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26 May 2015
Towards the simulation of
global crop productivity in
IMPACT with geospatial
data
Michael Marshall
World Agrof...
Global Crop Model Approaches
β–ͺ Local-scale models (e.g. DSSAT and APSIM) aggregated
via multiple-site calibration and aver...
Global crop model for IMPACT
𝐆𝐏𝐏 = 𝐟𝐬𝐭𝐫𝐞𝐬𝐬 𝛆 𝐦𝐚𝐱 𝐟 𝐀𝐏𝐀𝐑 𝐏𝐀𝐑
𝐟 𝐀𝐏𝐀𝐑 = 𝟏. 𝟐 βˆ— π„π•πˆ
𝐟𝐬𝐭𝐫𝐞𝐬𝐬 = 𝐦𝐒𝐧(𝐟 𝐓, 𝐟 𝐦)
𝛆 𝐦𝐚𝐱 =
𝟏
𝟏𝟐
𝐜 𝐦𝐚𝐱...
Model Calibration, Sensitivity Analysis,
and Validation
β–ͺ Identify most important parameters, remove
redundant or insignif...
Eddy Covariance Flux Towers
β–ͺ Three towers (2009-2014)
β–ͺ Alfalfa, rice, and
citrus orchard
β–ͺ 30-minute energy
balance and
...
Transects
60 m*
β–ͺ 10 quadrats per
frame (2011-2012)
─ Alfalfa, cotton,
maize, and rice
─ Biophysical data
─ Ground spectra...
Tasseled Cap Transformation
β–ͺ Weighted linear
combinations of
satellite reflectance
βˆ’ Brightness
βˆ’ Greenness
βˆ’ Wetness
β–ͺ F...
Index of Agreement (yellow = perfect match)
Biomass
Dark Soil
Light Soil
PlantsPlants
Reference
Data
β–ͺ AVHRR (5 km)
β–ͺ MODI...
Muchow (1990)
GPP β†’ Crop Yield
Prince et al. (2001) expressed yield in terms of GPP:
Yield =
RS Γ— HI
1 βˆ’ m0
Γ—
SOS
EOS
GPP
...
Need for systems simulation models in
Agroforestry Systems
Lloyd et al. (1990)
β–ͺ Agroforestry systems
large payoffs, but
c...
β–ͺ GPP and crop yield model validated at multiple
spatial scales in California
βˆ’ Thomas Gumbricht: MODIS-era GPP modeling
t...
Thank You
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7 icraf gfsm rome-mmarshall_05202015

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The Global Futures and Strategic Foresight (GFSF) team met in Rome from May 25-28, 2015 to review progress towards current work plans, discuss model improvements and technical parameters, and consider possible contributions by the GFSF program to the CRP Phase II planning process. All 15 CGIAR Centers were represented at the meeting.

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7 icraf gfsm rome-mmarshall_05202015

