1. Central American maize-bean systems and the changing climate Tortillas on the RoasterA. Schmidt, A. Eitzinger, K. Sonder, G. Sain, P. Lderach, J. Hellin, B. Rodriguez, M. Fisher, L. Rizo, S. OconCali, Colombia, October, 2012 Pic by Neil Palmer (CIAT). Funded by the The Howard G. Buffett Foundation

2. In Central America more than 1 million smallholder families depend onthe cultivation of maize and/or beans for their subsistence.Frequently there is a high vulnerability to extended drought periodsand extreme weather events such as hurricanes putting the foodsecurity of these smallholder families at risk.As climate already is changing by getting hotter & dryer, maize-beanfarmers in Central America will be forced to adapt to changes in cropsuitability to maintain food security.Tortillas on the Roaster seeks to predict locally specific changes inmaize bean production systems that people can act and respond toongoing climate change by concrete adaptation measures.2 3. Activity line and main objectives3 4. Methods: Climate dataProvide local scaleClimate predictions For current climate (baseline)we used historical climate data from WorldClimMeteorological stations on which WorldClim is based in the study areawww.worldclim.org Future climate: 21 global climate models (GCMs) fromIPCC (WCRP CMIP3) - SRES-A2, 2020 & 2050 Downscaling (CIAT Decision and Policy Analysis Working Paper, no. 1, delta-method)to provide higher-resolution (2.5 arc-minutes ~ 5 kilometer) 5. Methods: daily climate data Generate dailyclimate data for Generating characteristic dailyDSSATweather data with MarkSim**MarkSim was developed to generate precipitation data for tropical regions. We modified MarkSim for batch-processing. 6. Methods: Simulate Crop growing cycle Predict impact onDecision Support System for Agro technology Transfer (DSSAT)production systems(beans) Current yield Future yield (kg pro hectares)= expected impact on yield (+/-) 7. Methods: Target future interventions from predicted impact Identify (impact)Hot-spots Areas where the production systems of crops can be adapted Adaptation-Spots (more than 25% yield loss) Focus on adaptation of production system Areas where crop is no longer an option Hot-Spots (more than 50% yield loss) Focus on livelihood diversification New areas where crop production can be established Pressure-Spots (more than 25% yield gain) Migration of agriculture Risk of deforestation! 7 8. Methods: Socio-economic analysis Quantify socio- economicSocio-economic impact on farmers livelihoods consequencesVULNERABILITY to Climate Change (IPCC 2001) Degree of susceptibility and Exposureincapability of a system toDegree to which a system is confront adverse effects of exposed to significant variation climate changein climateSensitivityAdaptive capacity Degree to which a system is The ability of a system to adapt positively or negatively affected to climate change by climate related stimulus Focal group workshops on selected Hot- & Adaptation-spot-sites On Farm data collection by surveying based on livelihood indicatorsoff 5 assets: human, natural, social, physical, financial 9. results ResultsPic by Neil Palmer (CIAT). 10. Results: Predicted Climate Change in Central AmericaRESULTS19 GCM (IPCC 4th Assessment report CMIP3) scenario A2 CLIMATE CHANGE2 30 year mean periods 2010-2039 [2020], 2040-2069 [2050]For 2020: meanannual temp.increase1 - 1.1 CFor 2050: lessprecipitation( ~ -10%)mean temp.increase2.2 - 2.4Chottest day upto 35.6C(+ 2.4 - 2.6C)coolest nightup to 18.2C(+ 1.6 - 2C) 11. Results: Ground proofing and similar Climate patterns RESULTSCalculate Climate-Cluster from 19 bioclimatic variables to GROUND PROOFINGunderstand potential beans production areas Estimate current bean production areas by Kernel density using point data from *Beans-Atlas* Common beans atlas of the AmericasMichigan State University Suitability (crop to climate) analysis with EcoCrop 12. ResultsAssessment of beansPic by Neil Palmer (CIAT). 