the challenges of index-based insurancefor food security in developing countries editors: rené...

7/28/2019 The challenges of index-based insurancefor food security in developing countries Editors: Ren Gommes & Franoi

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Joint Research

Centre

The challenges o

index-basedinsurance

for food securityin developing countries Editors

Ren Gommes

and Franois Kayitakire


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European Commission

Joint Research Centre

Institute or Environment and Sustainability

Contact information

Franois Kayitakire

[email protected]

Institute or Environment and Sustainability

http://ies.jrc.ec.europa.eu/

Ren Gommes

[email protected]

Institute o Remote Sensing and Digital Earth, Chinese Academy o Sciences

http://english.irsa.cas.cn/


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2013

Joit Research Cetre

w I Rs Isu C Sy

The challegeso iex-base

israceor oo secrityi evelopig cotries

Proceedings of a technical workshop organised by the EC Joint

Research Centre (JRC) and the International Research Institute forClimate and Society (IRI, Earth Institute, Columbia University)

JRC Ispra, Italy, 2 a 3 May 2012

E yRe GOMMES

FraoisKAYITAKIRE


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ForeorOv s , w x su guu s sg vg us. Ts s x y x su s s skg su s s. M gy, su s x w guu v- s vs vy u s suy yg s vus sks v guu sk g sus, ug gu-

u su us.

Agricultural insurance aims to reduce the risk associated with crop pro-

duction and animal husbandry. With index insurance, settlements are

based on the value taken by an objective index rather than on a case-

by-case assessment o crop or livestock losses. Index insurance aims

to be aordable to all producers, including smallholders. Index insur-

ance could provide a saety net against weather-related risks or all

members o the arming community, thereby increasing ood security

and reducing the vulnerability o rural populations to weather shocks.

Index insurance, urthermore, can be combined with credits or insuredsmallholders, as the risk o non-repayment or lenders is reduced.

This boosts the use o agricultural inputs and equipment, leading to

increased and more stable crop production. It also makes participation

in the market or agricultural insurance and credit attractive or the

private sector, taking some o the burden to provide state-sponsored

insurance and credit systems away rom governments.

Realising the potential o weather index insurance or supporting agri-

cultural development, the European Commission has encouraged and

supported numerous schemes and initiatives, the most important one

being the Global Index Insurance acility jointly created by the In-

ternational inance Corporation (IC) and the International Bank or

Reconstruction and Development (IBRD) in 2008. The past years have

seen the emergence o a number o weather index insurance pilot pro-

jects, many o them in sub-Saharan Arica. During the same period,

noticeable progress has been made in research on the development

o better, more widely applicable indices, on the design and market-

ing o index insurance products, on orms o linkages to agricultural

credit, on ways or the successul operation o insurance schemes,

on suitable policy rameworks and on many other aspects o index

insurance. It was thereore timely to invite experts and stakeholders


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4 T gs x-s su su y vg u s

to meet to examine the state o weather index insurance rom various

angles, and to discuss some o the main challenges it aces.

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Hwv, w u s -, gs ssus u ys s y g-s s.I s quy s x su s jus g y y sus s u sks w g uv-y -sg vss.

Ks SDirector Sustainable Growth and Development

Directorate-General for Development and Cooperation EuropeAid


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CotetsOVERVIEW: FROM EARLY WARNING SYSTEMS TO INDEX INSURANCE 9

R Gs, s Kyk

MEETING CONCLUSIONS 15

JRC IRI

1 OPENING REMARKS 18

N Hu

2 RESPONSIBILITY IN INDEX INSURANCE: THE IMPORTANCEOF A SOLID SCIENCE BASE FOR FARMER-DRIVEN DESIGN 20

D E. Osg

3 INDEX-BASED INSURANCE: IDEAL SOLUTION OR DANGEROUSADVENTURE SOME REFLECTIONS 29

Ts Ls

I s

4 PRACTICAL LESSONS FROM IMPLEMENTING WEATHER INDEXINSURANCE FOR AGRICULTURE 32

Cs A

5 INDEX INSURANCE AND RISK MANAGEMENT FOR FOOD SECURITYAND RURAL DEVELOPMENT EXPERIENCE FROM IFAD AND WFP 38

Ey C, s Rs, Ns Bz, R Cu

6 INDEX INSURANCE IN AGRICULTURE THE (RE)INSURERS PERSPECTIVE 47

J H

7 CHALLENGES IN STRUCTURING AND PRICING INDEX-BASED RISK TRANSFERPRODUCTS IN EMERGING MARKETS: A REINSURERS PERSPECTIVE 55

M K

N xs

8 USING WEATHER INDEX INSURANCE TO COMBAT FOOD INSECURITY:MALAWI CASE 62

G Lv


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9 INSURING THE POOR: THE EXPERIENCE OF CATASTROPHIC AGRICULTURALINSURANCE IN PERU 68

Y Mz Vu

10 EVALUATION OF THE EXISTING RAINFALL INDEX INSURANCE EXPERIENCEIN ETHIOPIAS NYALA INSURANCE COMPANY (NISCO), ETHIOPIA 77

B Ts

11 INDEX-BASED CROP INSURANCE IN SENEGAL AND WEST AFRICA:SOME CONCERNS BASED ON ONGOING EXPERIMENTS 79

B Mu

12 PROTECTING PASTORALISTS AGAINST DROUGHT-RELATED LIVESTOCKMORTALITY: THE INDEX-BASED LIVESTOCK INSURANCE PROJECT (IBLI)IN NORTHERN KENYA 85

M Lg

13 WEATHER INDEX-BASED INSURANCE IN A CASH CROP REGULATED SECTOR:EX ANTE EVALUATION FOR COTTON PRODUCERS IN CAMEROON 91

A Ls, Bj Su, P Qu

14 FORECASTING THE MAJOR CROPS FOR AUSTRALIA AND CROP YIELDINSURANCE: AN INTEGRATED CLIMATE, BIOPHYSICAL AND REMOTESENSING APPROACH 101

As Pg, G H, Dv Rguz, P Dvs,As Dy, B Pw, P Bk

Ix sy

15 COMBINING MULTIPLE SOURCES OF CLIMATE INFORMATION TO GUIDETHE DESIGN OF WEATHER INDEX INSURANCE 112

Js D

16 FROM INFORMATION TO ACTIONABLE KNOWLEDGE: HOTSPOTS OF FOODINSECURITY IN EASTERN AFRICA 120

As Pg Dv Rguz, P Dvs, J Ds

17 DEMAND FOR DROUGHT INSURANCE IN ETHIOPIA 128

M Tss, As, S T. H, O Es

18 DEMAND FOR INDEX-BASED INSURANCE BY SMALLHOLDERS IN LIGHTOF EXISTING RURAL INSTITUTIONS IN AFRICA 133

Ax Ss


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7

19 AN INSIGHT ON FARMERS WILLINGNESS TO PAY FOR INSURANCEPREMIUM IN SOUTH INDIA: HINDRANCES AND CHALLENGES 137

Ks Ry Kkuu, Ku Ps, K Guv Ry, B Ask,

Uy Sk Ngu, Ss Xs, K Tu

20 DESIGNING AND IMPLEMENTING WEATHER INDICES: TOWARDS RESOLVINGTHE CHALLENGES OF WEATHER-BASED INDICES FOR AGRO RISK 146

Augus Bss, P Sjvsk, Cs P. Mg

21 ALTERNATIVE METHODS TO CONSTRUCT CONVENIENT INDICESWITH EXISTING DATA 156

Os Gusv

22 CHALLENGES TO SCALING UP INDEX-BASED INSURANCE PRODUCTSAND POSSIBLE SOLUTIONS 163

P M

Ix s gy

C s

23 CLIMATE SERVICES IN AGRICULTURE INSURANCES 166

Js C24 INDEX-BASED INSURANCE CHALLENGES AND ISSUES FOR WEATHER

DATA PROVIDERS 167

As Cvu

25 INDEX WEATHER INSURANCE FOR SMALLHOLDER FARMERS:WHO TAKES THE RISK? 172

Mys s, A Ezg, Jqu Dz N, S Ck, P L 172

26 CLIMATE AT DIFFERENT SPATIAL AND TEMPORAL SCALES 180

B Ly

27 MODEL OR OBSERVATION-BASED AGRICULTURAL WEATHER INDICES? 188

S Dg

28 CAPTURING THE SPATIAL VARIABILITY OF RAINFALL IN WEATHER-BASEDINDEX INSURANCE 195

H Gx, Dv I. . Gs

29 BEYOND SIMPLE, ONE-STATION RAINFALL INDICES 205

R Gs, Wgg G


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Avs s -s s

30 TEMPERATURE, SOWING DATES AND VARIETY AS CROP INSURANCE

INDEX FOR IRRIGATED WHEAT IN NORTHERN AND CENTRAL SUDAN 224Huss S. A, A Mk G G

31 THE POTENTIAL OF WEATHER HEALTH (WH) AND DAILY WEATHER ANDAGRICULTURE (DWA) CONCEPTS AS INSURANCE INDICES 230

V. R Ks Muy

32 REMOTE SENSING OF VEGETATION: POTENTIAL APPLICATIONS FORINDEX INSURANCE 238

M M, s Kyk, My E. Bw

33 FEASIBILITY OF USING THE FAO AGRICULTURAL STRESS INDEX SYSTEM (ASIS)AS A REMOTE SENSING-BASED INDEX FOR CROP INSURANCE 246

Os Rjs, Suk A

34 INTEGRATING DROUGHT AND SATELLITE-BASED INDICES IN SUPPORT OFRISK MANAGEMENT AND INSURANCE IN EAST AFRICA 254

Tsgy Tss

35 EARTH OBSERVATION-DERIVED YIELD ASSESSMENTS FOR INDEXINSURANCE IN AGRICULTURE LOGIC OF VARIABILITY SOURCES AND REALITY 263

Ks B, Kj Msk


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9

1. InTROduCTIOnS 1970s, y wg syss suy su y u- v ss sks. Hwv, s s u y wg vs, suy s vs y s g By, 2013. D vv wys k , vg us k w g s v v s svs, sy s u gy.

