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Acta Hortic. 1225. ISHS 2018. DOI 10.17660/ActaHortic.2018.1225.4 Proc. III All Africa Horticultural Congress Eds.: I.O.O. Aiyelaagbe et al.
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How future climatic uncertainty and biotic stressors might influence the sustainability of African vegetable production J.D.H. Keatinge1,a, D.R. Ledesma1, J.d’A. Hughes2, F.J.D. Keatinge3, S. Hauser4 andP.C.S.Traore51AVRDC - The World Vegetable Center, Shanhua, Tainan, Taiwan; 2International Rice Research Institute, LosBanos, Philippines; 3Department of Geography, University of Florida, Gainesville, Florida, USA; 4InternationalInstituteofTropicalAgriculture(IITA),Ibadan,Nigeria;5InternationalCropsResearchInstitutefortheSemi-AridTropics(ICRISAT),Bamako,Mali.Abstract
The study was conducted to determine whether likely global climaticuncertaintyinthefuturewillposesubstantiverisktosmall-scalevegetableproducersin Africa, and to considerwhether climate change threatens the development andsustainabilityof improvedvegetablehorticulturalsystems inAfrica.Annualaverageair temperature and rainfall totals were assessed over the period 1975-2014 or,wherepossible, forrainfall for longerperiodsapproaching100years; the trends inthesedatasetsweredeterminedthroughlinearregressiontechniques.Predictionsofthe likelyvaluesofannualaverageair temperatures in thenext25,50,75and100years were made. Considerable variability in trends is reported ranging fromextremely fastwarming inTunis,Tunisiacontrastingwithslightcooling inBamako,Mali.Annualvariabilityinrainfallwassubstantivebuttherewerenolong-termtrendsof consequence, evenwhen considered over the last 100 years. Consequently, thesustainabilityofvegetableproductionwillbe threatenedmostlybychanges inpest(e.g., weeds, insects, fungi, bacteria and viruses) damage to crops in small-scaleproduction systems. A call ismade for national governments to give these issuesenhanced priority in the distribution of future research and capacity-buildingresources,asmostof theseproduction stressorsareunder-researchedandevidentsolutionstosuchproblemsarenotcurrentlyavailable.
Keywords:annualtemperature,rainfallvariability,diseases,pests,horticulturalsystemsINTRODUCTIONThe potential for smallholder horticulturalists in Africa, very oftenwomen, to growthemselves out of poverty and to provide better nutrition for their families is greatlyincreasedwhentheyareabletopracticeeffectivevegetableproductionandmarketingfromtheirsmallholdingsandkitchengardens.(Afari-Sefaetal.,2012).Climateuncertaintyanditsimplications for changing potential biotic and abiotic stressors across the continent arepoorlyunderstood(Bebber,2015;Ebertetal.,inpress)andtheliteratureconcerningfutureprojections for vegetables is sparse. This issue is addressed for some specific locations(Keatinge et al., 2013) but the paucity of sites providing data cannot be deemed to becontinentally appropriate (Keatinge et al., 2014, 2015a). It has been suggested that themajorityofsmall-scalefarmersagreetheclimateinAfricaischangingfortheworse(Raoetal.,2011)andtherehavebeenfewattemptstointensifyhorticulturesustainablyexceptonkitchengardenscalelandholdings(Prettyetal.,2011).WehypothesizethatclimatechangethreatensthedevelopmentandsustainabilityofimprovedvegetablehorticulturalsystemsinAfrica(Ebertetal.,inpress),aswellasothercrops(Challinoretal.,2007;Paethetal.,2009).For trends in maize (Zeamays) production and other staples, the issue of climatechangeiswell-addressed.Ahighlydetailedanalysisemployingdatafrommorethan20,000 aE-mail: [email protected]
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historicalmaizetrialshasbeenreported(Lobelletal.,2011).Theirpaperindicatesanon-linearrelationshipbetweentemperatureandyield;foreachdegreedayabove30°C,thefinalyieldfallsby1%evenunderoptimalrain-fedconditions,andlossesarefurtherexacerbatedto 1.7% by a greater incidence of drought. Several papers call for adaptation of farmingsystems to address future climate scenarios based on the general circulation modelspromulgatedbytheIntergovernmentalPanelonClimateChange(IPCC,2007,2013)whichsuggestatleast2°Cglobalclimatewarmingoverthenext100yearswithmixedvariationsinannual rainfall. In the case of both maize and beans (Phaseolus vulgaris) in scenariosmodeling a 4°C increase in annual average temperature, continent-wide yield reductionsacrossAfricaweresubstantive–inthe10-25%categoryformaizeandgreaterthan50%forbeans–whichwouldhavenegativeconsequencesforsmallholderfarmers(Thorntonetal.,2011) and food security on the continent. Similar studies on sorghum,millet, groundnutandcassavaproductionreportpredictedyieldchangesofaroundminus10-20%bythemid-21st century (Schlenker and Lobell, 2010). An increased likelihood of severe drought andflooding events that would impact farmers severely has been discussed (Shongwe et al.,2009,2011;Raoetal.,2015).Southern Africa is projected to be vulnerable to increasing pressure of drought by2030 (Lobell et al., 2008) while the long term sustainability of the