Modelling cereal crops to assess future climate risk for family food self-sufficiency in southern Mali

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ContentslistsavailableatScienceDirect

Field

Crops

Research

j o ur na l h o me p a g e :w w w . e l s e v i e r . c o m / l o c a t e / f c r

Modelling

cereal

crops

to

assess

future

climate

risk

for

family

food

self-sufﬁciency

in

southern

Mali

Bouba

Traore

a,b,∗,1

_,

_Katrien

_{Descheemaeker}

c

_,

_Mark

_T.

_van

_Wijk

d

_,

_Marc

_Corbeels

e,f

_,

Iwan

Supit

g

_,

_Ken

_E.

_Giller

c

a_Institut_D’Economie_Rurale_(IER),_Programme_Coton,_SRA_N’Tarla_Bp:₂₈_Koutiala,_Mali

b_{International}_Crops_Research_Institute_for_the_Semi-Arid_Tropics_{(ICRISAT-Mali),}_BP₃₂₀_Bamako,_Mali

c_Plant_Production_Systems,_Wageningen_University,_P.O._Box_430,₆₇₀₀_AK_Wageningen,_The_Netherlands

d_Livestock_Systems_and_the_Environment,_{International}_Livestock_Research_Institute_(ILRI),_P.O._Box_30709,₀₀₁₀₀_Nairobi,_Kenya

e_Agro-ecology_and_Sustainable_{Intensiﬁcation}_of_Annual_Crops,_Centre_de_Coopération_{Internationale}_en_Recherche_Agronomique_pour_le_{Développement}

(CIRAD)-Av.Agropolis,34060Montpellier,France

f_Sustainable_{Intensiﬁcation}_Program,_{International}_Maize_and_Wheat_Improvement_Center_(CIMMYT),_P.O._Box_1041-00621,_Gigiri,_Nairobi,_Kenya

g_Earth_System_Science_and_Climate_Adaptive_Land_Management,_Wageningen_University_and_Research,_P.O._Box_47,₆₇₀₀_AK_Wageningen,_The_Netherlands

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received29April2016

Receivedinrevisedform7November2016 Accepted11November2016

Keywords:

Cropsimulationmodelling Plantingdate Fertilizeruse APSIM Sub-SaharanAfrica Climatechange

a

b

s

t

r

a

c

t

Futureclimatechangewillhavefarreachingconsequencesforsmallholderfarmersinsub-SaharanAfrica, themajorityofwhomdependonagriculturefortheirlivelihoods.Hereweassessedthefarm-levelimpact ofclimatechangeonfamilyfoodself-sufﬁciencyandevaluatedpotentialadaptationoptionsofcrop management.UsingthreeyearsofexperimentaldataonmaizeandmilletfromanareainsouthernMali representingtheSudano-SahelianzoneofWestAfricawecalibratedandtestedtheAgricultural Produc-tionSystemssIMulator(APSIM)model.Changesinfuturerainfall,maximumandminimumtemperature andtheirsimulatedeffectsonmaizeandmilletyieldwereanalysedforclimatechangepredictionsof ﬁveGlobalCirculationModels(GCMs)forthe4.5Wm−2_and_8.5_Wm−2_radiative_forcing_scenario_(rcp4.5 andrcp8.5).

InsouthernMali,annualmaximumandminimumtemperatureswillincreaseby2.9◦_C_and_3.3◦_C_by

themid-century(2040–2069)ascomparedwiththebaseline(1980–2009)underthercp4.5andrcp8.5 scenariorespectively.PredictedchangesinthetotalseasonalrainfalldifferedbetweentheGCMs,buton average,seasonalrainfallwaspredictednottochange.Bymid-centurymaizegrainyieldswerepredicted todecreaseby51%and57%undercurrentfarmer’sfertilizerpracticesinthercp4.5andrcp8.5scenarios respectively.APSIMmodelpredictionsindicatedthattheuseofmineralfertilizeratrecommendedrates cannotfullyoffsettheimpactofclimatechangebutcanbufferthelossesinmaizeyieldupto46%and51% ofthebaselineyield.Milletyieldlosseswerepredictedtobelesssevereundercurrentfarmer’sfertilizer practicesbymid-centuryi.e.7%and12%inthercp4.5andrcp8.5scenariorespectively.Useofmineral fertilizeronmilletcanoffsetthepredictedyieldlossesresultinginyieldincreasesunderbothemission scenarios.

Underfutureclimateandcurrentcroppingpractices,foodavailabilityisexpectedtoreduceforallfarm typesinsouthernMali.However,largeandmedium-sizedfarmscanstillachievefoodself–sufficiency ifearlyplantingandrecommendedratesoffertilizerareapplied.Smallfarms,whicharealreadyfood insecure,willexperienceafurtherdecreaseinfoodself-sufficiency,withadaptivemeasuresofearly plantingandfertilizeruseunabletohelpthemachievefoodself-sufficiency.Bytakingintoaccountthe diversityinfarmhouseholdsthatistypicalfortheregion,weillustratedthatcropmanagementstrategies mustbetailoredtothecapacityandresourceendowmentoflocalfarmers.Ourplace-basedfindingscan supportdecisionmakingbyextensionanddevelopmentagentsandpolicymakersintheSudano-Sahelian zoneofWestAfrica.

∗_{Corresponding}_author_at:_Institut_D’Economie_Rurale_(IER),_Programme_Coton,_SRA_N’Tarla_Bp:₂₈_Koutiala,_Mali.

E-mailaddresses:boubasiditraore@yahoo.fr,B.Traore@cgiar.org(B.Traore).

1 _Current_address:_{International}_Crops_Research_Institute_for_the_Semi-Arid_Tropics_{(ICRISAT-Mali),}_BP₃₂₀_Bamako,_Mali.

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1. Introduction

Climatechangewilladverselyaffectfoodproductioninmany regionsoftheworld,especiallyinsub-SaharanAfricawherealarge partofthepopulationfaceschronichunger(Lobelletal.,2008). Theobservedchangesinrainfallandtemperaturesincethe1970s are believed to have resulted in a decline of overall crop pro-duction(Barriosetal.,2008).Climatechangeprojectionsforthe WestAfricanregionindicateafurtherincreaseintemperatureof between1.1and4.8◦Candlargerdifferencesinrainfallbetween wetanddryseasonsattheendofthiscentury(IPCC,2013).

AreviewofclimatechangeimpactstudiesoncropyieldsinWest Africaillustratesalargedispersionofcropyieldchangesranging from−50%to+90%,withamedianyieldlossofabout11%(Roudier etal.,2011).PredictedimpactislargerinSudano-Sahelian coun-tries,withanaverageyieldlossof18%,comparedwiththecountries intheSouthernGuineaSavannahzonewherethepredictedyield lossis 13%(Sultanet al.,2013).Thisdifferenceis likelydue to thealreadydrierandwarmerclimateinthemorenortherly coun-triesofWestAfrica. Intheshortterm,(2010–2040),cropyields attheMaliannationallevelarepredictedtovarybetween−17% and+6%ofthecurrentyields(Buttetal.,2005a).Itisbelievedthat thenegativeimpactofclimatechangemainlyresultsfromeffects ofrisingtemperature,whichispredictedwithastrongersignal andlessuncertaintycomparedtoprecipitationchanges(Roudier etal.,2011).SchlenkerandLobell(2010)showedthatevenif rain-fallremainedconstantinsub-SaharanAfricabymid-century,crop yieldswoulddecreasebyabout15%duetothehighertemperatures reducingthelengthofthecropgrowthcycleandincreasingwater stressasaresultofgreatersoilwaterevaporationlosses.

Newevidenceofclimatechange(IPCC,2013)highlightstheneed toadaptcropping systems.For Mali,Butt etal. (2005b)argued thatbyimplementingadaptiveresponsessuchastheuseof high-temperature-resistantcropvarietiestogetherwithaddressingsoil fertilitydecline,economicgainscouldexceedlossesduetoclimate change.VariousadaptationoptionsthatMalianfarmerscurrently practicetocopewithclimatevariabilitycouldbeconsidered.In general,thesearetacticaladaptationsoffarmmanagement(such asshiftingtocroplandraces,alteringfertilization),butalsoinclude morestrategicadaptationsofalteredincome/assetmanagement (Chukuand Okoye,2009).A commonoperationaladaptationto addressclimateriskinsemi-aridandsub-humidregionsistoshift theplantingdatetocoincidewiththealteredstartof therainy season(Mulleretal.,2010).

