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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

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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

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What is CCAFS?

• Strategic partnership of international agriculture (CGIAR) and global change (Future Earth) research communities

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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

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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

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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

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Ris

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Food security early warning,

planning

Trade planning, strategic imports

Insu

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ayo

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Why CRAFT? Support adaptation opportunities

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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

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Basics of yield forecasting: Uncertainty

• Consider yields simulated with monitored weather thru current date, then sampled historic weather

• Uncertainty diminishes as season progresses

• Model error the non-climatic component

• Relative contribution of climate, model uncertainty changes through the season

forecast date harvest

season onset

Time of growing season

1 2

. . .

n

T Weath

er

data

year

monitored weather

historic weather

0.5

1.0

1.5

2.0

Gra

in y

ield

, Mg/

ha

1May 1Jun 1Jul 1Aug Harvest

Forecast Date

90th

75th

50th

25th

10th

1989 climatology-based Qld. Australia wheat forecast. Observed, and forecast percentiles. Hansen et al., 2004. Agric. For. Meteorol. 127:77-92

Un

ce

rta

inty

planting anthesis harvestTime

model uncertainty

climate uncertainty

SIM

UL

AT

ION

◄——— PREDICTION ———

Hansen, J.W., Challinor, A., Ines, A.V.M, Wheeler, T., Moron, V., 2006. Climate Research 33:27-41.

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Basics of yield forecasting: Reducing uncertainty

Unc

erta

inty

planting anthesis harvestTime

model uncertainty

climate uncertainty

Unc

erta

inty

planting anthesis harvestTime

2b. N-limited

3. Actual

1. Potential

pests, disease,micronutrients,toxicities

H, T, crop charac-teristics

water2a. Water-limited

??????

soil N dynamics,plant N use,stress response

photosynthesis,respiration,phenology

water balance,transpiration,stress response

Level of production Processes

nitrogen

after Rabbinge, 1993

• Reduce model error:

• Improve model

• Improve inputs

• Assimilate monitored state

• Greatest benefit late in season

• Reduce climate uncertainty

• Incorporate seasonal forecasts for remainder of season

• Greatest benefit early in season

Unc

erta

inty

planting anthesis harvestTime

model uncertainty

climate uncertainty

Unc

erta

inty

planting anthesis harvestTime

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Incorporating seasonal forecasts: Queensland wheat study (2004)

• WSI-type crop model

• PC1 of GCM (ECHAM4.5) rainfall, persisted SSTs

• Yields by cross-validated linear regression with normalizing transformation

• Probabilistic, updated

• Demonstrated yields more predictable than rainfall

• One of several potential methods tested

2 0 0 0 2 0 0 4 0 0 k m

C o r r e l a t i o n < 0 . 3 4 ( n . s . ) 0 . 3 4 - 0 . 4 5 0 . 4 5 - 0 . 5 0

0 . 5 0 - 0 . 5 5 0 . 5 5 - 0 . 6 0

0 . 6 0 - 0 . 6 5

> 0 . 6 5

Rain

Yield

Hansen, Pogieter, Tippett, 2004. Agric. For. Meteorol. 127:77-92

N

200 0 200 400 km

1 July

1 June

1 August

1 MayCorrelation

<0.34 (n.s.)

0.34-0.45

0.45-0.55

0.55-0.65

0.65-0.750.75-0.85

> 0.85

0.5

1.0

1.5

2.0

Gra

in y

ield

, M

g/h

a

1May 1Jun 1Jul 1Aug Harvest

Forecast Date

0.5

1.0

1.5

2.0

1May 1Jun 1Jul 1Aug Harvest

Forecast Date

1982 Queensland, Australia wheat yield forecast.

climatology only + GCM forecast

Forecast date

Gra

in y

ield

(M

g h

a-1

)

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Linking crop simulation models and seasonal climate forecasts statistically

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Time of year

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Versions:

• Windows 95+

• Linux batch

• Windows batch (for CRAFT)

Incorporating seasonal forecasts: CPT

Climate Predictability Tool (CPT) is an easy-to-use software package for making tailored seasonal climate forecasts.

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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)

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What CPT does

• Statistical forecasting

• Statistical downscaling

coarse resolution

fine resolution

statistical model

dynamical model

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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

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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

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CPT: Canonical Correlation Analysis (CCA)

July (top) and December (bottom) tropical Pacific sea-surface temperature anomaly, 1950-1999

December

July

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CPT: Which method?

Predictor Predictand (simulated yield)

Method

Point-wise Point-wise Multiple regression

Spatial pattern Point-wise Principal component regression

Spatial pattern Spatial pattern Canonical correlation analysis

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CCAFS structure: Yield forecast work flow

WEATHER

SOIL

CULTIVAR

MANAGEMENT

CROP MODEL (DSSAT CSM)

SIMULATED YIELDS

STATISTICAL MODEL

(CPT)

SEASONAL PREDICTORS

FORECAST YIELDS

AGGREGATION

CALIBRATION

CALIBRATED YIELDS

AGGREGATED YIELDS

OBSERVED YIELDS

3

4

1

2

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CROP SIMULATOR

IMPORT

PROCESS MANAGER

U

S

E

R

I

N

T

E

R

F

A

C

E

CROP MODEL MANAGER

CCAFS Modules

EXTERNAL ENGINES

INPUT/OUTPUT FILES

CENTRAL RDBMS

EXPORT

AGGREGATOR

CPT TOOL

SEASONAL FORECAST MANAGER

MS Windows MS .NET MySQL DB

CRAFT Architecture

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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

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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

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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.

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Management

Define input levels

– Field, Cultivar,

Planting, Irrigation,

Fertilizer – using

Management menu

from the menu bar.

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Project

• Search or select

existing project,

or create new

project

• Navigate to data

source form for

active run of

active project

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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.

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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

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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

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Visualization

• Displays user-

selected output

variables and

statistics

• Interactive grid

cell selection

• Display, map

results by grid

cell or polygon

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User interface summary

SPATIAL

DATA

MANAGEMENT DATA

PROJECT &

RUN SETUP

RUN PROJECT

RESULTS

Import default data sets – admin only

Import gridded user data sets

Export default data sets

Export gridded user data sets

Define Cultivar

Define Planting Dates

Define Irrigation Application

Define Fertilizer Application

Define Field History

Create a project

Search and Select Project & Run

Create Run(s)

Identify data sources

Apply UI based inputs

Run crop model

Run Seasonal Forecast module

Run Calibration module

Single Project Run

• Select project

• Select outputs to view

• View/Export Results

Compare Project Runs

• Select the two projects

• Select outputs to compare

• View/Export Results

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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