maize-based agro-ecosystemiscalledintoquestionandashorteningofthegrowingseasonisprojectedforthewesternpartsofsouthernAfrica(Shongweetal.,2009).Likewise, increasedtemperaturesand reduced rainfall in the Highveld region of South Africamay seriously undermine thelong termproductivityofmaize (WalkerandSchulze,2008).Reductions inyieldsofmorethan10%maybeexperiencedacrosssouthernAfricainthemediumterm(Zinyengereetal.,2013).TheseconcernsforsouthernAfricanfarmingsystemsarealsoreportedgenerallyandfor Zambia specifically by Twomlow et al. (2008), who call for immediate and intensivetrainingofsmallholderfarmerstosupplementtheiradaptivecapacitytoweathervariationandthustocopebetterwithfutureclimatechange.Anexaminationof longtermtrendsinrainfalldata(SternandCooper,2011)concludedover89years(excludingthefourprincipaldry seasonmonths) at Moorings Station in southern Zambia did not identify any simpletrends.Yet,whentheyearsweresplitbetweenElNino,ordinary,andLaNinaevents, thenthedailyprobabilityofrainwaslowerinElNinoyearsthaninotheryears.TheimportanceofElNinoyearsandtheirvariabilityintermsofprecipitationintheMara-SerengetiareaofKenyaandTanzaniahasbeenhighlightedbyOgutuetal.(2008),whoobservedthatgreaterrainfall was experienced in colder El Nino-Southern Oscillation (ENSO) events and lessrainfall in warmer ENSO event years, thus demonstrating the extreme difficulty indetermining specific trends in African rainfall resulting from overall global warming.Likewise,intermsofairtemperatureforthisregion,monthlyminimumtemperatureswereseen to increasesubstantiallyatNakuru,Kenya,yetnochangewasdiscernible inmonthlymaximum temperatures (Ogutu et al., 2012). Similarly, somewhat contradictory results intemperature trends are evident elsewhere, such as in Asia (Keatinge et al., 2014) andMesoamerica(Keatingeetal.,2016).InthecaseofeasternAfrica,anincreaseinmeanprecipitationratesisprojectedinthelong termand theremaybeagreaternumberofstormsofhigh intensity (Shongweetal.,2011).Studiesmodelingclimatescenariosup to2050withmaizeandbeancropssuggestthatlocaltopographicvariationsmayplayasubstantiveroleintherelativesustainabilityoffarming systems, with sites at higher elevation showing more yield gains than lossesresultingfromclimatechange(Thorntonetal.,2009).Yet, incommonwithearlierpapers,the importance of generating local, community-based efforts to increase the adaptivecapacityofsmallholderfarmersisstressedif theresource-poorarenotto incuradditionalhardship. Studies on maize (Cooper and Coe, 2011) represent the position of rain-fedagricultureineasternAfrica,specificallyatMakindu,Kenya,wherewitha3°Cprojectedrisein air temperature,most (80%)of growing seasonswould still show the same amount ofvariability in length of growing season that farmers currently experience. Thus, helpingfarmerstodevelopresilientsystemstodaywouldbeagoodstrategytoensuresustainabilityof their enterprises in the future.A clear, increasing trend inmaximumandminimumair
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temperaturessince1990wasobservedforEmbuinKenya(Raoetal.,2015).Atthislocationaverageannualair temperature increasedsignificantlybyaround0.4°Cwhenthe1980-89periodwascontrastedwith2001-2010,butnoclear trendwasobserved in theamountofrainfalleitherannuallyorseasonallyfrom1980-2010.ForwestAfrica, specifically for theCameroonianSahelian zone, a rising trend in airtemperaturewasobservedbetween1960and2008butno such trendwasevident in theprecipitation data, whichwas highly variable (Yengoh, 2013). Analyzing the results of 16studies it is concluded (Roudier et al., 2011) that yield reduction will be highly variableacrossarangeofstaplecrops,includingcereals,rootsandtubers,butisgenerallylikelytobelarge(averaging>10%yieldloss)acrossarangeofmedium-termtimehorizons(2030-2050onwards) with the negative effects of increased temperature beingmuch larger than theconsequencesofprojectedreductionsinrainfall.Yet,averysubstantialvariabilityinannualrainfallover the last100yearsorso in theSahelhasbeenreported(Sissokoetal.,2011).Thismaymake anydeterminationof future trends in rainfall exceedinglydifficult, thoughtheseauthorssuggestthatthedryingtrendofthelate20thcenturymighthavebeenrelatedtonaturalandhuman-producedgreenhousegasemissionsandhumanuseofaerosols.AlthoughitmaynotbeeasytoidentifycleartrendsinfutureprojectedtemperaturesandrainfallacrossAfrica,thepossibilityofincreasedrainfallby2050insomeareassuchastheEastAfricanhighlandshasbeensuggested(Cooperetal.,2009),aswellasincreasedairtemperature (Rao et al., 2015). A combination of higher temperatures and increasedmoisturewouldnotonlyaffectcropgrowthbutperhapsmoreimportantlymayinfluencethebehaviorofthemajorbioticconstraintstoproduction.Farrowetal.