T w s su s s s su sus

g sug y y v s gss, x W Bk, sv UN gss, s sus y s s, x, s y A, C . Osg s vu.

The idea is to tackle the problem at its roots and to transer, at least partially, production

risks rom armers to insurance institutions. Crop insurance schemes that have recently been

proposed in developing countries almost exclusively ocus on index-based approaches, an ap-

proach by which large numbers o armers can be insured or limited transaction costs. It is

generally believed that this potentially makes the insurance protable or private insurance

companies and aordable or ood producers, although some authors (e.g. Herbold in this vol-

ume) consider that development costs are substantially higher or index products than or clas-

sical multi-peril insurance.

The collection o short papers in this publication is the outcome o the workshop The Challeng-

es o Index-Based Insurance or ood Security in Developing Countries, jointly organised by the

Joint Research Centre o the European Commission (EC/JRC) (1) and the International Research

Institute or Climate and Society (IRI), Earth Institute, Columbia University (2). The JRC and IRI

collaborate in some technical areas o mutual interest , including ood security. Both institutions

recognise that repeated environmental shocks are a major threat or ood security in developing

1 M sy, Suy A S, Mg Aguu Rsus u MARS Isu

Ev Susy IES: s v ://s.j..u.u.

2 T IRI u ws C Rs Ev Dss, Isus S T.

Overvie: From early arig

systems to iex israceRen Gommes

JRC, [email protected]

Franois Kayitakire

JRC, [email protected]


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countries. Moreover, the induced risk prevents smallholder armers rom adopting investment

strategies that would have the potential to nally li them above the level o subsistence.

T IRI s g gs x Aguu Ix Isu AgII uus us v ssu s s AgII jsBw Osg., 2012; Cqu Osg, 2012; N ., 2011; Dku .,2009; Hu ., 2009.

Next to the AOs Global Inormation and Early Warning System (GIEWS), the USDAs oreign

Agricultural Service (AS), amine Early Warning Systems Network (EWS NET) and China s

CropWatch System (Wu et al ., 2010; Qiangzi Li et al., 2011; Bingang Wu and Qiangzi Li, 2012),

the JRC, through its unit or Monitoring Agricultural Resources (MARS) is one o the major global

players in the area o quantitative crop condition assessments (Baruth et al., 2007; Cantelaube

and Terres, 2005; Chavula and Gommes, 2006; Gommes et al., 2010; Kayitakire et al., 2010;Rojas Mora et al., 2005; Rojas Mora et al., 2011; Vancutsem et al., 2011).

When the MARS programme was established in the mid 1980s, a number o systematic stud-

ies were launched to test and eventually establish many o the methods that have now be-

come standard approaches in regional crop orecasting and monitoring, such as the geosta-

tistics o climate data, Geographic Inormation Systems (GIS), reliable techniques to estimate

crop water requirements, etc. An excellent account o the subject is given by Rijks et al. (1998).

Experience with crop insurance is available, but mostly in Europe (Bielza Diaz-Caneja et al.,

2009a; Bielza Diaz-Caneja and Colmenero, 2009b). It is interesting to note that Bielza Diaz-

Caneja et al. (2009) concluded that index insurance would be dicult to implement in Europe.

2. MEETInG OBJECTIVES And RATIOnALE

I y vg us, gss s w gv vs v skg x- sus guu su ss x ss. s xs, sss sus gv. Sv s s vu s su xs.

The meeting was based on the perception that indices are a pivotal component in index-based

crop insurance systems, because o: (i) the technical diculties in achieving a good statistical

relationship with losses, and the implications o this or mitigating the basis risk; (ii) the existing

trade-o between sophistication and transparency o methods; and (iii) the data sources, avail-

ability and reliability o data that constrain the development and operational use o the indices.

Moreover, as there is no guarantee that the best index (rom an agronomic point o view) ts the

best contract (rom an economic view), it is believed that only a multidisciplinary approach will

lead to the development o an insurance product, and the associated index, that is technically

sound, specically designed to target the key problems and risk management gaps that arm-

ers ace and where the insurance benets outweigh premiums. Through this broader approach,

the objective is to arrive at insurance packages with the greatest potential or reducing poverty,strengthening armers resilience to shocks o various origins and improving rural ood security.


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This is why the meeting involved some major partners rom the private sector (insurance and

reinsurance companies), development agencies, technical institutions dealing with the develop-

ment o comprehensive insurance schemes and experts in climate and crop-climate interac-

tions, who usually are responsible or the development o indices in collaboration with the otherplayers.

T g jvs u gg vvw xsg s, s -quy, u w s u gs, suss s s u suy. Pu ws qus x v v su ss ss s, s w s sy su syss.

3. STRuCTuRE OF THIS REPORT

T s s s s vvw suss uss g, jy y JRC IRI. T uss gg s susss. T s s x ss ss.

3.1. Sg s

Three presentations set the scene or the debates, starting with opening remarks by the MARS

unit head and the main host or the event, ollowed by the presentation o the philosophy at the

base o much o the work o IRI (i.e. that insurance products sold to smallholders in developing

countries will be air only i the armers participate in product development) and, nally, Munich

Re had a sober look at index-based insurance and spelled out some conditions or AgII to be

eventually successul.

3.2. I s

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3.3. N xs

T s s sv s vg sg -vs, s u AgII s y su s. T ss

, s s s vy s, -y su Pu vsk su Ky y s s Aus.


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3.4. Ix sy

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w g g AgII. T s s suss sw su s s Pg ., qus ssssg u y s Tss . Ss wgss y Kkuu . s-g I x, ssus gg g w u - Bss ., M. T y Gusv j Tuky u v s xs, u s ss v , s w s s, .

3.5. Ix s gy

T gs u s g u s s, ssy us, s v u Mg jvs JRC IRI w w ys sv y s ssus k w AgII, sy sssk. Ts s gu u gs Climate science Alternatives tosimple rainfall-based indices.

Climate sciece

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s AgII. Ty v v y NW Svs NWS Cvu su y WMO C, sk xsg u ss s .. I , AgII u j uy w svs g , s wys y y NWSs.

u s s s s us w vs, s w vk w sgg su us, ug u v-y Ly, v qus gu svs Dg, s vy Gx, Gs G.

Alteratives to simple raiall-base iices

S vs us s s. I, AgII s vy gy -xy, u g x s us s vs s s vs y u syss v s. T v x-s Su A G G I Ks Muy. Mss s w ssg us, w w u su sys uu AgII syss. Ty u s vy u y v w s. T ssg s -g w g svs M ., ssssg sss sRjs A usg, g gs, vs s vg

g Tss , vuy, kg ss v y s, sy wu k u quv w s s B Msk.


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13

T s w w su us g s ss vg s. My , su s sv u, s y Ts Ls Mu R u, u sussu

su us y s s s s s.

4. A wORd ABOuT THE ARTICLE PREPARATIOn PROCESS

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y w . I s y sg u s w - u, us s wys v.

5. REFEREnCES

Bu, B., Ry, A., Gvs, G. 2007, ISPRS Workshop Proceedings: Remote Sensing Sup-port to Crop Yield Forecast and Area Estimates, JRC 40702.

By, R. 2013, Mgg Rsk, Lkg Ey Wg Ey A. A CHus R, T Ry Isu I As, L, UK. 100 .

Bz Dz-Cj, M., C, C., D, C., Gg P, ., S, J., C,R. 2009, Risk Management and Agricultural Insurance Schemes in Europe, JRC RR, JRC 51982 EUR 23943 EN.

Bz Dz-Cj, M., G C, A. 2009, Evug P W- su v C-s Isu Ps, Ss J Ag Rs 71:3-11.

Bgg Wu, Qgz L, 2012, C g y gs x guu ss, I J. A E Osv. G- 16:101112.

Bw, M., Osg, D., Cquy, M. 2012, S-s su, Nature Geoscience 4,. 213214.

Cu, P., Ts, J. M. 2005, Ss w ss y g Eu, Tellus Series A Dynamic Meteorology and Oceanography, 57/A: 476487.

Cvu, A., Gs, R. 2006, Dv W Y Ix WXY Mz

C Isu Mw, AO, R, 19 . ://x-..g/SD/Rsv/Ag/M-w_WYX/MYZ_..


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Cqu, M., Osg, D. 2012, Ix Isu, Pu Ps, PsC ss,Journal of Risk and Insurance, 791:287300.

N, ., Vgs H, R., C, M., Cu, R., Hs, J., Osg, D. 2011, I-x Isu Mgg C-R Aguu Rsk: Tw Sg RsAg, Wks R, I Py Rs Isu R IPRI, Ws-g, DC.

Dku, T., G, A., Hs, J., Hus, E., Is, A., K, Y., Kusks, K., Ly, B.,Mjwz, M., Mu, M., Muy, C., N, M., Osg, D., Ps, N., Rs, A.,Sy, K., S, C., V, M. 2009, Designing Index-Based Weather Insurance for Farm-ers in Adi Ha, Ethiopia, R Ox A, IRI T R 0904.

