Addressing spatiotemporal variability in life cycle assessment: review focused on applications relevant to agriculture
Bibliographic record
Abstract
Agricultural life cycle assessment (LCA) studies often rely on aggregated, national-scale inventory data, which risks misrepresenting actual inventories and environmental impacts at regional or local levels. Variability in soil characteristics and climate conditions exacerbates this issue, particularly in field- or farm-level assessments. LCA accuracy improves when regionalized inventory data and updated methodologies are used, though practical implementation is often limited by the lack of standardized frameworks, spatially/temporally relevant inventory data, and software limitations. This study used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method to identify literature addressing spatiotemporal variability in LCA, and identification of software and/or techniques used in the domain. It evaluated methods currently used to incorporate such variability, highlighting the strengths and weaknesses of each. The contribution of the review is presented as the first systematic synthesis of spatial and temporal methodological approaches for agricultural LCA coupled with a practical decision-support framework for practitioners. Geographic information systems enhance LCA accuracy by modeling spatial and temporal patterns. Among the available tools, Brightway2, Temporalis, and OpenLCA are the most capable of dynamic and regionalized LCA, with Ecoinvent offering the most regionalized background data. While spatial differentiation is valuable, highly granular modeling (e.g., individual plant or row level) is often unnecessary for accurate results. However, detailed inventories are beneficial for specific applications like precision agriculture. Land use and soil organic carbon were the most commonly cited topics related to spatial and temporal variability. • Global review of agriculture LCA studies, focusing on spatiotemporal variability and current method strengths/weaknesses. • Brightway2, Temporalis, and OpenLCA among most comprehensive programs. • Ecoinvent provides the most regionalized life cycle inventories for background data. • Modeling impacts to a high granularity is not always necessary to obtain accurate and useful LCA results for most systems.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".