Using empirical and mechanistic models to predict crop suitability and productivity in climate change research

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Using well-established empirical and mechanistic models such as Ecocrop, Maxent, DSSAT to assess the impact of climate change on productivity and climate-suitability of crops and production systems.


  • 1. Using empirical and mechanistic models to predict cropsuitability and productivity in climate change researchAnton Eitzinger A.Eitzinger@cgiar.orgP. Laderach, C. Navarro, B. RodriguezDecision and Policy Analysis DAPA, CIAT Nairobi, June 13th 2013

2. Why crop modeling in climate change? assessing the impact of climate change onproductivity and climate-suitability of crops andproduction systems and understand the limitingfactors using well-established empirical and mechanisticmodels such as Ecocrop, Maxent, DSSAT, ..that allow for the incorporation of spatial data andfine-tuned biophysical dataHow? 3. Stations byvariable: 47,554precipitation 24,542tmean 14,835tmax y tminSources:GHCNFAOCLIMWMOCIATR-HydronetRedes nacionales-30.130.5Mean annualtemperature (C)012084Annualprecipitation (mm) 4. BPREC Generateinterpolated climatesurfaces usingANUSPLIN-SPLINAwith weather stationdata Cross validating (25iterations uncertaintyTMPUncertainty of climate data and models 5. BValidation of climate surface (25 iterations) 6. BCompare original worldclim with interpolated 7. GCMs are the only waywe can predict the futureclimateUsing the past to learnfor the futureGCM Global Climate Model 8. The Delta Method Use anomalies and discard baselinesin GCMs Climate baseline: WorldClim Used in the majority of studies Takes original GCM timeseries Calculates averages over a baseline andfuture periods (i.e. 2020s, 2050s) Compute anomalies Spline interpolation of anomalies Sum anomalies to WorldClim 9. Climate data For current climate (baseline)we used historical climate data from Future climate: global climate models (GCMs)from IPCC (AR5) SRES A2, A1B, .. Downscaling to provide higher-resolution (2.5 arc-minutes ~ 5 kilometer) 10. EcoCropThe database was developed 1992 by the Land and WaterDevelopment Division of FAO (AGLL) as a tool to identify plant speciesfor given environments and uses, and as an information systemcontributing to a Land Use Planning concept.In October 2000 Ecocrop went on-line under its own The database now held information on morethan 2000 species.In 2001 Hijmans developed the basic mechanistic model (also namedEcoCrop) to calculate crop suitability index using FAO Ecocropdatabase in DIVA GIS.In 2011, CIAT (Ramirez-Villegas et al.) further developed the model,providing calibration and evaluation procedures. 11. openSuitability modeling with EcocropEcoCrop, originally by Hijman et al. (2001), was further developed, providing calibration andevaluation procedures (Ramirez-Villegas et al. 2011).It evaluates on monthly basis if thereare adequate climatic conditionswithin a growing season fortemperature and precipitationand calculates the climatic suitability of theresulting interaction between rainfall andtemperatureHow does it work? 12. database held information on more than 2000species 13. What happens when Ecocrop model runs?1234567891011121 kilometer grid cells(climate environments)The suitability of a location (grid cell) for a cropis evaluated for each of the 12 potentialgrowing seasons.Growing season0 24 100 80 14. For temperature suitabilityKtmp: absolute temperature that will kill the plantTmin: minimum average temperature at which the plant will growTopmin: minimum average temperature at which the plant will grow optimallyTopmax: maximum average temperature at which the plant will grow optimallyTmax: maximum average temperature at which the plant will cease to growFor rainfall suitabilityRmin: minimum rainfall (mm) during the growing seasonRopmin: optimal minimum rainfall (mm) during the growing seasonRopmax: optimal maximum rainfall (mm) during the growing seasonRmax: maximum rainfall (mm) during the growing seasonLength of the growing seasonGmin: minimun days of growing seasonGmax: maximum days of growing season 15. Growing season: xx