13. Methods: Block diagram of impact assessment of beansPredict impact on production systems (beans) 14. Methods: DSSAT Simulation trialsPredict impact onDecision Support System for Agro technology Transfer (DSSAT) production systems (beans)Weather data input:Current climateAverage of 99 MarkSimdaily outputsFuture climateEnsemble of 19GCM & 99MarkSim outputs for 2020& 2050Runs: 17,800 points x 3 Planting date: Between 15th of April and 30th of June1 climates x 99 MarkSim-samples x 8 trials Variety 1: IB0006 ICTA-Ostua Variety 2: IB0020 BAT1289 Soil 1: IB00000005 (generic medium silty loam) Soil 2: IB00000008 (generic medium sandy loam) Fertilizer 1: 64 kg / ha 12-30-0 6 to 10 days after germination and 64 kg / ha Urea (46% N) at 22 to 25 days after germination. Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing and 64 kg/ha UREA at 22 to 30 days after germination. 15. Methods: Field trials to calibrate DSSATPredict impact on production systems Accompanying field trials in 5 countries to calibrate DSSAT (beans) For 2 DSSAT-varieties (IB0006 ICTA-Ostua, IB0020 BAT1289 INTA Fuerte Sequia, INTA Rojo, and To Canela 75 originating from Nicaragua ICTA Ostua and ICTA Ligero originating from Guatemala BAT 304 originating from Costa Rica SER 16, SEN 56, NCB 226, and SXB 412 originating from CIAT, Colombia. Sowing on: Primera (Beginning of June) Postrera (Beginning of September) After recollecting data during 2011results will be usedin a post-project-analysisto calibrate 2 initial DSSAT varietiesrun it again for trial sites and findspatial and temporal analogues 16. Results: yield change for year 2020 predicted by DSSAT (Primera)RESULTS IMPACT ON BEANS(average 8 trials) 17. Results: yield change for year 2020 (Primera) 8 trialsRESULTS COMPARE SIMULATIONSTrial 3 high performance / high impact Trial 7 medium high performance / less impactVariety 1: ICTA-OstuaVariety 1: ICTA-OstuaSoil 1: generic medium silty loamSoil 2: generic medium sandy loamFertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowingand 64 kg/ha UREA at 22 to 30 days after germination and 64 kg/ha UREA at 22 to 30 days after germination 18. Results: Specific country results for year 2020 (Primera)RESULTSBEANS IMPACTNicaraguaHighest impact (negative yield change) wouldbe expected on the dry corridor (Corredorseco) from Rivas, Granada up to Estel andMadriz.Improved yields are predicted for the Atlanticregion and Chontales which are traditionallyused for Apante-production (December - April) 19. Results: Specific country results for year 2020 (Primera) RESULTS BEANS IMPACT HondurasDry corridor continues its path up to Hondurasand El Paraiso, Francisco Morazan to Yoro.Ocotepeque is the only beans producingdepartment with in average increasing yields. 20. Results: Specific country results for year 2020 (Primera) RESULTS BEANS IMPACT El SalvadorHighest reduction in yield is expected to occurin the South-Eastern region in the departmentsCuscatlan.Impact in general is less compared to the other3 countries 21. Results: Specific country results for year 2020 (Primera) RESULTS BEANS IMPACT GuatemalaSome departments have high potential forfuture bean production regarding to changingclimate and perhaps because of their differentclimate zone.San Marcos (+38%), Totonicapn (+23%) andQuezaltenango (+31%) are high potentials forbeans production by 2020 (considering onlyclimate as factor) 22. Address uncertainty of DSSAT simulation RESULTS We calculated 4 different outcomes to address UNCERTAINTY uncertainty of DSSAT simulation a Relative yield change as average of 19 GCMs for 2020 b Average of the 1st quartile of GCMs c Average of 3rd quartile of GCMs d Breadth of GCMs agreeing in yield change prediction by DSSAT.Because of processing constraints we runDSSAT on a 15 kilometer buffer aroundsites selected for socio-economic analysis 23. maizAssessment of maizePic by Neil Palmer (CIAT). 