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Record W6981273356

Economic evaluation of agricultural adaptation strategies to weather events

2006· dissertation· en· W6981273356 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2006
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsCroppingClimate changeCrop yieldAgricultureYield (engineering)CropProductivityAgricultural productivityBaseline (sea)Extreme weather
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.249
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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