Earth
observation
for
drought
risk
financing
in
pastoral
systems
of
sub-Saharan
Africa
Francesco
Fava
1and
Anton
Vrieling
2Asclimate-relatedcrisesincreaseglobally,climaterisk
financingisbecominganintegralpartoffinancialprotection
andresiliencebuildingstrategiesofAfricancountries.
Drought-inducedcrisesresultindevastatinghumanimpactsandhigh
costsforvulnerablecountries,threateninglonger-term
investmentsanddevelopmentefforts.Whileearthobservation
(EO)hasbeenwidelyusedfordroughtearlywarning,new
opportunitiesemergefromintegratingEOdataandmethods
intoindex-baseddroughtriskfinancing(IBDRF)instruments.
Suchinstrumentsaimatsupportinganeffectiveandtimely
responseduringdroughtshocksandimprovingtheresilience
ofsmall-holderfarmersandlivestockkeepers.Thisreview
documentsthecurrentstatus,anddiscussesfutureprospects
andpotentialchallengesforEOutilizationinIBDRF
applicationsinsub-SaharanAfrica.Wefocusonpastoral
systems,whicharehotspotsintermsofvulnerabilitytoclimate
andenvironmentalchange,foodinsecurity,poverty,and
conflicts.Inthesesystems,EO-basedIBDRFinterventionsare
rapidlyscalingupaspartofnationalandinternationalrisk
managementstrategies.
Addresses
1InternationalLivestockResearchInstitute(ILRI),P.O.Box30709,
Nairobi00100,Kenya
2UniversityofTwente,FacultyofGeo-informationScienceandEarth
Observation,P.O.Box217,7500AEEnschede,TheNetherlands
Correspondingauthor:Fava,Francesco(f.fava@cgiar.org)
CurrentOpinioninEnvironmentalSustainability2020,48:44–52 ThisreviewcomesfromathemedissueonThedryland social-eco-logicalsystemsinchangingenvironments
EditedbyBojieFu,MarkStaffordSmithandChaoFu
Received:01June2020;Accepted:14September2020
https://doi.org/10.1016/j.cosust.2020.09.006
1877-3435/ã2020TheAuthor(s).PublishedbyElsevierB.V.Thisisan openaccessarticleundertheCCBYlicense(http://creativecommons. org/licenses/by/4.0/).
Introduction
Crisis risk financing refers to mechanisms that aim at reducingadversesocio-economicorecologicalimpactsof potentialcrises[1].Thiscanincludepaying toprevent
and reduce therisk, or to prepare for and respond to a shock. Climate risk financing (CRF) targets climate-related shocks (e.g. drought, floods and heat waves) andisbecominganintegralpartofclimaterisk manage-mentframeworksaskeycomponentsoffinancial protec-tionstrategicplanningforlowandmiddleincome coun-tries [2]. Multiple CRF approaches exist, including market-basedinstruments(e.g.insuranceschemes, cata-strophic bonds and swaps), contingent financing (e.g. credit), or budgetary tools (i.e. dedicated reservefunds or contingency budgets). These approaches are all designedtoincreasefinancialresiliencetoclimate-related crises,linkingtheresponseactionstopre-defined mech-anisms for timely release of financial resources. In this way,theyaimatensuringrapidandcost-effective prepa-ration,assistance,recoveryor reconstructionefforts. Droughts,definedasperiodswithwaterdeficitrelativeto normalconditions,areoneofthemostdisruptingnatural disasters, each year affecting millions of people world-wide with devastating impacts [3,4]. Severe droughts cause massive disruptions to national economies and dramaticimpactson thelivelihoodand foodsecurity of small-holder farmers and livestock keepers. Standard responsestodroughtinAfricancountries,suchas human-itariansupportin theformofcash or foodtransfers,are important instruments to support drought-affected vul-nerable populations. However, these responses have proventobeoftentooslow,cost-ineffective,andtofoster dependencyrather than resilience, especiallywhennot integratedintoholisticrisk managementstrategies[2]. Among the different CRF instruments, index-based approacheshavegainedconsiderabletractionoverthelast two decades, particularly for targeting drought shock impactsonAfricansmall-holderfarmingsystems. Index-baseddroughtriskfinancing(IBDRF)usestrigger mecha-nismsthatrelyonatransparentandobjectivelymeasured indicatorofdrought(i.e.theindex).Theunderlyingindex mustbehighlycorrelatedwithdrought-relatedeconomic lossestobeusefulintracking,and,therefore,transferring, therisk.Payoutsaremadewhentheindexvaluesfallbelow a pre-defined threshold, normally derived from historic indexrealizations.Whencomparedtoex-postloss verifica-tion(e.g.traditionalinsurance),IBDRFlimitsinformation asymmetry issues, such as adverse selection and moral hazards,3reduces transaction and verification costs, and
3Thisterminologyoriginatesfrominsuranceliterature:adverseselectionoccurswhenpotentialpolicyholdersmakedecisionsbasedoninformation
abouttheirriskexposurethatisnotavailabletotheinsuranceprovider.Moralhazardoccurswhenthepolicyholdersengageinactivitiesthatincrease theirexposuretorisk,leavingtheinsurerexposedtohigherriskthanhadbeenassessedforpremiumratedetermination.
