The CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) is a software platform that supports within-season crop forecasting through crop simulation modeling and incorporation of seasonal climate forecasts. It allows users to manage spatial data, run the DSSAT crop model, integrate seasonal climate predictions, perform spatial aggregation and probabilistic analysis, and visualize outputs. CRAFT is being developed by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) to provide decision support for adaptation planning related to climate risks and opportunities in agriculture.

1. The CCAFS Regional Agricultural
Forecasting Toolbox (CRAFT)
James Hansen, Theme 2 Leader
International Research Institute for Climate and Society
Herramientas para la Adaptación y Mitigación del Cambio Climático
en la Agricultura en Centroamérica
Panamá, 6-8 de Agosto 2013
Date
1

2. What is CRAFT?
•
•
Software platform to support within-season forecasting of crop
production; secondarily, risk analysis and climate change impacts
Functions:
•
•
•
•
•
•
•
•
Manage spatial data, crop simulation (currently DSSAT)
Integrate seasonal forecasts (CPT)
Spatial aggregation
Probabilistic analysis
Post-simulation calibration
Visualization
Analyses: risk, forecast,
hindcasts, climate change
Current version preliminary
2

3. What is CCAFS?
•
Strategic partnership of international agriculture (CGIAR)
and global change (Future Earth) research communities
3

4. What is CCAFS?
•
•
Strategic partnership of international agriculture (CGIAR)
and global change (Future Earth) research communities
World’s largest research program addressing the
challenge of climate change and food security
 Mechanism for organizing, funding
climate-related work across CGIAR
 Involves all 15 CGIAR Centers
 $67M per year
4

5. What is CCAFS?
•
•
•
Strategic partnership of international agriculture (CGIAR)
and global change (Future Earth) research communities
World’s largest research program addressing the
challenge of climate change and food security
5 target regions across the developing world
5

6. What is CCAFS?
•
•
•
•
Strategic partnership of international agriculture (CGIAR)
and global change (Future Earth) research communities
World’s largest research program addressing the
challenge of climate change and food security
5 target regions across the developing world
Organized around 4 Themes:
•
•
•
•
Adaptation to progressive change
Adaptation through managing climate risk
Pro-poor climate change mitigation
Integration for decision-making
6

7. Farmer
advisories
Input supply
management
Insurance
design
Risk
analysis
APPLICATION
Time of year
Food security
early warning,
planning
Trade planning,
strategic imports
Insurance
evaluation,
payout
Uncertainty (e.g., RMSEP)
marketing
harvest
anthesis
planting
seasonal
forecast
EVENT
Why CRAFT?
Support adaptation opportunities
growing season
7

8. Why CRAFT?
Meets an unmet need
•
•
•
•
Platform to facilitate research, testing and implementation
of crop forecasting methods
Target researchers and operational institutions in the
developing world
Accessible: free, open-source (eventually)
Adaptable: support multiple crop model families
8

9. •
Model error the nonclimatic component
Relative contribution of
climate, model uncertainty
changes through the
season
Hansen, J.W., Challinor, A., Ines, A.V.M, Wheeler,
T., Moron, V., 2006. Climate Research 33:27-41.
monitored
weather
.
.
.
historic
weather
model uncertainty
climate uncertain
n
T
season
onset
Time of growing season
forecast
date
harvest
90th
2.0
planting
1.5
75th
50th
25th
10th
anthesis
Time
harvest
◄——— PREDICTION ———
1.0
SIMULATION
•
Uncertainty diminishes as
season progresses
1
2
Grain yield, Mg/ha
•
Consider yields simulated
with monitored weather
thru current date, then
sampled historic weather
Weather data year
•
Uncertainty
Basics of yield forecasting:
Uncertainty
1989 climatology-based Qld.
Australia wheat forecast. Observed,
and forecast percentiles. Hansen et
al., 2004. Agric. For. Meteorol.
0.5
127:77-92
1May 1Jun 1Jul 1Aug
Harvest
9
Forecast Date

