ASACSSA Annual_Meeting_DSSAT_Update.pptx

Dssat workshop

1. 1 Agronomic Performance, Nutritional Value and Simulation of the Impacts of Climate Change on Growth and Yield of Brachiaria Species under Varying Locations in Central and Western Ethiopia Dissertation By: Fantahun Dereje Board of Advisoree Committee: Major Advisor: Gebreyohannes Berhane (PhD, Asso. Prof., AAU) Co-Advisors: Diriba Geleti (PhD, Senior Researcher, EIAR), Diriba Diba (PhD, Asso. Prof., WU) and Fekede Feyissa (PhD, Lead Researcher, EIAR)

2. IBSNAT Project; USAID, 1982- 193 Minimum Data Set Concept, 1983- 1986 Initial models included the CERES- Maize, CERES- Wheat and SOYGRO soybean models. Data standards for compatibility of models (1986, 1994) DSSAT v2.1 released in 1986 ICASA, 1994 - 2003 IDSSAT Version 3.5 released in 1998 (after project ended) DSSAT Cropping System Model, DSSAT v4 released in early 2004 DSSAT Version 4.02 in 2006, v4.5 in 2012, v4.6 in 2015 DSSAT Version 4.7 in 2017, v4.7.5 in 2019 2 • DSSAT Version 4.8 in 2021 (?) Some Historical Notes on DSSAT

3. Original scientific publications 3

4. Original User’s Guides 4

5. 5 Initial price: US $495 + shipping costs Updated price: US $195 + shipping costs Free download from DSSAT portal Free download & Open Source 3- clause BSD license Original Software

6. 6 The DSSAT Crop Modeling Ecosystem www.DSSAT.net

7. 7 DSSAT is not just a software program but an ecosystem of: Crop model users Crop model trainers Crop model developers Models for the most important food, feed, fiber, fuel, and vegetable crops Tools and utilities for data preparation Minimum data for model calibration and evaluation ICASA Data standards Application programs for assessing real- world problems

8. Country Downloads India 2493 China 1536 USA 1307 Pakistan 1127 Brazil 996 Iran 480 Argentina 473 Ethiopia 444 Indonesia 403 Thailand 328 Philippines 277 Germany 255 Peru 233 Colombia 212 Spain 197 Nigeria 179 Mexico 168 Italy 161 South Africa 156 United Kingdom 155 Total 15501 Countries 183 8

9. 9 DSSAT Interface & Organization

10. Input and Output Files Format 10

11. Cropping System Model (CSM) Structure 11

12. Cropping System Model (CSM) Structure 12

13. Plant Modules

14. Cropping System Model (CSM) Net Income Resource use Environmental Plant growth (grain, biomass, roots, etc.) Plant development (time to flowering, maturity, etc.) Yield Soil conditions (physical & chemical properties by layer) Weather (daily rainfall, solar radiation, max & min temperatures, …) Management events (sowing, irrigation, fertilizer, organic matter, tillage, harvest) Genetics (cultivar- specific parameters controlling growth and development) Crop Model Simulation 14

15. DSSAT Advancement Improve model performance Improve model functionality Improve portability Add new crops Add new crop modules Add new capabilities and process simulations Add new tools and utilities Develop new model applications Add new experimental data that encompass new environments and/or new management scenarios

16. Genetics in Crop Models Current crop models use empirical genotype specific parameters (GSPs) for cultivar environment interactions that are not linked to actual genes. These GSPs do not adequately include the genetic (G) and gene- by- environment interaction (G x E) effects on crop development, thus inherent limitations. Genetics in the DSSAT Cropping System Model Species coefficients Ecotype coefficients Cultivar coefficients Bridging the gap between biotechnology, breeding and crop management

17. Simulation of plant responses to temperature and photoperiod 1.0 Temp base Temp Max Temperature (°C) Daylength (h) Opt 1 Opt 02 CSDL PPSEN Model 1 /d =f(T) x f(D) Stage i = f(photothermal days) Cultivar Coefficients Species Coefficients CSM Genetic Coefficients

18. 18 Predicting time to flowering for dry bean based on QTL and Environmental Variables Stand Alone Gene- Based Model CSM-CROPGRO- Dry Bean

19. Gene- Based Model Integration diagram 19

20. Input Files T he reading of the file '.GEN' uses the line code (VAR#) and the TF (QTLs) with values of 1 and - 1. For each experiment 13 RILs/genotypes were used. BNGRO047.GEN CTFL1101.BNX If GENF is equal to Y the Gene- Based model can be executed. Otherwise, it will not affect the simulation. 20

21. Predicted versus Observed Flowering 21 Original Model Gene- based Model

22. Gene-based module integrated into the Main Model 22 Final Simulated Yield Original Model

23. Yield Forecasting Crop simulation models as a tool for yield forecasting DSSAT Crop Simulation Model Input data requirements Access to current and historical daily weather data Local soil characteristics Crop management Benefits Predict yield directly No dependence on satellite data Can be run locally – free!

24. Yield forecasting for wheat – Case Study Model evaluation - Akmola region Observed 1981 to 2015 Simulated 1984 to 2018 Simulated & Observed Historical Yield Yield Forecasting Historical Regional & FAO Yield

25. 2019 Wheat Yield Forecast Ensemble yield forecast - May 1, 2019 Current weather data January 1 to May 1, 2019 for Petropavlovsk Historical weather data for 1984 to 2018 Each line represents a different weather ensemble  Historical weather data  May 1  2019 weather data 

26. 2019 Wheat Yield Forecast Ensemble Forecast – Petropavlovsk Monthly forecast dates from April 1 to August 1 Predicted wheat yield variability and uncertainty is reduced for later forecast dates Accurate prediction on July 1 Significant change between June 1 and July 1 forecast dates The forecast improves as more current weather information becomes available

27. 2019 Wheat Yield Forecast Monthly Ensemble Forecast – Petropavlovsk Low rainfall in May & high rainfall in June

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29. Crop Yield Forecasting Using the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) in Ethiopia Kindie Tesfaye, Esayas Lemma, Robel Takele, Vakhtang Shelia , Addisu Dabale, Pierre C. Sibiry Traore, Gerrit Hoogenboom, Dawit Solomon GHACOF 58, May 27, 2021

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32. Perennial Forage Model-Alfalfa 32

33. 33 CROPGRO-Strawberry

34. 34 CROPGRO-Carinata

35. 35 CROPGRO-Quinoa

36. 36 MANIHOT- Cassava How do we model cassava?

37. MANIHOT- Cassava 37

38. 38 Energy balance

39. Time Series Calibration 39

40. Coupling Pests and Diseases 40

41. Challenges DSSAT Future? Free software Mixed language programming Open Source for the model and tools New tools and applications New platforms (Linux, iOS, Web) Linkages with other models using wrappers and docking technologies Driven by the availability of resources Driven by the interest of the user community

42. Challenges DSSAT Future? New crops: safflower, alfalfa, teff, sugarbeet, quinoa, chia, carinata, strawberry, hemp New crop modules: CROPGRO- Perennial, NWheat- Teff, CERES- Rice- Teff, SAMUCA- Sugarcane New processes: Salinity Plant P & Soil and plant K 2D soil model Greenhouse Gas Emissions Pest and disease coupling

43. DSSAT Portal www.DSSAT.net

44. DSSAT Development Sprint July 26- 30, 2021 @ International Fertilizer Development Center

45. DSSAT 2021 @ University of Georgia DSSAT 2021 @ TUM, Germany

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48. Questions? www.DSSAT.net 48