GC13I-0857: Designing a Frost Forecasting Service for ...continue to be engaged with service design...

Conclusions

Wewouldliketoacknowledgethesupportofthestakeholderswhoattendedtheworkshopandcontinuetobeengagedwithservicedesignandimplementation.

WewouldalsoliketorecognizeUniversityofAlabamainHuntsvilleEarthSystemScienceCenter,andtheRCMRDandSERVIRteamsfortheirsupport

Acknowledgements

Projectpartnersalongtheteavaluechainwerebroughttogetherfora3daystakeholdermappingandengagement

workshop

Service Planning Approach

Study Area

Kenya is the third largest tea exporter in the world, producing 10% of the world’s black tea. Sixty percent ofthis production occurs largely by small scale tea holders, with an average farm size of 1.04 acres, and anannual net income of $1,075. According to a recent evaluation, a typical frost event in the tea growing regioncauses about $200 dollars in losses which can be catastrophic for a small holder farm. A 72-hour frost forecastwould provide these small-scale tea farmers with enough notice to reduce losses by approximately 80 USDannually. With this knowledge, SERVIR, a joint NASA-USAID initiative that brings Earth observations forimproved decision making in developing countries, sought to design a frost monitoring and forecasting servicethat would provide farmers with enough lead time to react to and protect against a forecasted frostoccurrence on their farm. SERVIR Eastern and Southern Africa, through its implementing partner, the RegionalCentre for Mapping of Resources for Development (RCMRD), designed a service that included multiplestakeholder engagement events whereby stakeholders from the tea industry value chain were invited to sharetheir experiences so that the exact needs and flow of information could be identified. This unique eventallowed enabled the design of a service that fit the specifications of the stakeholders. The monitoring servicecomponent uses the MODIS Land Surface Temperature product to identify frost occurrences in near-real time.The prediction component, currently under testing, uses the 2-m air temperature, relative humidity, and 10-mwind speed from a series of high-resolution Weather Research and Forecasting (WRF) numerical weatherprediction model runs over eastern Kenya as inputs into a frost prediction algorithm. Accuracy and sensitivityof the algorithm is being assessed with observations collected from the farmers using a smart phone appdeveloped specifically to report frost occurrences, and from data shared through our partner networkdeveloped at the stakeholder engagement meeting. This presentation will illustrate the efficacy of our frostforecasting algorithm, and a way forward for incorporating these forecasts in a meaningful way to the keydecision makers – the small-scale farmers of East Africa.

AbstractTominimizefrostdamagetoteacropsbyprovidingfrost-

potentialmapstofarmers.4TousetheSERVIRserviceplanningframeworktodesigntheFrostMonitoringandForecastingServiceforKenya.

4Tounderstandtheflowofinformationanddecisionmakinglandscapeforthepartnersalongteavaluechain.

4Toengageregionalstakeholdersinco-developingandimplementating asuccessfulservice.

Objectives

EmilyC.Adams1,2,4,JamesWanjohi Nyaga3,WalterLeeEllenburg1,2,4,Ashutosh S.Limaye5,RobinsonM.Mugo3,AfricaIxmucane FloresCordova1,2,4,DanielIrwin4,JonathanCase5,SusanMalaso3,andAbsae Sedah6(1)UniversityofAlabamainHuntsville,EarthSystemScienceCenter,Huntsville,AL,UnitedStates,(2)NASA-SERVIRScienceCoordinationOffice,Huntsville,AL,UnitedStates,(3)RegionalCentreforMappingofResourcesforDevelopment,Nairobi,Kenya,(4)NASAMarshalSpaceFlightCenter,Huntsville,AL,UnitedStates,(5)ENSCOInc./NASAMarshalSpaceFlightCenter,HuntsvilleAlabama,(6)

KenyaMeteorologicalDepartment,CountyKericho,Kericho,Kenya

GC13I-0857:DesigningaFrostForecastingServiceforSmallScaleTeaFarmersinEastAfrica

NASAEarthScienceDivision|AppliedSciencesProgram|CapacityBuildingProgram

Questions?

emily.c.adams@nasa.govContact

Figure1. CountiesinKenyawhereteaisgrown(Bomet,Embu,Kericho,Kiambu,Kirinyaga,Kisii,Meru,Murang’a,Nandi,Nyamira,Nyeri,Tharaka-Nithi,Vihiga).

Figure2. StakeholdermapfortheFrostMonitoringandForecastingService.KeystakeholdersforsuccessfulservicedevelopmentincludeKenyaMeteorologicalDepartment(countyoffices),TeaResearchInstitute,KenyaDepartmentofAgriculture,KenyaTeaDevelopmentAuthority,CommunityBasedOrganizations,andInsuranceCompanies.

Figure5. Insitu frostobservationsandtheMODISLandSurfaceTemperaturewereusedtoproducealogisticregressionmodeltodeterminetheprobabilityoffrostoccurrence.TheresultsareloadedintotheFrostMapViewerwebpage.

Figure6.Asamplefrostpredictionmaptobesenttoendusers.TheUnifiedEnvironmentalMonitoringSystem(UEMS),whichincorporatestheNCARAdvancedResearchWRF(ARW),wasimplementedtoproduceadaily72-hrforecast.Variablesincluding2-mairtemperature,relativehumidity,and10-mwindspeedwereincorporatedintoanalgorithmtocalculatefrostpotential.Thisalgorithmiscurrentlyundersensitivitytestingandhindcast analysestoproduceamoreaccurateproduct.

Methodologies and Preliminary Results

FrostMonitoring

FrostForecasting

Methodologies and Preliminary Results (Left)Figure3. ServicePlanningLifecycle

Figure4. FrostMapperAppdevelopedbyRCRMDtocollectfieldobservationsoffrost

FrostMonitoring

ServicePlanningandStakeholderEngagement

https://ntrs.nasa.gov/search.jsp?R=20170011720 2020-04-23T08:00:35+00:00Z