applications of meteorology to agriculture

8/9/2019 Applications of Meteorology to Agriculture

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Chapter6

ApplicationsofMeteorologytoAgriculture

ThisChapterwasrewrittenandcoordinatedby

RobertStefanskiwithcontributionsfromT.Rusakova,Z.Shostak,E.Zoidze,SimoneOrlandini,

andNickHolden.

Aninternalreviewwasundertakenby

SimoneOrlandiniandNickHolden.

TheChapterwasexternallyreviewedby

TerryGillespie,PauloCaramoriandHiltonPinto


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6.1 Introduction

Theapplicationofmeteorologytoagricultureisessential,sinceeveryfacetofagriculturalactivitydependsontheweather.Thischaptercitesmanyexamplesoftheutilityofappliedmeteorologyinordertomakeusersawareofthepotentialbenefitsthatfarmerscangaintoimproveefficiencyandensuresustainabilityoftheirfarmmanagement,toprotectandensurethecontinuinghealthoftheircrops,livestock,andenvironment,toincreasetheiryieldandmarketvalueoftheircropsandtosolve selected operational problems. Other agricultural decision makers derive benefit fromagrometeorological applications such as government policy makers in ensuring adequate food

supplies,affordablefoodpricesfor theirconsumers,sufficientfarm incomefor their farmers,andreducingtheimpactofagriculturalpracticesontheenvironment.Decisionmakersininternationalagriculturalorganizationsalsouseapplicationsofmeteorologytoensurefoodsecurityandtoreacttopotentialfaminesituations.Atanylevel,theseobjectivescanonlybeachievedthroughactiveco-operation between National Meteorological and Hydrological Services (NMHSs), agriculturalextensionservices,farmersandtheirassociations,agriculturalresearchinstitutes,universities,andindustry.

Thetopicsgiveninthischapterwerebasedonaclassificationofapplicationsofmicrometeorologyto variousagricultural problemsgivenbyWMO Technical Note 119 (1972). They are groupedaccording to the general type of problem, namely, improving production; averting dangers toproduction;physiologyandgrowth;strategy;andtactics.ThesetopicscontinuedtoberelevantforthiseditionoftheGuideandonlysubtopicshavemodifieddependingonrecentapplications.ThephysiologyandgrowthtopicsarecoveredinChapter13.

Themainobjectiveofthechapterisgivetobriefoverviewsandexamplesofapplicationsatthelocalorfarmerlevel.However,therearealsoimportantapplicationsthatwillbediscussedatthe

government or international organization level. In selecting the examples of applications ofmeteorologytoagriculture,priorityhasbeengiventothosethatareusedoperationally.Also,anattempthasbeenmadetociteexamplesforthedifferentclimatesandregionsoftheworld.

6.1.1 Usersofagrometeorologicalinformation

The successful application of weather and climate information needs to integrate threecomponents:data,analysis,andusers,therefore,theultimategoalofanyapplicationistoserve

the needs of the users. A solid foundationofdata isa prerequisite for successful agriculturalmeteorologicalapplications(seeChapters1and2).Thenananalysisofthesedataisneededthattries to solve or address an agricultural problem. These analyses are described in this guide.Ultimatelytheusersofagrometerologicalinformationandapplicationsmustbekeptinmindwhendevelopingnewapplications.Theusercanbedefinedasanyagriculturaldecisionmakersuchasafarmer, extension agent, government official, media person, or the general public. Rijks andBaradas (2000) provide an overview of clients (users) of agrometeorological information They


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a few hours toa few days.These often involve decisions, based on the stateof the cropandcurrent or forecast weather, for such farm operations as cultivating, irrigating, spraying, andharvesting.

Holden and Ortiz (2003) provide a good description of macroclimate and mesoclimate in thecontextofagrometerologicalapplications.Macroclimateisthelargestandcoversbroadareasofacontinent (millionsofsquarekilometers)anddealswith theinteractionof large-scaletopography(mountain ranges, large lakes, and ocean influences) with air masses. At this scale, climatecharacteristicsshouldprovideinformationon thesuitabilityofa farmandwhetherthefarmcouldbeweather limitedbypest, disease, and operational timing problems.Mesoclimate reflects thefarmersviewoftheweatherexperiencedinaregion.Localsurfacefeaturessuchashills,smallmountains, large forests,or extensiveplains have adistincteffect at this scale.Acountrymayhaveoneortwomacroclimatezones,butitwillhavemanymesoclimates.Itisatthisscalethatspecificcalculationscanbemadetodefineagroclimaticregions.Atthesmallestscale,Rosenbergetal(1983)definesmicroclimateastheclimateneartheground,or in other words, the climate in which most much plants and animals live. In regards tometeorology,WMO (1972) regarded micrometeorology as dealing with the physical processestakingplacewithintheboundarylayersbetweenthetopoftheplant,tree,oranimalandthebottomoftherootsof thesoil.Mostof theapplicationsinthischapterarebasedonmicrometeorological

principles.ThemonographbyGordeevetal(2006)presentstheresultsofassessingthebioclimaticpotentialinRussia,thesurroundingstates,EuropeandUSA.Particularattentionisgivento theaccountofclimaticandagroclimaticpeculiaritiesintheseterritorieswhensolvingsomesocialandeconomicproblems.

6.1.3Benefitsderivedfromapplications

Manybenefitsresultfromtheapplicationofmeteorologicalservicestoagriculture.Theproductivityofaregionorofaparticularenterprisemaybeincreasedbythereductionofmanykindsoflossresultingfromunfavourableclimateandweather,andalsobythemorerationaluseoflabourandequipment.Greatereconomyofeffortisachievedonthefarm,largelybythereductionofactivitiesthat have little value or are potentially harmful. All of these increase the competitiveness ofproduction,reduceriskandhelptoreducethecostofthefinalproducts.

Inthedevelopedworld,asignificantportionofrecentworkinagriculturalmeteorologyhasshiftedfromincreasingyieldstoreducingtheenvironmentalimpactofagriculturalfertilizerandpesticideuseandcombatingpestsanddiseases.Inthedevelopingworld,muchofthefocusremainsonincreasing agricultural production but there is also an emphasis on sustainable agriculturalproductionandreducingtheimpactthepestanddiseases(i.e.desertlocusts).Th f ll i b i f l f i b fit f t l i l li ti f


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providedaframeworkforanalyzingcostsandbenefitsofagrometeorologicalapplicationsinplantprotection.