(2011)indicatethatrootrot(Pythiumspp.)wouldbeamoresevereconstrainttoproductionofbeanswithincreasedrainfallineasternAfrica–particularlyforthehighlandareassurroundingtheLakeVictoriabasin.Ground-truthingtoprovethispointwasnotaccomplishedbytheauthorsastherewasan insufficientdensityof rainfall recording stations in their region, yet logic suggests thatthis potential scenario must have some considerable validity for vegetables such as theSolanaceae(Bolandetal.,2004;Lucketal.,2011),whicharehighlysusceptibletosoil-bornediseases. Farrow et al. (2011) reviewed historical work and cite examples of some veryserious diseases in Africa that may become much more damaging in hotter and wetterconditions. This list includes diseases such as late blight (Phytophthora infestans) andcassavamosaicviruses,whicharehighlydamagingto leafyvegetables.Theeffectsofplantpests exacerbated by climate change will be difficult to determine (Bebber, 2015);considerable additional research investment will be required to untangle these highlycomplexrelationshipseffectively,andthentodesigntheappropriatecopingamendmentsforfarmingsystems(Garrettetal.,2011).Authors fromAVRDC-TheWorldVegetableCenterhavereportedthat,asaresultoflikely climate change, additional heat, drought and pest tolerance will be required fromcurrentandfuturevegetablebreedingprogramsforglobalspeciesinAfrica(DelaPenaandHughes, 2007; Keatinge et al., 2009; De la Pena et al., 2011) such as tomatoes (Solanumlycopersicum)(Fufaetal.,2011)andotherexoticSolanaceaesuchascapsicumsandchillis(Capsicumannuum)(Hansonetal.,2011).Theselectionandbreedingofhardy indigenousspecieswithtolerancetobioticandabioticconstraintsneedstobeamainstreamresearcheffortifsustainableandresilientvegetableproductionsystemsaretobedevelopedinAfrica(Keatingeetal.,2015b;Dinssaetal.,2016).MATERIALSANDMETHODSFor temperature data the siteswere selected for the quality and length of availabledata in those areas where vegetable horticulture is presently practiced and with dataexceeding30yearsinlength.Effortwasmadetoensuresiteselectionwaswidelydistributedacross Africa (Table 1; Figure 1). Annual mean temperatures were computed based onmonthlyaveragesperyear.Inrarecaseswheresomemonthshavemissingdata,themonthlyaveragesofthesamemonthsfromfiveimmediatelypreviousyearswereusedto fill inthegaps (Environment Canada, 2012). Data were collected from multiple sources, includingsomeusedinpreviousstudies(Keatingeetal.,2013,2014,2015a).
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Yangambi, DemocraticRepublicof Congo
Antananarivo,Madagascar
Bamako,Mali
Cairo,Egypt
Dakar,Senegal
Dar EsSalaam,Tanzania
Ghardaia,Algeria
Ibadan,Nigeria
Khartoum,Sudan
Nairobi,Kenya
Nouakchott, Mauretania
Port Elizabeth,South Africa
Potchefstroom,South Africa
Tunis,Tunisia
0 750 1,500 2,250 3,000375Kilometers
¯
Coordinate System: GCS WGS 1984Datum: WGS 1984Units: Degree Figure1.LocationofAfricanmeteorologicalsitesemployedintheanalysis.Data for annual average air temperature were downloaded from the UKMeteorologicalOfficeglobaldatabaseandaccessedthroughhttp://datamarket.com.Iftherewereminorexamplesofmissingdata in therecentpast fromthisdatasource, theseweredetermined from other records for the specific sites such as fromhttp://www.tutiempo.net/en/climate and http://www.wunderground.com. All data werecarefully examined for anomalies and missing values before sites were accepted forinclusion.Ateachsite, linearregressionlineswerefittedtothedatafortheperiods1975-2014as furtherdescribed(Keatingeetal.,2014).Thepossibilityofcurvilinear fits for thedatawasinitiallyconsidered,butlinearfitswerethemostappropriatesolutionintermsoffit(Figures2-11;Tables2and3).
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Table1. Location, elevation and years of temperature (T) and rainfall (R) data used foranalysisoflongtermrecordsatmeteorologicalstationsinAfrica.Meteorological station location Latitude Longitude Elev. (m) Data run T or R
T and R Antananarivo, Madagascar -18.00 +047.48 1279 1975-2014 T only Bamako (Sénou airport)1, Mali +12.53 -007.95 0380 1975-2014 T and R Bamako (old airport), Mali +12.65 -008.00 0350 1919-1974 R only Cairo Airport, Cairo, Egypt +30.13 +031.40 0064 1975-2014 T only Dakar (Yoff), Dakar, Senegal +14.73 -017.50 0027 1975-2014 T only Dar Es Salaam (Julius Nyerere airport, Tanzania) -06.87 +39.20 0055 1975-2014 T only Ghardaia, Algeria +32.40 +003.81 0450 1975-2014 T only Ibadan, Nigeria (IITA) Ibadan, Moor Plantation2
+07.43 +07.38
+003.90 +003.93
0227 0198
1975-2014 1901-1998
T and R R only
Khartoum, Sudan +15.60 +032.54 0382 1975-2013 T only Nairobi (Jomo Kenyatta airport), Kenya -01.31 +036.91 1624 1975-2014 T only Nouakchott, Mauretania +18.10 -015.95 0002 1975-2014 T only Port Elizabeth, South Africa -33.98 +025.60 0061 1975-2013 T and R Port Elizabeth, South Africa -33.98 +025.60 0061 1867-2010 R only Potchefstroom, NW University, S. Africa -26.74 +027.08 1349 1975-2014 T only Tunis, Carthage, Tunisia +36.83 +010.23 0003 1975-2014 T and R Tunis, Carthage, Tunisia +36.83 +010.23 0003 1895-2014 R only Yangambi (INERA), DR Congo +0.82014 +24.45627 0458 1912-2010 T and R 1Sénou is 15 km south of central Bamako which was the site of the old airport. 221 km apart with Moor Plantation west of Ibadan city and IITA north of Ibadan city. Table2. Trend analysis in annual average air temperature (°C) forAfricanmeteorologicalstations1975-2014.