Gs, R., Ds, H., M, L., C, A., Ty, B., Bg, R., Dw, M. A. A. 2010,Agg sg, C 6, WMO Guide to Agrometeorological PracticesGAMP, WMO N 134, Gv, 49 . ://www.ws.g/g/g/GAMP_C06..

Hu, M. E., Osg, D. E., Hss, U., M, A. , Bjw, H. s 2009, Index insur-ance and climate risk: Prospects for development and disaster management, C S-y N 2, I Rs Isu C Sy IRI, Cu Uvsy,NY, U Ss.

Kyk, ., L, O., Vg, P., B, M., T, V. 2010, Mg C Pu- Sys Zs IGAD Rg, JRC 62394.

Qgz L, Bgg Wu,Ku J, Qg Dg, Es, H., M Zg, 2011 Mz gs usg ENVISAT MERIS CBERS-02B CCD N C P, Cu-s Es Ag. 78: 208214.

Rjks, D., Ts, J. M., Vss, P. s 1998, Agrometeorological applications for regionalcrop monitoring and production assessment Accounts of the EU Support Group on Agro-

meteorologySUGRAM 199196, EUR 17735 EN, 505 .

Rjs M, O., R, ., D, J., L, O. 2011, Usg NDVI s uxy quy ssss ss A, International Journal of Remote Sensing,3212:32493265.

Rjs M, O., R, ., Ry, A. , Ng, T. 2005, R- Agg CY Mg Es A,Agronomie, 251:6377.

Vus, C., Pk, J.-., Kyk, . 2011, Dy Mg C As Su-S A Usg MODIS T Ss, JRC 65458.

Wu, B. . , Mg, J. H., L, Q.Z. 2010, A g g sys w ssg, Ts. ASABE, 533: 971-979.


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1. GEnERIC FIndInGS

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vg Cus, gs y JRC I Rs Isu C Sy IRI, ws Is, Iy, 2 3 My 2012. As w s-u s ssg g s, ys ussssu su s gv gss vs vv w g uy s u su - ss w w s .

Ps suss sks x-s w su WII g vws ss WII s s v v s js, w s s WII. I ws

gg su w sus y s y sg ssg u . I , sks suy sk su y uy k s v vy . I ws su us s s v vus, ug vs s s. Isu us s v ex postsk g v gu .

C sks s u qus sus WII s us s su w y s sk. x, ky qus s us vy xs s w suuk ss , s s usuy s su?A qus, suggs ug g, ws g- s s wy u g s su s u sk sus.

Dug ws uss s, s , v WII: s s us ug s g s w sg s sg y v. O sks w gg - vsk, sss ss ug s uss ws suggs s s sv g . Ps s gus su gv sv syss s ug su-

, sg w v y s. Ts wu v wy su-y sv u w-y j v s-gv sk

Meetig coclsiosJRC and IRI


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g syss. T ys w y sk-ks, s sks, sus u sk g s s ss gvs.

Bss sk ws gg s vus w WII, ss sk us w s-uss s wk su yus v w sss u s gg.

inally, participants in the workshop recommended that the WII community needs to accu-

mulate more experience and: (i) continue to build condence or a range o insurance prod-

ucts, technical options, methods and tools and their reliability (e.g. missing data estimation

and sophisticated simulation techniques); (ii) develop methods to eciently advocate, com-

municate, train and sell WII (rom armers to governments, under low and high technology

conditions); (iii) ensure that products are consistent and integrated packages optimised orall levels, whether they are simple rainall-based indices, yield indices or complex arm-

income indices.

2. SPECIFIC ISSuES

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2.1. S

It was mentioned that WII ofen works better (i.e. minimises basis risk) when the meso

scale and aggregated hazards are targeted, rather than individual armers. Care must be

taken to understand the most appropriate policyholder (e.g. a micro-nance institution or

a armer). It may be particularly eective, especially or armer level products, or indexes

to be bundled with complementary risk management tools that could address idiosyncratic

risks. This is also why satellites (which rely on relatively large pixel sizes) are seen as

a promising ingredient o indices, and why area-yield insurance schemes have the potential

to address many perceived or real shortcomings o pure WII, possibly in combination with

a basis risk und.

2.2. Cs

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2.3. R

W s us ssss sk sss s sv u

g s u s w su s s. I s s-sy WII uy u suss sv us suug w s g s ssy us us. Ssks 30 ys s ssy, w s v -g s s w s .g. 1520 ys.

2.4. Tg

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s, s w sus su uys w ss .g. s,ss, s .g. E N sg uus, k uu g s ug ys.

2.5. Ix gy

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g g s w, w w v u -g s vy , ss w gs k ssus g ss, sus s gu ss. W ws kwg su s v us s uy v, s su s usg ug s w s.

2.6. Tsy sy

T v s sy us vy v su sg sg g suy, w sy s ys. O y, g sy, sy y v gs s j u k WII. Isgy, s -v ss s su v y, u-g xs .. s sus. I , uy, v ug gu g sks, u gu ss u s ugu g ss, ug , guu xs TV w ss, sg ss s s, gu -uu uu us w sks, sy w s sus.


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I gv g su wks x-s su suy,

w sv ssss ug susss g -v g uss. A sus , , s wks -gs , , I wu k w ws u.

T g gs wks vy w w v ss JRC s Eu Csss -us s sv. T JRC vs s y -ss, w vw sug EU vs s Eu 2020 gs uvy s w s s, su sus uu. T JRC ys ky Eu- Rs A s s u-sy y wkg xsvy wg s gss M Ss, ss us ww.

I guu , wk uk svs guu s Eu U, u s v, ssg g ssus suy g.

suy s ky Eu Csss gs -uuwk g Eu 2020 jvs g- g vgsu gwg g u. usg u guuvs sug guu u, wk MARS U vs y -ss, y sssss s ug g syss.

W s gwg uyg/sg , ky vs s, ggy ssg gu 1. I s w vs MARS U v sgs, s s xus wks x-s s su s v.

I wy w y s wks, ug sv w v v- A v u .

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s u vw s w ss. T sus g, u s gs, su gss ws

1 Opeig remarksNeil Hubbard

Head o MARS UnitJoint Research Centre (JRC)[email protected]


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19

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Figure 1: Sus s, ggy ssg

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moellig


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KEYWORDS: iex israce, climate risk maagemet, climate aaptatio

SuMMARYI s g sgy v g us s g x-s su us. I s ss sys v ws svs, w y s w uy kw s, v us-y sg ss u s s -w ss sus v s. suss, s s w us w ss xg s s v gs.

1. InTROduCTIOn

U y, y v u x su u s g u-s s guy ss vy. T s wy u w s w s suy s k y gy, , suus.

Nw js v sv y s s; s su js v vy s.

2 Resposibility i iex

israce: The importaceo a soli sciece baseor armer-rive esigDaniel E. Osgood

Lead Scientist, Financial Instruments Sector TeamInternational Research Institute or Climate and Society

Columbia University Earth [email protected]


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21

As s gss us, ssy s sgy x su -js. I s-s s, s ss v x, - sus uyv us. A s ss, s ss s vy x -

u. As ss s, w us y v us-ss u s. Py x sus g qu.

A IRI, w sk g vs us ss ss v vs vg us. W v vv su-, s, sg, vu v - js v z us, wkg w s v s x suug us w s s s, g k-u, sg, qusv s. Ts uss s s v us su -s w v sg ug -v sss. W s u su

wk g y sss su s UN COP C Ps GHG Hu u gs u wk s v u ssus su s The Guardian, Nature, The New York Times, y Rus. I s x, wv s u svs ssus w v x wkg u s s s -v x su. S s x s uvw ssus su, u suss s s usvs, w ss s vu, s ws Vu 2 C Sy u 1, s x.

2. ISSuES, ExAMPLES And SCIEnCE

2.1. Ky s

Ix su s s s y ys , s g uy, w y wkg gs, u s s g u s s sk g-, v, . Bus x su s v v gs u sk s , us sy sg sk g vs. Is g u vs sk s , g w x su s w u vs w s sk u s s, w y ws w s su. T su us u sk -ss vy ug s, s s v sk g sys.

Iyg su su, w ug ssy -y sus, qus ss v us-g us, -s, sus, s. T ss us y-v ug w y k ws ssy

1 ://..u.u/s2


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sus. Oy s uy us gs y , y s-ks w us , v usg , sy us.

T ss us s u s s. I s wu -g guy w xs w v v s s us, kw s s w g, us ss, vus x-g s v gs w usu.

T g s s k su usu. I sus k y, s uky , v u sgy suss. I s s, su s k : yu k u su wuusg sg usu, yu sg y. T s sussu

x su js s y y uv vy ukus sk, y u sk g kg y su- ws k uv vy.

I a armer cannot repay a loan or inputs one year out o ve, the armer may orego hun-

dreds o dollars o potential prots in the other our years even i the drought never occurs.

W su s kg us sk g y us ug su s s k u us us, us s sy v su u. I s,

, qus w u s wg y su, u sg ss v su uks s su, w ss quy s w g us su s ys. Ts s vy susy g yg x s s sy w s ys, qus vy ss usg y s, s, sss.

2.2. Exs

Bgg w ss u 2003, BASIX j I v s s x x su vg w: s ws kg gu x-g w x ws ss , u us ws s- . T, gukg u ws sw su-,usg w ws s s s. Bus su ws y xsg u u s sk g uvy-g su, us ws gg su gu. T su ws s y s guu w sss s vv s wy ws y u s u s ss.

T y u s ss u su y, ug gu-

kg, ws y s s. S wk, y s v uv- v sgs v. S us y us k


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23

gg g sssy s sk g uvyg su. O us xs y su s.I s, , ug s s sg , y

ss w v k, v ug x su s s vy yug.