24. Methods/Results: DSSAT Simulation trialsPredict impact on production systems model runs were divided according to the two (maize)general soil types selected. best and worst (poor soil conditions) case scenarios 25. Methods/Results: DSSAT Simulation trialsPredict impact on production systems Maize yield differences between(maize)current climate and 2020s predicted poor soilgood soil 26. Results: Specific country results for year 2020RESULTSpoor soil scenario MAIZE IMPACTNicaraguaImpact for Nicaragua for the2020s and the poor soil scenarioon the country overall ispredicted to be a reduction of11% implying a production lossof 51,741 t compared to thelatest production statistics.good soil scenarioAreas like Masaya (-46%) andChinandega (-43%) would facehigher reductions while thelarger production areas likeJinotega (-9%), Matagalpa (-9%),Atlantico Sur (-1%) and Norte(-1%) are predicted to show lessreductions under the poor soilcondition scenario. 27. Results: Specific country results for year 2020RESULTSpoor soil scenario MAIZE IMPACTHondurasOverall losses for Hondurascompared to the 2009-2010production (available for 7regions) would amount to175,598 t of maize (poor soilconditions) an overall loss of30%. For the good soil and thegood soil scenario2020s losses overall are stillconsiderable with a total of69,534 t (12%). 28. Results: Specific country results for year 2020RESULTSpoor soil scenario MAIZE IMPACTEl SalvadorImpact for El Salvador for the2020s and the poor soil scenarioon the country overall ispredicted to be a reduction ofover 250,000 t of maize basedon the 2009-2010 productionyear.good soil scenarioAreas like La Paz (-74%), LaUnion (-44%), San Miguel (-43%), Usulutn (-40%), SanVicente (-39%), San Salvador (-35%) and Cabaas (-34%) wouldface higher reductions whileareas like Ahuachapan (-11%)and Chalatenango (-17%)arepredicted to show lessreductions under the poor soilcondition scenario. 29. Results: Specific country results for year 2020RESULTSpoor soil scenario MAIZE IMPACTGuatemalaImpact for Guatemala is softened by theconsiderable highland areas mainly in theWest of the country while drier areas likeparts of Petn, coastal areas in the South(Retalhulehu, Escuintla), and the Easternborder (Chiquimula and Jutiapa) wouldface considerable losses. Also the largestproducer in terms of area, Alta Verapaz, isgood soil scenariolittle affected due to slight increases underthe good soil scenario and only slightlosses under the poor soil conditionscenario. For the 2020s and the poor soilscenario on the country overall is predictedto be a reduction of 98,000 t incomparison with the latest productionstatistics.For the good soil scenario the overallbalance for the country is positive with4,247 t increase. 30. Socio-economic consequencesPic by Neil Palmer (CIAT). 31. Results: Hot-spots for maize or beans production areas in Central AmericaIdentify (impact) Hot-spots Message 1: We need to pick out where to start working! 32. Results: Selected 16 sites for socio-economic studyQuantify socio-economicconsequences 33. Result: Sample-site 1 - Texistepeque (Las Mesas), Santa Ana ,El Salvador Hot-spotLas MesasAltitude: 667 m(about 2188 feet)Hot-spot -141 kg/ha For 2020: 35 mm less rain (current 1605mm) mean temperature increase 1.1 C For 2050: Beans as most important income (sell 70% of harvest) 73mm less rain ( -5%) Climate variability (intense rain, drought), missing labor mean temperature increase 2.3 Chottest day up to 35 C (+ 2.6 C)& credits, high input costs, forces them to changescoolest night up to 17.7 C (+ 1.8 C) Increasing livestock displace crops into hillside areas Half of farmer rent their land Distance to market is far Mostly no road access in rainy season They buy inputs/sell produce from/to farm-stores(they call them: Coyotes)Message 2: Adaptation Strategies must be fine-tuned at each site!33 34. Result: Sample-site 2 Valle de Jamastran, Danl, Honduras Adaptation-spot Jamastran Altitude: 783 m (about 2568 feet) Adaptation-spot - 115 kg/haFor 2020: Active communities with already advanced agronomic 41 mm less rain (current 1094 mm) mean temperature increase 1.1 Cmanagement of maize-bean cropsFor 2050:80 mm less rain ( -7%) Favorable soil conditions and managementmean temperature increase 2.4 C Long-term technical assistance / training hottest day up to 34.2 C (+ 2.6 C)coolest night up to 17 C (+ 2.1 C) Irrigation schemes (e.g. 50 mz of 17 bean producers) Diversification options (vegetables, livestock) Market channels through processing