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.

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. Global Crop Model Approaches
▪ Local-scale models (e.g. DSSAT and APSIM) aggregated
via multiple-site calibration and averaging
− Complex
− High-input data requirement
− Locally accurate
− Site specificity (over-fitting)
− Non-linearity
▪ Large-area models (e.g. GLOPEM and GLAM)
− Simple
− Low-input data requirement
− Locally less accurate
− Generalizable
“Essentially, all models are wrong, but some are useful.”

3. Global crop model for IMPACT
𝐆𝐏𝐏 = 𝐟𝐬𝐭𝐫𝐞𝐬𝐬 𝛆 𝐦𝐚𝐱 𝐟 𝐀𝐏𝐀𝐑 𝐏𝐀𝐑
𝐟 𝐀𝐏𝐀𝐑 = 𝟏. 𝟐 ∗ 𝐄𝐕𝐈
𝐟𝐬𝐭𝐫𝐞𝐬𝐬 = 𝐦𝐢𝐧(𝐟 𝐓, 𝐟 𝐦)
𝛆 𝐦𝐚𝐱 =
𝟏
𝟏𝟐
𝐜 𝐦𝐚𝐱 − 𝚪
𝐜 𝐦𝐚𝐱 + 𝟐𝚪
𝐆𝐏𝐏 = 𝐟𝐬𝐭𝐫𝐞𝐬𝐬 𝜶 𝐦𝐚𝐱 𝐟 𝐀𝐏𝐀𝐑 𝐏𝐀𝐑
𝐟 𝐀𝐏𝐀𝐑 = 𝟏. 𝟐 ∗ 𝐄𝐕𝐈
𝐟𝐬𝐭𝐫𝐞𝐬𝐬 = 𝐦𝐢𝐧(𝐟 𝐓, 𝐟 𝐦)
𝛂 𝐦𝐚𝐱 = 𝟏. 𝟐𝟔
∆
∆ + 𝛄
Opti-LU
Opti-WU
𝐟 𝐓 = 𝐞
−
𝐓 𝐦𝐚𝐱−𝐓𝐨𝐩𝐭,𝐬𝐞𝐚𝐬𝐨𝐧𝐚𝐥
𝐓𝐨𝐩𝐭,𝐬𝐞𝐚𝐬𝐨𝐧𝐚𝐥
𝟐
𝐟 𝐌 =
𝐟 𝐀𝐏𝐀𝐑
𝐟 𝐀𝐏𝐀𝐑𝐦𝐱
εmax=0.06 mol·mol-1 (C4)

4. Model Calibration, Sensitivity Analysis,
and Validation
▪ Identify most important parameters, remove
redundant or insignificant parameters, and potentially
integrate new parameters
− Model performance statistics
− Monte Carlo Simulation
− Residual analysis
▪ Multiple scales of validation
− Plot (eddy covariance flux tower data)
− Field (non-destructive biomass transects)
− Landscape → global (high, moderate, and coarse
resolution remote sensing)

5. Eddy Covariance Flux Towers
▪ Three towers (2009-2014)
▪ Alfalfa, rice, and
citrus orchard
▪ 30-minute energy
balance and
meteorological data
▪ FAPAR (MODIS subset
tool:
http://daac.ornl.gov/)
▪ Daily GPP, RECO, and
NPP

6. Transects
60 m*
▪ 10 quadrats per
frame (2011-2012)
─ Alfalfa, cotton,
maize, and rice
─ Biophysical data
─ Ground spectra
─ Destructive
aboveground
wet biomass
─ Empirical (non-
destructive)
model-building
▪ CIMIS
1
2
3
5
4
6
10
7
8
9
Marshall and Thenkabail (2015)

7. Tasseled Cap Transformation
▪ Weighted linear
combinations of
satellite reflectance
− Brightness
− Greenness
− Wetness
▪ Fraction of
vegetation, bare soil,
and water
▪ Density
Kauth and Thomas (1976)

8. Index of Agreement (yellow = perfect match)
Biomass
Dark Soil
Light Soil
PlantsPlants
Reference
Data
▪ AVHRR (5 km)
▪ MODIS (500 m)
▪ Landsat (30 m)
▪ IKONOS (4 m), WorldView (1.85 m), and GeoEye (1.65 m)
Tasseled Cap
Biomass Estimation for Validation at
Remote Sensing Scales

9. Muchow (1990)
GPP → Crop Yield
Prince et al. (2001) expressed yield in terms of GPP:
Yield =
RS × HI
1 − m0
×
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. Need for systems simulation models in
Agroforestry Systems
Lloyd et al. (1990)
▪ Agroforestry systems
large payoffs, but
complex
▪ Systems simulation
− Can handle complex
systems
− Provide targeted and
effective intervention
− Tunable for “future”
climates
▪ Not widely used in
SSA, because of data
scarcity

11. ▪ GPP and crop yield model validated at multiple
spatial scales in California
− Thomas Gumbricht: MODIS-era GPP modeling
tool for California
− Patricia Masikate: Agro-forestry module (APSIM)
and estimates of RS, HI, and m0
− Erick Okuto: SOS and EOS simulation
▪ Next year (2016): Global GPP and crop yield model
development, calibration, and validation
− @ 5 km resolution 1982 – 2014 (+30 years)
Summary

12. Thank You