Tw ys , W Bk j Mw vg s x xsu gg v. Bu suu qus y s, ws - uvy-g kg. I s kg, s v ss gu-us ug x su u sy g g vy sks g s us j y gus, vs s, svgs, vsk. Aug s s su, us, -s, v x, u us su j y sw s suy s, sg sz j 1 0002 000

s s s suy CSP2 s, s w s ss sgs vg us Mw su vs.

Nw js v vg y s s. Ts js ss qu-y ss, us -v sg, uy su sk g s. Lgs vy gs g ss y vggxsg su syss, su s wks, s sus, wk sy gs, vs y vg -us g us s.

A y s g, gs A x su js u us s.Ts y, u js A v s uss s uyg su 2.I , js I v quky v g ss, g u 10 s y g w xsg, suss gv gs s 3.

At this level o scaling, it is becoming increasingly important to prevent large numbers o

armers rom being exposed to problematic insurance products. It is essential that a system

be developed in which armers themselves, who are the only ones who truly know their needs,

drive a multidisciplinary design process built on solid science and the real-world constraints o

insurance companies and development partners. or success, the poorest armers in the world

must interact with the researchers expanding the rontiers o the most advanced technologies.

2.3. S su

W ug s s ss x su, ky g s v us s s v k-x g gs.Is, w-- v s gu sg susss w

2 x: ://www.sygu.g/x.? gID=562; ://www.x.g/s/HARITA-quy--A-Ju-2011./_w/

3 ://.wk.g/x/u/?gPK=64165259&SPK=469372&PK=64165421&uPK=64166322&yID=000158349_20120305105832


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sss, s, s s . O, s-s s v kw s s u sy k , u us y uw e issues. In other cases, science products may have applications in which

they perorm very well, but these applications are not identied because the scientistsare ocused on an overly narrow insurance index correlation exercise and are not aware

o the set o options in the broader design problem involving insurance as a piece o a ull

package.

or example, because insurance typically provides payments at a rate o one in ve or ten

years, it is only the most severe years that need to be triggered or payouts. Tools that

may be inaccurate at quantiying yields in normal years might unction well when cong-

ured to identiy only the worst year out o ve.

It may be that the challenge is even simpler. In many cases, or the best production, thearmers challenge is to nd a portolio o crops that take advantage o as much time as

possible. In other words, the armer benets rom pushing the boundaries at the beginning

and end o the rainall season and is, thereore, highly vulnerable to the rainall season

starting late or ending early. Although not universal (e.g. in places with two rainall sea-

sons), it is common that all o the historical major loss years can be easily identied based

on the rainall ailing at the beginning or end o the season.

I the key role o the insurance is to help the armer take an expensive, but protable,

risk, the main reason the armer may be interested in insurance may be simply to provide

a meaningul payment to help them repay a loan in one o the years with an early or laterainall season ailure.

Thereore, technology may not be needed to measure precise yields, or exact rainall, but

instead ag a very obvious condition, such as the rainall season being too short. Ofen,

technologies work much more reliably on these kinds o tasks.

The challenge in this case is to nd out i the technology can play a useul role reliably. Sci-

ence does not necessarily have to provide the explicit trigger or the index, but instead pro-

vide the knowledge that the project is simpliying the problem correctly, that the, perhaps,

simple trigger being used does, in act, represent the key risk aced, and to bring the prod-

ucts up to a level o validation and reliability that can be quantied and communicated.

In some cases, a simple solution will be inadequate or inappropriate. It is important that

we use science and a dialogue with partners and armers to identiy the right tools or the

job, using sophisticated or simple solutions when appropriate.

The absence o comprehensive rainall and crop data remains a key constraint in scaling

insurance, since data is needed or index design and determining payouts. Index insurance

is not scalable i it only works in areas covered by existing rain gauges with long histories.

Many are hoping satellites could be the solution to this data poverty problem.


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25

However, satellite estimates are inaccurate and have many known ailings. They cannot

be applied blindly across all landscapes. or example, satellites ace dierent problems

interpreting a landscape with a lot o rocks, versus one covered by trees, or blanketed by

grasses, or with a lot o water. One side o a mountain range may be undamentally di-erent rom the other. Developing and validating the appropriate strategy or each loca-

tion is essential so that armers are not hurt by indexes based on bad data. This requires

a more sophisticated use o geospatial data than ever beore to link on-the-ground data,

experiences, needs, and concerns with advanced satellite and climate model inormation.

Both rainall and vegetation satellite indexes have been utilised in index insurance, with

mixed results. Keys to success tend to be validation o the products, and application o the

products in ways that they perorm well (e.g. the ailure o the end o the rainall season).

Ofen, it is not possible to accurately estimate rainall or crop yields. However, these same

tools are ofen reliable at agging that the there were no cold, heavy clouds over a villagewhen there should have been, indicating the rainall season ended early, or that the entire

landscape turned brown in response to the early end o the season.

Because satellite vegetation products are based on completely dierent inormation to

satellite rainall products, and have very dierent strengths and weaknesses, satellite

vegetation may be a good cross-check o how well satellite estimates o rainall catch the

bad years. However, initial diagnostics suggest there is much work to be done (4).

Many o the other abstracts in these proceedings will discuss exciting activity being un-

dertaken to address these issues. rom the IRI perspective, we have active projects withthe UN International Labour Organisation (ILO) and USAID (5) to move orward, and we are

building on our relationships with the JRC, NASA and other space agencies, and several

index insurance implementation groups to try to provide more robust solutions so that

projects can continue to be reliable as they expand. It is critical that multidisciplinary

eorts not only include implementers and social scientists, but also earth scientists in

a meaningul way.

One notable eort is the Google.org-unded work that the Ethiopian National Meteorology

Agency has done in partnership with Reading University and the IRI (6). Using their exper-

tise, knowledge o local rainall patterns, and weather station network, the Ethiopian NMA

has built a blended satellite/rain gauge gridded rainall product o unprecedented cover-

age and accuracy (7). The launch o this product has provided a very important source o

inormation or the R4/HARITA insurance eorts, which currently uses the US NOAA ARC

satellite rainall product to trigger payouts. Anyone online can access this advanced prod-

uct through the Ethiopian NMAs geospatial web server.

4 ://.u.u/us/=1002

5 ://www.suy.g//g/g/ -s-su---sy- ://.u.u//PjPss/EGAT_LYER_4_13.

6 ://.u.u/us/2012/__ _vg__svs__.

7 ://www.w./gs/us/u_/60_2_Tu_.


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Because o the high level o ground validation in the Ethiopian NMA product, it can make

estimates using satellites that have been in orbit or decades and are more accurate than

what has been done with the newest, most advanced satellites. This is extremely valu-

able or insurance because estimates using old satellites may be the only option to accessthe decades o uniorm historical data necessary or index design, pricing, and studying

climate trends. Ongoing eorts to bring this approach to other parts o Arica are likely to

yield valuable satellite rainall estimates or a wide range o projects.

In agricultural systems science, there are obvious connections to index insurance. Ofen,

a crop growth model can be directly applied as an index. However, it is essential that this

is done with care, as inappropriate models may have very little relationship to the losses

that the armer experiences (8), and some o the eatures o the models can ampliy meas-

urement noise dramatically, leading to expensive and inappropriate coverage (9). Ofen,

models require much more inormation than is available in an insurance application. Skil-ul application o models through meaningul interaction between scientists, implement-

ers and armers must be urther encouraged, so that they are validated, and assured to

lter noisy data and accurately target losses, as is described by other abstracts in this

proceedings.

Perhaps the most valuable role o agricultural systems science is in identiying the pro-

ductive opportunity blocked by risk that insurance might unlock, and providing insight and

validation or whatever index approach is under consideration.

The dismal science o economics has natural relevance in index insurance. However, ashas occurred with many other scientic disciplines, there has been a great deal o naive

application o economics, and it is very important to increase the sophistication o the link

between economics and index insurance. Much o the academic insurance discussion is di-

vorced rom the implementation experience. The economic discussion on armer demand

is conusing and must be more directly and coherently linked to policy and implementa-

tion issues. Much o the economics research ocuses on things that are easy to measure,

as opposed to important, in inorming projects. Economics, like agricultural systems, can

provide a great deal o wisdom on how to unlock productive opportunities, identiy com-

plementary risk management options, quantiy impacts, and provide insights on how to

oer the appropriate integrated package o tools.

Seasonal orecasting in climate science has an intimate link to index insurance. In a naive

insurance product, seasonal orecasts can undermine the nancial viability o the insur-

ance. or example, orecasts o drought can lead armers to buy insurance only when

there will be payouts, providing no premiums rom non-payout years to cover the payouts.

But, there is a synergy between orecasts and insurance that might provide benets

greater than either intervention alone. Crafed together, insurance may remove sucient

8 ://.u.u/~/IRI-CRMG-A-Isu-R-6-2007/IRI-CRMG-Ky-Tz-Mw-Isu-

R-6-2007.

9 ://.u.u/us/=875


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raming to design the insurance product , or that concretely builds insurance into an ad-

aptation program. Here, science has an important role to rebuild the index design and

packaging process to revolve around climate change eatures and the specic adaptation

issues and opportunities that exist.

3. COnCLuSIOnS

As x su s g ss, s sgy k u s s s su js. W us wys k su us us-u s s, v s gy g vy wu suy s s s u g w ug gu gu,

kwgg s. W us y w, w, u gy s suy us s su, k su sss, -su sks, s gu s ss.