Meteorological station location Intercept Slope R2 Significance Antananarivo, Madagascar -034.59 +0.027 0.45 P<0.01 Bamako (Sénou), Mali +059.83 -0.016 0.18 P<0.01 Cairo Airport, Cairo, Egypt -066.78 +0.044 0.56 P<0.01 Dakar (Yoff), Dakar, Senegal -038.89 +0.032 0.43 P<0.01 Dar es Salaam airport, Tanzania -007.42 +0.017 0.32 P<0.01 Ghardaia, Algeria -084.83 +0.054 0.61 P<0.01 IITA, Ibadan, Nigeria +004.59 +0.011 0.11 P<0.05 Khartoum, Sudan -033.95 +0.032 0.36 P<0.01 Nairobi (Jomo Kenyatta), Kenya -013.63 +0.016 0.23 P<0.01 Nouakchott, Mauretania -000.81 +0.013 0.06 ns Port Elizabeth, South Africa +019.89 -0.001 0.00 ns Potchefstroom, NW Uni. S. Africa -003.21 +0.010 0.03 ns Tunis, Carthage, Tunisia -117.90 +0.069 0.80 P<0.01 Yangambi (INERA), DR Congo1 -11.946 +0.0186 0.32 P<0.01
1To 1975-2010 only. ns = no significant difference.
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Table3. Long term trend analysis in annual rainfall totals for selected Africanmeteorologicalstations(yearsvariablebetweenstations).Meteorological station location Intercept Slope R2 Significance Bamako (Old airport + Sénou), Mali (1919-2014) +6829.06 -2.98 0.16 P<0.01 Bamako (Old Airport), Mali (1919-1974) -0959.77 +1.03 0.00 ns Bamako Sénou, Mali (1975-2014) +4380.14 -1.77 0.00 ns Ibadan (Moor Plantation + IITA), Nigeria (1901-2014 -1843.79 +1.58 0.03 ns Moor Plantation, Ibadan, Nigeria (1901-1998) -0359.47 +0.820 0.00 ns IITA, Ibadan, Nigeria (1975-2014) -8140.02 +4.748 0.02 ns Port Elizabeth, South Africa (1867-2014)1 +0569.63 +0.009 0.00 ns Port Elizabeth, South Africa (1975-2014)1 +9926.26 -4.69 0.11 ns Tunis Carthage, Tunisia (1895-2014)2 -1057.47 +0.768 0.05 ns Tunis Carthage, Tunisia (1975-2014)2 -0135.65 +0.300 0.00 ns Yangambi (INERA), DR Congo (1912-2010)3 -505.702 +1.147 0.01 ns
12011 missing value; 22000 and 2001 missing values; 3individual missing months replaced by long-term mean of the respective month. ns = no significant difference at P=0.01.
Figure2. Thetrendinannualaverageairtemperature(°C)atBamako(Senou)1975-2014.
Figure3.Thetrendinannualaverageairtemperature(°C)atIbadan(IITA)1975-2014.
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Figure4. Thetrendinannualaverageairtemperature(°C)atPortElizabeth1975-2014.NS=nosignificantdifferenceatP=0.01.
Figure5.Thetrendinannualaverageairtemperature(°C)atTunisCarthage1975-2014.
Figure6.Thetrendinannualaverageairtemperature(°C)atYangambi1975-2010.
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Figure7. A)Annualrainfalltotals(mm)1975-2014atBamako(Senou);B)Annualrainfalltotals (mm)1919-1974atBamako(Oldairport);C)Annual rainfall totals (mm)1919-2014atBamako(Oldairport+Senou).
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Figure8. A) Annual rainfall totals (mm) 1975-2014 at Ibadan (IITA); B) Annual rainfalltotals (mm) 1901-1974 at Ibadan (Moor Plantation); C) Annual rainfall totals(mm)1901-2014atIbadan(MoorPlantation+IITA).
