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Feature Articles

The Impact of Spatial Interpolation Techniques on Spatial Basis Risk for Weather Insurance: An Application to Forage Crops

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References

  • Agriculture and Agri-Food Canada (AAFC). 2012a. Evaluation of the agriInsurance, private sector risk management partnerships and wildlife compensation programs. Technical report. http://www.agr.gc.ca/eng/about-us/offices-and-locations/office-of-audit-and-evaluation/audit-and-evaluation-reports/agriculture-and-agri-food-canada-evaluation-reports/evaluation-of-the-agriinsurance-private-sector-risk-management-partnerships-and-wildlife-compensation-programs/?id=1367338599421.
  • Agriculture and Agri-Food Canada (AAFC). 2012b. Forage Statistics Technical report. http://www.agr.gc.ca/eng/industry-markets-and-trade/statistics-and-market-information/by-product-sector/crops/pulses-and-special-crops-canadian-industry/forage/forage-statistics/?id=1174494927045.
  • Bhunia, G. S., P. K. Shit, and R. Maiti. 2018. Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural Sciences 17(2): 114–26.
  • Black, E., E. Tarnavsky, R. Maidment, H. Greatrex, A. Mookerjee, T. Quaife, and M. Brown. 2016. The use of remotely sensed rainfall for managing drought risk: A case study of weather index insurance in Zambia. Remote Sensing 8(4): 342.
  • Boyd, M., J. Pai, Q. Zhang, H. Wang, and K. Wang. 2011. Factors affecting crop insurance purchases in China: The Inner Mongolia region. China Agricultural Economic Review 3(4): 441–50.
  • Brown, D. P., and A. C. Comrie. 2002. Spatial modeling of winter temperature and precipitation in Arizona and New Mexico, USA. Climate Research 22(2): 115–128. https://www.int-res.com/abstracts/cr/v22/n2/p115-128/.
  • Buxton, D. 1995. Growing quality forages under variable environmental conditions. In Proceedings of the Western Canadian Dairy Seminar, ed. J. Kenelly. Edmonton, AB, Canada: University of Alberta.http://www.wcds.ca/proc/1995/wcd95123.htm.
  • Cai, R., J. D. Mullen, J. C. Bergstrom, W. D. Shurley, and M. E. Wetzstein. 2013. Using a climate index to measure crop yield response. Journal of Agricultural and Applied Economics 45(4): 719.
  • Cao, X., O. Okhrin, M. Odening, and M. Ritter. 2015. Modelling spatio-temporal variability of temperature. Computational Statistics 30(3): 745–66.
  • Carter, M., A. de Janvry, E. Sadoulet, and A. Sarris. 2014. Index-based weather insurance for developing countries: A review of evidence and a set of propositions for up-scaling. Development Policies Working Paper, 111.
  • Chamberlain, S. 2016. rgbif: Interface to the global ’biodiversity’ information facility ’api’ (Computer software manual). R package version 0.9.5. http://CRAN.R-project.org/package=rgbif.
  • Chantarat, S., A. G. Mude, C. B. Barrett, and C. G. Turvey. 2009. The performance of index based livestock insurance: Ex ante assessment in the presence of a poverty trap. http://dx.doi.org/10.2139/ssrn.1844751.
  • Chen, D., T. Ou, L. Gong, C.-Y. Xu, W. Li, C.-H. Ho, et al. 2010. Spatial interpolation of daily precipitation in China: 1951–2005. Advances in Atmospheric Sciences 27(6): 1221–32.
  • Chen, F., and C. Liu. 2012. Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy and Water Environment, 10(3): 209–22.
  • Chien, Y.-J., D.-Y. Lee, H.-Y. Guo, and K.-H. Houng. 1997. Geostatistical analysis of soil properties of mid-west Taiwan soils. Soil Science 162(4): 291–98.
  • Clarke, D. J. (2016). A theory of rational demand for index insurance. American Economic Journal: Microeconomics 8(1): 283–306.
  • Clarke, D. J., D. Clarke, O. Mahul, K. N. Rao, and N. Verma. 2012. Weather- based crop insurance in India. World Bank Policy Research Working Paper no. 5985. Washington Dc: World Bank.
  • Cressie, N. (1988). Spatial prediction and ordinary kriging. Mathematical Geology 20(4): 405–21.
