Development of a Land Use Carbon Inventory for Agricultural Soils in the Canadian Province of Ontario
Notice bibliographique
Résumé
Globally, agricultural soils are being evaluated for their role in climate change regulation as a potential sink for atmospheric carbon dioxide (CO2) through sequestration of organic carbon as soil organic matter. Scientists and policy analysts increasingly seek to develop programs and policies which recognize the importance of mitigation of climate change and insurance of ecological sustainability when managing agricultural soils. In response, many countries are exploring options to develop local land-use carbon inventories to better understand the flow of carbon in agriculture to estimate its contribution to greenhouse gas (GHG) reporting. For instance, the Canadian province of Ontario does not currently have its own GHG inventory and relies on the Canada’s National Inventory Report (NIR). To address this, the province explored options to develop its own land-use carbon inventory to better understand the carbon resource in agricultural soils. As part of this undertaking, a gap analysis was conducted to identify the critical information gaps and limitations in estimating soil organic carbon (SOC) monitoring to develop a land-use carbon inventory (LUCI) for the cropland sector in Ontario. We conducted a review of analytical and modeling methods used to quantify GHG emissions and reporting for the cropland sectors in Canada, and compared them with the methods used in seven other countries (i.e., France, United Kingdom; Germany; United States of America, Australia, New Zealand, and Japan). From this comparison, four target areas of research were identified to consider in the development of a cropland sector LUCI in Ontario. First, there needs to be a refinement of the modelling approach used for SOC accounting. The Century model, which is used for Ontario’s cropland sector, can benefit from updates to the crop growth model and from the inclusion of manure management and other amendments. Secondly, a raster-based spatially explicit modelling approach is recommended as an alternative to using polygon-based inputs for soil data and census information for land management. This approach can leverage readily available Earth Observation (EO) data (e.g., remote sensing maps, digital soil maps). Thirdly, the contributions from soil erosion need to be included in inventory estimates of SOC emissions and removals from cropland. Fourth, establishment of an extensive network of long-term experimental sites to calibrate and validate the SOC models (i.e., CENTURY) is required. This can be done by putting in place a ground-truth program, through farmer-led research initiatives and collaboration, to deal with uncertainties due to spatial variability and regional climates. This approach would provide opportunities for farmers to collaborate on data collection by keeping detailed records of their cropping and soil management practices, and crop yields.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».