The results of studying the peculiarities of climate and weather conditions to optimize variouscultural practices (i.e., determinationofan advisable structure ofareasunder crops, dates andwaysofsoiltreatment,optimumperiodsanddosesoffertilizing)aimedatincreasedproductivityofplantgrowinginRussiacanbefoundinseveralpapersbyFedoseev(1979,1985).Theeconomicefficiencyofappliedagrometeorologicalrecommendationsisgiven.

OtherexamplescanbefoundinOmar(1980)andfromaconferenceontheeconomicbenefitsofmeteorologicalandhydrologicalservices(WMO,1994),

6.2Applicationsforgovernmentsorotherlargeadministrativebodies

Government or other large administrative bodies need quality and reliable assessments foroperational assessments of agricultural production. In regards to planning, this would involvequestions ofwhat kindofcropscould the countryeconomicallyproduceandwhere would theygrow them. These kinds of planning questions can be answered by macro and mesoscaleagroclimaticsurveys.

6.2.1OperationalAssessments

There are several examples of operational assessments of crop production that countries andinternationalagenciescarryout.Theseexampleshighlighttheutilityandneedofintegratingofdata, staff, and resources to produce reliable crop production assessments among differentagenciesatacountrylevelandamongdifferentcountries,NGOs,andotherorganizationsataninternationallevel.Bokenet.al.(2005)providesmanyexamplesofsuccessfuldroughtmonitoringandcropmonitoringapplicationsattheselevels.

The Food and Agriculture Organization of the United Nations (FAO) established the GlobalInformationandEarlyWarningSystem(GIEWS)in1975inresponsetotheglobalfoodcrisisoftheearly1970s.GIEWSprovidesinformationonfoodproductionandfoodsecurityforeverycountrybasedoncropmonitoring assessments that involve remote sensingandground-basedweatherstationdata(Mukhala,2005).AnumberoftheseassessmentsarebasedontheFAOCropWaterRequirementSatisfactionIndex(WRSI)modelthatdeterminesacumulativewaterbalancefor10day period from planting to maturity. WRSI is a combination of dynamic water balance andstatistical approaches and the index represents at any time of the growing season the ratiobetweentheactualandpotentialevaporation.TheWorldAgriculturalOutlookBoard(WAOB)oftheTheUnitedStatesDepartmentofAgriculture(USDA) is mandated to provide official monthly U.S. government forecasts of agriculturalcommoditiesthroughtheWorldAgriculturalSupplyandDemandEstimates(WASDE)publication(MothaandStefanski,2006).Thesesupplyanddemandestimatesarebasedonofficialcountry


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some practical problemsofplanningand organizationof agricultural production (Zhukov et.al.1989).

In Brazil, there are several centers generating daily updated agrometeorological information tosupportdecisionmaking, includingAgritempo (www.agritempo.gov.br), thatprovides informationforthewholecountry,andregionalcenterssuchasCepagri/Unicamp(www.cpa.unicamp.br)andIAC(www.ciiagro.iac.sp.gov.br)for thestateofSaoPaulo, IAPAR/SIMEPAR(www.iapar.br/sma)for the state of Parana and Ciram (www.chlimerh.rct.sc.br) for the state of Santa Catarina.Cooperativesoffarmersalsohavebeenprovidingagrometeorologicalinformationtotheirfarmersandfieldtechnicians(eg.www.fundacaoabc.org.br)(Pinto,2007).Other examples include Famine Early Warning System or FEWS (Rowland et. al., 2005), theEuropean Unions Monitoring Agriculture with Remore Sensing (Negre, 2006), the NationalAgriculturalMonitoringSysteminAustralia(Leedman,2007)andtheFarmweatherservice(Baier,2004)andagrometeorologicalservicesinKazakhstan(Baier,2004).

6.2.2 Agroclimaticsurveys

Concerning macroscale agroclimatic surveys, the joint inter-agency project on agroclimatologybetweenFAO,WMO,andUNESCOhasbeenverysuccessfulinproducing5publicationsandis

thefoundationofcurrentagroclimaticzoningstudies.Themostimportantpracticalapplicationsofmacroclimatic surveys include: choosing crops, varieties and domestic animals; determiningfavourable periods for sowing, hay-making and harvesting; establishing areas where dry-landfarming is possible and where irrigation has to be applied; planning afforestation and re-afforestation;findingtheoptimumrangeofclimaticvariablesforincreasingyieldsandagriculturalproduction in general; establishing potentials for the agricultural use of rangelands; anddeterminingrequirementsandpotentialitiesforefficientstorageandtransportationofcrops.

Theagroclimatologyinthesemi-aridandaridzonesoftheNearEastfocusedontheestimationof

theboundaryareaswheredry-landfarmingispossibleandwhereirrigationisneeded.(PerrindeBrichambautandWalln,1963).Theagroclimatologyofsemi-aridareasinWestAfricadealtwithdry-landfarminginWestAfricaandlengthofthegrowingseasonsthatisstrictlyassociatedtherainyseason(CochemandFranquin,1967).TheagroclimatologyofthehighlandsofeasternAfricafocusedonwhichcropwaterrequirementsweremetinthevariouslocalitiesofthethisarea(BrownandCochem,1973).TheagroclimatologyoftheAndeslookedattheuniqueeffectsofhigh elevation and high solar radiation input on crops (Frre et. al, 1978). The study of theagroclimatology survey of the humid tropics of South-east Asia. (Oldeman and Frre, 1982)

illustratedthe roleofagroclimatologyindeterminingstrategiesto increasefoodproductioninthehumidtropics.6.2.3 MesoscaleagroclimaticsurveysWith the advent of widespread computing capability (personal computers) and GeographicI f ti S t (GIS) li ti d li ti i h b id d Th


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Motroni(2002)focusedonthedevelopmentofamethodologytoassessclimaticandagroclimaticrisks. Land capabilitywas classified for Sardinia by using climate, geographic and soil data.Aclimaticriskindexwascomputedbasedon30yearaveragesofclimaticdata.

