Crop Insurance as a Risk Management Strategy in Bangladesh

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- identifying the time period during which risk is prevalent; and - identifying the weather index providing the best proxy for the weather exposure. This last step is the most critical in designing an index-based weather risk management strategy. Rather than measuring the actual impact on crop yields - or related fluctuations in demand, supply, or profitability - the index acts as a proxy for the loss experienced due to weather and is constructed from actual observations of weather at one or more specific weather stations. Location and Duration All weather contracts are based on the actual observations of weather variables at one or more specific weather stations. Transactions can be based on observations from a single station or a basket of several stations or on a weighted combination of readings from multiple stations like; - If an individual farmer is interested in purchasing weather protection for his particular crop, the index-based weather contract must be written on the weather station nearest the farmer’s land to provide the best possible coverage for the farmer client. - A larger grower, with several production regions, may be more interested in purchasing a weather contract based on several weather stations to reflect the weather conditions in all areas covered by the business. The grower’s risk management strategy can be either to purchase a weather contract on each of the identified weather stations or to purchase a single contract on a weighted average of several stations. All contracts have a defined start and end date to limit the period over which the underlying index is calculated. This calculation period describes the effective dates of the risk protection period during which relevant weather parameters are measured at the specified weather stations. For agricultural end users, the duration of the weather contracts will be determined by the specific requirements of their business. Contract duration has the flexibility to address individual end-user business exposures; which can be weekly, monthly, seasonal, and even multi-annual. Final settlement of the weather contracts typically occurs up to forty days after the end of the calculation period, once the collected weather data have been cross-checked and quality controlled by the relevant data-collecting body, usually the National Meteorological Service. Underlying Indexes A weather index can be constructed using any combination of measurable weather variables and any number of weather stations that best represent the risk of the agricultural end user. Common variables include temperature and rainfall, although transactions on snowfall, wind, sunshine hours, river flow, relative humidity, and storm/hurricane location and strength are also possible but used to be more complex. The normal process for designing an index-based weather insurance contract for an agricultural grower, for example, involves identifying a measurable weather index strongly correlated to crop yield. After gathering the weather data, an index can be designed by - Looking at how the weather variables have or have not influenced yield over time. - Discussing key weather factors with experts, such as agro-meteorologists and farmers; and/or 110
- Referring to crop growth models using weather variables as inputs for yield estimates or phenology models illustrating how weather variations relate to pest development. A good index must account for the susceptibility of crops to weather factors during different stages of development, the biological and physiological characteristics of the crop, and the properties of the soil. If a sufficient degree of correlation is established between the weather index and crop yield or quality, a farmer or an agricultural producer can insure his production or quality risk by purchasing a contract that pays if a specified undesirable weather event occurs or a specified desirable weather fails to occur. 26 25 24 23 22 21 INDIA Sayedpur Rangpur Dinajpur Rajshahi Ishurdi Chuadanga Jessore Satkhira Bogra Khulna Tangail Faridpur Mongla BMD Stations Mymensingh Dhaka Chandpur Madaripur Barisal Bhola Patuakhali Khepupara BANGLADESH INDIA Comilla M.Court Hatiya Feni B A Y O F B E N G A L Sylhet Srimangal Sitakunda Rangamati Sandwip Chittagong Kutubdia Coxs_bazar 89 90 91 92 INDIA Teknaf The index possibilities are limitless and flexible to match the exposure of the agricultural grower or producer, as long as the underlying data are of sufficient quality collected from adequate meteorological stations. For some meteorological parameters there are standards for adequacy of stations like precipitation. According to WMO, there should at least one meteorological station for 10,000 sq. km. Figure 7.1 on the previous page shows the location of meteorological stations along Bangladesh. There are 32 full-fledge meteorological stations with provision of measuring a number of climatic parameters. As it is seen from the figure that other than the North-Eastern part, i.e. Sylhet, Mymensingh and Brahman Baria the number of stations sound adequate for the rest of the country. Bangladesh Meteorological Department has future plan to install 12 more stations along the country. In that case it will be good enough to cover meteorological data for the entire country. Other than these, there are few more organizations which collect primary data of hydro-climatic parameters like SPARSO, BWDB, BIWTA, Agriculture Department, Department of Environment (DoE), LGED, etc. 111 BWDB Stations Figure 7.1 Weather Stations of BMD and Hydrological (Rainfall) stations of BWDB
