Regionalized life cycle inventory data collection and calculation for perennial forage production in Canada: methodological best practices and limitations
Notice bibliographique
Résumé
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 (N 2 O); 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 N 2 O 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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».