Consequential Life Cycle Assessment of Grain and Oilseed Crops: Review and Recommendations
Bibliographic record
Abstract
The field crop industry in Canada is a source of both significant economic benefits and environmental impacts. Environmental impacts include land and energy use, as well as greenhouse gas (GHG) and other emissions. Impacts also accrue upstream of the field in the product supply chain, from the production of such inputs as fertilizers and pesticides. There are currently two types of environmental life cycle assessment (LCA)—attributional LCA (ALCA) and consequential LCA (CLCA)—that may be used to study the life cycle impacts of products such as field crops. ALCA is a retrospective methodology that presents a snapshot of average, “status quo” conditions. CLCA is a prospective methodology that presents the potential implications of changes in a product system, including any associated market-mediated changes in supply or demand in other product systems. Thus, CLCAs can be used to assess large-scale changes in the field crop industry, including its relationship to other sectors and processes, such as the production of biofuel or of food for both human and animal consumption. The aim of this paper is to review and curate the knowledge derived through published CLCA studies that assessed the impacts of changes to field crop production systems on the life cycle resource use and emissions associated with the agricultural products, with a focus on their relevance to temperate climate conditions. The current study also highlights how previous studies, including ALCAs and farm management recommendations, can be used to inform the changes that should be studied using CLCA. The main challenges to conducting CLCAs include identifying the system boundaries, marginal products and processes that would be impacted by changes to field crop production. Marginal markets and product systems to include can be determined using economic equilibrium models, or information from local experts and industry reports. In order to conduct ISO-compliant CLCAs, it is necessary to include multiple relevant environmental impact categories, and to perform robust data quality and uncertainty analyses.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".