Petr(1991)describedamesoscaleagroclimaticclassificationschemeforCzechoslovakiabasedonahydrothermiccoefficient,HTC=R/(0.1TS10)whereRistherainfallsuminmillimetersandTS10isthedegree-daysabove10

oC.Suchanapproachcouldbeusedwithanydegree-daybasetemperatureandadaptedforspecificcrops.InBrazil,therehavebeenrecentnationwideeffortsinagroclimaticriskzoningforagriculturalcropsthatcharacterizes thepotential and climatic risks for several crops, includingmaize, soybeans,beans,wheat,barley,rice,cotton,coffee,cassavaanddifferentspeciesoffruits(Pinto,2007).TherecommendationsareusedbytheBrazilianGovernmenttoprovidefinancingtothefarmersatverylow rates. Furthermore, those who follow the recommendation of the agricultural zoning cancontractofficialinsuranceatspecialrates.Tobeeligibleforthebankcredit,thefarmersmustalsoadopt the best agronomical practices recommended by the extension service. These effortscombinedwithfarmersusingoptimumplantingdateshasincreasedtheproductivityofBrazilianagriculture(Pinto,2007).6.3 Applicationsforfarmersorgroupsoffarmers6.3.1 Improvementstoproduction

Asstated in the introduction, the original topicswerebased on aclassificationofapplication ofmicrometeorologytovariousagriculturalproblemsinWMOTechnicalNote119(1972).

6.3.1.1Irrigation

Initsbroadestterms,irrigationinvolveswaterbalancecalculationsbasedonrainfall,estimationofwater infiltration (effective rainfall), runoff, evapotranspiration (ET), and soil moisture. For soilmoisture,thereareseveralreliabledirectmeasurementssuchasmanualgravimetricandneutronprobe for routineapplication inagriculturalpractice(seeChapter2)and indirectmeasurementsbased on remotely sensed information (see Chapter 12). Early irrigation and soil moistureapplicationscanbefoundinHMSO(1967),BaierandRobertson(1965),Rider(1958),andStanhilletal(1968).Overtheyearstherehasbeenalargeamountofworkrelatedtoirrigation,especiallymeasuringandestimatingevapotranspiration.Anumberof textbooksprovidegoodoverviewsonthissubjectincludingRosenbergetal(1983).

Smith (2000) gives an overview of the widely accepted practical procedures that have beendevelopedbyFAOandotherstoestimatecropwaterrequirementsandyieldresponsetowaterstress. The methodologies of crop water requirements were first published in 1974 as FAOIrrigationandDrainagePaper24andrevisedin1977(DoorenbosandPruit,1977).Areviewand


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computersoftwareapplicationirrigationofgrassland intheNetherlands.Resultsshowthatusingthesystemcanreduceirrigationwaterby15-20%.

Kroes (2005) provides on overview of the Soil-Water-Atmosphere-Plant (SWAP) model thatintegrateswaterflow,solutetransport,andcropgrowth.TheSWAPmodelcanbeusedatthelocalscalebyfarmersandextensionagentsforirrigationdemand,potentials,andstrategiesandattheregionallevelbypolicymakersforspatialandsectoralirrigationstrategies.Venlinenetal(2005)usednumericalweatherforecastmodeldatatomodelsoilmoistureforinput into irrigation models. Potential evaporation was calculated using the Penman-Monteithequationbased ondata fromhigh resolution limitedareamodel. The datawere input into theAMBAVandSWAPirrigationmodels.6.3.1.2 Shelterfromthewind

WMOTechnicalNoteNo.59andChapter9ofRosenbergetal(1983)dealscomprehensivelywithwindbreaksandshelterbeltsandVanEimem(1968)discussedproblemsofshelterplanning.Grace(1977, as cited byRosenberg) provides an overview on the direct influences ofwindon plantgrowth.

Rosenbergetal(1983)defineswindbreaksasstructuresthatreducewindspeedandshelterbeltsasrowsoftreesplantedforwindprotection.Bothofthesecanreducephysiologicalstressesduetowindonplants and animals. They reviewed the literatureand found that shelter effectson themicroclimatearethefollowing:reducedpotentialandactualevapotranspiration;improvedinternalplant water relations (greater internal water potential and lower stomatal resistance), improvedopportunityforphotosynthesis,andfinallysheltergenerallyincreasesyield.Thesegeneralitiesaresubjecttovariationdependingonsoilmoistureandthebenefitsmaybemostdramaticindryyearsorundercriticalmoistureshortages.ExamplesofwidespreaduseofwindbreakincludetheGreatPlainsintheUSaftertheDustBowlyearsinthe1930s(Rosenbergetal,1983);theRhoneValleyinsoutheasternFrance,andtheNetherlands(VanEimem,1968).Marshall(1967)hasreviewedtheliteratureontheeffectofshelterontheproductivityofgrasslandsandfieldcrops,andshowedhow the proportional decrease ofwindspeedwith distance from the sheltercorrespondswithadecreaseinevaporation.Nighttime temperaturedecrease, relativehumidity,daytimeairandsoiltemperatureincreasevarywithdistancefromthebarrierbutdeclinetonoeffectatadistanceofabout12timestheheightoftheshelter.Inconjunctionwiththeseparameters,thegreatestsoilmoistureavailabilityandcropyieldarefoundinthezoneatadistanceof2to4timestheheightof

theshelter.Windbreaksreduceofforceofthewindintheshelteredzone.VanEimernetal(1964)showthatadensebarriermayprotectanareaabout10-15timestheheightdownwindandby increasingtheporosity to about 50%, the downwind influence can be increased to 20-25 times the height.Rosenbergetal(1983)statethatforthebestwindreductionandgreatestdownwindinfluencethe


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Colombia,CostaRica,Salvador,Guatemala,Uganda,Tanzania,andinthehigherelevationareasofnortheasternBrazil.InmostofBrazil,anunshadedcropfacilitatestheharvestingandnaturalgrounddryingof thecropasthemicroclimateissunnierhencewarmeranddrier.Inmostplaces,

coffeeberriesarehandpickedatthecherrystageandifthisstageisextendeditprovidesforaneasierandlongerharvestperiod.Therefore,shadingcanbeadvantageoussinceit increasesthecherrystageofthecropbecausethemicroclimateiscoolerandmoistwhichslowsthematurationprocess.Shadingalsoaidsinmaintaininghighsoilorganicmatter.CamargoandPereira(1994)also cite several characteristics of good shading trees and several other advantages anddisadvantages.Sheltersmayalsobeusedtoreduceproductionlossesfromlactatingdairycowsbecauseoftheheatloadduringthesummer(HahnandMcQuigg,1970).6.3.1.4 Greenhouses(Glassandplastic)Greenhouseshavebeenusedintemperateclimatesforover100yearsandservemainlytoreduceheatlossandpermitcompletecontroloverthewateringofplants.Recently,CO2enrichmentoftheatmospherehasbecomeanadditionaltechniqueingreenhousecultivation.AdetaileddiscussionongreenhousesispresentedinSeeman(1974)andinHoldenandOrtiz(,2003).Holden and Ortiz (2003) give many benefits derived from indoor agriculture (greenhouses):

protection againstdamagebyUV light; improved ambient temperatureconditions; protectionofcrops from adverse climatic conditions; increased productivity; reduced production costs;controllableharvestandbetterproductquality.Theyalsolistseveralclimateelementsthatmustbemanagedforgoodperformancefromgreenhouses.Thecoveringofthegreenhouseisimportantinregardstovisiblelighttransmissionforplantphotosynthesis.Knowledgeoftheclimatologysolarradiation, cloudiness, relative humidity, temperature andwind profilesof a greenhouse siteareimportant.