days (average of Gmin/Gmax) Temperature suitability (between 0 100%) Rainfall suitability (between 0 100%) Total suitability = TempSUIT * RainSUITIf the average minimum temperature in one of these months is 4C or less above Ktmp, it isassumed that, on average, KTMP will be reached on one day of the month, and the crop will die.The temperature suitability of that month is thus 0%. If this is not the case, the temperaturesuitability is evaluated for that month using the other temperature parameters.The overall temperature suitability of a grid cell for a crop, for any growing season, is the lowestsuitability score for any of the consecutive number of months needed to complete the growingseasonThe evaluation for rainfall is similar as for temperature, except that there is no killing rainfall andthere is one evaluation for the total growing period (the number of months defined by Gmin andGmax) and not for each month.The output is the highest suitability score (percentage) for a growing season starting in any monthof the year. 16. (climate) Suitability modellingA1B / 2030current 17. current A1B / 2030(climate) Suitability modelling 18. Change in climate-suitabilityassumptions on regional levellosses gains 19. Change in climate-suitability Lossesgains 20. Maximum entropy methods are very general ways to predict probabilitydistributions given constraints on their moments Predict species distributions based on environmental covariatesWhat is Entropy Maximization? You can think of Maxent as having two parts: a constraint component and an entropy component The output is a probability distribution that sums to 1 For species distributions this gives the relative probability of observingthe species in each cell Cells with environmental variables close to the means of the presencelocations have high probabilitiesMaxEnt model 21. B21Input: Crop evidence (GPS points)19 bioclimatic variables of current (worldclim) & future climateOutput:Probability of distribution of coffee (0 to 1)MaxEnt model 22. Bioclimatic variables for suitability modeling Bio1 = Annual mean temperature Bio2 = Mean diurnal range (Mean of monthly (max temp - min temp)) Bio3 = Isothermality (Bio2/Bio7) (* 100) Bio4 = Temperature seasonality (standard deviation *100) Bio5 = Maximum temperature of warmest month Bio6 = Minimum temperature of coldest month Bio7 = Temperature Annual Range (Bio5 Bi06) Bio8 = Mean Temperature of Wettest Quarter Bio9 = Mean Temperature of Driest Quarter Bio10 = Mean Temperature of Warmest Quarter Bio11 = Mean Temperature of Coldest Quarter Bio12 = Annual Precipitation Bio13 = Precipitation of Wettest Month Bio14 = Precipitation of Driest Month Bio15 = Precipitation Seasonality (Coefficient of Variation) Bio16 = Precipitation of Wettest Quarter Bio17 = Precipitation of Driest Quarter Bio18 = Precipitation of Warmest Quarter Bio19 = Precipitation of Coldest Quarterderived from monthly temperature & precipitation 23. Coffee suitability - Maxent Results Nicaragua 24. BResultsVariable AdjustedR2R2 due tovariable% of totalvariabilityPresentmeanChange by 2050sLocations with decreasing suitability (n=89.8 % of all observations)BIO 14 Precipitacin del mes ms seco 0.0817 0.0817 24.8 24.49 mm -3.27 mmBIO 04 Estacionalidad de temperatura 0.1776 0.0959 29.1 0.83 0.166BIO 12 Precipitacin anual 0.2057 0.0281 8.5 2462.35 mm -24.31 mmBIO 11 - Temperatura media del cuarto ms fro 0.2633 0.0576 17.5 20.11 C 1.86 CBIO 19 - Precipitacin del cuarto ms fro 0.2993 0.0155 4.7 169.13 mm -7.08 mmBIO 05 - Temperatura mxima del mes ms clido 0.3198 0.0102 3.1 28.45 C 2.30 CBIO 13 - Precipitacin del mes ms hmedo 0.2838 0.0205 6.2 450.27 mm 10.72 mmOtros - - 6.2Coffee suitability - Maxent Results Nicaragua 25. Ba Average of Q1 of GCMsb Average of GMSsc Average of Q3 of GCMsd Measure of agreement ofmodelse standard deviation of GCMsbceUncertainty of model output (Maxent) using 19 GCMs SRES A2 timeserie 2040 2069 (2050) 26. Decision Support System for Agro technology Transfer (DSSAT)+ 27. 