Vulnerability and Adaptation in Agriculture:

the Thai’s Experiences

• The study– Approach– Results

• Lessons learned and Potential improvement

• Implication

Approach

Controlled climate GHG 1% climate

Crop growth models

Yields (controlled climate)

Yields (GHG 1% climate)

Difference in yields

Climate Scenarios

•Using the simulation of the GCMs

–Transient (CO2 increases 1% per year)

•Various GCMs

–CCCMA

–ECHAM

–CSIRO

–HADLEY

From Global to Local

• Direct Interpolation

• Use 30 years monthly average to reduce

short term climate variation– 1990s(1960-89)

– 2020s(2010-2039)

– 2050s (2040-2069)

– 2080s(2070-2099)

• Develop daily climate scenarios for

specific locations

Crops: Models and Areas

•Crop models–CERES MAIZE, CERES RICE

•Crops and areas–Fragrant Rice in two provinces

–Maize in two provinces

Scenarios

• Four climate inputs from different GCMs

• Two crops x two areas

• With and without fertilizer

• With and without C effects on crop

• Four intervals ( 1990s, 2020s, 2050s and 2080s)

Some Results

Maize, Nakhonsawan, no fertilizer

-40

-30

-20

-10

0

2020 2050 2080

Perce

nt

CCCMA

ECHAM

CSIRO

Maize, Nakhonrachasima, no fertilizer

-50

-40-30

-20

-10

010

20

2020 2050 2080

perce

nt

CCCMA

ECHAM

CSIRO

- Rice, Roi et, no fertilizer

-40

-20

0

20

40

60

2020 2050 2080

Perce

nt

CCCMA

ECHAM

CSIRO

Rice, Surin, no fertilizer

-60

-50

-40

-30

-20

-10

0

2020 2050 2080

Perce

nt

CCCMA

ECHAM

CSIRO

Major observations

• Results from different GCMs vary quite a lot

• Yields tend to drop overtime

• Yields of maize are not much different between the two

provinces, the reverse is true for rice

• There are some positive carbon effects on yield

Lessons learned

• High uncertainty of climate models• Crop models need to be improved• Impacts could vary substantially between areas• Vulnerability analysis is not sufficient to be used

for adaptation study• Local climate data are limited

How to improve

• Reduce uncertainty of climate models– develop regional or sub-regional models

– improve downscaling methods

• Improve crop growth models• Cover the areas and crops adequately• Develop other approaches

Implications

• Research and development on vulnerability is urgently needed– Soft technology transfer

– Indigenous capacity building

• Sub-regional network is important to facilitate the technology development and capacity building

• A need for strong participation in international research and development on vulnerability

• Existing mechanisms make vulnerability and adaptation relatively much behind others