Inregardstotheconstructionmaterial,mostgreenhousescoveringaremadeofglass,fibreglass,andplasticwhileplasticagriculturaltunnelsarelesswidelyused(HoldenandOrtiz,2003).Inorderto choose the best covering suitable for a geographical location, the maximum,minimum andaverage temperatures, thepossibility of frost, theclimatologyof thewind and relativehumidity,rainfalldistributionandintensity,solarradiation,andspecificcropsneedtobetakenintoaccount.Aclimatologicalanalysisofthesolarradiation, temperatureandrelativehumidity isimportant forsitinggreenhouses.Theseparameterswilldeterminehowmuchinternalenvironmentalcontrolwill

beneededforoptimumplantgrowthdependingontheplantsgrown.Windspeedanddirectionareveryimportantfactorswhendesigningagreenhouse.Highwindscoulddamagethestructureorcoverings.Windisalsousedinsimplegreenhousedesignstomaintainthethermalbalancebyreducingenergycostsforheatingorcooling.Windventilationcanbeusedforbalancinginternaltemperaturesbyaircirculation,reducingrelativehumidity,croppollination,andreplenishingcarbondioxideandremovingoxygenforplantphotosynthesis.


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6.3.1.5 Groundcover(Mulching)

Soilmulchesofvariouskinds(i.e.strawcoverorartificialmaterialssuchasplastics)areusedtomodifytheheatandmoisturebalanceinthesoiltobenefittheplants.Davies(1975)providesagoodoverviewoftheeffectsofmulchingonplantclimateandyieldandstatesthatmulchesareparticularlyusefulinconversingmoisture,reducingtemperatureextremes,andminimizingerosion.Chapter 6 ofRosenberg etal (1983) provides several examples of variousmulches usedwithdifferentcrops.CamargoandPereira(1994)listthefollowingadvantagesofgrassstrawmulchingwiththecoffeecrop in Kenya: protects the soil from excessive heating that destroys the soil structure, lowerstemperaturesthatresultinlowerevaporationrates,providesorganicmattertothesoil,reducessoilerosion from heavy rainfalls; and minimizes weeds. They also cite studies that indicate thatmulchingcanreducethefrequencyofirrigation.Onthenegativeside,mulchingrequiresalargeamount ofgrass,andmore importantly, strawmulchescan aggravate frostproblems as the airtemperatureabovethemulchismuchwarmerduringthedayandcooleratnight.Themulchalsopreventsthegroundfromabsorbingheatduringtheday,whichissubsequentlyreleasedduringthenight.Studiesontheeffectofstrawmulchingonairtemperaturesindicatethatat5cmabovethegroundthemaximumtemperaturewas6.6Chigherandtheminimumwas1.7Clowerthanthe

bare groundCamargoand Pereira (1994).Gurnah and Mutea (1982; cited fromCamargoandPereira) tested different plasticcoverings on the soil temperature and concluded that onareassubjecttofrost,transparentplasticshouldbeusedandelsewherewhiteplasticshouldbeusedsinceitrespondsapproximatelytothesamethermalregimeasbaresoil.6.3.1.6 AnimalhousingMeteorologicaldataarerequiredwhenassessingwhetherandhowanimalhousingshouldbeput

intouse.Evaluationshouldalsotakeintoaccountthepotentialeconomicreturns,energycost,andavailability(Starr,1980).Animalhousingisundertakenbecauseofthethermalimbalanceresultsinadverseeffectsonanimalproductivity.Weather and climatecan determine the efficiencyof livestock productionbydirect and indirectinfluences (Starr, 1980). Direct influences affect the heat balance of the animal and includesextrememeteorologicalevents.Indirectinfluencesarediseaseandparasites.Excessiveheatorcold increases the metabolic energy required to maintain the animals body temperature thus

reducing the energy available for productivity. This energy imbalance is usually corrected byincreased feed, which is an increased cost to the farmer. The use of climatological data andanalysisisusefulinthiscase.Starr(1980)providesmanyexamplesofusingweatherandclimateinformationinapplicationofanimalhousing.Hementionsthatsiting,externalwind,temperature,and humidity affect both entry conditions and influencing patterns of internal air movement inenvironmentally controlled housing. Smith (1964 a, b, c) has shown the importance of the


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fruit has amajor impact on thequality of fruit during storage and that there have been manystudiesonmodellingapplematurityusingclimaticdata.

Pashairdis (1990) states thatmonitoring of the storageenvironmentandweather forecasts areused in many countries to determine the optimum conditions for potato tubers. In the formerCzechoslovakia,humidityandtemperaturedata(themeannumberofhoursexceedingthelimitsof3oC,7oC,and10oCduringthewintermonths)wereusedforthedesignandconstructionoflargepotato storage facilities. Friesland (2002) reviewed the scientific literature on the quality andstorageofgrapesandpotatoes.Hecitesthemostimportantmeteorologicalparametersforgrapequalityastemperatureandsolarradiationandlistsseveralqualitymodelsforthesecrops.Potatostoragequalityisdeterminedbyevaporation,transpiration,respiration,andgermination.6.3.1.7.2 Grain

Smith and Cough (1990) have revised the previous WMO Technical Note on this subject.Agrometeorologycanprovideguidancefortheconstructionofgrainstoragebasedonlocalclimatemodificationandenvironmental control. Storedgrain interacts with itsenvironment exchangingheat and moisture. The level of biologicalactivity ofgrainand potentially damagingorganismsmust be minimized. For safe storage, grain must be kept cool and dry, requirements that are

affectedbothby the characteristics of the building orstructure forhousingand by the externalenvironment.Heatuptakefromoutsidemustbeminimizedwhileheatlossfromstoragemustbemaximized. The moisture exchange between the grain and the external environment shouldgenerally lead toa reduction ingrain moisturecontent.When hot dry grain must be cooled topreventinsectinfection,theresultingmoistureincreasemustbekepttoanacceptablelevel.