Linkingclimatechangescenarioswithcropsimulation mod-ellingtoassesstheresponseofcropproductiontoclimatechange incombinationwithadaptivefarmmanagement,provides infor-mation that can enhance strategic decision-making by farmers andpolicymakerstoadapttothenovelchallengesofachanged climate(Rosenzweigetal.,2013).Thefewregionalimpact stud-iesthathave takeninto accountadaptationoptionsby farmers (Fraseretal.,2011)aredifﬁculttotranslateintoknowledgethat candrivelocalsolutions.Localstudieswithcropmodelsare gener-allybettersuitedtosupportlocallyappropriatedecision-making thanregionalapproaches (Fischeret al.,2005).Yetfew ofsuch locally-relevantstudiesexistinsub-SaharanAfrica(Tingemetal., 2009;Sultanetal.,2013).OnecropsimulationstudyinCameroon showedthatalteringplantingdateandcultivartypecanreducethe negativeimpactsofclimatechangeandevenincreasecropyields (TingemandRivington,2009).Asfarmsinsub-SaharanAfricaare verydiverse(Gilleretal.,2011),theimpactofclimatechangeand thefeasibilityofadaptationoptionsarelikelytodifferamong farm-ers.However,withclimatechangeimpactstudiesusuallyfocussing solelyattheﬁeldscale,farm-levelinformationthatisdisaggregated byfarmtypeisscarce.Thus,morelocallygroundedresearchon likelycropresponsestoclimatechangeandeffectsonfarm-level

indicatorsareneededtosupportdecision-makingonadaptation strategiestomitigatenegativeimpactsofchangingclimate.

Thisstudyaimedtobetterunderstandfutureclimatechange, itsimpactoncerealcropproduction,and thepotentialof adap-tationoptionsinsouthernMali.Ourspeciﬁcobjectiveswere:(i) tocalibrateandtestthecropmodelAPSIM(Agricultural Produc-tionSystemssIMulator)formaizeandmilletinSudano-Sahelian conditions;(ii)toanalysechangesinfuturerainfall,maximumand minimumtemperatureundertwoemissionscenarios leadingto a radiativeforcing of4.5Wm−2 _and _8.5_W_m−2 _by_mid-century

(2040–69);(iii)toassesstheimpactofclimatechangeonyields ofmaizeandmillet;(iv)toevaluatepotentialadaptationoptionsof cropmanagementand(v)toquantifythefarm-levelconsequences forfoodself-sufﬁciencyofdifferenttypesofsmallholderfarmersin southernMali.

2. Materialsandmethods

2.1. Site

We examined the effects of future climate on cereal crop production at N’Tarla(12◦35N, 5◦42W,302ma.s.l.). N’Tarlais representativeofthecereal cropproduction regioninMaliand occupies14%oftheterritory.Thisregionholdsabout40%ofthe totalpopulationand 50%ofthecultivablelandofMali(Deveze, 2006). The climate in southern Mali is typical of the Sudano-Sahelianzone ofWestAfrica.Averagelong-termannualrainfall atN’Tarlais846±163mm.TherainyseasonextendsfromMayto Octoberwithanaveragetemperatureof29◦C.Farmingsystemsin theregionaremixedagro-pastoralsystems,withcotton( Gossyp-iumhirsutumL.)asthemaincashcrop, inrotationwithcereals –maize(ZeamaysL.),pearlmillet(Pennisetumglaucum(L.)R.Br.), sorghum(Sorghumbicolor(L.)Moench),–andlegumes–groundnut (ArachishypogaeaL.)andcowpea(Vignaunguiculata(L.)Walp.).

2.2. Experimentaldataformodelcalibrationandtesting

We used dataon maize and millet froma ﬁeld experiment conductedoverthreeconsecutivegrowingseasonsfrom2009to 2011atN’Tarla.Theexperimentwasasplit-split-plotdesignwith threefactors(crop,variety,plantingdate)andfourreplicates.The mainplot treatmentwasthecrop(maize, pearlmillet). Onthe sub-plotsthree varietieswere tested,referred toas V1 and V2 forlongdurationvarietyandV3forshortdurationvariety(Table A1)andthreeplantingdatescoveredthepossiblerangeof plant-ingdatesinsouthernMali,referredtoasD1(earlyplantingdate), D2(mediumplantingdate)andD3(lateplantingdate).Eachyear, allplotsreceivedthreetonnesdrymatterperhectareofmanure (withanorganicmattercontentof44%andC/Nratioof12and organiccarboncontentof22%)andcrop-speciﬁcrecommended fer-tilizerdoses(IER/CMDT/OHVN,1998).Maizereceived85kgNha−1_,

26kgPha−1 _and₁₆_kg_K_ha−1_,_whilst_millet_received₃₉_kg_N_ha−1

andthesameamountsofPandKasmaize.Thesoilofthe experi-mentalsiteisaFerricLixisol(FAOsoilclassiﬁcation)with4%clay, 16%siltand80%sandcontentinthe0–40cmsoillayer.Thesoilis slightlyacidwithapHof5.6.Organiccarboncontentis2gkg−1_soil

(0–40cm).

For each variety of maize and millet,phenological develop-mentwasmonitoredandthedatesofemergence,endofjuvenile phase,floralinitiation,flagleafstage,flowering,startofgrainfilling andphysiologicalmaturitywererecorded.Leafareaindex(LAI)of maizeandmilletplantswasmeasuredat15,30,45,60and75days afterplanting.Cropswereharvestedafterphysiologicalmaturity andstoverandgraindrymatteryieldsweredetermined.The exper-imentwasdescribedindetailbyTraoreetal.(2014).

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Dailyrainfall,minimumandmaximumtemperatureand radi-ationwererecordedattheN’Tarlameteorologicalstationsituated atabout1kmfromtheexperimentalﬁeld.

2.3. Modelsimulations

TheAPSIM(AgriculturalProductionSystemssIMulator)model (Keatingetal.,2003)incombinationwithclimatemodeloutput oftheCoupled ModelIntercomparison ProjectPhase5 (CMIP5) (Tayloretal.,2012)wasusedtoassessclimatechangeimpactson maizeandmilletyields,andtoevaluatealteredplantingdatesand fertilisationasadaptationoptionstoclimatechange(seebelow, Section2.5).TheAPSIMMaize(7.4)andtheAPSIMMillet(7.4) mod-ulestogetherwiththesoilwater module(SoilWat)and thesoil nitrogenmodule(SoilN)ofAPSIMwereparameterizedandtested usingtheresultsoftheabovedescribedexperiment.Inthisway,we simulatedthegrowthofmaizeandmilletaslimitedbysoilwater andnitrogen.

2.3.1. Modelparameterization

ThevaluesforcropandsoilparametersinAPSIMweremeasured attheexperimentalsite,derivedfromliteratureorobtainedby cal-ibratingthemodelusingcropdevelopmentandgrowthdataofone year(2010)oftheexperiment(Tables1and2).Valuesfor pheno-logicalparametersforthedifferentmaizeandmilletvarietieswere basedoncalculatedthermaltimebetweenobservedphenological developmentstagesofthecrops(Table1).Basetemperature, maxi-mumgrainnumberperheadandgraingrowthratewerecalibrated basedonobservedbiomassandgrainproductiondataof2010.For thecoefﬁcientsoftheregressionfunctiondescribingtherelation betweentheareaofthelargestleafofmilletandthetotalnumber leaves,publishedvalueswereused(Akponikpèetal.,2010).

Soilwatercontentatthedrainedupperlimit(DUL),atthelower limit(LL),andatsaturation(SAT)werebasedonmeasurements per-formedattheexperimentalsite(Table2).Thebaresoilrunoffcurve numberwassetat40toaccountforlowrunoffduetotheﬂat topog-raphyoftheexperimentalsiteandthesoilwatercharacteristicsof asandysoil.Soilorganiccarbon,soilpHandsoilbulkdensitywere measuredattheexperimentalsite.Initialsoilconditionsforwater andsoilmineralNcontentweresetusingobserveddatafromthe experimentin2010,bothforthecalibrationandvalidationruns. Observedsoilwatercontentsmeasuredwithaneutronprobeat aweekbeforeplantingdatewere16mm,19mm,21mm,21mm, 43mm,and42mminthe0–0.1m,0.1–0.2m,0.2–0.3m,0.3–0.4m, 0.4–0.6mand0.6–0.8msoillayers,respectively.Initialvaluesfor mineralN(0–0.8m)were31kgha−1_for_NO−₃_and₁₄_kg_ha−1_for

NH+ 4.

