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

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.

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.

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  • Los escenarios de emisiones imponen condiciones para los modelos climáticos globales (basados en ciencias atmosféricas, química, física, biología, etc).Dividen el mundo el grillas y miran las relaciones entre factores que ocurren entre la atmósfera, los oceános, la superficie de la tierra. Por supuesto, hay cientos de procesos que salen de la comprensión de los modelos matemáticos así que estos modelos utilizan parametrizaciones para representar fenomenos incomprensibles. Son tan elaborados estos modelos que tienen que correrse en supercomputadoras. Entre más complejo sea el modelo, más factores tiene en cuenta y menos suposiciones usa. Se corre desde el pasado hasta el futuro
  • Can you please take area per altitude line out? This is very important is shows that there is no more area available further up and that coffee will compete even more with protected areas. PES discussion.If you cannot, explain to what does it pertain: current or 2050? It simply shows the area available at each altitude current and future. Just area per altitude.
  • The Decision Support System for Agrotechnology Transfer (DSSAT) is one of the most sophisticated crop simulation models currently available. Its advantages are the possibility to include specific information on weather, soils, plants, management and interactions of these factors.We ran DSSAT with available bean and maize variety calibration sets (2 fertilizer levels, 2 varieties, 2 soils, common smallholder conditions and management) to simulate current average yield and future expected yields. Results for current yields where ground-proofed through expert consultation throughout the region. In addition, field trials with recently introduced bean varieties with higher drought tolerance were conducted in order to obtain calibration data sets for more precise predictions.
  • We ran the model for all the four countries and mapped the results (in this case the differences between current and future (2020) bean production) for Central America.As we can see there are areas where yields will decrease dramatically whereas others are improving their production potential. The already described changes in climate conditions and their interactions with other location specific conditions determine crop production. Heat and drought stress and high night temperatures are the main culprits for these results. This is broadly sustained by scientific evidence. Some general findings are:Beans : Temperatures > 28/18 C (day/night) decrease biomass production, seed-set, seed number and size (less pods per plant, lessseed per pod, lower seed weight) Elevated CO2 also decreased seed-set Elevated CO2 increased biomass, but benefits of elevated CO2 decreased with increasing temperaturesMaize: High temperature stress decreases pollination and seed set in maize, mainly caused by decreased pollen viability and stigma receptivity High temperature stress decreases seed-set and kernel numbers perplant. High temperature stress also affects negatively kernel quality and density (protein, enzymes) Reproductive stages (pollen development, flowering, early grain filling)are relatively more sensitive to drought stress, drought decreases kernel number and dry weights. Maize needs 50% of the water in the period 10 days before to 20 days after initial flowering. Even with enough water temperature stress affects pollen development. Drought stress decreases kernels numbers and kernel size Higher night temperatures means higher losses from respiration thus biomass and yield lossesFrom the DSSAT results we can now identify the different type of intervention areas in the region (next slide)
  • As an example for a selected hot-spot location we presentTexistepeque / El Salvador where we find … (read the slide information)While we find several of these characteristics (e.g. coyotes as marketing channels) at other sites, each location shows also unique issues and combinations of factors and resources which make a specific fine-tuned adaptation strategies necessary. We pretend to build on several basic adaptation ideas which must be adapted to local conditions.
  • Our second example shows that climate change might open up opportunities for people with advanced adaptation strategies and who will quickly apply these strategies.Although Jamastran will also be challenged from changes in climate conditions their degree of organization, available infrastructure and training may allow them to take advantage of the 1,000 mm of annual rainfall at this site. The already installed irrigation schemes and market intelligence open up opportunities (time windows) to produce bean and other products for markets when e.g. beans are not available (March-May). Also seed production in the dry season could be very lucrative. However, the intelligent use of water resources will be decisive.

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

  • 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
  • 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?
  • Stations byvariable:• 47,554precipitation• 24,542tmean• 14,835tmax y tminSources:•GHCN•FAOCLIM•WMO•CIAT•R-Hydronet•Redes nacionales-30.130.5Mean annualtemperature (ºC)012084Annualprecipitation (mm)
  • BPREC• Generateinterpolated climatesurfaces usingANUSPLIN-SPLINAwith weather stationdata• Cross validating (25iterations• uncertaintyTMPUncertainty of climate data and models
  • BValidation of climate surface (25 iterations)
  • BCompare original worldclim with interpolated
  • GCMs are the only waywe can predict the futureclimateUsing the past to learnfor the futureGCM “Global Climate Model”
  • 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
  • Climate data• For current climate (baseline)we used historical climate data from WorldClimwww.worldclim.org• Future climate: global climate models (GCMs)from IPCC (AR5) – SRES A2, A1B, ..• Downscaling to provide higher-resolution (2.5 arc-minutes ~ 5 kilometer)http://ccafs-climate.org
  • 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 URLwww.ecocrop.fao.org. 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.
  • 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 precipitation……and calculates the climatic suitability of theresulting interaction between rainfall andtemperature…How does it work?
  • • database held information on more than 2000species
  • 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
  • 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
  • • 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.
  • (climate) Suitability modellingA1B / 2030current
  • current A1B / 2030(climate) Suitability modelling
  • Change in climate-suitability“assumptions on regional level”losses gains
  • Change in climate-suitability Lossesgains
  • • 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
  • B21Input: Crop evidence (GPS points)19 bioclimatic variables of current (worldclim) & future climateOutput:Probability of distribution of coffee (0 to 1)MaxEnt model
  • 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
  • Coffee suitability - Maxent Results Nicaragua
  • BResultsVariable AdjustedR2R2 due tovariable% of totalvariabilityPresentmeanChange by 2050sLocations with decreasing suitability (n=89.8 % of all observations)BIO 14 – Precipitación del mes más 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 – Precipitación anual 0.2057 0.0281 8.5 2462.35 mm -24.31 mmBIO 11 - Temperatura media del cuarto más frío 0.2633 0.0576 17.5 20.11 ºC 1.86 ºCBIO 19 - Precipitación del cuarto más frío 0.2993 0.0155 4.7 169.13 mm -7.08 mmBIO 05 - Temperatura máxima del mes más cálido 0.3198 0.0102 3.1 28.45 ºC 2.30 ºCBIO 13 - Precipitación del mes más húmedo 0.2838 0.0205 6.2 450.27 mm 10.72 mmOtros - - 6.2Coffee suitability - Maxent Results Nicaragua
  • 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)
  • Decision Support System for Agro technology Transfer (DSSAT)+
  • • For 2 DSSAT-varieties (IB0006 ICTA-Ostua, IB0020 BAT1289– “INTA Fuerte Sequia”, “INTA Rojo”, and “Tío 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
  • 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
  • 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
  • Statistical negative and positive outliers of predicted yield change by 2020
  • 31Areas where the production systems of crops can beadaptedAdaptation-SpotsFocus on adaptation of production systemAreas where crop is no longer an optionHot-SpotsFocus on livelihood diversificationNew areas where crop production can be establishedPressure-SpotsMigration of agriculture – Risk of deforestation!Identifying Impact-Hot-Spots and select sites for socio-economic analysis
  • 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
  • 33Message 3: There can be winners if they adapt quickly!Result: Sample-site 2 – Valle de Jamastran, Danlí, 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)
  • 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
  • • 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
  • 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