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Crop Insurance as a Risk Management
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Crop Insurance as a Risk Management
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Foreword The impacts of global warm
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ILO International Labor Organizatio
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3.6 Crop Insurance: some Terminolog
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Table 3.6 Advantages and challenges
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Figure 6.17 Organizational preferen
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Therefore, the main objective of th
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can be classified under different r
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National Reinsurance Scheme: The pu
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1.1 Statement of the Problem Chapte
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improving day by day and many of th
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Individual or area approach: To det
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Availability of Trained Personnel:
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FGD was mainly conducted among the
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outputs were critically analyzed an
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Comments specifically related to me
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Figure 3.1: Factors causing variati
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than ten times, while the global po
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Figure 3.8: Change in Palmer Drough
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yielding but drought resistant vari
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implement from the perspective of p
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Table 3.4: Present status of disast
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3.5 Limitation of Crop Insurance: P
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amount is measured by the extent of
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Contract (SUC). The premium rate fo
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Box 3.1: Examples of Parametric or
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Therefore, index products are a bit
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3.10 Crop Insurance Experience at D
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3.11.2 Canadian Experience Crop ins
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farmer was 150 percent of the crop
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segments of the population often en
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Chapter 4: Crop Insurance - Banglad
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development of insurance business i
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unanticipated catastrophic natural
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1 st stage ……………………
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Taka 80,000.00 60,000.00 40,000.00
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and requirements of the insurance h
Page 83 and 84: Defining risk-reduction measures by
Page 85 and 86: Chapter 5: Climate Change and Agric
Page 87 and 88: 21 1990 CYCLONE 39 127 171099 24289
Page 89 and 90: ppmv of CO2 combined with a 4 0 C i
Page 91 and 92: Crop Damage fully ( acre) 4000000 3
Page 93 and 94: Table 5.5: Partial listing of cyclo
Page 95 and 96: Figure 5.7: Monthly mean temperatur
Page 97 and 98: magnitude or an intense or high mag
Page 99 and 100: Historical monthly values Mean Stan
Page 101 and 102: 6.1 Field Survey Chapter 6: Crop In
Page 103 and 104: Son of BoroBari now day labor (Curt
Page 105 and 106: Figure 6.4: Drought prone regions o
Page 107 and 108: Figure 6.7: Response to disaster al
Page 109 and 110: Figure 6.10: Agro-ecological sub-re
Page 111 and 112: season. Sunamgonj, as Haor region,
Page 113 and 114: % of farmer % of farmer % of farmer
Page 119 and 120: 6.3 Farmer’s Views on Climatic Ef
Page 121 and 122: disastrous affect as such mode of l
Page 123 and 124: disbursement and Insurance Company
Page 125 and 126: Chapter 7: Summary Findings & Demon
Page 127 and 128: an incident occurred and farmers we
Page 129 and 130: Moral hazards: Theoretically moral
Page 131 and 132: duration of disaster is important h
Page 133: increased to 8.51% in 2003. Such a
Page 137 and 138: data, for example, can establish an
Page 139 and 140: particular gauge station can be com
Page 141 and 142: Figure 7.6 Defined danger level by
Page 143 and 144: Methodology to develop an index in
Page 145 and 146: Structuring the Product Once the in
Page 147 and 148: did not cross the Weather Index val
Page 149 and 150: methodology, and applicability can
Page 151 and 152: References Anderson, J. (2001). Ris
Page 153 and 154: Linnerooth-Bayer, J., Mechler, R. a
Page 156: This document is produced by Climat
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