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KEYWORDS: eakesses o iices, remote sesig, reptatio loss, traiig aaareess-raisig

SuMMARY

W Ix Isu WII guu s ugy . I g suw syss , x s s u-vss PPP. Psg s szs v xs w ys -s vg y w s s u w gvs.

A w su A U s u wy. T A Rsk Cy ARC s - s 15 us v . Hwv, u -ks, . A u k w WII s wkss -s; w v wu s xg WII us vg w. Cu s -x gy xg y u g xs, sy v y s us ssg us. Rsk su ysu k, w s ss y g wss-sg vs, s s sss, -su s, s w ss s-ks su, kg, s. I s ss skssy su y us s x su, s ws su v w su s sk u ss ys s. w, s ug WII v sg, w y vug u-u sks. Bu WII w k s w wy;w s s y s gy sussu su s s s.

3 Iex-base israce:

ieal soltio or agerosavetre Some reectiosThomas Loster

ChairmanMunich Re Foundation, Munich, [email protected]


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Internationalpartners


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KEYwORdS: eather, risk, agricltre, iex, israce

SuMMARY

Since the late 1990s, there has been a lot o discussion and debate about the promise and

potential uses o index-based agriculture insurance to manage weather risks in agriculture. The2005 World Bank publication Managing Agricultural Production Risk Innovations in Develop-

ing Countries set out, in some detail, the potential benets o index insurance and some early

examples o its application in developing countries. Since that time, a large amount o research

and piloting o the product has been undertaken both within and outside the World Bank, largely

targeting small armers. However, despite this experience and eort, there have been ew ex-

amples o successul scale-up at the armer level, unless achieved through policy or nancial

tools by governments and/or signicant nancial support rom donors. This note is based on the

recently released World Bank Discussion paper Weather Index Insurance or Agriculture: Guid-

ance or Development Practitioners, which is a summary o the ndings o almost 8 years o

research and development work into the topic.

1. InTROduCTIOn

Weather Index Insurance (WII) aims to protect armers against weather risks such as droughts and

oods. An index-based weather insurance policy links possible insurance payouts with the weather

requirements o the crop being insured: the insurer pays an indemnity whenever the realised value

o the weather index meets a specied threshold. Whereas payouts in traditional insurance pro-

grammes are pegged to actual crop damages, a armer insured under a weather index insurance

contract may receive a payout, or example, in the event o too little, or too much rain. In this case,rainall serves as a proxy or crop water need, which, in turn, is correlated with actual crop losses.

4 Practical lessos rom

implemetig eather iexisrace or agricltreCarlos Arce

Agricultural Risk Management TeamThe World [email protected]


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Some potential advantages o index insurance over traditional crop insurance include: the elimi-

nation o eld loss assessments; reduced inormation requirements and bureaucracy; greater

transparency that acilitates reinsurance; and lower costs and greater aordability or armers.

While insurance is generally the rst thing that comes to mind or most people when they think

o risk, it is important to understand that in agriculture, risk management mainly consists othree types o activity: mitigation, transer, and coping. Mitigation covers a number o tradition-

al activities. rom irrigation to hybrid seeds, rom vaccinations to pest control, many agricultural

activities are really the application o risk management approaches. Transer is the simple act

o passing ones nancial risk to a third party, who is prepared to accept it because they charge

a ee or premium or the service as, or example, in insurance and commodity price hedging.

Coping comes into play aer the act, when an individual takes physical or nancial measures

to deal with the impact o a risk once it is realised. Beore embarking on the use o a particular

product, it is imperative to identiy the risk and assess the potential application o one or a com-

bination o the above risk management activities, including insurance.

S 1990s, s suss u s uss x-s su guu vg us. T 2005W Bk u Managing Agricultural Production Risk Innovations in DevelopingCountries s u, s , s x su s yxs s vg us.

S , g u s g u s uk w us W Bk, gy gg s s. Hwv, ss x , v w xs sussu s-u v wu y s y gvs, sg

su s, . Ts s s y s Dsuss Aguu Ru Dv D gu 1.

Figure 1: Weather Index Insurance forAgriculture: Guidance for development

Practitioners, W Bk 2011


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2. LESSOnS LEARnEd

2.1. P-sy wk

Pre-easibility work is important, since data series may be incomplete or missing. The need or

accurate and reliable data or the establishment o the index creates major challenges in many

developing countries. or years, there has been underinvestment in meteorological services and

inrastructure in developing countries: as a result, data series are very oen simply not avail-

able. These data are necessary or the implementation o pilot programmes, and practitioners

should identiy whether these are available at the outset o the activity. In certain cases, there

may be alternative solutions, by, or example, constructing synthetic weather data by interpo-

lating eld and satellite-based observations to create a higher resolution data grid.

2.2. Implementation o index insurance schemes

The implementation o index insurance schemes is technically very challenging and there is

a lack o capacity in those countries where it would be most useul. There is currently a lack o

technical capacity in the insurance sectors o most developing countries, which is a constraint

to the scaling up and urther development o WII. While it is possible, on a pilot basis, to use

external consultants to design an index product and assist in roll-out, marketing, and sales,

such assistance is not possible on a wide scale, simply because o the lack o qualied proes-

sionals. It usually requires intricate mathematical modelling, data manipulation, and expertisein crop simulation (the study o how plants relate to environmental variables, including climate)

to design an index. Until such time as local capacity in these and other areas is developed, there

will be a heavy reliance on scarcely available international technical assistance. The techni-

cal expertise required also makes the product expensive and dicult to market to potential

policyholders.

2.3. armers actually want ull indemnity

The very nature o an index-based product creates the chance that an insured party may not

be paid when they suer loss. Index products do not oer exact indemnity: a armer can su-

er a loss and not receive a payout, either because the index was not triggered, or because the

loss was caused by a variable not covered by the index. This would be the case i, or example,

a armer had drought coverage and his crop was destroyed by a pest. Generally, armers want

indemnity rom loss, no matter what the cause.

It is possible to structure insurance with multiple indexes, but this increases the complexity o

the product and makes it dicult or armers to comprehend it. Basis risk is also a particular

problem or index products. It is requently caused by the act that measurements o a par-

ticularly variable, such as rain, may dier at the insurers measurement site and in the armers

eld. This also creates problems or insurance providers. Insurers ace serious reputationalproblems when armers pay premiums and suer losses not covered under the index. Insurers


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35

run the risk that such incidents negatively impact other products that they sell in the market.

Part o the reason the scaling up o index products has ailed is that both insurers and armers

suer rom this basis risk.

2.4. Practical challenges

There are practical challenges to product roll-out and the establishment o a sustainable pilot,

including the willingness o armers to pay. The roll-out o a product and its sale to potential

policyholders can be very time-consuming and complicated. In addition to all the technical chal-

lenges o establishing a weather index, it is not assured that armers will be willing or able to

pay premiums. Most armers in developing countries have extremely low disposable incomes

and a limited awareness o nancial products such as insurance. Given this, most are reluctant

to pay insurance premiums, as many do not monetise their crops especially i their govern-ment has a history o writing o debts or providing compensation. Add to this the complexity

o explaining how WII works and on what basis payouts are calculated and one can see that

demand is a real obstacle to scaling up.

While WII may not require local presence in respect to eld level assessments, it does require lo-

cal presence during product roll-out and sales. To ensure that armers understand the product,

extensive awareness activities and training are needed. The costs inherent in such a process

are prohibitive or most local insurance companies and, thereore, a major constraint to prod-

uct development. Likewise, banks and local partners may be hesitant to cooperate and it may

take a long time to market and sell the product to consumers in potentially remote armingcommunities.

2.5. Legal ambiguity

Legal ambiguity will continue to threaten the use o WII. It should be borne in mind that there

is a lack o clarity as to the regulatory and legal status o index-based products in nearly all

jurisdictions. Clearly, index products do not align with the traditional denition o insurance, as

they do not indemniy actual loss and a policyholder does not need to have an insurable interest

beore they purchase an index-based contract. Certain commentators have reerred to these

products as little more than gaming or lottery type activities. Without strict regulation, buyers

o these products will not have their interests protected by law.

3. COnCLuSIOnS

Ex w u s x, u s sg -s s vg us. T wg s w WII x-y us sw s.


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Large-scale commercial armig

or large-scale armers who have clearly identiable and insurable losses, and revenue streams

that enable them to pay premiums, WII can be an interesting option, especially where traditional

insurance is not available or too expensive.

Fiacig tool or social protectio schemes

WII may not be or the poorest o the poor, because o their inability to pay premiums or lack

o insurable interests, but the use o indexes at a district, regional, or national level can be

a useul tool to generate unds or social protection measures in the case o natural disasters.

Obviously, the issue o who will pay the premium and the establishment o distribution channels

are important parts o the product design.

Portolio risk maagemet or itermeiaries

Input suppliers, banks, and processors oen lend cash or product to a wide group o armersand are, thereore, exposed to the same production risks. The use o WII by such intermediary

stakeholders can be eective in reducing their exposure to certain risks. However, care should

be taken, as many risks that actually drive armer deault cannot be covered by WII (e.g. side

selling, price risk, and quality issues). In addition, there is a possibility that i a armer is aware

that their counterparty has insurance, the armer will be more likely to deault, even in cases

where deault is not due to a risk covered by the index.

Sovereig risk traser at the macro levelI a country is running a contingent risk that relates to a weather variable, the use o a WII

product can be extremely useul. Although at this level, the product is slightly dierent in nature a derivative, as opposed to insurance the general principles are the same. The payouts

received rom such a derivative could be used to stabilise budgetary shocks, purchase ood

or vulnerable populations, or nance social saety net programs. While this is a very attrac-

tive orm o risk management, countries oen cannot nance premiums, or they ace political

challenges in the use o public unds or nontangible and potentially risky premium payments.

