Regionalized life cycle inventory data collection and calculation for perennial forage production in Canada: methodological best practices and limitations
Bibliographic record
Abstract
Abstract Purpose In Canada, 95–99% of produced forages are consumed domestically each year, mainly by beef cattle. Despite their importance, their contribution to the Canadian livestock industry and associated ecosystem services has not been investigated. This study developed a life cycle inventory (LCI) of perennial forage production in Canada averaged from 2009 to 2018. Methods LCI data were sourced or calculated from up-to-date, regionally resolved sources and models. Inputs to perennial forage production included the following: concrete, steel, and plastic usage; machinery fuel consumption; electricity, natural gas, and water use for irrigation; and synthetic and organic fertilizer, lime, and herbicide use. Assessed emissions included ammonia and nitrous oxide (N2O); carbon dioxide from energy use; herbicide, nitrate, and phosphate losses; and soil carbon accumulation. Results were expressed per metric tonne of harvested perennial forage dry matter at provincial and regional scales—Western Canada [British Columbia (BC), Alberta (AB), Saskatchewan (SK), Manitoba (MB)] and Eastern Canada [Ontario (ON), Québec (QC)]. Results and discussion Rates of inputs varied, with generally lower nutrient but higher herbicide application in West vs. East. Irrigation was highest in BC, followed by AB and SK; energy consumption was highest in BC and lowest in QC. Higher N2O losses and nutrient losses via leaching and runoff in the East were partially due to greater soil moisture. Although total harvested perennial forage area declined from 6.43 to 5.23 million hectares from 2009 to 2018, these lands continued to accumulate soil carbon. The time period used to calculate average yields affected LCI estimates, as prairie yields were lower 1994–2003/1999–2008 due to drought. Furthermore, soil carbon sequestration estimates were affected by the annual change coefficients employed, underscoring the need for careful interpretation of LCI outputs. Results were compared to other studies and highlighted the importance of the choice of data and methods in creating LCI, and the need for transparency. Conclusions This first national LCI of perennial forage production in Canada provides a baseline for LCI inputs and outputs associated with this sector, highlighting provincial and regional differences. Outputs can be used to conduct future life cycle assessments to assess the environmental impacts of forage production and generate recommendations to improve sustainability, and for education and marketing purposes. This study demonstrates methodological best practices for LCI data mining and calculations, within available data and model limitations, thereby identifying gaps and providing a roadmap for other countries or sectors to develop detailed forage LCI.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".