incubated at 4°C. In contrast, CH 4 emissions peaked at 120% WHC and 20°C. Overall, incubating horse MBSA at 20°C and 60% WHC minimized tradeoffs between maximizing N concentration and availability and minimizing GHG emissions during decomposition, whereas we found no ideal decomposition conditions for poultry MBSAs. Core Ideas We found high N losses in flooded poultry manure‐based soil amendments (MBSAs) at 20°C. We found high GHG emissions in poultry MBSAs with lower moisture or temperature. There was an increase in horse MBSA total N concentration during incubations at lower moisture and 20°C.
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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".