Thesitingofstoragefacilitiesandtheirdesignandconstructionmaterialscanallbeinfluencedbymeteorologicalfactors.Forexample,inhotdryregionswithnorefrigeration,thereareadvantages

to storage facilities of high thermal capacity, with air space ventilated at night when the air iscoolest.Inwarmwetregionswithsmalldiurnaltemperatureranges,goodstoragemaybedifficulttodesign.Meteorologicalfactorsbecomemoreimportanttothefarmerwhennaturalairgraindryingsystemsareused.InthestateofOhiointheUS,forexample,bestresultsofshelledcornoccurwhentheclimaticconditionsprovidetemperaturesintherangeof-1to10 Candrelativehumiditiesintherangeof60to70%(Hansenetal,1990).InmostwesternOhiocounties,OctoberandNovemberprovidehighprobabilitiesforgoodclimaticconditionsfornatural-airdryingofsoybeansandshelledcorn.Thereisalsoanimpactofweatherconditionsduetorainfallbeforestoragethatcanbeimportant.Hightemperatureandrainthatoccursshortlybeforeharvestarethemostimportantdirectweathereffectsonspringbarleyquality(Freisland,2002).


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PaulodeMelo-Abreu(2005)providemorerecentoverviewsandexamplesofprotectionagainstfrostdamage.

Frost-riskmaps,datesof firstand lastfrostareasimple but useful applicationsofclimatedataapplied to agriculture. These maps are made at the macro to mesoscale and are useful forspecifyinggeneralplantingdatesforcerealcropsandassessmentofcropdamagewhencombinedwithphenologicaldata.

6.3.2.1.1.1Sites

Assessmentofpotentialsitesforfrost-sensitivecropsiscrucial,sinceitwilldiscouragegrowersfrom planting in frost-areas especially for high-value cropssuch as fruit treeand coffee crops.

Topoclimatologyandlocal-scaleagroclimaticzoningareimportanttoolsandmethodologiesinthisregard.Anearlyoverviewofconceptsandsomeexamplesaregivenin(MacHattieandSchnelle,1974). Gat et al (1997) describes agrotopoclimatology as being concerned with the localdifferencesinclimatearisingfromtopography,soil,andvegetationwithinauniformmacroclimaticzone(thiswasdefinedearlierinthischapterasbeingmesoclimate).Theyshowsomeexamplesofusingtopoclimatologicalanalysisindevelopingprobabilityoffrostoccurrencemapsovercomplexterrain.Withtheincreaseinavailabilityandspeedofpersonalcomputersinrecentyears,thesetypesofapplicationshaveincreased. (SeeChapter12,GISapplications).Oneexampleusesan

spatial interpolation methods to determine the spring frost hazard in the hilly areas of Frenchvineyardsbasedondigitalelevationdataandweatherstation(MadelinandBeltrando2005).

6.3.2.1.1.2Protectionagainstfrostdamage

Rosenburget.al.(1983)describestwokindsoffroststhatcanimpactcropsandthereforeneedtohavedifferent protection techniques. Advection frostusuallyoccurs during or aftera change inairmass,andisaccompanyingbystrongwinds(coldfront).Thenumberofprotection techniques

againstthiskindoffrostislimited.Radiationfrostoccursundertheinfluenceofahigh-pressuresystem and typically the winds associated with this kind of frost are very light. They list thefollowing frost protection methods: site selection, radiation interception, thermal insulation, airmixing, direct convective air heating, radiant heating, release of the heat fusion, and soilmanipulation.Mostofthesemethodsareonlyeffectiveagainstradiationfrostsbutsomecanbeapplicabletoboth.

Bagdonas(1978)describesmanydirectoractiveandindirectorpassivefrostprotectionmethods

taken frommostly Russian and European sources. Directmethods of frostprotection include:protective covers; smoke generation and artificial fogs; open-air heating of plants and areas;irrigation and sprinkling; and mixing air. Indirect methods include: biological methods such ashardening, seed treatment, selection of frost-hardy strains, development of new frost-hardyvarieties,andregulationofbuddevelopmentandecologicalmethodssuchascontrolofmineralnutritionandcropsiteselection.


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6.3.2.1.2 Hail

Hailcandestroyhigh-valuecropswithinashorttimeandmanycountrieshavethereforesoughttoreduceitsfrequencyortoreducethedamagethatitcauses.HurstandRumney(1971)giveashortsummaryoftheprotectionmethods thatinvolveaddingcondensationnucleito hail-formingclouds,bymeansofmissilesoraircraft,withtheobjectofproducingsmallhailstonesorsofthail.

Largereductionsofhailhavebeenclaimedbyanumberofgroups.AccordingtotheCommissiononAtmosphericScience,theweightofscientificevidencetodateisinconclusive,neitheraffirmingnordenyingtheefficacyofhailsuppressionactivities.ItisrecommendedthatinterestedpartiesconsulttheWMOStatementontheStatusofWeatherModificationforfurtherinformation.

6.3.2.2 Indirectweatherhazards6.3.2.2.1IntroductiontoCropandAnimalPest/Diseases

The application of meteorology to overcome the effects of pests and diseases on plants andanimalsinvolvesacompleteunderstandingofthecomplexlifecyclesofthepathogenanditshost,aswellastheenvironmentalconditionsthatinfluencegrowthanddevelopment.Plantpathologists

have developed a disease triangle with host (a susceptible animal or plant), environment(environmental conditions suitable for disease or pest establishment and development), anddisease (the presence of the disease orpest)at the apexes of the triangle (Figure1). Theseconceptshelptodescribethesituationforvirtuallyallknownpestsanddiseases.Allthreesidesofthetrianglemustexistforthepest/diseasetobeestablishedanddevelop.Ifoneofthesidesismissing, then establishment of the pest/disease will not occur. Meteorological factors are veryimportant for the growth and development of the host plant and animal species, for thepest/diseaseandtheairbornetransportofthepest/disease.Orlandini(1996)liststemperatures,solar radiation, precipitation, leaf wetnessand humidity, and wind as the majormeteorologicalfactorsinrelationtoplantpathology.Thedurationofleafsurfacewetnesscausedbydew,fogorrainisoftenacriticalvariablecontrollingthegerminationofdiseasespores.Itmustbecomputedfromstandardmeterologicaldataormeasuredwithleafwetnesssensors(Sentelhaset.al.,2004).Meteorologycanbeappliedviaobservationoftemperature,relativehumidity,andrainfall.Moresophisticated applications include numerical weather prediction models for wind direction andspeedforthediseaseorpest(seeFMDanddesertlocust).Forthehostorplantfoodsource,temperature can be used for phenological development, rates of infection, and disease/pest

survival(extremetemperatures).Rainfallisimportantforhostplantanddisease/pestdevelopment.Alloftheseaspectscanthenbemodelledandusedinoperationalapplicationsfortheagriculturaldecisionmaker.