Cotiget ace, as oppose to risk traser

Very similar to the previous application, the use o parametrics related to contingent nanc-

ing is another interesting use o the index model. However, in the case o contingent nancing,

a country aces slightly less risky premium payments, which are replaced by much smaller

commitment ees to access a line o credit, i needed. The main advantage or politicians is that

they do not have to pay relatively large amounts o money and potentially receive nothing in

return; with contingent nance, they have to pay back the credit used to compensate losses, but

only in the case o the risk being realised.

This paper describes lessons derived rom a number o agricultural index-based weather insur-

ance pilot activities by The World Bank and its partners. It should be noted, however, that the

eld continues to develop, evolve, and benet rom innovations in many technical areas, with

a number o new approaches being tested by other institutions. Weather index insurance aces

many challenges but i a number o obstacles are overcome, it might still hold some potential

or risk transer in the agricultural sector in developing countries.


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4. REFEREnCES And AddITIOnAL REAdInG

G, X. 2010, The Promise of Index Insurance, W Bk, Wsg, DC.

H, J., I, H., Sks, J. R., Syk, J. 2006, Risk Management in Agriculture for Natu-ral Hazards, ISMEA, R.

Hss, U. 2003, Innovative Financial Services for Rural India: Monsoon-Indexed Lending andInsurance for Smallholders, Aguu Ru Dv ARD Wkg P 9, W Bk, Wsg, DC.

IC 2009, Weather Index Insurance for Maize Production in Eastern Indonesia, I C, W Bk, Wsg, DC.

J, S., Sg, P., Aws, C. 2010, Rapid Agricultural Supply Chain Risk Assessment:A Conceptual Framework, W Bk, Wsg, DC.

Mu, O., Suy, C. J. 2010, Government Support to Agricultural Insurance, W Bk,Wsg, DC.

Mu, O., Sks, J. R. 2006, Piloting Index-Based Livestock Insurance in Mongolia, WBk s u w Jy Sks, 27 Juy 2011.

W Bk 2005,Managing Agricultural Production Risk Innovations in Developing

Countries, W Bk, Wsg, DC.

W Bk 2010,Jamaica: Towards a Strategy for Financial Weather Risk Management inAgriculture, W Bk, Wsg, DC.

W Bk 2010,Assessment of Innovative Approaches for Flood Risk Management andFinancing in Agriculture, Aguu Ru Dv Dsuss P 46, WBk, Wsg, DC.

W Bk 2011, Weather Index Insurance for Agriculture: Guidance for Development Prac-titioners, Aguu Ru Dv Dsuss P 5, W Bk, Wsg-, DC, 102 .


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KEYwORdS: iex Israce, risk maagemet, oo secrity, rral evelopmet

SuMMARY

H ws ugy v s s. Au 1.3 v ss y, 70 % s u s guu suvv. Ty v g s s y w , sg , g s, k ss. Rsk g s su s x- su -ss, ug su, v v v su ssg suy vy uy vs. Ts us ug-g s j u y IAD-WP W Rsk Mg y xs w ssg su v, sg u susy x su suy. Ous s w y - x su vus vs ug WP Ox A- R4Ru Rs Iv, gs g vv gukg svsk g v s sk u w svgs, sk s.

Iex israce a risk

maagemet or oo secritya rral evelopmet:Experiece rom IFAd a wFPEmily Coleman, Francesco Rispoli

International Fund or Agricultural Develop-ment (IFAD)

[email protected]; [email protected]

Niels Balzer, Richard Choularton

World Food Programme (WFP)[email protected]; [email protected]


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39

1. InTROduCTIOn

H ws ugy v s s. Au 1.3 v ss

y, 70 % s u s guu suvv. Ty v g s s y w , sg , g s, k ss. Ys yy w suss v. qu u - sss v xus us g s. Sg vss g-uu u, y gvs uv us, us, gv sus, v g vss svy g .

s u s, sks guu -g. Tssks g y sgy u s: 40 % -s vg us y w- s s W Bk,

2005. T s g x, g ys, x y g sk w sks y s us. Csvv js uss sg s v vs, u vy, u s, y 2050, g w s y 100 200 u -su ww Py, . 2009.

I g us xs, s svs s, u uss xy svv sk-kg sgs s y v w-g v g-squs su s vy, u, w u vs w xy.

I, sus v sw uss s u 30 % u yvsg w-sk, w-u vs Mu, 1995. T s v y g-u sk kg su s usg us v ss u -us g- v v. M- sus yy u s uss us y v svs w ys u. I vs s, s s vyg.

As su, uss y ss s-su gs sk,su s uy-s svgs g gus. Hwv, sks wvgs gs, su s ugs, quky vw s sgs. Tsuvv, uss s gv g sgs, su s ug su, vg s, sg uv sss ug s sss. W s sgs su s- suvv, y s uu y vsy v vs.


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2. wEATHER RISK MAnAGEMEnT FACILITY

2.1. Bkgu

T W Rsk Mg y WRM 1 s j v w w UNs gs, I u Aguu Dv IAD W Pg WP. Lu 2008 w su B M Gsu, WRM ws IADs x u WPs xs ss-sk u g. As su, s su v vv w sk g s v u vs u ug.

O wy WRM s s s y vg sug vv w sk g s, ug x su. Bug sss s s, WRM ky ss suss s uk, sus-y sy x su 2.

IAD WP w wkg yg ss ky ss ug vy ssy quy w . I s, WRMv u w s j us , w, ssg s x su

2.2. R ssg x su

Ivg W Rsk Mg Ws A: Evu R Ssg IxIsu s s j sg s sy x - s ssg gy u ss vu ugsk.

Cv w x su usg w ss y x su- usg y sy v su sg -ss, su s k w s vg qu y -. I y ss, s ss s sv u sg u su us x su.

R ssg s sg us v w su sus x , s s , gs g-s, y w us y vg s ss u y s. Ds s, s u vs sg vg su . T s g s j s, , s

1 ://www..g/u/w/

2 ://www..g/u/u/w.


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v sus x su us s s w ugs ug- s vg us. Ay sussuy -gs u xsg IAD WP gs su s R4 Ru Rs

Iv C 3.

T j w us Ws A s vuy ss w sk ss sg s x su, y s xsg ss k su gu-s w y , g w x , w guu ss. T j w, , ss s,s vu gs T 1, usg s s s WsA.

Table 1: Ivg W Rsk Mg: Evu R Ssg IxIsu Ws A Pj ss vs

Phases Mai activities

Phase 1: Detailed weather risk vulnerabilityassessment and mapping

One to our pilot areas selected or testing inPhase 3

Phase 2: Identication o remote sensingmethodologies or index insurance

Review o technologies, applications,constraints, opportunities; key players

Selection o up to our technologies to test

Phase 3: Development and testing omethodologies in pilot area(s)

Operation during cropping season

Ground evaluation

Phase 4: Overall assessment o results anddissemination

Relevance and application o remote sensingor index insurance

Integration o successul methodologies intoIFAD, WFP and wider programmes

By s s, vs s s j u s w x su, w u us usy. I sus s s, sus wu v sv s us su-S- A, uy S, w y s x su, v y k sg gss s . Aug y us s js ssg su x su, s x - w s gy : g s suy uvy;

g s s gs ug sks; sussg w -s; g gy WP IAD vs.


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3. R4 RuRAL RESILIEnCE InITIATIVE

3.1. Bkgu

T Ru Rs Iv R4 s j v U Ns W P-g WP Ox A Ox A u suss HARI-TA 3 y ug g s u us. T s vgsOx As xs ss sk u DRR, v s gg, s --wk js, s g, WPs y x w sy s, sss, s --wk, su.

T ss R4 s s sg gs gss. T WP,

ug s Sg P 200813, s vg sss, w s sss x WPs vs us g ug s,sgg ks, sg s - uvy ug s. Ox As sg s u y sv -gy ss uss vy, ug jus ug gs-s - usg y-ug sus v sus 4.

R4 vs w ug s, usg xsg gv-w uv sy s u ss sk s vy s x su- svs g v - k

gw. By wkg vs s, R4 ws su s vsy sk u w ks. Ts w su su s guu k vg us. I u, s w -sgy y su us w s v g su g w u s v . u s uu su y u v ss, -w guu -vs, gv gss, R4 uss g-, sus sus u vy ug y ug u sy.

sv, R4 us vs vu: w u u us v gs y su- u y s y u sk-u sus, s j, s u y ys su sk u suusy.

3 T H A Rsk Ts A HARITA s g sk-g wk v y OxA, R Sy Tgy s. HARITA s k w gu u sk g yg Es s s y su w w u. HARITA s sw sg sus sgw 200 uss vg 2009 v 13 000 uss 43 vgs 2011, y g

75 000 .

4 ://www.x.g/ssus/su


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43

3.2. R4 A

T R4 gg u sk gs u sk g sgs su- u uss s s u s, v suy v vs gu 1.

Risk rectio: Saety ets

T R4 s k u-s sy s, w v s xg wk uy us sss, w uy sk uvs sss gs sss v uvy.

W WP s wy kw vg sss vu us, ssks su ug sus g y vs . T WP vs su gv-u sy s u w. Sy s -su uss ss vs ss y vg , xg wk. I s R4, s wk s us u-g sks s y w vy, su s s w sv sk u s v.

I sk u vs, R4 s sk gs y uss g ssg ss ,

Figure 1: T u k R4 s

Risk Rectio

Risk Takig

Risk TraserRisk Reserves


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vsyg ug s vs v wy s us-s v suy: su sk s, svgs sk svs, s sk-kg.

