A new framework for simulating C decomposition and emissions from land applied biosolids and manures using the denitrification and decomposition model
Bibliographic record
Abstract
There is a need for modeling tools capable of estimating CO 2 emissions from land application of biosolids and manure. The Denitrification and Decomposition model (DNDC) was improved for this capacity by adding a separate manure C pool to disaggregate manure decomposition from the soil organic matter pools. The effect of soil temperature on soil organic matter decomposition was also improved. Data collected from two climatically distinct sites in Montreal (Quebec) and Truro (Nova Scotia) with corn were used to test DNDC for simulating yield, soil temperature and moisture, and CO 2 fluxes from soil amended with biosolids (mesophilic anaerobically digested, composted, or alkaline-stabilized). A third site in Harrow (Ontario) was used to verify the model for solid cattle manure applied to a corn-soybean field. Crop yields were well simulated by the improved model (rRMSE 4.1–30.1 %) for all sites. The model (0.78 ≤ d ≤ 0.93) outperformed the default version (0.61 ≤ d ≤ 0.9) in simulating CO 2 fluxes across all sites. Similarly, the model effectively simulated both soil temperature (d ≥ 0.88) and moisture (0.53 ≤ d ≤ 0.91). The addition of an independent biosolids/manure C pool in DNDC resulted in more accurate simulation of seasonal soil C decomposition and CO 2 emissions for alkalized and composted biosolids. The modified temperature function alleviated the over-prediction of CO 2 emissions shortly after biosolid application and improved the timing of emissions during the growing season. The enhanced model will help simulate best management practices for integrated crop-livestock-manure systems, reducing reliance on nitrogen fertilizer. • DNDCv.CAN was modified to include a new manure C pool • A modified temperature function alleviated early season CO 2 over-prediction • The improved model better simulated decomposition of organic amendments and CO 2 • Manure decomposition was improved but less so than for biosolids • The model enhancements increase confidence for simulating CO 2 emissions
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".