2.3.2. Modelevaluation

Themodelwastested againstdataofthetotal aboveground biomass,grainyieldandLAImeasuredin2009and2011.Model per-formancewasevaluatedgraphicallyandquantiﬁedbycalculating therootmeansquarederror(RMSE),withlowervaluesindicating bettermodelagreementwithobservedvalues,theR2_of_the

regres-sionbetweenobservedandsimulatedvalues,rangingfrom0to1, withhighervaluesexpressingabetterlinearrelationship,andthe modelefﬁciency(EF)(Willmottetal.,1985),rangingfrom−∞to1 withhighervaluesindicatingabetteragreementbetweenobserved andsimulatedvalues:

RMSE=

n i=1(Oi−Pi) 2 n EF=

n i=1

Oi−O¯

2−

n i=1

Pi−O¯

2

n i=1

Oi−_O¯

2

whereOiandPiaretheobservedandsimulatedvalues, ¯Oisthe

meanoftheobservedvaluesandnisthenumberofobservations.

2.4. Climatescenarios

WeusedthelatestCMIP5climatemodellingresultsforthe his-torical(1976–2005)andfutureclimatescenarios.Twogreenhouse gasemissionscenarioswereconsideredasdescribedinthe spe-cialreportonemissionscenarios(Nakicenovicetal.,2000).Under ahighRepresentativeConcentrationPathway(RCP8.5),the atmo-sphericCO2 concentrationwillriseto850ppmbytheendofthe

centuryandannualmeantemperatureinAfricawillincreaseby 3to6◦C(IPCC,2014).Inthercp4.5scenariotheatmosphericCO2

concentrationwillstabilizeat550ppmandtemperatureinAfrica willincreaseby2to5◦C(IPCC,2014).WedidnotaccountforCO2

fertilizationeffectsinthesimulationsandassumedthatmaizeand milletasC4plantsbeneﬁtrelativelylittlefromincreasedCO2

con-centrations(Tayloretal.,2014).Ithasbeenshownthatimpacts ofincreasedtemperaturescoupledwithsoilmoisturechangeswill largelyoverridethecompensatingeffectsofincreasedCO2oncrop

yields(RosenzweigandParry,1994).

2.5. Analysisofclimatechange,itsimpactsoncropyieldand effectsofadaptationoptions

Inordertospansomeoftheuncertaintyinclimateprojections, climatedataofﬁveGlobalCirculationModels(GCMs)(cnrm-cm5, ecearth, hadgem2-es, ipsl-cm5a-lr, mpi-esm-lr) were used.The particularGCMswereselectedﬁrstlybecausetheybelongtothelist ofCMIP5(CoupledModelIntercomparisonProject)models(Taylor et al.,2012)and secondlybecausedailybias-corrected datafor maximumand minimumtemperature andrainfall werereadily availableona0.5×0.5◦gridforthestudysiteovertheperiod1976 to2005forthepastclimateandoverthe2006to2099periodfor futureclimate.Radiationdatawasbias-correctedaccordingtothe methodofHaddelandetal.(2012).ThemethodofPianietal.(2010)

wasusedtobias-correcttemperatureandrainfall.Forboth meth-odstheWATCHforcingdatawasusedasabaseline(Weedonetal., 2011).

Futureminimumandmaximumtemperatureandrainfallwere comparedwiththebaselineconditionsbymeansofgraphical anal-ysesshowingmonthlyaveragesandrangesoftheselectedclimate indicatorsforthegrowingseason(fromthebeginningofMaytothe endofOctober).WecomparedeachGCMinordertounderstand theuncertaintyintheclimatedataandusedanalysisofvariance (ANOVA)totestforsigniﬁcantdifferencesbetweenGCMs.Forthe analysisoftheimpactsofclimatechangeoncropproductionwe usedsimulatedclimatedatafromtheindividualGCMsand subdi-videdtheentiresimulationperiodsoastohavecontinuousseries of30yearsforbaseline(1980to2009),andnear(2010to2039), mid(2040to2069)andend(2070to2099)ofthecentury peri-ods.Becauseofincreaseofuncertaintyinpredictedclimatedata towardstheendofcentury,wefocusedourcropmodelanalysis onthemid-centuryperiod.Thecropyieldspredictedwithclimate datafromtheﬁveGCMswerecomparedinordertounderstand theuncertaintyintheyieldpredictionsrelatedtotheuncertainty inthepredictedclimatedata.

Weﬁrstanalysedtheeffectsofclimatechangeunderthetwo emissionscenarios(rcp4.5andrcp8.5)oncropyieldforthelong (V1)andtheshortduration(V3)varietiesofmaizeandmilletwith earlyplantingdate(D1)andundercurrentfarmer’sfertilization

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Table1

Cropparametersforthelong(V1,V2)andshort(V3)durationvarietiesofmaizeandmilletusedintheAPSIMsimulations.

V1 V2 V3 Units Ref.

Maize Emergence-endjuvenile 307 290 263 ◦_C_days _Observed

Endjuvenile-ﬂoralinitiation 31 31 33 ◦_C_days _Observed

Flagleaf-ﬂowering 15 15 18 ◦_C_days _Observed

Flowering-startgrainﬁlling 191 191 185 ◦_C_days _Observed

Flowering–maturity 620 620 589 ◦_C_days _Observed

Daylengthphotoperiodtoﬂowering 12.5 12.5 12.5 hours Default

Daylengthphotoperiodforinsensitivity 24 24 24 hours Default

Basetemperature 10 10 10 ◦_C _Estimated

Grainmaximumnumberperhead 530 530 530 number Estimated

Graingrowthrate 8 8 9 mg/day Estimated

Radiationuseefﬁciency 1.6 1.6 1.6 g/MJ Default

Transpirationuseefﬁciency 0.009 0.009 0.009 kPa Default

Millet Emergence-endjuvenile 430 400 315 ◦_C_days _Observed

Endjuvenile-ﬂoralinitiation 112 112 112 ◦_C_days/h _Default

Flagleaf-ﬂowering 60 60 60 ◦_C_days _Observed

Flowering-startgrainﬁlling 100 100 100 ◦Cdays Observed

Flowering–maturity 548 457 508 ◦Cdays Observed

Regressionoflargestleafarea–intercept −807 −807 −807 mm2 _Akponikpè_et_al.₍₂₀₁₀₎

Regressionoflargestleafarea–slope 1137 1137 1137 mm2_/leaf _Akponikpè_et_al.₍₂₀₁₀₎

Grainnumberperhead 3500 2000 3200 grain/head Estimated

Graingrowthrate 0.42 0.42 0.42 mg/grain/day Akponikpèetal.(2010)

Radiationuseefﬁciency 1.3 1.3 1.3 g/MJ Akponikpèetal.(2010)

Transpirationuseefﬁciency 0.009 0.009 0.009 kPa Default

Table2

SoilparametersusedintheAPSIMsimulations.

Soildepth(cm)

Acronym 0–10 10–20 20–30 30–40 40–60 60–80 units Ref.

BulkDensity BD 1.57 1.57 1.72 1.67 1.74 1.65 g/cm3 _Measured

Volumetricsoilwatercontentatthelowerlimita _LL _0.148 _0.14 _0.171 _0.114 _0.086 _0.107 _mm/mm _Measured

Volumetricsoilwatercontentatthedrainedupperlimit DUL 0.283 0.272 0.304 0.283 0.206 0.25 mm/mm Measured

Volumetricsoilwatercontentatthesaturation SAT 0.317 0.305 0.337 0.331 0.226 0.273 mm/mm Measured

Soilwaterextractioncoefﬁcient KL 0.05 0.05 0.05 0.05 0.05 0.05 /days Estimated

Layerdrainageratecoefﬁcient SWCON 0.4 0.4 0.4 0.4 0.4 0.4 – Estimated

Inertfractionoforganiccarbon FINERT 0.2 0.2 0.35 0.35 0.899 0.89 – Sissoko(2009)

Non-inertfractionofcarboninmicrobialproducts FBIOM 0.04 0.02 0.02 0.01 0.01 0.01 – Sissoko(2009)

Soilalbedo SALB 0.13 – Sissoko(2009)

Stage1soilevaporationcoefﬁcient U 8.65 mm Sissoko(2009)

Stage2soilevaporationcoefﬁcient CONA 0.01 – Sissoko(2009)

Baresoilrunoffcurvenumber CN2BARE 40 – Sissoko(2009)

ReductioninCN2BAREduetocover CNRED 28 – Sissoko(2009)b

a_The_volumetric_soil_water_content_at_the_lower_limit_was_also_used_for_the_crop_lower_limit.

b _Sissoko₍₂₀₀₉₎_Analyse_des_ﬂux_d’eau_dans_les_systèmes_de_culture_sous_couverture_végétale_en_zone_Soudano_Sahelienne:_Cas_du_coton_semé_après_une_culture_de

sorgho/brachiariaausudduMali.ThèsedeDoctorat,MontpellierSupAgro,169pp.

practice(F1,being60kgNha−1_for_maize_and_no_N_for_millet).