Disease/Pest Triangle


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One important use of weather and climate information is in the field of Integrated PestManagement(IPM).IPMstrategiesincludenotusingchemicalunlessthereiseconomicdamagetothecrop.Therehavebeennumerousstudiesandmodelsontheinfluenceoftemperatureonplant and insect growth and development. Pruess (1983) gives an overview of degree-daymethodsforpestmanagement.WMOTechnicalNote192providesmanyexamplesofagrometeorologicalaspectsofoperationalcropprotection includingplantdiseases, insectpests,weeds,meteorological datarequirements,andapplicationofweatherforecasts.Das(1997)providesabriefoverviewof themeteorological

factorsrelatedtovegetablepestsanddiseases.Pedgley(1982)providesagoodoverviewof themeteorologyofwindbornepestsanddiseaseswithsomeexamples.Sectionsofthebookdealwithweatherat take-off,organismsstayingairborne,downwinddrift,insectflightwithinandabovetheboundarylayer,swarms,dispersionandconcentrations,andforecasting.

6.3.2.2.2 Croppests

Thereare many applications of the meteorology applied to crop pests. By using the concepts

related to the pest/disease triangle, applications focus on using temperatures to predict insectdevelopmentandhostplantdevelopment.Anyadditionalweatherparameterscanthanbeaddeddependingonthenatureofcroppest.

6.3.2.2.2.1 Desertlocust

Therehasbeenmuchworkonweatheranddesertlocusts.Earlystudiesincludemeteorologyanddesert locust migration (Rainey, 1963) and the accompanying training seminars (WMO,1963).Laterwork focused onmeteorology for locust control (WMO, 1991; Sadi, 1992; Ambar, 1992;

Pedgely,1997).

Pedgely (1997) givesagoodoverviewof themeteorological factors for locust control. Rainfalllargedeterminestheextentandintensityofbreedingthereforeisthemostimportantfactorinthedevelopmentofanoutbreakorplague.Rainfalllocationismoreimportantthanactualamountsandthisiswheresatelliterainfallestimatesareparticularlyuseful.Oncethereisasignificantrainfallevent(25mmormoreduringamonthortwo),thetendergrassvegetationinthedesertstartstogrowwhichisthemainfoodsourcefordesertlocusts.Itistheseabnormalrainfalleventsthatcan

triggerlocustoutbreaksandplagues.TherehavebeencasesintheArabianpeninsulawherealandfallingtropicalcyclonehasspurredalocustoutbreak,e.g.in1969.Temperatureaffectstherateofdevelopmentofallstagesofthedesertlocustlifecycle.Sincethispestisnativetohotdesertclimates,temperaturesgaininimportancetypicallywhenswarmtakeoffisneededonadailybasisorwhenthedesertlocustmigratesoutsidethedesertclimates.Likewise,winddirectionandspeedareneedforswarmflightandfortacticalsprayingapplicationsfromaircraftortheground.


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used to consider the effect of agrometeorological conditions on principal characteristics of theColoradobeetleactivitydurationofdevelopment,reproduction,anddeathofindividuals.Thepreliminaryamountofchemicaltreatmentsofpotatoiscalculatedbasedontheforecastedintensityof the Colorado beetle reproduction, then it iscorrected with the account ofobservedweatherconditions.Therehavebeenmanystudiesrelatingmeteorologicalfactorswithimportantcroppestssuchasthecottonleafwormandpinkbollworm(Omar1980),theColoradoPotatoBeetle(Hurst1975),variouspestsofsugarcane(Biswas1988),andcassavamitesNyiira(1980).6.3.2.2.3 Cropdiseases

Theapplicationofmeteorologyasanaidtothefarmerincombatingplantdiseasediffersaccordingto the mechanisms by which each pathogen is spread. The pathogen may be a year-roundresidentwhichincreasesandspreadswhenevertheweatherissuitableforitselfandthehostplant,e.g.thefungaldisease,potatoblight.Insomeareasapathogenmaynotbecapableofsurvivingtheyear,andmaynotreappearunlesstransportedinsufficientquantityfromadistantsource,e.g.blackwheatrust. Inrecent years, the development ofcropdiseasemodelshas focused onthehigh-economicvaluecropssuchasfruittrees,vineyards,andvegetablessincethemodelsneedmeteorologicalobservationinthefieldsettingsthatusuallyrequiretheestablishmentofautomatic

weatherstations.

6.3.2.2.3.1 PotatoBlight

There are two approaches available for the forecasting of late blight with a view to limitingagrochemicalusecomparedto7and10dayroutinespraying:

(1)Simple meteorological rules related to the life cycle ofPhytophthora infestans that userainfall,temperatureandhumidityover12-48hrperiodstopredictsporeproduction(critical

periods) and possibly subsequent periods when risk of infection is greatest. Thesemethodscanbeusedwitheitherhourlyobservationdataorwithsynopticweathermaps;

(2)Computerbaseddecisionsupportsystems(DSS),usuallyutilisingthesimplerulesthatrelyondatafromin-fieldautomaticweatherstationsoravailableasdigitalfilesfrom(usually)Internetsources.

The rules (Table 1) differ only in detail and require some regional or site-specific calibration.Merceretal(2004)indicatethatidealconditionsforsporeproductionarerelativehumidity>95%

andtemperature>10O

Catnightime,andforseriousinfectiontooccur,freewatermustbeavailableonthecropsurfacesorainfallandprolongedhighrelativehumidityarealsorequiredaftersporeproduction.Simplerulebasedmethodspredictcriticalperiodsfromlatespringuntillatesummerusingthefollowinggeneralrules:

(a)Minimumtemperature>10OCforaperiodofbetween12to48hours


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6.3.2.2.3.2 WheatDiseases

WMOTechnicalNote99(WMO,1969)providesanoverviewofthevariouswheatruststhatoccuraroundtheworldandthemeteorologicalfactorsthatthetransportofsporeanddiseaseoutbreaksforvarioustypesofwheatrusts.

