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Session 2 cassava model in dssat to support scheduled planting and high starch content use cases by patricia moreno

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The DSSAT modelling framework to support the SP and HS use cases by Patricia Moreno

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Session 2 cassava model in dssat to support scheduled planting and high starch content use cases by patricia moreno

  1. 1. www.iita.orgA member of CGIAR consortium Cassava model in DSSAT to support scheduled planting and high starch content use cases Patricia Moreno Gerrit Hoogenboom Senthold Asseng James Cock Myles Fisher Julian Ramirez-Villegas Luis Augusto Becerra
  2. 2. • Traditional agronomic approach: – Experimental trial and error Why Crop Models in Agriculture?
  3. 3. • Traditional agronomic approach: – Experimental trial and error • Systems Approach – Computer models – Experimental data • Understand  Predict Control & Manage – (H. Nix, 1983) •  Options for adaptive management and risk reduction Why Crop Models in Agriculture?
  4. 4. Application/ Analysis Control/ Management/ Decision Support DesignResearch Model Development Increased Understanding Model Test Predictions Prediction Research for Understanding Problem Solving Systems Approach Crop Simulation Model
  5. 5. DSSAT Crop Simulation Model 5 Net Income Resource useEnvironmental 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
  6. 6. www.iita.orgA member of CGIAR consortium Level 1: Running the model 1) Daily weather data: temperature, rainfall and solar radiation. 2) Soil data: Texture, % stones, bulk density, water retention (wilting point, field capacity, saturation), hydraulic conductivity, slope, color, organic carbon (if N is simulated). 3) Initial conditions: soil water content. If N is simulated: previous crop, N content of residues, depth and % incorporation of residues, N content in the soil. 4) Management: Planting date, plant density, cultivar characteristics, irrigation amount, fertilizer amount, organic manure composition. Data requirements
  7. 7. www.iita.orgA member of CGIAR consortium Level 2: Model evaluation Level 1 plus: 1) Treatments. 2) Yield and yield components. 3) General observations: Weed management, pest and disease occurrence, extreme weather events. Level 3: Model development Level 2 plus: 1) Growth analysis measurements: Biomass partitioning, number of leaves, LAI, nutrient concentrations in plant parts. 2) Soil water content. 3) Soil fertility: Organic carbon, N content. Data requirements
  8. 8. www.iita.orgA member of CGIAR consortium 1) Define different planting and harvesting dates. 2) Simulate quality of planting material  Initial weight of the planting stick. 3) Simulate varieties with different branching patterns and leaf development. 4) Growth of the plant under contrasting temperatures and rainfall patterns. 5) Effect of water stress on: • Development: branching and leaf formation. • Growth: Leaf area, biomass accumulation. 6) Simulations with nitrogen restrictions (under evaluation). What can we do with the model?
  9. 9. www.iita.orgA member of CGIAR consortium How does the model work? First aboveground growth • Priority leaf and stem growth. • After supply demand of carbohydrates to aboveground growth Additional carbohydrates accumulated in the storage roots
  10. 10. www.iita.orgA member of CGIAR consortium Cardinal point Whole plant & Germination Branching Potential Leaf size (1st part) Potential leaf size (2nd part) Leaf life Leaf expansion Tbase 13 13 13 13 Ecotype specific* Ecotype specific* Topmin 30 24 30 20 30 24 Topmax 35 24 35 35 30 24 Tmax 42.5 42.5 42.5 42.5 - - How does the model work? They change based on the process modelled (see table). Optimum maximum and maximum temperatures require revision  more trials at high temperatures.
  11. 11. www.iita.orgA member of CGIAR consortium n-2 n-2 n n n-1 n-1 Growingdegreedays accumulated(DABR) 1 2 3 Branching level How does the model work? B01ND: Difference between early (↓ value) and late (↑ value) branching varieties. B12ND: Constant slope of branching formation after the second branch.
  12. 12. www.iita.orgA member of CGIAR consortium (a) (b) How does the model work? Observed number of leaves at 3 different temperatures using chronological time (a) and thermal time (b). Source: Irikura ,1979
  13. 13. www.iita.orgA member of CGIAR consortium How does the model work? Simulated leaf appearance with different leaf formation rate (coefficient LNSLP), and model equation.
