ciat crop modeling_18may11

Ricky RobertsonEnvironment andProduction TechnologyDivision

InternationalFood PolicyResearch Institute

at CIAT: 18 May 2011

Process-based crop simulation models in support of global economic modeling

soiland water

plant above ground

plant below ground

weather

Processed based crop models try to mimic how plants respond to their environment

Sunshine

Rain/Irrigation

Soil properties

Growth of roots/stems/leaves/fruits

Water use/stress

Nutrient extraction

This presentation draws from experience with the DSSAT family of crop models from the user perspective

DSSAT consists of several framework pieces working together . . .

soil and water

plant above ground

plant below ground

weather

. . . each of which require parameters and data

soil characteristicsinitial conditions

variety attributes

plantingdate

real or generated

The outputs can be thought of in two ways

For economic modeling, “response” dominates, but sometimes “dynamic” is important (water management)

Dynamic: each day’s growth, water usage, etc.

Response: end results as determined by inputs

HWAH = 4400.30NUCM = 264.95NLCM = 113.41ETCM = 773.63

By running repeatedly, such models can be run globally when data are available

All of the details must be specified for each location

HC27rainfed

90 day spin up25% moisture content

maize990001

“April”

CSIRO/A2/2050

HC27rainfed

90 day spin up25% moisture content

maize990001

“April”

CSIRO/A2/2050

By running repeatedly, such models can be run globally when data are available

All of the details must be specified for each location

As users, there are interesting “what ifs” that can be done

Changes in yield under different climates (rainfed maize 990001; baseline/2000 to CSIRO/A1/2050/369ppm

CO2)

Highest yielding variety by location(irrigated rice, choosing among DSSAT generic

varieties)

The robustness or accuracy of results depend on the pieces

As a user, I can only look for obviously strange results

Location specific data

Environment models

Plant models

soiland water

plant above ground

plant below ground

weather

Data quality hinges on availability, geographic coverage, and consistency

soiland water

plant above ground

plant below ground

weather

Downscaling of climate data to local scales (to include sunshine and rainfall distribution; microclimates)

Soils probably provide the greatest opportunity for improvement

Users can make some observations to help model developers

soiland water

plant above ground

plant below ground

weather

Global scale modeling sometimes exposes strange behavior (e.g., root

water extraction in rice)

Calibration of varieties depends on quality and variability in experimental data (e.g., maize yield is highest

around Ames, IA)

FPU level yield and area projections

FPU boundaries

The yield projections are incorporated into the IMPACT economic model at a regional level

IMPACT runs on geographical units known as Food Production Units or FPUs

GCM/SRES scenario climate results are down scaled to 0.5 degree/5 minute resolution

FPU level yield and area projections

FPU boundaries

2000 June average minimum temperature

2050 CSIRO/A2 June average rainfall

Monthly averages are from Thornton and Jones’s FutureClim; daily weather is from DSSAT’s SIMMETEO

Planting months are chosen based on current and future climate conditions (a rule-based system)

FPU level yield and area projections

FPU boundaries

2000 Rainfed planting month

2050 CSIRO/A2 Rainfed planting month

Soils are represented by 27 generic soil profiles based on the harmonized FAO soil datasets

FPU level yield and area projections

FPU boundaries

Soil profiles color coded by location

Soil data must be matched to DSSAT-style soil profiles

The remaining inputs fall under management practices

FPU level yield and area projections

FPU boundaries

Choice of crop variety

Rainfed versus irrigated sources of water

Planting densities, row spacing, and transplanting details

Fertilizer types, amounts, and application dates

DSSAT generates projected yields for each location

FPU level yield and area projections

FPU boundaries

2000 Rainfed maize yield

2050 CSIRO/A2 Rainfed maize yield

Parallelization of the DSSAT runs results in major time savings

FPU level yield and area projections

FPU boundaries

roughly 1½ weeks on 80 processors

(5 crops,rainfed/irrigated,13 climates,15 arc-minute resolution)

serialparallelized

would takeroughly 96 weeks on a single processor

SPAM 2000 areas are used to weight the projected yields when aggregating to FPUs

FPU level yield and area projections

FPU boundaries

Rainfed maize physical area in 2000

The Spatial Production Allocation Model data are available from http://mapspam.info/

Vector FPU boundaries are placed over top of the raster yield projections

FPU level yield and area projections

FPU boundaries

2000 Rainfed maize yield with FPU boundariesin South Asia

Projected yields from DSSAT are aggregated up to the FPU-level for use in IMPACT

FPU level yield and area projections

FPU boundaries

By crop and rainfed/irrigated...

Find total SPAM area within FPU

Find total production (SPAM area × DSSAT yield) within FPU

Compute area-weighted-average yield as total production / total area