Potential impacts of climate change on corn, soybeans and barley yields in Atlantic Canada
Notice bibliographique
Résumé
In this paper, relationships between agroclimatic indices and average yields of grain corn (Zea mays L.), soybeans (Glycine max L. Merr.) and barley (Hordeum vulgare L.) in field trials conducted in eastern Canada are explored and then used to estimate potential impacts of climate change scenarios on anticipated average yields and total production of these commodities for the Atlantic region for the 2040 to 2069 period. Average yields of grain corn and soybeans were highly correlated (R 2 = 0.86 and 0.74, respectively) with average available crop heat units (CHU), with yields increasing by about 0.006 t ha -1 CHU -1 for corn and 0.0013 t ha -1 CHU -1 for soybeans. The explained variance was not improved significantly when water deficit (DEFICIT) was included as an independent variable in regression. Correlations between average yields of barley and effective growing degree-days (EGDD) were low (R 2 ≤ 0.26) and negative, i.e., there was a tendency for slightly lower yields at higher EGDD values. Including a second-order polynomial for DEFICIT in the regression increased the R 2 to ≥ 0.58, indicating a tendency for lower barley yields in areas with high water deficits and with water surpluses. Based on a range of available heat units projected by multiple General Circulation Model (GCM) experiments, average yields achievable in field trials could increase by about 2.6 to 7.5 t ha -1 (40 to 115%) for corn, and by 0.6 to 1.5 t ha -1 (21 to 50%) for soybeans by 2040 to 2069, not including the direct effect of increased atmospheric CO 2 concentrations, advances in plant breeding and crop production practices or changes in impacts of weeds, insects and diseases on yield. Anticipated reductions in barley yields are likely to be more than offset by the direct effect of increased CO 2 concentrations. As a result of changes in potential yields, there will likely be significant shifts away from production of barley to high-energy and high-protein crops (corn and soybeans) that are better adapted to the warmer climate. However, barley and other small grain cereals will likely remain as important crops as they are very suited for rotation with potatoes. There is a need to evaluate the potential environmental impacts of these possible shifts in crop production, particularly with respect to soil erosion in the region. Key words: Crop heat units, growing degree-days, water deficits, crop yields, climate change, Atlantic region
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».