Crop modelling with the DSSAT
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CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.

CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.

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Crop modelling with the DSSAT Crop modelling with the DSSAT Presentation Transcript

  • Crop modelling with the DSSAT September 2011
  • Why model?• Use for manipulations and experiments that are impractical,too expensive, too lengthy or impossible (in real-world socialand economic systems)• Address dynamic complexity (“emergent properties”) ofsystems in a way that reductionist science may not be able todo• Identify “best management” strategies (through optimization)• Study the long-term effects of options (predictions,projections)
  • Why model? -2-• Allow the researcher to control environmental andexperimental conditions• Allow hypothetical and exploratory situations to beinvestigated• Allow insight to be gained into the relative importance ofdifferent system elements• Assemble and synthesise what is known about particularprocesses Nicholson (2008)
  • What can models produce? Inputs Model Outputs“Predictions”• Point prediction: temperature in Kathmandu tomorrow• Behaviour: trends, patterns in space and time• Differences: system response with/without an intervention“Understanding”• Best bet: optimised performance of the system (N application rate)• Trade-offs: household income and range condition• Syntheses: what do we know about these processes, and which are stillblack boxes?
  • Reduced Oxidised Floodwatersoil layers soil zone A complicated system …
  • … but it can be modelled to a useful extent INPUTS CROP MODEL OUTPUTS Genotype information Based on mechanisms Biomass, yield Soil information of plant growth and Water use Weather information development (some Nitrogen useManagement information may be represented Carbon balance … empirically) … Things that apply Things that apply to to one particular the biophysical Use in situation (e.g. a world in general some way field plot)
  • Simulated and observed biomass accretion (kg DM/ha) for cowpea cultivarTVU 3046 grown in Griffin, Georgia, in 1998 canopy stem leaf Hoogenboom et al., 2000
  • Comparison of observed and simulated grain yield for 5 wheat models Simulated grain yield (t / ha) (a) AFRC-WHEAT2 (b) CERES-Wheat (c) Sirius (d) SUCROS2 (e) SWHEAT The solid lines represent the 1:1 relationshipJamieson et al., 1998 Observed grain yield (t / ha)
  • Production situation defining factors: CO21 potential radiation temperature crop characteristics - physiology, phenology - canopy architecture limiting factors: water2 nutrients attainable Yield-increasing measures reducing factors: weeds3 actual pests diseases Yield-protecting measures pollutants Production level (t/ha)
  • Production situation defining factors: CO21 potential radiation temperature crop characteristics - physiology, phenology - canopy architecture “Realism”factors: water limiting increases: nutrients2 attainable but so does complexity Yield-increasing measures reducing factors: weeds3 actual pests diseases Yield-protecting measures pollutants Production level (t/ha)
  • Crop modelling is 50 years old: some of it is “mature science”Crop model water balance in a layered soil (from late 1970s): Ritchie’s tipping bucket Transpiration Evaporation Rainfall, IrrigationRunoff Capillary rise Plant water uptake Bypass flow Deep drainage
  • DSSATDecision Support System for Agrotechnology Transfer
  • DSSAT v2.1 in 1989  DSSAT v4.5 2010About 2000 users in over 90 countries
  • Components of DSSATDATABASES MODELS APPLICATIONS Weather Validation / Crop Models Sensitivity Analysis Soil Seasonal Strategy SUPPORT SOFTWARE Analysis Genetics DSSAT User Interface Graphics Crop Rotation / Sequence Pests Weather AnalysisExperiments Spatial Analysis / Soil GIS Linkage Economics Experiments Pests Genetics Economics
  • DSSAT v4.5• Windows-based• Incorporates DSSAT CSM (+ Legacy Models)• Field scale• Data management tools • XBuild: Input crop management information in standard format • SBuild: Create and edit soil profiles • GBuild: Display graphs of simulated and observed data, compute statistics • ATCreate: Create and edit observations from experiments, formatted correctly • WeatherMan: Assist users in cleaning, formating, generating weather data • ICSim – Introductory tool to demonstrate potential yield concepts
  • DSSAT v4.5 Several different analytical capabilities • Sensitivity Analysis: vary soil, weather, management or variety characteristics for insight • Seasonal Analysis: multiple-year simulations to evaluate uncertainty in biophysical and economic responses • Rotation/Sequence Analysis: long-term simulations to analyze changes in productivity and soil conditions associated with cropping systems • Spatial Analysis: define spatially variable soil, weather, management characteristics across a field or region for analysis