enablesmoreeffectiveandtimelydistributionofpayouts. However,theseadvantagescomeatthecostofthe inher-entlyimperfectcorrelationbetweenindexandactualloss, alsoknownasbasisrisk[5].
Pastoral production systems dominate the African dry-lands,coverabout43%ofAfrica’slandmass,andarethe main livelihood for about 268 million people in these areas[6].Droughtis adistinctivefeatureand over mil-leniathepastorallivelihooddevelopedto dealwith this [7]. However, particularly in SSA, the combination of increasingrainfallvariability[8]andotherpressures(e.g. changes in land use and tenure, population growth, rangeland degradation [9]) is weakening the resilience of pastoral communities and the effectiveness of tradi-tional drought coping mechanisms (e.g. mobility, re-stocking).Severedroughtscanleadtocatastrophicherd lossesinpastoralregions,andassuchcausefood insecu-rityandpovertytrapdynamics,anddramaticallyreduce national GDPsof SSA countries[5].Consequently, the development and implementationof drought risk man-agementstrategiesforpastoralregions,includingIBDRF initiatives, isakey component of thepolicyagendasof developmentinstitutions andnationalgovernments. Thescarcityandpoorqualityofgrounddataandnational statistics in SSA pastoralregions[10] hasmadein most casesEarthobservation(EO)bysatellitestheonlyviable optionfor designing IBDRFinstruments for large-scale implementation. Thegrowingavailability andincreased qualityoflong-termEOdatasetsofrainfallproductsand vegetation indices [11] have been instrumental in designing indicesthat are thought to reliably represent drought risks. Furthermore, the close interlinkage betweenrangelandconditionandimpactsonproductive assets (i.e. livestock)and livelihoods has facilitated the designoffinancialtriggeringmechanismsthatlinkimpact to payouts.Therefore,datasetsobtainedthrough multi-temporalsatelliteimageryarecurrentlyakeycomponent of IBDRFinitiativesin pastoralregions.
However,whilstthescientificliteratureondrought mon-itoringisvast,onlyafewstudiesreviewedthe contribu-tion ofEO toinsurance[12,13],andcurrentlyno over-view exists of the IBDRF initiatives, challenges, and prospectsin sub-SaharanAfrica.
Evolution
of
IBDRF
in
pastoral
Africa
During the last decade IBDRF initiatives in African pastoraldrylandshavegainedsignificantmomentum,also thankstolargeinternationalinitiativessuchasthe InsuR-esilience Investment Fund [14] and the Global Index-InsuranceFacility(GIIF).WhileforseveralyearsIBDRF schemes have largely remained at the pilot level, con-strainedbythelimiteddemandforretailmicro-insurance products[15,16],theseschemesarenowgradually cover-ing larger areas, given their growing integration in
country-widesocialprotectionanddroughtrisk manage-ment programs. This caused a significant increase in volumeoffinancialtransactionsandagrowing participa-tionofinternationalre-insurancecompanies,which facil-itatesrisktransfertowardinternationalfinancialmarkets, and promotes larger investments by governments and internationalorganizations.
