Assessing The Impacts of Tillage, Manure Management, and Crop Rotation on Crop Growth and Soil Carbon Dynamics in Eastern Canada
Bibliographic record
Abstract
Highlights DNDCv.CAN was modified to simulate inverted soil & residue litter C deposition below the plow layer depth. The model reflects moldboard plowing with higher SOC at depth from residue burial. The DNDC framework enhancements captured tillage effects, with model outputs closely aligning with measured data. DNDC modifications offer scientific foundation for investigating soil carbon sequestration. Abstract. The effects of tillage-induced soil mixing and buried crop residues on soil organic carbon (SOC) changes at depth are inadequately represented in most models, especially when combined with different types of manure application. The objectives of this study were to improve the performance of the DeNitrification DeComposition (DNDC) model for assessing the impact of tillage (moldboard versus chisel plowing) combined with manure application on SOC sequestration at depth in cereal monoculture and cereal-perennial forage systems on a silty clay soil at Normandin, Quebec, and a corn-soybean-wheat rotation on a sandy loam soil at the Laval University agronomic research station in St-Augustin-de-Desmaures, Quebec. The two studies provided a comprehensive data suite of soil characteristics, crop yields, and soil organic carbon stocks across multiple depths under a combination of tillage and nutrient management practices. The DNDC model framework was enhanced by including soil inversion under moldboard plowing with allocation of crop residues to deep soil layers where they decompose more slowly. At Normandin, the model performed well in simulating barley yields under moldboard-manure management as per the average relative error (ARE = 4.7%) and the normalized root-mean-squared error (NRMSE = 7.1%) but demonstrated some challenges in simulating interannual hay yield variations perhaps due to the challenges in simulating winter kill. The accuracy of cumulative SOC simulations (0-10, 0-20, 0-30, and 0-50 cm) closely aligned with observed data, varying with fertilizer and tillage management, with (NRMSE) values ranging from 4.7% to 26.9% under forage rotation, and from 3.1% to 14.5% in barley monocropping, respectively in 2002 and 2010. As expected, the measured and modelled liquid dairy manure (LDM) in the hay rotations had higher SOC than the monoculture barley with dairy manure. DNDC effectively captured the trends in measured SOC stocks, showing significantly higher values in the deeper soil layers (0-30 to 0-50 cm) under moldboard plow and crop rotation compared to the top layers here chisel plow showed more SOC. Conversely, the effect of nutrient source on soil carbon was evident, with LDM maintaining higher SOC levels than mineral fertilizers under rotation, although the difference was less significant in the cereal monoculture. At the Quebec site, DNDC demonstrated excellent performance in estimating wheat yields (ARE of 0.6% and -7.6%), corn grain yields (ARE of 7.5% and 1.2%) and with moderate performance for soybean yields ARE of -13.2% and -11.9%) under calibration and validation, respectively. Overall, DNDC captured the carbon stock differences among management systems (ARE was -2%, and -8% for the calibration and validation, respectively) in the top 15 cm for the 8th year of the Quebec trial (2016). This study demonstrates the importance of assessing the impacts of management practices and manure application on SOC content across the entire soil profile, considering buried residues and tillage inversion.. These tillage applications are not usually considered in crop models but are important for the site specific and regional evaluation of strategies to promote soil carbon sequestration
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".