Seghietal (2000) providea survey ofmany crop diseasemodels including several onwheat.They describe EPIPRE as a system devised to support decision making in pest and diseasecontrolinwinterwheatwiththeaimofreducingpesticideuse.Thesystemintegratessixfungaldiseasesandthreeaphidpestsofwheat.Spirouil-Epurewasdeveloped inFranceandhasbeenused for many years to support extension services for brown wheat rust. The model usesmeteorologicaldataalongwithsomeagronomicandphenologicaldataandprovidesadviceonthe

datesforfirstfungicideapplicationwithinmicro-regionsandwell-definedcropzones.

6.3.2.2.3.3 AppleScabApplescabiscausedbythefungiVenturiainaequalisandisaneconomicallyimportantdiseaseforappleproducers.Postet.al(1963)providedoneoffirstoverviewsofthedisease,investigationsinvariouscountriesanddescriptionsoftheearlywarningsystems.MorerecentlyinGermany,theapplescabmodelASCHORFwasdevelopedandcanprovidepracticalrecommendationstoplantprotectionservicesandapplegrowers(Freisland,2005).Themodelledinfectionriskisdependant

ontemperatureandleafwetnessduration.Leafwetnessdurationiscalculatedbutnotmeasuredand is based on energy balance principles. The model uses a sliding 10-day time series byinputing data for the previous four days from the standard meteorological network and theninputtinggridpointdatafromnumericalweatherpredictionmodels.

6.3.2.2.3.4 DownyMildewDownymildew (Plasmoparavitcola)isoneofthemostimportantfungaldiseasesforwinegrapes(Vitisvinifera)andcanleadtoconsiderablelossesingrapeyieldandquality.Frieslandetal(2005)

developed the PERO model that calculates the start of infection of the grapevine diseasePeronosporathatisdeterminedbytemperatureandleafwetness.ThePEROmodelisbasedonlaboratory and field experiments and the inputs are hourly air temperature, relative humidity,calculated leafwetness, daily extreme temperatures, and daily rainfall. The model outputs areinfectiondatesandoilspotbalances(lesions)thatareusedforagrometeorologicaladvice.The PLASMO (Plasmopora SimulationModel) modelwas developed to simulate the biologicalcycle and the disease leaf area of grapevine downy mildew allowing for the best timing forfungicidetreatments(Orlandinietal2005).Datainputsarehourlytemperature,relativehumidity,rainfallandleafwetness.Theresultsareexpressedinpercentageofleafareacoveredbyoilspotlesion. ThePLASMOmodelhas been developed intocomputer program for distributionand isalsoavailableontheinternetforgreateraccess.Weatherdatacanbeuploadedtothemodelwebsiteforrunningofthemodel(Rossi,etal,2005).6 3 2 2 3 5 Other Applications


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6.3.2.2.4 AnimalPests/Diseases

Theapproachofthemeteorologisttoproblemsofanimaldiseaseisbasicallythesameasforplantdisease.Smith (1970)provides a good overview of practical linksbetweenweatherandanimaldiseasethatmaybewind-borne,parasitic,fungalortheresultofenvironmentalornutritionalstress.MorerecentreviewsincludeWMO(1989)andStarr(1980)thatdetailinternalanimalparasitesandcoldandhotweatherstress.Starr(1980)statesthattherearetwolinesofenquiryinusingclimateinformationasameasureofdiseaseincidence.Thefirstusesclimaticfactorstodevelopclimaticindicesknowntoinfluencethedevelopmentoftheanimalparasiteduringitslife-cycleoutsidetheanimal. The second usesbiological development rates, calculated from the studyofparasitesunder laboratory conditions in constant temperature chambers to determine the influence of

temperature variation on parasite development in actual field conditions. Besides the animaldiseases listed below forwhichmeteorological applicationshave been developed,Starr (1980)alsoprovidesinformationonnematodiriasisandparasiticgastro-enteritis(Starr,1980)

6.3.2.2.4.1 FootandMouthDisease

The 1968-69 FMD epidemic in the UK led to research that concluded meteorological factorsaccountforthemajorityofthevirusspreadingduringafootandmouthoutbreak(SmithandHugh-

Jones,1969;Wright,1969).Thevirusisspreadbybeingexhaledbyanimalsasthenucleiofwaterdroplets,andthenbeingdispersedbywinds.Themostimportantmeteorologicalfactorsarewind,humidityandrainfall;thesynopticsituationwilldeterminetosomedegreethedistanceoverwhichtheviruscanspread.Windspeedanddirectionwilldeterminethepatternofdispersionofthevirus.Stableatmosphericconditionsfavourdispersionoverlargedistancesbecauseverticaldistributionisminimised,while highwindsandturbulenceusually reduce the transportrange.Humiditywilldeterminethedurationthatthevirusremainsprotectedbyitswaterdroplet.Maximuminfectivityisassociatedwithrelativehumidity>60%(Murphyetal,2004).Rainfallwillinfluencewhenthevirus

isdepositedfromtheatmosphere.Inrainyconditionstheviruswillbedepositedonherbagewithinshortdistancesofthesourceratherthanmovingtoinfectotheranimals.

The more recent 2001UKepidemic has resulted in the development, utilisation and testing ofmodelsforpredictingthespreadofvirusbasedon(a)thepredictedviralloadatasourcelocation;(b)thepredictedspreadduetosurfaceweatherconditions(Glosteretal,2003;Mikkelsenetal,2003;McGrath and Finkle, 2001); and (c) latitude and topography.McGrath and Finkle (2001)noted that older models depended on synoptic observations and thus suffered error due topotentialremotenessofobservationstationsfromoutbreaksources.Mikkelsenetal(2003)tested

four dispersionmodels: (1) 10kmGaussianPlume (Glosteretal, 1981); (2) Nuclear AccidentModel(NAME,RyallandMaryon,1998);(3)RIsMesoscalePUFFmodel(RIMPUFF,Mikkelsenetal,1984);and(4)DanishEmergencyResponseModeloftheAtmosphere(DERMA,Srensen,1998).NAMEandDERMAarelongrangemodelsdrivenbynumericalweatherprediction(NWP)output,andproducedsimilarresultsdespitebeingdrivenbydifferentNWPmodels.Thelocal-scalemodels, driven by nearby observation data, were also used to analyse local infection. It was


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empirical approach. Spore traps and counts are now beingused toconfirm the meteorologicalevidence.