Table 1 summarizes operational IBDRF initiatives in
SSA.. These initiatives, initially launched as retail micro-level index-insurance schemes (IBLI), more recently have expanded their scope and modality of implementation, including fully subsidized insurance programstargetingvulnerablepastoralists(KLIP,SIIPE), sovereign-levelinsuranceschemes(ARC),andscalability mechanisms of shock responsive safety nets programs (HSNP, NUSAF). At the same time, IBRDF product design evolved from indices designed to assess an observedloss(i.e.theIBLIlivestockmortalityscheme), thustriggeringwhendroughtisalreadyimpacting pasto-ralistassets,toindicesdesignedtoidentifydeteriorating forage condition, thus triggering at the earlier drought stages,withtheaimofsupportingpastoraliststo protect their assets (i.e. livestock or livelihood) or countries implementingearlyresponseactions.
While fully operational initiatives in pastoral areas are concentratedlargelyinEastAfricaand,toalesserextent, in the Sahel, several insurance providers and organiza-tions arelaunching similar IBDRFsolutions on aretail basisacrossSSA, includinginNiger, ZambiaandSouth Africa.Inaddition,feasibilitystudiesarebeingconducted in Somalia,Senegal,Burkina Faso,andMali. Thus les-sons gatheredfrom early implementation efforts are of keyrelevancefor sustainablescaling ofIBDRFin SSA.
EO-based
index
design
in
operational
IBDRF
schemes
Similartomostdroughtearlywarningsystems[e.g.Refs.
17,18,19],existingIBDRFschemesforpastoralsystems predominantlyusevegetationindicesorrainfallestimates (RFE) as input (Table 1). These EO products should meet the fundamental operational requirements for IBDRF,whichinclude 1)fulltransparencyand accessi-bilityofthesourcedata,2)availabilityofhistoricalrecords forfinancialriskmodellingandpricingofideally20years ormore,3)nearreal-timeavailability,and4)anexpected remaining lifetime of at least a few years. Satellite-derivedRFEcanprovideadirectindicationof meteoro-logicaldrought,whichlargelydetermineswater availabil-ityin rangelands.While availabilityof drinkingwateris important for livestock health, monitoringthis for large areasiscumbersome,giventhatmuchwaterforlivestock comesfromwellsorsmallwaterbodiesthatcannoteasily bemonitoredwithEO data. RFEsare insteadtypically usedinIBDRFtoestimatetheavailablewaterfor vege-tation, for exampleusing simple water-balance models.
Thewater requirementsatisfactionindex (WRSI)used byARCissuchamodel;itestimatesevapotranspiration demandsofvegetationandcomparesthiswithasimple ‘bucket’ model of the soil that gets filled with rainfall [20,21].Twomaindrawbacksexistwhenusing rainfall-basedindicesforlarge-scalepastoralIBDRF:1)despite thegrowingavailabilityofgriddedrainfallproducts,the quality,accessibility,anddensityofrainfallstationdata in pastoral SSA is generally low [22,23], resulting in unknown orpoor accuraciesin these areas [24]; 2)the link between rainfall and the vegetation’s water avail-abilityiscomplex,anddependsonvegetation character-istics,soil,andrainfalldistribution[25,26],whichcannot becharacterizedsufficiently with10-daily rainfallsums [27].
For extensive pastoral systems, forage availability is a keydeterminantoflivestock health,asalternative feed resources are largely unavailable or inaccessible. To overcome drawbacks of RFEs, optical sensors onboard satellitescanbeusedtomeasurethevegetation’s reflec-tance. Dense healthy vegetation reflects much in near infrared(NIR),andlittleinredwavelengths,and spec-tral vegetation indices, like the normalized difference
vegetationindex(NDVI),usethistomonitorvegetation condition. Fordroughtmonitoring, NDVI imagesfrom coarse-resolutionsatellites(250mandup)aregenerally used,because1)longtimeseriesexisttodescribe long-termvariability inforageconditions,2)their dailydata collections allow for more cloud-free observations to describe vegetation changes throughout the season, and3)documentedevidenceexistsforastrong relation-ship between rangeland biomass and NDVI [28,29]. Requiredpre-processingstepstoeffectivelyuseNDVI for anomaly analysis include temporal compositing [transforming daily to, e.g. 10-day data, keeping the best-qualityobservationforeachpixel;30],and smooth-ingtofurtherreduceatmosphericeffects [31].
