Improving modelling estimations of soil organic carbon sequestration in manure-amended croplands
Bibliographic record
Abstract
Carbon sequestration reduces GHG emissions while improving soil fertility. In order for carbon sequestration through agriculture to be viable, however, accurate estimations of sequestration values are crucial in order to guide policy-making. Currently, Ontario’s provincial Ministry of Agriculture, Food and Rural Affairs (OMAFRA) uses sequestration values from the federal government’s farm-level greenhouse gas emission model (Holos), however these estimates fall short in one respect: a 2018 analysis demonstrated that manure application is not completely considered in the government’s estimates, which is a critical gap. The main purposes of our study were 1) to assess the accuracy of soil organic carbon estimations of process-based soil carbon models (Century and RothC) which were calibrated with data from long-term manure addition experiments in Ontario, and 2) to modify these models such that they were able to fully take manure application into account when estimating carbon sequestration in Ontario’s croplands, and determine whether this substantially increases model accuracy. The models’ estimations for soil organic carbon sequestration were respectively calibrated and validated using data from two long-term manure addition experiments in Ottawa and Harrow. By calibrating multiple models using multiple datasets, model-specific and site-specific biases were minimized. The statistical analyses consisted of a suite of tests that assess the modelling accuracy compared to baseline measured data: the coefficient of determination (R2), root mean square error (RMSE), average relative error (ARE), and the Nash-Sutcliffe efficiency statistic (NSE). As a result of these improved provincial estimates, Canadians will be better-informed about the greenhouse gas mitigation potential of long-term manure addition to croplands, which will help guide decisions made by policymakers as well as farmers. These improved provincial estimates will also be reported to Canada’s national greenhouse gas inventory, and will be ultimately disclosed to the UN’s Intergovernmental Panel on Climate Change (IPCC) in their global GHG summary report.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".