Economic evaluation of agricultural adaptation strategies to weather events
Notice bibliographique
Résumé
The purpose of this study is to assess the effects of weather events on agricultural production and the adoption of potential adaptation strategies to problems associated with climate variability for crop farmers in southwestern Ontario. The first step in assessing the appropriate adaptation strategies is to understand the sensitivity of crop yields to weather events. The effects of climatic and non-climatic factors on the mean and variance of corn, soybean and winter wheat yield were estimated with a panel data of 8 counties in southwestern Ontario over a period of 40 years. Average crop yields were determined largely by input use, soil quality and technological advances. Weather variables do enhance the explanatory power of the regression models of mean crop yield but productivity enhancements over time appear to offset annual fluctuations in weather. The estimated results suggest climate change may have an ambiguous effect on average crop yield. The projections would also depend on future technological developments, which have generated significant increases in yield over time despite changing annual weather conditions. While improving the tolerance and yield capability of crops through genetics and other technologies may be a public adaptation response to climate change, individual producers can also respond by re-allocating cropping area or by insuring their crops against yield loss. Area response functions were estimated for corn, soybeans and winter wheat for eight counties in Ontario over a 25 year period. The most important variables affecting acreage of corn, soybeans and winter wheat are expected profits. The decomposition of crop area response into both price and yield elasticity measures illustrated the importance of expected yield in the area allocation decision and consequently weather. Crop allocation will thus be used by farmers as an adaptation strategy to changes in climate even without changes in crop prices. The decision to use crop insurance as an adaptation strategy depends on economic factors, such as the expected distribution of crop prices and actual insurance costs, in addition to past and future expected weather, as summarized by crop yield. In contrast to previous studies on the demand for crop insurance, this study examines not only total participation but also the number of farmers who enter and exit the crop insurance program. The decomposition illustrates that the effect of a given variable is often muted by the aggregation. In addition, the approach distinguishes between price and yield variables rather than total returns and is consequently able to demonstrate that price variables are particularly important for farmers considering enrolling in crop insurance while yield variables and other risk management opportunities are more important for farmers who have been in the program but are deciding to exit. The result suggests moral hazard is reduced significantly by calculating the coverage yield level for an individual producer on the basis of a moving average of past yields for that farmer. While yield and its variance are particularly influential in the participation decision for farmers currently enrolled, its significant impact on the insurance decision for all farmers highlights the importance of crop insurance as a potential adaptation strategy to weather events.
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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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».