TotransformNDVIoralternativedroughtindicesintoa usefulindexforpastoralIBDRFschemes,threestepsare required:
1) Spatial aggregation; geographic units are normally larger than grid cells, both for operational reasons and to reflect that herds move. Aggregation within units generally incorporates amask of where range-landsoccur.
Table1
SummaryofoperationalIBDRFprogramstargetingpastoraldrylands,orderedchronologicallywithinsuranceprogramslistedfirstand non-insuranceprogramsatthebottom
Programa Years Scope Satelliteb
sensor/indicator
Indexc Area Households(nr)
IBLI 2010 2015 Retail micro-insurancefor droughtrelated livestockmortality
AVHRRNDVI Livestockmortality Marsabit(Kenya), Borana(Ethiopia)
5983
IBLI 2015–present Retail micro-insurancefor assetprotection
MODISNDVI z-scoreseasonalNDVI NorthernKenya andBorana (Ethiopia)
7000
KLIP 2015–present Subsidized insurancefor assetprotection
MODISNDVI z-scoreseasonalNDVI Northernand EasternKenya
18000
SIIPE 2017-present Subsidized insurancefor assetprotection
MODISNDVI z-scoreseasonalNDVI Somaliregion (Ethiopia)
5000
ARC 2017-present Sovereignlevel insurance
RFE–multiple datasets
WRSI(RFEbased) EastAfricaand Sahel
N/ad
ARC 2019-present Sovereignlevel insurance
MODISNDVI z-scoreNDVIorVCI EastAfricaand Sahel
N/ad
HSNP 2015-present Socialprotection scalability mechamism
MODISNDVI VCIrunningaverage NorthernKenya >100000
NUSAF 2017-present Socialprotection scalability mechanism
MODISNDVI NDVIpercentanomaly Karamoja (Uganda)
25000
a
IBLI=Index-basedLivestockInsurance,KLIP=KenyanLivestockInsuranceProgram,SIIPE=SatelliteIndex-InsuranceforPastoralistsinEthiopia, ARC=AfricanRiskCapacity,HSNP=HungerSafetyNetProgram,NUSAF=NorthernUgandaSocialActionFund.
bAVHRR=AdvancedVeryHighResolutionRadiometer.MODIS=ModerateResolutionImagingSpectroradiometer,NDVI=NormalizedDifference
VegetationIndex,RFE=RainfallEstimates(usingsatellitedata).
cZ-scoreissometimesreferredtoasstandardscore,WRSI=WaterRequirementSatisfactionIndex,VCI=VegetationConditionIndex. d
AsARCisasovereign-levelinsurancescheme,therecipientofthepayoutisthecountryandthenumberofhouseholdscoveredwilldependonthe country’scontingencyplans.ARCisofferinginsurancecoverforrangelandsintheentireSahelandHornofAfricaandplanstoofferitalsoinCentral andSouthernAfrica.TheNDVIproducthasbeenlaunchedinChad,Niger,Mali,Mauritania,andKenya.
2) Temporal aggregation; most schemes aim to assess seasonal forage scarcity, requiring expert or EO-derived [32] knowledge on rainfall/vegetation seasonality.
3) Normalization to compare the current index value againsthistoricindexrealizationsinpastyears.Multiple options exist, suchas forexamplez-scoring (subtract meananddividebystandarddeviation),linearscaling between minimum and maximum historic values [i.e. the vegetation condition index, or VCI; 33], or percentilecalculation.
Theterm‘indexdesign’inIBDRFrefersto:a)thechoice ofinputdata,b)theprecisemethodsforperformingthe above-mentionedthreesteps,c)thesequencingofthese steps, and d) the testing of the resulting index against drought-relatedlossestimates.Eventually,thechoicefor a design will at least partly depend on the IBDRF instrument’spurpose,satisfactionofstakeholderson his-toric and current index readings, and (scientific) back-groundof the‘designers’.Giventhatdroughtsnormally affectlargeregionsandaresimultaneouslycharacterized byreducedprecipitationandvegetationgrowth,itiswell possible that alternative designs provide similar index outcomesduringmaindroughtevents.
