A one-day Strategic Foresight Conference took place at IFPRI Headquarters in Washington DC on November 7, 2014. Participants from leading global modeling groups, collaborating CGIAR centers and research programs, and other partners reviewed new long-term projections for global agriculture from IFPRI and other leading institutions, examined the potential impacts of climate change and other key challenges, and discussed the role of foresight work in identifying and supporting promising solutions. Topics included: Long-term outlook and challenges for food & agriculture Addressing the challenges Foresight in the CGIAR Webcast video of morning sessions available on Global Futures program website here: http://globalfutures.cgiar.org/2014/11/03/global-futures-strategic-foresight-conference/

1. Investing in data for improved
modeling
Sika Gbegbelegbe
Strategic Foresight Conference
IFPRI, Washington DC, 7 November 2014

2. Outline
• Improving wheat and maize crop models:
process
• Linking crop and socio-economic models
• Integration with work of others in CIMMYT:
awareness

3. IMPROVING WHEAT CROP MODEL

4. Wheat – Context
• Objective: develop baseline crop models for
impact assessment studies (global and
regional scales); June 2011
• Data requirements:
– List of representative crop varieties
– Measured field trial data for crops: planting date;
anthesis and maturity dates; grain yield at
maturity; kernel weight; etc.

5. • Determined parameters of 1
representative cultivar for
each of 17 Mega-
Environments (MEs)
(data, expert knowledge and genetic info)
Calibration &
validation (IWIS)
Stat. analysis &
validation (IWIS)
Validation (IWIS)
ME Repr. cultivars
ME1 Seri M 82
ME1 PBW 343 (Attila)
ME2A Kubsa (Attila)
ME2B Tajan
ME 3 Alondra
ME 4A Bacanora (Kauz)
ME 4B Don Ernesto INTA
ME 4C HI 617 (Sujata)
ME 5A Kanchan
ME 5B Debeira
ME 6 Saratovskaya
ME 7 Pehlivan
ME 8A Halcon SNA
ME 8B Katya
ME 9 Bacanora (kauz)
ME 10 Bezostaya
ME 11 Brigadier
ME 12 Gerek 79

6. • Measured field trial data
for Seri M 82 and Kauz
from 1991 to 1995: grain
yield; days to anthesis;
days to maturity; kernel
weight ; aboveground
biomass; harvest index;
kernel number per m2
ME Repr. Cult. P1V P1D P5 G1 G2 G3 Phint
ME1 Seri M 82 20 94 564 22 39 1.0 120
ME 4A Bacanora (Kauz) 20 94 564 24 37 1.0 120
ME2A Kubsa (Attila) 20 94 564 22 40 1.0 120

7. Calibration: simulated vs. measured parameters for Seri M 82 and Kauz in Obregon, Mexico
(ME 1)
0
2000
4000
6000
8000
10000
Yield(kg/ha)
Yield-M Yield-S
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Yield(kg/ha)
Yield-M Yield-S

8. 0
2
4
6
8
10
12
0 4 8 12
Simulatedgrainyield(tonnes/ha)
Measured grain yield (tonnes/ha)
1:1 line
Seri-Ludh (ME 1)
Seri-Sud (ME 5)
Kauz-Ludh (ME 1)
Kauz-Sud (ME 5)
Kauz-Bang (ME 5)
Attl-Obre (ME 1)
Attl-Ludh (ME 1)
Attl-Sud (ME 5)
Attl-Chile (ME 2)
Evaluation I: Seri M 82, Kauz and Attila

9. 0
2
4
6
8
10
12
14
0 2 4 6 8 10 12 14
Simulatedgrainyield(tonnes/ha)
Measured grain yield (tonnes/ha)
1:1 line
Alondra-Brazil
Sujata-India
Kanchan-India
Kanchan-Bang
Debeira-Sudan
Debeira-Brazil
Katia-Iran
Katia-Turkey
Bezostaya-Turkey
Bezostaya-Iran
Evaluation II: other benchmark varieties

10. From site-specific to global simulations
• Global input data: adjustments
– Climate: measured data between 1980 and 2000;
rainfall; temperature; elevation
– Soil: initial conditions; adjustments
– Management:
• Varieties: done
• Fertilizer application: adjustments
• Irrigation application
• Planting months: adjustments
– Overlapping of MEs (with low average yields)

11. FAO annual yield data - average 1999-2001 (kg/ha)
0 2000 4000 6000 8000
Simulatedannualyield(kg/ha)
0
2000
4000
6000
8000
GER
FRA
CHN
RUS
USA
IRAN
MOR
Large producers
Medium producers
Small producers
1:1 Line
• Paper submitted to
‘Agricultural Systems’:
authors include modelers
from CIMMYT; breeders and
physiologists from CIMMYT
and ICARDA; modelers from
IFPRI

12. IMPROVING MAIZE CROP MODEL

13. Parameterization: benchmark maize
Environment Bench. cult.
Highland BH660
Wet upper mid-alt. WH403
Wet lower/up. mid-alt. SC403
Dry mid-altitude SC513
Wet lowland POI 30F32
Wet lower mid-altitude ZM521
USA and Canada Garst 8808
South America DKB 333B
Southern Europe A632 x W117
Europe (other) DEA
Chn, JPN, NKR, SKR CF1505
Middle east (cold) POI 31R88
Middle East + Egypt ZM521
South East Asia Suwan 3851
Australia and NWZL DeKalb XL82
Calibration
&
validation
Literature
(DSSAT)
• Challenge: measured
data (issues)
• Investment in data
collection: weather data;
field trial data; work
started in 2012

14. Baseline global maize production
• Efforts underway
to develop global
baseline maize
simulations:
different crop
management (e.g.,
split application of
fertilizer for rainfed
and irrigated
maize)
• Simulation against
FAOSTAT data 14
y = 0.52x + 1378.8
R² = 0.67
0
2000
4000
6000
8000
10000
12000
-3,000 2,000 7,000 12,000
SimulatedYield(kg/ha)
Yield from FAO (kg/ha) for major
producers
1:1

15. LINKING CROP AND SOCIO-
ECONOMIC MODELS

16. Linking crop and economic models
• Impact of 2012 weather extreme on maize
production in the USA and related effects on
global food security
• Bio-economic impact of climate change on
maize-based systems in Africa
• DT wheat in CWANA: do adoption pathways
matter?

17. Awareness of Foresight Modeling in
CIMMYT
• Increased awareness among Foresight modeling team:
economic; crop and spatial modelers
• Awareness within CIMMYT (Wheat and Maize CRPs)
– June 2011: one wheat breeder (with statistical background) out
of 3 was able to support (input data)
– January 2012: Global Futures meeting in Kenya (all centers)
• 2 wheat breeders (CIMMYT and ICARDA) involved
• Increased awareness for maize breeders and physiologists
– August 2013: CIMMYT-wide meeting on foresight modeling
(modelers from UF)
• Presentation of preliminary results: results from bio-economic
modeling
• Demands from breeders and pathologists: recommendation domains
for testing DT wheat (wheat); foresight on disease incidence (maize)