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 analoguesAccompanying field trials in 5 countries to calibrate DSSAT 28. Planting date: Between 15th of April and 30th of June1Variety 1: IB0006 ICTA-Ostua Variety 2: IB0020 BAT1289Soil 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 to25 days after germination. Fertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowing and 64kg/ha UREA at 22 to 30 days after germination.Weather data input:Current climateAverage of 99 MarkSimdaily outputsFuture climateEnsemble of 19GCM & 99MarkSim outputs for 2020& 2050Runs: 17,800 points x 3climates x 99 MarkSim-samples x 8 trialsDSSAT Tortillas on the Roaster in Central America 29. Results: yield change for year 2020 (Primera) 8 trialsTrial 3 high performance / high impactVariety 1: ICTA-OstuaSoil 1: generic medium silty loamFertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowingand 64 kg/ha UREA at 22 to 30 days after germinationTrial 7 medium high performance / less impactVariety 1: ICTA-OstuaSoil 2: generic medium sandy loamFertilizer 2: 128 kg/ha 18-46-0 Fertilizer application on sowingand 64 kg/ha UREA at 22 to 30 days after germination 30. Statistical negative and positive outliers of predicted yield change by 2020 31. 31Areas where the production systems of crops can beadaptedAdaptation-SpotsFocus on adaptation of production systemAreas where crop is no longer an optionHot-SpotsFocus on livelihood diversificationNew areas where crop production can be establishedPressure-SpotsMigration of agriculture Risk of deforestation!Identifying Impact-Hot-Spots and select sites for socio-economic analysis 32. 32 Beans as most important income (sell 70% of harvest) Climate variability (intense rain, drought), missing labor & credits,high input costs, forces them to changes 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)Result: Sample-site 1 - Texistepeque (Las Mesas), Santa Ana ,El SalvadorMessage 2: Adaptation Strategies must be fine-tuned at each site!Las MesasAltitude: 667 m(about 2188 feet)Hot-spot -141 kg/haFor 2020: 35 mm less rain (current 1605mm) mean temperature increase 1.1 CFor 2050: 73mm less rain ( -5%) mean temperature increase 2.3 C hottest day up to 35 C (+ 2.6 C) coolest night up to 17.7 C (+ 1.8 C)Hot-spot 33. 33Message 3: There can be winners if they adapt quickly!Result: Sample-site 2 Valle de Jamastran, Danl, Honduras Adaptation-spotJamastranAltitude: 783 m(about 2568 feet)Adaptation-spot -115 kg/ha Active communities with already advanced agronomicmanagement of maize-bean crops Favorable soil conditions and management Long-term technical assistance / training 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 problemsFor 2020: 41 mm less rain (current 1094 mm) mean temperature increase 1.1 CFor 2050: 80 mm less rain ( -7%) mean temperature increase 2.4 C hottest day up to 34.2 C (+ 2.6 C) coolest night up to 17 C (+ 2.1 C) 34. Decision support system modelling (for benchmark sites)Agronomic managementExpert & farmer surveyIntegrated crop-soil modeling160 LDSF sample sitesBaselinedomainsImpact2030 A1bExperimental[n] cultivars[n] fertilizer application[n] seasonsApplication domainsAnalysis of biophysical systems and simulating crop yield in relation to management factors. Combine thesemodels with field observations that allow adjustment of the models in the course of the growing season .Future24 GCMA1B (IPCC)CurrentworldClimValidation withavailable station dataDaily weather generatorMarkSIMWeatherstation data(daily)Climate datayieldsoil management 35. Downscaling is inevitable. Continuous improvements arebeing done Strong focus on uncertaintyanalysis and improvement ofbaseline data We need multiple approaches to improve theinformation base on climate change scenarios Development of RCMs (multiple: PRECIS not enough) Downscaling empirical, methods Hybrids We tested different methodologiesConclusions climate data 36. Conclusions crop models Ecocrop, when there is a lack oncrop information, for global orregional assessment Maxent, perennial crops withpresence only data (coordinates)available DSSAT, only for few crops (beans,maize, ), high data input demandand calibrated field experiments arenecessary We need to communicateuncertainty of model predictionsEmpiricalmodelsMechanisticmodels


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