10. Basics of yield forecasting:
Reducing uncertainty
Improve model
1. Potential
Improve inputs
Assimilate monitored state
2a. Water-limited
•
water
Greatest benefit late in season
Reduce climate uncertainty
•
H, T,
crop
characteristics
Incorporate seasonal forecasts
for remainder of season
2b. N-limited
3. Actual
Greatest benefit early in season
nitrogen
pests, disease,
micronutrients,
toxicities
photosynthesis,
respiration,
phenology
water balance,
transpiration,
stress response
soil N dynamics,
plant N use,
stress response
??????
after Rabbinge, 1993
model uncertainty
climate uncertainty
planting
anthesis
Time
harvest
Uncertainty
•
•
•
•
•
Processes
Level of production
Reduce model error:
Uncertainty
•
planting
anthesis
Time
harvest
10

11. Incorporating seasonal forecasts:
Queensland wheat study (2004)
Rain
•
•
•
WSI-type crop model
2.0
2.0
Grain yield, Mg/ha
•
climatology only
Grain yield (Mg ha-1)
•
•
PC1 of GCM (ECHAM4.5)
rainfall, persisted SSTs 1.5
Yields by cross-validated
1.0
linear regression with
normalizing transformation
Probabilistic, updated
0.5
N
1.5
1.0
Yield
1982 Queensland, Australia wheat yield forecast.
Correlation
1 May
1May 1Jun 1Jul 1Aug
0.5
1 June
Harvest 1May 1Jun 1Jul 1Aug
ForecastoDateation
C rrel
Forecast date Forecast
< 0.34 (n .s.)
0.34 - 0 .4 5
0.45 - 0 .5 0
0.50 - 0 .5 5
0.55 - 0 .6 0
0.60 - 0 .6 5
> 0. 6 5
Demonstrated yields more
predictable than rainfall
One of several potential
methods tested
Hansen, Pogieter, Tippett, 2004.
Agric. For. Meteorol. 127:77-92
+ GCM forecast
20 0
1 July
0
1 August
200
0
200
400 km
20 0
<0.34 (n.s.)
Harvest
0.34-0.45
Date
0.45-0.55
0.55-0.65
0.65-0.75
0.75-0.85
> 0.85
400 km
11

12. harvest
yn,1
yn,2
yn,3
.
.
.
n
.
.
.
yn,n-1
}
fitted statistical model
Weather data year
1
2
forecast
date
model
initialization
Linking crop simulation models and
seasonal climate forecasts statistically
ˆ
yk
Time of year
12

13. Incorporating seasonal forecasts:
CPT
Climate Predictability Tool (CPT) is an easy-to-use software
package for making tailored seasonal climate forecasts.
Versions:
• Windows 95+
• Linux batch
• Windows batch (for CRAFT)
13

14. Why CPT?
Address problems that arose in RCOFs:
•
•
•
Slow production made pre-forum workshops expensive
and prohibited monthly updates
Multiplicity, colinearity, artificial skill, lack of rigorous
evaluation made forecasts questionable
Little use of GCM predictions
(http://www.wmo.int/pages/prog/wcp/wcasp/clips/outlooks/climate_forecasts.html)
14

15. What CPT does
•
•
Statistical forecasting
Statistical downscaling
coarse resolution
statistical model
fine resolution
dynamical model
15

16. What CPT does
•
•
•
•
•
•
•
Statistical forecasting
Statistical downscaling
Designed to use gridded data (GCM output and SSTs)
as predictors
Uses principal components (PCs, or EOFs) as predictors
Rigorous cross-validation to avoid artificial skill
Diagnostics and evaluation
New multi-model support
16

17. CPT: Principal Components (PCs)
•
•
•
•
•
Explain maximum amounts of variance within data
Capture important patterns of variability over large areas
Uncorrelated, which reduces regression parameter errors
Few PCs need be retained, reducing dangers of “fishing”
Corrects spatial biases
First PC of Oct-Dec 1950 -1999
sea-surface temperatures
17

18. CPT: Canonical Correlation Analysis
(CCA)
July
July (top) and December (bottom)
tropical Pacific sea-surface
temperature anomaly, 1950-1999
December
18

19. CPT: Which method?
Predictor
Predictand
Point-wise
Point-wise
Multiple regression
Spatial pattern
Point-wise
Principal component regression
Spatial pattern
Spatial pattern
Canonical correlation analysis
(simulated yield)
Method
19