Sub-sequently,theeffectsofdifferentcroppingpracticesundercurrent andfutureclimatewereexploredbyinvestigatingeffectsof recom-mendedfertilizerrates(F2,85kgNha−1_for_maize_and₄₀_kg_N_ha−1

formillet),threeplantingdates(early(D1),medium(D2)andlate (D3)planting)andtwovarieties,i.e.ashort(V3)andalong(V1) durationvariety.

The impact of future climatechange on smallholder family foodself-sufﬁciencywasevaluatedbasedonthebalanceoftotal energyproducedandrequiredatthehouseholdlevel.Totalenergy produced (kcal) was calculated based on the total cereal pro-duction on the farm and a crop-speciﬁc grain energy content (FAO,1990).Energyrequirementswerecalculatedbasedonthe number of household members and the average daily energy requirementperperson(2450kcalperson−1_day−1 _for_adults_and

1775kcalperson−1_day−1 _for _children_under_the_age_of₁₁₎₍_FAO andINPhO,1993).

BasedonﬁndingsofSultanetal.(2013)indicatingthatmillet andsorghumyieldswillbesimilarlyaffectedbyclimatechange,we assumedthattheimpactofclimatechangeonmillet,estimatedin thisstudycanalsobeappliedtosorghum.Observedaverageyields

ofeachfarmtype(TableA2)andtotallandareaswereusedto deter-minethecurrent totalfarm production.Therelative impactsof futureclimatechangetogetherwiththeseobservedaverageyields wereusedtocalculatetheexpectedabsolutechangesinyieldand foodproductionunderthedifferentscenariosofclimatechange andadaptation.

Farmswerecategorisedintothreefarmtypes(TableA2)based onlandholding,ownershipoffarmingassets(plough,seeder,and cultivator),numberofcattleandtypeofcroppingsystem:alarge farmtype(6%ofthepopulation),amediumfarmtype(81%ofthe population)andasmallfarmtype(12%ofthepopulation) cultivat-ingrespectively18,10and4haofcropland(Djouaraetal.,2006). Forthefarmtype-speciﬁcadaptationoptionsweassumedthatthe largefarmtypewouldapplytherecommendedfertilizerrateand keepthecurrentearlyplantingpractice.Themediumfarmtypes wouldalsoapplyrecommendedfertilizerratesandplantearlyin thegrowingseason.Assmallfarmsusuallystruggletoplantontime becauseoflackofequipmentsuchasplough,seederanddraught animals,weassumedthatthistypeoffarmwouldplantatmedium plantingdate,butwouldapplyrecommendedfertilizerrates.

Bothsinglefactor(climate,variety,fertilizer,plantingdate)and interactioneffectsonsimulatedmaizeandmilletyieldswere

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esti-matedusingANOVAprocedures(Tukey’stest,atP≤0.05)(GenStat Edition14LibraryReleasePL18.2,VSNInternationalLtd.).

3. Results

3.1. Modelperformance

Modelperformanceformaizewasgood(Table3andFig.1). Sim-ulatedgrainyieldswererelativelyclosetoobservedvaluesforthe differentplantingdatesandvarietieswithR2_values_of_0.89_for₂₀₁₀

(modelcalibration)and0.69for2009and2011(modeltests).For totalabovegroundbiomasstheR2_values_were_lower:_0.55_for₂₀₁₀

and0.56for2009and2011(Fig.1).Modelefﬁciencyforgrainyield andtotalabovegroundbiomasswassatisfactoryforthethreemaize varieties(Table3).AlsoLAIwasadequatelysimulated(Fig.A1)with RMSEvaluesformaximumLAIof0.36,0.30and0.34forV1,V2and V3respectivelyandwithmodelefﬁciencyvaluesthatwerehigher than0.95forthethreevarieties(Table3).Plantextractablesoil watercontentsduringthegrowingseasonwerereasonablywell simulatedasindicatedbytheR2 _values_between_simulated_and

measuredvalues(0.69,0.72and0.58forrespectivelyV1,V2and V3in2010and0.52,0.29and0.43in2010,Fig.A1).

ModelperformanceformilletwassatisfactorywithR2_values_of

0.60and0.45forgrainyieldsimulations,respectivelyforthe cali-brationyear2010andthetwotestyears2009and2011(Fig.2).For totalabovegroundbiomass,R2_values_were_0.53_and_0.71_for

cali-brationandtestdatasetsrespectively.Asindicatedbythevaluesof RMSEandmodelefﬁciency,themodeladequatelysimulatedtotal abovegroundbiomass,grainyield,maximumLAIandLAIdynamics forthethreemilletvarieties(Table3)(Fig.A2).

3.2. Climatechangepredictions

Climatepredictionsfortheperiod2040–2069showedincreased minimum and maximum temperatures (Fig. A3). In the simu-latedbaselineclimateofthepast30years(1980–2009),average annual maximum and minimum temperatureswere 34◦C and 23◦C, respectively.Inthercp8.5scenarioaverageannual maxi-mumandminimumtemperaturesincreasedby2.9◦Cand3.3◦C bymid-century(2040–2069)ascomparedwiththebaseline.The strongest warming occurred in May withan increase of 3.1◦C and3.7◦Cinmaximumandminimumtemperature,respectively (Fig. A3).The increase inAugust and September(the period of ﬂoweringandmaturityofcerealcrops)was2.5◦Cand2.6◦Cfor maximum and minimumtemperatures, respectively. Compared withthercp8.5scenario,theexpectedwarmingbymid-century waslesspronouncedunderthercp4.5scenario(Fig.A3).

Predictedaverageannualrainfallfor2040–69didnotchange signiﬁcantlyfromthebaselinewitheitheremissionscenario,nor didaveragemonthlyrainfall(Fig.A3).Bymid-century,predicted averageseasonalrainfallamountwas890mmand945mm respec-tivelyforthercp4.5andrcp8.5scenarios.

Despitetheconsistenttrendsofincreasedpredictedminimum and maximum temperature compared withthe baseline, there werelargeandsigniﬁcantdifferences(P=0.001)betweentheﬁve GCMs (Fig.A4).Under thercp8.5scenario,thehighest growing seasontemperature wasobtainedwith theipsl model withan averageof28.6◦Cand38.5◦Crespectivelyforminimumand max-imumtemperature,whilethelowestgrowingseasontemperature wasobtainedwiththecnrmmodelwithanaverageof25.2◦Cand 35.2◦Crespectivelyforminimumandmaximumtemperature(Fig. A4).ThesamedifferencesbetweenGCMswereobservedunderthe rcp4.5scenario(Fig.A4).

3.3. Variabilityanduncertaintyingrainyieldpredictions

Under thebaselinescenariotheaveragecoefficientof varia-tionin simulated grainyieldwas21% and 15%respectively for maizeandmillet.Predictionsofmaizeandmilletgrainyields dif-feredbetweentheGCMs(Fig.3).Underthercp4.5scenario,average yieldsofthelongdurationvarietyofmaize(V1)simulatedwith cli-matedatafromthecnrmmodelweresignificantly(P<0.05)larger thanthosesimulatedwithclimatedatafromtheipslmodel,while yieldsobtainedwithhadgem2,mpiandecearthdatawere simi-lar(Table4).Thecoefficientofvariationinyieldoverthe30years simulationperiod(2040–69)variedbetween39%(cnrm)and68% (ipsl).Underrcp8.5ANOVAresultsindicatedthataveragemaize grainyieldssimulatedwithcnrmclimatedataweresignificantly (P<0.001)largerthanyieldssimulatedwithclimatedatafromthe othermodelsexcepthadgem2.Thecoefficientsofvariationinyields variedfrom39%(cnrm)to88%(ipsl).