  14. 14. www.iita.orgA member of CGIAR consortium How does the model work? Individual leaf size versus thermal time for 4 varieties in 3 temperatures. Source: Irikura ,1979 • LAXS: Maximum individual leaf size (coefficient). • LAXS reached at 900 GDD. • Optimum temperature changes: initially 24 °C until 900 GDD are accumulated. • After 900 GDD the optimum temperature is 20 °C.
  15. 15. www.iita.orgA member of CGIAR consortium 𝐼𝐼 𝐼𝐼0 = 1 − 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃 = 𝑒𝑒−𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘 Senescence due to shading How does the model work? Leaf duration is defined in 3 phases: Growth, active and senescence.
  16. 16. www.iita.orgA member of CGIAR consortium A. Unit node weight per canopy level (each 20 nodes) for 3 varieties. Source: Lian & Cock, 1979 Individualnodeweight(g) Canopy level How does the model work? A B B. Difference of unit node weight per canopy level (each 20 nodes) at 12 months after planting. Source: Lian & Cock, 1979
  17. 17. www.iita.orgA member of CGIAR consortium Individualnodeweight(g) Days after planting How does the model work? C. Logistic curve used to simulate node weight. Node weight is defined as a ratio of the maximum value observed for each variety. Source: Lian & Cock, 1979 C D. Simulated node weight for nodes in different cohorts. D
  18. 18. www.iita.orgA member of CGIAR consortium Publication Topic Location DSSAT Trial Varieties Veltkamp (1986) Growth and development different varieties Palmira (Colombia) CCPA7801 MCol-1684 MVen-77 MPtr-26 MCol-22 Veltkamp (1986) Growth and development different varieties Palmira (Colombia) CCPA8001 MMex-59 MCo-l638 Manrique (1992) Growth and development different temperatures (640 m) Mt. Haleakala, Maui (Hawai) HIMA8801 Ceiba Schulthess (1987) Gutierrez et al. (1988) Growth and development: water and N stress Ibadan (Nigeria) IIIB8301 TMS 30572 Lian & Cock (1979b) Growth and development different varieties Palmira (Colombia) CCPA7601 MCol-22 MMex-59 CMC40 El Sharkawy et al. (1998) El-Sharkawy & Cadavid (2002) Water stress (control treatment year 1- year 2) Santander de Quilichao (Colombia) CCQU9101 MCol-1684 CMC-40 MCol523-7 MCol507-37 Mejía et al. (1997) Water stress (Control treatment) Santander de Quilichao (Colombia) CCQU9402 MCol-1684 Porto (1983) Water stress (control treatments) Palmira, Santander de Quilichao (Colombia) CCPA8201 CCQU8201 MCol-1684 Data sets for calibration
  19. 19. www.iita.orgA member of CGIAR consortium Publication Topic Location DSSAT Trial Varieties Veltkamp (1986) Growth and development different varieties Palmira (Colombia) CCPA7901 CCPA8001 MCol-1684 MVen-77 MPtr-26 MCol-22 Porto (1983) Water stress Palmira, Santander de Quilichao (Colombia) CCPA8201 CCQU8201 MCol-1684 Connor & Palta (1981) Connor & Cock (1981) Connor et al. (1981) Water stress Santander de Quilichao (Colombia) CCQU7901 MCol 22 MMex 59 Manrique (1992) Growth and development different temperatures (282,1097 m) Mt. Haleakala, Maui (Hawai) HIMA8801 Ceiba El-Sharkawy & Mejia de Tafur (2010) Branching habit Santander de Quilichao (Colombia) CCQU9401 MCol22 Mejía et al. (1997) Water stress Santander de Quilichao (Colombia) CCQU9402 MCol-1684 El Sharkawy et al. (1998) El-Sharkawy & Cadavid (2002) Water stress (year 2) Santander de Quilichao (Colombia) CCQU9201 MCol-1684 CMC-40 MCol523-7 MCol507-37 Data sets for evaluation
  20. 20. www.iita.orgA member of CGIAR consortium Priorit y Parameter Output variable 2 B01ND: Thermal time from germination to first branching B01ND LAI Stem weight Aboveground biomass 3 B12ND: Mean thermal time between branches formation (after the first branch) B12ND LAI Stem weight Aboveground biomass 5 LAXS: Maximum individual leaf size during the growing period. LAI Aboveground biomass Harvest 6 SLAS: Specific leaf area. Leaf weight LAI Aboveground biomass 7 LLIFA: Active leaf duration. Leaf weight LAI 1 LNSLP: Leaf formation rate. Leaf number 4 NODWT: Individual node weight for the first stem of the shoot before branching at 3400 ˚Cd. Leaf weight Stem weight Aboveground biomass Harvest 8 NODLT: Mean internode length (cm) for the first stem of the shoot before branching Plant height Genetic coefficients = species + ecotype + cultivar Adjustment of parameters modifying cultivar and ecotype. Genetic coefficients
