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EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 6, Issue 6 (June 2019), Pages: 51-59


 Original Research Paper

 Title: Application of weather index-based insurance for paddy yield: The case of Malaysia

 Author(s): Yvonne Wong Jing Wen *, Raja Rajeswari Ponnusamy, Ho Ming Kang


 School of Mathematics, Actuarial and Quantitative Studies, Asia Pacific University, Kuala Lumpur, Malaysia

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 * Corresponding Author. 

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Agriculture is primarily vulnerable to vagaries of the weather. The impact of climate change is always intertwined with agricultural production. Therefore, in order to manage these losses, agricultural insurance should be initiated in Malaysia to the farmers to handle the financial risks associated with the impact of weather conditions on the crop yield. This study presents the results of a pilot scale investigation on weather index-based paddy insurance in five selected states in Malaysia. The aim of this study is to determine the appropriateness of this insurance model for each selected paddy cropping zone in Malaysia. Suitable weather indexes were chosen based on the relationship of these indexes and the paddy yield in each zone by employing the method of Ordinary Least Square regression and robust regression. The weather index-based insurance contract is designed based on the natural phenomenon and the time trend had been removed in order to reduce the basis risk. By investigating the relationship, a paddy insurance contract was then designed. However, the results showed that three paddy cropping zones are not suitable to uptake this index insurance as the regression models reported that the vagaries of weather did not cause a significant impact on the paddy yield of these states. This study reveals diversified insurance product design of each zone based on different weather indexes, which suggests that more weather variables should have to be taken into account in order to design a more robust weather-index insurance. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (

 Keywords: Weather index, Regression, Robust regression, Insurance

 Article History: Received 11 December 2018, Received in revised form 3 April 2019, Accepted 6 April 2019


No Acknowledgement.

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.


 Wen YWJ, Ponnusamy RR, and Kang HM (2019). Application of weather index-based insurance for paddy yield: The case of Malaysia. International Journal of Advanced and Applied Sciences, 6(6): 51-59

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 References (15) 

  1. Binswanger-Mkhize HP (2012). Is there too much hype about index-based agricultural insurance?. Journal of Development Studies, 48(2): 187-200.   [Google Scholar]
  2. Bokusheva R and Breustedt G (2012). The effectiveness of weather-based index insurance and area-yield crop insurance: How reliable are ex post predictions for yield risk reduction?. Quarterly Journal of International Agriculture, 51(2): 135-156.   [Google Scholar]
  3. Carter MR, Galarza F, and Boucher S (2007). Underwriting area-based yield insurance to crowd-in credit supply and demand. Savings and Development, 31(3): 335-362.   [Google Scholar]
  4. Chen YT (2011). Weather index-based rice insurance: A pilot study of nine villages in Zhejiang province, China. M.Sc. Thesis, Swiss Federal Institute of Technology Zurich, ETH, Zurich, Switzerland.   [Google Scholar]
  5. Cheong Y, Burkart K, Leitão P, and Lakes T (2013). Assessing weather effects on dengue disease in Malaysia. International Journal of Environmental Research and Public Health, 10(12): 6319-6334.   [Google Scholar] PMid:24287855 PMCid:PMC3881116
  6. Goodwin BK and Mahul O (2004). Risk modeling concepts relating to the design and rating of agricultural insurance contracts. The World Bank, Washington, D.C., USA.   [Google Scholar]
  7. He J, Zheng X, Rejesus RM, and Yorobe Jr JM (2019). Moral hazard and adverse selection effects of cost‐of‐production crop insurance: Evidence from the Philippines. Australian Journal of Agricultural and Resource Economics, 63(1): 166-197.   [Google Scholar]
  8. Iturrioz R (2009). Agricultural insurance. Primer Series on Insurance, The World Bank, Washington, D.C., USA.   [Google Scholar] PMCid:PMC2667749
  9. Lobell DB and Field CB (2007). Global scale climate–crop yield relationships and the impacts of recent warming. Environmental Research Letters, 2(1): 1-7.   [Google Scholar]
  10. Mahul O and Stutley CJ (2010). Government support to agricultural insurance: Challenges and options for developing countries. The World Bank, Washington, D.C., USA.   [Google Scholar]
  11. Nguyen MT (2013). Willingness to pay for area yield index insurance of rice farmers in the Mekong Delta, Vietnam. M.Sc. Thesis, Wageningen University and Research Center, Wageningen, Netherlands.   [Google Scholar]
  12. Osborne TM and Wheeler TR (2013). Evidence for a climate signal in trends of global crop yield variability over the past 50 years. Environmental Research Letters, 8(2): 1-9.   [Google Scholar]
  13. Poudel S and Shaw R (2016). The relationships between climate variability and crop yield in a mountainous environment: A case study in Lamjung District, Nepal. Climate, 4(1): 13-31.   [Google Scholar]
  14. Romero GH and Molina A (2015). Agriculture and adaptation to climate change: The role of insurance in risk management: The case of Colombia. Inter-American Development Bank, USA.   [Google Scholar]
  15. Smith V and Watts M (2009). Index based agricultural insurance in developing countries: Feasibility, scalability and sustainability. Bill and Melinda Gates Foundation, Seattle, Washington, USA.   [Google Scholar]