  • Main window in DSSAT v4.5
  • Selection of maizeexperiment, all treatmentsselected for simulation.Circle shows button forrunning the model andfor graphing results.
  • DSSAT4.5 graphics screens
  • Assessing Risk and Ways to Reduce it • Crop simulation models integrate the interaction of weather, soil, management and genetic factors • Use the crop simulation models to run “what if” scenarios • Develop alternate management practices that will benefit the farmer • Risk factors: weather and price uncertainty, two of the major sources
  • Context• Next season’s weather is uncertain• Variability in historical weather data can be assumed to describe uncertainty in next season’s weather• “Experiment” is run by specifying a possible management system over a number of prior years of weather data• Thus, a distribution of yields (& other outputs) is produced, converting uncertainty in weather into uncertainty in yield—for the specific management• Other management “treatments” can be simulated in the experiment
  • Using DSSAT to Analyze Uncertaintyq Simulate n years of the management being analyzed, using historical years of weather data and soil properties for the siteq Each year starts with the same initial soil conditionsq Each yield value is assumed to have an equal probability of happening in the future (assuming future weather statistical properties are the same)q Create cumulative probability distributionq Compute statistical properties (mean, variance, etc.)
  • Annual Yield Variability 5 4.5 4 3.5Yield, t/ha 3 2.5 2 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Years
  • Developing Cumulative Probability Distributions from Simulated ResultsYear Yield (t/ha) prob Cumulative 1 1.83 0.04 Ranked Yield Probability 2 2.78 0.04 0.3 0.04 1 3 1.9 0.04 0.9 0.08 1.1 0.12 0.9 4 2.3 0.04 5 4.7 0.04 1.3 0.16 1.69 0.2 0.8 6 2.4 0.04 7 1.3 0.04 1.75 0.24 1.83 0.28 0.7 8 4.1 0.04 1.9 0.32 9 3.5 0.04 0.6 2.05 0.36 10 0.3 0.04 2.3 0.4 11 2.6 0.04 2.4 0.44 0.5 12 2.05 0.04 Mean=2.65 t/ha 2.6 0.48 Cumulative Probability 13 3.04 0.04 2.75 0.52 0.4 14 3.28 0.04 2.78 0.56 15 1.69 0.04 3 0.6 0.3 16 0.9 0.04 3.04 0.64 Var=1.31 (t/ha)2 17 1.1 0.04 3.24 0.68 0.2 18 3.24 0.04 3.28 0.72 19 3.95 0.04 3.5 0.76 0.1 20 4.2 0.04 3.67 0.8 21 4 0.04 3.95 0.84 0 22 2.75 0.04 4 0.88 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 23 1.75 0.04 4.1 0.92 24 3.67 0.04 4.2 0.96 Yield, t/ha 25 3 0.04 4.7 1 Raw Ranked Cumulative Yield Data Yield Data Probability Distribution
  • CPFs of monetary return ($/ha) for three treatments
  • Planting date evaluation DAS CO32- Rainfed conditions 8000 Yield (kg ha1) - 6000 4000Simulated yields fordifferent planting 2000dates under rainfed 0(top) and irrigated DAS CO32- Irrigated conditions(bottom) conditions 8000 Yield (kg ha ) -1 6000 4000 2000 0 Feb-01 Feb-15 Mar-01 Mar-15 Apr-01 Apr-15 Planting date
  • Yield forecasting 7000 7000 a) AG9010 b) DKB 333B 6000 6000 5000 5000 Yield (kg ha ) Yield (kg ha ) -1 -1 4000 4000 3000 3000 2000 2000Average forecasted 1000 Simulated yield Observed yield 1000 Simulated yield Observed yieldyield and standard 0 0 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01deviation for 2002 Forecast date Forecast dateas a function of the 7000 7000forecast date and 6000 c) DAS CO32 6000 d) Excelerobserved yield (kg/ 5000 5000ha) for four maize Yield (kg ha) Yield (kg ha ) -1 -1 4000 4000hybrids 3000 3000 2000 2000 Simulated yield 1000 1000 Observed yield Simulated yield Observed yield 0 0 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 Forecast date Forecast date
  • DSSAT and other crop modelling systemsUsed in many different ways around the world: Crop management Precision agriculture Fertilizer management Sustainability studies Irrigation management Climate change studies Pest management Yield forecasting Tillage management Education Variety evaluation …
  • International climate change study: implications • Crop yields in mid- and high-latitude regions are less adversely affected than yields in low-latitude regions • Will simple farm-level adaptations in the temperate regions be able to offset the detrimental effects of climate change? • For the tropics, appropriate adaptations need to be developed and tested further at the household level; the role of genetic resources and information provision? • Regional impact analyses: discussion tomorrow
  • DSSAT v4.5 training• DSSAT training course sponsored by the University ofFlorida and ICRISAT, Hyderabad, 5-9 December 2011(open for applicants)• Possible: DSSAT training course at CRIDA during theweek of 13-17 February 2012
  • Prediction of milk production from cows consumingtropical diets Herrero (1997)
  • “All models are wrong, but some are useful”“… the practical question is, how wrongdo they have to be to not be useful.” - GEP Box
  • A simple interface for running complex crop models: