Impacts of potential climate change on selected agroclimatic indices in Atlantic Canada
Notice bibliographique
Résumé
Agroclimatic indices (heat units and water deficits) were determined for the Atlantic region of Canada for a baseline climate (1961 to 1990 period) and for two future time periods (2010 to 2039 and 2040 to 2069). Climate scenarios for the future periods were primarily based on outputs from the Canadian General Circulation Model (GCM) that included the effects of aerosols (CGCMI-A), but variability introduced by multiple GCM experiments was also examined. Climatic data for all three periods were interpolated to a grid of about 10 to 15 km. Agroclimatic indices were computed and mapped based on the gridded data. Based on CGCMI-A scenarios interpolated to the fine grid, average crop heat units (CHU) would increase by 300 to 500 CHU for the 2010 to 2039 period and by 500 to 700 CHU for the 2040 to 2069 period in the main agricultural areas of the Atlantic region. However, increases in CHU for the 2040 to 2069 period typically varied from 450 to 1650 units in these regions when variability among GCM experiments was considered, resulting in a projected range of 2650 to 4000 available CHU. Effective growing degree-days above 5°C (EGDD) typically increased by about 400 units for the 2040 to 2069 period in the main agricultural areas, resulting in available EGDD from 1800 to over 2000 units. Uncertainty introduced by multiple GCMs increased the range from 1700 to 2700 EGDD. A decrease in heat units (cooling) is anticipated along part of the coast of Labrador. Anticipated changes in water deficits (DEFICIT), defined as the amount by which potential evapotranspiration exceeded precipitation over the growing season, typically ranged from +50 to −50 mm for both periods, but this range widened from +50 to −100 mm when variability among GCM experiments was considered. The greatest increases in deficits were expected in the central region of New Brunswick for the 2040 to 2069 period. Our interpolation procedures estimated mean winter and summer temperature changes that were 1.4°C on average lower than a statistical downscaling procedure (SDSM) for four locations. Increases in precipitation during summer and autumn averaged 20% less than SDSM. During periods when SDSM estimated relatively small changes in temperature or precipitation, our interpolation procedure tended to produce changes that were larger than SDSM. Additional investigations would be beneficial that explore the impact of a range of scenarios from other GCM models, other downscaling methods and the potential effects of change in climate variability on these agroclimatic indices. Potential impacts of these changes on crop yields and production in the region also need to be explored. Key words: Crop heat units, effective growing degree-days, water deficits, climate change scenarios, statistical downscaling, spatial interpolation
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».