Opportunities
and
challenges
for
EO
contribution
to
IBDRF
RecenttargetedresearcheffortsforEOsupportto inno-vativedesignofIBRDFsolutions,madeintheframework of the programs listed in Table 1, helped to remove criticalbarriers for scalingIBDRFinitiatives in pastoral drylands. Here, we discuss four emerging trends in IBDRFwherethedemandforinnovativesolutionsfrom EOresearchisstrong.Whilewearguethatthequalityof IBDRF product design differsand thatEO-aided solu-tions canplayanimportant role to improve operational programs, we also emphasize that each solution is con-text-specificandneedstoconsiderthetrade-offbetween a)timingand accuracyof theassessment, b) cost-effec-tivenessoftheresponseaction,andc)thecomplexityof the interaction between drought shocks and the socio-ecological pastoral systems (Figure 1) with the goal of mitigatingimpactsandspeeding-uprecoveryintheshort term,whilebuildinglong-termsocialandenvironmental resilience.
EmergingEOdataproductsforIBDRF
EO data products used for IBDRF index design in pastoral areas have evolved over time in response to stakeholder feedbacksand with emergingtechnologies. For example,theARC productforextensiverangelands hasrecentlytransitionedfromWRSItoNDVI(Table1), meeting the demand of key stakeholders. However, besidesvegetationindex(e.g.NDVI)productsthat pro-videadirectmeasureofforagestatus,drought character-istics can be obtained, for example, from EO-derived
precipitation[35],soilmoisture[36,37],or evapotranspi-ration[38]products[forreviewsonEOdrought monitor-ing options see also Refs. 11,39]. A variety of these productshavealreadybeen proposedandinsomecases integratedintodroughtindex-insurancepilotprojectsfor crops inAfrica,for example,throughdataservice provi-sion by commercial EO companies [e.g. Refs. 40–43]. Theseeffortsprovidepromisingalternativedrought indi-cesforinnovativeIBDRFdesignbyaddressingmultiple phases of drought progression and thus merit further analysis.
IBDRFdesigncouldalsotakeadvantageofthe continu-ous streamof free10 30m resolutionEO datathatare providedbysatellitessuchasSentinel-1,Sentinel-2,and Landsat-8. Given their high observation frequency, timelyfine-scaleestimatesofseasonalityandforage con-ditionscanbeprovided[44],evenifcloudcoverremainsa concernfor short vegetationseasons [45].Arguably pas-toral IBDRF may not require fine-scale data because droughtsgenerallyaffectlargeareas, butwheredrought impactsdifferduetogreaterlandscapevariability(e.g.in agro-pastoral systems) they could prove beneficial. As historical archivesof 10 30mresolution dataare build-ing, and tools to analyze resulting large data amounts become commonplace [46], finer-scale droughtanalysis will likelyfinditswayintoIBDRF.
Remotesensingadvancesforimprovedproductdesign
Thespatialcomponentofbasisrisk(i.e.impactsarenot equallydistributedwithinageographicinsuranceunit)is arecurrentissueforIBDRFproductsinpastoralareas,as administrativeunitscannotfullyreflectthe agro-ecologi-cal variability and herd mobility patterns. This type of basisriskhasbeenreportedfor someinsuranceunitsin Kenya and Ethiopia, especially at the fringe between agro-pastoral and extensive pastoral systems. To deal with this issue, administrative units could be replaced by more meaningful ecological units, for example, defined based on similar temporal behavior of NDVI [47]. In addition, high resolution EO data can help to improve rangeland mapping and characterization [48], allowing to better spatially focus coarser-resolution droughtindicesontheareaswithininsuranceunits that mattermostforforageproduction.