industries Advanced infrastructure (electricity, roads) Need to optimize water use efficiency Credit problems Message 3: There can be winners if they adapt quickly!34 35. Socio-economicResults: Focal groups resultsQuantify socio-Focal Groups were carried out in Honduras, El Salvador, and Nicaragua since unexpected economicclimatic events (flooding in Oct-Nov 2011) prevented us from implementing focal groups inGuatemala. consequences Main activities and trends 35 30 25 Mentions ( %) 20 15 1050El Salvador Honduras NicaraguaMaize Beans Sorghum & maicillo Vegetables Cattle Poultry &eggs Rice Fruits Coffee Pork 36. Socio-economicResults: Focal groups resultsQuantify socio-Focal Groups were carried out in Honduras, El Salvador, and Nicaragua since unexpected economicclimatic events (flooding in Oct-Nov 2011) prevented us from implementing focal groups inGuatemala. consequences Farmers perceptions point to economic as well as climatic events as main drivers of perceived trends 70 60 50Mentions (%) 40 30 20 100 El SalvadorHonduras NicaraguaClimate related (1) Economics/finance (2)Lack o fResouces (3) Other (4) 37. Socio-economic results capitalsResults: Focal groups LivelihoodQuantify socio-Focal Groups were carried out in Honduras, El Salvador, and Nicaragua since unexpectedeconomicclimatic events (flooding in Oct-Nov 2011) prevented us from implementing focal groups inGuatemala.consequences(a)70 60 (b)8070 50 60Mentions (%) Mentions (%)50 4040 3030 2020 10 1000El SalvadorHondurasNicaraguaEl Salvador HondurasNicaragua Own Rent LoanPotable Irrigation No treated (wells)(c)80 70 60 (a) Forms of land tenureMentions (%) 50 (b) Water availability 40 30 (c) Main road types 20 100El SalvadorHondurasNicaraguaAll year Dry season only 38. Socio-economicResults: Focal groupsresults Quantify socio-(a)5045 economic40 consequences35Menciones (%)30252015 Perceptions of10 5 future threats and 0 El Salvador Honduras NicaraguaopportunitiesClimate events Natural resourcsFinancial and economic resources Social eventsCatastrophic events60(b) 5040Menciones (%) (a) future threats30 (b) future opportunities2010 0 El SalvadorHonduras Nicaragua Public investmentStrengthening of human & social resources Sustainable development projects Change of activities 39. Socio-economic resultsResults: Socio-economic analysis Quantify socio- Quantity and value of maize and beanseconomic consequencesproduction losses in 2020 60,000Maize/beans value of production losses (000 us$)50,709 50,00045,623 40,000 30,000 20,00017,476 10,0008,622 0NicaraguaHonduras El SalvadorGuatemalaEstimated value of maize&beans production losses at 2020 (us$) Summary of predicted types of changes on country level 40. Methods: Socio-economic analysis Quantify socio- economic Household vulnerability consequencesPic by Neil Palmer (CIAT). 41. Socio-economic results - Household EXPOSUREMethods: Socio-economic analysisQuantify socio- I II IIIeconomicImpact on land productivityGCC Consequences at hotspot levelAdjustment factor at thehousehold level Indicator: Exposure level of themaize/beans cropping systemconsequences(predicted by the(estimated) (High, Medium, Low) biophysical model) Exposure level of the maize/beans cropping system The adjustment level at the farming system (Household exposure)1.Relative change in bean yield predicted by the biophysical model(as shown in previous slides)2.Conservation technologies / Inclination 42. Classes of maize/beans cropping system exposure (%)Classes maize production exposure (%)10203040506070809040 6080 0 0 20 100 100 (c) (a) El RosarioEl RosarioSan Felipe San Felipe El Salvador El SalvadorSan RafaelSan RafaelIpala Ipala San Manuel ChaparronHigh High San Manuel Chaparron Guatemala GuatemalaPatzicia PatziciaMedium MediumAlaucaAlaucaLow LowJamastranJamastran HondurasOrica HondurasOrica Results: Socio-economic analysis La Hormiga La HormigaSan Dionisio San Dionisio Nicaragua Nicaragua Totogalpa TotogalpaClasses of beans production exposure (%)20 406080 0 100120 (b) El Rosario San FelipeEl SalvadorSan Rafael Socio-economic results - Household EXPOSURE Ipala HighSan Manuel ChaparronGuatemalaPatzicia