The Impact Of Agroclimatic Variables On Crop Insurance Claims In Saskatchewan
2017
- 177Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage177
- Downloads108
- Abstract Views69
Thesis / Dissertation Description
The study investigated the impact of agroclimatic variables on the loss cost of hard red spring wheat (HRSW), durum, barely and canola in Saskatchewan. Using daily data on temperature and precipitation, we estimated the water balance or soil moisture using American Society of Civil Engineers standard reference evapotranspiration formula. Accounting for model uncertainty by using Bayesian modeling averaging (BMA), we find that loss cost is influence by monthly temperature and water balance. We find that water balance in June and August impact loss cost of the HRSW, durum, barley and canola. Depending on the crop, one percent increase June water balance, above its long-term average, decreases loss cost between 0.35 percent and 0.64 percent while a one percent increase in August water balance, above its long-term average, increases the loss cost between 0.24 percent and 0.36 percent. A one percent increase in water balance variability increases the loss cost between 0.35 percent and 0.66 percent.Temperature also affects loss cost, depending on the crop and month. For the early stage of the growing season, a percent increase in GDD increases loss cost between 0.75 and 1.99 percent. However, at the later stages of the growing season, a one per increase in GDD decreases loss cost between 0.7 percent and 2.25 percent.We find that BMA, in general, outperforms OLS model for out-sample-forecast. Lastly, we find that the forecasted premium rate based on weather probabilities from BMA predictors performed better than simple or 10 year moving average.
Bibliographic Details
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