Harmonizing methods to account for soil nitrous oxide emissions in Life Cycle Assessment of agricultural systems
Why this work is in the frame
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Bibliographic record
Abstract
Worldwide greenhouse gas emissions (GHG) reached 59 Gt of CO 2 eq in 2019 and agricultural soils are the primary source of N 2 O emissions. Life cycle assessments (LCA) have been successful in assessing GHG from agricultural systems. However, no review and harmonization attempt has been focused on soil N 2 O emissions, despite the need to improve LCA methodologies for assessing GHG in agricultural LCA. We therefore undertook a review and harmonization of existing methods to account for soil N 2 O emissions in LCA of agricultural systems and products: i) to compare current methods used in LCA; ii) to identify advantages and iii) disadvantages of each method in LCA; iv) to suggest recommendations for LCA of agricultural systems; v) to identify research needs and potential methodological developments to account for soil N 2 O emissions in the LCA of agricultural systems. In this paper, we consider as soil N 2 O emissions, those originated from soils in relation to fertilisers (organic and manufactured), crop residues, land use/land management change, grassland management, manure and slurry applications and from grazing animals. The approach adopted was based on two anonymous expert surveys and a series of expert workshops ( n = 21) to define general and specific criteria to review LCA methods for GHG emissions used in LCA of agricultural systems. A broad list of keywords and search criteria was used as the research involved GHG assessment in agricultural LCA. Reviewed papers and methodology were then assessed by LCA and soil N 2 O emission experts ( n = 14). >25,000 scientific papers and reports were identified, 1175 were screened, 263 included in the final review and 31 scientific papers were related to soil N 2 O emissions. The results showed that a high level of accuracy corresponded to a low level of applicability and vice versa, following the assessment framework developed in this work through participatory approaches. The choice of LCA methods, critical for high quality LCA of agricultural systems, should be based on the assessment objectives, data availability and expertise of the LCA practitioner. However, it is preferable to use DNDC model after calibration and validation or direct field measurements, considering system effects. When necessary data are lacking, IPCC tier 2 methodology where available should be used, otherwise 2019 IPCC Tier 1 methodology. This LCA method development should be synchronous with improvements of quantification methods and the assessment of a wider range of agricultural management practices and systems. • Methods used in life cycle assessment (LCA) of agricultural systems to account for soil N 2 O need to be improved. • A method harmonization was carried out and recommendations for soil N 2 O emissions were identified in agricultural LCA. • The results showed that a high level of accuracy corresponded to a low level of applicability and vice versa. • DNDC, DAYCENT and direct measurements scores high on accuracy to assess soil N 2 O emissions in LCA of agricultural systems. • Alternative to DNDC and measurements are IPCC Tier 1 or 2 methodologies to assess soil N 2 O emissions in agricultural LCA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it