MediumAlauca Low (a) Exposure level of maize (b) Exposure level of beans JamastranHonduras Orica (c) Exposure level of maize/beansLa Hormiga economic San Dionisio consequencesNicaragua Quantify socio- Totogalpa 43. Socio-economic results - Household SENSITIVITYResults: Socio-economic analysisQuantify socio-economicconsequences Stages in the estimation of the sensitivity of livelihoods sources indicator Importance of the system maize/bean farm income MaizeBeans 100120 Classes of beans importance in farms income (%)Classes of maize importance in farms income (%)90801007080605060403040202010 0 0 Totogalpa Orica Patzisia San Manuel Chaparron El RosarioSan Felipe San Rafael La HormigaIpalaSan DionisioAlauca JamastranSan Manuel Chaparron San Felipe OricaSan RafaelPatzisia TotogalpaEl Rosario Ipala La HormigaSan DionisioAlauca JamastranEl SalvadorGuatemala Honduras NicaraguaEl Salvador GuatemalaHondurasNicaraguaHigh Medium LowHighMediumLow 44. Classes of household sensitivity (%) Classes of maize sensitivity (%) 2040 60 80 0 2040 60800 100(c) (a)El RosarioEl RosarioSan FelipeSan Felipe El SalvadorEl Salvador San RafaelSan Rafael IpalaIpala San Manuel ChaparronSan Manuel ChaparronHighHighGuatemala Guatemala Patzisia PatzisiaMediumMedium AlaucaAlaucaLowLowJamastranJamastranOrica HondurasOrica Honduras Results: Socio-economic analysis La Hormiga La HormigaSan DionisioSan DionisioNicaragua NicaraguaTotogalpa TotogalpaClasses of beans sensitivity (%)(b) 20 6080 040100El Rosario San FelipeEl SalvadorSan Rafael Socio-economic results - Household SENSITIVITYIpala HighSan Manuel ChaparronGuatemala Patzisia Medium Alauca Low JamastranOricaHonduras(a) Households sensitivity of maize(b) Households sensitivity of beansLa Hormiga economic San Dionisio consequences Quantify socio-Nicaragua(c) ) Households sensitivity of maize/beans Totogalpa 45. Socio-economic results - Household ADAPTABILITYMethods: Socio-economic analysisQuantify socio-economic Stages used to estimate the household adaptive capacity consequences 46. Classes of natural captal availabilty (%) Classes of physical capital availabilty (%) 60800 2040 10020 40 60800100120(c)(a) El Rosario El Rosario San Felipe San FelipeSan Rafael San RafaelEl SalvadorEl Salvador Ipala IpalaSan Manuel Chaparron San Manuel ChaparronLowLow PatzisiaPatzisiaGuatemalaGuatemalaMediaMediaHighHighAlauca Alauca Jamastran JamastranOricaHonduras OricaHondurasResults: Socio-economic analysisLa Hormiga La Hormiga San Dionisio San DionisioNicaraguaNicaragua TotogalpaTotogalpaClasses of credit access (%)20406080 0100(b)El RosarioSan FelipeSan RafaelEl SalvadorSocio-economic results - Household ADAPTABILITY Ipala (c) Natural capital (a) Physical capital LowSan Manuel Chaparron Stages used to estimate the household adaptive capacityGuatemala Fair AlaucaJamastran OricaHonduras (b) Financial capital (credit access)La Hormigaeconomic San DionisioconsequencesQuantify socio-NicaraguaTotogalpa 47. Socio-economic results - Household ADAPTABILITYResults: Socio-economic analysis Quantify socio- Stages used to estimate the household adaptive capacityeconomic100 consequencesClasses of human captal availabilty (%) 80 60 40 20(a) 0 San Manuel ChaparronIpalaOricaPatzisiaTotogalpaEl RosarioSan Rafael La HormigaSan Felipe San DionisioAlaucaJamastran El Salvador GuatemalaHondurasNicaraguaLow Media High 100Classes of social capital availability (%)8060(b)4020 0(a) Human capitalOricaPatzisia El Rosario San Rafael AlaucaLa Hormiga TotogalpaSan Felipe IpalaSan DionisioJamastran San Manuel Chaparron(b) Social capital El Salvador GuatemalaHonduras Nicaragua LowMediaHigh 48. Socio-economic results - Household ADAPTABILITYResults: Socio-economic analysisQuantify socio-economicconsequencesHouseholds adaptive capacity 100 Classes of households capacity of adaptation 80 60 40 20 (%)0 Totogalpa OricaPatzisiaSan Rafael La Hormiga El Rosario San Felipe IpalaSan DionisioAlauca San Manuel Chaparron JamastranEl Salvador GuatemalaHonduras Nicaragua LowMediaHigh 49. Socio-economic results - Household VULNERABILITYResults: Socio-economic analysisQuantify socio-economic Households