Payouttimingandtemporalaggregationisanothercritical componentofproductdesign.Triggeringearlyactionfor expectedadverseeventscanmakedisasterresponsemore cost-effective [49,61]. Because drought is aslow-onset shock,indicatorscanbedesignedthatdetectearlystages of thedrought progression[11].EO has supportedthe anticipationoftheresponsebyeffectivelycharacterizing between-yearvariabilityofforageconditionsearlierinthe season through shortening temporal aggregation win-dows, with the aim of designing assetprotection insur-ance products[49,62,63].Thishasbeenafundamental
step for the IBLI and KLIP programs to increase the uptakeandgainpoliticalsupporttowardgeographic scal-ing.However,indicesthatallowforearlytriggeringmust alsobebackedbyefficient payoutdeliverymechanisms toavoidnegative impactsonprogram sustainability. Morerecently,forecastingapproachesbasedontimeseries analysisandmachinelearningtechniqueshavebeen devel-opedtopredicttime-integrated(seasonal)NDVIanomalies fromlagged NDVI,rainfall,andclimateindices[64–68]. Whileresearch ondroughtforecasting modelsisgaining momentum, particularly for early warning systems (e.g. HSNP, Table 1), the implementation of forecasts into IBDRFinstruments is challenginggiventheforecasts’ large uncertaintyatlocalscalesandlongertimelags[69].Using forecastsasatriggeringindexcouldthusincreasebasisrisk andleadtooperationalimplementationchallenges(e.g.in caseoffalsealarmandunneededresponse)[61,70]. More-over,decidingonthresholdsforresponsetriggeringcould becomplexbecausethisrelatesnotonlytothelevelofthe impact, but also to itsprobability. A differentchallenge emerges when forecastsare not directly used but could nonethelessaffectIBDRFinstrumentssuchasinsurance.
Forexample,sinceinsurancepremiumsarebasedon his-toricalrealizationsoftheindex,reasonablyaccurate fore-castsmadebeforeinsurancesales[possiblythrough indige-nous indicators: 71] could lead to adverse selection if prospectivepurchasers have informationabout expected payoutsthattheproduct’spricingdoesnotaccountfor[e.g. Ref.15].Overall,whileanticipatoryriskfinancingbasedon forecastsisapromising innovationin IBDRF, its opera-tionalimplementationwouldrequireacarefulassessment ofassociatedrisksandeffectiveharmonizationwithother IBDRFinstruments.
QualityassuranceofIBDRF
Basis riskremains acriticalconcern for thequality and sustainabilityofIBDRFschemes.Foroperational initia-tivesafewrecentstudiescomparedresultsfrommultiple index designs [41,47,49,50]. Notwithstanding, these studieshighlightedthatevaluationoftheresulting indi-cesremainscomplexgiventhescarcityofand/ordifficulty tocollectgood-qualityin-situdataondroughtoutcomes. While EO product development and improved index design havepotentialto reducebasis risk,ultimately it remainsan empirical questionwhether this potentialis
Figure1 DROUGHT PROGRESSION IN PASTOROL SOCIO-ECOLOGICAL SYSTEMS TIMING & ACCURACY
COST-EFFECTIVENESS COST OF RESPONSE
TYPE OF RESPONSE
ACCURACY OF ASSESMENT / BASIS RISK TRIGGER TIMING
Current Opinion in Environmental Sustainability
GroundandsatelliteEOapplicationscancontributetomonitordroughtprogressionanditsecologicalandsocio-economicimpactstosupport context-specificdesignofIBDRFinstruments.TheupperpartoftheFigureillustratestheprogressionofdroughtimpactsfollowingMishraand Singh[34].Whiledroughtevolvesfrommeteorologicaltosocio-economic,IBRDFdesignneedstoconsidertrade-offstomaximizethe social-ecologicalbenefits,whileaccountingforeconomiccoststoascertainlong-termsustainabilityoftheinstruments.Forexample,anEO-basedindex designedwithanearlytriggerisexpectedtobemoreaccurateindetectingmeteorologicaloragriculturaldrought,butmightbelesseffectivein assessingsocio-economicimpacts.Thissourceofbasisriskmay(ormaynot)becounterbalancedbythesavingsineconomiccostsassociated withassetprotectionandearlyresponse.Theseassumptionsarecontext-specificandproductqualityassessmentisthusfundamentaltoevaluate trade-offs.
realized.Thisquestiongoesbeyondthetraditional accu-racy assessment approachesin the EO domain (e.g. an evaluationifsoilmoistureisaccuratelyrepresented ina soil moisture product),as it should encompass also the socio-economicvalueoftheproposedsolution[49],and be formalized through quality metrics, minimum stan-dards and robust assessment frameworks [1,51]. The overarchinggoalshouldbetodesignrigorousquantitative metricsandapproachestoassessandcomparetheutility of IBDRFinterventions.