  • Dick, W., and A. Stoppa. 2011. Weather index-based insurance in agricultural development: A technical guide. International Fund for Agricultural Development (IFAD).
  • Dirks, K., J. Hay, C. Stow, and D. Harris. 1998. High-resolution studies of rainfall on Norfolk Island: Part II: Interpolation of rainfall data. Journal of Hydrology 208(3): 187–93.
  • Eischeid, J. K., P. A. Pasteris, H. F. Diaz, M. S. Plantico, and N. J. Lott. 2000. Creating a serially complete, national daily time series of temperature and precipitation for the western United States. Journal of Applied Meteorology 39(9): 1580–91.
  • El Kenawy, A., J. I. López-Moreno, S. M. Vicente-Serrano, and F. Morsi. 2010. Climatological modeling of monthly air temperature and precipitation in Egypt through GIS techniques. Climate Research 42(2): 161–76.
  • SRI Environmental Systems Research Institute ESRI. (2016). Arcgis desktop (Computer software manual). Redlands, California. http://www.arcgis.com.
  • Erdogan, S. 2009. A comparision of interpolation methods for producing digital elevation models at the field scale. Earth Surface Processes and Landforms 34(3): 366–76.
  • Erhardt, R. J., and R. L. Smith. 2014. Weather derivative risk measures for extreme events. North American Actuarial Journal 18(3): 379–93.
  • Golden, L. L., C. C. Yang, and H. Zou. 2010. The effectiveness of using a basis hedging strategy to mitigate the financial consequences of weather-related risks. North American Actuarial Journal 14(2): 157–75.
  • Heimfarth, L. E., and O. Musshoff. 2011. Weather index-based insurances for farmers in the North China Plain: An analysis of risk reduction potential and basis risk. Agricultural Finance Review 71(2): 218–39.
  • Hengl, T. 2009. A practical guide to geostatistical mapping. 2nd ed. Vol. 52. Luxembourg: Office for Official Publications of the European Communities.
  • Herdendorf, C. E. 1982. Large lakes of the world. Journal of Great Lakes Research 8(3): 379–412.
  • Hofstra, N., M. Haylock, M. New, P. Jones, and C. Frei. 2008. Comparison of six methods for the interpolation of daily, European climate data. Journal of Geophysical Research: Atmospheres 113(D21).
  • Holdaway, M. R. 1996. Spatial modeling and interpolation of monthly temperature using kriging. Climate Research 06(3): 215–25.
  • Jensen, N. D., C. B. Barrett, and A. G. Mude. 2016. Index insurance quality and basis risk: Evidence from northern Kenya. American Journal of Agricultural Economics 98(5): 1450–69.
  • Jiang, W., and J. Li. 2014. The effects of spatial reference systems on the predictive accuracy of spatial interpolation methods. Geoscience Australia, GeoCat 76314, Record 2014/01.
  • Joseph, V. R., and L. Kang. 2011. Regression-based inverse distance weighting with applications to computer experiments. Technometrics 53(3) 254–65.
  • Kilibarda, M., T. Hengl, G. Heuvelink, B. Gräler, E. Pebesma, M. Perčec Tadić, et al. 2014. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. Journal of Geophysical Research: Atmospheres 119(5): 2294–313.
  • Kuzyakova, I., V. Romanenkov, and Y. V. Kuzyakov. 2001. Geostatistics in soil agrochemical studies. Eurasian Soil Science 34(9): 1011–7.
  • Li, J., and A. D. Heap. 2008. A review of spatial interpolation methods for environmental scientists. GeoCat 68229, Record 2008/23 Canberra: Geoscience Australia.
  • Li, J. L., J. Zhang, C. Zhang, and Q. G. Chen. 2006. Analyze and compare the spatial interpolation methods for climate factor. Pratacultural Science 8.
  • Lin, J., M. Boyd, J. Pai, L. Porth, Q. Zhang, and K. Wang. 2015. Factors affecting farmers’ willingness to purchase weather index insurance in the Hainan Province of China. Agricultural Finance Review 75(1): 103–13.
  • Mahul, O., and J. Skees. 2007. Managing agricultural risk at the country level: The case of index-based livestock insurance in Mongolia. Washington, DC: World Bank.
  • Mair, A., and A. Fares. 2010. Comparison of rainfall interpolation methods in a mountainous region of a tropical island. Journal of Hydrologic Engineering 16(4): 371–83.