Formillet,ANOVAresultsindicatedsignificant(P<0.01) differ-encesinyieldsobtainedwithclimatedatafromdifferentGCMs bothforthercp4.5andrcp8.5scenarios.Under rcp4.5the aver-ageyieldssimulatedwithdatafromtheecearthweresignificantly (P<0.01)largerthanyieldssimulatedwithclimatedatafromthe cnrmandhadgem2modelswhileaverageyieldsobtainedwithipsl andmpidataweresimilar.Thecoefficientofvariationin simu-latedyieldsvariedfrom11%(hadgem2)to16%(cnrm).Underthe rcp8.5scenario,averageyieldssimulatedwithclimatedatafrom ecearthweresignificantly(P<0.001)largerthanyieldssimulated withclimatedatafromallotherGCMs.

WithresultsfromallGCMspooled,theaveragecoefﬁcientof variationofpredictedmaizegrainyieldoverthe30years simula-tionperiodwas53%and60%forrcp4.5andrcp8.5,whileformillet itwas14%forrcp4.5and12%forrcp8.5respectively.

For maize therangein simulatedyieldsbetweenGCMs was 395kgha−1_and₇₈₁_kg_ha−1_respectively_under_rcp4.5_and_rcp8.5

whileformilletitwas134kgha−1 _and₂₁₃_kg_ha−1 _{respectively.}

Therefore,itseemsthattheuncertaintyinmaizeandmilletyield predictionsresultedfrombothGCMandRCPdifferences.

3.4. Yieldpredictionsunderclimatechangewithcurrentfertilizer practices

Forthelongduration variety(V1)ofmaize atearlyplanting date (D1) themedian predictedgrain yield in thebaseline cli-matescenarioandwithcurrentfertilizerapplicationrate(F1)was 2213kgha−1 ₍_Fig.₃_)._For_future_climate_during_the_mid-century

period(2040–69),themedianpredictedgrainyielddeclinedby59% and67%underthercp4.5andrcp8.5scenariosrespectively.A sim-ilarimpactofclimatechangewaspredictedfortheshortduration maizevariety(V3)atearlyplanting(D1),showingadecreaseof50% and58%underthercp4.5andrcp8.5scenariorespectively,relative tothemedianpredictedbaselineyieldof1733kgha−1₍_Fig.₃_).

Com-paringthetwomaizevarietiesatearlyplanting(D1),themedian grainyieldofthelongdurationvariety(V1)was22%largerthanthat oftheshortdurationvariety(V3)underthebaselineclimate.The differencesbetweenvarietiesweresmallerthan5%underfuture climateforboththercp4.5andrcp8.5scenarios.

Themedianpredictedgrainyieldunderthebaselineclimate was1199kgha−1 _for _the_long_duration_variety_(V1)_of_millet_at

earlyplanting(D1)andwithcurrentfertilizerapplicationrate(F1) (Fig.3).Therewasnopredictedyieldlossunderrcp4.5whilefor thercp8.5scenario,thepredictedyieldlosswas4%(Fig.3).Forthe shortdurationvariety(V3)ofmilletatearlyplanting(D1),median predictedgrainyieldunderthebaselinescenariowas1101kgha−1

(Fig.3).Underfutureclimate,medianpredictedyieldlosswas0% forthescenariorcp4.5and6%for thescenariorcp8.5. Compar-ingbothmilletvarietiesatearlyplanting(D1)revealedthatthe

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2010

Observed total biomass (kg ha-1)

2000 4000 6000 8000 10000 S im ula te d t o ta l b io m as s ( k g ha -1 ) 2000 4000 6000 8000 10000 V1 V2 V3 2009 and 2011

2000 4000 6000 8000 10000

2010

Observed grain yield (kg ha-1)

0 1000 2000 3000 S im ul at ed gr ai n yi el d ( k g ha -1 ) 0 1000 2000 3000 V1 V2 V3 2009 and 2011

Observed grain yield (kg ha-1)

0 1000 2000 3000 V1D1 V1D2 V1D3 V2D1 V2D2 V2D3 V3D1 V3D2 V3D3

c)

d)

a)

b)

rRMSE = 0.15 R2_{= 0.89} rRMSE = 0.24 R2 = 0.69 rRMSE = 0.26 R2 = 0.55 rRMSE = 0.22 R2 = 0.56

Fig.1.Comparisonofobservedandsimulatedgrainyieldandtotalabovegroundbiomassofmaizein2010forAPSIMmodelcalibration(a,c)andin2009and2011formodel testing(b,d).V1andV2:Longdurationvariety;V3:Shortdurationvariety;D1,D2andD3correspondrespectivelytoearly(June),medium(July)andlate(August)planting date.The1:1lineisindicatedineachgraph.

2010

3000 6000 9000 12000 15000 Si m ul at ed t ot al bi on ass (kg h a -1) 3000 6000 9000 12000 15000 V1 V2 V3 2010 Observed yield (kg ha-1) 0 500 1000 1500 2000 S im ul at ed gr ai n yi el d ( k g ha -1 ) 0 500 1000 1500 2000 V1 V2 V3 2009 and 2011 Observed yield (kg ha-1) 0 500 1000 1500 2000 V1D1 V1D2 V1D3 V2D1 V2D2 V2D3 V3D1 V3D2 V3D3

c)

a)

b)

3000 6000 9000 12000 15000 2009 and 2011

d)

rRMSE = 0.30 R2= 0.60 rRMSE = 0.24 R2_{= 0.45} rRMSE= 0.13 R2_{= 0.53} rRMSE = 0.22 R² = 0.71

Fig.2.Comparisonofobservedandsimulatedgrainyieldandtotalabovegroundbiomassofmilletin2010forAPSIMmodelcalibration(a,c)andin2009and2011formodel testing(b,d).V1andV2:Longdurationvariety;V3:Shortdurationvariety;D1,D2andD3correspondrespectivelytoearly(June),medium(July)andlate(August)planting date.The1:1lineisindicatedineachgraph.

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Table3

Modelperformancestatisticsformaizeandmilletgrainyield(kgha−1_),_total_above_ground_biomass_(kg_ha−1₎_and_maximum_leaf_area_index_(LAI_m2_m−2_).

V1 V2 V3

RMSE EFF RMSE EFF RMSE EFF

Maize grainyield 397 0.54 497 0.15 463 0.38

totalabovegroundgroundbiomass 1333 0.65 1549 0.30 933 0.42

LAI 0.36 0.95 0.30 0.98 0.34 0.95

Millet grainyield 249 0.39 380 0.32 295 0.54

totalabovegroundgroundbiomass 1226 0.77 1316 0.64 2040 0.31

LAI 0.68 0.79 0.75 0.74 0.43 0.85

RMSE:Rootmeansquareerror,EFF:efﬁciencyofthemodel;V1andV2:Longdurationvariety;V3:Shortdurationvariety.

2D Graph 12 cnrm_ecearth hadg em2 ipsl mpi M ill et y ie ld ( k g h a -1 ) 0 400 800 1200 1600 2D Graph 13 cnrm_ecearth hadg em2 ipsl mpi M ai ze y ie ld ( k g ha -1) 0 500 1000 1500 2000 2500 3000 V1D1_rcp4.5 V1D1_rcp8.5 V1D1_rcp4.5 V1D1_rcp8.5 GCM cnrm_ecearth hadg em2 ipsl mpi V3D1_rcp4.5 V3D1_rcp4.5 Plot 1 GCM vs Col 35 Col 31 vs av ma bl v3d1 V3D1_rcp8.5

SED SED SED SED

cnrm_ecearth hadg

em2 ipsl mpi

V3D1_rcp8.5 SED

Fig.3. Boxplotsofpredictedgrainyieldofmaizeandmilletundercurrentfarmer’spractice(F1;60kgNha−1_for_maize_and_no_N_for_millet)_for_ﬁve_selected_GCMs_over_the

period2040–2069underclimatescenariosrcp4.5andrcp8.5.V1D1:Longdurationvarietyatearlyplantingdate;V3D1:Shortdurationvarietyatearlyplantingdate;SED:

Standarderrorofthedifferencebetweenmeans.Thestarsrepresentaveragesimulatedgrainyieldvalueunderthebaselineclimate(1980–2009).

Table4

Averagesimulatedgrainyield(kgha−1₎_under_farmer’s_management_practice_for_the_long_duration_variety_(V1)_of_maize_(early_planting_and_application_of₆₀_kg_N_ha−1₎_and

millet(earlyplantingandnoNapplied)fortheﬁveselectedGCMsduringtheperiod2040–2069underclimatescenariosrcp4.5andrcp8.5,withtherespectivecoefﬁcients

ofvariation(%)betweenbrackets.