A main challenge is collecting and analyzing relevant reference data on drought outcomes, given the limited formal data sets [52]. Potential data sources include multi-year forage biomass measurements [53], drought recallexercises[52],andrepetitivehouseholdsurveyson droughtoutcomessuchaslivestockmortality[49]orchild nutrition[54].Giventhecost-intensiveandlabor-intensive nature of collecting such data, ground-based EO approachescanprovideausefuladdition.Agoodexample istherepetitiveobservationofthesamevegetation,either bypermanentcameras[55,56],orthroughcrowdsourcing platforms[57].Forcrops,thishasalreadyledtotheideaand implementation of ‘picture-based-insurance’ whereby farmersrepeatedlycollectpicturesoftheirfieldsfor verifi-cation of insuranceclaims[58]. Thisidea could be extended to rangeland and livestock conditions, possibly taking advantage of computer-visionbased automationof body scoringtechniques[59,60].Increasedeffortsinreference datacollectionareurgentlyneededtoanswertheempirical questionandprovidequalityinsuranceproducts.
Socio-economicandenvironmentalimpactevaluation
With the geographic expansion and growing number of households covered, the need for impact evaluation of IBDRFinitiativeshasincreased.Traditionallyimpact eval-uationofdroughtcrisesonsmall-holderfarmersand live-stock keepers largely focused on socio-economic factors through mixed qualitative-quantitative household-level surveys [72].However,the highcosts andcomplexityof such surveys haveprevented asystematic integrationof impactassessmentstudies in IBDRF initiatives, so that only fewrobustassessmentsareavailableinpastoralregions[e.g. Ref.73].Inaddition,environmentalimpacts have been poorlyconsidered so far, while scalingIBDRFinterventions to a large number of households might have relevant ecological impacts. For example, reduced herd losses may result in increased grazing pressure on rangelands [74,75],althoughempiricalstudiessuggestonthecontrary thatinsuredpastoralistskeepsmallerherdsbecausethey reducetheiruseoflivestockasprecautionarysavings[76]. EOapproachesforrangelandhealthmonitoringarewell established [e.g. Ref. 77]. However, only few studies, focused on land restoration, have integrated these approachesinto impactassessment of largeprogramsin drylands[78,79].Recentliteraturealsoshowedpotential
for assessing food security and household wealth [80,81,82], but pointed out that satellite EO capacity to directly monitor socio-economic indicators of house-hold wellbeing and rapid land use dynamics during drought events (e.g. land accessibility, land tenure changes, migration)is limited. Impact evaluation could beimprovedbycombiningsatelliteEOinformationwith in-situcollectedenvironmentalandsocial-economicdata [83], which are increasingly available via mobile and crowdsourcing platforms also in rural Africa [e.g. Refs.
84,85,86].Thiscanbesupportedbyadvancesinmachine learning algorithms, allowing to extract patterns and understandspatialconnectionsfromdiversedatasources [87]. Datadriven approachesshould be, however, used with caution and framed within a robust set of causal hypotheses, taking into account the cross-scale interac-tionsbetweenphysicalandsocio-economicfactors[81].
Conclusions
IBDRF initiatives are scaling up as part of the policy agendasofSSAcountriesanddevelopmentorganizations onresiliencebuilding,povertyreduction,andsustainable development.EOplaysakeyroleinsustainingthistrend in pastoral drylands, with the potential of significant societalandpolicyimpactintheregion.Innovationsfrom groundandsatelliteEOtechnologiescouldcontributeto design more accurate, cost-effective, and harmonized droughtriskfinancingprograms,aswellastoassesstheir effectiveness during shocks and their longer-term impacts. However,thiscan onlybeachievedif theEO communitydoesnotlimititsroletoproviding technolog-icalsolutionsandservices,butratherbecomesanintegral partoftheinterdisciplinaryframeworktoaddressdrought risk management in complex socio-ecological systems, understandingsynergiesandtrade-offsbetweenresearch, operationalimplementation,and policyformulation.
Conflict
of
interest
statement
Nothingdeclared.
Acknowledgements
TheCGIARResearchprogramonLivestockandcontributorstothe CGIARTrustFundareacknowledgedforsupportingthisresearch.Anton VrielingwasfundedbytheDutchResearchCouncil(NWO),Spacefor GlobalDevelopment(WOTRO)programme,aspartofthe CGIAR-Netherlandspartnership.WethankNathanielJensen(ILRI)andJames Hassell(SmithsonianGHP)fortheirvaluablecommentsandinputsonthe manuscript.
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