Assessment of greenhouse gas emissions from soybean cropping system: A case study of Ontario in Canada
Bibliographic record
Abstract
Soybean (Glycine max L.) is the fourth largest Canadian field crop covering around 2×106 hectares of land. Soybean crops take up more than 50% of Canada’s cultivated areas and contribute significantly to greenhouse gas (GHG) emissions in cropping system. The present study assesses GHG emissions from Ontario soybean fields. In this study, the crop districts of Ontario were divided into five categories: southern Ontario, western Ontario, central Ontario, eastern Ontario, and northern Ontario, and a general model for assessing emissions was developed. Emissions from the manufacturing and transportation of nitrogen/phosphorus (N/P) fertilizer, emissions from field operations, emissions from herbicide usage, and both direct and indirect emissions from agricultural lands were considered the major sources of GHGs. The results showed that total GHG emissions were around 7×105 Mg CO2-eq in 2018. The largest emission contributor was agricultural land, with emissions of 5.3×105 Mg CO2-eq, accounting for 77% of the total emissions. Moreover, GHG emissions were significantly influenced by environmental conditions. As precipitation/evapotranspiration (Pr/PE) decreased, total GHG emissions declined from southern Ontario to central Ontario. In southern Ontario (high Pr/PE), GHG emissions based on crop yield were 492 kg CO2-eq per hectare of seeding area, which was 46% greater than those in northern Ontario (low Pr/PE). GHG emissions from agricultural lands were the highest contributor to total GHG emissions among the four emission sources in all crop districts. Fertilizer N inputs accounted for the largest portion of agricultural land emissions in southern and central Ontario, while in other regions, crop residue N input was the largest source. A multivariate factorial analysis was also used to estimate the effect of uncertain parameters on system performance, and the main impacts, and their interactions were identified. The resutls demonstrated that farming practices had the most significant impact on total GHG emissions. Understanding the detailed impacts of these elements and their interactions can help determine major factors that influence total GHG emissions and allow us to implement appropriate strategies to mitigate agricultural GHG 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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".