GCM Maize Millet rcp4.5 rcp8.5 rcp4.5 rcp8.5 ipsl 1014a₍₆₈₎ ₇₀₈a₍₈₈₎ ₁₂₆₉abc₍₁₂₎ ₁₂₄₁b₍₅₎ hadgem2 1273ab₍₅₂₎ ₁₂₅₈cd₍₄₅₎ ₁₁₉₈ab₍₁₁₎ ₁₁₆₁ab₍₉₎ mpi 1306ab₍₅₀₎ ₈₆₄ab₍₆₃₎ ₁₂₈₂bc₍₁₂₎ ₁₁₂₈a₍₁₅₎ ecearth 1377ab₍₅₇₎ ₁₀₉₃bc₍₆₄₎ ₁₃₀₉c₍₁₇₎ ₁₃₄₁c₍₁₉₎ cnrm 1409b₍₃₉₎ ₁₄₈₉d₍₃₉₎ ₁₁₇₅a₍₁₆₎ ₁₁₇₂ab₍₁₄₎

Differentsuperscriptletter(a,b)percolumnindicatesigniﬁcantdifferences(Tukey’stest,P<0.05)inthemeanvalue.

medianpredictedyieldofthelongdurationvariety(V1)was8% largerthanthatoftheshortdurationvariety(V3)underthe base-lineclimate.Thisdifferenceremainedsimilarunderfutureclimate ofthemid-centuryperiodforbothemissionscenarios.

Theyielddeclinewasmainlycausedbytheimpactofthe pre-dictedtemperatureincrease,whilepredictedchangesinrainfall onlyhad a minoror noeffectunder both scenarios rcp4.5and rcp8.5.ThisisillustratedinFig.4forrcp8.5,showingthata

ﬁc-titiousclimatescenario,inwhichhistoricalrainfalliscoupledwith futuretemperatureaspredictedbyGCMs,hadthesameeffecton simulatedmaizeandmilletgrainyieldsasthefuturescenariowith bothfuturetemperatureandrainfallaspredictedbyGCMs.

Linear regression analysis indicated a significant (P<0.001) shorteningofthepredictedtimetofloweringbymid-centuryfor bothvarietiesofmaizeandmillet(Fig.5).Forthelongduration variety(V1)ofmaize,thesimulatedtimetofloweringwas

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short-Maize yield (kg/ha) 0 700 1400 2100 2800 Cu mm ul ati ve pr ob ab ili ty of y ie ld ( % ) 0 20 40 60 80 100 Baseline rcp8.5 rcp8.5 with historical rain

Millet yield (kg/ha)

0 400 800 1200 1600 2000 Baseline rcp8.5 rcp8.5 with historical rain a) b)

Fig.4. Cumulativeprobabilityfunctionsofsimulatedgrainyieldofthelongdurationvariety(V1)atearlyplanting(D1)formaize(a)andmillet(b)undercurrentfarmer’s practice(F1;60kgNha−1_for_maize_and_no_N_for_millet)_and_under_the_historical_{(1980–2009)}_climate_(baseline),_the_combined_future₍₂₀₄₀_to₂₀₆₉₎_rainfall_with_future

temperature(rcp8.5),andthecombinedhistoricalrainfallwithfuturetemperature(rcp8.5withhistoricalrain).SimulatedyielddataincluderesultsfromﬁveselectedGCMs over30years.

enedby2.5daysand4daysunderrcp4.5andrcp8.5respectively, whilefortheshortdurationvariety(V3)theshorteningwas2and 3daysovertheperiod2010–2069.Formilletthesimulatedtime toﬂoweringwasshortenedby2daysforboththelong(V1)and short(V3)durationvarietiesunderscenariorcp4.5.Underrcp8.5, theshorteningwas4and3daysforthelong(V1)andshort(V3) durationvarietyrespectivelyovertheperiod2010–2069.

3.5. Evaluatingadaptationoptionstofutureclimatechange 3.5.1. Maize

Therewasasigniﬁcantinteractioneffectofclimateonthe fertil-izer−maizegrainyieldrelationship(Table5).Predictedgrainyield lossesunderfuturemid-centuryclimatewere51%and57%with currentfarmerpracticeoffertilization(F1,60kgNha−1₎_under_the

rcp4.5andrcp8.5scenariosrespectively.Withrecommended fer-tilizerapplication(F2,85kgNha−1₎_simulated_yield_losses_under

rcp4.5andrcp8.5werereducedtorespectively46%and51%. Atearly(D1)andmedium(D2)plantingthesimulatedclimate changeimpactswereayieldlossof,respectively,47%and53%for rcp4.5andof53%and60%forrcp8.5.Atlateplanting(D3),which resultedinverylowgrainyields,therelativeyieldlosseswereabout 70%underbothemissionscenarios(Table5).

Whereas recommended fertilizer rate application improved grainyieldsunderboththecurrentandfutureclimate,the pre-dictedeffectwasnegligibleifplantingwaslate(D3)(Fig.6).Also, thepredictedfertilizer effectwasstrongerforthelongduration variety(V1)ascomparedwiththeshortdurationvariety(V3)both forthebaselineandthefutureclimatescenarios.Ifplantingwas delayedstrongly(D3),theshortdurationvariety(V3)wasagood alternative,whereaswithaslightdelayofplanting(D2),thelong durationvariety(V1)stillyieldedmore.Whenplantingwasdelayed underthefutureclimate,thedifferencesbetweenthemaize vari-etiesdissipated(Fig.6).

3.5.2. Millet

Simulatedgrainyield obtained withrecommended fertilizer practiceF2(39kgNha−1₎_was_larger_than_with_farmer_practice_(F1,

0kgNha−1₎_by_about_29%_under_baseline_climate,_by_22%_under_the

rcp4.5scenarioandby20%underthercp8.5scenario(Table5).If therecommendedfertilizerrateswereapplied,thepredictedyield

lossesunderfarmerpracticeduetoclimatechangewereoffsetfor bothemissionscenarios(Table5).Withrecommendedfertilizer,a yieldlossof8%and15%waspredictedatearly(D1)andmedium (D2) plantingunderboth emission scenarios. Delayingplanting fromD1toD3causedastrongyieldlossacrossthebaselineand futureclimates.Asimilarsigniﬁcanteffectofclimatechangeon grainyieldwaspredictedforthelongduration(V1)andshort dura-tion(V3)varieties,withsimulatedyieldlossesof10%and14%,and 11%and17%underthercp4.5andrcp8.5scenariosrespectively (Table5).

Astrongyieldincreasewaspredictedwithrecommended fertil-izerrates(F2)forthelongdurationvariety(V1),especiallyatearly planting(D1)underthecurrentclimate(Fig.6).Underboth emis-sionscenariosfertilizerwaspredictedtolargelyoffsetthenegative effectofclimatechangeonthelongdurationmilletvariety(V1). Fortheshortdurationvariety(V3)ontheotherhand,applying rec-ommendedfertilizerratesreversedtheclimatechangeeffectfor D1andD2,butnotforD3underbothemissionscenarios.

3.6. Impactoffutureclimatechangeonfamilyfood self-sufﬁciency

Under thecurrent climateconditions,the foodneedsofthe largeand medium farms weresatisfiedby on-farm production whilethesmallfarmtypedidnotachievethis (Table6).Under future climate and current cropping practices, food availability wasreducedforallfarmtypes,butlargefarmsstillachievedfood self-sufficiency(Table6).Themediumfarmsdroppedbelowthe self-sufficiencythresholdandsmallfarmsexperienceda further decreaseinfoodself-sufficiency.Underfutureclimateconditions, largefarmsincreasedtheirfoodself-sufficiencystatusby apply-ingrecommendedfertilizerrates(Table6).Mediumfarmsraised foodself-sufficiencyabove100%byadvancingplantingfromthe currentmedium plantingdate(D2) toearlyplantingdate(D1). Applyingtherecommended fertilizerrates incombination with earlyplantingfurtherincreasedfoodproduction,whereasapplying recommendedfertilizerrateswithoutearlierplantingwas insuffi-cienttoreachfoodself-sufficiency.Forsmallfarms,plantingearlier and/orapplyingtherecommendedfertilizerrateswasinsufficient toachievefoodself-sufficiencyunderfutureclimateconditions.