  21. 21. www.iita.orgA member of CGIAR consortium Priority Parameter Output variable 1 PARUE: PAR conversion factor (g dry matter/MJ) Leaf weight Stem weight Aboveground biomass Harvest 4 TBLSZ: Base temperature for leaf development (˚C) LAI 2 BRxF: Branch number per fork at fork x (1-4). Leaf weight LAI Aboveground biomass 3 KCAN: PAR extinction coefficient Leaf weight LAI Genetic coefficients = species + ecotype + cultivar Adjustment of parameters modifying cultivar and ecotype. Genetic coefficients
  22. 22. www.iita.orgA member of CGIAR consortium (a) LAI, (b) Total weight (kg/ha), (c) aboveground biomass (kg/ha), (d) yield (kg/ha) at 282 m (orange), 640 (blue), 1097 m (green) (Data from Manrique (1992)) a b c d Evaluating the model
  23. 23. www.iita.orgA member of CGIAR consortium Trigger: Water content < Field capacity Evaluating the model
  24. 24. www.iita.orgA member of CGIAR consortium Evaluating the model
  25. 25. www.iita.orgA member of CGIAR consortium ↑ altitude ↓ temperature ↓ leaf area ↑ # branches ↑ shading ↓ leaf duration Evaluating the model
  26. 26. www.iita.orgA member of CGIAR consortium Variety: TMS 30572 Location: IITA, Ibadan Source: Schulthess,1987, Gutierrez, 1988 The model in Nigeria Data collected: Individual leaf size, number of leaves, biomass partitioning and growth rates. Part I PhD thesis: Analysis of cassava growth and development in Nigeria.
  27. 27. www.iita.orgA member of CGIAR consortium Variety: TMS 30572 Location: IITA, Ibadan Source: Schulthess,1987, Gutierrez, 1988 The model in Nigeria Dry seasonDry season
  28. 28. www.iita.orgA member of CGIAR consortium Variety: TMS 30572 Location: IITA, Ibadan Source: Schulthess,1987 The model in Nigeria Dry season Dry season
  29. 29. www.iita.orgA member of CGIAR consortium Variety: TMS 30572 Location: IITA, Ibadan Source: Schulthess,1987 The model in Nigeria Dry season Dry season Dry season Dry season Dry season Dry season Different planting dates
  30. 30. www.iita.orgA member of CGIAR consortium LAI Importance quality of data Observed data of LAI and leaf weight do not show similar tendency although it would be expected. Leaf weight
  31. 31. www.iita.orgA member of CGIAR consortium Further work • Evaluate performance of the model with data collected on years 1 & 2. • Challenge: Weather data collection and soil analysis. • Model is simulating dry matter, what about fresh weight and dry matter content variability. • Develop algorithms to represent starch content dynamics. • Data workshop: What do we have available for the crop modeling activity in the ACAI project?
  32. 32. www.iita.orgA member of CGIAR consortium PhD Objectives Patricia Moreno 1. Understand the dynamics and mechanisms of modeling the dry matter distribution and starch content in cassava and other storage crops. 2. Determine the relationships between starch and dry matter accumulation with environmental and management variables in cassava.
  33. 33. www.iita.orgA member of CGIAR consortium PhD Objectives Patricia Moreno 3. Develop a module that simulates the dynamics of dry matter and starch content for cassava as a function of environmental variables and management 4. Identify best management practices for small- holder farmers in East and West Africa that optimize dry matter and starch content in cassava
  34. 34. www.iita.orgA member of CGIAR consortium
  35. 35. www.iita.orgA member of CGIAR consortium OYSCG A

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