6.3.2.2.4.3 Fascioliasisinsheep

Fascioliasis(commoncalledliverflukedisease)isaparasiticdiseasecausedbyFasciolahepaticathat affects sheep. The complicated life-cycle consists of infected sheep passing flukeeggs indungthathatchintofree-swimminglarvaintheopenpastureandinfectthefluke'sintermediatehost,asnail,Lymneatrunculata.Thisisthemostsensitivestageofthelifecycle.Thelarvrewilldieiftheycannotenterasnailwithin24hours.Ollernshaw(1966)describesawetnessindex(Mt)basedonmonthlyrainfall,potentialtranspiration,andthenumberofraindaysthatwasdevelopedinEnglandandWales.Theindexisacculumatedoveraseason,andbasedoncomparisonwith

historicaldiseasestatistics,thresholdsfortreatmentscanbeestablished.Part I of Gibson (1978) provides an updated and very through overview of using weatherinformationinthevariousmodelsandmethodsofthisdiseaseinEuropeandstatesthatthemostimportantmeteorologicalfactorsinthedevelopmentof Fasciolahepaticaaretemperaturesabove10Cforthedevelopmentoftheparasiteinsidethesnailhostandthepresenceoffreewater.Starr(1980) also lists several analytical and simulation models that predict parasite populations inpastureandinthehost.Hesitestheuseofanalyticalmodelsforstrategicdiseasecontrolpolicies

andsimulationmodelsfortacticalcontrolprocedures.InPartIIofGibson(1978),thereareseveralexamplesofusingweatherinformationtostudyand/ormodelnematodiriassisinsheep,tapeworms,ticks,andnematodes.

6.4 Otherapplications

Therearemanyotherapplicationsofmeteorologyforagriculturebesidesthosealreadymentioned.Theyare covered in otherchapters indetail becauseof their importance.One importantgroup

relatestothephysiologyandgrowthofplants,fromgerminationto finalyield.Theseareaffectedsomewhatbytheapplicationsalreadydealtwith,forexample,irrigation,shelter,coveranddisease.Otherapplicationscanbecited,includingtheuseofdegree-daysorotherindicestodeterminethephenologicalstagesofcropssuchasflowering,reproduction,andmaturity.Thesestagesareveryimportant for pest/diseasemanagement. Typically, growing-degree-day or heat-unit calculationsaremadeby subtractinga threshold temperature from the averagedaily temperatureand thenaccumulatingtheseunitsovertimetomodelplantandinsectdevelopment.Thesimplestformonadailybasisis: Degree-day=[(Tmax+Tmin)/2]Tbase. Robertson (1983) describes these calculations as temperature-remainder models (TRIM) andprovidesanextensiveoverviewofthevariousmethodologiestocalculatecropdevelopment.SeeChapters(5and13)foramoredetailedoverviewoftheseconcepts.Anothergroupofapplicationsconcerns field operations Since cultivation drilling spraying and harvesting are all highly weather


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Table1.Examplesofcriticalperiodpredictionmethods(thisisnotanexhaustivelist)indicatingthetypesofmodificationmadeforvariousregionswherepotatoesaregrown:

Method Temperature Humidity Other Rainfall Reference

Dutchrules1926EUROPE

>10

O

Cminatnight 4hrsbelowdewpointatnight 8/10thsfollowingday Followed by >0.1 mmrain vanEverdingen(1926)

Beaumontperiods1947UK

>10OCminforaminimum48hrperiod 75%during the 48 hrperiod

Beaumont(1947)

Irishrules1953IRELAND

>10OCminfora12hrperiod >90%forthe12hours Free moisture on leaves for 2hrsafterthe12hrperiodortherainfallcriterion

Betweenthe7thand15thhours around the endofthe12thhourofthe12hrperiod

Keane(1982)

Hyrerules1954

NE-USA

5 day average 10OCminfor2x24hrperiods >90% for 11 hours ineachofthe2periods

Smith(1956)

NegativePrognosis

1966

GERMANY

Usestemperaturebandsandmultipliesthe hrs in each band by a weightingfactor

>10and90%orarainfalllimitfor 4 hr blocks. Athigher temperatureusea10hrblock

Can subtract form risk whenRHis0.1 mmrainfallorRHlimitfor4hrblocks. At higher

temperature use a 10 hrblock

Ullrich and Schrodter(1966)

Youngrules1978SOUTHAFRICA

>10and70% at 2pmduringeach of the 24 hrperiods

van Rij and du Preez(2004)

Forsundrules

1983

NORWAY

Maximum17-24OC

Minimum>10OC

for2x24hrperiods

>75% at noon duringeach24hrperiod

>0.1mm during each 24hrperiod

Forsund(1983)

Winstelrules1993

BELGIUM

Phase 1: Average daily >10 and 10OC.

Phase2:Maximumdaily>23and90% Winstel(1993)

WashingtonStaterules

1996

NW-USA

Rainfall indicators calculated forperiodswhenminimum>5OCduringApril and May and then July andAugust

Calculatesprobabilityofayearbeinganoutbreakyearusingdiscriminant functions andbinarylogisticregression

Dayswith>0.25mmandtemperature>5OC.

Johnsonetal(1996)


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Table2:ExamplesofDSStypesystemsforpredictionandmanagementofblightoutbreaks(afterBoumaandHansen,1999)

Model Countryande-mail

Originaldevelopmentyear

Maintargetusers Input Output

Televis [email protected]

1957 FarmersAdvisors Weatherdata Epidemiologicaldata

Guntz-Divoux Francefredec.nord.pas-de-

[email protected]

1963 AdvisorsExtensionservice Weatherdata

Adviceline

Simphyt GermanyBkleinhenz.lpp-mainz@

agrarinfo.rpl.de

1982 Plantprotectionservice,Extensionofficers

WeatherdataFielddata

FielddataAdvice

ProPhy [email protected]

1988 FarmersAdvisorsExtensionofficers WeatherdataFielddataOtherdata

WeatheroverviewsFielddataAdvice

Plant-Plus [email protected]

1990 FarmersAdvisorsSuppliersProcessors

Micro-climateCrop+productinformation

DiseasemapsFungicideprotectionperiods

I.P.I Spain 1990 AdvisorsFarmers Weatherdata 1stspraytimingEpidemiologicaldata

NegFry DenmarkJensG.Hansen@

agrisci.dk

1992 FarmersAdvisors WeatherdataFielddata

1stspraytimingFungicideapplications

PhytoPRE+2000

SwitzerlandHansrudolf.forrer@

fal.admin.ch

1995 AdvisorsFarmers,Plantprotectionservice

WeatherdataFielddataOtherdata

RegionaldataFielddata

Guntz-Divoux

[email protected]

1996 AdvisorsExtensionservice Weatherdata

Adviceline

applications of meteorology to agriculture

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