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Figure9. A) Annual rainfall totals (mm) 1975-2014 at Port Elizabeth; B) Annual rainfalltotals(mm)1867-2014atPortElizabeth.For rainfall data, the literature suggested that trends at all levels from continental,regional,nationalandindividualsitesareveryhardtodeterminewithanyrealconfidence(Thomaset al., 2007;Sissokoet al., 2011).Asa result, only individual locationswith longrecordsofhighquality(longerthan90years)wereselectedforillustrativepurposesofthevariabilityinthiscriticalparameter.Thesedatasetsweremadeavailablefromthedirectlycollected and locally acquired records of international agricultural research institutesincluding IITA (the International Institute of Tropical Agriculture), ICRISAT (theInternationalCropsResearchInstitutefortheSemi-AridTropics),ICARDA(theInternationalCenterforAgriculturalResearchintheDryAreas),INERA(InstituteNationalpourlesEtudesetRechercheAgricole) and from thewebsitehttp://geekwright.com/modules/gwreports/index.php.Fortwoofthesesites(BamakoandIbadan)itwasnecessarytocombinethedatafrom two closely associated sites to create a sufficiently long data run to realisticallyexaminethetrendsinrainfall.Thus,atBamako,datafromthesitesoftheold(1919-1974)and new airports (1975-2014) were amalgamated. Similarly, at Ibadan the records fromMoorPlantation(1901-1974)wereaddedtothoseoftheheadquartersofIITA(1975-2014).Linearregressionanalysiswascarriedoutonboththeamalgamatedandnon-amalgamateddata sets for these locations (Table 3) to demonstrate the potential legitimacy of theamalgamations.Thequalityof theotherdatasetsatmostof theother locationsshown in
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Table1weregenerally toopoor for inclusion in theanalysis, thusreducingthenumberofactivesitesusedforrainfalltrendanalysis.
Figure10.A)Annual rainfall totals (mm)1975-2014atTunisCarthage (missing2000and2001);B)Annualrainfalltotals(mm)1895-2014atTunisCarthage(missing2000and2001).
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Figure11.A) Annual rainfall totals (mm) 1975-2010 at Yangambi, province oriental, DRCongo;B)Annualrainfalltotals(mm)1912-2010atYangambi,provinceoriental,DR Congo; C) Annual rainfall totals (mm) 1912-1974 at Yangambi, provinceoriental,DRCongo.RESULTSThevariabilityandtrendsinaverageannualairtemperature1975-2014areshownin
A
B
C
y = 2.9745x - 4131.5R² = 0.0258; p<0.1605, NS, n=36
0
500
1000
1500
2000
2500
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Total Rainfall, Yangambi, (1975 - 2010)
y = 1.7319x - 1640R² = 0.0513; p<0,271, NS, n=99
0
500
1000
1500
2000
2500
1910 1930 1950 1970 1990 2010
Total Rainfall, Yangambi, (1912 - 2010)
y = 2.8478x - 3799.2R² = 0.0511; p<0.2188, NS; n=63
0
500
1000
1500
2000
2500
1910 1920 1930 1940 1950 1960 1970 1980
Total Rainfall, Yangambi, (1912 - 1975)
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Table2for14sites.AverylargevariabilityintrendsandR2valueswasobserved(Figures2-6). Sites such as Tunis and Ghardia in North Africa had data trends indicating extremelyrapid increases in temperature. Sites including Cairo, Khartoum, Dakar and Antananarivoshowed positive but lesser increases in temperature trends. The data for Nairobi, Dar esSalaam,Ibadan(IITA)andYangambiindicatedsmallpositivetrends.ResultsforNouakchott,Potchefstroom and Port Elizabeth showed no significant trends and Bamako had a smallsignificantnegative trend.R2 values ranged froma very close linear fit (0.8) atTunis to averypoor fit (0.0)atPortElizabethbut therewasnosuggestion fromvisual inspectionofthesedatathatothernon-linearmodelswouldhavebeenmoreappropriate(Figures5and4,respectively).UsingthetrendspresentedinTable2(1975-2014)projectionsweremadeforperiodsof25,50,75and100yearsintothefutureassumingthatthetrendsremainedconstantfortheprojectedtimeperiod(Table4).Thefiguresarepresentedasaclearerwaytocomparethe results at different locations in the termsusuallypresentedbymajor globalmodelingefforts (IPCC,2007).Averysubstantialvariationwasevident, ranging from6.9°Cper100years(2015-2115)atTunis,to4.5°CinCairo,and1.1°CatIbadan(IITA).ForPortElizabeththetrendwasnotsignificantandthustheprojectionisfornochange;atBamakothetrendwas negative, and the projected change in 100 years was-1.6°C.Table4. Increases inaverageannualair temperature(°C)projectedby25,50,75and100yearsupto2115assumingthatthetrendspresentedinTable2remainconstant.Meteorological station location 2015-2040 2015-2065 2015-2090 2015-2115 Antananarivo, Madagascar +0.66 +1.33 +1.99 +2.65 Bamako (Sénou), Mali -0.40 -0.80 -1.20 -1.60 Cairo Airport, Cairo, Egypt +1.11 +2.23 +3.34 +4.45 Dakar (Yoff), Dakar, Senegal +0.79 +1.59 +2.38 +3.17 Dar es Salaam