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T im e to f lo w er in g ( d ay s) 50 55 60 65 70 75 Baseline V1D1 Baseline V3D1 Future V3D1 Future V1D1

rcp4.5 with maize rcp8.5 with maize

Year 1970 1995 2020 2045 2070 Year 1970 1995 2020 2045 2070 Ti me t o fl ow er in g (d ay s) 40 45 50 55 60

rcp4.5 with millet rcp8.5 with millet

a) b)

c) d)

Fig.5.Simulatedtimetoﬂowering(days)fortheV1(longduration)andV3(shortduration)varietiesofearlyplanted(D1)maize(a,b)andmillet(c,d),undercurrent farmer’spractice(F1;60kgNha−1_for_maize_and_no_N_for_millet)_during_the_period_2010-69_compared_to_the_baseline_period_{(1980–2009)}_for_two_future_climate_scenarios

(rcp4.5and8.5).SimulateddataincluderesultsfromﬁveselectedGCMs.

Table5

Averagesimulatedyield(kgha−1₎_and_ﬁrst_order_interaction_effect_of_fertilizer_application,_planting_date_and_variety_by_climate,_comprising_the_current_and_the_projected

mid-century(2040–2069)climateunderthercp4.5andrcp8.5scenarios.

Maize Millet

Factor Baseline rcp4.5 rcp8.5 Baseline rcp4.5 rcp8.5

Fertilizer F1 1554 768 669 1010 935 884 F2 1857 845 764 1303 1138 1064 P 0.001 0.001 SED 23 9 Planting D1(Early) 2320 1240 1082 1462 1375 1313 date D2(Medium) 2107 985 844 1062 937 867 D3(Late) 689 194 225 945 797 743 P 0.001 0.001 SED 28 11

Variety V1(Longduration) 1844 840 712 1248 1123 1067

V3(Shortduration) 1566 772 722 1065 950 881

P 0.001 0.055

SED 23 9

FormaizeF1=applicationof60kgNha−1asfarmerpractice,F2=applicationof85kgNha−1asrecommendedpractice.FormilletF1=applicationof0kgNha−1asfarmer

practice,F2=applicationof40kgNha−1asrecommendedpractice.D1,D2andD3arerespectivelyearly(June),medium(July)andlate(August)plantingdate.V1andV3

arelongandshortdurationvariety.Rcp4.5andrcp8.5arethelowandhighemissionscenarioforthemid-centuryperiod(2040–2069),SED:standarderrorofthedifference

betweenmeans.

4. Discussion

Underbothemissionscenarios(rcp4.5andrcp8.5)temperatures are predicted to increase in southern Mali with strong nega-tiveconsequencesforcropproductivity,especiallymaize.Ifcrop managementisimproved(ifdelaysinplantingdateareavoided, recommendedfertilizationratesusedandthebestperformingcrop varietieschosen),thelossincropyieldduetoclimatechangecan becompensatedandeventurnedintoanincreasecomparedwith currentyields.Wenowdiscussourmainﬁndingsinmoredetail, anddrawconclusionsontheconsequencesforsmallholderfamily foodself-sufﬁciency.

4.1. ClimatechangeinsouthernMali

ThecurrenttrendofclimatewarminginsouthernMali(Traore etal.,2013)willcontinue andevenincrease inspeed,resulting in an increase of maximum and minimum temperature of 2.9 and 3.3◦C respectivelybythemid-centuryperiod (2040–2069). Thisresultiscorroboratedacrossthethreemainecologicalzones (Sudanian,SahelianandSahelo−Saharan)ofWestAfrica( CEDEAO-ClubSahel/OCDE/CILSS, 2008). As a consequence, temperature thresholdswithnegativeeffects oncropyieldsthat used tobe reachedrarely,arenowattainedmorefrequently(Stottetal.,2004). Thistrendwillintensifybymid-centuryifgreenhousegas emis-sions continue unabated(Stottet al.,2011).Thegeographically widespreadwarmingtrendinWestAfricaincludesatendencyof

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V1 Baseline rcp4.5 rcp8.5 Yie ld ( k g ha -1 ) 0 1000 2000 3000 4000 b) V3 Baseline rcp4.5 rcp8.5 D1F1 D1F2 D2F1 D2F2 D3F1 D3F2 c) V1 Baseline rcp4.5 rcp8.5 Y ie ld ( k g ha -1 ) 0 500 1000 1500 2000 _d) V3 Baseline rcp4.5 rcp8.5 SED SED SED SED a)

Fig.6. Averagesimulatedgrainyieldofthelong(V1)andshort(V3)durationvarietiesofmaize(a,b)andmillet(c,d)underthebaselineclimateandunderthetwofuture climatescenarios(rcp4.5and8.5)forthemid-century(2040-69)period.FormaizeF1=applicationof60kgNha−1_as_farmer_practice,_F2₌_application_of₈₅_kg_N_ha−1_as

recommendedpractice.FormilletF1=noNappliedasfarmerpractice,F2=applicationof40kgNha−1_as_recommended_practice._D1,_D2_and_D3_correspond_respectively_to

early(June),medium(July)andlate(August)planting.SED:Standarderrorofthedifferencebetweenmeans.

Table6

Futureclimatechangeimpactonthefoodself-sufﬁciencyoflarge,mediumandsmallfarmtypes.

Croppingpractice Climate Maize

(kgfarm−1₎ Millet (kgfarm−1₎ Sorghum (kgfarm−1₎ Foodself-sufﬁciency(%,

expressedinkcalterms)

Largefarm Currentpractice Baseline 2451 4927 7390 176

rcp4.5 1605 4457 6685 152

rcp8.5 1538 4542 6163 146

Adaptationoption Fertilizer rcp4.5 1944 6149 9224 206

rcp8.5 1939 6063 9094 204

Mediumfarm Currentpractice Baseline 3650 1503 3506 103

rcp4.5 2679 1393 3251 87 rcp8.5 2568 1386 3235 85 Adaptationoption F2*D1 rcp4.5 4851 2022 4979 141 D1 4005 1907 4696 126 F2 2986 1477 3447 94 F2*D1 rcp8.5 4840 1979 4874 139 D1 3838 1897 4672 124 F2 2979 1446 3375 93

Smallfarm Currentpractice Baseline 693 1802 970 41

rcp4.5 678 1746 940 40 rcp8.5 650 1694 912 39 Adaptationoption F2*D2 rcp4.5 756 1974 1148 46 D2 497 1862 992 40 F2 558 1771 1031 40 F2*D2 rcp8.5 754 1932 1091 45 D2 482 1662 963 37 F2 577 1734 979 39

Currentpracticefertilization(F1)is60kgNha−1_for_maize_and_no_N_for_millet_and_sorghum_for_all_farm_types._Recommended_fertilizer_(F2)_for_maize_is₈₅_kg_N_ha−1_and

40kgNha−1_for_millet_and_sorghum._Early_(D1)_and_medium_(D2)_planting_date_with_long_duration_variety_was_considered_as_current_practice_for_the_large_and_medium_farm

typerespectivelyandlateplantingandshortdurationvarietywasconsideredascurrentpracticeforthesmallfarmtype.Forthelargefarmstheadaptationoptionwasthe

recommendedfertilizerrate,keepingearlyplanting.Forthemediumandsmallfarmtypesadaptationoptionsweretherecommendedfertilizerrateandplantingearlierin

thegrowingseason.Thelattermeantmovingfrommedium(D2)toearly(D1)plantingdateforthemediumfarmtypeandfromlate(D3)tomedium(D2)plantingdatefor

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drierregionstowarmupmorerapidlythanwetterregions(Dai etal.,2004).

Wefoundnoclearchangesinthemonthlyorannualamounts ofrainfallforthenearfuturewhileoverWestAfricathereislow tomediumconﬁdenceinprojectedchangesofrainfall(Niangetal., 2014).Extremelydryandwetyearswilllikelybecomemore fre-quentduringthe21stcentury(Daietal.,2004)andtheincrease oftemperatureinfuture willlikelyleadtohighersoil evapora-tionlossesandthusdryingoftopsoillayers,therebypotentially increasingtheintensityanddurationofdroughts(Trenberth,2011).

4.2. Effectofclimatechangeonmaizeandmilletyields

Bothlong and shortduration maize and millet varietiesare negativelyaffectedbyclimatechange.Ourmodelpredictions sug-gestthatthetemperatureincreasewillsubstantiallyreducegrain yields,especially ofmaize,stronglyaffectingfoodproductionin theSudano-Sahelianzone. Ourﬁndings resultingfroma locally calibrated and tested cropgrowth model add evidence to sev-eralstudies that have predicted the potentialimpact of future climatechangeontheperformanceofsub-SaharanAfrica agricul-ture(Barriosetal.,2008;Sultanetal.,2013;Serdecznyetal.,2016). Wefoundalargevariabilityinfutureclimateimpactonyieldof maizeandmillet.Thislargevariabilityisinlinewithﬁndingsof

Fischeretal.(2001)andParryetal.(2004)showingthatthe mag-nitudeofcropyieldresponsestoclimatechangeinsub-Saharan Africavariedconsiderablyfrom−98%to+16%althoughinmost casespredictedchangesarenegative(Challinoretal.,2007).This largevariabilityinpredictedyieldsislargelyexplainedbyspatial variabilityandbydifferencesinthetypeofclimateandcropgrowth simulationmethodsused.

