Spatially resolved inventory and emissions modelling for pea and lentil life cycle assessment
Notice bibliographique
Résumé
Pulses are an increasingly popular source of plant-based protein among food manufacturers, food service providers, and retailers globally. Canada is currently a global leader in pulse production and exports. Given the large geographical scope of their production within Canada, pulses are produced under a variety of conditions reflecting differences in soil, climate, and characteristic management practices. In order to understand the influence of regional factors on the impacts of pulse production, high-resolution, regionalized life cycle assessments (LCAs) were carried out using both region-specific input/output data and emissions modelling. Six hundred Canadian pea and lentil farmers were surveyed to collect detailed data regarding characteristic farm inputs, yields and management practices at the reconciliation unit level of spatial resolution, which reconciles Canadian provincial borders with terrestrial ecozones based on soil and climate factors. The process-based model DeNitrification DeComposition (DNDC) was used to estimate regionally specific N emissions. This was compared to emissions estimates generated using the IPCC Tier II empirical models, which are typically used to estimate greenhouse gas emissions for national inventory reports. The main contributors to the life cycle environmental impacts of pea and lentil production were fertilizer and fuel use. This was consistent across all levels of regional aggregation including the total Prairie province average, as well as ecozone and provincial levels. There was variation in the magnitude of the impacts in each region assessed, which was mainly attributable to differences in yield, as well as reported fertilizer application rates and related emissions and fuel use for field operations. Significant differences were found in N emissions estimates between the DNDC and IPCC models, the magnitude of which varied by region and by the N emissions model employed. DNDC was found to provide better-resolved emissions estimates at the ecozone scale. This demonstrates the relevance of regionally specific emissions modelling since local soil and climate conditions had a large impact on the emissions estimates. In addition, the levels of uncertainty in the models were generally higher at the provincial and prairie province scale than at the ecozone scale. This may indicate that farming practices and associated resource/environmental impacts are more strongly influenced by soil and climate conditions than by provincial standards and guidelines. These results underscore the necessity of spatially resolved data collection and modelling to provide accurate estimates of the environmental impacts of crop production and to support more sustainable management of arable crop production.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».