airport, Tanzania +0.42 +0.84 +1.26 +1.68 Ghardaia, Algeria +1.34 +2.68 +4.02 +5.36 IITA, Ibadan, Nigeria +0.28 +0.55 +0.83 +1.11 Khartoum, Sudan +0.80 +1.61 +2.41 +3.21 Nairobi (Jomo Kenyatta), Kenya +0.41 +0.83 +1.24 +1.65 Nouakchott, Mauretania 0 0 0 0 Port Elizabeth, South Africa 0 0 0 0 Potchefstroom, NW Uni. S. Africa 0 0 0 0 Tunis, Carthage, Tunisia +1.72 +3.44 +5.15 +6.87 Yangambi (INERA), DR Congo +0.46 +0.93 +1.40 +1.86 RainfalltrendsareshowninTable3andFigures7-11.Recordscreatedbylocalstationcombinations were deemed to be legitimate as there were few differences observablebetweenthetrendsshownat thevarious locallyadjacentsites(Figures6a-cand7a-c).Allsites examined in the standard recording period for this paper 1975-2014 showed nosignificanttrends,withrelativelyhighannualvariabilityandthusverylowR2values.Whenextended periods greater than 90 years were examined, the same results were observed(Figures 7c, 8c, 9b, 10b, 11b) with the minor exception of Bamako, which showed asignificantbutveryslightlynegativetrend.Thismighthavebeenduetothesplittingofthetwoairportsites,asneithersitewhentakenaloneshowedasignificanttrend(Figures7a-c).TheR2inthisinstance(Figure7c)wasalsoquitelow(0.16).AsR2valuesweregenerallylow,the likelihood of spurious significant results is recognized as possible. Thus the rigorousstatistical confidence requirement of P<0.01 was used to confirm the significance of thetrends.DISCUSSIONFuture climatic uncertainty and its influence on biotic stressors will be profound
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(Bolandetal.,2004;Garrettetal.,2006).SuchfactorsmaycompromisethesustainabilityofAfrican vegetable production. This influence on productivitywill come from considerableandunpredictable, usuallypositive, variation in long term temperature trends indifferentlocations. There is continuing large inter-year variability in rainfall totals that show nosignificant long-term trends in the modern era (1975-2014) or even when viewed overmuchlongerspansofover100years.Increasedairtemperaturesanduncertainrainfallwillcauseadditionaldifficultiesforsmall-scalevegetableproducersduetolikelyincreasesinweedgrowth,inpestnumbersandtheir epidemiology, changes in disease spectra, in greater pathogenicity, and in increasedaggressiveness and greater viral virulence and mutation. These complex interactionsbetweenweeds, insects, pathogens and climate are currently poorly understood (Scherm,2004;Gregoryet al.,2009).Major insectpests, suchaspodborers that infest legumeandfruitvegetables,willbeabletocompleteadditionalgenerationsinaseason.Thiswillalsobetrueformajorviralvectorssuchaswhiteflies,aphidsandthrips(Hansonetal.,2011).Theimpact will occur not only on global vegetables such as tomato, eggplant, cabbages andgreenbeansbutalsoonawiderangeofAfricanindigenous/traditionalvegetables(Keatingeetal.,2016).Furthermore,theimpactwillincludedamagetoAfricanstaplecropsofwhichpartcanbeusedassecondaryvegetables,suchascowpealeavesandgreenpods(YangandKeding,2009),cassavaleaves(UfuanAchidietal.,2005),pigeonpeaasgreenpeasandevengreenmaizeforroasting(McCann,2001).TemperatureandrainfalltrendsTheconsiderablevariabilityintemperaturetrendsshownbetweenlocationsinAfrica(1975-2014)wasnotunexpectedassuchvariabilityhasbeenshowntohaveexistedgloballyfor the period 1975-2011 (Keatinge et al., 2014). In addition, a historical study involvinglocations distributed globally with at least 100 years of data (Keatinge et al., 2015a)demonstrated similar results. Furthermore, in a study of locations in a much moreconcentrated geographic area –Mesoamerica – such differences in temperature at nearlyadjacentlocationshavebeenreported(Keatingeetal.,2015b).Thecausesofsuchvariabilityare notwell understood, but factors influencing rural albedo and heat diffusivity such asincreasedurbanizationorthegrowingextentofhigh-albedostructuresatairports(Keatingeet al., 2013), extremechanges in landuse suchasdeforestation inCentral andNorthwestAfrica (Paeth et al., 2009), the introduction of major irrigation schemes (Baigorria et al.,2007), localizedhighconcentrationsofairpollution(Keatingeetal.,2015a),andelevationand aspect within the context of mountain chains (Keatinge et al., 2015b) have all beenconsidered,withoutanyfullysatisfactoryexplanationemergingfromthedifferentanalyses.The results in this paper do not contradict studies by other authors predictingtemperature increases in the future, such as at Embu in Kenya (Rao et al., 2015), ortemperaturevariability inyearsassociatedwiththeElNino-Southernoscillation(Ogutuetal., 2008).However, the existing literaturedoesnothighlight the inter-location