Improving the DNDC Model for Estimating Decomposition and Carbon Dioxide Emissions from Biosolids and Manure-Amended Fields
Bibliographic record
Abstract
<b>Highlights</b> <list list-type=bullet><list-item> DNDCv.CAN was modified to include a new manure C pool with its own decomposition rate </list-item><list-item> The improved model better simulated decomposition of organic amendments and CO<sub>2</sub> emissions </list-item><list-item> A modified temperature function alleviated the early season CO<sub>2</sub> over-prediction for biosolids </list-item><list-item> The simulation of decomposition of cattle manure was also improved but not so drastically as for biosolids </list-item><list-item> The model enhancements increase confidence for simulating manure and biosolid management </list-item></list> <b><sc>Abstract.</sc></b> The objective of this study was to improve and validate the Denitrification and Decomposition (DNDC) model for simulating CO<sub>2</sub> emissions from land application of biosolids and manure. A separate manure C pool was added to the DNDC framework to disaggregate manure decomposition from the soil organic matter pools. Decomposition rates of biosolids were estimated using measurements of organic material. The effect of soil temperature on soil organic matter decomposition was also improved using an arctangent function. Data collected from two climatically distinct sites in Montreal (Quebec) and Truro (Nova Scotia) in 2017–2019 with continuous corn was used to test the model in simulating crop yield, soil temperature and moisture, soil CO<sub>2</sub> fluxes amended by biosolids (mesophilic anaerobically digested, composted, and alkaline-stabilized biosolids), urea, and unfertilized control. Data from the third site was used to verify the model with solid cattle manure (SCM) and inorganic fertilizer (IF) applied to a corn-soybean rotation field in Harrow, Ontario (2012-2015). Both default and improved models were calibrated using the data from IF for the Ontario site, and soil surface spread treatments for Montreal and Nova Scotia while SCM, control and soil-incorporated treatments were used for validation. Crop yields were well simulated by the improved model [relative root mean squared error rRMSE (4.1 – 30.1%) for all the three sites. The improved DNDC model (Wilmott d coefficient 0.72 ≤ d ≤ 0.96) outperformed the default version (0.61 ≤ d ≤ 0.9) in simulating CO<sub>2</sub> fluxes across all the three sites. Similarly, the statistical results showed that the model effectively simulated both soil temperature (d ≥ 0.88) and moisture (0.53 ≤ d ≤ 0.91) across the sites. The addition of an independent biosolids/manure C pool in DNDC resulted in more reasonably simulated decomposition rates for alkalized and composted biosolids to better match observed CO<sub>2</sub> emissions. The modified temperature function alleviated the over-prediction of CO<sub>2</sub> emissions shortly after biosolid application and greatly improved the timing of emissions during the growing season. The revised model also better differentiated the CO<sub>2 </sub>emissions between biosolids types and urea treatments. These model enhancements will enable us to simulate best management practices for integrated crop-livestock-manure management systems, optimizing nutrient cycling across farm systems to enhance the sectors' sustainability-profitability and resiliency, minimizing reliance on nitrogen (N) fertilizer derived from fossil fuels.
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 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".