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Quantifying Climate Risk Using Crop Insurance Data: The Case of Spring Frost Damage in Korean Apple Orchards. (2021). Kim, Seung Min ; Han, Ji Wan.
In: 2021 Annual Meeting, August 1-3, Austin, Texas.
RePEc:ags:aaea21:313937.

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  1. • Auffhammer, M. (2018). Quantifying economic damages from climate change. Journal of Economic Perspectives, 32(4), 33-52.

  2. • Cesaraccio, C., Spano, D., Snyder, L., & Duce, P. (2004). Chilling and forcing model to predict bud-burst of crop and forest species. Agricultural and Forest Meteorology 126, 1-13.
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  3. • Kim, SO., Chung, U., Kim, SH., Choi, IM., & Yun JI. (2009). The Suitable Region and Site for ‘Fuji’ Apple Under the Projected Climate in South Korea. Korean Journal of Agricultural and Forest Meteorology, 11(4), 162-173.
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  4. • Mendelsohn, R. Nordhaus, W. D., & Shaw, D. (1994). The impact of global warming on agriculture: A Ricardian analysis. American Economic Review, 94(4), 753-771.

  5. • Ramalho, E. A., Ramalho, J. J., & Murteira, J. M. (2011). Alternative estimating and testing empirical strategies for fractional regression models. Journal of Economic Surveys, 25(1), 19-68.

  6. • Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences of the United States of America, 106(37), 15594-15598.
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  7. • Zalom F. G., Goodell, P., Wilson, L. T., Barnett, W. W., & Bentley, W. J. (1983). Degree-days: The calculation and use of heat units in pest management. • The independent variables identified via the FRM are used as inputs to a Random Forest Classifier machine learning method. This method is used to predict future spring frost risks under different scenarios and conditions (see below). • RF model achieves 94.3% overall accuracy, capturing 92.3% of actual spring frost occasions and 94.9% of non-spring frost occasions. Fig 2. A confusion matrix for prediction of spring frost damage probability using a random forest model Predicted ෡ ? Actual ? 0 1 0 1 71.3% 23% 3.8% 1.9% Fig 4. Benefits of sprinkler based on RCP 4.5(left) and 8.5(right) Fig 5. Comparing spring frost risk at different regions based on RCP 4.5(left) and 8.5(right) Reduction of spring frost probability Spring frost probability
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  8. Fig 1. Dates of expected full bloom in ‘Fuji’ apple in South Korea based on A1B scenario (Kim et al., 2009). • Spring frost (or late frost) at the blooming season reduce both productivity and quality of fruits. • Climate change entailing warming winter could end up exposing fruit flowers to the deadly late-winter chill by advancing blooming dates.
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