20. CCAFS structure:
Yield forecast work flow
1
WEATHER
CROP MODEL
(DSSAT CSM)
SOIL
SEASONAL
PREDICTORS
SIMULATED YIELDS
2
STATISTICAL
MODEL
(CPT)
CULTIVAR
MANAGEMENT
4
FORECAST YIELDS
OBSERVED YIELDS
CALIBRATION
3
AGGREGATED YIELDS
AGGREGATION
CALIBRATED YIELDS
20

21. CRAFT Architecture
CCAFS Modules
PROCESS MANAGER
CROP MODEL MANAGER
AGGREGATOR
SEASONAL FORECAST MANAGER
IMPORT
EXPORT
CENTRAL
RDBMS
EXTERNAL ENGINES
CROP SIMULATOR
U
S
E
R
I
N
T
E
R
F
A
C
E
MS Windows
MS .NET
MySQL DB
CPT TOOL
INPUT/OUTPUT FILES
21

22. Steps: yield forecast run
• Step 1 – Prepare/Review Data Sets
• Step 2 – Create Project & Run
• Step 3 – Link Data Sources
• Step 4 – Enter Crop Management Data
• Step 5 – Setup & Execute Crop Model Run
• Step 6 – View Crop Model Run Results
• Step 7 – Seasonal Forecast Run
• Step 8 – View Forecast Yield Results
22

23. Home Page
• Main Menu
• Connect
application to
desired database,
and test
• Lists 5 most recent
projects
• Project Name link
will direct the user
to the current state
of the workflow
23

24. Data Upload
•
•
•
•
CCAFS pre-loaded
with default data
(currently South
Asia).
Users can upload
data: crop mask,
cultivar, fertilizer,
field history, planting,
irrigation mask &
management, soil.
These data are input
data to DSSAT and
CPT engines during
run.
Version control of
user-supplied data.
24

25. Management
Define input levels
– Field, Cultivar,
Planting, Irrigation,
Fertilizer – using
Management menu
from the menu bar.
25

26. Project
• Search or select
existing project,
or create new
project
• Navigate to data
source form for
active run of
active project
26

27. Data Sources
• Customize run
by selecting
uploaded or
default data
sources
• Drop-down lists
of previously
uploaded data
• This screen is
not shown if
Default is
selected when
creating Project
and Run.
27

28. Apply Inputs
• Tabbed details of
available Levels,
and a grid to show
the applied levels
for specified ‘Type
of Data’
• Green = input
applied,
• Pink = input not
applied
• Mandatory fields
must be applied
28

29. Run Project
• Executes current
active project with
configured data
sources and applied
inputs
• On successful
execution, prompts
to save run results,
view result maps
• 2 steps:
•
•
Crop simulation
Seasonal forecast
29

30. Visualization
• Displays userselected output
variables and
statistics
• Interactive grid
cell selection
• Display, map
results by grid
cell or polygon
30

31. User interface summary
SPATIAL
DATA
MANAGEMENT
DATA
PROJECT &
RUN SETUP
RUN
PROJECT
RESULTS
Import
default data
sets – admin
only
Define Cultivar
Create a
project
Run crop
model
Search and
Select Project
& Run
Run
Seasonal
Forecast
module
Single Project Run
• Select project
• Select outputs to
view
• View/Export
Results
Import
gridded user
data sets
Define Planting
Dates
Define Irrigation
Application
Create Run(s)
Export default
data sets
Define Fertilizer
Application
Export
gridded user
data sets
Define Field
History
Identify data
sources
Apply UI based
inputs
Run
Calibration
module
Compare Project
Runs
• Select the two
projects
• Select outputs to
compare
• View/Export
Results
31

32. Major planned enhancements
•
•
•
•
•
Generalize locations, grid schemes, user inputs
Crop model interoparability (AgMIP)
Additional crop models:
•
•
•
•
•
•
APSIM
AquaCrop
ORYZA2000
SARA-H
InfoCrop
…
Hindcast analysis and validation statistics
De-trending and post-simulation calibration
32