Risk traser: Aorable israce

T R4 ws s s w su y ug u uy sk u js.

Large-scale weather-related shocks, such as drought, can oen be beyond the capacity o poor

households and their communities to manage. Through R4, cash-poor households can purchase

insurance through their own labour: insurance-or-work. When a drought or other weather dis-

aster occurs, insurance payouts are provided through existing saety net programmes, stream-

lining the process by leveraging established delivery channels: the cash or ood-or-work pay-ment mechanisms already in place. This graing o insurance on to existing mechanisms has

been identied as a best practice or scaling up insurance or poor rural households because it

provides necessary coverage and incentives or productive investments (Hazell, 2010).

Over the last decade, micro-insurance has increasingly been recognised as a key element in

helping the rural poor deal with risk. Aordable risk management products, such as agricultural

insurance, reduce the uncertainty and impact o weather-related disasters and the livelihood

threats that they pose. With well-designed micro-insurance products, compensation or weath-

er-related losses enables productive assets to be replaced and stimulates aster recovery. The

predictability o income can reduce negative risk coping strategies and stimulate rural house-holds to invest in activities and technologies that oer a higher rate o return (Skees, 2008).

B- uy s s uy us su, uy s, u. I Ox As HARITA xs E 2010, 27 % su s w us w s y uy s w su--wk s j .

T v su--wk s s s vss sy s w- sk s sg u R4v. Mv, R4 s y g g k su g us, xg ss svs s. I suss-u, g k u g wu w ss su , x ss svs.

Risk-takig

Bs s ss : y w k usks v vs ss xs sg sks.

Isu uk s, gvg s y vs ss, -

ss w gs s guu uvy. Assg vsg uv sss, su s vsk, s ss ug gu


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v y s--wk gs , s w-uss, y su yu. Bu s vss s vu sks, ug vy: R4 , s vuy s ss

ug sk u vs, svgs, su.

Risk reserves

T R4 s s ss s-s svgs u sk svs.

Svgs y w uss u sg s vsg vs, u s s u gs s- s s ussy w sks. Gu svgs u vu uss v u , g s s-su s uy. R4

w wk w s usg v s su us svgs, ug Ox As xsv x s .

Cu s s g 500 000 su vu uss v5 ys u us, ug E Sg, w g sk g- suy g w ug y xsg sy s su u uss.

R4 is recognised by key WP partners as one o the most promising models or addressing hun-

ger using an integrated risk management approach in line with the current G8 and G20 discus-

sions. USAID has, in act, already provided USD 8 million to WP to implement R4 in Senegal.

4. REFEREnCES And RESOuRCES

Hz, P., As, J., Bz, N., Hsu Cs, A., Hss, U., Rs, . 2010, ThePotential for Scale and Sustainability in Weather Index Insurance for Agriculture and Rural

Livelihoods, I u Aguu Dv W Pg.

L-By, J., M, R. , Suz, P., V, M. 2009, Dug Isu Suss- s Mw, Natural Hazards Observer, V. XXXIII, N 5, Nu Hzs C,Uvsy C.

M, J. 1995, I Sg Csu Sg,Journal of EconomicPerspectives, V. 9.

Py, M., Evs, A., Rsg, M. W., W, T. 2009, Climate Change and Hunger Re-sponding to the Challenge, W Pg, R, Iy.

Sks, J. R., C, B. 2008, The Potential of Weather Index Insurance for Spurring a Green

Revolution in Africa, s Py Sgy Cvg A G Rvu A AGRA, 2325 Ju 2008, N, Ky.


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W Bk 2005, Managing Agricultural Production Risk Innovations in DevelopingCountries, W Bk, Wsg, DC.

5. AddITIOnAL REAdInG And LInKS

Bs, M. V., Tss, M., Tk, T. 2010, Cuy-Bs Sus C Css E, C Cg I Dv: Mkg Dv CM Ev, JICA Rs Isu.

Hu, E. M., Osg, D. E., Hss, U., M, A. , Bjw, H. s 2009, Index insur-ance and climate risk: Prospects for development and disaster management, I

Rs Isu C Sy IRI, Cu Uvsy, Nw Yk, U Ss.

Tss, M., V, M. 2009, Estimating the Demand for Micro-Insurance in Ethiopia, OxA, ss y I Lu Ogs U NsC Dv u.

Ts, W., Ps, N., Gkss, A., Mu, K. 2008, Microinsurance DemandAssessment in Adi Ha, suy ss y Ox A.

Ps, N., Muy, C. 2009, Index insurance games in Adi Ha Tabia, Tigray Regional State,

E, suy ss y Ox A.

Ps, N. 2009, Livelihoods Coping and Microinsurance in Adi Ha, Tigray, Ethiopia.

Dku, T., G, A., Hs, J., Hus, E., Is, A., K, Y., Kusks, K., Ly, B.,Mjwz, M., Mu, M., Muy, C., N, M., Osg, D., Ps, N., Rs, A.,Sy, K., S, C., V, M. 2009, Designing Index-Based Weather Insurance for Farm-ers in Adi Ha, Ethiopia, Ox A, IRI T R 09-04.

:// www..g/u/u/w.

:// www..g/u/u/WII__gu.

://www..g/u/u/w.

:// www..g/u/w/

:// www.w.g/ss-sk-u

:// www.x.g/ssus/su


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47

KEYWORDS: agricltral israce, iex-base agricltral israce, iex isr-ace, eather iex israce, area-yiel israce, pblic-private partership, PPP,

remote sesig, crop groth moel

SUMMARY

Ix su s g s w s w. Mu gss s v x su, sy u sg. Nvss, g xs x su, sy vg s, v u. Ts s y u su ws sug u vs s, w u su wk vg ss s. T s su wk s u-v -s PPP w s, s, su usy kg s.u, w-gg s ws vs: v,-y x us s. Tg vs w -u y sssss, us g y-s su sus. T sss, s su us ss y sssss.

1. InTROduCTIOn

Ix su s g s w s w. T jv xsu s sg su v s gg w s yk ss , us g s svgs y-s su us.

6 Iex israce i

agricltre The (re)isrersperspectiveJoachim Herbold

Munich Reinsurance [email protected]


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Mu gss s v x su, sy u sgs T 1.

Table 1: Ix su us g

Proctio Type o iex Perils covere Applicatio

Crop Meteorological trigger One to two selected perilsonly

India (precipitation), Canada (heat

units in corn), pilot projects in several

developing economies (c. Hazell et

al., 2010)

Area yield trigger All climatic risks as

a package

India, USA

Vegetation index (remote sensing) Only indirect Not yet applied or arable crops

Multiple actors in crop growthmodel

Selected perils only Experimental stage

Grassland Meteorological trigger One to two selected perilsonly

Canada, USA

Vegetation index (remote sensing) Only indired Canada, USA, Spain

Livestock Based on measured livestock

mortality

Indirect all risks Mongolia

Based on vegetation index Indirect all risks Kenya (pilot)

Nvss, g xs y x su sv k sk s sus vg s v u.T u x su, s vy u u s -u, v v u w . I s , ss s v-s vsg u s w s. Ts ysss uk sv sk-k, su su svy.


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49

2. MISCOnCEPTIOnS COnCERnInG IndEx InSuRAnCE

2.1. T su s su u

I ss sgg su sus guu vgus, s v sk s k su u v s s. T su u x-s suu y-s su u sv k ss svs s g.

E guu su u s u suwk. T, s ss usu sys-v -

g w su u s . I us: sys s u .

T s sys s ws.

Istittioal rameork i the orm o a pblic-private partership (PPP)

T PPP s u s, s, su usy k-g s 1. T s u s s sg u g wk vg -g us s sss. T vv jsks guu su sys s ss.

Eective orgaisatioal set-p

s , s s usu v v. A -su s y sussu s g s kw-w v vgus.

Respose to heterogeeos strctres i the agricltre o evelopig cotries

T jv guu su sys s v susus s ss s, u g s.

Eciet marketig chaels

I k su s-v v su, u s su k w guu su.

1 gs PPP us sks, s H 2010 H 2012.


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2.2. Lw s ss x us?

T gu v s x us v w s

ss. A s k, wv, vs wg.

Dv ss susy g x us, s u sgs x vy y . u, gs us y x us.

Mkg ss y s ss s v -us g s. I us w, su ssu g g u.

Css ss jus sy w s xus, ss jus g ssy .

O ss g ss s v.

I us, s v x us vv w s ss.Hwv, y u s s ss u s sw qus.

2.3. W vs x su?

Ix su w x su u y v y ss,

v gs, s gss y s, su usy, kg s gvs. T v vs gg y j us.

3. OVERESTIMATEd POTEnTIAL OF wEATHERIndEx InSuRAnCE

I suss, wg sgs w-gg s vk.

Rvy w s w s 60% w gg y u-y vs: s u s ss sk g vu u sk su, sy sss u u, w y s, y s y.

W ss su v gg . Ty su - s wk. Hwv, s qus .

Ps u s us.


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As squ s sgs, w-gg s s u w . u, sg-u js ss gs , s-w su ss s v vy u.

4. wHERE dOES IndEx InSuRAnCE wORK And wHEREdOES IT nOT wORK?

4.1. v

u, s -y us w-ggs us y s u, ss sk s w ys ssss y s g ss s su s -. Ys s wg.

O g u : s s s wys, u s ys.

R sg u ys : s ss v su -gy sg y ssss .

Y ssss y -ssg gy gw s

uu S 5.1.

gss u, vg x su s ssg gy x su s.