  1. 1. 26 May 2015 Towards the simulation of global crop productivity in IMPACT with geospatial data Michael Marshall World Agroforestry Centre United Nations Ave, Gigiri P.O. Box 30677-00100 Nairobi, Kenya +254207224244 m.marshall@cgiar.org
  2. 2. Global Crop Model Approaches β–ͺ Local-scale models (e.g. DSSAT and APSIM) aggregated via multiple-site calibration and averaging βˆ’ Complex βˆ’ High-input data requirement βˆ’ Locally accurate βˆ’ Site specificity (over-fitting) βˆ’ Non-linearity β–ͺ Large-area models (e.g. GLOPEM and GLAM) βˆ’ Simple βˆ’ Low-input data requirement βˆ’ Locally less accurate βˆ’ Generalizable β€œEssentially, all models are wrong, but some are useful.”
  3. 3. Global crop model for IMPACT 𝐆𝐏𝐏 = 𝐟𝐬𝐭𝐫𝐞𝐬𝐬 𝛆 𝐦𝐚𝐱 𝐟 𝐀𝐏𝐀𝐑 𝐏𝐀𝐑 𝐟 𝐀𝐏𝐀𝐑 = 𝟏. 𝟐 βˆ— π„π•πˆ 𝐟𝐬𝐭𝐫𝐞𝐬𝐬 = 𝐦𝐒𝐧(𝐟 𝐓, 𝐟 𝐦) 𝛆 𝐦𝐚𝐱 = 𝟏 𝟏𝟐 𝐜 𝐦𝐚𝐱 βˆ’ πšͺ 𝐜 𝐦𝐚𝐱 + 𝟐πšͺ 𝐆𝐏𝐏 = 𝐟𝐬𝐭𝐫𝐞𝐬𝐬 𝜢 𝐦𝐚𝐱 𝐟 𝐀𝐏𝐀𝐑 𝐏𝐀𝐑 𝐟 𝐀𝐏𝐀𝐑 = 𝟏. 𝟐 βˆ— π„π•πˆ 𝐟𝐬𝐭𝐫𝐞𝐬𝐬 = 𝐦𝐒𝐧(𝐟 𝐓, 𝐟 𝐦) 𝛂 𝐦𝐚𝐱 = 𝟏. πŸπŸ” βˆ† βˆ† + 𝛄 Opti-LU Opti-WU 𝐟 𝐓 = 𝐞 βˆ’ 𝐓 π¦πšπ±βˆ’π“π¨π©π­,𝐬𝐞𝐚𝐬𝐨𝐧𝐚π₯ 𝐓𝐨𝐩𝐭,𝐬𝐞𝐚𝐬𝐨𝐧𝐚π₯ 𝟐 𝐟 𝐌 = 𝐟 𝐀𝐏𝐀𝐑 𝐟 𝐀𝐏𝐀𝐑𝐦𝐱 Ξ΅max=0.06 molΞ‡mol-1 (C4)
  4. 4. Model Calibration, Sensitivity Analysis, and Validation β–ͺ Identify most important parameters, remove redundant or insignificant parameters, and potentially integrate new parameters βˆ’ Model performance statistics βˆ’ Monte Carlo Simulation βˆ’ Residual analysis β–ͺ Multiple scales of validation βˆ’ Plot (eddy covariance flux tower data) βˆ’ Field (non-destructive biomass transects) βˆ’ Landscape β†’ global (high, moderate, and coarse resolution remote sensing)
  5. 5. Eddy Covariance Flux Towers β–ͺ Three towers (2009-2014) β–ͺ Alfalfa, rice, and citrus orchard β–ͺ 30-minute energy balance and meteorological data β–ͺ FAPAR (MODIS subset tool: http://daac.ornl.gov/) β–ͺ Daily GPP, RECO, and NPP
  6. 6. Transects 60 m* β–ͺ 10 quadrats per frame (2011-2012) ─ Alfalfa, cotton, maize, and rice ─ Biophysical data ─ Ground spectra ─ Destructive aboveground wet biomass ─ Empirical (non- destructive) model-building β–ͺ CIMIS 1 2 3 5 4 6 10 7 8 9 Marshall and Thenkabail (2015)
  7. 7. Tasseled Cap Transformation β–ͺ Weighted linear combinations of satellite reflectance βˆ’ Brightness βˆ’ Greenness βˆ’ Wetness β–ͺ Fraction of vegetation, bare soil, and water β–ͺ Density Kauth and Thomas (1976)
  8. 8. Index of Agreement (yellow = perfect match) Biomass Dark Soil Light Soil PlantsPlants Reference Data β–ͺ AVHRR (5 km) β–ͺ MODIS (500 m) β–ͺ Landsat (30 m) β–ͺ IKONOS (4 m), WorldView (1.85 m), and GeoEye (1.65 m) Tasseled Cap Biomass Estimation for Validation at Remote Sensing Scales
  9. 9. Muchow (1990) GPP β†’ Crop Yield Prince et al. (2001) expressed yield in terms of GPP: Yield = RS Γ— HI 1 βˆ’ m0 Γ— SOS EOS GPP Where SOS is start of season, EOS is end of season, RS is the root-to-shoot ratio, HI is the harvest index, and m0 is grain moisture content RS, HI, and m0 are constant by crop type (Xin et al. 2013) OR
  10. 10. Need for systems simulation models in Agroforestry Systems Lloyd et al. (1990) β–ͺ Agroforestry systems large payoffs, but complex β–ͺ Systems simulation βˆ’ Can handle complex systems βˆ’ Provide targeted and effective intervention βˆ’ Tunable for β€œfuture” climates β–ͺ Not widely used in SSA, because of data scarcity
  11. 11. β–ͺ GPP and crop yield model validated at multiple spatial scales in California βˆ’ Thomas Gumbricht: MODIS-era GPP modeling tool for California βˆ’ Patricia Masikate: Agro-forestry module (APSIM) and estimates of RS, HI, and m0 βˆ’ Erick Okuto: SOS and EOS simulation β–ͺ Next year (2016): Global GPP and crop yield model development, calibration, and validation βˆ’ @ 5 km resolution 1982 – 2014 (+30 years) Summary
  12. 12. Thank You

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