  • Major, J. 1999. Index hedge performance: Insurer market penetration and basis risk. In The financing of catastrophe risk, ed. K. A. Froot, pp. 391–432. Chicago: University of Chicago Press.
  • Makaudze, E. M., and M. J. Miranda. 2010. Catastrophic drought insurance based on the remotely sensed normalised difference vegetation index for smallholder farmers in Zimbabwe. Agrekon 49(4): 418–32.
  • McCarl, B. A., X. Villavicencio, and X. Wu. 2008. Climate change and future analysis: Is stationarity dying? American Journal of Agricultural Economics 90(5): 1241–7.
  • Mobarak, A. M., and M. R. Rosenzweig. 2012. Selling formal insurance to the informally insured [February 1, 2012]. Yale Economics Department Working Paper No. 97; Yale University Economic Growth Center Discussion Paper No. 1007. http://dx.doi.org/10.2139/ssrn.2009528.
  • Nalder, I. A., and R. W. Wein. 1998. Spatial interpolation of climatic normals: Test of a new method in the Canadian boreal forest. Agricultural and Forest Meteorology 92(4): 211–25.
  • Norton, M., D. Osgood, and C. G. Turvey. 2010. Weather index insurance and the pricing of spatial basis risk. Selected Paper prepared for presentation at the Agricultural & Applied Economics Association's 2010 AAEA, CAES & WAEA Joint Annual Meeting, Denver, CO, July 25–27.
  • Norton, M. T., C. Turvey, and D. Osgood. 2012. Quantifying spatial basis risk for weather index insurance. Journal of Risk Finance 14(1): 20–34.
  • Nusret, D., and S. Dug. 2012. Applying the inverse distance weighting and kriging methods of the spatial interpolation on the mapping the annual precipitation in Bosnia and Herzegovina. Ph.D. diss. International Environmental Modelling and Software Society (iEMSs).
  • Odening, M., and Z. Shen. 2014. Challenges of insuring weather risk in agriculture. Agricultural Finance Review 74(2), 188–99.
  • Okhrin, O., M. Odening, and W. Xu. 2013. Systemic weather risk and crop insurance: The case of China. Journal of Risk and Insurance 80(2): 351–72.
  • Paulson, N. D., and C. E. Hart. 2006. A spatial approach to addressing weather derivative basis risk: A drought insurance example. Selected Paper prepared for presentation at the American Agricultural Economics Association Annual Meeting, Long Beach, CA, July 23–26.
  • Porth, C. B., L. Porth, W. Zhu, M. S. Boyd, K. S. Tan, and K. Lui. 2018. Remote sensing applications for insurance: A predictive model for pasture yield in the presence of systemic weather [14 June 2018]. https://ssrn.com/abstract=3195389 or http://dx.doi.org/10.2139/ssrn.3195389.
  • Porth, L., and K. S. Tan. 2015. Agricultural insurance — More room to grow? The Actuary 12(2): 36–41.
  • Rowley, R. J., K. P. Price, and J. H. Kastens. 2007. Remote sensing and the rancher: Linking rancher perception and remote sensing. Rangeland Ecology & Management 60(4): 359–68.
  • Shields, D. A. 2015. Agricultural Disaster Assistance. CRS Report RS21212 Washington, DC: Congressional Research Service.
  • Shope, C. L., and G. R. Maharjan. 2015. Modeling spatiotemporal precipitation: Effects of density, interpolation, and land use distribution. Advances in Meteorology 2015, 174196. https://doi.org/10.1155/2015/174196.
  • Sun, Y., S. Kang, F. Li, and L. Zhang. 2009. Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin Oasis of Northwest China. Environmental Modelling & Software 24(10): 1163–70.
  • Turvey, C. G., and M. K. Mclaurin. 2012. Applicability of the normalized difference vegetation index (NDVI) in index-based crop insurance design. Weather, Climate, and Society 4(4): 271–84.
  • Veness, C. 2011. Calculate distance and bearing between two latitude/longitude points using Haversine formula in javascript. http://www.movable-type.co.uk/scripts/latlong.html.
  • Weng, Q. 2006. An evaluation of spatial interpolation accuracy of elevation data. Berlin: Springer.
  • Wu, T., and Y. Li. 2013. Spatial interpolation of temperature in the United States using residual kriging. Applied Geography 44: 112–20.
  • Zhu, Q., and H. Lin. 2010. Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes. Pedosphere 20(5): 594–606.

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