A vvw s x-s su s gv T 2.

4.2. Ms v

Table 2: C s x su s us

Cotry Type o proctio

Iex proct Premim[millio ]

Liability[millio ]

Farmersisre[millio]

Area isre[millio ha]

India

(2009/10)

Crop

Crop

Area yield (NAIS)

Meteo trigger (WBCIS)

185

76

23.7

2.3

USA (2011) Crop

Grassland

Grassland

Area yield (GRP)

Meteo trigger (Precipit)

Vegetation index (NDVI)

333

77

4

3879

355

26

2.1

12.5

1.4

Spain (2010) Grassland Vegetation index (NDVI) 13 109

Sources: AIC 2010, IRDA 2010, RMA, Agsgu 2010.


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O s ggg v, x vg v, -y g ggs s, ug vgs -y ggs v S 4.1 y.

A xy v u suu u gg usg u .g. g 20 % -y gg.

T vg s-v s ss sk s . Hwv, w su y g vu s, xs v s, s s sv. I s us s ssu ss su g.

5. OuTLOOK On TECHnOLOGICAL AdVAnCES

Tg vs w y-s su sus guus, u v v ss ssss.

A gs w y ssss ss w v u gss s . x, ssg gy s v u v gw s v u y ssss-s. Hwv, sus wys v ss-k gs gu

u y . I uu, w ss su u y -g vs vss s.

5.1. R ssg

As ssg u: , y ss, ssss g ss vs vg sus, su us .g. s vg- s ys.

T gs uu s ws.

Ieticatio o crop type

I x s w ss uu s, ss u s.

Assessig yiels

I uu, y ss ssss g ys s, ss u s w su uy v uv suu s gus,w susqu -y us.

I w u u ssss vu ys. T uy vs su g qu suusy g s


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su. T, vy sg s gy s squ. As sz s ssss w u.

5.2. C gw s

u gss y ssss v usg ssg vg- sus s u s gw s. Rs su sus wg ss.

Csg y vy s squ x w vs: us u ug ys v x w zsug.

Dvg y s w w u s . Dg vy w g ys sss s-

s/vu ys. Ts u vy g gwg s.

5.3. Au g y

Au g vs y wy us gy s vss v us. Nvss, u vs ssy,y wg s: - u-ss g,

s, uy

6. COnCLuSIOnS

Ix su us k guu su us su wk: sys gs u .

Aguu su syss s u-v ss w uuv.

T w-gg s s vs: v, s -y us.

Pu s s v v v w s-u s vu s.

Nw u gy w vs y-s suus ss ssss. Rs su us ss y sssss.


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7. REFEREnCES

AIC Aguu Isu Cy I L 2010, Annual Report 20092010,

Nw D.

IRDA (Insurance Regulatory and Development Authority India) (2010),Annual Report 200910,

Hyderabad.

Agsgu 2010, Informe annual 2010, M, 144 .

Hz, P., As, J., Bz, N., Hsu Cs, A., Hss, U., Rs, . 2010, ThePotential for Scale and Sustainability in Weather Index Insurance for Agriculture and Rural

Livelihoods, I u Aguu Dv W Pg,

R, Iy.

H, J. 2010, C su vg s sus sussv, Rural 21, Vu 44 N 4/2010, . 1418.

H, J. 2012, Nw s guu su vg s, Fi-nance for food, Ks Wuu KW ., Ds K, Sg Pusg,Hg.

RMA Rsk Mg Agy ://www3..us.gv/s/s/s..


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55

KEYWORDS: agricltre, oo secrity, ie israce, risk traser, emergig markets

SuMMARY

As part o risk mitigation strategies, nancial risk transer solutions, or example crop insurance,can help to secure armers income and reduce their livelihood risk in case o crop ailure. In

recent years, index-based agricultural risk transer products have become increasingly popular

in emerging markets, having the relative advantages o ast loss settlement as well as cost-

eective and simple administration compared to indemnity-based products. However, with the

exception o India, the markets or such index-based risk transer schemes are still too small to

be commercially sustainable. The major challenges or urther market development and scaling

up o index-based risk transer solutions in emerging markets are briey highlighted here. These

challenges can be grouped into three areas: (i) data availability and data quality; (ii) basis risk

and simplicity o payout denitions; and (iii) eective and ecient distribution.

1. InTROduCTIOn

Aguu s ky sy y s vg us w sg- u g suss g. Csquy, suy s j w u vg vsg sw s y, s y x H A.

Avg suy qus s g guu vu , u-

g ss us, sg, s, guu xs svs . Iu, ss u suu s s sy k w ss

7 Challeges i strctrig a

pricig iex-base risk traserprocts i emergig markets:a reisrers perspectiveMarcel Kttel

Swiss Re Corporate [email protected]


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qu sk g. Aguu sk s sus, x su, g s sk g sgs vu u v sks.

W y-s us g guu su k,y vus gs gg ks ug k quy ss juss, g sv ss, z. Ts ssus gy v y x-s sus w yu s s u xs su y s.

T s y us s s w s su s y sss. Hwv, w, y-s ss, ss juss vs s s s vu sss, s x-s ss

s y -g yu us k s -s ss su s s quy. T sug w u sss yus x-s us s kw s ss sk s s js sussu sg u x-s sk s ss.

I su , ug, y-s us, y s s sssk s yus vu ss juss ssss ssys y sg s, v ug, ug g x, ss sk su ss s s .

W x-s us w kw v ks sug -s sks, y y y gg ks. A yss j gs uy v x-s sk ssus s, , w s w u s y sus s.

Ts vw j gs us x-s ss ggks s s us x vg su s- 90 % x-s su ss su-S A.

2. CHALLEnGES TO SCALInG uP IndEx-BASEdRISK TRAnSFER PROduCTS

Gy, j gs uy x-s sk s sus gg ks gu s: , ss sk, su.


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2.1. D

Considering that the payout is not based on the armers actual yield but rather on a proxy

index, the data used to construct such an index are o critical importance. As described ear-lier, the most common data types are weather and (area aggregated) yield data. Independent

o the type, market standards as established by risk-takers operating mainly in/out o OEDC

countries (Table 1, top row) require data to be o sucient length (2030 years or weather,

1520 years or yield data), quality (ew gaps, homogeneous), and availability (costs, timeli-

ness or loss settlement). The reality in emerging markets is oen very dierent rom these

standards (Table 1, bottom row) making the structuring and pricing o index-based products

very challenging or even impossible: weather data are oen only available or 1015 years

(even less or yield data) and there are requently gaps, a lack o inormation about data his-

tory/quality and possible inhomogeneities, or example due to changes in the location, instru-

mentation or surroundings. urthermore, as long historical weather time series are oen onlyavailable rom locations remote rom the agricultural production areas, new weather stations

are occasionally installed closer to the armers elds and proposed to be used or settlement.

This has, however, the disadvantage that the weather regime o this new site is largely un-

known, thus making the accurate structuring and pricing very dicult or even impossible tasks.

or yield data, common challenges, though oen not limited to emerging markets only, are the

non-standardised data collection process and the large inter-annual changes in yield due to,

or example the use o ertiliser and other inputs. This makes it very dicult to determine the

expected yield which is to be used as reerence to assess the armers production shortall.

There exist, however, opportunities to, at least partly, overcome these data issues: or example,the oen sparse and short data time series rom ground weather stations can be complemented

by gridded inormation rom, or example, satellites. Such gridded data could represent a major

step orward as they oen provide high resolution in both space and time, and generally cover

a suciently long time such that they can be used in index-based risk transer products. Recent

projects such as, or example, HARITA in Ethiopia have successully used gridded weather data.

To urther explore the potential o gridded data collaboration between research institutions,

product developers, and risk takers should be intensied.

Table 1: Mk ss ss y sk-ks OECD us

data Basis Risk distribtio

OECD-marketstandard

20-30 years No gaps Homogeneous Near real-time

Validated

Minimized

Understood by all stakeholders

Distribution channels dedicated to insurance

(e. g. retail insurance agents)

No selling past inception of risk calculation

period

Good time management

Emergingmarketreality

10-I 5 years

Lots of gaps

Inhomogeneous

Settlement weather

station distant rom

historical weather station

Not validated

Minimized based on data from

similar regions

Not understood by all

stakeholders

Distribution through non-insurance channels

Selling past inception date of risk

calculation period

Sub-optimal time management as products

are new and/or sales period dictated by

marketing o bundled product


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2.2. Bss sk

W x-s sk s us v vg wg jv,

s, s ss, y v ss sk. Gy,ss sk s s g ssu w y-s s w sus ss sks y g sgus w s: x-ss sk, w s u s y sss us y y s su y x; ss sk, w ss s w u s s u gwg ss; s sssk, w s gg su x u.

T s quy s ss sk, s y . Assu sss v, x s s s w

w y gs; , ss sk su ss s w us T 1.

A g s xy x s: s ss sk, y s us sg s vus gwg ss,s w v s s u w, us kg -sgy u sks, ug su s ,s y, s, us x.

I v s ss sk ssus, u vs su sv su

g v w s s ys sg x s y sy su u s .

2.3. Dsu

Gv usuy s u , s su u s- wy ggg sks su y s sus. B s-u ggg y sus , jusy ss su sss, js su s.

Ts s j g u s w suu. Csquy,y xsg u wks, x M Isus MIs guuu vs, us su x-s sk s us y u-g w sus w us su s ss. W w su s s ss s, s us - gs, x w, u sus, ss x-s sk s us g x u su ssy usg vs s T 1. u, s y sk-s s, vs, u vs, sus, sus vv x-s sk s ss, sss s s y s

u s u gwg ss.

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