Thenegativeimpactsof increasedtemperaturesonyieldsof maize,andtoalesserextentmillet,highlighttheimportanceof copingwithglobalwarming,especiallyforresource-limited small-holderfarmers.IntheSudano-Sahelianregion,poorsoilfertility andinappropriatemanagementpracticesareamajorcauseoflow cropproductivity.Separatingthesefactorsfromeffectsofclimate changeandvariabilityisnotstraightforward(Simeltonetal.,2013), butourresultsshowedthatcurrentcroppingsystemswithlow fertilizerinput (e.g.millet), areless sensitivetoclimatechange thancroppingsystemswithhigherfertilizerinput(e.g.maize).As croppingsystemsintensify,goodmanagementintermsoftimely plantingandchoosingadaptedvarietiesbecomesessentialtocope withclimatechange.

Withnoclear predictedchange inrainfall in ourstudy,the predictedclimateimpactwasprimarilyrelatedtoincreased tem-perature.However,apossiblerainfallchangemayhaveanimpact onthecropresponse totemperature increase.For example,for amillet varietyinNiger, itwaspredictedthatchanges in rain-fallstronglyaffectedthenegativeeffectsofincreasedtemperature (+1.5◦C),witha59%versus26%cropyieldlossfordecreasingand increasingrainfallrespectively(SalackandTraore,2006).Similarly,

Roudieretal.(2011)showedthatrainfallchanges,stilluncertain inclimateprojections, havethepotentialtoaggravate or mod-erateimpactdue totemperaturedependingonwhetherrainfall decreasesorincreases.

Our resultsshowedthat akey cause oflower cropyieldsis thereductionofthetimetoflowering,therebyreducingthe effec-tiveperiodduringwhichbiomassandassimilatebuild-upbefore grainfillingcantakeplace.Wefoundthatbymid-centuryforthe rcp4.5andrcp8.5scenariosrespectively,28%and38%ofdaily max-imumtemperaturesduringthegrowingperiodwerehigherthan thethresholdvalueof38◦CabovewhichAPSIMsimulatestheeffect ofheatstressonthenumberofmaizegrains.Forbothscenarios, 57%ofthedailymeantemperatureswereabovethethresholdvalue of30◦CmaizegrainfillingisnegativelyaffectedinAPSIM(Fig.A5).

Simulatedphotosynthesisisalsonegativelyaffectedbyincreased temperatures:bymid-century8%ofdailymeantemperatureswere abovethethresholdvalueof35◦Cforbothcrops.Formaizethe threeprocesses(grainformation,grainﬁllingandphotosynthesis) wereaffectedbythepredictedtemperatureincreases,whilefor milletonlyphenologyandgrainﬁllingwereaffected.Thelarge dif-ferenceinthepredictedyieldresponsesbetweenmaizeandmillet toclimatechangeindicatesthattheeffectofthepredicted temper-atureincreaseongrainnumberislarge.

Althoughwithcurrentpracticesmaizeproductivitywasmore stronglyimpactedbyclimatechangethanmilletproductivity,poor yields(lessthan1200kgha−1₎_were_obtained_more_frequently_with

milletthanwithmaize,bothforlongandshortdurationvarieties (Fig.4).Itmeansthatthetrendofexpansionofmaizecultivation (Soumaréetal.,2004)islikelytocontinue,despitethepotential strongdeclineinpredictedyieldsunderclimatechange.

4.3. Adaptationoptionsforfamilyfood-sufﬁciency

Toachieve familyfoodself-sufficiencycurrentcrop manage-mentstrategiesneedtobeimproved.Theeffectivenessofchanging plantingdateasanadaptationoptionvariesaccordingtofarmtype (Table3).Early plantingis animportantoptiontoachieve food self-sufficiencyforthemedium-sizedfarms.Otherstudies(Kamara etal.,2009)havealsoshowntheimportanceofplantingdatefor enhancingcropproductivity.Theadoptionofearlierplantingwill toagreatextentdependonimprovementofcurrentplanting tech-niques,whichcurrentlyrelyonarudimentaryseederthathasnot evolvedsinceitsintroductionin1960(FAO,2008).Moreefficient seederswouldallowfasterplantingofalargerareaallowing farm-erstotakeadvantageofthefirstrains(Traore,2014).

Althoughfarmersareawareofthebeneficialeffectsofmineral fertilizer on crop productivity, access to a secure and afford-ablesupplyremains themainconstraint.Besidesincreasingthe amounts of fertilizer used, fertilizer use efficiencyneeds to be improved(deRidderetal.,2004).Forinstance,micro-dosingor hillapplicationcanimprovefertilizeruseefficiency,resultingin increasedcropyieldsandfarmer’sincome(Sawadogo-Kaboréetal., 2009).Yetthesmallerfarmersremainfoodinsecureevenwhen improvingtheplantingdate fromlatetomediumand applying therecommendedfertilizerratesbecauseoftheirlimitedcropped area.Achievingfoodsecurityforthisgroupoffarmerswillrequire off-farmactivitiesgiventheirlandconstraints.Bydisaggregating theclimatechangeimpactassessment byfarmtype, adaptation optionsandstrategiescanbetailoredtospecificfarmcontexts,thus increasingtheirrelevanceandlikelihoodofadoption.

4.4. Uncertaintyinprojectionsanditsimplications

Ourresultsshowedlargeuncertaintyinyieldpredictionswith the different GCMs and RCPs both for maize and millet. This uncertaintyprimarilystemsfromthevariationsinthewayGCMs respond tochanges in atmospheric forcing (Teng et al., 2012), whichisassociatedwiththestructureoftheclimatemodels,model parameterization,andalsowithspatialresolution.Basedonthese uncertainties,itcanbeinferredthatresultsobtainedwithagroup orensembleofGCMsaremorereliablethanresultsobtainedwith individualGCMs.Assengetal.(2013)showedthattheuncertainty inclimatechangeimpactprojectionsalsoresultsfromthechoice ofcropmodels.Itisthereforesuggestedtousemulti-cropmodel ensemblesinasimilarwayastheuseofmultiGCMensembles. Ourresultsobtainedwithasinglecropgrowthsimulationmodel shouldthereforebeinterpretedwiththislimitationinmind.

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5. Conclusion

With both emission scenarios, i.e. the worst case scenario (rcp8.5)andaloweremissionscenario(rcp4.5),temperaturesin southern Mali will continue to increase. The rise in tempera-turehasnegative consequences for crop productivityand food self-sufficiency.Ourmodelpredictionssuggestthatunderfuture climatechange,thelargeandmediumfarmscanremainfood self-sufficient.Adaptationofcropmanagementthroughearlyplanting, increasedfertilizerratesandadaptedcropvarietiesisessentialto copewithclimatechange.Smallerfarmsarelikelynottoachieve foodself-sufficiencyunderanyofthesemanagementscenariosand willneedoff-farmemploymentorsomeformofsocialsupport. Decisionmakingbyextensionanddevelopmentagentsandpolicy makerscanbesupportedbytheseplace-basedfindingsthatapply totheSudano-SahelianzoneofWestAfrica.

Acknowledgements

We thank the International Development Research Centre (IDRC)andDepartmentforInternationalDevelopment(DFID)for fundingthroughtheClimateChangeAdaptationinAfrica(CCAA) GranttotheUniversityofZimbabwe.Additionalfundingfromthe InstitutD’EconomieRuraleduMaliisgratefullyacknowledged.We thanktheWorldClimateResearchProgramme’sWorkingGroup onCoupled Modelling, which is responsible for CMIP, and the climatemodellinggroups(CNRM-CM5,ECEARTH,HADGEM2-ES, IPSL-CM5A-LR,MPI-ESM-LR)formakingavailabletheirmodel out-put. We are grateful to Neil Huth, Myriam Adam, Irenikatche AkponikpèandDilysMacCarthyforhelpwiththeAPSIMmodel.

AppendixA. Supplementarydata

Supplementarydataassociatedwiththisarticlecanbefound,in theonlineversion,athttp://dx.doi.org/10.1016/j.fcr.2016.11.002.

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