variability,whichisclearlyevident.Whenlarge-scalemodelingisappliedtolargernumbersofdatasetsinAfrica,andglobally,suchlocalvariabilityisaveragedoutandthisthenresultsinthe2+°Cincreasesper50-100yearspredictedbytheworkinggroupsoftheIPCC(IPCC,2007,2013).Eveninthisverysmallsampleof13locationstheaverageincreasepredictedper100yearsisinthesame2-3°Crange.However,whethersuchaveragesarenowgermanetothefutureplans of vegetable breeders and crop protection specialists remains a moot point. Forexample,shouldvegetablebreedersinPotchefstroomignoretheneedforspecificextraheattolerance in their cultivars over the next 25 years, as no increase in annual average airtemperatureispredicted?ShouldthoseinTunisbeultra-awareofthisfactorasincreasesintemperaturearepredictedtobelarge(Table4)?Theresultsgiveninthispapersuggestthatjustsuchpolicieswouldbelogical,althoughifoverallwarmingdriversaretooperateonaconsistent global scale then perhaps less variability between sites might be the futureoutcome.However,thereisnoevidencetosuggestthattobethecaseinAfrica.The rainfall results presented in this paper offer a much clearer picture. Theconclusion that no significant differences were observed in seasonal long-term trends at
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MooringsStationinZambiawith89yearsofdata(SternandCooper,2011)andlikewiseforstations around Nakuru in the Masai Mara/Serengeti game reserves region ofKenya/Tanzaniabut foraquiteshortrunofdata (Ogutuetal.,2008,2012)areconfirmedcontinentally for the fewstationswithgood long-termrecordsof consistentquality.Giventheclearannualvariabilityinrainfalltotalsatallthelocationsexamined,itwouldprobablyrequireamuchlongerdataset(>200years)toascertainhistoricaltrendsandsuchdatasetsarenotavailable.Therefore, forpurposesof thispaperweassume there isnosubstantivetrendintotalannualrainfalldiscernibleinanylocationinAfrica,butfarmersneedtobeveryconcerned about rainfall variability on a year-to-year basis. This assumption would besupportedbytheanalysisof longtermSaheliandata(Sissokoetal.,2011).This,ofcourse,does not rule out the likelihood of an increase in extreme drought and floodingweathereventsinawarmingworldpredictedbyShongweetal.(2009,2011)andRaoetal.(2015)forSouthernandEasternAfrica.ImplicationsforbioticstressorsandthesustainabilityofAfricanvegetableproductionAlthoughannualaverageairtemperatureisaverybluntinstrumentforthepredictionofplantgrowth/pestinteractions(SchermandvanBruggen,1994),itisstilloneofthemostconservative/reliable meteorological variables (which is freely available) for reflectingchangewithinsystems.Temperatureisrecognizedasacriticalclimatevariabledetermininginsectpest-cropinteractions(Bolandetal.,2004).Giventhevariationbetweentemperaturetrends shown at different locations (Table 4) and that plant breeding programs, wherepossible, will be aiming atmore than small niche environments, it is necessary to targethigher levels of resistance to expected plant stressors than was previously consideredacceptableinavarietysuitableforrelease(Afari-Sefaetal.,2012).Forexample,‘Tengeru97’isanopenpollinatedfreshmarkettomato(Solanumlycopersicum)linebredbyAVRDC-TheWorldVegetableCenterandreleasedasavarietybytheTanzaniangovernment20yearsago(Ojiewo et al., 2010). It is now commonly grown throughout East Africa fromEthiopia toZimbabwe and sold by a range of private sector seed companies. However, its eventualsuccessorswiththepotentialtofollowinthe‘Tengeru’seriesmustincorporatenewmaterialfromtheAVRDCbreedingpipeline,whichisdesignedtohavefurtherimproved,pyramidedresistance to pathogens. These include early and late blight (Alternaria solani andPhytophthora infestans, respectively),Tomato yellow leaf curl virus (TYLCV;Begomovirus),anthracnose fungal diseases (Colletotrichum spp.) and bacterial wilt (Ralstoniasolanacearum).Farmerswillalsoneedtoadoptbetter,safeagriculturalpractices,which,inthe case of tomato,will include the use of grafted seedlings, integrated pestmanagementpractices,andifpossible,sufficientprotectionmeasuressuchasnethousestopreventvirusvectors coming into contact with the plants. Increasing annual temperatures have thepotential to magnify the risk of yield reduction (Sheu et al., 2009; Tsai et al., 2011),particularly when it is appreciated that whitefly (Bemisia tabaci) – the insect vector forTYLCV–canproduceanextrathreegenerationsinseasonswhenaverageairtemperaturesincreaseby1°C(Hansonetal.,2011).Likewise,suchincreasedtemperaturescanextendormodifythegeographicandaltitudinalrangeofpests(Jaramilloetal.,2011),oradvancethetimingofaphidmigrationsintocrops(Scherm,2004).Other insectspeciesseverelydamagingto importantvegetables inAfrica,andwhichmay becomemore numerous andmore destructive with increased temperatures, includespider mites (Tetranychidae spp.; Rosenzweig et al., 2001), pod borers (Maruca vitrata,Heliocoverpaarmigera;Sharmaetal.,2010),leafminers(Liriomyzaspp.andTutaabsoluta;Allacheetal.,2015),diamondbackmoth(Plutellaxylostella;Grzywaczetal.,2010)andmanyothers. All these species are capable of very substantive damage to field and greenhousevegetableyieldseitherdirectlyorindirectlythroughvirustransmissionfrominsectssuchasthrips (Thysanoptera spp.), which transmits Tomato spotted wilt virus in a range ofvegetables(Moritzetal.,2004).Concernoverstressorsencouragedbyhigherair temperaturesshould includemajorweed species. These are both native and invasive in Africa such as Imperata cylindrica