vulnerabilityconsequences10080 Classes of vulnerabilility (%)604020 0 San Manuel ChaparronTotogalpaPatzisiaOricaEl RosarioSan Rafael Ipala La Hormiga San Felipe San Dionisio AlaucaJamastran El Salvador Guatemala Honduras NicaraguaHigh Medium Low 50. Adaptation- & Mitigation strategies 51. Result: Local Adaptation- Mitigation strategiesWe derived five principal strategies for adaptation at farm level Sustainable intensification: Aimed at increasing physicalproductivity while preserving natural resources (land andwater) in productive systems (eco-efficiency). Diversification: Increases the amount of consumptionsources and income from agriculture Expansion: Expands the endowment of different types ofcapitals Increasing off-farm income: Increase the importance ofsources of income from more secure out-of-the-householdactivities. Out of agriculture as a livelihood strategy: The householdleaves agriculture as a source of income and consumption.51 52. Result: Local Adaptation- Mitigation strategiesSustainable intensificationIncrease rain water use efficiency! Improved soil and pest management Socially integrated soil and pest management with coordinated actionsacross the community and national actors. Irrigation and water-catchment Extent production into drought season with lower temperatures usingirrigation and water-catchment systems. Improve plant nutrition management water use efficiency can be increased by 15-25% through adequatenutrient management Genetic improvement for heat stress and drought tolerance Breeding for common bean improvement in Central America for severalstresses associated with climate change.52 53. Result: Local Adaptation- Mitigation strategiesDiversificationIncrease consumption sources and income from agriculture! Agua-Agro-Silvo-Pastoral Systems Nutrient cycling is enhanced through the integration of crops andanimals resulting in higher crop yields. Improved soil and water quality and increased biodiversity Lower greenhouse gas emissions and increased carbon sequestration Trees and shrubs offer sources of bio-energy Fruits, nuts, horticulture nursery stock, wood fiber and livestock shelter Opportunities for restoration of degraded lands Allow for livestock integration 53 54. Result: Local Adaptation- Mitigation strategiesExpansion Expansion of land occupation & expansion of the endowment of natural, physical, financial, human and social capitals on farm level! Natural shift to Apante areas To avoid deforestation, increase effectiveness of bean production by optimal management of abioticstress and biotic constraints through a multidimensional farming system approach. Start with farmers awareness building to climate change mitigation and build up conservation incentivesfor farmer groups. Converting grazing land into cropland Controlled agricultural land use shift (caused by changing climate patterns) inside existing agriculturalfrontiers in Central America by using improved forages for livestock and convert liberated grazing landinto cropland. The land tenure complex Long-term land lease is not common, perspectives investments in sustainable soil and watermanagement will not to be made Policy interventions are urgently needed Expansion of human and social capital Learning framework for farmer groups Bring Climate Change research to the ground Generate site-specific adaptation- and mitigation strategies and share them spatially with concreteincentives among farmer communities.54 55. Result: Local Adaptation- Mitigation strategiesIncreasing off-farm income Central American smallholders traditionally generate off-farm income during e.g. coffee harvest, in processing facilities or mostly for women. These are temporal activities during the dry season associated with migration. Remittances are also an important source of off-farm income and largely spent on consumption.Out of agriculture In general, rural areas provide limited opportunities for income generation which leads to migration to urban areas or outside Central America. 55 56. Thank youa.eitzinger@cgiar.orgp.laderach@cgiar.orghttp://dapa.ciat.cgiar.org/withwithout Climate Adaptation Strategies