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(MacDonald, 2004); Striga and Orobanche spp. (Parker, 2009), and Chromolaena odorata(Kriticos et al., 2005;Norgrove andHauser, 2015). Thesemajorweed species are alreadyextremely difficult to control under small-scale farmer conditions in Africa and highertemperaturesandCO2 levels inawarmingcontinentcouldeasilyexacerbate thesechronicproblems.We believe our hypothesis that climate change threatens the development andsustainability of improved vegetable horticultural systems in Africa is supported by theevidencepresentedinthispaper.Plantbreeders,entomologistsandplantpathologistswillnowneedtogivecarefulattentiontowhetherlocalclimateuncertaintyandweathereventsare an increasingly significant factor across the broad locations atwhich they are aimingtheir improvedvegetable linesforrelease.Considerationneedstobegiventobreedingfordrought, flooding and salinity tolerance. Vigilance will be needed to monitor for theemergence of adapted pests andmutated pathogens,with new viruses being a particularcaseinpointasvegetablesareparticularlysensitivetothesestressors.Thus,itisvitalforallnationalgovernmentsinAfricatotakethepotentialrisktotheirhorticulturalindustriesasaserious matter of national priority. They must be more generous and consistent in theirsupport of horticultural research and capacity building in horticulture. No action in thisregard can only lead to increased poverty, malnutrition, and failure to attain the targetsoutlined in UN Sustainable Development Goal 2 (Improving Agricultural Systems andReducingRuralPoverty).ACKNOWLEDGEMENTSWewishtoacknowledgethekindcollaboratorswhoprovideddataforthisstudyfromIITA, ICRISAT and ICARDA and to their associated National Meteorological Servicecolleagues.Wealsowish to acknowledge the generous, long termdonors toAVRDC -TheWorldVegetableCenterandtoIITA,ICRISATandICARDAthatmadethisexploratoryworkpossible.LiteraturecitedAfari-Sefa, V., Tenkouano,A.,Ojiewo, C., Keatinge, J.D.H., andHughes, J.A. (2012). Vegetable breeding inAfrica:constraints, complexityandcontributions towardsachieving foodandnutritional security.FoodSecur.4,115–127https://doi.org/10.1007/s12571-011-0158-8.Allache, F., Bouta, Y., and Demnati, F. (2015). Population development of the tomato moth Tuta absoluta(Lepidoptera:Gelechiidae)ingreenhousetomatoinBiskra,Algeria.J.CropProtect.4,509–517.Baigorria,G.A., Jones, J.W.,Shin,D.W.,Mishra,A.,andO’Brien, J.J. (2007).Assessinguncertainties incropmodelsimulationsusingdailybias-correctedRegionalCirculationModeloutputs.Clim.Res.34, 211–222https://doi.org/10.3354/cr00703.Bebber,D.P. (2015).Range-expandingpestsandpathogens inawarmingworld.AnnuRevPhytopathol53 (1),335–356https://doi.org/10.1146/annurev-phyto-080614-120207.PubMedBoland,G.J.,Melzer,M.S.,Hopkin,A.,Higgins,V., andNassuth,A. (2004). Climate changeandplantdiseases inOntario.Can.J.Pl.Path.26(3),335–350https://doi.org/10.1080/07060660409507151.Challinor,A.,Wheeler,T.R.,Garforth,C.,Craufurd,P.Q.,andKassam,A.(2007).Assessingthevulnerabilityoffoodcrop systems inAfrica to climate change.Clim.Change83 (3),381–399https://doi.org/10.1007/s10584-007-9249-0.Cooper,P.J.M.,andCoe,R.(2011).Assessingandaddressingclimate-inducedriskinsub-SaharanAfricanrainfedagriculture.Exp.Agric.47(02),179–184https://doi.org/10.1017/S0014479711000019.Cooper,P.J.M.,Rao,K.P.C.,Singh,P.,Dimes,J.,Rao,K.,Dixit,P.,andTwomlow,S.J.(2009).Farmingwithcurrentandfutureclimaterisk:Advancinga“HypothesisofHope”forrainfedagricultureinthesemi-aridtropics.JournalofSemi-AridTropicalAg.Res.7,1–19.DelaPena,R.C.,andHughes,J.d’A.(2007).Improvingvegetableproductivityinavariableandchangingclimate.J.Semi-AridTropicalAg.Res.4,1–22.DelaPena,R.C.,Ebert,A.W.,Gniffke,P.A.,Hanson,P.,andSymonds,R.C.(2011).Geneticadjustmenttochangingclimates:Vegetables.InCropAdaptiontoClimateChange,S.S.Yadav,R.J.Redden,J.L.Hatfield,H.Lotze-Campen,andA.E.Hall,eds.(London,UK:JohnWileyandSons),p.396–410.
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